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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020167Research ArticleGenetics/Genomics/Gene TherapyMus (Mouse)α-Actinin-4-Mediated FSGS: An Inherited Kidney Disease Caused by an Aggregated and Rapidly Degraded Cytoskeletal Protein α-Actinin-4 Mutations in Kidney DiseaseYao June 1 Le Tu Cam 1 Kos Claudine H 1 Henderson Joel M 2 Allen Phillip G 3 Denker Bradley M 1 Pollak Martin R [email protected] 1 1Renal Division, Department of Medicine, Brigham and Women's Hospital and Harvard Medical SchoolBoston, MassachusettsUnited States of America2Department of Pathology, Brigham and Women's Hospital and Harvard Medical SchoolBoston, MassachusettsUnited States of America3Hematology Division, Department of Medicine, Brigham and Women's Hospital and Harvard Medical SchoolBoston, MassachusettsUnited States of America6 2004 15 6 2004 15 6 2004 2 6 e16719 12 2003 7 4 2004 Copyright: © 2004 Yao et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Protein's Role in Progressive Renal Disease Focal segmental glomerulosclerosis (FSGS) is a common pattern of renal injury, seen as both a primary disorder and as a consequence of underlying insults such as diabetes, HIV infection, and hypertension. Point mutations in theα-actinin-4 gene ACTN4 cause an autosomal dominant form of human FSGS. We characterized the biological effect of these mutations by biochemical assays, cell-based studies, and the development of a new mouse model. We found that a fraction of the mutant protein forms large aggregates with a high sedimentation coefficient. Localization of mutant α-actinin-4 in transfected and injected cells, as well as in situ glomeruli, showed aggregates of the mutant protein. Video microscopy showed the mutant α-actinin-4 to be markedly less dynamic than the wild-type protein. We developed a “knockin” mouse model by replacing Actn4 with a copy of the gene bearing an FSGS-associated point mutation. We used cells from these mice to show increased degradation of mutant α-actinin-4, mediated, at least in part, by the ubiquitin–proteasome pathway. We correlate these findings with studies of α-actinin-4 expression in human samples. “Knockin” mice with a disease-associated Actn4 mutation develop a phenotype similar to that observed in humans. Comparison of the phenotype in wild-type, heterozygous, and homozygous Actn4 “knockin” and “knockout” mice, together with our in vitro data, suggests that the phenotypes in mice and humans involve both gain-of-function and loss-of-function mechanisms. Transgenic experiments in mice suggest that the human kidney disorder, focal segmental glomerulosclerosis (FSGS), is a result of both gain- and loss-of-function mechanisms ==== Body Introduction In humans, ACTN4 mutations cause a form of focal segmental glomerulosclerosis (FSGS) (Kaplan et al. 2000). This lesion, which describes a pattern of injury characterized by regions of sclerosis in some renal glomeruli, is a common finding in kidney disease from a wide range of primary disorders, including HIV infection, diabetes, and hypertension (Ichikawa and Fogo 1996; Somlo and Mundel 2000). The four α-actinin genes encode highly homologous proteins that normally form approximately 100 kDa head-to-tail homodimers. While the best-defined function of α-actinin-4 is to cross-link and bundle actin filaments, α-actinins have been found to interact with a large and diverse set of other proteins (Honda et al. 1998; Takada and Beggs 2002). α-Actinin-2 and α-actinin-3 are located predominantly in the sarcomere, while α-actinin-1 and α-actinin-4 are widely expressed. In the human kidney, only α-actinin-4 expression is detected (Kaplan et al. 2000). Human ACTN4-associated FSGS is inherited in an autosomal dominant pattern. By contrast, mice homozoygous for Actn4 null alleles have glomerular disease, while heterozygous Actn4 null mice have no readily apparent phenotype (Kos et al. 2003). Transgenic mice harboring a mutant Actn4 targeted to the glomerular podocyte have an FSGS-like lesion, although it is not clear whether this is due to the dominant effect of the mutant Actn4 per se or to dysregulated Actn4 expression in this cell (Michaud et al. 2003). We previously showed that FSGS-associated ACTN4 mutations increase the binding of α-actinin-4 to F-actin (Kaplan et al. 2000). This was confirmed independently by different methodology (Michaud et al. 2003). However, the relationship between altered actin binding and disease is unclear. Of particular interest is whether the human disease is due to a gain-of-function effect of mutations on protein function or due to a partial loss of normal function. We therefore performed a series of experiments to help us understand the biological consequences of mutations in ACTN4. We demonstrate here that mutant α-actinins exhibit altered structural characteristics, localize abnormally, and have significantly diminished half-life. By developing a mouse model harboring a disease-associated point mutation, we confirm the pathologic effect of this mutation on glomerular function. Our results suggest that the major effects of Actn4 mutations are protein misfolding and accelerated degradation, leading to loss of normal α-actinin-4 function, α-actinin-4 aggregation, and progressive kidney disease. Results Mutant α-Actinin-4 Conformation In overlay assays (performed as per Chan et al. 1998; data not shown), mutant α-actinin-4 is able to bind both mutant and wild-type α-actinin-4. Since mutant α-actinin-4 polypeptides are able to interact, we used sucrose gradient assays to examine whether the mutant α-actinin-4 dimerizes normally. We produced radiolabeled in vitro translated wild-type and mutant α-actinin-4 (K228E, T232I, R235P) and subjected these proteins to centrifugation in 5%–20% sucrose gradients. We found that all of the wild-type α-actinin-4 eluted as a single peak, as expected. By contrast, approximately 80% mutant α-actinin-4 eluted at the same peak as in wild-type, while about 20% of the mutant protein eluted much more quickly and peaked in the first fraction. We then used a 10%–40% sucrose gradient in an attempt to better characterize this small peak. Again, in multiple experiments, about 20% of the mutant α-actinins eluted in the first fractions. α-Actinin has a sedimentation coefficient of 6.4 (Feramisco and Burridge 1980), and both wild-type and the majority (approximately 80%) of mutant actinins migrated in this position. However, a significant fraction of mutant α-actinin-4 had a sedimentation coefficient equal to or greater than 11.3, the sedimentation coefficient of catalase (Figure 1). This extremely high sedimentation rate suggests either the presence of large α-actinin-4 multimers or the existence of large (and perhaps insoluble) aggregates. When these experiments were performed in the presence of a large excess of cold α-actinin, this rapidly sedimenting fraction of mutant α-actinin was unchanged, showing that the abnormally conformed mutant α-actinin-4 could not be shifted back into normally sedimenting mutant/wild-type heterodimers (Figure 1). Figure 1 Mutant α-Actinin-4 Sediments Abnormally (A) Pattern of sedimentation of α-actinin-4 in a 10%–40% sucrose gradient. Results shown are for wild-type α-actinin-4, K228E α-actinin-4, and K228E α-actinin-4 after addition of excess cold (wild-type) α-actinin. A fraction of the mutant α-actinin-4 sedimented at least as quickly than the highest molecular weight marker, catalase, which has a sedimentation coefficient of 11.3. This was observed with all of the other mutants tested as well (data not shown), but never with labeled wild-type α-actinin-4. Addition of cold α-actinin did not alter the sedimentation pattern seen with the mutant form of α-actinin-4. (B) Results illustrated graphically. Units are arbitrary. α-Actinin-4 Behavior in Cells We performed both transfection and nuclear injection studies in a conditionally immortalized podocyte cell line to look at the effect of disease-associated mutations on α-actinin-4 localization. Irrespective of the method used to express the mutant α-actinins in cells, we found altered localization of the mutants. Consistent with the altered sedimentation observed in vitro, mutant α-actinin-4 formed localized aggregates when expressed in cells. We used video microscopy to view the fate of the green fluorescent protein (GFP)–α-actinin-4 after nuclear injection of the cDNA. Similar results were observed in three independent sets of experiments. In each experiment, 15–35 cells were microinjected in the nucleus with plasmid DNA; of these, five to ten cells showed signal at 4–6 h after injection. Consistently, the mutants behaved abnormally, were unevenly distributed in the cell cytoplasm, and were much less dynamic compared with the wild-type proteins (Figure 2A; see also Videos S1–S3). These findings were consistent with the indirect immunofluorescence (IF) studies performed in Actn4 K228/K228 mice (see below). Figure 2 Mutant α-Actinin-4 Behavior in Cells (A) Mutant and wild-type α-actinin-4 show different localization and dynamics when expressed in a conditionally immortalized differentiated mouse podocyte cell line. Differentiated podocytes were injected in the nucleus with equal concentrations of expression plasmid for GFP fusions of mutant and wild-type actinins. At 2–4 h after injections, cells were imaged and both phase and fluorescence images recorded as described in the Materials and Methods. To illustrate changes in distribution of the fluorescence signal, three fluorescence images each 1 min apart were overlaid as red, green, and blue panes. Areas of fluorescence that were the same in all panes show as white, while dynamic areas are indicated by the color. The top panel indicates the initial phase and overlain dynamic fluorescence images of wild-type α-actinin-4, while the bottom two panels illustrate characteristic results for mutants K228E and T232I at 3 min time intervals. (See Videos S1–S3.) (B) Transfections in podocytes derived from mutant and wild-type mice. When transfected into conditionally immortalized podocytes of all three α-actinin-4 genotypes (+/+, K228E/+, or K228E/K228E), wild-type GFP–α-actinin-4 shows diffuse cytoskeletal localization. Mutant GFP–α-actinin-4 shows a similar alteration in localization when expressed in these three cells types. We transfected conditionally immortalized podocytes derived from Actn4 +/+, Actn4 K228E/+, and Actn4 K228E/K228E mice with either wild-type GFP–α-actinin-4 or K228E mutant GFP–α-actinin-4 (Figure 2B). The wild-type GFP–α-actinin-4 showed diffuse cytoplasmic localization in podocytes of all three genotypes. By contrast, mutant α-actinin-4 showed a similar aggregated appearance in all three cell types. This suggests the absence of a strong dominant effect of the mutant protein on the wild-type, as the endogenous K228E actinin does not alter the localization of the wild-type GFP-tagged protein. We developed α-actinin-4 “knockin” mice using the methods of homologous recombination in embryonic stem cells. Previously we developed an Actn4 “knockout” mouse (Kos et al. 2003). As indicated schematically in Figure 3A, mating these mice with germline Cre transgenic mice produced offspring in which an intronic loxP-flanked neomycin resistance cassette had been excised. We bred these heterozygous mice (Actn4 K228E/+) to generate litters with wild-type, heterozygous, and Actn4 K228E/K228E mice. We genotyped mice by testing for the presence or absence of an engineered silent EarI site as described previously (Kos et al. 2003). The homozygous K228E mice, in contrast to the Actn4-deficient model, demonstrated normal levels of Actn4 mRNA expression (Figure 3B). RT-PCR and sequencing of the transcript confirmed that the K228E allele was in fact expressed in the homozoygous mice. However, in multiple tissues tested (kidney, lung, leukocytes, brain), as well as in fibroblasts derived from these mice, α-actinin-4 protein expression was markedly reduced, with an approximately 90% reduction in protein expression in Actn4 K228E/K228E homozygotes and an approximately 50% reduction in Actn4 K228E/+ heterozygotes (Figure 3D). Lymphocytes from a human subject heterozygous for one ACTN4 K2228E mutation similarly had an approximately 50% reduction in the amount of α-actinin-4 compared with related and unrelated controls (Figure 3E). Figure 3 “Knockin” Mouse Model (A) Targeting construct. As we have described elsewhere (Kos et al. 2003), targeting initially resulted in a “knockout” allele, due to disruption of normal transcription, presumably by the intronic loxP-flanked neomycin resistance cassette. After breeding to Cre transgenic mice, this neomycin cassette was excised, as illustrated. (B) Northern blot analysis using kidney total RNA illustrates that the expression of the Actn4 transcript in K228E/K228E is similar to the expression in wild-type mice. (C) RT-PCR and sequencing of the relevant portion of Actn4 exon 8 confirms that the transcript in mice homozygous for the targeted allele contains the desired point mutation (top, wild-type; bottom, targeted). (D) Western blot showing markedly decreased expression of α-actinin-4 protein in K228E/K228E homozygous mice and moderately decreased expression in heterozygotes. Shown are blots using protein from cultured fibroblasts. Results were similar using protein extracted from lung, brain, liver, and kidney (data not shown). β-actin control is shown below. (E) Western blot showing expression of α-actinin-4 in lymphocytes from a human K228E/+ heterozygote (Kaplan et al. 2000) and three wild-type controls (two related, one unrelated). β-actin control is shown below. α-Actinin-4 Degradation We observed decreased mutant α-actinin expression in an immortalized knockin mouse fibroblast homozygous for the Actn4 K228E mutation, compared with the wild-type fibroblast (Figure 3D). To help determine the fate of the mutant α-actinin, we performed pulse and pulse–chase studies. We labeled Actn4 K228E/K228E fibroblasts, Actn4 K228E/+ fibroblasts, and wild-type fibroblasts with [35S]methionine for different timepoints (pulse) following incubation in methionine-deficient medium. In order to trace the newly synthesized 35S-labeled α-actinins, we used an α-actinin-4-specific antibody to immunoprecipitate α-actinin-4 from the cell lysates. (We detected no α-actinin-4 in the cell pellets.) As shown in Figure 4A, we found that at any timepoint, there was less mutant than wild-type α-actinin-4 synthesized. However, as shown in Figure 4B, the rates of increase in labeled α-actinin-4 were similar, suggesting that the low expression level of mutant α-actinin-4 is not due to a defect in synthesis. We then labeled the mutant and wild-type fibroblasts with [35S]methionine for 3 hours (pulse), following incubation in methionine-deficient media, and then incubated the cells in media containing excess cold methionine (chase) for different timepoints to follow the degradation of the newly synthesized α-actinin-4. As shown in Figure 4C and 4D, mutant α-actinin-4 degraded at a much faster rate than did the wild-type protein. The estimated half-life of mutant α-actinin-4 is about 15 h, while the half-life of the wild-type α-actinin-4 is much greater than 30 h. Three replicate experiments gave the same results. The rapid degradation of the K228E mutant α-actinin-4 is reversed by treatment with lactacystin (Figure 4E), indicating that this mutant form of α-actinin-4 is degraded through the ubiquitin–proteasome pathway. Figure 4 α-Actinin-4 Synthesis and Degradation (A and B) Synthesis of α-actinin-4 by wild-type and K228E/K228E fibroblasts. The rate of increase in the accumulation of mutant and wild-type α-actinin-4 is similar, as indicated by the superimposed shapes of the synthesis curves. (C and D) Pulse–chase experiments showing degradation of α-actinin-4 in K228E/K228E cells. Half-life of wild-type α-actinin-4 is greater than 30 h. Half-life of mutant α-actinin-4 is approximately 15 h. (E) Half-life of K228E mutant α-actinin-4 is restored to near-normal levels by the addition of lactacystin. Shown is labeled α-actinin-4 levels, expressed as a percentage of α-actinin-4 at time 0 and at 16 h and in the presence of 2.5 μM lactacystin in DMSO or in DMSO alone. In Vivo Phenotype We performed standard histologic analyses of kidneys from Actn4 K228/K228E mice, as well as Actn4 K228/+ and wild-type littermates. In Actn4 K228/K228E mice as old as 13 mo, we saw no abnormalities by light microscopy with periodic acid–Schiff (PAS) and hematoxylin-and-eosin (H & E) staining. All of the 11 Actn4 K228/K228E kidneys examined by electron microscopy had abnormalities in podocyte structure. Typically, these consisted of focal areas of foot process effacement (Figure 5A). By contrast, we found mild abnormalities in one of 13 wild-type and one of nine Actn4 K228E/+ littermates. Mice were typically genotyped at or shortly before the time of weaning (at approximately 3 wk of age). Only 10% (23 of 231) of the offspring of crosses between heterozygous mice were homozygous for the K228E change, suggesting increased peri- or neonatal lethality in the homozygous mice, similar to what we have observed in Actn4 null mice (Kos et al. 2003). In Actn4 K228/K228E mice, we frequently observed the appearance of abnormal electron-dense structures in the podocyte cell bodies (Figure 5D). Figure 5 In Vivo Phenotype Electron micrographs from Actn4 wild-type (A) and Actn4 K228E/K228E mice (B–D). As shown, Actn4 K228E/K228E mice were found to have abnormalities that were typically focal, with some areas of podocyte foot process effacement (B), as well as areas that appeared essentially normal (C). Bottom image ([D] using tannic acid counterstaining) illustrates electron-dense deposits observed in several podocyte cell bodies in Actn4 K228E/K228E mice. No such deposits were observed in wild-type or heterozygous mice. We measured urine protein excretion in mice of five different genotypes: wild-type (Actn4 +/+), heterozyogtes for either a null or K228E allele (Actn4 +/– and Actn4 K228E/+), and homozoygotes for either a null or K228E allele (Actn4 –/– and Actn4 K228E/K228E). Results were quite variable within each genotypic group of mice (likely reflecting differences in age and genetic background). However, the overall pattern of protein excretion was similar in the Actn4 +/+, Actn4 +/–, and Actn4 K228E/+ mice, while both groups of homozygous mutant mice (Actn4 –/– and Actn4 K228E/K228E) had significantly greater—and similar—degrees of proteinuria (Figure 6B). We did not see significant differences in serum creatinine levels or blood urea nitrogen levels (BUN) between mice of the three genotypes (Figure 6A), nor did we identify any single mutant mouse with significant BUN or creatinine elevations. Figure 6 Biochemical Characteristics of Mutant Mice (A) Average BUN and creatinine levels in Actn4 K228E/– (n = 12), Actn4 +/+ (n = 8), and Actn4 K228E/K228E (n = 12) mice at the time of sacrifice. Differences were not statistically significant. Error bars show standard deviation. (B) Summary of proteinuria in wild-type, Actn4 +/–, Actn4 K228E/–, Actn4 –/–, and Actn4 K228E/K228E mice, measured by albumin dipstick. Distribution of measurements are illustrated graphically for each genotype. To determine whether the K228E point mutation altered α-actinin-4 localization in vivo, we performed indirect IF studies. As shown in Figure 6A, α-actinin-4 appears to be mislocalized and aggregated in Actn4 K228E/K228E kidneys. By contrast, we see no difference in the expression of slit-diaphragm proteins ZO-1 and nephrin. The merged ZO-1/Actn4 images show overlapping patterns of expression in the wild-type and Actn4 K228E/+ mice, but clearly distinct expression patterns in the Actn4 K228E/K228E mice. We examined α-actinin-4 localization in a human ACTN4 K228E/+ kidney biopsy sample (Figure 6B). Although we are cautious in our interpretation, given the availability of only one biopsy sample, there appears to be a more punctate appearance to the α-actinin-4 distribution, consistent with the existence of some α-actinin-4 aggregates in human heterozygotes. Discussion We have previously shown that dominantly inherited point mutations in the α-actinin-4 gene ACTN4 cause a form of human glomerular disease (Kaplan et al. 2000). We have also shown that mice lacking α-actinin-4 expression develop a severe glomerular lesion (Kos et al. 2003). Here we have further explored the biochemical and cell biologic alterations caused by disease-associated α-actinin-4 mutations. Human α-actinin-4-associated FSGS is characterized by a dominant pattern of inheritance. Affected individuals typically develop disease in adulthood. Some individuals develop progressive renal failure, others develop moderate proteinuria, while a few show no evidence of kidney dysfunction well into adulthood. This contrasts with other recently elucidated inherited disorders of the podocyte caused by mutations in the slit-diaphragm proteins nephrin and podocin, where disease typically presents in the neonatal period or in childhood and follows a recessive pattern of inheritance (Kestila et al. 1998; Boute et al. 2000). Mice lacking slit-diaphragm proteins CD2AP and Neph-1 similarly present with very early-onset nephrosis (Shih et al. 1998; Donoviel et al. 2001). Furthermore, in contrast to the typically diffuse podocyte foot process effacement observed in kidney biopsies from individuals with these recessive forms of disease, individuals with ACTN4-associated FSGS have focal podocyte abnormalities, nonnephrotic levels of proteinuria, and slowly progressive adult-onset disease leading to significant (and often end-stage) renal failure in adulthood. These phenotypic differences themselves suggest a different mechanism of disease from what is observed with slit-diaphragm protein defects. Our earlier experiments suggested that mutant α-actinin-4 binds actin filaments more strongly than wild-type α-actinin-4 in vitro. However, this may reflect a propensity toward oligomerization rather than increased F-actin binding per se. The finding that the mutant α-actinin-4 forms aggregates with greatly decreased half-life suggests two possible models to explain the human (and mouse) disease. One model would explain the development of podocyte damage as a direct effect of protein aggregation and the toxic effects of such aggregation, as is observed in several degenerative neurologic conditions such as Alzheimer and Parkinson diseases (Horwich 2002). The second model explains the disease as a loss-of-function disease, reflecting the increased rate of mutant α-actinin-4 degradation. We do not regard these models as mutually exclusive. In fact, we believe it likely that the development of disease may involve both of these mechanisms. It is interesting to note that α-actinin-4-mediated kidney disease bears some similarities to the adult-onset neurodegenerative condition Huntington disease. In Huntington disease, dominant mutations that lead to expanded polyglutamine tracts cause neurodegeneration. The mutant huntingtin protein is misfolded and forms aggregates that are thought to have dominant, toxic effects on neuron function (Bucciantini et al. 2002; Bates 2003). These proteins also play critical and nonredundant roles in the relevant organs: analogous to what we have observed in α-actinin-4-deficient mice, mice with reduced huntingtin expression show abnormal brain development and perinatal lethality (White et al. 1997). In contrast to humans, mice heterozygous for α-actinin-4 mutations do not have overt disease. We suspect that in humans, over a timespan of several decades, the combination of decreased α-actinin-4 expression and the formation of aggregates ultimately proves toxic. We suggest that the relatively short life of mice compared with that of humans may be the major explanation of this difference. Not all humans carrying disease-associated mutations develop clinical disease (Kaplan et al. 2000). Disease, in both human and murine heterozygotes, likely requires a “second hit,” either in the strict genetic sense of a second mutation in the relevant cell type or a “physiologic hit,” such as elevated blood pressure, renal toxin exposure, or vascular disease, to name three examples. We suspect that renal stresses will uncover deleterious renal phenotypes in heterozygous mice, similar to the disease observed in humans. With aging and gradual accumulation of aggregates in the terminally differentiated podocyte, we believe that humans with ACTN4 mutations become increasingly susceptible to minor insults. We note also that mice, unlike humans, express α-actinin-1 in podocytes (Kos et al. 2003). This may help stabilize mutant α-actinin-4 or may produce more redundancy, giving the mouse glomerulus greater protection to perturbations in this pathway. We note that, as shown in Figure 7, the appearance of α-actinin-4 is more punctate in the kidney from a K228E heterozygous individual than a control, consistent with the existence of aggregates in heterozygous humans. This effect, however, is subtle, and is consistent with the lack of any detectable renal phenotype in several humans who carry disease-associated ACTN4 mutations (Kaplan et al. 2000). Although the number of ACTN4 mutations we have found is small, we have not detected any human disease-associated ACTN4 mutations to date that lead to premature termination, suggesting that simple haploinsufficiency may not by itself cause disease (our unpublished data). Figure 7 In Situ Protein Localization (A) IF studies of glomerular protein expression in Actn4 +/+, Actn4K228E/+ , and Actn4 K228E/K228E mice. As indicated, expression of α-actinin-4, ZO-1, and nephrin is shown, as is a merged image of α-actinin-4 and ZO-1 expression. (B) Glomerular expression of α-actinin-4 in normal human kidney and in an individual heterozygous for a K228E mutation. As indicated in Figure 5 and in the text, heterozygous K228E Actn4 knockin mice have no clear phenotype either by histologic analysis at the light and electron microscopic levels or by analysis of urine protein and serum creatinine. Even at more advanced ages, the Actn4 K228E/+ mice appear normal. By contrast, we observe clear glomerular phenotypes in both Actn4 –/– and Actn4 K228E/K228E mice. Our genetic observations are consistent with our biochemical observations. Specifically, we believe that α-actinin-4 mutations lead to a reduction in normal α-actinin-4 activity and to protein aggregation and that glomerular phenotypes reflect both loss of normal α-actinin-4 function and toxic effects of aggregated α-actinin-4. While the disease observed in homozygous Actn4 K228E/K228E mice may primarily reflect loss of function resulting from rapid α-actinin-4 degradation, heterozygous humans may show slow development of podocyte damage from the effects of α-actinin-4 aggregation. Is alteration of α-actinin-4 expression or conformation a cause or a mediator of secondary forms of kidney disease? These seem plausible hypotheses given the data presented here, together with results from other investigators showing alterations in α-actinin-4 levels in association with proteinuria in certain animal models (Shirato et al. 1996; Smoyer et al. 1997). This suggests that physiologic processes that alter the expression, conformation, or both of this (and other) cytoskeletal proteins, either at the protein or the transcript level, might be amenable to interventions that restore normal patterns of protein expression. Materials and Methods Cell and cell culture Mouse podocytes were kindly provided by Dr. Peter Mundel (Albert Einstein College of Medicine, Bronx, New York, United States) and cultured as described previously (Mundel et al. 1997). In brief, undifferentiated podocytes were cultured in RPMI-1640 (Cellgro, CellGenix, Freiburg, Germany) medium containing 10% fetal calf serum (FCS) and 20 U/ml γ-interferon (γ-IFN) at 33°C and 5% CO2. Differentiated podocytes were cultured in the medium containing no γ-IFN at 37°C. Additional conditionally immortalized podocytes from “knockin” litters were generated exactly as described previously (Mundel et al. 1997). Fibroblasts were derived from lungs dissected from newly sacrificed adult mouse littermates of different genotypes. Cells were propagated in culture and immortalized by transfection with an SV-40 large T-antigen expression plasmid using Fugene 6 transfection reagent (Roche, Basel, Switzerland). Protein extraction Fibroblasts were allowed to grow to confluence, then scraped off the tissue culture plate in the presence of cold phosphate-buffered saline (PBS) and spun at 3,000 rpm at 4°C for 10 min. Lymphocytes were isolated from whole blood with Histopaque-1077 solution (Sigma, St. Louis, Missouri, United States) following the manufacturer's instructions. The pellets were resuspended in ice-cold lysis buffer (150 mM NaCl, 50 mM Tris [pH 8.0], 1% Triton X-100, 1 mM Na-orthovanadate, 4 μM microcystin, and protease inhibitor). We collected the supernatant and estimated the protein concentration using either the Bradford method or equalizing the protein concentration in different lysates by Western blot using β-actin as a standard. Transient transfection and Immunocytochemistry We mutated a wild-type pBluescript-GFP-ACTN4 clone using a QuickChange kit (Stratagene, La Jolla, California, United States) to create clones harboring each of three disease-associated mutations (K228E, T232I, S235P) (Kaplan et al. 2000). These mutant and wild-type α-actinin-4 clones were transfected into podocytes using Fugene 6 (Roche). Cells 60 h after transfection were fixed in 2% paraformaldehyde and 4% sucrose in PBS for 5 min and then permeabilized in 0.3% Triton for 5 min. Fixed cells were blocked with 2% FCS, 2% BSA, 0.2% fish gelatin in PBS for 60 min and incubated with anti-α-actinin-4 antibody, and rabbit antigen–antibody complexes were visualized with fluorochrome-conjugated secondary antibodies. Sucrose gradient studies Sucrose gradients of 5%–20% and 10%–40% were made using a buffer containing 0.02 M Tris–HCl (pH 7.5), 0.15M NaCl, 0.1 mM EDTA, and 0.2 mM of DTT and were internally calibrated with BSA, carbonic anhydrase, and catalase. In vitro translated and radiolabeled wild-type and mutant α-actinin-4 (K228E, T232I, S235P) were loaded onto the gradient, centrifuged at 40,000 rpm for 15 h at 4°C, and eluted into 0.2 ml fractions. Eluates were analyzed by SDS-PAGE and autoradiography. Pulse–chase studies Immortalized mouse fibroblasts were incubated in methionine-free MEM medium containing 10% FCS (dialyzed overnight using 12K-14K SPECTRA/POR porous membrane in normal saline at 4°C) and 25 mM HEPES for 20 min at 37°C. [35S]Methionine was added to a final concentration of 0.1 mCi to pulse the cells. Cells were pulse labeled for 0, 15, 30, 60, 120, 180, and 240 min. To study the degradation of α-actinin-4, cells were pulsed for 3 h and then chased for 0, 3, 6, 12, 20, 24, and 30 h with excess cold methionine. We used anti-α-actinin-4 antibody recognizing the N-terminus to precipitate α-actinin-4 from the cell lysates. Protein A–sepharose beads (Pierce Biotechnology, Rockford, Illinois, United States) were preincubated with the anti-α-actinin-4 antibody for 2 h at 4°C and then incubated with the lysates overnight at 4°C. The beads were washed with lysis buffer and resuspended in SDS-PAGE loading buffer. Samples were resolved on a 10% polyacrylamide gel and visualized by exposure to radiographic film. For lactacystin treatment, cells were first pulsed for 3 h as above, followed by addition of 2.5 μM lactacystin dissolved in DMSO (A. G. Scientific, San Diego, California, United States) or 0.125% DMSO alone with cold methionine. Nuclear DNA injection and imaging For injection and imaging, cells were cultured on MatTek Corporation (Ashland, Massachusetts, United States) 35 mm coverslip dishes in F12 media (BioFluids, BioSource International, Carmarillo, California, United States) without phenol red and supplemented with 10 mM HEPES and antibiotics. Plasmid DNA at 0.5–2.0 ng/nl was injected in cell nuclei using a Narishige (Lake Forest, California, United States) IM-200 picoliter pressure injection system. OD microcapillary glass pipettes (1.0 mm) were pulled to a fine tip using a Narishige PB-7 needle puller. Cells were maintained at 37°C using a modified Harvard Apparatus (Hopkinton, Massachusetts, United States) microscope incubator mounted on a Nikon (Tokyo, Japan) Diaphot 300 inverted microscope. Images were collected using a Princeton Instruments MicroMax 1300Y cooled CCD camera (Roper Scientific, Tucson, Arizona, United States). Excitation and emission wavelengths were controlled using dichroic and bandbass filters from Omega Optical (Brattleboro, Vermont, United States) and a Sutter Instrument (Novato, California, United States) Lambda 10–2 filter wheel image acquisition. Device control and postacquisition processing were done with Isee Imaging Software (Inovision Corporation Raleigh, North Carolina, United States). Actinin dynamics display To display the changes in GFP–actinin distribution over time, images collected at 1 min intervals were used sequentially as red, green, and blue channels of a RGB composite image. Areas of signal that did not change have equal representation in each of the channels and generate a white signal in the final image. Areas of signal that did change on a minute-to-minute basis are indicated by either a red, green, or blue hue. For example, a region rich in red would indicate an signal present in the first image, but absent in the two sequential images, indicative of a withdraw or loss of signal in that region. Generation of K228E mutant mice Previously, we described the development of a mouse model lacking detectable Actn4 expression (Kos et al. 2003). These mice harbor a germline mutation in exon 8 of Actn4 encoding a K228E substitution, as well as an intronic loxP-flanked neomycin resistance cassette. We bred these mice to transgenic mice with germline expression of Cre recombinase (129-TgN(Prm-Cre)58Og; Jackson Laboratory, Bar Harbor, Maine, United States). We verified excision of the neomycin resistance cassette by PCR. Heterozygous mice were crossed to obtain mice homozygous for the K228E substitution. Mice were genotyped for the K228E as described previously (Kos et al. 2003). We used Trizol reagent to extract RNA from kidneys for RT-PCR and for Northern blot analysis. Mouse phenotyping Freshly harvested kidneys were fixed in Bouin's solution. H & E and PAS staining were performed using standard methodology. Electron microscopy was performed after fixation in Karnovsky's media using standard diagnostic protocols. For the electron micrographs, all of the glomeruli imaged were from as deep into the renal cortex as possible. Indirect IF studies were performed using standard methods (Kos et al. 2003). Urine microalbumin was assessed by a reader blinded to mouse genotype using albumin dipsticks (Albustix, Bayer, Leverkusen, Germany). BUN and creatinine measurements were performed at the clinical chemistry laboratory at Brigham and Women's Hospital. Supporting Information Video S1 Podocytes Following Nuclear Injection of Wild-Type GFP-α-Actinin-4 cDNA (14.8 MB MOV). Click here for additional data file. Video S2 Podocytes Following Nuclear Injection of K228E GFP-α-Actinin-4 cDNA (11.8 MB MOV). Click here for additional data file. Video S3 Podocytes Following Nuclear Injection of T232I GFP-α-Actinin-4 cDNA (10.9 MB MOV). Click here for additional data file. Accession Numbers GenBank accession numbers for genes and proteins discussed in this paper are NM_004924 and NP_004915 (human ACTN4; also LocusLink ID 81) and NM_021895 and NP_068695 (mouse Actn4; also LocusLink ID 60595). OMIM numbers are 603278 (FSGS-1) and 604638 (ACTN4). These experiments were supported by National Institutes of Health grants DK59588 (to MRP), GM55223 (to BMD), and GM057256 (to PGA). We thank Larry Holzman and Peter Mundel for gifts of anti-synaptopodin and anti-nephrin antibodies and Dr. Mundel for the podocyte cell line. We thank Kazufumi Honda and Carol Otey for the GFP–α-actinin-4 clone. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. JY, PGA, BMD, and MRP conceived and designed the experiments. JY, TCL, CHK, JMH, and PGA performed the experiments. JY, JMH, PGA, BMD, and MRP analyzed the data. JH, PGA, and MRP contributed reagents/materials/analysis tools. JY and MRP wrote the paper. Academic Editor: Peter Mundel, Albert Einstein College of Medicine Abbreviations BUNblood urea nitrogen FCSfetal calf serum FSGSfocal segmental glomerulosclerosis GFPgreen fluorescent protein H & Ehematoxylin and eosin IFimmunofluorescence IFNinterferon PASperiodic acid–Schiff PBSphosphate-buffered saline ==== Refs References Bates G Huntingtin aggregation and toxicity in Huntington's disease Lancet 2003 361 1642 1644 12747895 Boute N Gribouval O Roselli S Benessy F Lee H NPHS2 , encoding the glomerular protein podocin, is mutated in autosomal recessive steroid-resistant nephrotic syndrome Nat Genet 2000 24 349 354 10742096 Bucciantini M Giannoni E Chiti F Baroni F Formigli L Inherent toxicity of aggregates implies a common mechanism for protein misfolding diseases Nature 2002 416 507 511 11932737 Chan Y Tong HQ Beggs AH Kunkel LM Human skeletal muscle-specific alpha-actinin-2 and -3 isoforms form homodimers and heterodimers in vitro and in vivo Biochem Biophys Res Commun 1998 248 134 139 9675099 Donoviel DB Freed DD Vogel H Potter DG Hawkins E Proteinuria and perinatal lethality in mice lacking NEPH1, a novel protein with homology to NEPHRIN Mol Cell Biol 2001 21 4829 4836 11416156 Feramisco JR Burridge K A rapid purification of alpha-actinin, filamin, and a 130,000-dalton protein from smooth muscle J Biol Chem 1980 255 1194 1199 7356657 Honda K Yamada T Endo R Ino Y Gotoh M Actinin-4, a novel actin-bundling protein associated with cell motility and cancer invasion J Cell Biol 1998 140 1383 1393 9508771 Horwich A Protein aggregation in disease: A role for folding intermediates forming specific multimeric interactions J Clin Invest 2002 110 1221 1232 12417558 Ichikawa I Fogo A Focal segmental glomerulosclerosis Pediatr Nephrol 1996 10 374 391 8792409 Kaplan JM Kim SH North KN Rennke H Correia LA Mutations in ACTN4 , encoding alpha-actinin-4, cause familial focal segmental glomerulosclerosis Nat Genet 2000 24 251 256 10700177 Kestila M Lenkkeri U Mannikko M Lamerdin J McCready P Positionally cloned gene for a novel glomerular protein—nephrin—is mutated in congenital nephrotic syndrome Mol Cell 1998 1 575 582 9660941 Kos CH Le TC Sinha S Henderson JM Kim SH Mice deficient in alpha-actinin-4 have severe glomerular disease J Clin Invest 2003 111 1683 1690 12782671 Michaud JL Lemieux LI Dube M Vanderhyden BC Robertson SJ Focal and segmental glomerulosclerosis in mice with podocyte-specific expression of mutant alpha-actinin-4 J Am Soc Nephrol 2003 14 1200 1211 12707390 Mundel P Heid HW Mundel TM Kruger M Reiser J Synaptopodin: An actin-associated protein in telencephalic dendrites and renal podocytes J Cell Biol 1997 139 193 204 9314539 Shih NY Li J Karpitskii V Nguyen A Dustin ML Congenital nephrotic syndrome in mice lacking CD2-associated protein Science 1999 286 312 315 10514378 Shirato I Sakai T Kimura K Tomino Y Kriz W Cytoskeletal changes in podocytes associated with foot process effacement in Masugi nephritis Am J Pathol 1996 148 1283 1296 8644869 Smoyer WE Mundel P Gupta A Welsh MJ Podocyte alpha-actinin induction precedes foot process effacement in experimental nephrotic syndrome Am J Physiol 1997 273 F150 F157 9249603 Somlo S Mundel P Getting a foothold in nephrotic syndrome Nat Genet 2000 24 333 335 10742089 Takada F Beggs AH Alpha-actinins. In: Creighton TE, editor. Encyclopedia of molecular medicine 2002 New York John Wiley 122 127 White JK Auerbach W Duyao MP Vonsattel JP Gusella JF Huntingtin is required for neurogenesis and is not impaired by the Huntington's disease CAG expansion Nat Genet 1997 17 404 410 9398841

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PLoS Biol. 2004 Jun 15; 2(6):e167

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PLoS Biol

10.1371/journal.pbio.0020167

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020168SynopsisCell BiologyImmunologyMolecular Biology/Structural BiologyAnimalsInformation Transport across a Membrane synopsis6 2004 15 6 2004 15 6 2004 2 6 e168Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Specific Interface between Integrin Transmembrane Helicesand Affinity for Ligand ==== Body From a biochemical perspective, a living cell is a collection of molecules jampacked into a confined space by a flexible barrier, called the plasma membrane. A diverse array of proteins embedded in the plasma membrane act as conduits between the cell interior and its external environment, conveying nutrients, metabolites, and information. The life of a cell—as well as that of any multicellular organism—depends on a cell's ability to communicate with its neighbors, both near and far. One way cells do this is with transmembrane receptors outfitted with both extracellular and intracellular domains that mediate information flow between the cell's external and internal environment. One class of transmembrane receptors, called integrin receptors, specializes in interacting with and binding to other cells and the extracellular matrix, a complex of molecules surrounding cells that provides structural support. By integrating various components of the extracellular matrix, integrins (also known as adhesion receptors), play an important role in such diverse processes as cell differentiation, programmed cell death, wound healing, and metastasis. Association between integrin α and β subunit transmembrane domains Integrins can be regulated by signals within the cell to bind to their ligands with either low or high affinity. While a multitude of integrin ligands have been identified and the general mechanics of both the extracellular and intracellular domains of these receptors are known, exactly how a signal crosses the receptor's transmembrane segment to regulate affinity has remained obscure. Now, Bing-Hao Luo, Timothy Springer, and Junichi Takagi have taken a mutational approach to shed light on the inner workings of the transmembrane segment and to explain how it transmits information. Much of what we know about the function of integrins has come from studying the crystal structures and models obtained from structural analysis. These analyses have generated information not only about the structure and composition of the extracellular and intracellular domains of integrins, but also about the conformational changes that accompany signaling events. Integrins contain a large extracellular domain, a transmembrane segment, and a relatively short intracellular “tail.” Integrins are heterodimers—molecules that contain two subunits composed of different amino acids—made up of an α chain and a β chain. Tight association of the two subunits is associated with an inactive, or low-affinity, state of the extracellular ligand-binding domain. Separation of the intracellular subunits is associated with a dramatic conformational change and activation of the extracellular domain, changing a bent structure with a downward-pointing ligand-binding site into an extended one with an outwardly stretched ligand-binding site. This mechanism differs from most transmembrane signaling molecules, which usually achieve activation through association with their target molecules. To investigate how the transmembrane segment mediates these changes, Luo, Springer, and Takagi systematically replaced amino acids in both the α and β transmembrane domains of the heterodimer with cysteines, creating the potential for binding interactions through a chemical reaction, disulfide bond formation, between the two subunits. By analyzing 120 possible cysteine pairs, the researchers not only confirmed the structure of the transmembrane region as helical but also mapped the proximal amino acid residues between the helices. To understand how the helical transmembrane domains transmit signals, the team introduced activating mutations in the amino acids of the α subunit cytoplasmic tail. Using this approach, they observed the loss of the contact between the subunits, indicating a separation of the transmembrane helices. Furthermore, when disulfide bond formation occurred, linking the transmembrane segments together, activation was suppressed. While previous models had proposed various modes of subunit movements, including hinge- and piston-like models, these results strongly support the notion that lateral separation of the subunits is the driving force behind the signal. As many diseases arise from defects in integrin adhesion, understanding the conformation and mechanism of integrin activation could suggest promising avenues for drug development aimed at correcting such defects.

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PMC423142

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2021-01-05 08:21:11

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PLoS Biol. 2004 Jun 15; 2(6):e168

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PLoS Biol

10.1371/journal.pbio.0020168

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020169PrimerCell BiologyImmunologyMolecular Biology/Structural BiologyAnimalsIntegrin Bidirectional Signaling: A Molecular View Integrin Bidirectional SignalingQin Jun Vinogradova Olga Plow Edward F 6 2004 15 6 2004 15 6 2004 2 6 e169Copyright: © 2004 Qin et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Specific Interface between Integrin Transmembrane Helicesand Affinity for Ligand Cells receive and send signals across the plasma membrane using the integrin family of receptors. What is it about their structure that can mediate their function? ==== Body It goes without saying that the cellular plasma membrane effectively creates a barrier between the inside (intracellular area) and outside (extracellular area) of the cell it defines. In order for the cell to sense and respond to its environment (including other cells and the supporting structures that comprise the extracellular matrix [ECM]) and for the environment to influence cell function (including cell growth and movement), bidirectional signaling across the plasma membrane has to be mediated by receptors and other structures. About two decades ago, it became widely appreciated that many of the cell surface receptors that mediate cell–cell and cell–ECM interactions were structurally and functionally related, and the term “integrins” was coined to reflect the capacity of members of this family to integrate the extracellular and intracellular environment (Hynes 1987). Integrin-mediated interactions are vital to the maintenance of normal cell functioning because of their ability to mediate inside-out (intracellular to extracellular) and outside-in (extracellular to intracellular) signaling. Integrin dysfunctions are associated with numerous human disorders such as thrombosis, atherosclerosis, cancer, and chronic inflammatory diseases. Despite a total of nearly 30,000 integrin-related articles in the literature, intensive effort—more than 200 articles per month—continues to focus on understanding the roles of integrins in both physiological and pathological processes. The Integrin Family The integrin family comprises 20 or more members that are found in many animal species, ranging from sponges to mammals (Hynes 2002). They consist of two distinct, associated subunits (noncovalent heterodimers), where each subunit (α, β) consists of a single transmembrane domain, a large extracellular domain of several hundred amino acids (composed of multiple structural domains), and typically, a small cytoplasmic domain of somewhere between 20–70 residues (Figure 1). The extracellular domains bind a wide variety of ligands, whereas the intracellular cytoplasmic domains anchor to cytoskeletal proteins. In this manner, the exterior and interior of a cell are physically linked, which allows for bidirectional transmission of mechanical and biochemical signals across the plasma membrane, and leads to a cooperative regulation of cell functions, including adhesion, migration, growth, and differentiation. A central topic in the integrin research over the past decade has been the mechanism of inside-out activation (Liddington and Ginsberg 2002). In their resting state, integrins normally bind the molecules that activate them with low affinity. Upon stimulation, a cellular signal induces a conformational change in the integrin cytoplasmic domain that propagates to the extracellular domain. Integrins are transformed from a low- to a highaffinity ligand binding state. Such inside-out regulation of integrin affinity states is distinct from the outside-in signaling observed upon activation of most other transmembrane receptors (e.g., growth factor–growth factor receptor interactions), including integrins. The inside-out signaling protects the host from excessive integrin-mediated cell adhesion, which could, for example, lead to spontaneous aggregation of blood cells and have profound pathological consequences. Figure 1 A Model for Integrin Inside-Out Activation and Clustering Cellular stimulation induces a conformational change in talin that exposes its talin head domain. The talin head domain binds to the β cytoplasmic tail, which displaces the α tail from its complex with the β tail, which in turn leads to an unclasping and a membrane-associated structural change of the cytoplasmic face (Vinogradova et al. 2002, 2004). Notice the proposed shifted membrane interface for both membrane-proximal helices before and after unclasping (green bars), which suggests a “fanning-out” unclasping process (Vinogradova et al. 2004). The unclasping initiates the opening of the integrin C-terminal stalks—including the transmembrane domains (Luo et al. 2004)—which is necessary for the switchblade shift of the extracellular headpiece from the bent to the extended form for high-affinity ligand binding (Takagi et al. 2002). The α subunit is in blue and the β subunit is in red. The ligated integrins cluster, possibly via oligomerization of transmembrane domains (Li et al. 2003). The model was generated based on the crystal structure of αvβ3 extracellular domain (Xiong et al. 2001) and the nuclear magnetic resonance structure of the cytoplasmic domain (Vinogradova et al. 2002, 2004) with the helices extending to the transmembrane domain. The Heads and Tails of Inside-Out Signaling Mutational studies provided the initial hints that disruption of the non-covalent clasp between α and β cytoplasmic tails is clearly the event within the structure of the integrin that initiates inside-out signaling. Point mutations in the α and β cytoplasmic tails that are near the membrane or deletion of either region result in constitutive activation of the receptor (O'Toole et al. 1991, 1994; Hughes et al. 1995). Mutating a single specific residue in the cytoplasmic tail of either subunit led to integrin activation, but a double mutation, which would have allowed retention of a salt bridge between the subunits, did not (Hughes et al. 1996)—suggesting that integrin inside-out activation is dependent upon regulation of the interaction between the two subunits. In support of this hypothesis, peptides corresponding to α and β cytoplasmic tails have been shown to interact with each other (Haas and Plow 1996). Since these original observations, there has been an intensive effort to understand the mechanism for regulation of integrin activation by the cytoplasmic region (for a recent review, see Hynes 2002). On the road toward this goal, Ginsberg and colleagues discovered that the head domain of a cytoskeletal protein—talin—plays a key role in binding to integrin β cytoplasmic tails and inducing integrin activation (Calderwood et al. 1999). Many other intracellular proteins bind to the α and β cytoplasmic tails (Liu et al. 2000), but the importance of talin in integrin activation is particularly convincing since it has been confirmed by multiple laboratories (Vinogradova et al. 2002; Kim et al. 2003; Tremuth et al. 2004) using various methods including overexpression and gene knockdown (siRNA) approaches (Tadokoro et al. 2003). In 2001, Springer and coworkers provided evidence for a model by which separation of the C-terminal portions of the α and β subunits results in inside-out activation. They showed that replacement of the cytoplasmic-transmembrane regions by an artificial linkage between the tails inactivates the receptor, whereas breakage of the clasp activates the receptor (Lu et al. 2001; Takagi et al. 2001). Shortly thereafter, the model gained direct and strong experimental support from a structural analysis in which the membrane-proximal helices of the two subunits were found to clasp in a weak “handshake” that could be disrupted by talin or constitutively activating mutations (Vinogradova et al. 2002). The model has been further verified by other biophysical studies (Kim et al. 2003) and extended to other integrins (Vinogradova et al. 2004). Since the membrane-proximal regions of integrin α and β cytoplasmic tails are highly conserved, the generalization of this signaling mechanism to all integrins was to be anticipated. A dynamic image of how such cytoplasmic unclasping occurs at the membrane surface can now be modeled (Figure 1) (Vinogradova et al. 2004). Straightening Out the Outside On the extracellular side, ground-breaking insights were provided when the crystal structure of the extracellular domain of integrin αvβ3 (the nomenclature identifies the particular α and β subunits) was determined (Xiong et al. 2001). In addition to the exquisite structural details, the overall conformation was surprisingly bent (Figure 1), which contrasted with structures revealed by the earlier electron micrographic studies that showed an extended, stalk-like structure (Weisel et al. 1992). Springer and coworkers used a series of biochemical/biophysical experiments to suggest that the bent structure represents an inactive form of integrin (Takagi et al. 2002), whereas activation induces a switchblade shift that converts the bent form to the extended form (Figure 1). A molecular picture has emerged for integrin insideout activation where a cellular signal induces the conformational change of talin exposing its head domain allowing it to bind to the integrin β cytoplasmic tail. This interaction unclasps the complex between the cytoplasmic tails, which then allows a conformational shift in the extracellular domain from a bent to a more extended form for high-affinity ligand binding (Figure 1) (Takagi et al. 2002). The activated integrins may then undergo clustering whereby the transmembrane domain of each type of subunit (the α or β) interacts with itself—called homotypic oligomerization of the transmembrane domains (Figure 1) (Li et al. 2003). Ligand occupancy and receptor clustering initiates outside-in signaling that, in turn, regulates a variety of cellular responses (see below). The three steps in Figure 1 occur as part of a dynamic equilibrium, and perturbation of any step can shift the equilibrium, leading to transient, partial, or permanent integrin activation/inactivation depending on the extent of perturbation. For example, deletion of aIIb cytoplasmic tail completely removes the clasp and permanently activates the receptor (O'Toole et al. 1991), whereas a particular disease mutation may only impair the clasp and partially activate the receptor (Peyruchaud et al. 1997). While the model in Figure 1 is based on direct structural evidence for the cytoplasmic face (Vinogradova et al. 2002; Kim et al. 2003) and the extracellular domain (Takagi et al. 2002), the changes in the transmembrane region remained speculative. In this issue of PLoS Biology, Luo et al. (2004) provide what is, to our knowledge, the first experimental evidence for the transmembrane domain separation, an event suggested by the model shown in Figure 1. By selectively altering the residues that can interact with one another, the authors defined a specific transmembrane domain interface in resting αIIbβ3 and showed that this interface is lost upon activation of this integrin. Backed by extensive structural and biochemical data on the integrin cytoplasmic/extracellular domains, this transmembrane domain study takes the next vital step toward a more complete understanding of the unclasping mechanism for integrin activation. Although the energy required for lateral separation of the transmembrane domains in membrane appears to be high, the third step in Figure 1 (clustering via transmembrane domain oligomerization) may compensate for it. Filling in the Pieces Despite the molecular level of our understanding of integrin activation, a number of key questions remain unresolved. Although we know that the membrane-proximal clasp on the integrin cytoplasmic face controls the integrin activation, the distal side of either the α or β cytoplasmic tails may also play a role in integrin activation, since other mutations indicate that the C-terminal membrane distal region is important in regulating integrin activation via a mechanism that is yet unknown. Thus, the picture for the cytoplasmic face-controlled inside-out activation may be substantially more complicated than specified in Figure 1. There may exist other factors, such as negative regulators, in cells that bind to the cytoplasmic tails or their complex, and control the conformational change required for integrin activation. Also, there may be pathways other than the talin-mediated one that lead to integrin activation. Structures of the integrin cytoplasmic face bound to talin and the many other proteins known to bind to the cytoplasmic tails of integrins will undoubtedly provide further insights. In the transmembrane region, although there is ample evidence for heterodimeric transmembrane domain association (Adair and Yeager 2002; Schneider and Engelman 2003; Gottschalk and Kessler 2004; Luo et al. 2004) and dissociation upon integrin activation (Luo et al. 2004), a definitive structural view is missing. Some studies have proposed that homo-oligomerization is essential for inducing integrin activation (Li et al. 2003). However, the data provided by Luo et al. do not appear to support this model. On the extracellular side, while the C-terminal unclasping and separation of the cytoplasmic and transmembrane regions appears to relieve the structural constraint and may allow the unbending of the extracellular domain to attain the high-affinity ligand binding state (Takagi et al. 2002), a thorough molecular understanding of this process awaits high resolution structures of the intact receptor in inactive and active forms. What About Outside-In? Upon the inside-out activation, integrins bind to specific extracellular matrix proteins. However, for the integrins to grip tightly to the extracellular matrix to mediate cell adhesion and migration, the integrin cytoplasmic domains must be anchored to the cytoskeleton (Giancotti and Ruoslahti 1999). This is achieved by “outside-in” signaling, i.e., when an integrin binds to the extracellular ligand, it clusters with other bound integrins, resulting in the formation of highly organized intracellular complexes known as focal adhesions that are connected to the cytoskeleton. The focal adhesions incorporate a variety of molecules, including the cytoplasmic domains of the clustered integrins, cytoskeletal proteins, and an extensive array of signaling molecules. The high local concentrations of these molecules facilitate cascades of downstream intracellular responses via protein–protein interactions, which are linked to the cytoskeleton as well as to complex intracellular signaling networks. Although many intracellular components involved in outsidein signaling have been identified, and much has been learned about various signaling pathways involved in outside-in signaling (Giancotti and Ruoslahti 1999), a molecular view of how the various events occur in time and space is still very uncertain. In particular, little structural insight has been obtained for early outside-in intracellular events following ECM–integrin binding, e.g., upon ECM engagement. How is the integrin cytoplasmic domain connected to the cytoskeleton? How is this connection regulated during cell adhesion and migration? The next wave of structural information may provide insights into these important and fertile areas of investigation. This work was supported by National Institutes of Heath grants to JQ and EFP, and an American Heart Association Scientist Development Grant to OV. Jun Qin, Olga Vinogradova, and Edward F. Plow are at the Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, Ohio, United States of America. Jun Qin and Olga Vinogradova are in the Structural Biology Program, and Edward F. Plow is at the Joseph J. Jacobs Center for Thrombosis and Vascular Biology. E-mail: [email protected] (JQ). Abbreviation ECMextracellular matrix ==== Refs References Adair BD Yeager M Three-dimensional model of the human platelet integrin alpha IIbbeta 3 based on electron cryomicroscopy and x-ray crystallography Proc Natl Acad Sci U S A 2002 99 14059 14064 12388784 Calderwood DA Zent R Grant R Rees DJ Hynes RO The talin head domain binds to integrin beta subunit cytoplasmic tails and regulates integrin activation J Biol Chem 1999 274 28071 28074 10497155 Giancotti FG Ruoslahti E Integrin signaling Science 1999 285 1028 1032 10446041 Gottschalk KE Kessler H Evidence for hetero-association of transmembrane helices of integrins FEBS Lett 2004 557 253 258 14741377 Haas TA Plow EF The cytoplasmic domain of αIIb β3 : A ternary complex of the integrin α and β subunits and a divalent cation J Biol Chem 1996 271 6017 6026 8626385 Hughes PE O'Toole TE Ylanne J Shattil SJ Ginsberg MH The conserved membrane-proximal region of an integrin cytoplasmic domain specifies ligand binding affinity J Biol Chem 1995 270 12411 12417 7759482 Hughes PE Diaz-Gonzalez F Leong L Wu C McDonald JA Breaking the integrin hinge: A defined structural constraint regulates integrin signaling J Biol Chem 1996 271 6571 6574 8636068 Hynes RO Integrins: A family of cell surface receptors Cell 1987 48 549 550 3028640 Hynes RO Integrins: Bidirectional, allosteric signaling machines Cell 2002 110 673 687 12297042 Kim M Carman CV Springer TA Bidirectional transmembrane signaling by cytoplasmic domain separation in integrins Science 2003 301 1720 1725 14500982 Li R Mitra N Gratkowski H Vilaire G Litvinov R Activation of integrin alphaIIbbeta3 by modulation of transmembrane helix associations Science 2003 300 795 798 12730600 Liddington RC Ginsberg MH Integrin activation takes shape J Cell Biol 2002 158 833 839 12213832 Liu S Calderwood DA Ginsberg MH Integrin cytoplasmic domain-binding proteins J Cell Sci 2000 113 3563 3571 11017872 Lu C Takagi J Springer TA Association of the membrane proximal regions of the alpha and beta subunit cytoplasmic domains constrains an integrin in the inactive state J Biol Chem 2001 276 14642 14648 11279101 Luo B-H Springer TA Takagi J A specific interface between integrin transmembrane helices and affinity for ligand PLoS Biol 2004 2 e153 10.1371/journal.pbio.0020153 15208712 O'Toole TE Mandelman D Forsyth J Shattil SJ Plow EF Modulation of the affinity of integrin alphaIIIb beta3 (GPIIb-IIIa) by the cytoplasmic domain of alphaIIIb Science 1991 254 845 847 1948065 O'Toole TE Katagiri Y Faull RJ Peter K Tamura R Integrin cytoplasmic domains mediate inside-out signal transduction J Cell Biol 1994 124 1047 1059 7510712 Peyruchaud O Nurden AT Milet S Macchi L Pannochia A R to Q amino acid substitution in the GFFKR sequence of the cytoplasmic domain of the integrin IIb subunit in a patient with a Glanzmann's thrombasthenia-like syndrome Blood 1998 92 4178 4187 9834222 Schneider D Engelman DM GALLEX, a measurement of heterologous association of transmembrane helices in a biological membrane J Biol Chem 2003 278 3105 3111 12446730 Tadokoro S Shattil SJ Eto K Tai V Liddington RC Talin binding to integrin beta tails: A final common step in integrin activation Science 2003 302 103 106 14526080 Takagi J Erickson HP Springer TA C-terminal opening mimics ‘inside-out’ activation of integrin alpha5beta1 Nat Struct Biol 2001 8 412 416 11323715 Takagi J Petre BM Walz T Springer TA Global conformational rearrangements in integrin extracellular domains in outside-in and inside-out signaling Cell 2002 110 598 611 Tremuth L Kreis S Melchior C Hoebeke J Ronde P A fluorescence cell biology approach to map the second integrin-binding site of talin to a 130 amino acid sequence within the rod domain J Biol Chem 2004 In press Vinogradova O Velyvis A Velyviene A Hu B Haas T A structural mechanism of integrin alpha(IIb)beta(3) “inside-out” activation as regulated by its cytoplasmic face Cell 2002 110 587 597 12230976 Vinogradova O Vaynberg J Kong X Haas TA Plow EF Membrane-mediated structural transitions at the cytoplasmic face during integrin activation Proc Natl Acad Sci U S A 2004 101 4094 4099 15024114 Weisel JW Nagaswami C Vilaire G Bennett JS Examination of the platelet membrane glycoprotein IIb-IIIa complex and its interaction with fibrinogen and other ligands by electron microscopy J Biol Chem 1992 267 16637 16643 1644841 Xiong JP Stehle T Diefenbach B Zhang R Dunker R Crystal structure of the extracellular segment of integrin alpha Vbeta3 Science 2001 294 339 345 11546839

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PLoS Biol. 2004 Jun 15; 2(6):e169

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PLoS Biol

10.1371/journal.pbio.0020169

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020170Correspondence and Other CommunicationsCell BiologyScience PolicyEthics as Our Guide CorrespondenceCook Michael 6 2004 15 6 2004 15 6 2004 2 6 e170Copyright: © 2004 Michael Cook.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Reason as Our Guide Beyond Therapy Scientists and Bioethics Councils A Voice for Research, a Voice for Patients Taking the Stem Cell Debate to the Public Ethereal Ethics In response to the Blackburn and Rowley essay on the President's Council on Bioethics, several thought-provoking opinions on ethical challenges in biomedical research are expressed by prominent stakeholders ==== Body Blackburn and Rowley's (2004) criticism of a report on embryonic stem cell research from the President's Council on Bioethics (2004) is puzzling. Where is the bioethics? The nub of their complaint is that some details of the report have been partisan and have distorted ‘the potential of biomedical research and the motivation of some of its researchers’. No doubt their quibbles are well-founded, as every committee report is a compromise. However, it does not follow that if the benefits of embryo stem cell research had been presented more persuasively and in greater detail, then the case for ‘non-commercial, federal, peer-reviewed funding’ would be unassailable. Such a view appears to be based squarely on a utilitarian view of the moral status of embryos: that the good flowing from destructive research outweighs the evil of embryo destruction. Far from being a neutral scientific analysis, this expresses a commitment to the proposition that biomedical progress is more important than the defence of human life. If twentieth century philosophy of science has taught us anything, it is that the aspiration to pure scientific objectivity is a dangerous illusion. Research programs always embody philosophical and moral assumptions that must be openly defended. If Blackburn and Rowley want government support for embryo stem cell research, they must justify their bioethical approach and not hide behind a smokescreen of indignation over Blackburn's unwilling departure from the Council. BioEdge, Australasian Bioethics Information, Sandy Bay, Tasmania, Australia E-mail: [email protected] ==== Refs References Blackburn E Rowley J Reason as our guide PLoS Biol 2004 2 e116 10.1371/journal.pbio.0020116 15024408 President's Council on Bioethics Monitoring stem cell research 2004 Available at http://bioethics.gov/reports/stemcell/index.html via the Internet. Accessed 24 March 2004

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2021-01-05 08:26:26

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PLoS Biol. 2004 Jun 15; 2(6):e170

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PLoS Biol

10.1371/journal.pbio.0020170

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020172ObituaryNeuroscienceHomo (Human)Choices: The Science of Bela Julesz Bela Julesz: ChoicesSiegel Ralph M 6 2004 15 6 2004 15 6 2004 2 6 e172Copyright: © 2004 Ralph M Siegel.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Highlights of Bela Julesz's scientific career in visual neuroscience ==== Body Throughout his career, Bela Julesz created new scientific disciplines by remarkable combinations of seemingly disparate approaches. The selection of his major discipline, which would eventually be called visual neuroscience, may have been serendipity or choice. When the unexpected Soviet invasion of Hungary in 1956 spurred his emigration to the United States, Bela Julesz, with his Hungarian doctorate in engineering, joined the numerous mathematical luminaries working at AT&T Bell Laboratories, such as John Tukey, Harry Nyquist, Claude Shannon, and John Kelly. One of the projects underway at the time was the creation of long random-number binary sequences that did not repeat. Bela told the story that he was assigned the problem of testing these number generators; he decided to use the best pattern recognizer that he knew of—the human visual system. The random bits of zeros and ones drawn from the random number sequences were plotted as sequential rows in an image. Any repeats, any correlations across space, would be instantly seen by the human visual system as patterns in the random dots. What caused Bela to choose this unusual approach to looking for patterns, combining computers and vision? His doctoral thesis research in network theory and television signals clearly influenced him, but it was quintessential Bela to give himself a hand up into a new field by building on his base of knowledge, moving in a new and unexpected direction using mathematical and psychological insight. He termed this talent “scientific bilingualism” (Julesz 1994). Bela Julesz, in front of a picture from his and A. Michael Noll's computer art exhibition, “Computer-Generated Pictures,” held at the Howard Wise Gallery, New York City, in 1965. Photograph courtesy of Rutgers University This success in exploiting the visual system, and the intellectual freedom intrinsic to the design of Bell Labs, provided Bela with the opportunity to use these new random dot patterns to explore the visual system. Most of us know well that we can use the small differences in the images in each eye to see depth. Sir Charles Wheatstone showed in 1838 that if two different perspective images were observed through a stereoscope so that each eye observed only one view, a startlingly realistic three-dimensional image occurred. Oliver Wendell Holmes, stereoscope enthusiast, wrote of the experience that “the shutting out of surrounding objects, and the concentration of the whole attention, which is a consequence of this, produce a dreamlike exaltation…in which we seem to leave the body behind us and sail away into one strange scene after another, like disembodied spirits” (Holmes 1861). The basis of this three-dimensional perception was hotly debated between Wheatstone and fellow physicist Sir David Brewster. (Though it may seem odd for physicists to concern themselves with the physiology of optics, this was felt to be a natural extension of the study of the physics of optics.) Brewster opined that perspective was the source of the apprehension of an object's shape. Wheatstone insisted that the images in the each eye had identifiable landmarks that were combined to assign depth to the landmarks. Bela read much of the literature of that time, and he must have seen two greats as wrestling without either finding the overwhelming hold to pin down the other. More than one hundred twenty years after Brewster and Wheatstone, Bela realized that his random dot patterns could be used to probe this question. What Bela did was create a pair of identical random dot patterns. When viewed binocularly through a stereoscope (i.e., fused), they would be seen as a single surface. Then Bela took a central region from the right random dot pattern and displaced it minutely to the right. Now when the two patterns were fused, the central square was not seen double, but after a moment or two, eerily moved into depth, behind the surrounding region. In 1960, Bela's experiment with what eventually became known as Julesz random dot stereograms unambiguously demonstrated that stereoscopic depth could be computed in the absence of any identifiable objects, in the absence of any perspective, in the absence of any cues available to either eye alone. It was a perfect combination of psychological and mathematical insight and technology that solved this puzzle. (It is an interesting aside that Bela sent his first report to the Journal of the Optical Society of America, where it was rejected; the Bell Labs Technical Journal holds the now classic paper [Julesz 1960]. The Journal of the Optical Society of America published Bela's second paper [Julesz 1963].) The stereoscope had existed 125 years. Bela proposed in his book Foundations of Cyclopean Perception (1971) that early in the vision process the two images from the two eyes were combined to form a single view, imbued with inherent depth information. The perceptual “cyclops within us” was proposed to analyze the visual world first, before the motion, color, and contrast systems began their perceptual operations. Bela's book is full of powerful visual experiments that make this point irrefragably; from his psychophysical analysis, binocular vision forces unexpected constraints on the rest of vision, Q.E.D. Foundations of Cyclopean Perception is still considered one of the classics of modern psychophysics and continues to have profound relevance to both those entering the field and established investigators—over thirty years after its publication. At the time of his death, Bela had begun working on a second edition. His success in determining the sequence of visual processing using random dot stereograms led Bela to propose that the anatomical hierarchy of the visual system could be understood in part through visual psychophysics—he termed this approach “psychoanatomy.” His ingenious use of the stereogram established a new approach in the field of vision research and presaged the now common use of carefully controlled computational techniques in brain science. By this time Bela's reputation was established, and in 1983, he received a prestigious MacArthur Fellowship—the “genius award.” He used the funds for travel, including an annual peregrination to the California Institute of Technology, where I first met Bela in 1985. His seminars and lecture courses were enthusiastically received and endorsed by countless students, post-doctoral trainees, and faculty, as evidenced both by his formidable reputation and through the numerous citations of his work. His approach to presenting his research was modest and gently self-deprecating. He always encouraged young scientists; his joy and passion in their science were transmitted both through his warm persona and his suggestions of directions for future study. His insights guided my development of random dot kinematograms (i.e., movies) to examine how motion could be used to construct three-dimensional form (Siegel and Andersen 1988). He collaborated with Derek Fender, David Van Essen, and John Allman at the California Institute of Technology on the combination of the computer, the psychophysical approach, and the physiological experiment. Bela was a fount of ideas, each building on the prior's advance. His later passions were explorations of texture and attention, notably with Jonathan Victor and Dov Sagi. Bela's appealing hypothesis that textons (putative elements of textures) are represented at a cellular level is now questionable (Julesz et al. 1978). Bela was groping for an overarching computational theory for the representation of random geometry, but none was to be had. Nonetheless, the texton elements served useful duty in the demonstration that there were two stages to early vision—an effortless phase preceding attention and a guided identification phase (Sagi and Julesz 1985). Many contemporary laboratories examining vision, studying either perception or the activity of neurons, now incorporate designed, complicated, yet highly controlled stimuli that have evolved (knowingly or not) from Bela's original forays in the 1960s and 1970s. His continuing impact was recognized by his election to the National Academy of Science in 1987. In 1989, Bela retired from Bell Labs (by then he was a department head) and joined the Department of Psychology at Rutgers University to establish the Laboratory of Vision Research. Bela continued investigating mechanisms of form, texture, and stereopsis; his presence led to numerous studies into the implications of his original findings as well as new investigations into computational vision. His collaborations greatly aided the establishment of neuroscience at Rutgers. Bela wrote Dialogues on Perception (1995), a wide-ranging intellectual effort, in which he uses classic dialectics to question both his own successes and those of his chosen field. In the book one reads of two competing intellects, a Bela who believes in his contributions to science and another Bela who is constantly belittling and judging his contributions. Throughout his career Bela Julesz was able to add language after language to his research imperative, becoming a true scientific polyglot. Although his arrival in the United States was propelled by political events beyond his control, his intellectual directions followed a chosen path “less traveled by, and that has made all the difference.” In 1956, an engineer set out from Hungary. By 2003, his unique combination of mathematical precision combined with deep biological insight had carried him to elegant solutions for seemingly intractable problems in visual neuroscience. Bela was always in dialogue, often with others, and often with himself. In the process, he would gently drive each of us, and himself, forward to our final destination of understanding the brain. Bela Julesz died on December 31, 2003, forty-seven years to the day after starting at Bell Laboratories. Ralph M. Siegel is an associate professor in the Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, United States of America. E-mail: [email protected] ==== Refs References Holmes OW Sun-painting and sun sculpture Atlantic Monthly 1861 8 13 29 Julesz B Binocular depth perception of computer-generated patterns Bell Labs Tech J 1960 39 1125 1162 Julesz B Stereopsis and binocular rivalry of contours J Opt Soc Am 1963 53 994 999 14047836 Julesz B Foundations of cyclopean perception 1971 Chicago University of Chicago Press 406 Julesz B Dialogues on perception 1994 Cambridge (Massachusetts) MIT Press 304 Julesz B Gilbert EN Victor JD Visual discrimination of textures with identical thirdorder statistics Biol Cybern 1978 31 137 140 728493 Sagi D Julesz B Fast noninertial shifts of attention Spat Vis 1985 1 141 149 3940055 Siegel RM Andersen RA Perception of three-dimensional structure from motion in monkey and man Nature 1988 331 259 261 3336437

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10.1371/journal.pbio.0020172

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020176Research ArticleBioinformatics/Computational BiologyBiophysicsNeuroscienceHomo (Human)Electroencephalographic Brain Dynamics Following Manually Responded Visual Targets EEG Dynamics Following Visual TargetsMakeig Scott [email protected] 1 Delorme Arnaud 1 Westerfield Marissa 2 Jung Tzyy-Ping 1 Townsend Jeanne 2 Courchesne Eric 2 3 Sejnowski Terrence J 1 4 1Swartz Center for Computational Neuroscience, Institute for Neural ComputationUniversity of California at San Diego, La Jolla, California, United States of America2Department of Neurosciences, University of California at San DiegoLa Jolla, California, United States of America3Children's Hospital Research Center, San DiegoCalifornia, United States of America4Howard Hughes Medical Institute, Computational Neurobiology LaboratorySalk Institute for Biological Studies, La Jolla, CaliforniaUnited States of America6 2004 15 6 2004 15 6 2004 2 6 e17630 11 2003 12 4 2004 Copyright: © 2004 Makeig et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Deconstructing Brain Waves: Background, Cue, and Response Scalp-recorded electroencephalographic (EEG) signals produced by partial synchronization of cortical field activity mix locally synchronous electrical activities of many cortical areas. Analysis of event-related EEG signals typically assumes that poststimulus potentials emerge out of a flat baseline. Signals associated with a particular type of cognitive event are then assessed by averaging data from each scalp channel across trials, producing averaged event-related potentials (ERPs). ERP averaging, however, filters out much of the information about cortical dynamics available in the unaveraged data trials. Here, we studied the dynamics of cortical electrical activity while subjects detected and manually responded to visual targets, viewing signals retained in ERP averages not as responses of an otherwise silent system but as resulting from event-related alterations in ongoing EEG processes. We applied infomax independent component analysis to parse the dynamics of the unaveraged 31-channel EEG signals into maximally independent processes, then clustered the resulting processes across subjects by similarities in their scalp maps and activity power spectra, identifying nine classes of EEG processes with distinct spatial distributions and event-related dynamics. Coupled two-cycle postmotor theta bursts followed button presses in frontal midline and somatomotor clusters, while the broad postmotor “P300” positivity summed distinct contributions from several classes of frontal, parietal, and occipital processes. The observed event-related changes in local field activities, within and between cortical areas, may serve to modulate the strength of spike-based communication between cortical areas to update attention, expectancy, memory, and motor preparation during and after target recognition and speeded responding. A new analysis considers EEG signals, not as average responses, but as individual event-related perturbations in ongoing dynamical brain processes ==== Body Introduction The waking brain updates and fulfills intentions through brain processes that operate within and across multiple brain areas to integrate perception, association, and action. Fulfillment of intentions is facilitated by features of these and other processes that support informed anticipation of and selective attention to events and their probable consequences. The dynamics of ongoing electroencephalographic (EEG) activity recorded from the human scalp differ markedly with state of attention and intention (Makeig and Inlow 1993; Worden et al. 2000), yet most event-related EEG research has assumed that the effects of events on EEG signals emerge out of a flat baseline, as in the typical averaged event-related potential (ERP). The electrophysiological consequences of stimulus events spread quickly in the brain. By 50–150 ms, sensory stimulus information is widely distributed (Hupe et al. 2001), perturbing ongoing patterns of local field activity in many brain areas (Klopp et al. 2000). There is little chance, therefore, that any but still earlier ERP features occur within single brain areas. The adequacy of time-domain ERP averaging for modeling macroscopic brain dynamics also depends on the assumption that the cortical sources of EEG activity contributing to and not contributing to average ERP waveforms are somehow distinct. The scalp topographies of unaveraged EEG and averaged ERP data may, however, be quite similar (Makeig et al. 2002), strongly suggesting that areas contributing to ongoing EEG signals may also contribute to ERP averages. EEG processes not contributing to response averages may also be affected by experimental events, and several types of dynamic EEG response processes are not reflected in ERP averages (Pfurtscheller and Aranibar 1977; Makeig 1993; Makeig et al. 2002). A more comprehensive model of event-related brain dynamics is therefore needed to capture features of EEG signals that index the dynamic interplay between spatially coherent brain processes supporting anticipation of, attention towards, associations to, and behavioral responses following experimental events. The above considerations suggest that event-related EEG dynamics may be better modeled as coordinated event-related perturbations in the statistics of multiple, intermittently active local field processes. Since the volume-conducted scalp projections of such processes generally overlap, they cannot be separately identified in the scalp-recorded data. An alternate approach we adopt here is to separate the contributions of local field processes by using distinct differences in their time courses (Makeig et al. 1996, 1997; Jung et al. 2001a). Following stimuli belonging to an anticipated but infrequently presented category, the averaged ERP is dominated by a broad vertex-positive peak often called P300 after its earliest appearance in auditory responses (Sutton et al. 1965; for a review see Soltani and Knight, 2001). Results of ERP (Ruchkin et al. 1990) and brain lesion studies (Halgren et al. 1980; Knight et al. 1989) and of functional imaging experiments (Ford et al. 1994; Ebmeier et al. 1995; Ardekani et al. 2002) strongly suggest that P300 is actually a late positive-going response complex that sums effects of local field perturbations in several brain regions. A more inclusive model of the event-related EEG brain dynamics occurring in such data should consider, therefore, how target stimulus presentations and subject motor responses perturb the dynamics of ongoing EEG signals both within and across single subjects. Here, we present such a model. Results The independent component analysis (ICA) method provides a complete decomposition of single-trial (or continuous) EEG data, separating the data into distinct information sources. As the results presented below show, the amount of information about cortical dynamics provided by this method is large. Here, we detail for the first time dynamics occurring within single trials of the classes of maximally independent EEG processes whose event-related activities contribute to 31-channel visual target responses recorded during a test of spatial selective visual attention (Figure 1). Figure 1 Task Display Subjects fixated on a cross above which five boxes were constantly displayed. In each 76-s task block, one of these (grey box) was colored differently. The location of this covertly attended box varied pseudorandomly across blocks. A series of disks were presented briefly in any of the five boxes in a random order. Subjects were asked to respond with a thumb button press as quickly as possible whenever a disk appeared in the attended box. Response Dynamics of the Scalp Recordings: ERPs and ERP Images To orient readers used to analyses of the raw scalp-channel data, we first present conventional ERP results at selected channels. As we previously reported, in these experiments performance level was high; more than 95% of targets were followed by a button press within the allotted response window (150–1000 ms). Mean subject-median reaction time (RT) was 352 ms. The average ERP time locked to onset of the target stimulus followed by a subject button press contained the expected late positive complex or P300 positivity following early stimulus-locked peaks conventionally termed P1, N1, P2, and N2 (Figure 2A). The scalp topography of the late positive complex varied continuously through its extent (Figure 2A, scalp maps). Figure 2 Grand Mean and RT-Sorted Single-Trial Responses at Sites Fz and Pz (Left) Stimulus-locked grand mean and (right) response-locked grand mean responses to target stimuli. (A and B) Grand mean responses at all 29 scalp channels (colored traces), plus scalp maps at indicated latencies. (C–F) Grand moving mean single-trial responses from all 15 subjects, at frontocentral site Fz (C and D) and at central parietal site Pz (E and F), plotted in ERP-image format and sorted by subject RT (curving dashed trace in left column; vertical solid line in right column, plotted at the mean subject-median response time of 352 ms). ERP-image units: z = microvolts divided by root-mean-square microvolts in the (−1000 ms to 0 ms) channel baseline EEG of the same subject after removal of eye and muscle artifact components from the data. Vertical smoothing window: 300 trials. Grand mean normalized responses are shown below each image. In the grand average of the same epochs, each time-locked to the subject response (Figure 2B), the early response-locked peaks became smeared out, and the P300 and succeeding negative dip more concentrated. In two-dimensional “ERP-image” plots of the 8,413 single trials from all 15 subjects (Figure 2C–2F), potential fluctuations in single trials are shown as color-coded horizontal lines, here normalized by channel baseline variability then sorted (across all trials) by RT and smoothed (vertically) with a 300-trial moving average. The ERP images clearly show that the early visual response peaks at central posterior site Pz (Figure 2E) were time-locked to stimulus onset, while the late positivity at Pz immediately followed the button press (compare Figure 2E and 2F) except in the trials with the quickest RTs. Over half of these were contributed by two fast-responding subjects whose responses, unlike those of the other 13 subjects, preceded P300 onset. At frontocentral channel Fz, however, the late positivity in the stimulus-locked grand average (Figure 2C, bottom) was largely composed of two response-locked positive peaks, separated by 200 ms, that, together with intervening and flanking negativities, could be partially modeled by a two-cycle, 5-Hz wavelet (Figure 2D). The single P300 peak at Fz in the stimulus-locked ERP (Figure 2A) “smears out” the two-cycle pattern that is captured clearly in the response-locked average (Figure 2B and 2D), while highlighting a concurrent, broader, and possibly stimulus-locked positivity in faster-RT trials (Figure 2C). Event-related spectral perturbations Figure 3 summarizes the grand mean time course of changes from prestimulus baseline in log spectral EEG power at all the scalp channels time-locked to button presses (solid vertical line) across the EEG frequency range. During and just prior to the button press, an approximately 3-dB increase in low-theta-band power peaked (red area) near 4 Hz in bilateral central and posterior cortex. This increase remained significant (p < 0.01) for 14 of the 15 subjects even after the subject-mean ERP was subtracted from each trial (data not shown). Figure 3 Changes in Mean Scalp Spectral Power Time-Locked to the Subject Response The solid vertical line indicates moment of the motor response (shown at the grand mean subject median RT, 352 ms). Color scale: decibel change from prestimulus baseline. Image shows signed-RMS power changes across all 29 scalp channels prior to removal of all but the largest eye artifacts. Scalp maps show the scalp topography of the spectral power change in decibels relative to baseline. Note (A and B) the broad posterior low-theta- and anterior higher-theta-band maxima at the button press, (C, D, and F) the bilateral central alpha and beta blocking, (E) the central lateral postresponse beta increase, and (G) the increase in low-frequency eye artifacts at the end of the record. A concurrent but weaker theta power increase near 6 Hz (Figure 3B) was maximal at frontocentral and parietal scalp sites. The theta increase at these sites was accompanied by a blocking of mu activity around 10-Hz and 22-Hz, maximal at the left and right central scalp but also widespread over posterior scalp (Figure 3C, 3D, and 3F). Following the button press, a late central bilateral increase in beta activity (maximal at 16–18 Hz) appeared (Figure 3E). Figure 3 was computed prior to performing ICA and removing eye movement artifacts. The diffuse, far frontal increase in 3–10 Hz activity that began 500 ms after the button press (Figure 3G) doubtless reflects increased subject eye activity following the target response. ICA Decompositions Conventionally, characterizing the sources of ERP (Figure 2) or event-related spectral perturbation (ERSP) (Figure 3) processes is thought to be difficult because the scalp sensors are relatively far from the actual brain sources and therefore each sums the volume-conducted activities of several source areas. Moreover, the biophysical inverse problem of determining the potential source distribution from a given scalp map is in general severely underconstrained, with many mathematically correct but physiologically different solutions. Nonetheless, infomax ICA, applied to the concatenated single trials for each subject, after removing trials containing out-of-bounds or uncharacteristic artifacts, decomposed the whole set of concatenated EEG signals into 31 spatially fixed, maximally temporally independent component processes, and the scalp maps associated with many of these processes resembled the scalp projections of synchronous activity in either one or sometimes two nearly bilaterally symmetric cortical patches Component contributions to the single-trial EEG signals Figure 4 shows two single trials at site Pz (black traces) from one subject after removal of six eye and muscle artifact components. Projected activities of the three independent components most strongly contributing to each trial are shown as thin colored traces and accompanying scalp maps. Since infomax ICA provides a complete linear decomposition, the observed data (black traces) are in each case the sum of the remaining 25 (31 minus six) component projections, including the three component projections shown. In the upper trial, and typically, the single-trial P300 at Pz was accounted by ICA as summing contributions from at least two independent EEG processes. Component IC1 for this subject (ranked first by amount of total EEG variance accounted for) was later included in the parietal “P3b” component cluster (described below) on the basis of its scalp map and activity power spectrum. Figure 4 Independent Component Decompositions for Two Single Trials Black traces indicate two of 561 single target-response trials from one subject at scalp site Pz (upper right scalp map). Solid vertical lines indicate stimulus onsets; dashed vertical lines indicate button presses. A prominent late positivity occurred in the upper trial. All 561 1-sec, 31-channel EEG epochs time-locked to target stimuli were concate-nated and decomposed by infomax ICA, yielding 31 maximally independent data components. Colored traces show the projections (in microvolts) to this scalp channel of the three (nonartifact) independent components contributing the largest variance to each postresponse data window, linked to (individually scaled) maps of their scalp topographies. Component numbers (IC1–IC6), ranked by total EEG variance accounted for, and cluster affiliations (P3f, P3b, FM, P3b, Rα) are indicated above the scalp maps. Note differences in the time courses of IC1. While component IC1 accounted for the largest part of the P300 peak in the upper trial, in the lower trial the same IC1 process showed mixed low alpha and beta activity with a smaller postmotor response positivity. Note that the positive postresponse contribution of IC1 in this trial (thin blue trace) was sometimes larger than the observed positivity in the whole-channel data (thick black trace). At these times, some of the other 24 components contributed negative potentials to the signal at this scalp channel, partially canceling the IC1 positivity in the recorded data. Thus ICA, applied to the continuous or concatenated single-trial data, may actually recover more of the actual projected signals than are available in the single-channel data. Independent component clusters Cluster analysis, applied to the normalized scalp topographies and power spectra of all 465 components from the 15 subjects (see Materials and Methods), identified at least 15 clusters of components having similar power spectra and scalp projections. These component clusters also showed functionally distinct activity patterns. Six distinct component clusters (data not shown) accounted for eye blinks, horizontal eye movements, and left and right temporal muscle noises, respectively. These were effectively removed from the activity of the other component clusters by the ICA decomposition and are not further considered here. Equivalent dipole locations Figure 5 shows the results of modeling the grand mean scalp maps for each of the nine independent component clusters as the projection of an equivalent dipole. Residual scalp map variances unaccounted for by these models were relatively small (range: 0.87% to 9.55%; mean: 4.93%), though the equivalent dipole locations for the individual clustered components were not all tightly clustered, as shown by the spatial standard deviation ellipses. Figure 5 Mean Component Cluster Equivalent Dipole Locations The mean scalp map for each of the nine component clusters could be well fit by a single equivalent dipole (mean residual variance: 4.8%). The figure shows the locations and orientations of these dipoles, as determined by BESA, plotted on the spherical head model, with ellipses showing the spatial standard deviations of the locations of the equivalent dipoles for the individual components in the cluster. The location of the equivalent dipole for a radially oriented cortical source patch (or here, effective sum of patches) is typically deeper than the cortical patch itself (Baillet et al. 2001). The equivalent dipoles for individual components in the P3b cluster (data not shown) were scattered across parietal and central cortex (as indicated in Figure 5 by P3b's larger spatial standard deviation). Therefore, the equivalent dipole for the mean scalp map of the P3b cluster was unnaturally deep and represented the center of the active cortical source distribution only symbolically. The mean equivalent dipole location for the cluster designated “P3f” was estimated chiefly from the two periocular electrodes. Moreover, the complicated geometry of the frontal skull cannot be well fit to the spherical head model used here. Thus, the mislocalization of the P3f cluster equivalent dipole below the orbitofrontal brain surface should not be taken literally. The spatial standard deviations of the other component cluster dipoles were smaller. Their equivalent dipoles indicate the respective dominant cortical regions of their source domains. Though the mean cluster scalp map for the central occipital alpha cluster (Cα) could be fit satisfactorily by a single equivalent dipole located in the central occiput, for several of the cluster components a better model of the component scalp map was obtained from a symmetric dipole pair in left and right pericalcarine cortex (data not shown). Component Cluster Dynamics Mean dynamics properties of nine nonartifact component clusters are summarized in Figures 6–9, each of which shows the mean scalp map and response-locked ERP image, activity and ERP spectra, and ERSP for one or more component clusters. Because of the complexity of the results, we report and interpret the nine component clusters in four groups based on shared dynamic features. Figure 6 Far-Frontal and Parietal Component Clusters Contributing to the P300 (A–D) Far-frontal component cluster accounting for the preresponse (P3f) positivity. (E–H) Broad parietal component cluster accounting for part of the postresponse (P3b) positivity. The periresponse energy increase for these processes peaks at below 5 Hz. (A and E) The mean component scalp map. (B and F) The whole-data (black traces) and cluster-accounted (red fill) ERP envelopes (minimum and maximum voltage channel values at each time point), plus (inset) the power spectrum of the whole EEG (black traces) and the whole response-locked average ERP (red fill). The lower edge of the red fill shows actual ERP power, the upper edge, the phase-random EEG spectrum required to produce the observed average ERP spectrum by phase cancellation. The difference between the upper edge of the red fill and the actual EEG spectrum (black trace) reflects phase consistencies across trials in the single trial data. (C and G) ERP-image plot of the color-coded single trials time-locked to the response (solid vertical line) and sorted by RT from stimulus onset (dashed line). Trials normalized by dividing by the standard deviation of component activity in the 1-s prestimulus baseline. (D and H) The component mean ERSP showing mean event-related changes in (log) spectral power across data trials time-locked to the response (solid line). Here, median stimulus delivery time is indicated by the dashed line. Figure 9 Three Posterior Alpha-Rhythm Component Clusters Panels as in Figure 6. (A–D) Left posterior alpha (Lα) component cluster. (E–H) Central posterior alpha (Cα) component cluster with characteristic trapezoidal scalp projection, consistent with a bilateral, pericalcarine equivalent dipole source model, and demonstrating prolonged phase resetting following stimulus onset (curving dashed trace). (I–L) Right posterior alpha (Rα) component cluster. Note (C) the relative absence of the alpha-ringing pattern in the Lα cluster activity and the (D, H, and L) relatively weak postresponse alpha blocking in these clusters. Two clusters contributing to the late positive response (P3f and P3b) Two component clusters made distinct contributions to the late positive complex of the target ERP. After subtracting the larger back-projected scalp-data contributions of components accounting for blinks and saccadic eye movements, the response-locked ERP at both periocular channels contained a broad, approximately 2-μV positive-going scalp potential peaking on average 39 ms before the recorded button press. Figure 6A–6D shows the mean scalp map and dynamic properties of a cluster of ten independent components from ten subjects that together largely accounted for an ERP feature whose time course was highly similar to the peak we labeled P3f (for P3-frontal) in an earlier report on decomposing the matrix of 25 condition ERPs from these experiments (Makeig et al. 1999a). Note, in the ERP image (Figure 6C), the absence of sharp excursions not regularly time-locked to experimental events, which would mark blinks or lateral eye movements. Such activity at far frontal and periocular channels was effectively separated out by ICA into artifact components (data not shown). Instead, as shown in Figure 6B, the P3f component cluster accounted for nearly all the positivity occurring before the button press (designated P3f by Makeig et al. 1999a), particularly in shorter latency-response trials (Figure 6C). The P3f cluster-mean response-locked positivity began near 150 ms, consistent with direct neurophysiological evidence that by 150 ms after stimulus onset, visual information is spread throughout the brain by a complex web of afferent and efferent connections (Klopp et al. 2000; Hupe et al. 2001). Subtracting the button travel time (approximately 25 ms, roughly estimated from electromyographic recording during one experimental session) and the neuromuscular conduction time (approximately 15 ms) suggested that the P3f peak at 39 ms before the button press occurred very near the moment of the subcortical motor command. It is thus tempting to speculate that the P3f process should originate in frontal structures involved in motivated decision making and response selection, such as orbitofrontal cortex (Ikeda et al. 1996), though the sparse spatial sampling of the present data does not allow more specific conclusions. The far-frontal (P3f) component cluster response appears similar to the 280-ms “P2a” peak noted in responses to (foveal) visual “oddball” stimuli by Potts et al. (1998). Potts and Tucker (2001) reported that P2a was maximal near the eyes but can be recorded over most of the face and may also be found in attention conditions involving no subject button press. The scalp map of the ERP-derived P3f component derived by ICA applied to the 25 condition ERPs (Makeig et al. 1999a) also included bilateral parietal features not seen here in the P3f component cluster. Evidently, the temporal and far-frontal projections joined in the ERP-derived P3f component map did not cohere in the much more extensive single-trial data and so were separated by ICA applied to the concatenated single-trial data. This detail points to the advantage of decomposing a sufficient number of unaveraged data trials over decomposing even a relatively large set of averaged responses. Figure 6E–6H shows the mean scalp map and activity patterns of a bilaterally distributed cluster of 15 components (from nine subjects) that projected most strongly to posterior and central scalp sites and made a substantial contribution to the slow postmotor P300 or P3b positivity. Examination of the raw ERP waveforms of the six subjects not contributing to this cluster suggested the absence of a typical central parietal positivity in their target responses. The mean cluster scalp map (Figure 6E) resembled that of the response-locked parietal ERP peak itself (see Figure 2B). The ERP image of the normalized single-trial component activity (Figure 6G) includes an early series of small, positive and negative, wave fronts following the (sorted) time of stimulus delivery (dashed curve) by fixed delays. These are followed by a large response-locked positivity (red area) accounting for 62% of postre-sponse ERP variance at 300 ms for site Pz. The P3b cluster positivity is clearly smaller in late-response trials (Figure 6G, top), consistent with the Pz data (Figure 2F). The mean cluster ERSP (Figure 6H) reveals a significant (3-dB) post-response low-theta power increase. The significance of the stimulus-locked P300 (or P3b) peak over central parietal cortex has long been debated. Our results clearly show (Figure 2E) that in these experiments, this peak was time-locked to and predominantly followed the motor response. P3b onset occurred at about the moment of the motor command, coincident with the P3f peak. It could not, therefore, index activity involved in making the motor decision or action, as this is seemingly associated with the P3f process. The equivalent dipole distribution of the P3b cluster was broad, the strongest commonality being dipole orientation toward the central parietal scalp. Between-subject variability in locations of P3b generators have also been reported by researchers using other source localization methods (Moores et al. 2003). It is possible that more advanced three-dimensional component clustering methods, applied to decompositions from more subjects and more data channels, might allow further distinctions among processes in this cluster. Central midline clusters Figures 7 and 8 show mean properties of four classes of components producing the two-cycle postresponse evoked-response pattern seen clearly in the response-locked data at site Fz (see Figure 2D). Principal among these were two component clusters (Figure 7) projecting maximally to the frontal midline (FM) and central midline (CM) scalp, respectively. Figure 7 Two Mediofrontal Independent Component Clusters Showing a Postmotor Theta Response Pattern Panels as in Figure 6. (A–D) FM cluster of components often exhibiting a theta-band peak in their activity spectra. (E–H) CM component cluster projecting maximally to the vertex. Figure 8 Two Mu Rhythm Component Clusters Also Showing the Postmotor Theta Response Pattern Panels as in Figure 6. (A–D) Left mu rhythm (Lμ) component cluster with mu characteristic 10-Hz and 22-Hz peaks in the activity spectrum. (D) Following the button press, this activity is blocked. (E–H) Corresponding right mu rhythm (Rμ) component cluster. In the RT-sorted FM cluster ERP image (Figure 7C), the two negative wave fronts follow the curving trace marking stimulus onsets, the second of these merging with the earlier RT-locked negativity. Though the vertex-maximum CM component cluster (Figure 7E–7H) also exhibited the postmotor theta feature (Figure 7F and 7G, red arrows) with the mid frontal and mu rhythm clusters (see below), it contributed little to the broader (P3b) positivity produced mainly by the central parietal (see Figure 6E) and central occipital (Figure 9) component clusters. Though the equivalent dipole locations of components in the two midline clusters were somewhat overlapping (see Figure 5), their mean equivalent dipole locations were generally consistent with sources in or near the dorsal anterior cingulate and cingulate motor areas, respectively (Ullsberger and von Cramon 2003). These areas are implicated by fMRI and neurophysiological experiments as participating in motor response selection and anticipation of the consequences of events, including those involving self-perceived errors (Shima and Tanji 1998; Luu and Tucker 2001; Manthey et al. 2003; Ullsperger and von Cramon 2003). The phase/latency of the postmotor theta burst appeared to be consistent across quicker and slower responses. The theta burst appears to resemble other reported FM EEG activity patterns: theta bursts or trains (fmθ) appearing during mental concentration (Mizuki et al. 1980; Gevins et al. 1997; Uchida et al. 2003) and brief bursts of theta activity linked to and following the error-related negativity (Luu and Tucker 2001; Luu et al. 2004), an ERP peak whose latency matches the first negativity in the FM cluster postresponse ERP (at approximately 60 ms). Inverse source modeling has placed the generating cortical domain of the ERN and fmθ in or near the dorsal anterior cingulate. In this ICA decomposition, however, the two-cycle postmotor theta burst pattern appeared not only in the FM cluster, but also in the CM, mu, and parietal clusters (see below). The scalp map of the CM cluster (Figure 7E) resembles scalp maps of the “P3a” or “P3novel” ERP peaks seen, e.g., when unique and unexpected stimuli are included in a randomly alternating sequence of target and nontarget stimuli (Courchesne et al. 1975; Polich and Comerchero 2003). Here, however, the CM cluster made only a small contribution to the stimulus-locked target ERP. Mu rhythm clusters The left and right mu rhythm component clusters (Lμ and Rμ in Figure 8) exhibited the defining feature of mu rhythms—distinct spectral peaks near 10 Hz and 22 Hz that are strongly blocked following movements, with equivalent dipoles located roughly over hand motor cortex (and/or adjacent postcentral somatosensory areas), and oriented roughly orthogonal to the directions of the central sulci. Both the ERP and ERSP peaks were larger in the left mu cluster (contralateral to the response hand) than in the right. In common with the midline clusters, the mu component clusters contained the two-cycle postmotor theta pattern (Figure 8D) concurrent with a mean theta power increase (Figure 8H). They also made slower, positive-going contributions to the parietal ERP, particularly to the late “slow wave” phase of the stimulus-locked P300 complex that, unlike the main (P3b) peak, exhibits a polarity reversal over the central scalp (Simson et al. 1977). In a previous ICA analysis of ERPs from these experiments, the late slow wave phase of the stimulus-locked P300 complex was confined to a single component (Makeig et al. 1999a), but here it was separated into distinct left and right mu rhythm processes in at least ten of the subjects. More detailed source analysis of magnetoencephalographic mu rhythms has assigned their source mainly to somatosensory cortex (Forss and Silen 2001). Thus, the postresponse slow positivity (Figure 8C) of the Lμ cluster, larger following slower responses, might index tactile feedback from the hand and button surface (Makeig et al. 1999a). Posterior alpha clusters Figure 9 shows the dynamics of three clusters of components projecting to the posterior scalp. Each had a distinct near 10-Hz alpha frequency peak in its activity spectrum, most pronounced in components of the central cluster (Figure 9F, inset). The stimulus-locked ERP contributions of the two lateral posterior alpha clusters, shown as sloping wave fronts in Figure 9C and 9K, included an early stimulus-locked peak accounting for most of the P1 ERP peak (near 145 ms) and for part of the succeeding N1 peak, which summed contributions from several clusters. In the central alpha component cluster, the initial stimulus-locked response feature was followed by a train of approximately 10-Hz stimulus-locked waves. These can be said to be produced by partial phase resetting of the intermittent alpha activity of these components following stimulus onsets, since they were accompanied by no mean increase in alpha power. The central alpha cluster also made an appreciably broad, triangular contribution (Figure 9F and 9G) to the P300 positivity, while the contributions of the lateral clusters to the response-locked ERP, beginning just before the button press, were small and narrow. The mean response-related ERSPs for these three clusters were weak (Figure 9D, 9H, and 9L), and their postresponse alpha and beta blocking were brief and weak, compared to the two mu clusters (see Figure 8). The lateral clusters, but not the central cluster, exhibited a low beta increase above baseline (near 14 Hz) beginning near the button press. While the existence of multiple alpha rhythms has long been noted, ICA here neatly separated their activities and identified their complete, overlapping scalp maps based on the relative independence of their activity patterns in the unaveraged data. The central posterior alpha processes had a stronger alpha-band peak than lateral posterior alpha components, and showed longer-lasting phase resetting following visual stimulus onsets (Figure 9G). The longer phase memory implied by the prolonged phase resetting is compatible with longer bursts of alpha activity in these components. Possibly, the distinct dynamics of the central and lateral posterior clusters may serve different though still unknown purposes. The “trapezoidal” signature of several of the central posterior component scalp maps (Figure 9E) is compatible with a model comprising two equivalent dipoles located symmetrically in left and right pericalcarine cortex. To be fused into a single infomax ICA component, activity in both hemispheres must have been largely synchronous with negligible phase delay. Alpha-band activity in two cortical areas can indeed be synchronous if the two areas are densely connected, here most likely via corpus callosum. Synchronization of bilateral generator regions via dense callosal coupling might also support the observed sharp (around 10-dB) alpha peak in the activity spectra of these components. In these data, the lateral posterior alpha components were always unilateral and never bilateral. Possibly this may reflect the lower density of direct connections between these areas. Early ERP features in these experiments appeared predominantly contralateral to the stimulus locations. No doubt this was because these stimuli were presented above and usually lateral to fixation. In other data, we have noted that visual ERPs time-locked to foveally presented stimuli usually contain a bilateral posterior P1/N1/P2 complex (Makeig et al. 2004). Component ERP Contributions Together, the nine component clusters accounted for 91.1% of the variance of the response-locked grand mean ERP at all channels in the 1000 ms following stimulus onset, as well as for 90.8% of the variance of the stimulus-locked grand mean ERP. Figure 10A and 10B shows the envelopes (most positive and negative channel values, across all channels, at each time point) of the stimulus-locked and response-locked grand mean ERPs (black) and the envelope (red fill) of the summed back-projections to the scalp of the components comprising the nine clusters. The normalized grand average activity time courses for the nine clusters are shown in Figure 10C–10H, for comparison with the time courses of the grand mean ERP (Figure 10A and 10B). Figure 10 Component Time Courses and Summed Scalp Projections Summed projections (A and B) to the grand mean ERP average of all trials time-locked to stimulus onsets (left) and to subject responses (right), plus (C–H) grand mean normalized activity time courses of each of the nine independent component clusters, scaled and separated into the same cluster groupings as in Figures 6–9. For comparison with the stimulus-locked responses (left), response-locked data epochs (right) are shown aligned to the mean subject-median response time (352 ms, dashed line in left panels). Note that stimulus-locked component cluster ERP activity first appeared in the lateral posterior alpha clusters (at 100 ms). Onset of the stimulus-locked ERP of the P3b cluster at about the same time was soon followed by the far-frontal P3f cluster onset (near 120 ms, Figure 10C). The stimulus-locked ERP deflection began at the same moment in the four postmotor theta clusters (Figure 10E). Six of the nine clusters had a negative peak in their stimulus-locked ERP average near 200 ms, confirming the spatial complexity of the N1 peak, as indicated by invasive measures (Klopp et al. 2000) and comparable to previous analysis of nontarget epochs from this data set (Makeig et al. 2002). In the response-locked cluster ERPs, note that the P3f cluster activity appeared to begin early, while response-related activity in the P3b cluster ERP diverged from baseline 10–20 ms before the P3f peak, concurrent with a posterior-negative peak in the left mu cluster ERP. The posterior-positive peaks in the response-locked ERPs of both mu clusters, the early shoulder of the P3b cluster ERP peak, the central cluster ERP slow wave, and the negative-going peak of the FM cluster ERP all occurred together, about 100 ms after the P3f peak. Figure 11 shows the individual and summed independent component cluster contributions to the grand mean ERP at sites Fz and Pz. At Pz, no component cluster contributed more than a third of peak parietal P300 amplitude in either the stimulus-locked or response-locked ERPs (Figure 11C and 11D). The largest cluster contribution to the peak at Fz was also from the P3b cluster, which contributed about half its peak amplitude (Figure 11A and 11B). The P3f cluster contributed at best a third. These results cast doubt on claims that the target-response P300 peak at Fz predominantly indexes frontal activity. Figure 11 Cluster Projections to the Scalp ERP Component cluster contributions (in microvolts, thin traces) to the grand mean stimulus-locked (left) and motor-response-locked (right) target ERPs at scalp sites Fz (top) and Pz (bottom), plus their summed contributions (thick traces). Although the P3b cluster makes the largest contribution to the evoked responses at both scalp sites, its contribution does not outweigh the summed contributions of the other clusters. Post-Response Theta Synchronization Phase coherence analysis of consistent phase relationships between the FM and CM clusters, and between the FM and left mu clusters, time-locked to and following the motor response showed that significant theta phase coherence appeared in the data, even after the respective component ERPs were subtracted from each trial, indicating a transient postresponse phase linkage between these otherwise maximally independent processes. Figure 12 illustrates this phenomenon in the time domain using phase-sorted ERP-image plots. Sorting trials by the phase (with respect to the button press) of the postmotor theta activity of the FM cluster (Figure 12A) and then imaging the single-trial activities of the CM cluster in the same trial order (Figure 12B) induced partial theta phase ordering on the CM data (i.e., the slightly diagonal wave fronts in Figure 12B). The converse procedure (Figure 12C) gave a similar result (Figure 12D). Figure 12 Phase Coupling of Theta Components: Time-Domain View (A) ERP-image view of baseline-normalized, response-aligned single-trial activity time series of components in the FM cluster, sorted (top-to-bottom) by phase at 4.87 Hz in a window centered 89 ms after the button press. Vertical smoothing: 400 trials. Units: microvolts normalized by dividing by the standard deviation of component single-trial baseline activity. The curving vertical trace (left) shows a moving mean of stimulus onset times; the central vertical line, the time of the button press. Data band pass in all panels: 0.1–40 Hz. (B) Exporting the same trial sorting order from (A) to CM cluster components (from the nine subjects contributing components to both clusters) demonstrates the significant partial theta phase coherence (r is approximately 0.3) between the two clusters in the postresponse time/frequency window. Note the induced (top-down, left-to-right) slope of the latency of the two (orange) positive-going CM cluster theta wave fronts. (C) Phase-sorted ERP image, as in (A), of the normalized CM cluster trials. (D) FM cluster component trials sorted in the same trial order as (C). Again, the partial theta-band phase coherence of the two clusters in the postresponse period is reflected in the diagonal (blue) negative-going wave fronts of the FM cluster data. Video 1 is an animation representing the joint response-related theta-band dynamics occurring in and between all nine component clusters (Delorme et al. 2002). The figure shows an analysis window centered 89 ms after the button press. Note that the transient theta phase coherence between the mu, parietal, and midline components was selective: phase coherence between FM and CM, FM and Lμ, and Rμ and P3b clusters (indicated by linking cylinders) were significant, whereas no significant phase linkage occurred in this time period between the FM and P3b clusters, nor between the CM and Lμ clusters. This selectivity diminishes the possibility that the observed transient phase linkages were produced by appearance of postresponse EEG activity not separated out by ICA into a separate component and therefore misattributed by the ICA model to nearby independent components. The results presented here, however, do not allow us to completely discount this possibility. Discussion ICA used the temporal information contained in the single-trial EEG time courses to identify and separate maximally independent processes. These were associated with overlapping scalp maps and time courses whose distinctive features were no longer blurred by volume conduction as in the scalp electrode data. The nine independent component clusters here identified by their similar scalp projections and activity spectra resemble classes of EEG phenomena long described by neurologists from observation of paper data displays—central and lateral alpha, left and right mu, and FM theta rhythms. By cleanly separating the EEG contributions of these processes, ICA allowed exploration of their individual and joint event-related dynamics. Our finding of selective theta synchronization between FM, motor, and parietal processes (Video 1) was only possible using ICA. The clear separation of “alpha ringing” in the stimulus-locked response from the other ERP features (see Figure 10G) also illustrates the power of ICA to separate temporally and functionally distinct activities that are generated in different brain areas but project to the same scalp channels. The nine clusters largely reproduced the component clusters we obtained previously from ICA decomposition of brief (100-ms) poststimulus time windows following nontarget and target stimuli in these experiments (Makeig et al. 2002). The major difference in the two sets of clusters was the inclusion, here, of a parietal P3b component cluster. In both analyses, the clustering omitted many small components with “noisy” scalp maps and time courses, and also omitted “outlier” components specific to single subjects. After removing clear ocular and muscle artifact components from the raw data, however, the nine identified EEG component clusters together accounted for over 90% of the grand mean ERP variance (over all channels) as well as almost 60% of variance in the whole EEG. By contrast, the ERP data themselves accounted for only 6% of poststimulus EEG variance, supporting our claim that this analysis presents a more complete model of the event-related EEG dynamics occurring in these data than the averaged ERP. Cluster Localization The group mean equivalent dipole locations shown in Figure 5 only symbolize the actual distribution of the component source domains. The relation between processes derived by ICA from scalp data and processes seen in invasively recorded cortical data are not yet clear. For example, the equivalent dipole localization of the P3b cluster-mean scalp map does not correspond directly to all the cortical areas noted to generate P3b-like local field potentials in implanted presurgical epileptic patients by Halgren et al. (1995a, 1995b)—parts of the superior parietal lobule, inferior frontal, and temporal cortices, as well as the limbic medial temporal lobe. However, in general it is difficult to infer the cortical distribution of a cortical source domain from equivalent dipole location. Clearly, more inclusive methods of ICA component source localization fitted to actual subject cortical geometry (Dale and Sereno 1993) may be useful for further research. It is not clear, however, whether “hot spots” recorded by sparsely implanted intracranial electrodes necessarily map activity that dominates the scalp EEG dynamics; hot spots might arise from focal or more spatially diffuse activities in other areas. ERSPs Production of scalp EEG signals requires partial synchronies in local field activity extending across the relatively large (2-cm or more) domains of neuropile (Nunez 1981). These must be dynamically maintained, and might well be perturbed by biological systems and mechanisms that implement and reinforce “top-down” cognitive decisions such as, as here, to selectively attend and respond to relatively infrequent visual stimuli while ignoring frequent nontargets (Giesbrecht et al. 2003). Posterior alpha, in particular, increases promptly when visual attention shifts between hemifields or between visual and auditory stimulus streams (Worden et al. 2000). Most of the spectral perturbations appearing in this analysis have been previously reported in some form, for instance, the alpha blocking following visual stimuli cueing visual attention and mu blocking accompanying cued finger movements (Hari et al. 1997; Pfurtscheller et al. 2000). A late beta increase following target responses has also been reported (e.g., Makeig 1993). ERP Influence Traditionally, averaging event-locked EEG data from single scalp channels to form an ERP is assumed to reject, by random phase cancellation, “background” EEG rhythms whose statistics are tacitly assumed to be unaffected by experimental events. To test the effect of the average ERP on the observed spectral perturbations, we computed the cluster ERSPs again after removing the component mean ERP from each trial (data not shown). All the effects shown in Figures 6–9 remained significant. Common ERSP Features Note that many of the cluster ERSPs (Figures 6–9) share common features. How is this possible? First, “independent” components returned by infomax decomposition of EEG data are never perfectly independent, but are instead those found by infomax to be maximally independent. This is not a mere play on words, but an advantageous feature of infomax decomposition that allows it to separate activity from different cortical areas even when the independence of synchronous activities within those areas is not unbroken or absolute. Second, the infomax independence metric is weighted toward separation by phase differences rather than by power spectral differences. The observed spectral perturbations may reflect, in part, common modulatory influences of central neurotransmitter-labeled brainstem systems involved in orienting and arousal, which project widely to cortex and are known to change the spectral properties of cortical field activity following novel or meaningful events (Aston-Jones et al. 2001; Fries et al. 2001; Hasselmo et al. 2002). Functional Significance of the Postmotor Theta Bursts At and after the button press, a mean frontocentral theta power increase appeared in all 15 subjects' data. It was partially phase coherent in four of the component clusters and was not eliminated by removing the subject-mean ERP from each trial. Local bursts of theta-band activity are widely distributed on the human cortex (Kahana et al. 1999) and associated with cognitive function (Caplan et al. 2003). In hippocampus, an association between theta phase and high-frequency “sharp wave” activity has been observed in nonhuman animals (Csicsvari et al. 2003). In turn, high-frequency activity can index the organization of spike timing of similarly tuned neurons into brief near-synchronous volleys more likely to trigger further spikes in common target neurons (Fries et al. 2001; Salinas & Sejnowski 2002). Following nontarget stimuli in this experiment, FM components exhibited weak “theta ringing” (partial poststimulus theta phase locking) not accompanied by increased theta power (Makeig et al. 2002). Here, following targets, a two-cycle period of increased theta activity appeared, time-locked to the motor response and weakly phase coherent between FM, parietal, and motor areas. Coherent theta activity might enhance the speed, salience, and reliability of spike-based communication between these and other brain areas connected with them, including hippocampus and related limbic structures such as the amygdala (Seidenbacher et al. 2003). The result might be facilitation of information transfer to and from memory structures about the event and its anticipated consequences, and selective retuning of attentional states in relevant cortical areas based on anticipatory evaluation of the consequences of the cued motor response, including readjusted sensory and motor expectancies. The postmotor theta bursts seen here following correct speeded responses very likely are tightly linked to the ERP feature with strong theta-band energy that follows highly speeded manual (or foot) responses in the Erickson flanker task (Holroyd et al. 1995). Luu and Tucker (2001) have suggested that the so-called error-related negativity in the response-locked ERP following responses the subject knows immediately to be in error partly represents partial phase locking of transiently increased theta-band activity in FM and other sources (Luu et al. 2004). A similar negative-going ERP feature has been reported following negative feedback whose valence is not known in advance (Gehring and Willoughby 2002; Ruchsow et al. 2002). Luu and Tucker (2001) also reported the appearance of enhanced theta activity in the ERP above somatomotor cortex following known-error responses. Our animation of the event-related theta-band dynamics in our data (see Video 1) demonstrates that correct speeded button presses produce partially synchronized theta-band increases in frontal (but not central) medial and contralateral somatomotor process clusters. Mean coherence phase lag between the midline clusters suggests that the postmotor theta activity in the FM cluster components leads that of the CM components by about 8 ms, a physiologically plausible value whose statistical reliability should be tested on a larger data set. Postmotor Theta and P300 Elbert and Rockstroh (1987) have proposed that cortical surface positivities in general index periods of relative neural depolarization and concomitant insensitivity (“disfacilitation”) of the involved cortex to distal input, possibly explaining the concurrent attentional blink (McArthur et al. 1999; Kranczioch et al. 2003) and amplitude decrease in the auditory steady-state response (Rockstroh et al. 1996). von Stein et al. (2000), on the other hand, have reported that following delivery of visual targets to cats, coherence in the theta band occurred between the output layer of a higher and the input layer of a lower visual cortical area. They did not observe this coherence following nontarget stimuli. Thus, the postmotor P300 and the theta burst response may have complementary functions: to decrease bottom-up environmental sensitivity and to concurrently apply the results of top-down processing to cortical perceptual areas. Although probably not available for observation in scalp data, the postmotor theta-band synchronization might extend to limbic areas (hippocampus, amygdala, and others) and play a role in memory updating (e.g., of learned/remembered “context”) following goal-directed actions (cf. Seidenbecher et al. 2003). Thus, we suggest that the postmotor theta burst may reflect directly “context-updating” processes previously proposed to be indexed by the broad P300 positivity (Dien et al. 2003). Evoked Responses In this speeded response paradigm at least, the ERP P300 positivity was nearly strictly time-locked to and predominantly followed the motor response. The P300 positivity was, here, indeed a late positive complex of potentials generated in several brain areas, confirming results of invasive recording (Smith et al. 1990) and clinical group-difference studies (Potts et al. 1998). ICA decomposed the unaveraged EEG signals composing the target-response ERP into several classes of brain EEG processes originating predominantly in frontal, central, parietal, and occipital cortex. This result adds to longstanding doubts about the specificity of ERP peak measures. In particular, it shows that parietal sources may account for less than half of the peak amplitude of the stimulus-locked positive peak at Pz (see Figure 11C), the most commonly used index of P300 magnitude. Altogether, we found four component clusters contributing to the P300 maximum at Pz—in descending order, central parietal, left and right mu, and central occipital alpha EEG processes. As well, the (3-fold) largest EEG contributor to the stimulus-locked P300 positivity at Fz is volume-conducted from the same parietal (P3b cluster) sources, casting strong doubt on the specificity of peak amplitude at Fz for indexing frontal function. Phase Resetting As in our previous single-trial analysis of some of these data (Makeig et al. 2002), partial phase resetting of ongoing intermittent alpha and theta EEG processes contributes to some features of the average ERP. However, partial phase resetting is not a sufficient model for all the ERP features. For example, the central posterior alpha cluster actually showed a postmotor response decrease in alpha power during its prolonged alpha-ringing ERP contribution (see Figure 9G and 9H), whereas the monopolar parietal P300 (or P3b) ERP feature was associated with an event-related increase in both whole EEG variance in the central parietal channel (Pz; data not shown) and in component activity variance of the P3b, Cα, and Lμ clusters that contributed the most to it. The central posterior alpha-ringing feature of the stimulus-locked ERP can be parsimoniously described as arising through alpha phase resetting. The P300/P3b feature, on the other hand, might better conform to the usual conception of an ERP as measuring evoked response activity added to ongoing EEG activity. Note in Figure 6H, however, that the low-frequency energy of the P3b cluster component activities increased by less than 3 dB at 3 Hz, demonstrating that the “baseline” activity of these processes included slow-wave processes with similar spectral characteristics. The postmotor theta burst phenomenon comprised both a frequency-specific power increase and significant (though partial) phase locking through its two-cycle ERP duration. The variety of these ERP features suggests that assuming a strict dichotomy between evoked and phase-reset activities is unproductive. Rather, each ERP feature may be usefully and better characterized as summing event-related perturbations of various sorts in the ongoing activities of one or more localized cortical EEG processes. To produce a reproducible peak or peaks in the average ERP, these perturbations should involve some degree of (partial) phase locking of the contributing process activities to the time-locking events in one or more frequency regions. At very low (near-DC) frequencies, event-related “phase locking” implies event-related “sign locking.” For example, the P300 would never appear in ERP averages at all if it were negative-going in half the trials. Similarly, there need be no strong dichotomy between evoked phenomena that involve partial phase locking and so contribute to average ERPs, and induced phenomena that may involve changes in spectral amplitudes but do not show phase locking and thus do not contribute to average ERPs. Rather, it is important to realize that most event-related EEG dynamics have both induced and evoked aspects. Event-Related Brain Dynamics The results presented here confirm that extensive, complex, and flexible information concerning links between cognitive processes and macroscopic brain dynamics are available in noninvasive high-density EEG data. Availability of more comprehensive analysis techniques, such as that introduced here, should make EEG (and related magnetoencephalographic) data analysis of increasing interest both to cognitive neuroscientists and to neurophysiologists, as event-related EEG dynamic models complement observations of slow-changing hemodynamics while greatly expanding the restricted spatial information available from single- and multineuron spike recordings. Materials and Methods Task design ERPs were recorded from subjects who attended to randomized sequences of filled disks appearing briefly inside one of five empty squares that were constantly displayed 0.8 cm above a central fixation cross (see Figure 1) following Townsend and Courchesne (1994). The 1.6-cm square outlines were displayed on a black background at horizontal visual angles of 0 ° ± 2.7 ° and 0 ° ± 5.5 ° from fixation. During each 76-s block of trials, one of the five outlines was colored green and the other four blue. The green square marked the location to be attended. This location was varied in random, counterbalanced order across blocks. In each block, 100 stimuli (filled white disks) were displayed for 117 ms within one of the five empty squares in a pseudorandom sequence with interstimulus intervals of 250 to 1000 ms (in four equiprobable 250-ms steps). Subjects and task Fifteen right-handed volunteers (ages 19 to 53 y, mean = 30; 12 male, three female) with normal or corrected-to-normal vision participated in the experiment. Subjects were instructed to maintain fixation on the central cross while responding only to stimuli presented in the green-colored (attended) square. Subjects were required to press a thumb button held in their right hand as quickly as possible following stimuli presented in the attended location (see Figure 1). Thirty blocks of trials were collected from each subject, yielding 120 target and 480 non-target trials at each location. Subjects were given approximately 2-min breaks between blocks. EEG recordings EEG data were collected from 29 scalp electrodes mounted in a standard electrode cap (Electro-Cap International, Eaton, Ohio, United States) at locations based on a modified International 10–20 System, and from two periocular electrodes placed below the right eye and at the left outer canthus. All channels were referenced to the right mastoid with input impedance less than 5kΩ. Data were sampled at 512 Hz with an analog pass band of 0.01–50 Hz. To further minimize line noise artifacts, responses were digitally low-pass filtered below 40 Hz prior to analysis. Trials containing electrooculographic potentials larger than 70 μV or amplifier blocking were rejected, and brain responses to stimuli presented at each location in each attention condition were stored separately. Responses to target stimuli were analyzed only when (as in nearly all cases) subjects responded 150–1000 ms after target onset. The few targets followed by no such button press were not considered in the present analysis. Previous analysis Analysis of average ERP and some single-trial data from these experiments have been reported previously. Makeig et al. (1999a) first reported ICA decompositions of late (P300) target responses in a 5 × 5 matrix (five stimulus locations by five attended locations) of grand mean visual stimulus ERPs from these experiments. They reported three maximally independent ERP components of interest which they labeled P3f, P3b, and Pmp. Makeig et al. (1999b) applied the same multiple-ERP analysis to the first 250-ms period following stimulus onsets and demonstrated that distinct contributions to the N1 ERP peak were generated in the right hemisphere 9 ms earlier, on average, than in the left. ICA separates component processes mixed in scalp data based on their relative temporal independence, which should be maximally expressed in the unaveraged data. Systematic application of ICA to unaveraged data from these experiments was first demonstrated for non-target stimulus trials (Makeig et al. 2002). In that analysis, a 100-ms poststimulus period (150–250 ms after stimulus onset) was extracted from each of the over 3,000 trials for each subject, and these data were concatenated and decomposed by ICA. Some information about the target stimulus trials was also presented in Jung et al. (2001b). Here we report the results of comparing ICA decompositions of roughly 600 1-s target-response trials from each of 15 subjects. Independent component analysis Infomax ICA (Bell & Sejnowski 1995; Makeig et al. 1996) is one of a family of algorithms that exploit temporal independence to perform blind separation of underlying data sources. Lee et al. (1999) have shown that these algorithms have a common information-theoretic basis, differing chiefly in the form of distribution assumed for the sources, which may not be critical (Amari 1998). Infomax ICA finds, by natural gradient ascent, a square “unmixing” matrix that maximizes the joint entropy (Cover and Thomas 1991) of a nonlinearly transformed ensemble of zero-mean input data vectors. Maximizing joint entropy implies, under reasonable assumptions (Bell and Sejnowski 1995), minimizing mutual information among the component activities. This means that information about the simultaneous activity values of any number of the components gives minimum information about the concurrent activity values of any other components. Independent component activities are minimally correlated, both in standard second-order and in higher-order senses. That is, they each appear to ICA to be “free-running” and in this sense act as separate sources of information in the data. The power of infomax source separation in a growing range of signal processing applications derives from its basic aim to identify information sources in data, in contrast to previous root-mean-square estimation methods that aim simply to model data variance (Jung et al. 2001a). Natural-gradient logistic infomax ICA in the automated form we use here (the runica algorithm, Makeig et al. 1997) can accurately decompose mixtures of component processes having symmetric or skewed distributions without requiring nonlinearities specifically tailored to them, and can be usefully applied to EEG data from 100 or more channels. The number of time points required for training may be as few as several times the number of unmixing weights (usually the square of the number of channels), though using more (clean) training data are preferable. In turn, the number of channels must be at least equal to (and preferably larger than) the number of interpretable components to be separated (Makeig et al. 1999a). The success of ICA applied to EEG data is strictly determined by the degree to which EEG dynamics fit the ICA model. The first requirement, that the underlying sources mix linearly in the electrode recordings, is assured by the biophysics of volume conduction at EEG frequencies (Nunez 1981). The assumption of relative spatial stationarity of EEG sources is compatible, at least, with evidence of brain modularity from anatomy and functional imaging. The assumption of relative independence of the source signals is compatible with physiological models that emphasize local, short-range intracortical and radial thalamocortical coupling in the generation of local electrical synchronies in the EEG range (Salinas and Sejnowski 2001). The ultimate validity of the assumptions above in any data set cannot be guaranteed a priori. The consistency and physiologic plausibility of the results of ICA decompositions, such as we present here, including their often tight linkage to behavioral and cognitive variables, are strong indirect evidence for the workability of the assumptions and of the ICA model. Direct physiological testing of the model and its physiologic assumptions will require development of multiscale recording methods. Meanwhile, yet more flexible (but also more intricate) ICA models of EEG activity are possible (e.g., Anemueller et al. 2003). It remains to be seen, however, whether the information gain offered by such models exceeds the loss of statistical power associated with their higher complexity. As first demonstrated by simulations (Makeig et al. 2000), when training data consist of fewer large source components than channels, plus many more small source components, as might be expected in actual EEG data, large source components are accurately separated into separate output components, with the remaining output components consisting of mixtures of smaller source components. In this sense, performance of the infomax ICA algorithm degrades gracefully as the amount of noise in the data increases. For more details about applying ICA to ERP and EEG data, see Makeig et al. (1999a, 2002) and Jung et al. (2000a, 2000b). Here, the runica algorithm (available for download with the EEGLAB toolbox of Delorme and Makeig [2004a] from http://sccn.ucsd.edu/eeglab) was applied to sets of 400 to 600 1-s trials (31 channels, 256 time points) time-locked from −200 ms before to +800 ms after onsets of target stimuli presented at any of the five stimulus locations (see Figure 1). Target trials in which the subject did not respond with a button press (fewer than 5%) were removed from the data. Learning batch size was 50. Initial learning rate began near 0.0004 and gradually reduced to 10−6 during 50–150 training iterations that required about 30 min of computer time. Results of the analysis were relatively insensitive to the exact choice of learning rate or batch size. Reducing the stopping weight change from 10−6 to 10−7 did not appear to change the resulting decompositions qualitatively, although when decomposing data from many more channels, we have since noted an advantage to continuing to train until weight change falls below 10−7. Component clustering Commonly in ERP research, neural activity expressed in periocular data channels is ignored for fear of mislabeling eye-activity artifacts as brain activity. Some ICA components of EEG records can be clearly identified as accounting primarily for eye movements, line or muscle noise, or other artifacts through their characteristic scalp maps and activity time courses (Makeig et al. 1996; Jung et al. 2000a, 2000b). Subtracting the projections of artifactual components from averaged or single-trial data can eliminate or strongly reduce these artifacts while preserving the remaining nonartifactual EEG phenomena in all of the data channels. ICA thus makes it possible to examine periocular EEG activity apart from eye movements. Here, the total of 465 (31 times 15) component maps and mean activity log spectra from the 15 subjects were clustered by applying a modified Mahalanobis distance measure (Enghoff, 1999 see Appendix of Jung et al. 2001b) to vectors coding differences in the component 31-channel (x, y) map gradients and activity log spectra after reduction to 12 and five dimensions respectively by principal component analysis. Cluster membership was in a few cases then further adjusted by eye for uniformity. Clustering based on scalp map gradients and activity spectra, as reported here, is one of several possible component clustering approaches, whose relative advantages have not yet been explored. Event-related spectral dynamics To examine stimulus- and response-induced changes in the EEG spectrum, we computed ERSP transforms (Makeig 1993) for each channel and each clustered independent data component using the publicly available EEGLAB toolbox (Delorme and Makeig 2004a, 2004b). ERSPs show changes in decibels from baseline in spectral power across a broad frequency range (here, 3–50 Hz). The time/frequency analysis used Hanning-windowed sinusoidal wavelets of 3 cycles at 3 Hz, rising linearly to about 15 cycles at 30 Hz. This modified wavelet transform was selected to optimize the trade-off between temporal resolution at lower frequencies and stability at higher frequencies. Constructing surrogate data sets by shuffling the data epoch subwindows used to construct the time-locked spectral average allowed choosing an initial within-subject significance cutoff (not corrected for multiple comparisons) of p < 0.01. To construct between-subject mean ERSPs, we used binomial statistics to select a significance cutoff based on the minimum number of subjects required to have significant differences (in the same direction) from baseline at a given time/frequency point (p < 0.0001). ERSP transforms of the data were computed at each channel and then for each clustered data component. To test for partial phase locking (i.e., nonrandom phase relationships) between EEG processes and the occurrence of experimental events across trials, we used intertrial phase coherence (Makeig et al. 2002). To test the presence of nonrandom phase relationships (possibly including fixed delays) between activities in different (maximally) independent components, we performed event-related phase coherence analysis (Makeig et al. 2002; Delorme et al. 2002), again with a single-subject bootstrap significance threshold of p < 0.01 (uncorrected) between pairs of independent components (from the same subject) included in each pair of independent component clusters (defined below). To exclude the possibility that the observed phase linkages arose only from common phase locking of the portion of the single-trial data constituting the ERP, we also subtracted the concurrent mean ERP from each trial before computing phase coherence. Functions to compute and plot the time/frequency measures used here are also available in the EEGLAB toolbox. Equivalent dipole modeling Both simple anatomic considerations and observed results of ICA decomposition suggest that cortical EEG sources may be usefully modeled as patches of cortex with partially synchronous local field activities. In brief, the high local coupling density of both excitatory and, particularly, inhibitory neurons in cortex means that local field potentials sufficiently synchronous to create measurable EEG signals should tend to extend through a compact spatial domain—roughly speaking, a patch of cortex of unspecified extent. Through volume conduction, partially synchronous activities within cortical source patches produce far-field potentials throughout the brain and on the scalp. The distribution of the scalp electrical field produced by such a source patch is nearly identical to that of a small dipolar potential element whose geometry is like a tiny flashlight battery oriented perpendicular to the cortical surface. This “battery” is termed the equivalent dipole for the cortical source. Here, we used a relatively simple and well-known method for fitting the positions and orientations of equivalent dipoles in a four-shell spherical head model for each component (BESA; Megis Software, Munich, Germany). To reduce the time required to process the 485 component maps, we used a version (3.0) of the BESA software that allowed batch processing to fit single-dipole models to each component scalp map. (A dipfit tool set by R. Oostenveld, producing equivalent results, is now available at http://sccn.ucsd.edu/eeglab/dipfit.html). Some bilaterally symmetric component maps were better fit with symmetric dual-dipole models. The successful fits of single-dipole models for many of the clustered components is compatible with their generation within a single, compact patch of cortex, while bilateral dual-dipole models are compatible with tightly coupled oscillatory activity (without net phase delay) in two bilaterally symmetric cortical patches densely connected via corpus callosum or common subcortical drive. To distinguish the relative regional locations of the component clusters, the scalp maps for the individual components in each cluster were first oriented similarly (e.g., so as to all be positively correlated), normalized, and averaged. These cluster mean maps were then fit with single-dipole models to roughly illustrate the regional distinctions between the sources of the component clusters. Finally, the cluster-mean dipoles and event-related time/frequency information measured by ERSP, intertrial phase coherence,and event-related phase coherence analysis of all the single trials were visualized together in three dimensions using an animated display developed by Delorme et al. (2002). Video 1 Postmotor Theta Dynamics An animation representing grand mean patterns of event-related dynamics in the theta band. Black traces in the lower panel show the envelope of the grand mean ERP time-locked to the subject button press; the dotted vertical line shows the median time of stimulus onset (response minus 352 ms). Theta dynamics computed in a window (center frequency 4.87 Hz, 3-cycle Hanning taper) centered (blue vertical line) 89 ms after the button press (red vertical line). Each sphere in the upper panel represents the location of the equivalent dipole for a component cluster. Approximate projections of the equivalent-dipole locations are shown in shadow on three planes from an average magnetic resonance image (Montreal Neurological Institute). Log spectral power changes (relative to prestimulus baseline) are indicated by the sizes of the spheres (see key, bottom right). Nongrey sphere colors indicate consistent intertrial phase locking (intertrial phase coherence). Colored cylinders joining spheres indicate significant event-related phase coherence between cluster components. (4.26 MB MOV) This report was supported by The Swartz Foundation, the National Institutes of Health (NINDS NS34155 and NIMH MH36840), and the Howard Hughes Medical Institute. Thanks to Stefan Debener for detailed comments. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. JT and EC conceived and designed the experiments. MW performed the experiments. SM, AD, and MW analyzed the data. SM, AD, T-PJ, and TJS contributed reagents/materials/analysis tools. SM wrote the paper. Academic Editor: Rainer Goebel, Universiteit Maastricht Abbreviations CMcentral midline EEGelectroencephalographic ERPevent-related potential ERSPevent-related spectral perturbation FMfrontal midline ICAindependent component analysis RTreaction time ==== Refs References Ardekani B Choi S Hossein-Zadeh GA Porjesz B Tanabe JL Functional magnetic resonance imaging of brain activity in the visual oddball task Brain Res Cogn Brain Res 2002 14 347 356 12421658 Amari S Natural gradient works efficiently in learning Neural Comput 1998 10 251 276 Anemueller J Sejnowski TJ Makeig S Complex independent component analysis of frequency domain electroencephalographic data Neural Netw 2003 16 1311 1323 14622887 Aston-Jones G Chen S Zhu Y Oshinsky ML A neural circuit for circadian regulation of arousal Nat Neurosci 2001 4 732 738 11426230 Baillet S Mosher JC Leahy RM Electromagnetic brain mapping IEEE Signal Proc Mag 2001 18 14 30 Bell AJ Sejnowski TJ An information-maximization approach to blind separation and blind deconvolution Neural Comput 1995 7 1129 1159 7584893 Caplan JB Madsen JR Schulze-Bonhage A Aschenbrenner-Scheibe R Newman EL Human theta oscillations related to sensorimotor integration and spatial learning J Neurosci 2003 23 4726 4736 12805312 Courchesne E Hillyard SA Galambos R Stimulus novelty, task relevance and the visual evoked potential in man Electroencephalogr Clin Neurophysiol 1975 39 131 143 50210 Cover TM Thomas JA Elements of information theory 1991 New York John Wiley 542 Csicsvari J Jamieson B Wise K Buzsaki G Mechanisms of gamma oscillations in the hippocampus of the behaving rat Neuron 2003 23 311 322 Dale AM Sereno MI Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: A linear approach J Cogn Neurosci 1993 5 162 176 23972151 Delorme A Makeig S EEGLAB: MATLAB toolbox for electrophysiological data analysis. San Diego(California): Institute for Neural Computation, University of California at San Diego. Available: http://sccn.ucsd.edu/eeglab via the Internet 2004a Accessed 10 Feb 2004 Delorme A Makeig S EEGLAB: An open-source toolbox for analysis of EEG dynamics J Neurosci Methods 2004b 134 9 21 15102499 Delorme A Makeig S Fabre-Thorpe M Sejnowski TJ From single-trial EEG to brain area dynamics Neurocomputing 2002 44–46 1057 1064 Dien J Spencer KM Donchin E Localization of the event-related potential novelty response as defined by principal components analysis Brain Res Cogn Brain Res 2003 17 637 650 14561451 Ebmeier KP Steele JD MacKenzie DM O'Carroll RE Kydd RR Cognitive brain potentials and regional cerebral blood flow equivalents during two- and three-sound auditory “oddball tasks.” Electroencephalogr Clin Neurophysiol 1995 95 434 443 8536572 Elbert T Rockstroh B Threshold regulation: A key to the understanding of the combined dynamics of EEG and event-related potentials J Psychophysiol 1987 4 317 333 Enghoff S Moving ICA and time-frequency analysis in event-related EEG studies of selective attention. Technical Report INC-9902 1999 La Jolla (California) Institute for Neural Computation, University of California at San Diego Ford JM Sullivan EV Marsh L White PM Lim KO The relationship between P300 amplitude and regional gray matter volumes depends upon the attentional system engaged Electroencephalogr Clin Neurophysiol 1994 90 214 228 7511503 Forss N Silen T Temporal organization of cerebral events: Neuromagnetic studies of the sensorimotor system Rev Neurol (Paris) 2001 157 816 821 11677402 Fries P Reynolds J Rorie A Desimone R Modulation of oscillatory neuronal synchronization by selective visual attention Science 2001 291 1560 1563 11222864 Gehring WJ Willoughby AR The medial frontal cortex and the rapid processing of monetary gains and losses Science 2002 295 2279 2282 11910116 Gevins A Smith ME McEvoy L Yu D High-resolution EEG mapping of cortical activation related to working memory: Effects of task difficulty, type of processing, and practice Cereb Cortex 1997 7 374 85 9177767 Giesbrecht B Woldorff MG Song AW Mangun GR Neural mechanisms of top-down control during spatial and feature attention NeuroImage 2003 19 496 512 12880783 Halgren E Squires NK Wilson CL Rohrbaugh JW Babb TL Endogenous potentials generated in the human hippocampal formation and amygdala by infrequent events Science 1980 210 803 805 7434000 Halgren E Baudena P Clarke JM Heit G Liegeois C Intracerebral potentials to rare target and distractor auditory and visual stimuli. 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II: Medial, lateral and posterior temporal lobe Electroencephalogr Clin Neurophysiol 1995b 94 229 250 7537196 Hari R Salmelin R Makela JP Salenius S Helle M Magnetoencephalographic cortical rhythms Int J Psychophysiol 1997 26 51 62 9202994 Hasselmo M Hay J Ilun M Gorchetchnikov A Neuromodulation, theta rhythm and rat spatial navigation Neural Netw 2002 15 689 707 12371520 Holroyd CB Dien J Coles MG Error-related scalp potentials elicited by hand and foot movements: Evidence for an output-independent error-processing system in humans Neurosci Lett 1998 242 65 68 9533395 Hupe J-M James A Girard P Lomber SG Dayne BR Feedback connections act on the early part of the responses in monkey visual cortex J Neurophysiol 2001 85 134 145 11152714 Ikeda A Luders HO Collura TF Burgess RC Morris HN Subdural potentials at orbitofrontal and mesial prefrontal areas accompanying anticipation and decision making in humans: A comparison with Bereitschaftspotential Electroencephalogr Clin Neurophysiol 1996 98 206 212 8631280 Jung T-P Makeig S Humphries C Lee TW McKeown MJ Removing electroencephalographic artifacts by blind source separation Psychophysiology 2000a 37 163 178 10731767 Jung T-P Makeig S Westerfield M Townsend J Courchesne E Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects Clin Neurophysiol 2000b 111 1745 1758 11018488 Jung T-P Makeig S McKeown MJ Bell AJ Lee T-W Imaging brain dynamics using independent component analysis P IEEE 2001a 89 1107 1122 Jung T-P Makeig S Westerfield M Townsend J Courchesne E Analysis and visualization of single-trial event-related potentials Hum Brain Mapp 2001b 14 166 185 11559961 Kahana MJ Sekuler R Caplan JO Kirschen M Madsen JR Human theta oscillations exhibit task dependence during virtual maze navigation Nature 1999 399 781 784 10391243 Klopp J Marinkovic K Chauvel P Nenov V Halgren E Early widespread cortical distribution of coherent fusiform face selective activity Hum Brain Mapp 2000 11 286 293 11144757 Knight RT Scabini D Woods DL Clayworth CC Contributions of temporal-parietal junction to the human auditory P3 Brain Res 1989 502 109 116 2819449 Kranczioch C Debener S Engel AK Event-related potential correlates of the attentional blink phenomenon Brain Res Cogn Brain Res 2003 17 177 187 12763203 Lee TW Girolami M Bell AJ Sjenowski TJ A unifying information-theoretic framework for independent component analysis Comput Math Appl 2000 39 1 21 Luu P Tucker DM Regulating action: Alternating activation of midline frontal and motor cortical networks Clin Neurophysiol 2001 112 1295 1306 11516742 Luu P Tucker D Makeig S Frontal midline theta and the error-related negativity: Neurophysiological mechanisms of action regulation Psychophysiology 2004 In press Makeig S Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones Electroencephalogr Clin Neurophysiol 1993 86 283 293 7682932 Makeig S Inlow M Lapses in alertness: Coherence of fluctuations in performance and the EEG spectrum Electroencephalogr Clin Neurophysiol 1993 86 23 35 7678388 Makeig S Bell AJ Jung T-P Sejnowski TJ Independent component analysis of electroencephalographic data. In: Touretzky D, Mozer M, Hasselmo M, editors. Advances in neural information processing systems, Volume 8 1996 Cambridge (Massachusetts) Massachusetts Institute of Technology Press 145 151 Makeig S Jung T-P Ghahremani D Bell AJ Sejnowski TJ Blind separation of auditory event-related brain responses into independent components Proc Natl Acad Sci U S A 1997 94 10979 10984 9380745 Makeig S Westerfield M Jung T-P Covngton J Townsend J Functionally independent components of the late positive event-related potential during visual spatial attention J Neurosci 1999a 19 2665 2680 10087080 Makeig S Westerfield M Townsend J Jung T-P Courchesne E Functionally independent components of early event-related potentials in a visual spatial attention task Philos T Roy Soc B 1999b 354 1135 1144 Makeig S Jung T-P Ghahremani D Sejnowski TJ Independent component analysis of simulated ERP data. In: Nakata T, editor. Integrated human brain science 2000 Amsterdam Elsevier Makeig S Westerfield M Jung T-P Enghoff S Townsend J Dynamic brain sources of visual evoked responses Science 2002 295 690 693 11809976 Makeig S Debener S Onton J Delorme A Mining event-related brain dynamics Trends Cogn Sci 2004 8 204 210 15120678 Manthey S Schubotz RL von Cramon DY Premotor cortex in observing erroneous action: An fMRI study Brain Res Cogn Brain Res 2003 15 296 307 12527103 McArthur G Budd T Michie D The attentional blink and P300 Neuroreport 1999 10 3691 3695 10619668 Mizuki Y Tanaka M Isozaki H Nishijama H Inanaga K Periodic appearance of theta rhythm in the frontal midline area during performance of a mental task Electroencephalogr Clin Neurophysiol 1980 49 345 351 6158411 Moores K Clark C Hadfield JL Brown GC Taylor DJ Investigating the generators of the scalp recorded visuo-verbal P300 using cortically constrained source localization Hum Brain Mapp 2003 18 53 77 12454912 Nunez P Electric fields of the brain: The neurophysics of EEG 1981 Oxford Oxford University Press 484 Pfurtscheller G Aranibar A Event-related cortical desynchronization detected by power measurements of scalp EEG Electroencephalogr Clin Neurophysiol 1977 42 817 826 67933 Pfurtscheller G Neuper C Krausz G Functional dissociation of lower and upper frequency mu rhythms in relation to voluntary limb movement Clin Neurophysiol 2000 111 1873 1879 11018505 Polich J Comerchero M P3a from visual stimuli: Typicality, task, and topography Brain Topogr 2003 15 141 152 12705810 Potts G Tucker DM Frontal evaluation and posterior representation in target detection Brain Res Cogn Brain Res 2001 11 147 156 11240117 Potts G Hirayasu Y O'Donnell BF Shenton ME McCarley RW High-density recording and topographic analysis of the auditory oddball event-related potential in patients with schizophrenia Biol Psychiatry 1998 15 982 989 Rockstroh B Muller M Heinz A Wagner M Berg D Modulation of auditory responses during oddball tasks Biol Psychol 1996 43 41 55 8739613 Ruchkin DS Johnson R Canoune HL Ritter W Hammer M Multiple sources of P3b associated with different types of information Psychophysiology 1990 27 157 176 2247547 Ruchsow M Grothe J Spitzer M Kiefer M Human anterior cingulate cortex is activated by negative feedback: Evidence from event-related potentials in a guessing task Neurosci Lett 2002 14 203 206 Salinas E Sejnowski TJ Correlated neuronal activity and the flow of neural information Nat Rev Neurosci 2001 2 539 550 11483997 Seidenbecher T Laxmi TR Stork O Paper H-C Amygdalar and hippocampal theta rhythm synchronization during fear memory retrieval Science 2003 301 846 850 12907806 Shima K Tanji J Role for cingulate motor area cells in voluntary movement selection based on reward Science 1998 282 1335 1338 9812901 Simson R Vaughn HG Ritter W The scalp topography of potentials in auditory and visual Go/Nogo tasks Electroencephalogr Clin Neurophysiol 1977 43 864 875 73454 Smith ME Halgren E Sokouk M Gaudena P Musolino A The intracranial topography of the P3 event-related potential elicited during auditory oddball Electroencephalogr Clin Neurophysiol 1990 76 235 248 1697255 Soltani M Knight R Neural origins of the P300 Crit Rev Neurobiol 2001 14 199 224 Sutton S Braren M Zubin J John ER Evoked-potential correlates of stimulus uncertainty Science 1965 150 1187 1188 5852977 Townsend J Courchesne E Parietal damage and narrow “spotlight” spatial attention J Cogn Neurosci 1994 6 220 232 23964973 Uchida S Maehara T Hirai N Kawa K Shimizu H Theta oscillation in the anterior cingulate and beta-1 oscillation in the medial temporal cortices: A human case report J Clin Neurosci 2003 10 371 374 12763350 Ullsperger M von Cramon DY Error monitoring using external feedback: Specific roles of the habenular complex, the reward system, and the cingulate motor area revealed by functional magnetic resonance imaging J Neurosci 2003 23 4308 4314 12764119 von Stein A Chiang C Konig P Top-down processing mediated by interareal synchronization Proc Nat Acad Sci U S A 2000 97 14748 14753 Worden MS Foxe JJ Wang N Simpson GV Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha-band electroencephalography increases over occipital cortex J Neurosci 2000 20 RC63 10704517

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020177Correspondence and Other CommunicationsCell BiologyScience PolicyScientists and Bioethics Councils CorrespondenceMcLaren Anne 6 2004 15 6 2004 15 6 2004 2 6 e177Copyright: © 2004 Anne McLaren.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Reason as Our Guide Beyond Therapy Ethics As Our Guide A Voice for Research, a Voice for Patients Taking the Stem Cell Debate to the Public Ethereal Ethics In response to the Blackburn and Rowley essay on the President's Council on Bioethics, several thought-provoking opinions on ethical challenges in biomedical research are expressed by prominent stakeholders ==== Body I read with interest the article in a recent issue of PLoS Biology by Elizabeth Blackburn and Janet Rowley, two of the scientific members of President Bush's Council on Bioethics. Invited by the President to serve on this Council, they say that it was ‘a difficult invitation to accept’. Maybe, but that they did accept the invitation is to be applauded. As the Council's report ‘Monitoring Stem Cell Research’ states, ‘fairness in ethical evaluation and judgment depends on … fair and accurate description of the relevant facts of the case at hand’. In other (fewer) words, sound ethics requires a solid base in sound science. It is crucial that any bioethics committee or council made up of ten to twenty members should include at least two or three scientists broadly acquainted with the field in general, and with recent published findings. I was only sorry to read that Elizabeth Blackburn (who works in California but is a Fellow of the Royal Society, the United Kingdom Academy of Sciences) had her Council term terminated by White House directive on February 27, 2004. Of course, any bioethics committee or council (and I have served on several such, both in the UK and elsewhere in Europe) is likely also to include philosophers, lawyers, theologians, sociologists, and probably ‘lay’ people of appropriate interests. The scientists may well find that other members of the group have ‘strong opposing views’ on ethical issues, as well as on the costs and benefits of technologies arising from biomedical research. Elizabeth Blackburn and Janet Rowley were assured, both by Leon Kass, the chairman of the Council, and by President Bush himself, that their voices would be heard and integrated into the Council statements. It is therefore disappointing to learn that, in the ‘Beyond Therapy’ report (which I have not yet read), their requests for revision of certain aspects was declined. Were they not offered the option of a brief minority report? It would be expected in such circumstances that dissenting opinions would be recorded (as was done, for example, in the 1984 UK report by the Committee on Human Fertilisation and Embryology chaired by Mary Warnock (1984), and in some of the Opinions offered by the European Group of Ethics to the European Commission). This would be particularly appropriate, and indeed essential, when recommendations are put forward. The ‘Monitoring Stem Cells Research’ report (which I have read) contains no recommendations, but includes a rather comprehensive survey of the various ethical positions relating to human embryonic stem cell research, a historical account of the development up to the present time of federal law and policy, and a chapter on recent (almost entirely United States) developments in human stem cell research and therapy. The scientists must have contributed substantially to this section of the report. Emphasis is put on the need for research on both adult and embryonic stem cells, since at present there is no way to assess which approach has the more promising therapeutic potential for which diseases. Some funding figures are given: on human embryonic stem cell research the US National Institutes of Health spent $10.7 million in 2002 and $17 million in 2003, with an estimated total spent by US companies of $70 million, while in the same two years the National Institutes of Health spent $170 million in 2002 and $181.5 million in 2003 on adult stem cell research. However, it is not obvious that there are any US scientists wanting to work on human embryonic stem cells within the constraints of US federal funding who are prevented from doing so by lack of money. To my mind, the major deficiency in the ‘Monitoring Stem Cells Research’ report is the almost complete lack of reference to what Elizabeth Blackburn and Janet Rowley correctly call ‘years of rigorous and careful research in animal models’. Some mention is made of experiments with human embryonic stem cells in immunologically handicapped mice, but in any such model both the stem cells and the mice are difficult to work with. Much of the science-based optimism that human embryonic stem cells may eventually prove of therapeutic value springs from the results of experiments with mouse embryonic stem cells in intact mice. Curiously, only a single such experiment is cited: an impressive but somewhat recondite piece of work from Jaenisch's laboratory (Rideout et al. 2002), using cloned and genetically modified mouse embryonic stem cells to treat a form of mouse hepatitis. A wider consideration of work on animal models, together with some emphasis on the potential use of human embryonic stem cells for toxicity testing and drug design by pharmaceutical companies, is in part what Elizabeth Blackburn and Janet Rowley believe ‘would help the public and scientists better assess the content of the report’. If they requested inclusion of such material, it is unfortunate that their requests were declined. Wellcome Trust/Cancer Research UK Gurdon Institute, Cambridge University, Cambridge, United Kingdom E-mail: [email protected] ==== Refs References Blackburn E Rowley J Reason as our guide PLoS Biol 2004 2 e116 10.1371/journal.pbio.0020116 15024408 Committee on Human Fertilisation and Embryology Report of the committee of enquiry into human fertilisation and embryology 1984 London Her Majesty's Stationery Office 103 p Available at http://www.bopcris.ac.uk/img1984/ref2900_1_1.html via the Internet. Accessed 21 April 2004 President's Council on Bioethics Beyond therapy: Biotechnology and the pursuit of happiness 2003 Available at http://bioethics.gov/beyondtherapy/index.html via the Internet. Accessed 29 February 2004 President's Council on Bioethics Monitoring stem cell research 2004 Available at http://bioethics.gov/reports/stemcell/index.html via the Internet. Accessed 24 March 2004 Rideout WM Hochedlinger K Kyba M Daley GQ Jaenisch R Correction of a genetic defect by nuclear transplantation and combined cell and gene therapy Cell 2002 109 17 27 11955443

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10.1371/journal.pbio.0020177

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020180SynopsisBioinformatics/Computational BiologyBiophysicsNeuroscienceHomo (Human)Deconstructing Brain Waves: Background, Cue, and Response synopsis6 2004 15 6 2004 15 6 2004 2 6 e180Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Electroencephalographic Brain Dynamics Following Manually Responded Visual Targets ==== Body Light waves from an awaited signal—a white circle—arrive at the subject's eye; within a fraction of a second, the subject's thumb presses a button. Between eye and thumb lies the central nervous system, its feats of perception, integration, and response largely opaque to scientific scrutiny. Imaging techniques like magnetic resonance imaging can detail brain anatomy but can only broadly show changes in activity levels occurring over seconds—indirect echoes of brain function. Electrodes stuck to the scalp record coordinated neuronal symphonies, and wires inserted among neurons can capture the single-cell firing patterns of the individual instruments of the neural orchestra. But how these electrical signals map to information processing within and across neural circuits remains blurry. A new analysis sharpens the focus by separating individual brain wave patterns, measured from multiple sites across the scalp, into nine distinct process classes, each centered in an anatomically relevant brain area and producing predictable patterns as human subjects receive visual cues and produce responses. Schematic representation of the source and strength of task-related EEG signals. The animation can be accessed online at http://dx.doi.org/10.1371/journal.pbio.0020176.v001 Scalp electroencephalograms (EEGs) are dominated by waves of synchronized neuronal activity at specific frequencies. Decades of research have associated wave patterns recorded at different scalp regions with different states of alertness—attending, drowsy, sleeping, or comatose; eyes open or closed—and gross abnormalities, such as seizure, brain damage, and tumor. In order to separate EEG responses to specific events from background, state-related activity, researchers repeat an experiment like the button-press exercise tens or hundreds of times and average the EEG across trials. By averaging out background activity, this technique reveals a characteristic waveform, called an event-related potential (ERP). It differs by electrode location, but often contains a large positive wave that peaks 300 milliseconds or more after an awaited visual cue. In the current paper, Scott Makeig et al. argue that ERP averaging removes important information about ongoing processes and their interactions with event-related responses. Instead of averaging multiple recordings from each of 31 electrode sites, the authors applied an algorithm that seeks independent signal sources contributing to the individual tracings. The researchers measured signal source activities by the frequency and phase of wave patterns and source locations by comparing signal strength and polarity at different electrodes. Altogether, the researchers identified nine classes of maximally independent sources, each having similar locations and activities across subjects. The results dovetail neatly with prior anatomical and functional observations. This analysis demonstrates that average waveforms identified in ERP studies probably sum multiple, separate processes from several brain regions. In particular, the large positive ERP seen 300 milliseconds or more after a visual cue reflects different waveforms from frontal, parietal, and occipital cortex—areas involved in task planning, spatial relationships and movement, and visual processing, respectively. In addition, this study showed a two-cycle burst of activity in the 4–8 (theta) frequency band after button presses—another common ERP feature. The theta activity was coordinated across several signal sources, and localized to areas associated with planning and motor control. Notably, the planning component seemed to lead the motor signal. Suppression or resynchronization of several EEG processes followed the visual cue or button press. The authors theorize that such coordination might influence the speed or impact of communication between brain areas and help retune attention after significant events. Using this approach in more subjects, and under differing conditions, could provide an unprecedented glimpse of how the brain translates perception and planning into action. The results suggest that EEG data contain an untapped richness of information that could give researchers and clinicians a new window into thought in action.

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PMC423148

CC BY

2021-01-05 08:21:11

no

PLoS Biol. 2004 Jun 15; 2(6):e180

utf-8

PLoS Biol

10.1371/journal.pbio.0020180

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020181Correspondence and Other CommunicationsCell BiologyScience PolicyBeyond Therapy … LetterSinsheimer Robert 6 2004 15 6 2004 15 6 2004 2 6 e181Copyright: © 2004 Robert Sinsheimer.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Reason as Our Guide Ethics As Our Guide Scientists and Bioethics Councils A Voice for Research, a Voice for Patients Taking the Stem Cell Debate to the Public Ethereal Ethics In response to the Blackburn and Rowley essay on the President's Council on Bioethics, several thought-provoking opinions on ethical challenges in biomedical research are expressed by prominent stakeholders ==== Body It is indeed regrettable that a distinguished and thoughtful scientist such as Elizabeth Blackburn should have been dismissed from the President's Council on Bioethics. Scientific perspectives such as hers are surely needed on this committee. Her dismissal was apparently the consequence of her disagreement with some of the text of the Council's report, “Beyond Therapy: Biotechnology and the Pursuit of Happiness” (2003). The thrust of this report is that some of the directions of current biological research will, if carried to fulfillment, result in major changes in the nature of human life—changes that the report regards with foreboding. In their essay, Drs. Blackburn and Rowley (2004) try to bypass these concerns with the argument that we really are not able to accomplish any of these changes yet and, indeed, some may never be possible. I would suggest that as scientists we should face these issues forthrightly. We should not seek refuge in presentday uncertainties. The authors of the report are not naïve nor ignorant. Yes, if these lines of research are successful, their outcome will change the nature of human life. As an example, consider current research into the causes of aging. Clearly, we do not at present know how to achieve major increases in the human life span (although we are able to do so in lower life forms). But it is plausible that we will learn how to do so. And surely a, say, doubling of the human life span would change the nature of human life. Likewise, if we learn to modify the human gene pool so as to produce exceptional individuals or to alter human capabilities, or if powerful drugs are developed that may commandeer the human psyche, the nature of human life will be altered. But so be it. The nature of human life has changed repeatedly and profoundly in the past—with the invention of agriculture, with the invention of writing, with the development of machines and mechanical power, with the advent of modern science and medicine. The nature of human life is different in 2004 a.d. from what it was in 1000 a.d. or 46 b.c. or 5000 b.c. or 10,000 b.c., and it will change again in the future. The concerns expressed in the report are earnest, and they should be confronted in earnest. Department of Molecular, Cellular, and Developmental Biology, University of California at Santa Barbara, Santa Barbara, California, United States of America E-mail: [email protected] ==== Refs References Blackburn E Rowley J Reason as our guide PLoS Biol 2004 2 e116 10.1371/journal.pbio.0020116 15024408 President's Council on Bioethics Beyond therapy: Biotechnology and the pursuit of happiness 2003 Available at http://bioethics.gov/reports/beyondtherapy/index.html via the Internet. Accessed 19 April 2004

15208726

PMC423149

CC BY

2021-01-05 08:26:26

no

PLoS Biol. 2004 Jun 15; 2(6):e181

utf-8

PLoS Biol

10.1371/journal.pbio.0020181

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020182Correspondence and Other CommunicationsCell BiologyScience PolicyA Voice for Research, a Voice for Patients CorrespondencePerry Daniel 6 2004 15 6 2004 15 6 2004 2 6 e182Copyright: © 2004 Daniel Perry.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Reason as Our Guide Beyond Therapy Ethics As Our Guide Scientists and Bioethics Councils Taking the Stem Cell Debate to the Public Ethereal Ethics In response to the Blackburn and Rowley essay on the President's Council on Bioethics, several thought-provoking opinions on ethical challenges in biomedical research are expressed by prominent stakeholders ==== Body In the very thoughtful essay “Reason as Our Guide” by Drs. Elizabeth Blackburn and Janet Rowley (2004), the authors highlight a key concern with the reports published by the President's Council on Bioethics—the lack of credible scientific information being passed on to policy makers. Blackburn and Rowley point out many areas of the report “Monitoring Stem Cell Research” that needed correction from a scientific standpoint. While it is impossible to include every suggestion in a report that seeks to draw consensus from a large panel of members, in a heated, political debate like that surrounding embryonic stem cell research and therapeutic cloning, providing the most accurate and complete scientific information to policy makers is crucial. Unfortunately, with the recent dismissal of Dr. Blackburn from the Council, there will now be one less voice for scientific research and for the potential the research holds for curing disease and alleviating the suffering of millions. Speaking for the Coalition for the Advancement of Medical Research, our concern is not only the small number of researchers on the Council and lack of complete scientific data being shared with policy makers, but the absence of patient representation on the Council itself. With the exception of public comment periods, patient organizations have no voice in the work of the Council as it discusses issues that profoundly impact them. Now, with one less member standing up for research and thus patients, our concern grows even stronger. The Blackburn and Rowley essay also correctly points out that there is more published work on adult stem cell research because of a “paucity of funding for research using embryonic stem cells.” Despite this lack of federal and private funding, advances continue to be made—but just think of the advances we could have had if only there were a supportive federal policy that encouraged embryonic stem cell research instead of stifling it. We hope—in light of scientific advances made over the past several years and the strong support of the scientific community, including the National Institutes of Health, the Health and Human Services Department, and the National Academy of Sciences—that the President will reevaluate the current federal policy for stem cell research and consider easing the restrictions. We commend Drs. Blackburn and Rowley for trying to set the record straight in their essay, and applaud their efforts to stand up for medical research, which has the potential to benefit us all. Coalition for the Advancement of Medical Research, Washington, District of Columbia, United States of America E-mail: [email protected] ==== Refs References Blackburn E Rowley J Reason as our guide PLoS Biol 2004 2 e116 10.1371/journal.pbio.0020116 15024408 President's Council on Bioethics Monitoring stem cell research 2004 Available at http://bioethics.gov/reports/stemcell/index.html via the Internet. Accessed 24 March 2004

15208727

PMC423150

CC BY

2021-01-05 08:21:11

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PLoS Biol. 2004 Jun 15; 2(6):e182

utf-8

PLoS Biol

10.1371/journal.pbio.0020182

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020183FeatureDevelopmentEcologyEvolutionZoologyAnimalsInsectsEverything You Always Wanted to Know about Sexes FeatureWhitfield John 6 2004 15 6 2004 15 6 2004 2 6 e183Copyright: © 2004 John Whitfield.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.What defines a sex? Although we tend to think there are only two - males and females - there are many different ways to mix and match the attributes of sexes ==== Body From a human perspective, sexes seem a relatively simple thing to get one's head around—there are females, and there are males. But our perspective seems biased and narrow when applied to life as a whole, says evolutionary biologist Laurence Hurst of the University of Bath, United Kingdom.“If you were a single-celled alga sitting in a pond, you wouldn't see the world as splitting into males and females.” In fact, different species have evolved a bewildering number of ways to mix and match the attributes of sexes. Some do not have males and females, but have adaptations that mean each individual performs a specific role during sex. There are other species of which every member is sexually equivalent, but individuals nevertheless divide into groups for the purposes of mating. And in some species, individuals make both eggs and sperm (Box 1). This biological diversity has produced a semantic muddle among biologists—everyone who thinks about the evolution of sexes seems to have a slightly different take on what a sex is. “The literature is highly confusing—we need to clarify our terminology,” comments Rolf Hoekstra, a geneticist at the University of Waageningen in the Netherlands. As things stand, there are three main aspects to the definition of a sex: who you are, who you can mate with, and who your parents are. The third part of this trinity—parental number—shows the least variation in nature. No known organism needs more than one mother and one father. But even this assumption is now starting to break down at the level of biological systems. In a recently discovered hybrid system within the harvester ant genus Pogonomyrmex, queens must mate with two types of males to produce both reproductive individuals and workers (Figure 3). These ants are the first species known which truly has more than two sexes—with colonies effectively having three parents— argues Joel Parker of the University of Lausanne, Switzerland. Figure 3 An Ant with Three Sexes? (A) Two males from the harvester ant genus Pogonomyrmex, one from each genetic strain. In a recently discovered hybrid system, queens must mate with both types of males to produce reproductives and workers. Photo courtesy of Charles Hedgcock, Charles Hedgcock Photography, Tucson, Arizona, United States. (B) Hybrid workers emerging from a nest. Photo courtesy of Veronica Volny, University of California, Berkeley, California, United States. Parker's ideas might reactivate evolutionary biologists' interest in sexes, which has lain somewhat dormant since the 1990s. It could also provide a new route to experiments— something often lacking in the field. Not everyone agrees that it makes sense to define the ants' genetic quirks as new sexes. Each ant is still only a mix the genes from no more than two parents, after all. But Parker believes that our current ideas about mating systems may not be adequate to describe the ingenuity of evolution. “Until you see a three-sex system, you don't know what it'll look like,” he says. Little and Large To address whether these ants have more than two sexes, we first need to look at other candidates for sexes, their numbers in different species, and how these systems evolved. One thing biologists do agree on is that males and females count as different sexes. And they also agree that the main difference between the two is gamete size: males make lots of small gametes—sperm in animals, pollen in plants—and females produce a few big eggs. But researchers also think that before males and females evolved, sex occurred between organisms with equal-sized gametes, a state called isogamy. Evolutionarily speaking, an isogamous species faces two pressures. Individuals can make more smaller gametes, thus increasing their potential number of offspring, or they can make fewer bigger gametes, thus giving their offspring a better start in life by providing them with more resources. Theoretical analyses suggest that this pressure is particularly great if being big carries large benefits, making isogamy unstable. The original identical gametes will evolve towards the opposite ends of the size spectrum. In many species, however, one size of gamete still fits all. The organisms that have hung on to isogamy are found among the less complex branches of life, such as fungi, algae, and protozoa. This might be because large gametes, yielding well-funded zygotes, are likely to be more strongly selected if the resulting offspring needs to grow into a large and complex organism. The benefits of large gametes in simple and unicellular organisms are not so obvious. Some support for this hypothesis comes from the algae belonging to the group Volvocales. The variation in gamete size within each species matches its degree of complexity. For example, the unicellular species Chlamydomonas rheinhardtii is isogamous, while Volvox rouseletti, which lives in balls of up to 50,000 cells, has large and small gametes (Figure 4). Figure 4 Four Different Species of Volvocales Algae (A) Gonium pectorale, (B) Eudorina elegans, (C) Pleodorina californica, and (D) Volvox carteri. These are unicellular organisms that live in colonies and have both large and small gametes. Photo courtesy of Aurora M. Nedelcu, from the Volvocales Information Project (http:\\www.unbf.ca\vip\index.htm). The Opposite of Sexes? The question of sexes, and their number, is complex in isogamous species. Such species still typically comprise different groups for mating purposes. They have genes that allow them to mate with everyone except those belonging to the same “mating type” (this is presumably to avoid inbreeding and to produce offspring that are genetically diverse to cope with environmental change or biological enemies). Species with mating types, rather than males and females, aren't limited to two interbreeding groups: the ciliate protozoan Tetrahymena thermophila has seven, and the mushroom Schizophyllum commune has more than 28,000, for example. Some biologists call these mating types sexes; others think that, in the absence of traits other than sexual compatibility or the lack thereof, it makes more sense to view species with many mating types as having no sexes, rather than lots. Yet most isogamous species have only two mating types. This seems perverse—it excludes half the population as potential mates without gaining the benefits of specialization in sexual biology. With William Hamilton, Hurst came up an explanation for this apparent inefficiency. Two-group mating systems, they proposed, evolved as a way for genes in the nucleus to police the DNA in organelles. Cellular structures with their own genomes, such as mitochondria and chloroplasts, can divide more rapidly than the cells that house them. If the inheritance of organelles was biparental, selfish mutations in their DNA could spread rapidly, Hurst and Hamilton showed. A nuclear gene that enforces uniparental inheritance of organelles, along with a label that allows such cells to recognize each other so that their nuclear genes can share the benefits of cytoplasmic policing, should be favored. The mating biology of isogamous species offers considerable support for this idea. The aforementioned C. rheinhardtii, for example, comes in two mating types called plus and minus. When the two fuse, the plastid of the minus cell is detroyed. Most isogamous species that fuse cells have a similar mechanism. Male-killer parasites such as Wolbachia, a parasite of arthropods, show the selection pressure that intracellular passengers can exert (see also the primer by Wernegreen in the March issue of PLoS Biology). And cellfusion experiments hint that biparental inheritance of organelles does indeed cause problems, says Hurst. “Hybrids are often rubbish, but they can be better if a drug is administered that inhibits the mitochondria of one cell line.” The species that have lots of mating types, such as ciliate protozoa, exchange nuclear DNA, but not cytoplasm, and hence not intracellular organelles. Since individuals are freed from the need to police their organelles or keep out parasites, selection favors the widest assortment of possible mates, and thus the evolution of a large number of mating types so that one's own type—which one can't mate with—is a small subset of the population. It is possible to imagine species with cytoplasmic policing likewise having many mating types, but such a situation would be much more prone to break down and be invaded by selfish agents than one with two clearly defined types, which is what we usually see in nature. Some have argued that cytoplasmic policing might also be a selective force for different-sized gametes. Sperm could be small so that they do not import mitochondria into the egg. More than a decade after he devised it, Hurst's is still the leading hypothesis explaining the number of mating types in a species. But experimental evidence remains frustratingly elusive. “I wouldn't say I was entirely satisfied,” says Hurst. “We've got all these ideas, and they turn out to be quite hard to test—there's no simple thing one can do on a single species.” There are species where the uniparental inheritance of organelles is not so strictly enforced, says Hoekstra, such as yeasts and plants. “It's not easy to see if selection [on organelles] is strong enough,” he says. Three's Company Yet even in a species such as S. commune, with its thousands of mating types, each sexual encounter involves only two cells. Nor are we likely to find a species that defies this pattern. The technical difficulties of combining more than two sets of genetic information into one individual, and of parceling out that information during meiosis, must be vast, says Brian Charlesworth of the University of Edinburgh. “We've reached the point of two cells fusing, and stuck with that; two cells are probably just as good as three,” he says. The ant colonies that Parker suggests have three parents are a hybrid of the species Pogonomyrmex rugosus and P. barbatus. The hybrids have not yet been classed as a new species, but they are well established across the southwestern United States, and there is no evidence of contemporary gene flow between hybrids and their parent species. Each ant has one parent if it is male, because male ants are produced from unfertilized eggs, or two if it is female. But each sex also comes in two genetic strains. If a queen mates with a male of her own strain, her offspring will be queens, and if she mates with a male from the other strain, the sperm will give rise to workers. So, for a colony to function fully it—and the queens it produces, because workers raise queens—must have two fathers and one mother. And if any one group were to disappear, the population as a whole would go extinct—unlike fungal mating types, where it's easy to imagine that the species would carry on if a few disappeared. “If you lose any one, the whole thing collapses,” says Parker. “It's really different from any other system.” So, Parker argues, Pogonomyrmex has four sexes: the males and females of each strain. The idea is particularly potent if one views a social insect colony as a “superorganism,” with the workers equivalent to the cells of a body. It's as if a female mates with one male to produce her offspring's somatic cells, and another to produce its germ cells. The ants form chaotic mating swarms, so most queens have no problem mating multiply and getting sperm from males of both strains, although one would expect that males would strongly favor mating with females of their own strain. It's not known how the system originated. Separating the worker and reproductive castes by genetics—other social insects do this by environment, that is, by rearing workers and reproductives differently—may allow selection to operate more efficiently on each lineage, and the workers may benefit from hybrid vigor: field researchers report them as being highly aggressive. In an echo of Hurst's hypothesis, the system also mixes mitochondrial and nuclear genes differently in queens and workers. Some evolutionary biologists, such as Charlesworth, do not consider Pogonomyrmex's mating types sexes, arguing that to define sexes in yet another way only confuses the picture further. “[The ants] are an interesting system, but I wasn't persuaded by Parker's interpretation,” Charlesworth says. “I'm not a fan of the idea that it's useful to use the word ‘sex’ to describe compatibility between mating types—it muddies the waters.” Others are more positive towards Parker's interpretation: “It deserves to be taken seriously,” says evolutionary biologist Eörs Szathmáry of the Collegium Budapest in Hungary. “He's thrown a stone in the water—now we need to see what kinds of ripples it makes. You can't falsify a definition in the way you can a hypothesis; what determines their fate is whether people find them useful or not.” Species in which some individuals give up their reproductive opportunities to form part of a breeding group, such as slime molds, might have a system similar to that of the ants, Parker believes. “There may be hidden mating incompatibilities,” he says. “Now [that] people know to look, we're going to start seeing more of these systems.” Figure 1 Two Individuals of Pseudobiceros bedfordi About to Have a Sperm Battle Species of the flatworm genus Pseudobiceros are hermaphroditic and have two penises that are used to inject sperm into the partner. P. bedfordi is exceptional in that it applies sperm onto the partner's skin rather than injecting it. Photo courtesy of Nico Michiels. Figure 2 Scars of Sex (A) Streaks of sperm (St) received after a mating interaction in the hermaphroditic flatworm, Pseudobiceros bedfordi. (B) Received sperm appears to “burn” holes (H) in the receiver. Some (unknown) component of the ejaculate dissolves the skin tissue. Sc, scar tissue. (C) Exceptional case where an individual received a large amount of sperm somewhere in the middle of the body, resulting in a large hole (asterisk). The the body subsequently tore in two. Individuals like these are occasionally found in the field and can regenerate much of their body. Photo courtesy of Nico Michiels. Box 1. The Best of Both Worlds? One option for dividing up the sexes is “both”—hermaphroditism. This might seem like an ideal solution—everyone becomes a potential partner, and everyone can bear offspring. In practice, however, hermaphroditism is uncommon among multicellular animals. The reasons are similar to those explaining why evolution favors unequal-sized gametes—once sexes have evolved, it's better to commit all one's resources to one role or the other, rather than try and be a jack-of-all-trades. After all, there are many good uses for mating resources other than simply producing eggs or sperm. An animal could defend a territory or provide parental care, for example. Hermaphroditism, however, is useful if one's sexual options are severely limited. In particular, it can be favored when encounters with potential mates are extremely rare. It makes no sense for an animal to invest heavily in the biological equipment of maleness, say, if it will have almost no opportunities to use it: better to hedge your bets. Animals with low or unpredictable population densities and those that are immobile, have poor senses, or lack long-distance signalling are often hermaphroditic. These include sponges, worms—whether flat, nematode, or annelid—and many molluscs (and, of course, plants, the majority of which are hermaphroditic). Most hermaphrodites still need to find at least one mate in their lifetimes: the cost of inbreeding prevents self fertilization from becoming common. Hermaphroditic animals have some weird sexual adaptations. Helix aspersa snails shoot calcareous love darts into one another. And when the marine flatworm Pseudobiceros bedfordi mates, each worm has two penises, which they fence with in a battle to smear one another with sperm without being fertilized themselves in the process (Figures 1 and 2). Such oddities result when the mating opportunities of a hermaphroditic species increase, and specialization starts to become more favorable, says evolutionary biologist Nico Michiels, of the University of Muenster in Germany. In a species with two separate sexes, males and females often have different ideas about whether a mating is a good idea—males tend to be keener, females tend to be choosier. The result can be an evolutionary arms race, with each sex evolving adaptations that help them get their way. By exercising mate choice, each sex has some influence on the types of adaptations that evolve—anything too outlandish is unlikely to be favored. This counterbalance is not present in hermaphrodites, however. Rather than having one half of a species resist a particular mating strategy, the whole species is just as likely to adopt it. “Hermaphrodites run into awkward and bizarre mating conflicts,” says Michiels. Michiels believes that hermaphroditism was the ancestral state for animals, and thinks that we might be able to find the relics of this past in contemporary species with separate sexes. To test these ideas, he is searching for groups containing closely related hermaphroditic and bisexual species. Such taxa are very rare, however. John Whitfield is a freelance science writer based in London, United Kingdom. E-mail: [email protected] ==== Refs Further Reading Allen JF Separate sexes and the mitochondrial theory of ageing J Theor Biol 1996 180 135 140 8763364 Cahan SH Keller L Complex hybrid origin of genetic caste determination in harvester ants Nature 2003 424 306 309 12867980 Hurst LD Why are there only two sexes? Proc R Soc Lond B Biol Sci 1996 263 415 422 Hurst LD Hamilton WD Cytoplasmic fusion and the nature of sexes Proc R Soc Lond B Biol Sci 1992 247 189 194 Birkhead TR Møller AP Mating conflicts and sperm competition in simultaneous hermaphrodites Sperm competition and sexual selection 1998 London Academic Press 219 254 Parker GA Baker RR Smith VGF The origin and evolution of gamete dimorphism and the male-female phenomenon J Theor Biol 1972 36 181 198 Parker JD A major evolutionary transition to more than two sexes? Trends Ecol Evol 2004 19 83 86 16701233 Randerson JP Hurst LD A comparative test for the evolution of anisogamy Proc R Soc Lond B Biol Sci 2000 268 879 884 Wernegreen JJ Endosymbiosis: Lessons in conflict resolution PLoS Biol 2004 2 e68 10.1371/journal.pbio.0020068 15024418

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PMC423151

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2021-01-05 08:26:26

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PLoS Biol. 2004 Jun 15; 2(6):e183

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PLoS Biol

10.1371/journal.pbio.0020183

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020184SynopsisGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyVirologyVirusesHomo (Human)Initiation of DNA Replication: The Genomic Context Synopsis6 2004 15 6 2004 15 6 2004 2 6 e184Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Plasticity of DNA Replication Initiation in Epstein-Barr Virus Episomes ==== Body Every time a cell divides, it must first duplicate its entire genome. Barring the occasional error, the daughter cells inherit identical copies of the parent cell's genome. With a typical human cell containing almost 9 feet of DNA made of 3 billion base pairs crammed into a nucleus about 5 microns (.0002 inches) in diameter, that's no small feat. To accomplish the job, cells engage specialized teams of protein machines, each performing different tasks during the various stages of DNA replication: initiation, duplication, quality control, and repair. Much of what we know about the molecular mechanisms of DNA replication comes from studies of bacteria. In the bacterial genome, which consists of several million base pairs, replication begins at a single site, spanning about 100 base pairs. The regulation and mechanisms of replication, even in the compact bacterial genome, are so complex that 51 years after Watson and Crick reported that the structure of DNA “immediately suggests” a mechanism for its replication, biologists are still working out the details and regulation of that mechanism. Linearized EBV episome imaged by fluorescent microscopy and aligned with the corresponding genomic map Before duplication, aptly named initiator proteins bind to DNA at replication initiation sites and break the bonds holding the complementary base pairs together, separating the double helix locally into single strands and creating two Y-shaped junctions at either end called replication forks. At each replication fork, a complex of proteins continues the business of unzipping the DNA and using the exposed single strands as templates to generate complementary daughter strands. What controls when and how individual initiation sites are activated in mammalian cells has remained obscure. Is initiation restricted to specific sites? Do specific DNA sequences control initiation events locally? Examining individual molecules of fluorescently labeled replicating DNA, Paolo Norio and Carl Schildkraut report that initiation events are not controlled by individual initiation sites but occur throughout the genome. And the activation of these sites appears to depend on what's happening at the genomic level. Using a novel technique called single molecule analysis of replicated DNA (SMARD), Norio and Schildkraut use the Epstein Barr virus (EBV) in human B cells as a model system for studying DNA replication. During the latent stage of infection, the EBV genome exists as an episome—a circular piece of extrachromosomal DNA. It replicates only once per cell cycle, during the DNA synthesis stage, and uses its host's replication machinery to do so. Using nucleotide analogs that can be detected by immunofluorescence (since the analogs attract antibodies that are fluorescently labeled), the researchers can determine the position, direction, and density of the replication forks, and then determine how replication starts, progresses, and terminates. Norio and Schildkraut studied replication using two strains of the EBV virus grown in human B cells, their natural target. Previous studies, which had largely focused on the activity of individual initiation sites, had suggested that different EBV strains vary in how initiation sites are activated and that specific initiation sites or regions likely regulate replication. Looking at larger genomic regions, Norio and Schildkraut found something different: not only do initiation sites occur throughout the genomes, but their activity “differs dramatically” in the two EBV strains and even within a strain. Differences were seen in the order of initiation site activation, in the direction of replication fork movement, and in the speed of duplication in different parts of the genome. While the two largely similar viral genomes do show some genetic differences, the authors dismiss the idea that these local differences could explain the observed variations in replication control. It's more likely, they conclude, that epigenetic modifications (such as changes in chromatin structure) produce the differences in the order and frequency of activation of initiation sites across genomic regions. It seems that initiation events are not restricted to specific genomic areas, and experimentally induced loss of individual initiation sites does not significantly affect EBV genome replication (because other sites take up the slack). This redundancy provides flexibility in determining which sites are activated. Since the EBV genome uses human replication machinery to duplicate its genome, these findings likely apply to DNA replication in mammalian cells as well. The very survival of the cell—and the health of the organism it inhabits—depends upon the faithful replication of the genome. Using processes that operate at the genomic level may afford cells the means to manage an unwieldy genome, and perhaps, more importantly, guarantee their genes safe passage to the next generation.

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PMC423152

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2021-01-05 08:26:26

no

PLoS Biol. 2004 Jun 15; 2(6):e184

utf-8

PLoS Biol

10.1371/journal.pbio.0020184

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020185SynopsisBioinformatics/Computational BiologyDevelopmentDrosophilaComputation Approach Shows Robustness of the Striped Pattern of Fruitfly Embryos Synopsis6 2004 15 6 2004 15 6 2004 2 6 e185Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Topology and Robustness in the Drosophila Segment Polarity Network A Calculus of Purpose xx ==== Body Since the days of ancient Greece, mathematics has been used to describe the world in the hopes of identifying underlying laws of nature. Physicists have long relied on mathematics to understand the behavior and interaction of particles too small to observe directly. Since it's not always possible to determine the behavioral properties of a single atom or electron, physicists characterize the behavior of these particles in terms of probability and the law of averages. Likewise, it's not always easy to tell how a single protein contributes to the behavior of a cell or organism. Faced with increasingly immense datasets—from genomes, proteomes, gene expression networks, cell signaling pathways, and more—biologists are turning to the tools of higher mathematics. High-throughput technologies like genome sequencers and microarrays generate a global picture of genomic or cellular activity, but such datasets have a high noise-to-signal ratio—the details are often subject to multiple interpretations. One way computational methods can help separate the signal from the noise is by determining the likelihood of a given set of interactions and presenting a range of possible network behaviors. When sufficient information about a biological pathway is available, experimental evidence can enhance modeling approaches to help refine the nature and role of putative network behaviors. (To learn more about computational biology, see the essay “A Calculus of Purpose,” by Arthur Lander, also in this issue of PLoS Biology.) Cell behaviors for segment polarity patterning One model system ripe for computational analysis is the fruitfly Drosophila melanogaster, genetically the best-understood multicellular organism. Drosophila development proceeds through a complex series of both sequential and simultaneous events. An elaborate network of genetic interactions transforms a single-cell Drosophila egg into a multicellular embryo with 14 discrete segments. These segments are the result of a series of hierarchical decisions, as one set of genes induces the characteristic expression pattern of another set: “gap” genes direct the striped expression pattern of “pair rule” genes, which induce expression of segment polarity genes, whose messenger RNA (mRNA) and protein products produce the characteristic 14-segment polarity pattern. While the molecules and pathways that generate the segment polarity pattern are well known, little is known about the quantitative nature of their interactions: in what concentrations do the components (for example, mRNAs and proteins) exist and what parameters (for example, binding constants, transcription rates, and gene product life spans) govern their interactions? Four years ago a group of researchers led by George von Dassow developed a model of the genetic interactions that define segment polarity, called the segment polarity network. The model used a parameter set of 48 numerical values for each computer simulation of the segment polarity pattern. Since quantitative information about the network was unavailable, the group used random values for each of the parameters, repeating the simulation for nearly 250,000 different random parameter sets. The model proved remarkably robust—the network output was largely insensitive to variation in parameter values, with a surprisingly large fraction of random parameter sets generating the desired segment polarity pattern. That so many random variables could produce the pattern means either that almost any set of parameter values can work or that only a few of the parameters are important. Now, Nicholas Ingolia reveals the mechanism accounting for this robustness and bolsters the model with recent experimental evidence. To investigate the reason for the original model's robustness, Ingolia asked whether the parameters of the model could be deconstructed into the properties of individual cells. It's known, for example, that the stable expression of two genes, called wingless (wg) and engrailed (en), within specific cells of a “prepattern” laid down early in embryogenesis is converted into the segment polarity pattern by an intercellular signaling network. Wg and en operate through positive feedback loops that activate their own expression, a process that is destined to end up with individual cells in one of two stable states of gene expression (an outcome called bistability). Since each stable state is intrinsically robust—that is, resistant to changing parameters—Ingolia hypothesized that the parameters that generate the robustness of the segment polarity pattern in von Dassow's model are those that produce this bistability. Using computational methods to simulate the behavior of individual cells, Ingolia shows that individual cells in the original model adopt three different stable states of wg and en expression. The overall pattern of the model, as well as its insensitivity to parameter variation, Ingolia concludes, emerges from the stable expression states of single cells. Parameters that do not produce bistability within single cells, Ingolia found, almost never generate the correct pattern, while those that do produce bistability are much more likely than randomly chosen parameters to generate the striped segment pattern. When Ingolia added new experimental variables to the model—the signaling protein produced by the sloppy-paired gene and its interactions with en—he could reduce the fraction of parameter sets that satisfied the bistability requirement but nonetheless failed to produce the segment polarity pattern, refining the model to reflect the realities of the cell. Such computational approaches are allowing biologists to gain valuable insights into the real-world properties and behavior of staggeringly complex biological networks. It's been over 2,000 years since Pythagoras proposed that the laws of heaven and earth reflect a numerical harmony rooted in mathematical laws. Whether that notion holds for biology, bit by bit the tools of higher mathematics are peeling back the layers of complexity to identify underlying properties of living systems.

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2021-01-05 08:21:11

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PLoS Biol. 2004 Jun 15; 2(6):e185

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PLoS Biol

10.1371/journal.pbio.0020185

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020186SynopsisEcologyEvolutionInfectious DiseasesMicrobiologyVirologyVirusesEubacteriaHomo (human)PlasmodiumYeast and FungiEcology Drives the Global Distribution of Human Diseases Synopsis6 2004 15 6 2004 15 6 2004 2 6 e186Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Ecology Drives the Worldwide Distribution of Human Diseases ==== Body It's no surprise that the Amazonian rainforest contains far more species than, say, the Siberian tundra. Over 50% of the world's species live in tropical rainforests, which cover just 6% to 7% of the earth's terrestrial surface. That the number of marine and terrestrial species declines with distance from the equator is a well-documented phenomenon called the latitudinal species diversity gradient. What's proven challenging, however, is figuring out what drives this pattern. Over 30 hypotheses have been proposed in the past two decades, but only four have garnered serious attention. These four focus on variables relating to area and energy factors, geographic constraints, and habitat diversity. Understanding the factors—both contemporary and ancient—responsible for the diversity gradient could help answer one of the fundamental questions in evolutionary ecology: what regulates species diversity? But teasing out the likely mechanisms behind this diversity has practical implications as well: mounting evidence suggests that ecological and climatic conditions influence the emergence, spread, and recurrence of infectious diseases. Global climate change is likely to aggravate climate-sensitive diseases in unpredictable ways. The number of pathogen species increases towards the equator Increasingly, public health programs aimed at preventing and controlling disease outbreaks are considering aspects of the ecology of infectious diseases—how hosts, vectors, and parasites interact with each other and their environment. The hope is that by understanding how ecological factors impact the global distribution of parasitic and infectious diseases, public health officials can predict and contain future outbreaks. Even though parasitic and infectious organisms account for a major fraction of the biological diversity on the planet, few studies have analyzed the factors affecting the spatial distribution of these organisms or attempted to quantify their contribution to biodiversity. In this issue, Vanina Guernier, Michael Hochberg, and Jean-François Guégan address the influence of ecological factors on the biological diversity and distribution of parasitic and infectious diseases and find that climatic factors are the most important determinant of the global distribution of human pathogens. The current understanding of human disease and availability of complete datasets on many parasitic and infectious diseases, the researchers explain, present a unique opportunity to explore the relationship between parasitic and infectious disease species richness (defined in their study as total number of pathogens within a given country's borders) and latitude. This information, in turn, can help identify potential factors that affect diversity gradients. After compiling epidemiological data on 332 different human pathogens across 224 countries, Guernier et al. used sophisticated statistical modeling methods to identify and characterize the influence of a number of potential contributing factors on species richness. After adjusting the model to control for cofactors that might influence the relationship between latitude and species richness indirectly rather than directly (cofactors such as the size of countries and demographic, economic, and environmental variables), the researchers confirmed that, on average (seven times out of ten), tropical areas harbor a larger number of pathogen species than more temperate areas. In other words, the species richness of human pathogens follows the same pattern seen in other species. These results, Guernier et al. argue, suggest that the latitudinal species diversity gradient “might be generated in large part by biotic interactions.” This in turn indicates that current estimates of species diversity, which ignore parasites and infectious organisms, are “substantially underestimated.” The authors went on to explore groupings of individual pathogen species within larger parasitic and infectious disease communities along the gradient and found that species present at northern latitudes are a subset of those present in equatorial areas, rather than a different set of species (a phenomenon called “nestedness”). Since nestedness is strongly associated with latitude, which is typically used as a proxy for a range of climatic factors, the researchers investigated the relationship between various climatic variables and pathogen diversity. The climatic variable most strongly correlated with diversity was the maximum range of precipitation of a region. The finding that climatic factors are largely responsible for the spatial distribution of human pathogens has important implications for predicting and managing future infectious disease outbreaks. These results counter the conventional assumption that socioeconomic conditions are the most important factor in controlling disease, indicating that global climate change could have far more significant effects on global patterns of disease, with diseases once relegated to the tropics migrating to temperate zones, for example. Identifying the links between ecology and disease, however, could lay the foundation for effective preventive strategies.

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PMC423154

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2021-01-05 08:21:11

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PLoS Biol. 2004 Jun 15; 2(6):e186

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PLoS Biol

10.1371/journal.pbio.0020186

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020187Book Reviews/Science in the MediaDevelopmentScience PolicyEscape Velocity: Why the Prospect of Extreme Human Life Extension Matters Now Book Reviewde Grey Aubrey D. N. J 6 2004 15 6 2004 15 6 2004 2 6 e187Copyright: © 2004 Aubrey D. N. J. de Grey.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Should we be considering the social and economic ramifications of a society where life-span could be limitless? ==== Body The biogerontologist David Sinclair and the bioethicist Leon Kass recently locked horns in a radio debate (http://www.theconnection.org/shows/2004/01/20040106_b_main.asp) on human life extension that was remarkable for one thing: on the key issue, Kass was right and Sinclair wrong. Sinclair suggested, as have other experts, including his mentor Lenny Guarente and the National Institute on Aging advisory council member Elizabeth Blackburn, that Kass and other bioconservatives are creating a false alarm about life extension, because only a modest (say, 30%) increase in human life span is achievable by biomedical intervention, whereas Kass's apprehensions concern extreme or indefinite life extension. Kass retorted that science isn't like that: modest success tends to place the bit between our teeth and can often result in advances far exceeding our expectations. Coping with Methuselah consists of seven essays, mostly on the economics of life extension but also including one essay surveying the biology of aging and one on the ethics of life extension. The economic issues addressed are wide ranging, including detailed analysis of the balance between wealth creation by the employed and wealth consumption in pensions and health care; most chapters focus on the United States, but the closing chapter discusses these issues in a global context. Each essay is followed by a short commentary by another distinguished author. Within their own scope, all of these contributions are highly informative and rigorous. Dishearteningly, however, all echo Sinclair's views about the limited prospects for life extension in the coming decades. In my opinion, they make three distinct oversights. The first concerns current science. Sinclair and several other prominent gerontologists are presently seeking human therapies based on the long-standing observation that lifelong restriction of caloric intake considerably extends both the healthy and total life span of nearly all species in which it has been tried, including rodents and dogs. Drugs that elicit the gene expression changes that result from caloric restriction might, these workers assert, extend human life span by something approaching the same proportion as seen in rodents—20% is often predicted—without impacting quality of life, and even when administered starting in middle age. They assiduously stress, however, that anything beyond this degree of life extension is inconceivable. I agree with these predictions in two respects: that the degree of life extension achieved by first-generation drugs of this sort may well approach the (currently unknown) amount elicitable by caloric restriction itself in humans, and that it is unlikely to be much exceeded by later drugs that work the same way. In two other ways, however, I claim they are incorrect. The first error is the assumption of proportionality: I have recently argued (de Grey 2004), from evolutionary considerations, that longer-lived species will show a smaller maximal proportional life-span extension in response to starvation, probably not much more than the same absolute increase seen in shorter-lived species. The second error is the assertion that no other type of intervention can do better. In concert with other colleagues whose areas of expertise span the relevant fields, I have described (de Grey et al. 2002, 2004) a strategy built around the actual repair (not just retardation of accumulation) of age-related molecular and cellular damage—consisting of just seven major categories of ‘rejuvenation therapy’ (Table 1)—that appears technically feasible and, by its nature, is indefinitely extensible to greater life spans without recourse to further conceptual breakthroughs. Table 1 Strategies for Engineered Negligible Senescence The seven categories of accumulating molecular or cellular side effects of metabolism whose possible contribution to age-related mammalian physical or cognitive decline is an established school of thought within contemporary biogerontology, and the foreseeable therapies that can repair or obviate them The second oversight made both by the contributors to Coping with Methuselah and by other commentators is demographic. Life expectancy is typically defined in terms of what demographers call a period survival curve, which is a purely artificial construction derived from the proportions of those of each age at the start of a given year who die during that year. The ‘life expectancy’ of the ‘population’ thus described is that of a hypothetical population whose members live all their lives with the mortality risk at each age that the real people of that age experienced in the year of interest. The remaining life expectancy of someone aged N in that year is more than this life expectancy minus N for two reasons: one mathematical (what one actually wants, roughly, is the age to which the probability of survival is half that of survival to N) and one biomedical (mortality rates at each age, especially advanced ages, tend to fall with time). My spirits briefly rose on reading Aaron and Harris's explicit statement (p. 69) of the latter reason. Unfortunately, they didn't discuss what would happen if age-specific mortality rates fell by more than 2% per year. An interesting scenario was thus unexplored: that in which mortality rates fall so fast that people's remaining (not merely total) life expectancy increases with time. Is this unimaginably fast? Not at all: it is simply the ratio of the mortality rates at consecutive ages (in the same year) in the age range where most people die, which is only about 10% per year. I term this rate of reduction of age-specific mortality risk ‘actuarial escape velocity’ (AEV), because an individual's remaining life expectancy is affected by aging and by improvements in life-extending therapy in a way qualitatively very similar to how the remaining life expectancy of someone jumping off a cliff is affected by, respectively, gravity and upward jet propulsion (Figure 1). Figure 1 Physical and Actuarial Escape Velocities Remaining life expectancy follows a similar trajectory whether one walks off a cliff or merely ages: the time scales differ, but one's prognosis worsens with time. Mild mitigation of this (whether by jet propulsion or by rejuvenation therapies) merely postpones the outcome, but sufficiently aggressive intervention overcomes the force of gravity or frailty and increasingly distances the individual from a sticky end. Numbers denote plausible ages, at the time first-generation rejuvenation therapies arrive, of people following the respective trajectories. The escape velocity cusp is closer than you might guess. Since we are already so long lived, even a 30% increase in healthy life span will give the first beneficiaries of rejuvenation therapies another 20 years—an eternity in science—to benefit from second-generation therapies that would give another 30%, and so on ad infinitum. Thus, if first-generation rejuvenation therapies were universally available and this progress in developing rejuvenation therapy could be indefinitely maintained, these advances would put us beyond AEV. Universal availability might be thought economically and sociopolitically implausible (though that conclusion may be premature, as I will summarise below), so it's worth considering the same question in terms of life-span potential (the life span of the luckiest people). Figure 1 again illustrates this: those who get first-generation therapies only just in time will in fact be unlikely to live more than 20–30 years more than their parents, because they will spend many frail years with a short remaining life expectancy (i.e., a high risk of imminent death), whereas those only a little younger will never get that frail and will spend rather few years even in biological middle age. Quantitatively, what this means is that if a 10% per year decline of mortality rates at all ages is achieved and sustained indefinitely, then the first 1000-year-old is probably only 5–10 years younger than the first 150-year-old. The third oversight that I observe in contemporary commentaries on life extension, among which Coping with Methuselah is representative, is the most significant because of its urgency. First-generation rejuvenation therapies, whenever they arrive, will surely build on a string of prior laboratory achievements. Those achievements, it seems to me, will have progressively worn down humanity's evidently desperate determination to close its eyes to the prospect of defeating its foremost remaining scourge anytime soon. The problem (if we can call it that) is that this wearing-down may have been completed long before the rejuvenation therapies arrive. There will come an advance—probably a single laboratory result—that breaks the camel's back and forces society to abandon that denial: to accept that the risk of getting one's hopes up and seeing them dashed is now outweighed by the risk of missing the AEV boat by inaction. What will that result be? I think a conservative guess is a trebling of the remaining life span of mice of a long-lived strain that have reached two-thirds of their normal life span before treatment begins. This would possess what I claim are the key necessary features: a big life extension, in something furry and not congenitally sick, from treatment begun in middle age. It is the prospect of AEV, of course, that makes this juncture so pivotal. It seems quite certain to me that the announcement of such mice will cause huge, essentially immediate, society-wide changes in lifestyle and expenditure choices—in a word, pandemonium—resulting from the anticipation that extreme human life extension might arrive soon enough to benefit people already alive. We will probably not have effective rejuvenation therapies for humans for at least 25 years, and it could certainly be 100 years. But given the present status of the therapies listed in Table 1, we have, in my view, a high probability of reaching the mouse life extension milestone just described (which I call ‘robust mouse rejuvenation’) within just ten years, given adequate and focused funding (perhaps $100 million per year). And nobody in Coping with Methuselah said so. This timeframe could be way off, of course, but as Wade notes (p. 57), big advances often occur much sooner than most experts expect. Even the most obvious of these lifestyle changes—greater expenditure on traditional medical care, avoidance of socially vital but risky professions—could severely destabilise the global economy; those better versed in economics and sociology than I would doubtless be even more pessimistic about our ability to negotiate this period smoothly. Overpopulation, probably the most frequently cited drawback of curing aging, could not result for many decades, but the same cannot be said for breadth of access irrespective of ability to pay: in a post-9/11 world, restricted availability of rejuvenation therapies resembling that seen today with AIDS drugs would invite violence on a scale that, shall we say, might be worth trying to avoid. Am I, then, resigned to a future in which countless millions are denied many decades of life by our studied reluctance to plan ahead today? Not quite. The way out is pointed to in Lee and Tuljapurkar's (1997) graph of the average wealth consumed and generated by an individual as a function of age, reproduced in Coping with Methuselah (p. 143). Once AEV is achieved, there will be no going back: rejuvenation research will be intense forever thereafter and will anticipate and remedy the life-threatening degenerative changes appearing at newly achieved ages with ever-increasing efficacy and lead time. This will bring about the greatest economic change of all in society: the elimination of retirement benefits. Retirement benefits are for frail people, and there won't be any frail people. The graph just mentioned amply illustrates how much wealth will be released by this. My hope, therefore, is that once policy makers begin to realise what's coming they will factor in this eventual windfall and allocate sufficient short-term resources to make the period of limited availability of rejuvenation therapies brief enough to prevent mayhem. This will, however, be possible only if such resources begin to be set aside long enough in advance—and we don't know how long we have. Aubrey D. N. J. de Grey is in the Department of Genetics at the University of Cambridge, Cambridge, United Kingdom. E-mail: [email protected] Book Reviewed Aaron HJ, Schwartz WB, editors (2004) Coping with methuselah. Washington (District of Columbia): Brookings Institution Press. 296 pp. ISBN (paperback) 0-8157-0039-3. US$19.95. Abbreviations AEVactuarial escape velocity ==== Refs References Alzheimer A Uber eine eigneartige Ehrankung der Himrinde Allg Z Psychiatr Psychish-Gerichtliche Med 1907 64 146 148 Barzilai N She L Liu BQ Vuguin P Cohen P Surgical removal of visceral fat reverses hepatic insulin resistance Diabetes 1999 48 94 98 9892227 Brody H Organization of the cerebral cortex III J Comp Neurol 1955 102 511 556 14381544 Cutler RG Giacobini E The dysdifferentiation hypothesis of mammalian aging and longevity The aging brain: Cellular and molecular mechanisms of aging in the nervous system 1982 New York Raven Press 1 19 de Grey ADNJ Bioremediation meets biomedicine: Therapeutic translation of microbial catabolism to the lysosome Trends Biotechnol 2002 20 452 455 12413818 de Grey ADNJ The unfortunate influence of the weather on the rate of aging Gerontology 2004 In press de Grey ADNJ Ames BN Andersen JK Bartke A Campisi J Time to talk SENS: Critiquing the immutability of human aging Ann N Y Acad Sci 2002 959 452 462 11976218 de Grey ADNJ Campbell FC Dokal I Fairbairn LJ Graham GJ Total deletion of in vivo telomere elongation capacity: An ambitious but possibly ultimate cure for all age-related human cancers Ann N Y Acad Sci 2004 In press Harman D The biologic clock: The mitochondria? J Am Geriatr Soc 1972 20 145 147 5016631 Hayflick L The limited in vitro lifetime of human diploid cell strains Exp Cell Res 1965 37 614 636 14315085 Kass DA Shapiro EP Kawaguchi M Capriotti AR Scuteri A Improved arterial compliance by a novel advanced glycation endproduct crosslink breaker Circulation 2001 104 1464 1470 11571237 Lee R Tuljapurkar S Death and taxes: Longer life, consumption, and social security Demography 1997 34 67 81 9074832 Manfredi G Fu J Ojaimi J Sadlock JE Kwong JQ Rescue of a deficiency in ATP synthesis by transfer of MTATP6, a mitochondrial DNA-encoded gene, to the nucleus Nat Genet 2002 30 394 399 11925565 Monnier VM Cerami A Nonenzymatic browning in vivo: Possible process for aging of long-lived proteins Science 1981 211 491 493 6779377 Rao MS Mattson MP Stem cells and aging: Expanding the possibilities Mech Ageing Dev 2001 122 713 734 11322994 Schenk D Barbour R Dunn W Gordon G Grajeda H Immunization with amyloid-beta attenuates Alzheimer-disease-like pathology in the PDAPP mouse Nature 1999 400 173 177 10408445 Strehler BL Mark DD Mildvan AS Gee MV Rate and magnitude of age pigment accumulation in the human myocardium J Gerontol 1959 14 430 439 13835175 Szilard L On the nature of the ageing process Proc Natl Acad Sci U S A 1959 45 35 45

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2021-01-05 08:26:26

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PLoS Biol. 2004 Jun 15; 2(6):e187

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PLoS Biol

10.1371/journal.pbio.0020187

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020188Correspondence and Other CommunicationsCell BiologyScience PolicyTaking the Stem Cell Debate to the Public CorrespondenceZon Leonard I Zoloth Laurie Kadereit Suzanne 6 2004 15 6 2004 15 6 2004 2 6 e188Copyright: © 2004 Zon et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Reason as Our Guide Beyond Therapy Ethics As Our Guide Scientists and Bioethics Councils A Voice for Research, a Voice for Patients Ethereal Ethics In response to the Blackburn and Rowley essay on the President's Council on Bioethics, several thought-provoking opinions on ethical challenges in biomedical research are expressed by prominent stakeholders ==== Body In their essay in the April 2004 issue of PLoS Biology, Elizabeth Blackburn and Janet Rowley (2004), two distinguished cellular biologists and members of the President's Council on Bioethics, strongly question the scientific foundation of two reports from the Council (President's Council on Bioethics 2003, 2004). The Council on Bioethics was formed by executive order “to advise the President on bioethical issues that may emerge as a consequence of advances in biomedical science and technology.” An open discussion between ethicists and scientists is critical to the advisory system. The recent administrative dismissal of Dr. Blackburn from the Council is very alarming. By stacking the deck with conservative opinions, and not accurately discussing the scientific issues, the Bioethics Council has become irrelevant to the scientific community and presents a jaundiced view to the public. Stem cell research and its applications have the potential to revolutionize human health care. Recent polls show support for embryonic stem cell research, even with conservative voters. The public, as the major benefactor of biomedical research and the target population of beneficial clinical advances, has the right to a fact-based discussion of the science regarding stem cells. It is therefore time that the debate on stem cell research, with its risks and benefits, be taken to the public. A debate on stem cell research restricted to the President's Council on Bioethics is a disservice to the public. Nearly three decades ago, the advent of recombinant DNA technology and in vitro fertilization (IVF) techniques, raised similar concerns regarding research. Contrary to apprehensive expectations, recombinant DNA technology has boosted enormous advances in the health care and pharmaceutical industry. IVF evolved to be a widely accepted, safe medical procedure, with over one million healthy babies born by IVF and related treatments. Similarly, once stem cells are successfully used in the clinic, most of today's political and ethical issues will evaporate. The International Society for Stem Cell Research (ISSCR), a society whose membership encompasses the bulk of the stem cell research brain trust, holds the position that research on both adult and embryonic stem cells will guarantee the fastest progress in scientific discovery and clinical advances. The ISSCR also strongly opposes reproductive cloning and supports the National Academy of Science's proposal to develop voluntary guidelines to encourage responsible practices in human embryonic stem cell research. One of the original recommendations of the President's Council on Bioethics was a four-year moratorium on stem cell research. The purpose of this moratorium was theoretically to open a large, national discourse on the topic of stem cell research, a debate intended to bring all sides into thoughtful reflection on the issue. To that end, the ISSCR has repeatedly and consistently offered an open forum for all sides in the debate at our conferences, and has carefully offered invitations to join our society and to speak at our annual meeting to members of the President's Council, including colleagues whose opposition to stem cell research has been clear. None have accepted. Dr. Kass, in particular, has received several direct appeals but has turned down every such opportunity to make his case to the researchers who arguably are his discourse partners, from whom he could learn much, and whom he should be actively engaged in teaching. It is tragic that voices of dissent and debate are stilled, for it is this very quality of open debate that is at the heart of both the scientific method and an ethically directed American democracy—surely a goal that we all share. Howard Hughes Medical Institute, Chevy Chase, Maryland, United States of America Children's Hospital, Harvard Medical School, Cambridge, Massachusetts, United States of America International Society for Stem Cell Research, Northbrook, Illinois, United States of America Center for Genetic Medicine, Northwestern University, Evanston, Illinois, United States of America *To whom correspondence should be addressed. E-mail: [email protected] ==== Refs References Blackburn E Rowley J Reason as our guide PLoS Biol 2004 2 e116 10.1371/journal.pbio.0020116 15024408 President's Council on Bioethics Beyond therapy: Biotechnology and the pursuit of happiness 2003 Available at http://bioethics.gov/reports/beyondtherapy/index.html via the Internet. Accessed 19 April 2004 President's Council on Bioethics Monitoring stem cell research 2004 Available at http://bioethics.gov/reports/stemcell/index.html via the Internet. Accessed 24 March 2004

15208732

PMC423156

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2021-01-05 08:21:11

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PLoS Biol. 2004 Jun 15; 2(6):e188

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PLoS Biol

10.1371/journal.pbio.0020188

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020189Correspondence and Other CommunicationsCell BiologyScience PolicyEthereal Ethics CorrespondenceLovell-Badge Robin 6 2004 15 6 2004 15 6 2004 2 6 e189Copyright: © 2004 Robin Lovell-Badge.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Reason as Our Guide Beyond Therapy Ethics As Our Guide Scientists and Bioethics Councils A Voice for Research, a Voice for Patients Taking the Stem Cell Debate to the Public In response to the Blackburn and Rowley essay on the President's Council on Bioethics, several thought-provoking opinions on ethical challenges in biomedical research are expressed by prominent stakeholders ==== Body It is a great pity when vested interest and dogma dominate what should be a well-informed and rational debate. The essay by Elizabeth Blackburn and Janet Rowley (2004), about the output and the workings of the President's Council on Bioethics, therefore prompted in me a strong reaction of sadness and despair, although I have to admit not one of surprise. In the United Kingdom, we have had an almost continuous debate since the mid 1980s on topics relating to research on early human embryos. I myself have been involved in some of this debate, especially over the last few years, relating to human embryonic stem cells and nuclear transfer. I will not dwell on the political outcomes of this debate, which are widely known, but I want to stress that it has been one that has been very well informed, with contributions from all sides, including many highly respected moral philosophers and bioethicists. These include notable individuals such as Dame Mary Warnock and bodies such as the Nuffield Bioethics Council, who have been especially valuable because of their independence. So why are the conclusions reached by bioethicists in the UK, who are generally supportive of research involving human embryos, different from those of the President's Council on Bioethics? The same scientific information is available on both sides of the Atlantic. The rules of logic are the same. So it has to be the way the information is interpreted or filtered. This implies bias or vested interest or the input of dogma that is based on belief rather than rational thought. Some examples of this are discussed in the Blackburn and Rowley essay, and they are very worrying. The scare mongering about preimplantation genetic diagnosis is ridiculous—simple mathematics shows that it is implausible to use this technique to screen the usual number of embryos obtained in one round of in vitro fertilisation for more than two or three genetic traits, while we know that intelligence must rely on many more. I am a great fan of science fiction, but I can recognise it as such. I worry that some members of the President's Council seem unable to do this. Many of these daft ideas were already promoted in a book by Francis Fukuyama (2002), and while they can be a harmless way of promoting debate, they should not be included in documents meant to inform policy makers. It is certainly very unfortunate if the input of real science in the Council is to be reduced. The scientific issues are complex. For example, we certainly do not know nearly enough about either adult or embryonic stem cells to say which will be the best for therapies, and of course it is possible that both will turn out to be useful for different problems. Both also offer exciting new ways to explore human disease and the influence of genetics and environment without having to rely on human experimentation. But any committee looking into what is ethically acceptable has to be provided with a balanced view of what will be possible in the near future. There is no point in being too speculative, in part because it is also difficult to predict what will be ethically acceptable in the future. If cures come from the use of human embryonic stem cells, then I suspect that there will be widespread acceptance, as happened with heart transplants and with in vitro fertilisation, both of which were initially greeted with horror by many. It is impossible to have an informed debate without accurate and appropriate information, and there seems little point in having a debate that is not informed. Because of various sensitivities, it seemed to me before the creation of the President's Council on Bioethics that for far too long the issues relating to embryo research had not been considered properly within the United States. The President's Council was therefore an opportunity to redress this situation. But from the evidence I fear it will not succeed. Moreover, it does the general public a disservice to pretend to have a serious committee exploring issues of bioethics when that committee fails to live up to the ideals of impartiality and rationality. Division of Developmental Genetics, Medical Research Council National Institute for Medical Research, London, United Kingdom E-mail: [email protected] ==== Refs References Blackburn E Rowley J Reason as our guide PLoS Biol 2004 2 e116 10.1371/journal.pbio.0020116 15024408 Fukuyama F Our posthuman future: Consequences of the biotechnology revolution 2002 New York Farrar, Straus, and Giroux 272

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PLoS Biol. 2004 Jun 15; 2(6):e189

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PLoS Biol

10.1371/journal.pbio.0020189

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020190PrimerEvolutionGenetics/Genomics/Gene TherapyInfectious DiseasesZoologyHomo (Human)PrimatesWhat's So Hot about Recombination Hotspots? Recombination HotspotsHey Jody 6 2004 15 6 2004 15 6 2004 2 6 e190Copyright: © 2004 Jody Hey.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Absence of the TAP2 Human Recombination Hotspot in Chimpanzees A DNA Recombination "Hotspot" in Humans Is Missing in Chimps Recombination is a nearly ubituitous feature of genomes; where and when it occurs can provide insights about its evolution and can affect our ability to identify genes that cause disease ==== Body Consider a piece of text, either this one that you are now reading or any other. Surely they are all pretty much alike, in so far as they are all run-on strings of characters. In this same sense, we can envision that all DNA strands are alike because all are monotonous polymers with the same general chemical makeup. Indeed, this is how we think of DNA when considering its basic function of inheritance, in which all parts of all chromosomes must be duplicated and then passed from one cell generation to the next. The capacity for inheritance is fundamentally a consequence of DNA's general molecular structure, and not of its sequence per se, as Watson and Crick (1953), and indeed Muller (1922) long before them, well appreciated. Muller did not know that genes are made of DNA, but he did realize that, whatever genes were made of, they must have a general capacity to replicate, regardless of the information they carry (Muller 1922). But sequence does matter when DNA fulfills its other, more directly functional role. When the DNA that makes up a gene is exposed and expressed, when a gene is serving its individual function, then the detailed sequence means all. So where does recombination (Box 1) fit in? Is recombination something that happens to DNA generally? Or does it happen to particular sequences? Bacteria have their chi (χ) sequence, which is a specific series of eight base pairs in the DNA of the bacterial chromosome that stimulate the action of proteins that bring about recombination (Eggleston and West 1997). Similarly, the immunoglobulin genes of mammals have recombination signal sequences that are involved in V-J joining—a kind of somatic recombination involving the joining of a variable gene segment and a joining segment to form an immunoglobulin gene (Krangel 2003). But does normal meiotic recombination depend on the local DNA sequence? In yeast, as well as mammals (mice and humans), the answer is partly yes, for it is clear that chromosomes have local recombination hotspots where crossing over is much more likely to occur than in other places on the chromosome. Recombination hotspots are local regions of chromosomes, on the order of one or two thousand base pairs of DNA (or less—their length is difficult to measure), in which recombination events tend to be concentrated. Often they are flanked by “coldspots,” regions of lower than average frequency of recombination (Lichten and Goldman 1995). Diverse Implications of Recombination Hotspots: The Study of Meiosis and the Mapping of Human Disease Alleles Recombination hotspots are of strong interest to at least two quite different groups of biologists. For geneticists and cell biologists who study meiosis, the existence of recombination hotspots offers a way to learn what other processes are associated with recombination. This is partly how we know that homologous crossovers in yeast and other eukaryotes are initiated by the cleavage of single chromosomes, called “double-strand breaks” (Box 1). It turns out that because of this causal linkage, the hotspots for doublestrand breaks and the hotspots for recombination are one and the same (Game et al. 1989; Sun et al. 1989; Keeney et al. 1997; Lopes et al. 1999; Allers and Lichten 2001; Hunter 2003). For population geneticists, much of the interest in recombination hotspots comes from their possible effect on the patterns of DNA sequence variation along human chromosomes and from the possibility that these patterns could be used to map the position of alleles that cause disease. When multiple copies of the DNA sequence of a gene, or of a larger region of a chromosome, are aligned, they reveal the location and distribution of variation at individual nucleotide positions—single nucleotide polymorphisms (SNPs). Each particular sequence, or haplotype, will carry a configuration for the SNPs for that region (Figure 1). Investigators have long known that SNPs that are adjacent or near each other tend to be highly correlated in their pattern and to exhibit strong linkage disequilibrium (Box 1). It is this linkage disequilibrium that enables scientists to map the locations of mutations that cause heritable genetic diseases. If alleles that cause a disease have the same kind of linkage disequilibrium with nearby SNPs as SNPs generally have with each other, then one could search for genes with disease alleles by looking for a pattern of SNPs that is found only in people who have the disease. This general method for mapping disease alleles is called “association mapping,” and it is basically a search for linkage disequilibrium between disease alleles and other SNPs. Whether or not association mapping works depends on the actual patterns of linkage that occur among SNPs in human populations, and these patterns depend in turn on how much recombination has occurred in the past (as well as on other demographic and mutation processes). Figure 1 A Hypothetical Example of Eight Aligned Haplotypes for All the SNPs Found in a Region Base positions that are not variable are not shown. Blocks of adjacent SNPs that revealed no evidence of historical recombination are flanked by vertical red bars. Two longer haplotype blocks are indicated in green. The presence of historical recombination was discerned by the four-gamete criterion. In brief, if the haplotypes for two SNPs, each with two bases (e.g., A/G at one SNP and C/T at the second), reveal all four possible combinations (i.e., A-C, A-T, GC, and G-T) then this is evidence that there has been a recombination event between these SNPs in the history of the sample of haplotypes (Hudson and Kaplan 1985). If haplotype blocks are long, then it is possible to represent much of the haplotype diversity using just a small sample of the SNPs found within that region. With the advent of larger human haplotype data sets, it has become clear that there are often fairly long regions with very high linkage disequilibrium (Daly et al. 2001; Patil et al. 2001; Gabriel et al. 2002). This pattern of variation has been characterized as occurring in “haplotype blocks,” which are apparent regions of low recombination (or high linkage disequilibrium). Figure 1 shows a hypothetical example of haplotype blocks among eight haplotypes for a series of SNPs found over a region of a chromosome. Given diverse evidence of recombination hotspots in humans, a much discussed question is whether recombination hotspots play a large role in the formation of the pattern of haplotype blocks (Wang et al. 2002; Innan et al. 2003; Phillips et al. 2003; Stumpf and Goldstein 2003). The occurrence of haplotype blocks has inspired the HAPMAP project (http://www.hapmap.org/), which has the goal of identifying a subset of SNPs that capture most of the relevant linkage information in the human genome (IHC 2003). If one had a subset of all common SNPs, with one or two per haplotype block, then this subset would contain much of the available information for association mapping of disease alleles. The Evolution of Recombination and (Possibly) Recombination Hotspots Recombination is a nearly ubiquitous feature of genomes, and a great many theories have been put forward to explain why it would be evolutionarily advantageous for genes to regularly break with one another to join new genes (Barton and Charlesworth 1998). By and large these theories predict that recombination should occur more often where genes occur in higher concentration and that it should happen less often in areas of the genome where genes are spaced far apart. This expectation is roughly born out in the human genome, where recombination rates are higher in regions of the genome with higher gene density (Fullerton et al. 2001; Kong et al. 2002). To consider the possible evolutionary advantages of individual recombination hotspots, we can draw from theory on the evolution of recombination modifiers. In particular, recent population genetic theory has brought to light some fairly general circumstances for which mutations that raise recombination rates would be favored by natural selection (Barton 1995; Otto and Barton 1997; Otto and Barton 2001; Otto and Lenormand 2002). The basic idea is that linkage disequilibrium can easily occur (for many reasons) between two (or more) polymorphic sites that are under selection. When this occurs, an allele that raises the recombination rate (and decreases the linkage disequilibrium) can cause selection to act more efficiently. If an allele that is under positive or negative selection always occurs with an allele at another locus that is also under selection (i.e., the two loci are in strong linkage disequilibrium), then selection cannot act on one locus independently of the second locus. As new, multilocus configurations of beneficial alleles are generated (by recombination) and increase in frequency by selection, the modifiers of recombination that caused the production of those beneficial configurations increase in frequency with them. A key piece of evidence supporting this kind of theory of the evolution of recombination is directional selection, like that which occurs in artificial selection experiments, which often generates a correlated elevation in recombination rates (Otto and Lenormand 2002). Connecting these ideas about the evolution of recombination modifiers to the question of recombination hotspots, we come to the possibility that individual hotspots may have arisen as a byproduct of linkage disequilibrium between genes on either side of the hotspot that were under selection. This situation would create a kind of selection pressure favoring recombinant haplotypes and thus also favoring those chromosomes that happen to have a high recombination rate between the selected genes. If true, then we might expect local recombination rates (i.e., hotspots and coldspots for recombination) to fluctuate in location and intensity, in ways that would be hard to precisely predict without knowing what genes have been under selection and what patterns of linkage disequilibria there may have been. In this light, the paper by Ptak et al. (2004) in this issue of PLoS Biology is especially interesting. They report that chimpanzees do not have a recombination hotspot in the TAP2 region where humans have a fairly well characterized recombination hotspot (Jeffreys and Neumann 2002). Ptak et al.'s is a statistical study of linkage disequilibrium in the TAP2 region of chimpanzees and humans, and is less direct than the sperm-typing study of Jeffreys and Neumann (2002). However the contrast in linkage patterns between humans and our closest relatives suggests that recombination hotspots can evolve fairly quickly. Functional Constraints on Recombination Hotspots As appealing as the recombination modifier theory of recombination hotspots may be, there is circumstantial evidence that argues against it and that suggests that recombination hotspots are not directly the byproduct of selection on alleles in linkage disequilibrium. Particularly important in this regard is that some wellstudied organisms (notably the worm Caenorhabditis elegans and the fruitfly Drosophila melanogaster) have not shown evidence of recombination hotspots. If we compare these organisms with yeast and mammals, which do show hotspots, we gain some more insight into the factors affecting the evolution of hotspots. Recall that double-strand breaks are the sites where recombination is initiated during meiosis, and that this is true regardless of the presence of hotspots for both phenomena. Apparently it is the case in yeast and mammals that both recombination and double-strand breaks are also prerequisites for the proper formation of the synaptonemal complex (SC) (Figure 2) and thus for proper orientation of the spindle apparatus and accurate segregation of chromosomes during meiosis (Paques and Haber 1999; Lichten 2001; Hunter 2003; Page and Hawley 2003). In contrast, neither double-strand breaks nor recombination appear to be required for the formation of the SC in D. melanogaster or C. elegans (Zickler and Kleckner 1999; MacQueen et al. 2002; McKim et al. 2002; Hunter 2003; Page and Hawley 2003). Double-strand breaks and recombination do indeed co-occur in these model organisms, and are required for proper chromosome segregation, but they occur after the formation of the SC. Both of these species have broad chromosomal regions where crossing over occurs at higher rates than others, but there have been no reports of local recombination hotspots. Figure 2 The Synaptonemal Complex (A) Model of the SC. Lateral elements (light blue rods) of homologous chromosomes align and synapse together via a meshwork of transverse filaments (black lines) and longitudinal filaments (dark blue rods). The longitudinal filaments are collectively referred to as the “central element” of the SC. Ellipsoidal structures called recombination nodules (gray ellipsoid) are constructed on the central region of the SC. As their name implies, recombination nodules are believed to be involved in facilitating meiotic recombination (crossing over). The chromatin (red loops) of each homologue is attached to its corresponding lateral element. Because there are two “sister chromatids” in each homologue, two loops are shown extending laterally from each point along a lateral element. (B) Top: Set of tomato SCs. Chromatin “sheaths” are visible around each SC. Bottom: Two tomato SCs. The chromatin has been stripped from the SCs, allowing the details of the SC to be observed. Each SC has a kinetochore (“ball-like” structure) at its centromere. Recombination nodules, ellipsoidal structures found on the central regions of SCs, mark the sites of crossover events (see inset). Images and legend courtesy of Daniel G. Peterson, Mississippi Genome Exploration Laboratory, Mississippi State University, Mississippi State, Mississippi, United States (http://www.msstate.edu/research/mgel/index.htm). Recombination during meiosis seems to be required for proper chromosome segregation; however, in those organisms where recombination and double-strand-break hotspots occur, these phenomena are also required for proper formation of the SC. It is as if the recombination machinery has been partly co-opted for chromosome alignment in some eukaryotes more so than in others. The implication of these findings is that recombination hotspots are byproducts of other functional constraints associated with the recombination process. This does not rule out the evolutionary theory of recombination modifiers, or that the location and intensity of recombination hotspots may evolve rapidly, but it does suggest that we may not need to invoke the evolutionary modifier theory to explain the existence of recombination hotspots. Conclusions Recombination hotspots co-occur with double-strand-break hotspots in some eukaryotes, and together these phenomena appear to play an important role in the formation of the SC in those organisms. Given the limited phylogenetic occurrence of recombination hotspots (i.e., their occurrence in some, but not all, species), general theories for the evolution of recombination may not be very helpful for understanding the existence of recombination hotspots. However, in those species where they do occur, it is quite possible that recombination hotspots do evolve in location and intensity. Furthermore, the presence of recombination hotspots in humans may have large effects on the length of local patterns of linkage disequilibrium (haplotype blocks) and thus on our ability to map disease alleles by their association with other markers. Box 1. Glossary Double-strand break: A break in both strands of a DNA molecule, as distinguished from a break in just one strand. Linkage disequilibrium: A pattern of association between two SNPs or two loci that each have multiple alleles, such that pairs of particular SNPs or alleles, one from each locus, tend to co-occur within individuals or genomes more often than would be expected if the loci are sorting independently of each other. Recombination: The process of one double-stranded DNA molecule joining with another; specifically in the context of meiosis, the process of two homologous chromosomes exchanging large portions of their DNA (this is also called “crossing over”). Jody Hey is in the Department of Genetics at Rutgers University, Piscataway, New Jersey, United States of America. E-mail: [email protected] Abbreviations SCsynaptonemal complex SNPsingle nucleotide polymorphism ==== Refs References Allers T Lichten M Differential timing and control of noncrossover and crossover recombination during meiosis Cell 2001 106 47 57 11461701 Barton NH A general model for the evolution of recombination Genet Res 1995 65 123 144 7605514 Barton NH Charlesworth B Why sex and recombination? Science 1998 281 1986 1990 9748151 Daly MJ Rioux JD Schaffner SF Hudson TJ Lander ES High-resolution haplotype structure in the human genome Nat Genet 2001 29 229 232 11586305 Eggleston AK West SC Recombination initiation: Easy as A, B, C, D. …chi? Curr Biol 1997 7 R745 R759 9382825 Fullerton SM Bernardo Carvalho A Clark AG Local rates of recombination are positively correlated with gc content in the human genome Mol Biol Evol 2001 18 1139 1142 11371603 Gabriel SB Schaffner SF Nguyen H Moore JM Roy J The structure of haplotype blocks in the human genome Science 2002 296 2225 2229 12029063 Game JC Sitney KC Cook VE Mortimer RK Use of a ring chromosome and pulsed-field gels to study interhomolog recombination, double-strand DNA breaks and sister-chromatid exchange in yeast Genetics 1989 123 695 713 2693206 Hudson RR Kaplan NL Statistical properties of the number of recombination events in the history of a sample of DNA sequences Genetics 1985 111 147 164 4029609 Hunter N Synaptonemal complexities and commonalities Mol Cell 2003 12 533 535 14527398 [IHC] International HapMap Consortium The International HapMap Project Nature 2003 426 789 796 14685227 Innan H Padhukasahasram B Nordborg M The pattern of polymorphism on human Chromosome 21 Genome Res 2003 13 1158 1168 12799351 Jeffreys AJ Neumann R Reciprocal crossover asymmetry and meiotic drive in a human recombination hot spot Nat Genet 2002 31 267 271 12089523 Keeney S Giroux CN Kleckner N Meiosis-specific DNA double-strand breaks are catalyzed by Spo11, a member of a widely conserved protein family Cell 1997 88 375 384 9039264 Kong A Gudbjartsson DF Sainz J Jonsdottir GM Gudjonsson SA A high-resolution recombination map of the human genome Nat Genet 2002 31 241 247 12053178 Krangel MS Gene segment selection in V(D)J recombination: Accessibility and beyond Nat Immunol 2003 4 624 630 12830137 Lichten M Goldman AS Meiotic recombination hotspots Annu Rev Genet 1995 29 423 444 8825482 Lichten M Meiotic recombination: Breaking the genome to save it Curr Biol 2001 11 R253 R256 11413012 Lopes J Tardieu S Silander K Blair I Vandenberghe A Homologous DNA exchanges in humans can be explained by the yeast double-strand break repair model: A study of 17p11.2 rearrangements associated with CMT1A and HNPP Hum Mol Genet 1999 8 2285 2292 10545609 MacQueen AJ Colaiacovo MP McDonald K Villeneuve AM Synapsis-dependent and -independent mechanisms stabilize homolog pairing during meiotic prophase in C. elegans Genes Dev 2002 16 2428 2442 12231631 McKim KS Jang JK Manheim EA Meiotic recombination and chromosome segregation in Drosophila females Annu Rev Genet 2002 36 205 232 12429692 Muller HJ Variation due to change in the individual gene Am Nat 1922 56 32 50 Otto SP Barton NH The evolution of recombination: Removing the limits to natural selection Genetics 1997 147 879 906 9335621 Otto SP Barton NH Selection for recombination in small populations Evolution Int J Org Evolution 2001 55 1921 1931 Otto SP Lenormand T Resolving the paradox of sex and recombination Nat Rev Genet 2002 3 252 261 11967550 Page SL Hawley RS Chromosome choreography: The meiotic ballet Science 2003 301 785 789 12907787 Paques F Haber JE Multiple pathways of recombination induced by double-strand breaks in Saccharomyces cerevisiae Microbiol Mol Biol Rev 1999 63 349 404 10357855 Patil N Berno AJ Hinds DA Barrett WA Doshi JM Blocks of limited haplotype diversity revealed by high-resolution scanning of human Chromosome 21 Science 2001 294 1719 1723 11721056 Phillips MS Lawrence R Sachidanandam R Morris AP Balding DJ Chromosome-wide distribution of haplotype blocks and the role of recombination hot spots Nat Genet 2003 33 382 387 12590262 Ptak SE Roeder AD Stephens M Gilad Y Pääbo S Absence of the TAP2 human recombination hotspot in chimpanzees PLoS Biol 2004 2 e155 10.1371/journal.pbio.0020155 15208713 Stumpf MP Goldstein DB Demography, recombination hotspot intensity, and the block structure of linkage disequilibrium Curr Biol 2003 13 1 8 12526738 Sun H Treco D Schultes NP Szostak JW Double-strand breaks at an initiation site for meiotic gene conversion Nature 1989 338 87 90 2645528 Wang N Akey JM Zhang K Chakraborty R Jin L Distribution of recombination crossovers and the origin of haplotype blocks: The interplay of population history, recombination, and mutation Am J Hum Genet 2002 71 1227 1234 12384857 Watson JD Crick FH Molecular structure of nucleic acids: A structure for deoxyribose nucleic acid Nature 1953 171 737 738 13054692 Zickler D Kleckner N Meiotic chromosomes: Integrating structure and function Annu Rev Genet 1999 33 603 754 10690419

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2021-01-05 08:21:11

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PLoS Biol. 2004 Jun 15; 2(6):e190

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PLoS Biol

10.1371/journal.pbio.0020190

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020192SynopsisEvolutionGenetics/Genomics/Gene TherapyPrimateHomo (Human)A DNA Recombination “Hotspot” in Humans Is Missing in Chimps Synopsis6 2004 15 6 2004 15 6 2004 2 6 e192Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Absence of the TAP2 Human Recombination Hotspot in Chimpanzees What's So Hot about Recombination Hotspots? ==== Body When Francis Collins and Craig Venter reported the draft sequence of the human genome in 2001, Collins described the so-called book of life as more of a life sciences encyclopedia. In it, we can find our evolutionary history written in the fossil record of our DNA, a parts manual listing the genes and proteins needed to build and operate a human being, and a medical text, gleaned from the genetic variants linked to human disease. Unfortunately, he added, the texts are written in a language “we don't entirely know how to read yet.” Since then, biologists have made great progress in extracting meaning from the human genome. Humans are 99.9% alike genetically, and that 0.1% makes all the difference in terms of appearance, personality, and susceptibility to disease. That 0.1% promises to shed light on the evolutionary forces that control genetic variation as well as the genetic origins of human disease. The TAP2 region harbors a recombination hotspot in humans. What about in chimpanzees: hot or cold? Very small genetic variations—including differences of a single DNA base, called single nucleotide polymorphisms, or SNPs—occur through random mutations. Individuals have two of each chromosome (one from the mother and one from the father), and the combination of SNPs found together on one chromosome can change through the random shuffling of genetic material between the two chromosomes when sperm and egg cells are produced during meiosis. By studying the location and frequency of this reassortment in the genome, biologists hope to understand how recombination affects the overall pattern of SNP variation and how these patterns relate to human disease. An international collaborative effort called the HapMap Project aims to identify the most common SNP associations within chromosomes, known as haplotypes, and then determine which haplotypes are associated with disease. This approach relies on what's known as “linkage disequilibrium”—the nonrandom association of alleles (gene variants) at different locations on a chromosome—to facilitate their search for candidate disease genes. Adjacent SNPs show strong linkage disequilibrium, which means that researchers can select a limited number of SNPs as markers for a haplotype and test their association with disease rather than testing each SNP. Patterns of linkage disequilibrium depend on the rate of recombination—higher recombination rates typically cause less linkage. Demographic factors and chance also affect levels of linkage disequilibrium; while both vary across populations, it has been thought that recombination rates do not. Recombination appears to favor specific genomic regions, termed hotspots, but the observation that the recombinant chromosomes are not passed down in equal proportions suggests that recombination hotspots may be short-lived, appearing as transient blips on the evolutionary radar. Exploring this possibility, Susan Ptak et al. compared a well-studied recombination hotspot in humans, called TAP2, with a similar region in our closest evolutionary cousins, chimpanzees, to see whether they are similarly endowed. Since recombination occurs relatively rarely, researchers have relied largely on indirect methods to determine regional recombination rates. Though recent advances have made sperm analysis in humans more practical (though still technically challenging), such techniques are less feasible with chimps because collecting large amounts of sperm from individual males might compromise their success in mating competition or reduce the genetic diversity of endangered chimp populations. Here, the researchers used an indirect approach to estimate recombination rates from the patterns of linkage distribution, which “reflect the rate and distribution of recombination events in the ancestors of the sample.” They focused on chimps from a single subspecies because the reported high level of genetic differentiation between subspecies could skew estimates of recombination rate variation. Analysis of the TAP2 region revealed 47 SNPs in the human and 57 in the chimp, with an overall lower level of linkage disequilibrium in humans: strong linkage was seen only in adjacent pairs of SNPs in humans, but was found in both adjacent and more distant pairs in the chimps. Using a statistical approach to characterize recombination rate variation between the two species, Ptak et al. found “extremely strong support” for rate variation in humans but found strong evidence against such variation in chimps. Humans and chimps diverged from a common ancestor five to six million years ago and differ at only 1.2% of base pairs on average. That the recombination hotspot does not exist in both species suggests that hotspots are not stable and can evolve fairly quickly. If recombination rates within a small genomic area—at the level of a few thousand bases—can change in such a short time frame between such closely related species, Ptak et al. reason, they may do so within species, too. Such a prospect has important implications for the HapMap Project and disease association studies that rely on linkage disequilibrium. While haplotypes offer a shortcut for identifying candidate disease genes based on typing a certain number of markers, the number of markers required depends on the strength of linkage disequilibrium. If recombination rates differ across human populations, as these results suggest, then the strength of linkage disequilibrium will too—which means that association studies might need to adjust the number of markers needed to flag candidate disease genes in different populations.

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PMC423159

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2021-01-05 08:21:11

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PLoS Biol. 2004 Jun 15; 2(6):e192

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PLoS Biol

10.1371/journal.pbio.0020192

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020193SynopsisBioinformatics/Computational BiologyCell BiologyGenetics/Genomics/Gene TherapySaccharomycesCombining Measures to Characterize Subcellular Machinery Synopsis6 2004 15 6 2004 15 6 2004 2 6 e193Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Integrative Analysis of the Mitochondrial Proteome in Yeast ==== Body Understanding how the cell functions—or breaks down—implies an understanding of the assembly lines, transportation systems, and powerhouses that keep it running. Global approaches are needed to identify the numerous proteins essential to each cellular machine. But which techniques are best? Lars Steinmetz and colleagues applied and evaluated a variety of methods to define mitochondrial proteins, and report that sets of complementary approaches are needed to characterize a cellular subsystem. Zooming in on mitochondria (Image by Peter Seibel, design by Shayna Roosevelt) Yeast mitochondria make an ideal subject for study. About two-thirds of the estimated 700 mitochondrial proteins have been identified to date, leaving fertile ground for new finds. The researchers previously compiled a list of 477 proteins with compelling evidence for mitochondrial involvement. This list provides a well-defined reference set against which to test protein-finding methods. Moreover, the well-studied and accessible yeast genome is well-suited for exploration and genetic manipulation. Since mitochondria are very similar among all eurkaryotes (organisms whose cells have nuclei), the results will prove relevant across species. Steinmetz and colleagues triangulated results from multiple techniques to identify new candidate mitochondrial proteins. They compared the reference protein list to their new data from protein, mRNA, and gene knockout studies, and to 19 published datasets from other researchers, to evaluate the success of different techniques at finding known mitochondrial proteins. Then they combined evidence across studies to identify a set of proteins that likely characterizes most of the mitochondrial machinery. The researchers first identified proteins from yeast mitochondria using a technique called liquid chromatography mass spectrometry, which separates the proteins by water insolubility (also called hydrophobicity), then identifies each by the mass and molecular charge of its constituents. By comparing this approach with others, the authors show that this proteomic technique alone is by no means comprehensive, nor error-free. Mass spectrometry is biased toward finding more abundant proteins, and the purified mitochondria can contain contaminants from elsewhere in the cell. To address these issues, the authors compared their protein data with a protein study from another group, the reference protein set, and a recent subcellular localization study. Potential mitochondrial proteins identified by more than one protein approach were more likely to localize to mitochondria in the localization study than were proteins identified by only one approach. This finding suggests that, compared to either method alone, a combination of protein and localization measures can more robustly identify proteins residing in mitochondria. But since mitochondria, as the cell's power plants, are integrated into other cellular machinery, the authors argue, methods targeting proteins that are physically located to mitochondria should be complemented with functional approaches. Proteins with mitochondrial roles, regardless of concentration or location in the cell, are better identified by approaches that associate mRNA expression or gene deletion—which removes proteins or renders them inoperable—with changes in mitochondrial function. By comparing results from multiple methods against the reference protein list, the researchers evaluated the likelihood that each protein was mitochondrial. They compiled a list of 691 top candidates. This multi-technique analysis easily outperformed any single study in terms of its ability to identify proteins in the reference set, and of the proportion of known versus unconfirmed proteins located. As mitochondria are well-conserved across species, the results provide a candidate gene list for finding human counterparts that might be associated with mitochondrial disorders. Future studies can use this analysis to evaluate which research methods are likely to be most informative in other cell systems. This paper demonstrates the power of combining techniques with differing strengths in order to zero in on proteins that might elude any single approach, resulting in a more complete parts list for specific cellular machinery.

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PMC423160

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PLoS Biol. 2004 Jun 15; 2(6):e193

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PLoS Biol

10.1371/journal.pbio.0020193

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020194SynopsisGenetics/Genomics/Gene TherapyMus (Mouse)A Protein's Role in Progressive Renal Disease Synopsis6 2004 15 6 2004 15 6 2004 2 6 e194Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. α-Actinin-4-Mediated FSGS: An Inherited Kidney Disease Caused by an Aggregated and Rapidly Degraded Cytoskeletal Protein ==== Body Focal segmental glomerulosclerosis (FSGS) made a brief media splash last year when a kidney transplant forced NBA superstar Alonso Mourning into early retirement. Mourning's condition elicited a flood of calls from fans offering their kidneys, but most people with kidney disease are not so lucky. Some 56,000 patients await transplants; many have waited over five years. FSGS, which underlies about 25% of the 60,000 kidney-related deaths each year, causes inflammation and irregular scarring in the glomeruli, clusters of blood vessels in the kidney that filter toxins from the blood. These lesions, which allow protein and blood to escape into the urine, cause progressive kidney failure. FSGS commonly occurs as an outgrowth of various primary disorders, including obesity, HIV infection, diabetes, and hypertension. Though it's not clear what causes FSGS, this form of renal pathology is becoming more common. By using the genes underlying inherited forms of FSGS as probes, scientists hope to uncover the mechanisms that unleash the disease and to find ways to stem the damage. Mutant α-actinin-4 is mislocalizes and aggregates in renal glomeruli Mutations in the ACTN4 gene, which encodes a protein called α-actinin-4, cause an inherited form of FSGS. The protein normally remodels actin filaments, the primary structural component of muscle and cytoskeleton. Having a single mutated copy of the gene can cause FSGS in humans, though it is unclear how. In this issue of PLoS Biology, Martin Pollak and his colleagues at Brigham and Women's Hospital at Harvard Medical School use a three-pronged approach to figure out how the defective protein wreaks renal havoc and how these physiological changes lead to FSGS. Using biochemical analysis, cell-based studies, and a newly developed “knockin” mouse model, the researchers report that FSGS-related mutations cause α-actinin-4 to engage in various aberrant behaviors that ultimately rob the protein of its function and poison cells. In previous experiments, Pollak's team had discovered that some families with the inherited form of FSGS carried mutations in ACTN4. In these individuals, the disease appeared to strike podocytes (glomeruli epithelial cells) first. While engineering mice designed to carry mutations in this gene, the researchers created mice that lacked detectable Actn4 expression. These “knockout” mice developed severely damaged podocytes and progressive glomerular disease. In the current experiments, the researchers returned to their “knockin” mice, which carry two copies of the mutation found in the families with inherited FSGS. They also generated “normal” mice and mice harboring one normal and one mutant copy of the gene. In the biochemical experiments, the researchers investigated the mutant protein's binding behavior. Typically, two α-actinin-4 proteins form a twosome without incident, but here the mutants behaved badly, assuming improper structural conformations and forming aggregates rather than pairs. Next, Pollak and colleagues introduced the genes with the Actn4 mutation into podocytes, using a variety of methods, to see where in the cells the expressed proteins turned up. They also introduced fluorescently labeled mutant and normal Actn4 genes into podocytes that were grown from the three mouse types: normal proteins were diffused throughout the cytoplasm in each cell type, but the mutant proteins showed an uneven distribution. Analysis of various tissues taken from the knockin mice revealed normal levels of mRNA transcripts—indicating normal gene transcription—but “markedly reduced” α-actinin-4 protein levels. The mutant proteins, it turns out, were manufactured normally but were degraded far more quickly than normal proteins. Electron microscopy showed that podocytes in the kidneys of the knockin mice had structural defects, while the mice themselves had significantly higher levels of protein in their urine than mice with one or two normal copies of the gene did. The finding that FSGS-associated Actn4 mutations produce α-actinin-4 aggregates with significantly reduced life span, the authors explain, suggests two possible mechanisms of initiating disease: aggregation and the toxic affects of aggregation could injure podocytes, or loss of α-actinin-4 function caused by rapid degradation of the protein could produce injury. Pollak and colleagues argue that both factors likely play a role: Actn4 mutations lead to both reduced α-actinin-4 activity and protein aggregation, and the loss of protein function and the toxic effects of protein aggregation produce glomerular injury. As inherited renal disease typically emerges later in life, it may be that α-actinin-4 aggregation causes incremental but cumulative podocyte damage over time. So what does this mean for patients with progressive renal disease? While these findings may not translate into clinical applications anytime soon, they do suggest that therapies aimed at repairing the structure or expression of these essential cytoskeletal proteins might return the renegade proteins to the fold.

0

PMC423161

CC BY

2021-01-05 08:21:11

no

PLoS Biol. 2004 Jun 15; 2(6):e194

utf-8

PLoS Biol

10.1371/journal.pbio.0020194

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020195SynopsisBiophysicsGenetics/Genomics/Gene TherapyNeuroscienceMus (Mouse)A Method for Studying Calcium Dynamics in Transgenic Mice Synopsis6 2004 15 6 2004 15 6 2004 2 6 e195Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Functional Fluorescent Ca2+ Indicator Proteins in Transgenic Mice under TET Control ==== Body Calcium makes up just 2% of the human body, and 99% of it is sequestered in bones and teeth. The remainder exists within and around cells, influencing a variety of cellular processes, from transcription and cell survival to nerve signaling and muscle contraction. Calcium inhabits the intra- and extracellular space as a gradient, with extracellular concentrations some 1,000 times greater than those inside the cell. These gradients are maintained by calcium pumps. Calcium signaling operates mostly through voltage- and ligand-gated calcium channels (ligands are signaling molecules), both in the plasma membrane of the cell and in the membranes enclosing intracellular organelles. Signaling is typically initiated by an influx of calcium across the plasma membrane or by the release of calcium from an organelle, such as the endoplasmic reticulum. In neuron-to-neuron signaling, calcium signaling helps convert electrical signals into chemical signals in the form of neurotransmitters. The arrival of an electrical signal at a nerve terminal opens the many calcium channels in the nerve terminal, admitting a stream of calcium ions. The increased intracellular calcium concentration in turn releases the resident neurotransmitters accumulated in the nerve terminal, converting the electrical signal into a chemical signal. As neurotransmitters bind to their receptors in the next target neuron, they change the cell's membrane potential, prompting the neuron to generate an electrical signal, thereby converting the chemical signal back into an electrical one and completing the signaling circuit. Since calcium dynamics mediates most neuronal information flow, it can be used as a general measure of neural activity. Calcium-indicator-expressing mouse on a Ca2+ activity odor map (Image by Rolf Sprengel) Through an elaborate network of electrical activity, the brain encodes, combines, and interprets signals to process information about the world. Simultaneous measurement of this activity in multiple brain locations has provided valuable insight into how neural networks function. But since electrical recordings can't pinpoint activity in the fine branches of individual neurons or pick up biochemical (nonelectrical) signals, researchers are increasingly turning to approaches that measure calcium concentrations as a proxy for neuron activity. Now Mazahir Hasan et al. have created mice engineered to stably express two different kinds of fluorescent calcium indicator proteins (FCIPs) in the brain (the fluorescence produced by these proteins can be seen when the brain is viewed with a two-photon microscope). Because the indicators are incorporated into the mouse genomes, this approach offers the possibility of targeting specific cells (by using promoters that specify which cells the genes should be activated in), allowing researchers to map the activity of select neuron populations. Fluorescent proteins can be incorporated into a gene of interest to help researchers track that gene's protein in living tissue. FCIPs report calcium concentrations by changing fluorescence when they bind to calcium. Introducing calcium indicators into neural tissues was largely impractical, often failing to target specific cell types, until these genetically engineered indicators were developed in the late 1990s, allowing the desired specificity. While FCIPs have been used to good effect in worms, fruitflies, and zebrafish—and just recently in mouse muscle—they had not been stably and functionally expressed in the mammalian brain until now. To deliver the FCIPs to the mouse brain, Hasan et al. used a regulatory promoter called Ptet (in the tetracycline system), which offers the possibility of targeting the expression of the FCIPs in different neural populations. To test the functionality of the proteins, they used fluorescence microscopy to analyze neurons from mouse lines and found high levels of FCIP expression. The real test, however, was whether the FCIPs could fulfill their promise as a probe for calcium activity. When the authors electrically stimulated brain slices from a mouse line expressing moderate to high levels of FCIP (electrical stimulation is known to increase intracellular calcium concentration), fluorescence increased rapidly following the stimulus. Significant changes in FCIP fluorescence were also observed when live mice responded to odor stimulation. That “fast and robust” FCIP signals were detected in live animals responding to sensory stimulation, the authors argue, proves the promise of FCIPs as a reporter on the activity of select neural populations in living systems. And since these indicator proteins retain stable functional expression over time (8- to 12-week-old mice continued to express the proteins), they could help researchers track neuronal activity over extended periods. While a variety of bugs remain to be worked out with FCIPs—it's unclear, for example, why only the Ptet promoter generates high levels of FCIP expression in the brain and why not all neurons in a given population express the proteins—Hasan et al. demonstrate that the tetracycline system supports stable expression of the calcium indicators. The FCIP approach avoids the complications of invasive techniques like surgically administering dyes and produces a more interpretable signal, since the cell populations are already known. Because FCIPS can be used in living animals, they can reveal where and when neurons are firing. And because FCIP mice can be crossed with mice containing mutations in genes important for neural function, this method could reveal how specific genes contribute to the construction of neural networks.

0

PMC423162

CC BY

2021-01-05 08:21:11

no

PLoS Biol. 2004 Jun 15; 2(6):e195

utf-8

PLoS Biol

10.1371/journal.pbio.0020195

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020195SynopsisBiophysicsGenetics/Genomics/Gene TherapyNeuroscienceMus (Mouse)A Method for Studying Calcium Dynamics in Transgenic Mice Synopsis6 2004 15 6 2004 15 6 2004 2 6 e195Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Functional Fluorescent Ca2+ Indicator Proteins in Transgenic Mice under TET Control ==== Body Calcium makes up just 2% of the human body, and 99% of it is sequestered in bones and teeth. The remainder exists within and around cells, influencing a variety of cellular processes, from transcription and cell survival to nerve signaling and muscle contraction. Calcium inhabits the intra- and extracellular space as a gradient, with extracellular concentrations some 1,000 times greater than those inside the cell. These gradients are maintained by calcium pumps. Calcium signaling operates mostly through voltage- and ligand-gated calcium channels (ligands are signaling molecules), both in the plasma membrane of the cell and in the membranes enclosing intracellular organelles. Signaling is typically initiated by an influx of calcium across the plasma membrane or by the release of calcium from an organelle, such as the endoplasmic reticulum. In neuron-to-neuron signaling, calcium signaling helps convert electrical signals into chemical signals in the form of neurotransmitters. The arrival of an electrical signal at a nerve terminal opens the many calcium channels in the nerve terminal, admitting a stream of calcium ions. The increased intracellular calcium concentration in turn releases the resident neurotransmitters accumulated in the nerve terminal, converting the electrical signal into a chemical signal. As neurotransmitters bind to their receptors in the next target neuron, they change the cell's membrane potential, prompting the neuron to generate an electrical signal, thereby converting the chemical signal back into an electrical one and completing the signaling circuit. Since calcium dynamics mediates most neuronal information flow, it can be used as a general measure of neural activity. Calcium-indicator-expressing mouse on a Ca2+ activity odor map (Image by Rolf Sprengel) Through an elaborate network of electrical activity, the brain encodes, combines, and interprets signals to process information about the world. Simultaneous measurement of this activity in multiple brain locations has provided valuable insight into how neural networks function. But since electrical recordings can't pinpoint activity in the fine branches of individual neurons or pick up biochemical (nonelectrical) signals, researchers are increasingly turning to approaches that measure calcium concentrations as a proxy for neuron activity. Now Mazahir Hasan et al. have created mice engineered to stably express two different kinds of fluorescent calcium indicator proteins (FCIPs) in the brain (the fluorescence produced by these proteins can be seen when the brain is viewed with a two-photon microscope). Because the indicators are incorporated into the mouse genomes, this approach offers the possibility of targeting specific cells (by using promoters that specify which cells the genes should be activated in), allowing researchers to map the activity of select neuron populations. Fluorescent proteins can be incorporated into a gene of interest to help researchers track that gene's protein in living tissue. FCIPs report calcium concentrations by changing fluorescence when they bind to calcium. Introducing calcium indicators into neural tissues was largely impractical, often failing to target specific cell types, until these genetically engineered indicators were developed in the late 1990s, allowing the desired specificity. While FCIPs have been used to good effect in worms, fruitflies, and zebrafish—and just recently in mouse muscle—they had not been stably and functionally expressed in the mammalian brain until now. To deliver the FCIPs to the mouse brain, Hasan et al. used a regulatory promoter called Ptet (in the tetracycline system), which offers the possibility of targeting the expression of the FCIPs in different neural populations. To test the functionality of the proteins, they used fluorescence microscopy to analyze neurons from mouse lines and found high levels of FCIP expression. The real test, however, was whether the FCIPs could fulfill their promise as a probe for calcium activity. When the authors electrically stimulated brain slices from a mouse line expressing moderate to high levels of FCIP (electrical stimulation is known to increase intracellular calcium concentration), fluorescence increased rapidly following the stimulus. Significant changes in FCIP fluorescence were also observed when live mice responded to odor stimulation. That “fast and robust” FCIP signals were detected in live animals responding to sensory stimulation, the authors argue, proves the promise of FCIPs as a reporter on the activity of select neural populations in living systems. And since these indicator proteins retain stable functional expression over time (8- to 12-week-old mice continued to express the proteins), they could help researchers track neuronal activity over extended periods. While a variety of bugs remain to be worked out with FCIPs—it's unclear, for example, why only the Ptet promoter generates high levels of FCIP expression in the brain and why not all neurons in a given population express the proteins—Hasan et al. demonstrate that the tetracycline system supports stable expression of the calcium indicators. The FCIP approach avoids the complications of invasive techniques like surgically administering dyes and produces a more interpretable signal, since the cell populations are already known. Because FCIPS can be used in living animals, they can reveal where and when neurons are firing. And because FCIP mice can be crossed with mice containing mutations in genes important for neural function, this method could reveal how specific genes contribute to the construction of neural networks.

0

PMC423163

CC BY

2021-01-05 08:21:10

no

PLoS Biol. 2004 Jun 15; 2(6):e229

latin-1

PLoS Biol

10.1371/journal.pbio.0020229

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020173Research ArticleBiotechnologyIn VitroDNA Display I. Sequence-Encoded Routing of DNA Populations Sequence-Encoded Routing of DNAHalpin David R 1 Harbury Pehr B [email protected] 1 1Department of Biochemistry, Stanford University School of MedicineStanford, CaliforniaUnited States of America7 2004 22 6 2004 22 6 2004 2 7 e1734 2 2004 13 4 2004 Copyright: © 2004 Halpin and Harbury.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. DNA Display II. Genetic Manipulation of Combinatorial Chemistry Libraries for Small-Molecule Evolution DNA Display III. Solid-Phase Organic Synthesis on Unprotected DNA Harnessing DNA-Based Technology for Drug Discovery Translating DNA into Synthetic Molecules Recently reported technologies for DNA-directed organic synthesis and for DNA computing rely on routing DNA populations through complex networks. The reduction of these ideas to practice has been limited by a lack of practical experimental tools. Here we describe a modular design for DNA routing genes, and routing machinery made from oligonucleotides and commercially available chromatography resins. The routing machinery partitions nanomole quantities of DNA into physically distinct subpools based on sequence. Partitioning steps can be iterated indefinitely, with worst-case yields of 85% per step. These techniques facilitate DNA-programmed chemical synthesis, and thus enable a materials biology that could revolutionize drug discovery. Resin-linked oligonucleotides are described that efficiently partition subpools of DNA based on sequence, enabling DNA- programmed chemical synthesis ==== Body Introduction Subsequent to the discovery of DNA as the information-carrying blueprint for biopolymer assembly, the possibility has existed for its utilization to program molecular processes devised by man. DNA is an attractive material for several reasons. It provides very high information density: a micromolar solution of thousand-base DNA fragments can store 106 bits per femtoliter. The information is amplifiable, so that a single molecule can be copied to produce a measurable quantity of nucleic acid. A large collection of enzymatic tools (e.g., polymerases, helicases, recombinases, and restriction enzymes) and man-made tools (e.g., oligonucleotide synthesizers, thermal cyclers, and purification kits) exist to manipulate DNA. Several technologies take advantage of these facts. For example, patterned DNA fragments have been used to direct self-assembly of nucleic acid objects (Seeman 2003), to follow the fate of cells in complex populations (Shoemaker et al. 1996), to localize substrates and catalysts for “lab on a chip” experiments (Winssinger et al. 2002), and for DNA computing (Braich et al. 2002). More recently, the idea has been advanced that patterned DNAs could be used to direct small-molecule synthesis (Harbury and Halpin 2000; Gartner and Liu 2001), providing a genetic code for organic chemistry. A fundamental difficulty in using DNA to program molecular events is transducing the information contained within a nucleic acid sequence into a corresponding physical outcome. One general scheme to link DNA identity to a downstream process relies on sequence-specific partitioning. This self-separation is accomplished straightforwardly by hybridization of DNA molecules to immobilized oligonucleotides. Once spatially separated, the different pools of nucleic acid can be subjected to different processing steps. Thus, the sequence of a DNA fragment determines its fate. For multistep procedures, sequential hybridizations to multiple subsequences within a DNA molecule are required. Iterative partitioning of DNA molecules is equivalent to routing the molecules through a network, with each sequence taking a unique path. Routing small quantities of DNA requires high-yielding, high-fidelity, and repeatable preparative hybridization. Although a vast literature exists on DNA hybridization for analytical purposes, literature on preparative applications, where DNA must be recovered after hybridization, is quite limited. Major precedents include RNA purification over polyrA binding resins (Aviv and Leder 1972) or tRNA binding resins (Tsurui et al. 1994), and an electrophoresis-based selection procedure used in DNA computing (Kenney et al. 1998). However, none of these methods is suitable for routing, either because they are not efficient, not repeatable, or difficult to interface with a downstream physical outcome. Here we present a practical method to autoroute DNA libraries through multiple decision points of a tree-type network, making DNA-programmed assembly processes possible. Results Our experimental scheme is illustrated in Figure 1. A population of DNA “genes” consisting of catenated coding positions is constructed. Defined sets of “codon” sequences exist at the first position (a1, b1, and so on), second position (a2, b2, and so on), and subsequent positions. Codons present in one coding position are mutually exclusive of the codons at any other position. The identity of the first codon determines the fate of the gene at the first decision point of the network, the second codon at the second decision point, and so forth. Figure 1 Routing DNA through Networks (A) Structure of a simplified nine-member routing gene library. The ssDNA consists of 20-base noncoding regions (black lines Z1–Z4) and 20-base coding positions (colored bars [a,b,c]1–3). All library members contain the same four DNA sequences at the four noncoding regions. At each of the three coding positions, three mutually exclusive codons, (a,b,c)n, are present for a total of twenty-seven different routing genes. Resin beads coated with an oligonucleotide complementary to one codon (anticodon beads; gray ball at left) capture by hybridization ssDNAs containing the corresponding codon. (B) To travel through the network, the ssDNA library starts on one or multiple DEAE columns (black column on left) and is hybridized to a set of anticodon columns (red, green, and blue columns) corresponding to the set of codons in the first coding position. The genes are thus physically partitioned into subpools based on sequence identity and can be processed accordingly. Each subpool is subsequently transferred to a distinct DEAE column, completing the first step through the network. The hybridization splitting, processing, and transfer are repeated for all subsequent coding regions. After completion of the final step, the library is concentrated on a reverse-phase column (RP; black-and-white column on right) and eluted for solution manipulation. Genes are “read” by hybridization to a set of anticodon columns. Each anticodon column displays an oligonucleotide complementary to one codon sequence, and a complete set of columns comprises the complements to all codons at a single coding position. Genes bind to the columns by codon–anticodon base pairing, and are thus partitioned. To read a subsequent coding position, the genes are first transferred to a nonspecific DNA binding resin, regenerating an unhy-bridized state. This DNA is then hybridized to a new set of anticodon columns. By a series of such reading cycles, the sequence of a gene guides it through the network. DNA Routing Genes For DNA routing genes, we adopted a modular design adaptable to networks of varying depth and width. We chose codons consisting of 20 bases, catenated to neighboring codons through 20-base noncoding regions (Figure 1). To prevent aberrant codon–anticodon pairing, all sequences were taken from a set of more than 10,000 distinct 20mers that do not crosshybridize in microarray experiments (Giaever et al. 2002). The work reported here utilized 340-base fragments that specified routes through a tree with eight hierarchical levels and ten branches per level. Each of the 108 unique genes contained routing instructions for eight decision points. Construction of the gene library proceeded in two stages (Figure 2A). Initially, 160 40-base oligonucleotides comprising a codon and an adjacent noncoding region were synthesized. We assembled sets of 16 of these 40mers (for example, the oligonucleotides corresponding to codons a1, a2, . . . , a8) into ten 340-base genes (“all a,” “all b,” etc.). The ten different genes were subcloned. Eight 60-base segments were then PCR amplified from an equimolar mixture of the parental plasmids. Each segment consisted of a coding position and two adjacent noncoding regions. The eight degenerate products were spliced together into 340-base fragments by primerless PCR, thus producing a library of 108 complexity. In principle, collective assembly of the 160 40mer oligonucleotides would have created the library in one step. In practice, the two-stage approach provided better control over codon distributions in the final gene population. Figure 2 Construction and Diversification of Routing Gene Populations (A) Overlapping complementary oligonucleotides that span an entire gene (for example [Z–a]1–8 and a1–8′–Z2–9′) were assembled into full gene products (“all a,” “all b,” etc.) by primerless PCR and subcloned. Equivalent amounts of the ten resulting plasmids (a1–8, … , j1–8) were mixed and used as template for eight separate PCR reactions with noncoding region primer pairs (Zi/Zi +1′) that flanked a single coding position. The eight degenerate PCR products (Zn−xn−Zn +1) were assembled into a library of 108 different genes by primerless PCR (right). (B) To generate ssDNA, a T7 promoter (pT7) was appended to the 3′ end of the double strand DNA library. The minus strand of the library was transcribed using T7 RNA Polymerase (T7 RNAP), and reverse transcribed from a Z1 primer using MMLV Reverse Transcriptase (MMLV RT) in a coupled reaction. The resulting DNA/RNA heteroduplex was treated with sodium hydroxide to hydrolyze the RNA, providing ssDNA. The noncoding regions play an instrumental role in the construction and handling of genes. By providing conserved crossover points, they facilitate the modular generation of highly complex populations from a small number of starting oligonucleotides. For the same reason, the noncoding regions make it possible to diversify existing gene sets by recombination. The noncoding regions also place codons in the correct coding position, ensuring that all genes incorporate one codon per branch point of a network. The existence of a well-defined “reading frame” results from the fact that anticodon columns only hybridize to DNA subsequences at a specified coding position, and not to codons elsewhere in the gene. To obtain hybridization-capable nucleic acid, the duplex DNA genes must be converted to a single-stranded form. (Possibly, duplex DNA hybridization to oligonucleotides through D-loops could be driven by RecA and ATP [Shortle et al. 1980]). A number of approaches for generating single-stranded DNA (ssDNA) have been described (for example Nikiforov et al. 1994; Williams and Bartel 1995; Pagratis 1996; Ball and Curran 1997), but we found most of them unsuitable for large-scale work. A modified nucleic-acid-sequence-based amplification protocol (Compton 1991) ultimately proved most expedient. Thus, we appended a T7 polymerase promoter to duplex DNA routing genes by PCR amplification with appropriate primers (Figure 2B). This material was used as the substrate for a coupled transcription/reverse-transcription reaction to generate DNA/RNA heteroduplexes. Hydrolysis of the RNA strand of the heteroduplexes with sodium hydroxide provided nanomole quantities of high-quality single-stranded DNA. Oligonucleotide Hybridization Chromatography Synthesis of anticodon columns involves covalent attachment of oligonucleotides to a solid phase. Thiol-containing and amine-containing oligonucleotide modification reagents are commercially available, and either should facilitate coupling to an appropriately activated resin. However, pilot experiments indicated that amide linkages were more easily prepared than thioether linkages. The deprotection protocols for oligonucleotide-linked sulfhydryl moieties were more complex than for amine moieties, and sulfhydryl-modified oligonucleotides were prone to oxidation and general loss during manipulation steps. Amine-modified oligonucleotides were easier to work with and were thus used for production of anticodon columns. It proved necessary to desalt crude oligonucleotides over reverse-phase cartridges before coupling. As candidate solid phases, we tested commercially available chromatography resins made of polystyrene (Magnapore macroporous chloromethylpolystyrene beads, Argogel-NH2, epoxide-activated Poros 50 OH), methacrylate (Ultralink Biosupport Mediumand Iodoacetyl), and agarose (N-hydroxysuccinimide[NHS]-activated Sepharose, carbonyl diimidazole-activated Sephacryl S-1000). 20-base modified anticodon oligonucleotides were coupled to the resins. Quantification by reverse-phase chromatography of the uncoupled oligonucleotide remaining in solution provided a measure of reaction progress (Figure 3). An underivatized ten-base oligonucleotide was included in all coupling reactions to control for nonspecific loss of nucleic acid. Figure 3 Anticodon Column Synthesis (A) Jeffamine 1500 (compound 1) was reacted with glutaric anhydride, and the singly acylated linker (compound 2) was purified over a HiTrap SP column. Purified compound 2 was coupled to NHS-activated Sepharose (gray ball). Treatment of the linkered resin compound 3 with TBTU/NHS, and subsequent incubation with a 5′-amino modified oligonucleotide (NH2-DNA), completed the synthesis of an anticodon column. (B) Refractive index FPLC chromatograms of PEG compounds 1 and 2 before and after purification by cation-exchange chromatography. Linker compound 1 migrates as a bisamine (green trace) while compound 2 migrates as a monoamine (red trace). (C) HPLC chromatograms of a 5′-aminated 20-base oligonucleotide (NH2-20mer) and a nonaminated ten-base oligonucleotide control (10mer) incubated with TBTU/NHS activated resin compound 3. Chromatograms of the starting material (black) and supernatant after 12 h (red) are shown. An unknown side-product of the coupling reaction (NH2-20mer side-product) is labeled. To test hybridization properties, 50 μl columns of the derivatized resins were loaded with 1 nmol each of a complementary 20-base oligonucleotide and a noncomplementary ten-base oligonucleotide by cyclical flow in high-salt buffer. After column washing, bound oligonucleotides were eluted with deionized water. The specificity and efficiency of hybridization were evaluated by high performance liquid chromatography (HPLC) analysis of the load, flow-through, and elute fractions (Figure 4). By this assay, none of the initial resins functioned for preparative DNA fractionation, either because they failed to bind DNA well (Argogel-NH2, epoxide-activated Poros 50 OH, NHS-activated Sepharose, and Biosupport Medium) or because they bound DNA without sequence specificity (Magnapore beads and Iodoacetyl). Figure 4 Linker Effects on Hybridization The hybridization to anticodon columns of a ten-base noncomplementary oligonucleotide and a 20-base complementary oligonucleotide was analyzed by HPLC. Chromatograms of the hybridization load (blue), flow-through (red), and elute (black) are shown. (A) shows the anticodon column that was synthesized by coupling the anticodon oligonucleotide directly to NHS-activated Sepharose. (B) shows the anticodon column that was synthesized by coupling the anticodon oligonucleotide to NHS-activated Sepharose through a PEG linker. Following an observation that long polyethylene glycol (PEG) linkers dramatically improve hybridization to DNA on polypropylene surfaces (Shchepinov et al. 1997), we synthesized an approximately 100-atom modified PEG spacer to sit between the resin and the anticodon oligonucleotide (see Figure 3). The synthetic scheme utilized inexpensive, commercially available reagents and ion-exchange chromatography for purification. Efforts to attach the spacer to Biosupport Medium were unsuccessful, but the spacer coupled readily to NHS-activated Sepharose and to carbonyl diimidazole-activated Sephacryl S-1000. The linkered Sephacryl and Sepharose materials immobilized amine-modified oligonucleotides to final densities of approximately 90 nmol per milliliter of resin (Figure 3C). Anticodon columns containing 50 μl of either resin efficiently and reversibly hybridized to 1 nmol of a complementary 20-base oligonucleotide, while exhibiting unmeasurable binding to a noncomplementary ten-base oligonucleotide (Figure 4). Subsequent experiments were carried out with the Sepharose-based resin. The hybridization columns proved extremely robust, withstanding over 30 hybridization cycles, treatment with 10 mM sodium hydroxide, and exposure to dimethylformamide (DMF) without a detectable decrease in perfor-mance. Fidelity of Routing We next investigated how buffer conditions and temperature influenced the accuracy and yield of 340-base ssDNA hybridization to anticodon columns. For these experiments, a single radiolabeled DNA gene was diluted 10-fold into an excess (50 pmol) of an unlabeled routing gene library, and loaded onto a 250-μl diethylaminoethyl (DEAE) Sepharose column. The DEAE Sepharose column was placed in a closed 3-ml fluid circuit containing ten anticodon columns, of which only one complemented a codon within the radiolabeled DNA. Hybridization buffer was pumped over the system in a direction that placed the complementary anticodon column distal to the DEAE Sepharose column (Figure 5, left). After hybridization, the flow-through was collected, and bound nucleic acid was eluted off of each column in the system. The quantity of radiolabeled DNA present in each fraction was determined by scintillation counting. Figure 5 Cyclical Multistep Routing (Left) Genes are transferred from the “n−1” step DEAE columns to the “n” step anticodon columns by connecting all columns in series and cyclically pumping a high-salt buffer through the system with a peristaltic pump (gray box) for 1 h at 70 °C and 1 h at 46 °C. (Right) Genes are transferred from an “n” step anticodon column to an “n” step DEAE column by connecting the two columns in series and cyclically pumping 50% DMF through the system for 1 h at 45 °C. Arrows indicate the direction of flow. By varying the temperature (25 °C to 70 °C), salt identity (sodium chloride, lithium chloride, or tetramethylammonium chloride) and salt concentration (10 mM to 2 M) of the hybridization buffer, we determined that 1.5 M sodium chloride in a phosphate buffered solution with pH 6.5 at 45 °C provided the most robust hybridization behavior over multiple codon sequences. In addition, an initial high temperature step (70 °C) and the presence of a DEAE column inline proved critical to achieving uniformly high yields (Table 1). The high temperature and DEAE column may serve to break up structures in the DNA genes that inhibit association with anticodon columns. Consistent with previous microarray data (Shoemaker et al. 1996), addition of 20-base oligonucleotides complementary to the noncoding regions improved hybridization efficiency. The hybridization kinetics were fast, approaching equilibrium to within 5% in less than an hour at 46 °C. Using optimal hybridization conditions, 90% or more of the input radiolabeled DNA was routed to the correct anticodon column irrespective of the sequence pair used. Table 1 340-Base ssDNA Hybridization Efficiencies and Specificities A radiolabeled “all b” gene was hybridized to anticodon columns corresponding to one coding region (see Figure 5, left). The fraction of input radiolabel recovered from each component of the system is reported, as measured by scintillation counting. Cold DNA: an unlabeled library of 106 genes was added to the hybridization reaction in 10-fold excess over radiolabeled DNA. 70 °C Step: hybridization was carried out at 70 °C for 1 h before cooling to 45 °C. FT: radiolabel recovered from hybridization flow-through. DEAE: radiolabel recovered from an inline DEAE column. a′–j′: radiolabel recovered from the specified anticodon column or pair of anticodon columns. Lost: input radiolabel not recovered from any component Serial Multistep Routing In order to route a DNA fragment through successive levels of a hierarchical tree, multiple hybridization steps are required. DNA from parental anticodon columns must be isolated and hybridized to anticodon columns corresponding to the daughter-node branches. The manipulations must be highly efficient to ensure good routing yields through trees with many levels. We investigated several schemes for accomplishing iterative hybridizations. Our initial strategies utilized a multi-step procedure with three columns (anticodon, DEAE, and reverse-phase), linear transfer formats, and centrifugal evaporation. These three-column strategies did not prove to be high-yielding. We eventually observed that efficient iterative hybridizations could be accomplished with only two columns, using cyclic column-to-column transfers. Thus, a parental anticodon column with bound DNA was placed in a liquid circuit with a 250-μl DEAE-Sepharose column (Figure 5, right). A 50% DMF solution was pumped over the system, breaking interactions between the anticodon column and bound DNA, and promoting the binding of free DNA to the anion-exchange resin. (DNA bound to DEAE columns can be conveniently interfaced with an encoded process, such as covalent transformation by solid-phase organic chemistry [Halpin et al. 2004]). Subsequently, the DEAE-Sepharose column with bound DNA was placed in a liquid circuit with a set of anticodon columns corresponding to the branches of the daughter node. As described above, a high-salt buffer was pumped cyclically over the system to elute DNA from the anion-exchange resin, and to promote hybridization to the new set of anticodon columns. The two reciprocal transfers constitute one hybridization cycle and can be repeated indefinitely. The iterative hybridizations proceed with very high DNA recoveries (greater than 95% for anticodon to DEAE and greater than 90% for DEAE to anticodon) for several reasons. First, the columns “see” a large volume of liquid flow in the cyclic format, although the total volume of buffers used is small. Second, because the transfers are column-to-column, losses associated with manipulation of dilute DNA solutions do not occur. The two-column strategy makes it practical to iterate successive hybridizations, with worst-case overall yields of 0.85n for n hybridizations. The final requirement was to isolate DNA as a concentrated, salt-free aqueous solution upon completion of routing. For this purpose, DNA bound to anticodon columns was eluted with a small volume of an (ethylenedinitrilo)tetraacetic acid (EDTA) solution and precipitated. Alternatively, DNA bound to DEAE-Sepharose columns was transferred to a reverse-phase cartridge by cyclically pumping a high-salt buffer over the two columns in series. DNA on the reverse-phase cartridge was then washed with deionized water, eluted in acetonitrile/water, concentrated by evaporation, and desalted over a microcentrifuge gel-filtration column. Discussion Several technical improvements to our protocols are possible. The hybridization conditions could be further optimized to increase yields and shorten times, perhaps by the addition of proteins such as Escherichia coli SSB or RecA (Nielson and Mathur 1995). For procedures involving multiple generations of a gene population, a T7 promoter must be appended repeatedly to the library. Incorporation of an RNAZ module into the routing genes would eliminate this step by providing a permanent T7 promoter (Breaker et al. 1994). Conceivably, ssDNA production could be rendered unnecessary by using peptide nucleic acids as capture oligonucleotides or as complements to the noncoding regions. Peptide nucleic acids have been reported to invade DNA duplexes, forming more stable heteroduplexes (Kuhn et al. 2002). Routing of DNA populations provides a general way to exploit DNA as a programming medium. For example, a routing approach has been utilized to compute solutions to the traveling salesman problem (Braich et al. 2002). In order to obtain the answer, it was necessary to isolate DNA fragments containing a defined set of subsequences through iterated hybridizations. By increasing the speed and yield of such isolation steps, the tools described here should aid DNA computing advances. The preparative hybridization protocols will also facilitate purification of defined genomic sequences and primary mRNA transcripts for the study of nucleic acid modifications, and for the analysis of adjunct proteins. One advantage of iterated hybridization in this context is that it increases the overall specificity of purification, in much the same way that successive amplifications with nested primers increase the specificity of PCR reactions. The technique could also be applied to the isolation of nucleoprotein complexes, such as telomerase, that have been tagged with nucleic acid epitopes. Finally, the fates of individual molecules undergoing a process of covalent assembly can be programmed by routing. For example, the protocols presented here were used to direct the split-and-pool synthesis of a combinatorial chemistry library (Halpin and Harbury 2004). That work involved routing genes through a tree with six levels and ten branches per level. In order to program large libraries of very low-molecular-weight compounds, routing through shallow trees with thousands of branches per level will be required. Adaptation of the anticodon columns to a microarray format would achieve this goal in a practical manner. Such massively parallel DNA-directed chemistry has the potential to revolutionize modern drug discovery. Materials and Methods Materials. O,O′-bis(3-aminopropyl) polyethylene glycol of average molecular weight 1500 Da (compound #14535-F, also called Jeffamine 1500) and all other chemicals and solvents were purchased from Sigma-Aldrich (St. Louis, Missouri, United States). DEAE columns were prepared by pipetting approximately 250 μl of DEAE Sepharose Fast Flow resin (#17-0709-01; Amersham Biosciences, Little Chalfont, United Kingdom) into empty TWIST column housings (#20-0030; Glen Research, Sterling, Virginia, United States). DNA library assembly. 160 40mer oligonucleotides were synthesized using standard phosphoramidite chemistry. The 5′ 20 bases of each oligonucleotide consisted of a noncoding region sequence, while the 3′ 20 bases consisted of a codon sequence. Oligonucleotides were purified by electrophoresis on 15% denaturing acrylamide gels. The oligonucleotides were divided into ten sets exclusive to a-type codons (a1–a8), b-type codons (b1–b8), and so forth. Each set of 16 oligonucleotides was assembled into a 340-base fragment by primerless PCR (Stemmer et al. 1995). Assembly reactions contained 1 μl Vent DNA Polymerase (#0254; New England Biolabs, Beverly, Massachusetts, United States), 1X Vent buffer, 250 μM each dNTP, and 0.1–10 pmol of each oligonucleotide, and were run for 20 to 35 cycles (unless otherwise noted, PCR reactions had a volume of 100 μl). In a second PCR step, the assembly products were amplified from 1 μl of the assembly reaction using 0.2 nmol of each end primer. The amplified genes were purified on 2% NuSieve (#50081; FMC BioProducts, Rockland, Maryland, United States) agarose gels and subcloned between the SphI and EcoRI sites of the pET24A plasmid (#70769-3; Novagen, Madison, Wisconsin, United States). To construct a full library, the ten plasmids were mixed in equal proportions and used as template for eight PCR reactions. The primers were Z1/Z2′ for the first reaction, Z2/Z3′ for the second reaction, and so forth. The resulting 60-base-pair products were purified on 3% NuSieve agarose gels. Following quantification by densitometry, 120 ng of each fragment was used in a single 50-μl, ten-cycle primerless PCR reaction to assemble a library. The assembly products were amplified using 1 μl of the assembly reaction as template and 0.2 nmol of each end primer. The final library was subcloned, and 36 isolates were sequenced to verify the presence of the expected codon distribution at each coding position. Preparation of ssDNA. ssDNA was generated using a modified NASBA reaction (Compton 1991). Duplex DNA template (1–10 pmol) was transcribed/reverse-transcribed in a 200-μl reaction containing 1 nmol primer, 40 mM Tris (pH 8.3), 20 mM magnesium dichloride, 40 mM potassium chloride, 10% DMSO, 5 mM DTT, 0.1 mg/ml BSA, 3.5 mM each rNTP, 2.5 mM each dNTP, 1000 U MMLV RNAseH minus reverse transcriptase (for example #M3682; Promega, Madison, Wisconsin, United States), 100 U T7 RNA Polymerase (for example Promega #P2075), and 2 U of pyrophosphatase (New England Biolabs #MO296). To prepare radiolabeled ssDNA, a primer kinased with γ-33P ATP was used. Reactions were incubated for 12 h at 42 °C. Following the enzymatic step, RNA was hydrolyzed by addition of sodium hydroxide to 100 mM and heating of the reaction tube for 2 min at 100 °C. The solution was subsequently neutralized with acetic acid and spun in a benchtop microfuge at 16,000 g for 2 min to remove precipitated material. The supernatant was transferred to a fresh tube, brought to 50 mM EDTA, and ethanol precipitated. ssDNA product was purified by electrophoresis on 4% denaturing acrylamide gels. Excised gel bands were crushed and rotated overnight in 3–6 ml 5 mM Tris (pH 8.0), 500 μM EDTA, and 500 μM EGTA. Acrylamide was removed by spin column filtration, and the solution volume was reduced to 800 μl by centrifugal evaporation. Samples were phenol/chlorofom extracted, ethanol precipitated, and resuspended in water. Purification of bisamine linker (compound 1). The crude Jeffamine material was purified by fast protein liquid chromatography cation-exchange chromatography over a 5-ml Hi-Trap SP column (Amersham Biosciences #17-1152-01). In early work, 1-ml batches of a 250-mg/ml aqueous solution were loaded onto the column in 50 mM acetic acid, washed with load buffer, and bumped off with 1 M lithium chloride. Subsequently, we developed a gradient protocol. The material was loaded in water, and the product was eluted with a linear water–hydrogen-chloride gradient (0–30 mM hydrogen chlo-ride over 15 column volumes) at 6 °C monitored by refractive index detection (RID-10A; Shimadzu, Tokyo, Japan). After every fifth injection, the column was washed with 1.5 M sodium chloride to remove a yellow residue and was then reequilibrated in deionized water. Pooled fractions of the bisamine peak were brought to pH 10 by addition of solid sodium carbonate, and the purified Jeffamine product (compound 1) was extracted into methylene chloride. The combined organic layers were dried over sodium sulfate, and solvent was removed by rotary evaporation. Yields of the pale yellow solid were 40% based on the weight of crude starting material. Synthesis of amine-acid linker (compound 2) One mole equivalent of 1.5 M glutaric anhydride in dioxane was added to a briskly stirred 250-mg/ml aqueous solution of purified Jeffamine (compound 1). After 30 min, the crude reaction product was injected in 1-ml batches over a 5-ml Hi-Trap SP column and eluted as described above. Pooled fractions of the monoamine peak were brought to pH 7 by addition of solid sodium bicarbonate, and the purified linker product (compound 2) was extracted into methylene chloride, dried over sodium sulfate, and obtained as a pale yellow solid by rotary evaporation. Yields were 35% to 50% based on the weight of purified Jeffamine starting material. A compound similar to the purified linker compound 2 is commercially available (#0Z2W0F02; Nektar Therapeutics, San Carlos, California, United States). Synthesis of linkered resin (compound 3). To prepare resin compound 3, compound 1 or compound 2 was dissolved at 300 mg/ml in DMF/200 mM DIEA. The linker solution was incubated with one volume equivalent of drained NHS-activated Sepharose (Amersham Biosciences #17-0906-01). The suspension was rotated at 37 °C for 72 h, washed over a plastic frit with DMF to remove excess linker, and incubated with 1 M ethanolamine in DMF for an additional 12 h at 37 °C. The product resin was washed and stored at 6 °C. Resins coupled to compound 1 were further treated by incubation with an equal volume of DMF containing 100 mM glutaric anhydride and 15 mM pyridine at 37 °C under rotation for 48 h. Resin activation (compound 4). Typically, TBTU (320 mg) and NHS (115 mg) were dissolved in 4 ml of DMF/500 mM DIEA, and drained compound 3 (1 ml) was added. The suspension was rotated at 37 °C for 1 h. Product resin was washed with the following sequence (20 ml of each): ethyl acetate, tetrahydrofuran, ethanol, water, 5 M sodium chloride, water, and DMF. Resin activation was performed just prior to oligonucleotide coupling. Construction of anticodon columns Twenty-base capture oligonucleotides were synthesized using standard phosphoramidite chemistry, with the addition of a C12-methoxytritylamine modifier at the 5′-end (Glen Research #10-1912). Following ammonia cleavage and drying, the oligonucleotides were desalted over C18 Sep-Pak cartridges (#WAT020515; Waters Corporation, Milford, Massachusetts, United States). Purification proceeded according to the manufacturer's instructions, but a deionized water wash was inserted before the final elution step to remove residual TEAA. Coupling reactions of oligonucleotides to resin were carried out in low-binding 0.65-ml microcentrifuge tubes (#11300; Sorenson Bioscience, Salt Lake City, Utah, United States). Ten nanomoles of a capture oligonucleotide and 10 nmol of a nonaminated 10mer control oligonucleotide in a 40-μl aqueous solution were mixed with 160 μl of DMF/200 mM DIEA. Fifty microliters of drained resin compound 4 was added, and the suspension was rotated at 37 °C for 12 h. Reaction progress was monitored by HPLC. Supernatant aliquots (20 μl) were injected onto a 4.6-mm × 25-cm Varian Microsorb-MV 300-5 C18 column (#R0086203C5; Varian, Palo Alto, California, United States) and eluted with a linear water–acetonitrile gradient (0%–45% acetonitrile in five column volumes) in the presence of 0.1 M TEAA (pH 5.2) at 50 °C. After 12 h, the resin was pelleted by centrifugation at 100 g for 1 min, supernatant was removed, and the resin was incubated with 1 M ethanolamine in DMF for an additional 12 h at 37 °C. The derivatized resins were loaded into empty DNA synthesis column housings (#CL-1502-1; Biosearch Technologies, Novato, California, United States). Oligonucleotide hybridization. Hybridization was performed in a closed system consisting of an anticodon column, male tapered luer couplers (Biosearch Technologies #CL-1504-1), capillary tubing (Amersham Biosciences #19-7477-01), silicone tubing (#8060-0020; Nalgene Labware, Rochester, New York, United States), tubing connectors (Amersham Biosciences #19-2150-01, #18-1003-68, and #18-1027-62), and a peristaltic pump (Amersham Biosciences #18-1110-91). Approximately 1 ml of hybridization buffer (60 mM sodium phosphate (pH 6.5), 1.5 M sodium chloride, 10 mM EDTA, and 0.005% Triton X-100) containing 400 pmol of a complementary 20-base oligonucleotide and 400 pmol of a noncomplementary ten-base oligonucleotide was cyclically pumped through the system at 0.5 ml/min for 1 h in a 46-°C water bath. DNA was eluted off the anticodon column with 4 ml of 1 mM EDTA (pH 8.0) and 0.005% Triton X-100 heated to 80 °C. Flow-through and elute fractions were analyzed by HPLC as described above (0%–18% acetonitrile in five column volumes). ssDNA hybridization. ssDNA was loaded onto a DEAE-Sepharose column as described (Halpin et al. 2004). Anticodon columns were connected in series to the DEAE column using male tapered luer couplers, capillary tubing, silicone tubing, and tubing connectors. Approximately 3 ml of hybridization buffer containing 1 nmol of each oligonucleotide complementary to the noncoding regions was cyclically pumped over the system at 0.5 ml/min for 1 h at 70 °C, 10 min at 37 °C, and 1 h in a 46-°C water bath within a 37-°C room. Hybridized DNA was transferred back to a fresh DEAE column, or eluted with 4 ml of 1 mM EDTA (pH 8.0) and 0.005% Triton X-100 heated to 80 °C. For analysis purposes, the hybridization flow-through, the DEAE resin, and the anticodon column elutes were mixed with 10 ml of scintillation cocktail (Bio-safe 2, Research Products International, Mount Prospect, Illinois, United States) and shaken vigorously. Counting was performed using the 35S preset channel of a scintillation counter (Beckman Instruments, Fullerton, California, United States). Anticodon to DEAE DNA transfer. DEAE and anticodon columns were connected in series using male tapered luer couplers, 3.16-mm manifold tubing (#39-628; Rainin, Oakland, California, United States), and tygon tubing 3603. Using a peristaltic pump (Minpuls2; Gilson, Middleton, Wisconsin, United States), approximately 7 ml of a 50% DMF solution was flowed cyclically over the columns at 3 ml/min for either 1 h at 45 °C or 12 h at 25 °C. Endpoint isolation of DNA. DEAE columns were connected in series to C8 SepPak columns (Waters #WAT036775) using male tapered luer couplers, tygon tubing 3603, and tubing connectors. Approximately 6 ml of 50 mM ethanolamine (pH 10.0), 1.5 M sodium chloride, 1 mM EDTA, and 0.005% Triton X-100 was cyclically pumped over the columns at 1 ml/min for 1 h at 50 °C. The Sep-Pak columns were then washed with 12 ml of 100 mM TEAA (pH 6.5) followed by 12 ml of water. ssDNA was eluted from the Sep-Pak column with 4 ml of 50% acetonitrile heated to 80 °C. Samples were concentrated by centrifugal evaporation to a volume of approximately 30 μl and desalted over G25 Sephadex spin columns (Sigma-Aldrich #G-25-150). We thank Shivani Nautiyal and S. Jarrett Wrenn for critical reading of the manuscript and helpful discussions throughout the course of this work, Elizabeth Zuo for oligonucleotide synthesis and mass spectrometry analysis, and Mai Nguyen for oligonucleotide synthesis. DRH is a Howard Hughes Medical Institute Predoctoral Fellow. This work was funded by a Stanford Office of Technology Licensing Research Incentive Award (#127P304 to PBH) and a Burroughs-Wellcome Fund New Investigator in the Pharmacological Sciences Award (#1001162 to PBH). Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. DRH and PBH conceived and designed the experiments. DRH performed the experiments. DRH and PBH analyzed the data and wrote the paper. Academic Editor: Gerald Joyce, Scripps Research Institute Abbreviations DEAEDiethylaminoethyl DMFdimethylformamide EDTA(ethylenedinitrilo)tetraacetic acid HPLChigh performance liquid chromatography NHS N-hydroxysuccinimide PEGpolyethylene glycol ssDNAsingle-stranded DNA ==== Refs References Aviv H Leder P Purification of biologically active globin messenger RNA by chromatography on oligothymidylic acid-cellulose Proc Natl Acad Sci U S A 1972 69 1408 1412 4504350 Ball JK Curran R Production of single-stranded DNA using a uracil-n-glycosylase-mediated asymmetric polymerase chain reaction method Anal Biochem 1997 253 264 267 9367514 Braich RS Chelyapov N Johnson C Rothemund PW Adelman L Solution of a 20-variable 3-sat problem on a DNA computer Science 2002 296 499 502 11896237 Breaker RR Banerji A Joyce GF Continuous in vitro evolution of bacteriophage RNA polymerase promoters Biochemistry 1994 33 11980 11986 7522554 Compton J Nucleic acid sequence-based amplification Nature 1991 350 91 92 1706072 Gartner ZJ Liu DR The generality of DNA-templated synthesis as a basis for evolving non-natural small molecules J Am Chem Soc 2001 123 6961 6963 11448217 Giaever G Chu A Ni L Connelly C Riles L Functional profiling of the Saccharomyces cerevisiae genome Nature 2002 418 387 391 12140549 Halpin DR Harbury PB DNA display II: Genetic manipulation of combinatorial chemistry libraries for small-molecule evolution PLoS Biol 2004 In press Halpin DR Wrenn SJ Lee JA Harbury PB DNA display III: Solid-phase organic synthesis on unprotected DNA PLoS Biol 2004 In press Harbury PB Halpin DR Board of Trustees of the Leland Stanford Junior University DNA-templated combinatorial library chemistry United States patent application WO 99-US24494 2000 Kenney M Ray S Boles TC Mutation typing using electrophoresis and gel-immobilized acrydite probes Biotechniques 1998 25 516 521 9762449 Kuhn H Demidov VV Coull JM Fiandaca MJ Gildea BD Hybridization of DNA and PNA molecular beacons to single-stranded and double-stranded DNA targets J Am Chem Soc 2002 124 1097 1103 11829619 Nielson KB Mathur EJ Stratagene Method for producing primed nucleic acid templates United States patent 5,773,257 1995 6 6 Nikiforov TT Rendle RB Kotewicz ML Rogers YH The use of phosphorothioate primers and exonuclease hydrolysis for the preparation of single-stranded PCR products and their detection by solid-phase hybridization PCR Methods Appl 1994 3 285 291 8038696 Pagratis NC Rapid preparation of single stranded DNA from PCR products by streptavidin induced electrophoretic mobility shift Nucleic Acids Res 1996 24 3645 3646 8836196 Seeman NC DNA in a material world Nature 2003 421 427 431 12540916 Shchepinov MS Case-Green SC Southern EM Steric factors influencing hybridisation of nucleic acids to oligonucleotide arrays Nucleic Acids Res 1997 25 1155 1161 9092624 Shoemaker DD Lashkari DA Morris D Mittmann M Davis RW Quantitative phenotypic analysis of yeast deletion mutants using a highly parallel molecular bar-coding strategy Nature Genet 1996 14 450 456 8944025 Shortle D Koshland D Weinstock GM Botstein D Segment-directed mutagenesis: Construction in vitro of point mutations limited to a small predetermined region of a circular DNA molecule Proc Natl Acad Sci U S A 1980 77 5375 5379 6254078 Stemmer WP Crameri A Ha KD Brennan TM Heyneker HL Single-step assembly of a gene and entire plasmid from large numbers of oligode-oxyribonucleotides Gene 1995 164 49 53 7590320 Tsurui H Kumazawa Y Sanokawa R Watanabe Y Kuroda T Batchwise purification of specific tRNAs by a solid-phase DNA probe Anal Biochem 1994 221 166 172 7985789 Williams KP Bartel DP PCR product with strands of unequal length Nucleic Acids Res 1995 23 4220 4221 7479087 Winssinger N Ficarro S Schultz PG Harris JL Profiling protein function with small molecule microarrays Proc Natl Acad Sci U S A 2002 99 11139 11144 12167675

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020174Research ArticleBiotechnologyIn VitroDNA Display II. Genetic Manipulation of Combinatorial Chemistry Libraries for Small-Molecule Evolution Small-Molecule BreedingHalpin David R 1 Harbury Pehr B [email protected] 1 1Department of Biochemistry, Stanford University School of MedicineStanford, CaliforniaUnited States of America7 2004 22 6 2004 22 6 2004 2 7 e1744 2 2004 11 2 2004 Copyright: © 2004 Halpin and Harbury.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. DNA Display III. Solid-Phase Organic Synthesis on Unprotected DNA DNA Display I. Sequence-Encoded Routing of DNA Populations Harnessing DNA-Based Technology for Drug Discovery Translating DNA into Synthetic Molecules Biological in vitro selection techniques, such as RNA aptamer methods and mRNA display, have proven to be powerful approaches for engineering molecules with novel functions. These techniques are based on iterative amplification of biopolymer libraries, interposed by selection for a desired functional property. Rare, promising compounds are enriched over multiple generations of a constantly replicating molecular population, and subsequently identified. The restriction of such methods to DNA, RNA, and polypeptides precludes their use for small-molecule discovery. To overcome this limitation, we have directed the synthesis of combinatorial chemistry libraries with DNA “genes,” making possible iterative amplification of a nonbiological molecular species. By differential hybridization during the course of a traditional split-and-pool combinatorial synthesis, the DNA sequence of each gene is read out and translated into a unique small-molecule structure. This “chemical translation” provides practical access to synthetic compound populations 1 million-fold more complex than state-of-the-art combinatorial libraries. We carried out an in vitro selection experiment (iterated chemical translation, selection, and amplification) on a library of 106 nonnatural peptides. The library converged over three generations to a high-affinity protein ligand. The ability to genetically encode diverse classes of synthetic transformations enables the in vitro selection and potential evolution of an essentially limitless collection of compound families, opening new avenues to drug discovery, catalyst design, and the development of a materials science “biology.” The authors use DNA "genes" to direct the synthesis of combinatorial chemistry libraries, and show in an in vitro selection experiment that specific drugs can be developed ==== Body Introduction Creation of molecular function represents a fundamental challenge. Nature accomplishes the task through evolution, iterating cycles of selection, amplification, and diversification. Multiple generations of selective pressure and reproduction transform a diverse population into one consisting only of molecules fit to survive. Life on this planet thus emerged from a limited chemical palette, comprising proteins, nucleic acids, sugars, lipids, and metabolites. Over the last two decades, technologies that recapitulate this process in the test tube have been developed, and have produced an amazing collection of biopolymers with unprecedented recognition and catalytic properties (reviewed in Roberts and Ja 1999). At present, however, these in vitro selection techniques cannot be applied to compounds of nonbiological origin and have therefore not affected most areas of molecular discovery. The question arises: what would become possible if in vitro selection were applied to chemical populations of arbitrary composition? High-throughput screening of combinatorial chemistry (HTS-CC) libraries represents a first approximation to small-molecule evolution, in that the process roughly mimics the diversification and selection components of evolution. However, amplification and iteration have no functional equivalents in HTS-CC techniques, placing practical limits on library complexity. Amplification and iteration are critical for identifying vanishingly small amounts of material from a complex population. Moreover, these processes make possible the application of bulk selections rather than serial screens to assay libraries, vastly increasing throughput. Accordingly, typical HTS-CC libraries rarely exceed 106 unique members (Dolle 2003), whereas the biopolymer libraries used for in vitro selection experiments generally comprise 109–1013 unique members (Roberts and Ja 1999). A state-of-the-art high-throughput screening facility, capable of performing 300,000 tests per day, would require 9 millennia to screen a typical in vitro selection library (Morais 2003). If the success of molecular discovery correlates with library complexity, then in vitro selection of combinatorial chemistry libraries for functional molecules will be far more powerful than screening. In order to apply in vitro selection to combinatorial chemistry libraries, each compound must be associated with a gene that specifies its structure. DNA has been utilized previously to record the synthetic history of individual beads in a split-and-pool combinatorial synthesis, but the DNA tags could not direct subsequent resynthesis of the corresponding compound (Brenner and Lerner 1992). More recently, hybridization-induced proximity strategies for DNA-templated organic synthesis have been described, but their use has not yet been reported for the synthesis of complex libraries (Gartner and Liu 2001 and references therein). In this manuscript we present and demonstrate a general method for the in vitro selection and evolution of combinatorial chemistry libraries (Harbury and Halpin 2000). Results Strategy In vitro selection requires iterated rounds of three steps: conversion of genes to gene products, selection of gene products, and gene amplification (Figure 1). The last two steps, selection and amplification, are similar between all forms of in vitro selection. However, conversion of genes to gene products poses a unique problem for the in vitro selection of small molecules. Whereas enzymes convert genetic material into the natural biopolymers, no machinery exists to directly translate genes into small molecules. Figure 1 The In Vitro Selection Cycle Experiments are initiated with a nucleic acid library (colored DNA). The sequence of each gene directs the synthesis of a corresponding gene product (colored ball) that is physically linked to its encoding nucleic acid. The gene products are subjected to selection, for example, through binding to an immobilized macromolecule (cyan widget at bottom). The nucleic acid encoding selected gene products is amplified and used as input for a subsequent cycle. In general, small-molecule libraries are synthesized by the split-and-pool method which is illustrated in Figure 2 (Furka et al. 1991; Thompson and Ellman 1996). A mixture of supports (the inert material on which small molecules are built, typically polystyrene beads) is randomly split into subpools. A distinct chemical building block is then coupled to the supports in each subpool, after which the supports are pooled together and mixed. Splitting, coupling, and pooling are repeated until the library synthesis is complete. The series of subpools into which a support partitions determines what chemical building blocks are added to the support. Thus, the trajectory that a support takes through a split-and-pool synthesis is essentially a molecular recipe. If a support could predetermine its own trajectory, it would encode the synthesis of the small molecule ultimately attached to it. Predetermining support trajectories can be accomplished by using a DNA library as the support material, and by directing the splits through hybridization. The DNA sequence of each support then governs its subpool path, and acts as a genetic blueprint for a small molecule. Figure 2 Split-and-Pool Synthesis of a Combinatorial Chemistry Library A mixture of solid supports (balls with rotated “L” at top) is randomly split into subpools. A distinct chemical building block (red, green, or blue ball) is coupled to the supports in each subpool. The supports are repooled and mixed. This process of splitting, chemistry, and pooling is iterated until the library synthesis is complete. The small molecules ultimately synthesized are combinations of the different building blocks (colored circles, squares, and diamonds). As highlighted by the black bead, the path taken by a support through the split-and-pool synthesis (right, middle, left) determines the small molecule synthesized on it (blue ball, green square, red diamond). The number of reactions performed is the sum of the number of subpools in each split (3 + 3 + 3 = 9). The number of unique small molecules generated is the product of the number of subpools in each split (3 × 3 × 3 = 27). The construct we chose for our DNA support library is shown in Figure 3A. The single-stranded DNA (ssDNA) includes a unique reactive site at its 5′ end, upon which a small molecule is synthesized. The DNA sequence contains 20-base “codons” flanked by 20-base noncoding regions. Within the DNA support library, sequence degeneracy exists at the coding positions. The set of codons in each DNA support specifies a small-molecule synthesis by directing the splitting of the ssDNA into appropriate subpools. The noncoding regions enable genetic recombination of support sequences by PCR (Halpin and Harbury 2004). Figure 3 Chemical Translation (A) Schematic showing the structure of the DNA support library. Small molecules are synthesized at the 5′ end of 340-base ssDNA genes. The ssDNA consists of 20-base noncoding regions (black lines labeled Z1–Z7) and 20-base coding positions (colored bars labeled [a–j]1–6). All library members contain the same seven DNA sequences at the seven noncoding regions. At each of the six coding positions, ten mutually exclusive DNA codons, (a–j)n, are present, for a total of 60 different sequences. Each coding region specifies the addition of a single subunit to a growing small molecule. A unique reactive site (in this case a primary amine) for small-molecule synthesis is attached to the 5′ end of the ssDNA through a polyethylene glycol linker (squiggly line). Resin beads coated with an oligonucleotide complementary to one codon (anticodon beads, gray ball at right) capture by hybridization ssDNAs containing the corresponding codon. (B) Chemical translation is a split-and-pool synthesis, with splitting directed by DNA hybridization. A ssDNA library is hybridized to a set of anticodon columns (gray balls) corresponding to the set of codons present at a single coding position. The ssDNA genes partition into subpools based on sequence identity. Distinct chemical subunits (colored balls) are coupled to the DNA in each subpool. Finally, the DNA is repooled, completing the encoded addition of one subunit to the growing small molecule. The process of hybridization splitting, chemistry, and pooling is repeated for all subsequent coding regions. (C) Schematic product of chemical translation. The sequence of the small-molecule subunits (colored balls) corresponds to the sequence of codons (colored bars) in the ssDNA gene. Our scheme for DNA-directed split and pool synthesis is shown in Figure 3B. The library is first split by hybridization to a set of anticodon columns complementary to the different 20-base sequences present at the first coding position. Distinct chemical building blocks are coupled to each subpool, and the library is repooled. The process is repeated, but the splitting is directed by a subsequent coding position. Each coding position comprises a set of codons that differ in sequence from the codons at all other coding positions. Consequently, splitting is always directed by hybridization at one intended coding region, and not by codons elsewhere. Small molecules are synthesized directly on their encoding DNAs, maintaining the physical linkage between gene and gene product (Figure 3C). Direct conversion of genes into small-molecule gene products, combined with selection and amplification steps, enables the in vitro selection of small-molecule libraries. Reduction to Practice We first developed a Sepharose-based resin derivatized with anticodon oligonucleotides complementary to codon sequences (Halpin and Harbury 2004). We tested the resin by hybridizing a library consisting of seven ssDNA sequences to a corresponding set of seven different anticodon columns (Figure 4). There was little crosshybridization, which ensures that DNA genes will be accurately translated. Analysis of splitting efficiencies by a scintillation counting assay of radiolabeled ssDNA showed that 90% or more of the ssDNA inputs were recovered from the correct hybridization columns for all tested sequences (Halpin and Harbury 2004). The resin is also robust. We have not observed any loss in efficiency with over 30 cycles of hybridization and elution. Figure 4 Sequence-Directed Splitting Seven serially truncated ssDNAs differing in sequence at one coding position (illustrated at left of gel, number of bases indicated) were hybridized to seven anticodon columns (cylinders at top of gel). The load (lane 1), flow through (lane 2), and column elutes (lanes 3–9) were analyzed by denaturing polyacrylamide gel electrophoresis. We next addressed chemical synthesis on unprotected DNA. Use of a solid phase in small-molecule synthesis allows for the application of excess reagents, to drive reactions to completion, and simplifies product purification (Merrifield 1963). To realize these advantages, we carried out synthetic steps while DNA was noncovalently bound to diethylaminoethyl (DEAE) Sepharose resin (Halpin et al. 2004). DEAE Sepharose was chosen for solid-phase synthesis because it adsorbs DNA reversibly and in a sequence-independent manner and because it behaves well in organic solvents. Incubation of immobilized DNA with the appropriate reagents results in addition of a building block, completing one step in the synthesis of a small molecule. Following the chemical step, DNA is eluted from the solid phase and manipulated in solution. As an initial chemistry, we chose 9-Fluorenylmethoxycarbonyl [Fmoc]–based peptide synthesis. Figure 5 shows the results of solid-phase peptide synthesis on DNA using Fmoc-protected succinimidyl esters (Anderson et al. 1963; Carpino and Han 1970; Halpin et al. 2004). Synthesis of the [Leu]enkephalin pentapeptide on an aminated 20-base oligonucleotide (Figure 5B) yielded a highly pure [Leu]enkephalin-DNA conjugate. A nonaminated oligonucleotide internal control was not altered by the chemistry, ruling out nonspecific chemical modification of DNA. Over 90% of the recovered nucleic acid was the intended [Leu]enkephalin-DNA conjugate (the overall recovered yield was 60%). The results correspond to a 98% efficiency for each amino acid coupling step. Figure 5 Peptide Synthesis on DNA (A) Structure of the [Leu]enkephalin–DNA conjugate. (B) High performance liquid chromatography chromatogram of the [Leu]enkephalin peptide synthesized using succinimidyl ester chemistry on a 20-base oligonucleotide modified with a 5′ primary amine (20mer). A 10-base oligonucleotide without the 5′ primary amine (10mer) was included in the reactions as a control for nonspecific DNA modification. The red and blue traces are the DNA before and after chemistry, respectively. The mass of the major product peak (42-min retention time) matches the expected mass of the [Leu]enkephalin–DNA conjugate. (C) Electromobility shift assay of peptides synthesized on 340-base ssDNA. Conjugates were eletrophoresed on a native agarose gel in the absence (lanes 1, 3, 5, and 7) or presence (lanes 2, 4, 6, 8, and 9) of the [Leu]enkephalin-binding antibody 3-E7. [Leu]enkephalin (L) or a scrambled sequence (S) was synthesized on a 5′ amino-modified 20-base oligonucleotide, which was subsequently used as a primer for PCR (lanes 1–4), or directly on 5′ amino-modified 340-base ssDNA, which was subsequently converted to dsDNA (lanes 5–9). Addition of free [Leu]enkephalin peptide (lane 9) competes away binding. Synthesis of [Leu]enkephalin on a 340-base ssDNA support, capable of encoding an eight-step synthesis, was analyzed using an electromobility shift assay and the enkephalin-specific 3-E7 antibody (Hwang et al. 1999). Figure 5C shows that 3-E7 shifts the majority of the [Leu]enkephalin-DNA (approximately 85% when standardized to a positive control), showing that the biological activity of the peptide is maintained while attached to DNA. The 3-E7 antibody does not shift a scrambled-DNA peptide conjugate containing the same amino acids as [Leu]enkephalin but in a different order. Finally, free [Leu]enkephalin peptide eliminates the shifting of [Leu]enkephalin-DNA by 3-E7, demonstrating the specificity of the shift. Our chemical translation strategy requires repeated hybridization-directed splitting and coupling of chemical building blocks to DNA. Two different solid phases were utilized for these tasks. To efficiently transfer DNA from anticodon columns to DEAE Sepharose columns, we cyclically pumped 50% dimethylformamide (DMF) over the columns connected in series (Figure 6). Conversely, to transfer DNA from DEAE Sepharose columns back to anticodon columns, we used a high salt buffer in a closed system. In both cases, a large effective buffer volume flows over each column, which allows the DNA transfer processes to approach thermodynamic equilibrium. These column-to-column transfers remove intermediate storage tubes and require little solvent, minimizing loss of DNA. Figure 6 Reduction to Practice Chemical translation requires iteration of a chemistry step and two column-transfer steps. ssDNA is transferred from anticodon columns to DEAE Sepharose columns by cyclically pumping 50% DMF through a pair of columns (one hybridization, one DEAE) attached in series for 1 h at 45 °C. Chemistry is performed on ssDNA bound to each DEAE column. ssDNA is transferred from DEAE columns to anticodon columns by cyclically pumping a 1.5-M NaCl buffer through all DEAE columns and all anticodon columns associated with the next coding position for 1 h at 70 °C and 1 h at 46 °C. Efficiencies for each step are indicated in red. In Vitro Selection of a Chemically Synthesized Library To test and validate our general strategy, we applied in vitro selection to a primarily nonnatural peptide library, with the goal of identifying a high-affinity ligand for the monoclonal antibody 3-E7 (Meo et al. 1983). Isolation of 3-E7 ligands is a well-defined in vitro selection problem characterized previously (Cwirla et al. 1990; Barrett et al. 1992). We designed our library to contain at least one known 3-E7 ligand, [Leu]enkephalin. The [Leu]enkephalin peptide binds to 3-E7 with an affinity of 7.1 nM, and its size (five residues) was well-suited for our experiments. An initial DNA support library consisting of ten distinct sequences (“all a,” “all b,” etc.) was diversified 105-fold by PCR recombination to generate a support library with a complexity of one million, as verified by DNA sequencing (Halpin and Harbury 2004). This library was chemically translated into acylated pentapeptides using Fmoc-protected succinimidyl esters. The peptide library included ten different monomers at each position (Figure 7A). The first five positions comprised one of ten amino acids (β-alanine, D-alanine, D-leucine, D-tyrosine, 4-nitro-phenylalanine, glycine, leucine, norleucine, phenylalanine, or tyrosine). The N-terminus was left unmodified or was acylated with one of nine acids (acetic, benzoic, butyric, caproic, glutaric, isobutyric, succinic, trimethylacetic, or valeric). After library synthesis and conversion of the ssDNA into duplex form, the library was subjected to selection using the 3-E7 antibody. The selected DNA was PCR amplified and used as input for the subsequent round of synthesis and selection. Figure 7 In Vitro Selection of a Nonnatural Peptide Library (A) Library building blocks. Proteinogenic building blocks are shown in green. (B) Approximately 70 DNA genes from each round of selection were sequenced, and the results are summarized as a histogram plot. The x-axis indicates the number of amino acid residue matches to [Leu]enkephalin encoded by a library sequence. The y-axis indicates the library generation (0, starting material; 1, after round one selection; 2, after round two selection). The z-axis indicates the number of sequences encoding a particular number of matches (x-axis) in a particular round (y-axis). (C) The top row reports the round two library consensus sequence, which matches [Leu]enkephalin. The second row reports the percentage of round two library clones that encode the [Leu]enkephalin amino acid at each residue position. The third row reports the identity and frequency of the most commonly occurring non-[Leu]enkephalin subunit at each position. In order to monitor library convergence, DNA from the starting material (round 0) and from after one (round 1) or two (round 2) selection generations was subcloned, and approximately 70 different isolates from each round were sequenced (Figure 7B). In round 0, none of the sequences encoded more than three residues in common with [Leu]enkephalin. Two sequences from round 1 encoded five [Leu]enkephalin residues, and one sequence encoded four residues. Of the round 2 sequences, twenty encoded full-length [Leu]enkephalin, thirty-four encoded single mutants, and eleven encoded double mutants. Only three round 2 sequences encoded less than four [Leu]enkephalin residues. The round 2 consensus peptide sequence matched [Leu]enkephalin (Figure 7C). Previous work has shown that the N-terminal residues (Tyr-Gly-Gly-Phe) are responsible for most of [Leu]enkephalin's affinity for the 3-E7 antibody (Meo et al. 1983; Cwirla et al. 1990). We observed high sequence conservation at these residues, recapitulating the earlier results. To assess generality, we carried out a second [Leu]enkephalin in vitro selection experiment using a peptide library of the same size but constructed with a completely different “genetic code.” Every codon in the alternate library coded for an amino acid different from the one it coded for in the first library. The [Leu]enkephalin codon series in the first library was b1-j2-b3-c4-h5-i6, whereas in the alternate library it was d1-b2-g3-g4-i5-f6. Two rounds of selection enriched the alternate [Leu]enkephalin DNA gene 105-fold (data not shown). The data suggest that little, if any, DNA sequence encoding bias exists in our system. Further, they illustrate the reproducibility of the technology. Together, the results demonstrate conclusively that the DNA display strategy can be used for the in vitro selection of synthetic chemical libraries. Discussion Previous efforts to expand the scope of in vitro selection have utilized nonnatural bases or amino acids incorporated into DNA, RNA, and peptide libraries using polymerases and the ribosome (Bittker et al. 2002; Li et al. 2002; Forster et al. 2003). Such efforts are limited by the extent to which enzymes will tolerate novel monomers. In addition, enzymes can only produce polymers chemically and topologically similar to their natural products, which are not well-suited for all applications. An alternative strategy for expanding the chemical diversity of gene products exploits DNA-templated synthesis, where hybridization-induced proximity promotes covalent bond formation (Gartner and Liu 2001). One great advantage of proximity-based DNA-directed synthesis is its ability to accommodate multiple reactions in “one pot.” However, there are several significant disadvantages. Each building block must be attached to an oligonucleotide, which is both expensive and labor intensive. All chemistry must proceed under conditions compatible with DNA hybridization, ruling out many organic solvents, high pH, and high temperature. Finally, there may be a limitation to the number of steps that can be encoded by the proximity approach. While an impressive array of chemical reactions has been accomplished by this method (Gartner et al. 2002), its use for in vitro selection has not been reported. DNA display is a general method for the in vitro selection of synthetic combinatorial chemistry libraries. The system is modular, so that chemistry and selection protocols can be easily changed. It can take advantage of existing combinatorial chemistry technology as well as chemical transformations previously carried out in the presence of unprotected DNA (Gartner et al. 2002; Summerer and Marx 2002). Solid-phase, solution-phase, enzymatic, and proximity effect reaction formats are all suitable. We have developed an extensive set of tools to adapt new chemistries for in vitro selection (Halpin et al. 2004). In addition to diverse chemistries, many different library architectures are also possible. The library reported here was synthesized in six encoded steps with ten distinct building blocks per step. However, essentially any combinatorial scheme can be accommodated. The 20-base codon sequences used here were taken from a larger set (>10,000) of 20-base sequences experimentally verified to exhibit orthogonal hybridization properties (Giaever et al. 2002). As a first approximation, the highest possible fold enrichment per round of selection can be determined by considering its relationship to translation fidelity and the signal-to-noise ratio of the selection. Fold enrichment (E) is defined as the geometric increase in the fraction of target molecules in a library that results from a single round of synthesis and selection. Fidelity (F) is defined as the fraction of genes recovered from a completed library synthesis that have been correctly translated. The signal-to-noise characteristic of a selection (S/N) is defined as the ratio of the fraction of target molecules selected to the fraction of nontarget molecules selected. In most cases, the fold enrichment reduces to the simple expression at the right of Equation 1 where f0 denotes target gene fraction in the selection input and p denotes the probability of a nontarget gene being mistranslated to the target gene product. Biological systems have such high fidelity that F can be considered to equal one. However, the fidelity of chemical translation processes is the product of hybridization specificity and chemistry efficiency raised to the power of the number of steps. It is important to consider these parameters when adapting new chemistries and selections to the DNA display format. Equation 1 can help determine the minimum number of rounds required for library convergence, and thus the feasibility of a proposed in vitro selection experiment. For example, a library synthesized with a fidelity of 0.01 and subjected to a selection with a S/N of 1000 would give a 10-fold enrichment per round at best. If the library included 1012 unique members, at least 12 rounds would be required to achieve convergence. In addition to influencing convergence rates, fidelity also limits achievable library complexity. The maximum effective library complexity corresponds to the product of Avagadro's number, the moles of library, and the fidelity. Based on our observed 90% hybridization efficiency and 95% chemistry efficiency, extension of the library reported here to 13 synthetic steps would produce 1012 distinct small molecules per 30 pmol of DNA starting material, a quantity easily manipulated in a microcentrifuge tube. Diversification between rounds of selection by recombination makes possible in vitro evolution of libraries with complexities exceeding the physical library size. Thus, a “best” molecule can be pinpointed without exhaustive testing of all potential species. Starting with a working population of compounds that sparsely sample a chemical space, molecules containing parts of an optimal molecular solution often have a selective advantage relative to siblings, and become enriched. Subsequent recombination processes splice together fragments from the numerous partially optimal molecules to form a globally optimal molecule. Thus, the best structure is found, even if the odds were negligible that it existed in the initial working population. The same principle accounts for the striking success of gene shuffling in protein engineering (Kurtzman et al. 2001) and of the genetic algorithm optimization procedure in computer science (Forrest 1993). Recombination of a DNA display library by DNA shuffling (Stemmer 1994), which was used here to diversify the initial DNA library (Halpin and Harbury 2004), would enable the in vitro evolution of synthetic libraries with complexities exceeding 1013. Prospectus DNA display enables the use of genetic tools such as complementation analysis and backcrossing to analyze small-molecule populations. The approach can be used to study molecular evolution without potential biases resulting from experiments restricted to RNA, DNA, and peptide polymers. A general scientific problem that will be directly addressed is the relationship between small-molecule library complexity and the quality of molecules discovered. With biopolymers, more complex libraries yield higher-affinity ligands (Takahashi et al. 2003). However, many have argued that increasing small-molecule library complexity will not produce higher quality “hits” (Breinbauer et al. 2002). This judgment is based on the paucity of viable drug candidates that have emerged from even the most complex combinatorial chemistry libraries. Analysis of “hits” from increasingly diverse small-molecule populations (as much as 106-fold more complex than current synthetic libraries) will test the validity of this belief. Drug discovery would represent one important application for a small-molecule in vitro selection technology. While the cost of drug discovery has increased continuously over the last decade (from less than $15 billion for research and development in 1996 to more than $25 billion in 2002), the number of new molecular entities approved by the FDA has steadily dropped, from 56 in 1996 to 17 in 2002 (Hall 2003). A fast, inexpensive, and generally accessible procedure for the in vitro selection of druggable small-molecule libraries would accelerate the early stages of drug development. The nonnatural peptide chemistry in this work was developed as a proof of principle, but may nevertheless have practical applications in medicine. For example, the nonribosomal peptide drugs vancomycin and cyclosporin are a widely used antibiotic and immunosuppressant, respectively (Walsh 2002). Annual joint sales of the nonnatural gonadotropin-releasing-hormone peptide analogues gosarelin and leuprolide exceed $2 billion (Klabunde and Hessler 2002). DNA display offers an immediate approach for the in vitro selection of general polyamide libraries that include such compounds (Halpin et al. 2004). Future extensions of DNA display include the development of massively parallel array-based splitting strategies for the in vitro selection of low-molecular-weight small-molecule libraries (for example a library built in three synthetic steps with 10,000 building blocks per step). Massively parallel syntheses will produce compounds that conform better to Lipinski's “rule of five” (Lipinski et al. 1997) and presumably will thus be more druggable. Beyond drug discovery, DNA display can be applied to the engineering of chemical switches, the discovery of transition metal catalysts for aqueous and nonbiological environments, and the identification of enzyme-specific ligands for activity-based profiling. Because the system is inexpensive, is easily implemented by a single individual, and requires only common laboratory equipment, in vitro selection and eventual evolution of large synthetic chemical populations should become a broadly accessible tool. Materials and Methods Materials. The 3-E7 antibody was purchased from Gramsch Laboratories (Schwabhausen, Germany). PANSORBIN cells were purchased from Calbiochem (San Diego, California, United States). BSA was purchased from New England Biolabs (Beverly, Massachusetts, United States). Yeast tRNA was purchased from Ambion (#7119, Austin, Texas, United States). The [Leu]enkephalin peptide and all oligonucleotides were purchased from the Stanford PAN Facility (Stanford, California, United States). Chemistry. Solid-phase peptide synthesis was carried out as previously described (Halpin et al. 2004). 5′ amino-modified ssDNA (#10-1912-90, #10-1905-90, #10-1918-90, Glen Research, Sterling, Virginia, United States) was noncovalently bound to DEAE Sepharose Fast Flow resin (# 17-0709-01, Pharmacia-LKB Technology, Uppsala, Sweden) packed into TWIST column housings (Glen Research #20-0030-00). DNA was loaded onto the columns in 10 mM acetic acid, 0.005% Triton X-100 buffer. To accomplish amino acid additions, columns were washed with 3 ml of DMF and subsequently incubated with 62.5 mg/ml Fmoc succinimidyl esters in 300 μl of coupling solvent (22.5% water, 2.5% DIEA, and 75% DMF) for 5 min. Excess reagent was washed away with 3 ml DMF, and the coupling procedure was repeated. The Fmoc-protecting group was then removed by two 1-ml treatments with 20% piperdine in DMF, one for 3 min and one for 17 min (Carpino and Han 1970). Finally, the columns were washed with 3 ml of DMF followed by 3 ml of DEAE Bind Buffer (10 mM acetic acid, 0.005% Triton X-100). Anhydride couplings followed the same procedure except that a 3-ml water wash was added after DNA loading to remove remaining acetic acid. Columns were incubated with 10 mM of each anhydride (100 mM for trimethylacetic anhydride) in 500 μl of DMF for 30 min. 20-base oligonucleotide–peptide conjugates were eluted off DEAE columns with 2 ml of DEAE Elute Buffer (1.5 M NaCl, 50 mM Tris pH 8.0, and 0.005% Triton X-100). 340-base ssDNA-peptide conjugates were eluted with 2 ml of Basic Elute Buffer (1.5 M NaCl, 10 mM NaOH, and 0.005% Triton X-100) heated to 80 °C. For synthesis of libraries, a 2-ml PBS wash was added at the end of each amino acid coupling step to remove remaining anionic reagents. Following the last coupling step in the library synthesis, free oligonucleotides were separated from 340-base DNA supports by washing with 2 ml of DEAE Elute Buffer. Electromobility shift assay. The electromobility shift assay was performed as previously described (Hwang et al. 1999). No plasmid DNA was added to the samples. Antibody 3-E7 (0.5 μg) was added to the “antibody plus” samples. Samples were run on a 2% NuSieve (#50081, FMC Bioproducts, Rockland, Maryland, United States) agarose gel for 1 h at 100 V in TBE. 840 μM peptide was used to compete away binding to the peptide–DNA conjugate. Selection. ssDNA was converted to double-stranded DNA (dsDNA) by one-cycle PCR with a single end primer. The 50-μl PCR reaction contained 20 μM primer, 200 μM of each dNTP, 5 mM MgCl2, 1X Promega Taq reaction buffer, and 5 U of Taq DNA polymerase (#M1661, Promega, Madison, Wisconsin, United States). The PCR program was 94 °C for 2.5 min, 58 °C for 1 min, and 72 °C for 15 min. The dsDNA–peptide conjugates were incubated with PANSORBIN cells in 50 μl of Selection Buffer (TBS, 0.1% BSA, and 0.1 μg/μl yeast tRNA) at 4 °C for 1 h to preclear conjugates that nonspecifically bind to the cells. Then, preclear beads were pelleted by centrifugation and removed. Antibody 3-E7 (0.5 μg) was added to the supernatant and allowed to incubate for 1 h at 4 °C. The solution was then mixed with fresh PANSORBIN cells for 1 h at 4 °C. The cells were pelleted and washed at 25 °C three times with 500 μl of Wash Buffer (TBS, 0.1% BSA, 0.1 μg/μl tRNA, and 350 mM NaCl), followed by a single wash with 500 μl of Selection Buffer. The dsDNA–peptide conjugates were eluted by incubation of the cells with 50 μl of 200 μM [Leu]enkephalin in Selection Buffer for 1 h at 25 °C. Selected genes were amplified from 10 μl of elute supernatant with 25-cycle PCR reactions. General. High performance liquid chromatography analysis of DNA–peptide conjugates, synthesis of anticodon columns, hybridization and transfer of DNA, library assembly, ssDNA generation, and library isolation were performed as previously described (Halpin and Harbury 2004; Halpin et al. 2004). We thank Shivani Nautiyal and S. Jarrett Wrenn for critical reading of the manuscript and helpful discussions throughout the course of this work; Elizabeth Zuo for oligonucleotide synthesis and mass spectrometry analysis; and Mai Nguyen for oligonucleotide synthesis. DRH is a Howard Hughes Medical Institute Predoctoral Fellow. This work was funded by a Stanford Office of Technology Licensing Research Incentive Award (#127P304 to PBH) and a Burroughs-Wellcome Fund New Investigator in the Pharmacological Sciences Award (#1001162 to PBH). Conflicts of Interest. The authors have declared that no conflicts of interest exist. Author Contributions. DRH and PBH conceived and designed the experiments. DRH performed the experiments. DRH and PBH analyzed the data and wrote the paper. Academic Editor: Gerald Joyce, Scripps Research Institute Abbreviations DEAEdiethylaminoethyl DMF N,N-dimethylformamide dsDNAdouble-stranded DNA Fmoc9-Fluorenylmethoxycarbonyl HTS-CChigh-throughput screening of combinatorial chemistry ssDNAsingle-stranded DNA ==== Refs References Anderson GW Zimmerman JE Callahan FM N-hydroxysuccinimide esters in peptide synthesis J Am Chem Soc 1963 85 3039 Barrett RW Cwirla SE Ackerman MS Olson AM Peters EA Selective enrichment and characterization of high-affinity ligands from collections of random peptides on filamentous phage Anal Biochem 1992 204 357 364 1443536 Bittker JA Phillips KJ Liu DR Recent advances in the in vitro evolution of nucleic acids Curr Opin Chem Biol 2002 6 367 374 12023118 Breinbauer R Vetter IR Waldmann H From protein domains to drug candidates: Natural products as guiding principles in the design and synthesis of compound libraries Angew Chem Int Ed Engl 2002 41 2879 2890 12203413 Brenner S Lerner RA Encoded combinatorial chemistry Proc Natl Acad Sci U S A 1992 89 5381 5383 1608946 Carpino LA Han GY 9-fluorenylmethoxycarbonyl function, a new base-sensitive amino-protecting group J Am Chem Soc 1970 92 5748 5749 Cwirla SE Peters EA Barrett RW Dower WJ Peptides on phage: A vast library of peptides for identifying ligands Proc Natl Acad Sci U S A 1990 87 6378 6382 2201029 Dolle RE Comprehensive survey of combinatorial library synthesis: 2002 J Comb Chem 2003 5 693 753 14606800 Forrest S Genetic algorithms: Principles of natural selection applied to computation Science 1993 261 872 878 8346439 Forster AC Tan ZP Nalam MNL Lin HN Qu H Programming peptidomimetic syntheses by translating genetic codes designed de novo Proc Natl Acad Sci U S A 2003 100 6353 6357 12754376 Furka A Sebestyen F Asgedom M Dibo G General method for rapid synthesis of multicomponent peptide mixtures Int J Pept Protein Res 1991 37 487 493 1917305 Gartner ZJ Liu DR The generality of DNA-templated synthesis as a basis for evolving non-natural small molecules J Am Chem Soc 2001 123 6961 6963 11448217 Gartner ZJ Kanan MW Liu DR Expanding the reaction scope of DNA-templated synthesis Angew Chem Int Ed Engl 2002 41 1796 1800 19750721 Giaever G Chu A Ni L Connelly C Riles L Functional profiling of the Saccharomyces cerevisiae genome Nature 2002 418 387 391 12140549 Hall S Revitalizing drug discovery Technol Rev 2003 106 38 45 Halpin DR Harbury PB DNA display I. Sequence-encoded routing of DNA populations PLoS Biol 2004 2 e173 10.1371/journal.pbio.0020173 15221027 Halpin DR Lee JA Wrenn SJ Harbury PB DNA display III. Solid-phase organic synthesis on unprotected DNA PLoS Biol 2004 2 e175 10.1371/journal.pbio.0020175 15221029 Harbury PB Halpin DR Board of Trustees of the Leland Stanford Junior University DNA-templated combinatorial library chemistry United States patent application WO 99-US24494 2000 Hwang B Smider V Chu G The use of electrophoretic mobility shift assays to study DNA repair. In: Henderson D, editor. DNA repair protocols: Eukaryotic systems 1999 Totowa (New Jersey) Humana Press 103 120 Klabunde T Hessler G Drug design strategies for targeting G-protein-coupled receptors Chembiochem 2002 3 929 944 Kurtzman AL Govindarajan S Vahle K Jones JT Heinrichs V Advances in directed protein evolution by recursive genetic recombination: Applications to therapeutic proteins Curr Opin Biotech 2001 12 361 370 11551464 Li SW Millward S Roberts R In vitro selection of mRNA display libraries containing an unnatural amino acid J Am Chem Soc 2002 124 9972 9973 12188645 Lipinski CA Lombardo F Dominy BW Feeney PJ Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings Adv Drug Deliv Rev 1997 23 3 25 Meo T Gramsch C Inan R Hollt V Weber E Monoclonal antibody to the message sequence Tyr-Gly-Gly-Phe of opioid peptides exhibits the specificity requirements of mammalian opioid receptors Proc Natl Acad Sci U S A 1983 80 4084 4088 6191329 Merrifield RB Solid phase peptide synthesis: 1. Synthesis of a tetrapeptide J Am Chem Soc 1963 85 2149 2154 Morais RC Mind the gap Forbes 2003 172 58 60 Roberts RW Ja WW In vitro selection of nucleic acids and proteins: What are we learning? Curr Opin Struct Biol 1999 9 521 529 10449375 Stemmer WPC Rapid evolution of a protein in vitro by DNA shuffling Nature 1994 370 389 391 8047147 Summerer D Marx A DNA-templated synthesis: More versatile than expected Angew Chem Int Ed Engl 2002 41 89 90 12491447 Takahashi TT Austin RJ Roberts RW mRNA display: Ligand discovery, interaction analysis and beyond Trends Biochem Sci 2003 28 159 165 12633996 Thompson LA Ellman JA Synthesis and applications of small molecule libraries Chem Rev 1996 96 555 600 11848765 Walsh CT Combinatorial biosynthesis of antibiotics: Challenges and opportunities Chembiochem 2002 3 125 134 11921390

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020175Research ArticleBiotechnologyIn VitroDNA Display III. Solid-Phase Organic Synthesis on Unprotected DNA SPOC on Unprotected DNAHalpin David R 1 Lee Juanghae A 2 Wrenn S. Jarrett 1 Harbury Pehr B [email protected] 1 1Department of Biochemistry, Stanford University School of MedicineStanford, California, United States of America2Department of Chemistry, Stanford University School of Humanities and SciencesStanford, CaliforniaUnited States of America7 2004 22 6 2004 22 6 2004 2 7 e1754 2 2004 13 4 2004 Copyright: © 2004 Halpin et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. DNA Display II. Genetic Manipulation of Combinatorial Chemistry Libraries for Small-Molecule Evolution DNA Display I. Sequence-Encoded Routing of DNA Populations Harnessing DNA-Based Technology for Drug Discovery Translating DNA into Synthetic Molecules DNA-directed synthesis represents a powerful new tool for molecular discovery. Its ultimate utility, however, hinges upon the diversity of chemical reactions that can be executed in the presence of unprotected DNA. We present a solid-phase reaction format that makes possible the use of standard organic reaction conditions and common reagents to facilitate chemical transformations on unprotected DNA supports. We demonstrate the feasibility of this strategy by comprehensively adapting solid-phase 9-fluorenylmethyoxycarbonyl–based peptide synthesis to be DNA-compatible, and we describe a set of tools for the adaptation of other chemistries. Efficient peptide coupling to DNA was observed for all 33 amino acids tested, and polypeptides as long as 12 amino acids were synthesized on DNA supports. Beyond the direct implications for synthesis of peptide–DNA conjugates, the methods described offer a general strategy for organic synthesis on unprotected DNA. Their employment can facilitate the generation of chemically diverse DNA-encoded molecular populations amenable to in vitro evolution and genetic manipulation. A method is presented that makes possible the use of standard organic reaction conditions and common reagents in the presence of unprotected DNA, an important step in enabling DNA-directed chemical synthesis and drug discovery ==== Body Introduction A number of strategies have been proposed recently to enable the in vitro selection and evolution of chemical libraries (Harbury and Halpin 2000; Gartner and Liu 2001). These new approaches to molecular discovery rely upon DNA-directed synthesis, whereby a physical linkage is established between DNA “genes” and respective nonbiological synthetic “gene products.” Such encoded syntheses proceed through a repeated series of two associated steps: reading of sequence information in the DNA and execution of an appropriate chemical transformation. A fundamental obstacle in all cases is covalently constructing a synthetic entity in the presence of unprotected DNA. One approach takes advantage of hybridization to induce proximity between reactants covalently attached to oligonucleotides. “Reading” is accomplished by the hybridization of the reactant conjugates to a DNA template, whereas synthetic execution results from the reactants being positioned closely together. The strategy has been demonstrated for several types of chemistries (Orgel 1995; Bruick et al. 1996; Xu et al. 2001; Gartner et al. 2002a; Li et al. 2002). However, these proximity methods are necessarily limited to reaction conditions compatible with DNA hybridization, precluding a large number of potential chemical transformations. Moreover, only synthetic reagents that have been precoupled to DNA can be used. Rather than tailoring reactions to the narrow window of hybridization conditions, DNA reading and chemical transformation can be carried out in chronologically distinct steps (Halpin and Harbury 2004b). The DNA is first physically partitioned into subpools by hybridization, accomplishing the reading step. An appropriate reaction is then carried out on each physically separate subpool. As such, the chemical process can take place under DNA-denaturing conditions, permitting the use of organic solvents, high pH, and elevated temperature. Although DNA exhibits limited solubility in nonaqueous solvents, its immobilization on a solid phase can be exploited to access such environments. With a solid-phase chemistry approach, a large existing body of known chemical transformations becomes accessible to DNA-encoded synthesis. Solid-phase approaches also facilitate rapid, efficient, small-scale (nanomole) syntheses. Reagents can be used in vast excess, postreaction work-up involves only washing of the solid phase, and solution-phase manipulation steps that lead to material losses are avoided. However, the conventional attachment of the small molecule to a solid phase through an irreversibly cleavable linker is not adequate, because DNA must repeatedly come on and off the solid phase during reading steps. Here we report a detailed strategy for carrying out solid-phase organic chemistry on unprotected DNA that is suitable for encoded library synthesis by a partitioning approach. Furthermore, we demonstrate a number of tools for adapting new chemistries, and we use those tools to develop comprehensive methods for peptide synthesis on unprotected DNA. Results Solid-Phase Synthesis Resin We first had to choose a solid-phase material that exhibited several critical properties: reversible, efficient binding and release of unprotected DNA; robust solvent integrity; and resistance to chemical modification. The first requirement narrowed our focus to resins that noncovalently bind DNA. A number of resins were tested and excluded due to poor bind–release properties (diethylaminoethyl [DEAE] silica, Macro-Prep ceramic hydroxyapatite, and quaternary amine anion exchange resins). Others exhibited extensive compression in organic solvent (Sephacryl S-1000, macroporous methacrylate) or poor reswelling during organic to aqueous solvent transitions (Poros 50 HQ). Reverse-phase resins were excluded because they would presumably not retain DNA in many organic solvents. DEAE Sepharose, a tertiary amine anion exchange resin, exhibited excellent bind and release properties. By a high performance liquid chromatography (HPLC) assay, oligonucleotides were immobilized and eluted quantitatively in small volumes (Figure 1). Single-stranded DNA (ssDNA) molecules as long as 340 bases were also bound and eluted with high efficiency (data not shown). No severe resin compression was observed using a number of solvents, including H2O, methanol (MeOH), dimethyl sulfoxide, N,N-dimethylformamide (DMF), ethyl acetate, and dichloromethane. Lastly, Sepharose has been used previously as a material for solid-phase synthesis (Tegge and Frank 1997; Nakaie et al. 2003). All subsequent work was carried out with DEAE Sepharose. Figure 1 Peptide Coupling to DNA Supports (A) Fmoc-based peptide coupling reaction to an aminated 20-base oligonucleotide (NC20) where X represents a succinimidyl or EDC/HOAt-activated ester. (B) HPLC chromatograms of a nonaminated 10-base (10mer) and an aminated 20-base (NC20) oligonucleotide. HPLC traces show DEAE column load (solid black) and elute (broken black). DEAE column elutes after succinimidyl ester–mediated (solid red) or EDC/HOAt-mediated (broken red) Fmoc-Leu coupling and Fmoc deprotection are shown. The resulting amino acid–DNA conjugate is denoted (Leu-NC20). (C) Chemical transformations are carried out using small DEAE Sepharose columns and syringes (left). Washes are facilitated by a vacuum manifold with chemically resistant stopcocks (right). Peptide Chemistry We studied 9-fluorenylmethyoxycarbonyl (Fmoc)–based peptide synthesis because it has a well-established solid-phase precedent and offers a challenge in diverse chemical functionality (Figure 1A). As a DNA support, we chose a 20-base oligonucleotide modified with a 5′ primary amine (NC20; Figure 2) so that coupling reactions could be readily assayed by HPLC. NC20 and an unmodified 10-base oligonucleotide control (10mer) were bound to a DEAE Sepharose column, washed with DMF, and incubated with an Fmoc–amino acid succinimidyl ester solution in a closed system (see Figure 1C). A solution of 20% piperidine in DMF was then used to remove the Fmoc group. The DNA was eluted and compared to starting material via an HPLC mobility assay (Figure 1B). Coupling and Fmoc-deprotection proceeded with high efficiency (95% or greater) in 30 min. Furthermore, the internal control oligonucleotide was unmodified and fully recovered, ruling out nonspecific DNA modification. Further experiments indicated that addition of the first amino acid proceeds more efficiently than subsequent additions. We therefore optimized coupling for the addition of a second amino acid to an oligonucleotide already acylated with leucine (Leu-NC20). Nearly quantitative coupling was observed for all amino acids, and all conjugates were verified by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) (Table 1). However, the succinimidyl esters did not efficiently acylate proline residues. Figure 2 DNA Support Structure and Modified Amino Acids (A) Peptide synthesis is carried out on DNA modified with a 5′ C12 amino (NC20) or a 5′ PEG amino (NP20) linker. (B) Fluorescent lysine derivative (compound 1, Fmoc-Lys[coumarin]-OH) and BME/DBU labile protecting groups for lysine (compound 2, Fmoc-Lys[Ns]-OH), arginine (compound 3, Fmoc-Arg[Ns]-OH), and histidine (compound 4, Fmoc-His[CNP]-OH). Table 1 Amino Acid Coupling Efficiencies to Leu-NC20 Dde, 1-(4,4-dimethyl-2,6-dioxocyclohex-1-ylidene)ethyl; NorLeu, norleucine Footnotes denote deviations from standard coupling conditions: (a) 4 × 5 min, (b) 80% DMF in MeOH, (c) 40% DMF in MeOH, (d) 20% DMF in MeOH and 50 mM HOAt, (e) 20% DMF in MeOH, and (f) 100% DMF and 50 mM HOAt Efficiencies report the fraction of recovered DNA that was converted to product, and are indicated to the nearest 5%. Column headings “Succinimidyl Ester” and “In Situ Activation” indicate the method of activation. MALDI-MS denotes observed and calculated masses of reaction products. Reactions were performed with 100 pmol of Leu-NC20 starting material. Quantitative coupling is reported as ≥ 95%. Conjugates with N-terminal His(Trt) were not detectable by MALDI-MS, but acetylation of the N-terminus gave accurate mass measurements (data not shown) We next explored the possibility of in situ activation for peptide coupling. In a first pass, 1-(3-[dimethylamino]propyl)-3-ethylcarbodiimide hydrochloride (EDC) out-performed other activating reagents examined; other reagents gave poor coupling yields (benzotriazole-1-yl-oxy-tris-pyrrolidino-phosphonium hexafluorophosphate [PyBOP]), resulted in the formation of undesired side products (2-[1H-benzotriazole-1-yl]-1,1,3,3-tetramethyluronium tetrafluoroborate [TBTU]), or led to poor recovery of DNA (dicyclohexylcarbodiimide [DCC] and diisopropylcarbodiimide [DIC]). Thirty-minute EDC coupling reactions were typically less than 50% efficient without the addition of acylation catalysts such as N-hydroxysuccinimide, 1-hydroxy-7-azabenzotriazole (HOAt), or N-hydroxybenzotriazole (Nozaki 1997). Of these, HOAt was superior, bringing coupling efficiencies above 90%. A number of reaction solvents were examined, including H2O, DMF, MeOH, isopropanol, dichloromethane, and mixtures thereof. In general, MeOH gave the best results, with DMF only slightly worse, followed by isopropanol then H2O. For most amino acids, exceptional coupling was achieved with 30-min coupling times using 50 mM Fmoc–amino acid-OH, 50 mM EDC, and 5 mM HOAt (see Figure 1B; Table 1, Figure S1). Importantly, amino acids activated in situ with EDC acylated proline efficiently. We observed equally efficient coupling using a more hydrophilic polyethylene glycol (PEG) linker (NP20; see Figure 2), which might be better suited for biological applications and in vitro selections (Halpin and Harbury 2004b). To examine whether the coupling conditions generalized to longer DNA fragments, we used an aminated 340-base ssDNA as the support. After coupling, the eluted amino acid–DNA conjugates were digested with nuclease P1, a 3′-to-5′ exonuclease that cleaves all but the 5′ phosphodiester bond of our ssDNA constructs. The 5′-terminal nucleotide, which maintains the linker and synthetic peptide product, was separated from the other nucleoside monophosphates by HPLC and verified by electrospray ionization mass spectrometry. Amino acid coupling to the 340-base ssDNA proceeded with efficiencies comparable to those observed with oligonucleotides (data not shown). In some cases, the sensitivity of this HPLC assay was increased using fluorescence detection. For these experiments, we synthesized a fluorescent lysine derivative, Fmoc-Lys(coumarin)-OH (see Figure 2B, compound 1), that was incorporated as the C-terminal amino acid. The fluorescence signal allowed sensitive monitoring of subnanomolar quantities of product. By mass spectrometry and HPLC criteria, the peptide synthesis procedures did not damage oligonucleotides. As a more stringent test for possible DNA damage, we synthesized a pentapeptide on a 340-base ssDNA support and examined the ability of the conjugate to act as a template for primer extension. Polyacrylamide gel electrophoresis analysis of the radiolabeled extension products showed no truncated fragments (Figure 3), providing further evidence that the synthetic procedures are DNA-compatible. Figure 3 Peptide–DNA Conjugate As Template for DNA Synthesis 5′ PEG amino-modified 340-base ssDNA was loaded onto two DEAE Sepharose columns. The pentapeptide [Leu]enkephalin was synthesized on one column using EDC/HOAt and Fmoc–amino acids. The DNA was eluted, desalted, and used as template for radiolabeled primer extension reactions. Denaturing polyacrylamide gel electrophoresis analysis of reaction products shows no difference between ssDNA (control) and [Leu]enkephalin–ssDNA ([Leu]enk) templates. Side Chain Protection Typically, acid-labile groups are used to protect reactive amino acid side chains in Fmoc peptide synthesis. Given the instability of DNA in strong acids, it was necessary to identify alternate protecting group strategies. In all cases, the protecting groups were required to be stable during peptide coupling procedures and removed under alternate DNA-compatible conditions. The carboxylic acid side chains of aspartic and glutamic acid are usually protected as esters during Fmoc peptide synthesis. Sterically bulky esters are required to suppress piperidine-induced imide formation, which leads to undesired side chain peptide bonds. We discovered that the conventional tert-butyl (tBu) esters, which are normally cleaved with trifluoroacetic acid, could be removed at pH 6.5 in an aqueous solution at 70 °C. This gentle condition offers a convenient approach for acid deprotection. To verify that the thermolytic tert-butyl ester deprotection did not proceed through intramolecular imide formation, we coupled either Fmoc-Asp(tBu)-OH or Fmoc-Asp-OtBu to Leu-NC20, followed by Fmoc-Phe-OH. The main and side chain isomers of these tripeptide–DNA conjugates were resolved by HPLC after removal of the t-butyl group. Interconversion of the isomers during deprotection was undetectable (less than 5%). When the experiment was repeated using a 10 mM NaOH solution for tert-butyl ester deprotection (where imide formation would be expected), interconversion of the side and main chain isomers was observed. These results indicate that the thermolytic deprotection maintains the regiochemistry of the initial peptide bonds. At this time, we have little other data that speak to the mechanism of deprotection. Protection of the primary amine side chain of lysine prevents the formation of branched peptides. 2-Nitrobenzenesulfonamide (nosylamide) protection was particularly attractive as a means for lysine protection because the protecting group is base-stable and removed under conditions known to be DNA-compatible (Fukuyama et al. 1995). The lysine side chain was nosylated in good yield to produce Fmoc-Lys(Ns)-OH (see Figure 2B, compound 2). The nosyl (Ns) group was removed quantitatively from Lys(Ns)-containing peptide–DNA conjugates by β-mercaptoethanol (BME)/1,8-diazabicyclo[5.4.0]undec-7-ene (DBU) in a DMF solution at 60 °C. Arginine does not absolutely require side chain protection (Table 1). However, we observed a marked decrease in coupling efficiency as multiple unprotected arginine residues were added at adjacent positions in a synthetic peptide (first Arg approximately 95%; second Arg approximately 50%; third Arg less than 10%). These difficult transformations were accomplished in high yield by repeated high-temperature couplings (4 × 30 min, 37 °C). However, protection of the guanidino group of arginine would offer a more general solution. Given that the conventional arginine protecting groups are sulfonamides, we speculated that nosylamide protection could be applied. We synthesized Fmoc-Arg(Ns)-OH (see Figure 2B, compound 3) from Boc-Arg-OH and found that it coupled well (Table 1) and was quantitatively deprotected under the BME/DBU conditions. Side chain protection of histidine is essential to prevent acyl-transfer reactions and to suppress L,D-racemization. We found that the trityl (Trt) group of His(Trt) is rapidly removed under the thermolytic conditions used to deprotect tert-butyl esters. However, the trityl group is not ideal in aqueous conditions because of its hydrophobicity and extreme lability at high temperatures. To offer a more robust solution, we sought an H2O-compatible histidine protecting group. The 2,4-dinitrophenyl group, widely used in Boc peptide synthesis, is more hydrophilic than trityl and is removed under the nosyl-deprotection conditions. Unfortunately, 2,4-dinitrophenyl is not stable to the piperidine used for Fmoc removal (Garay et al. 1997). After testing a number of nitro-phenyl derivatives, we found that the 4-cyano-2-nitro-phenyl (CNP) group exhibits the appropriate reactivity. We synthesized Fmoc-His(CNP)-OH (see Figure 2B, compound 4) in one step from Fmoc-His-OH. The CNP group is stable to 20% piperidine in DMF and is deprotected fully with BME/DBU. Histidine racemization is a well-recognized concern in peptide synthesis. To assay the extent of racemization occurring during histidine coupling, we synthesized the oligonucleotide–dipeptide conjugate His(CNP)-Ala-NC20 using either L- or D-Fmoc-His(CNP)-OH. The diasteriomeric dipeptide–oligonucleotide products were resolvable by reverse-phase HPLC. Neither L-His nor D-His coupling resulted in detectable racemization. The experiment was repeated using L- and D-Fmoc-His(Trt)-OH with the same result. Fmoc-Cys(StBu)-OH was employed as the protected form of cysteine. The tert-butyl thioether coupled efficiently (Table 1) and was deprotected under the same conditions used to deprotect lysine, arginine, and histidine (CNP). Although we were unable to recover free thiol-containing peptides from our HPLC system for unknown reasons, we could alkylate the deprotected thiol side chain and recover the thioether-containing peptide–oligonucleotide conjugates. Polypeptide Synthesis To characterize multistep syntheses (Gartner et al. 2002b), we prepared a number of peptide–DNA conjugates, ranging from two to 12 amino acids in length and varying in sequence and degree of side chain protection required (Protocol S1). In all cases, absolute yields of conjugates with n amino acids exceeded (0.9)n (Figure S2). The HPLC-purified conjugates were analyzed by Edman degradation. Peptide sequencing data were unambiguous and in full agreement with the intended synthetic peptide sequences (Table 2), ruling out side chain modifications that could not be detected by MALDI-MS. Table 2 Sequenced Peptide–DNA Conjugates Peptides are written with the N-terminus to the left. N/A, not applicable Beyond the physical and chemical characterization of the peptide–oligonucleotide conjugates, it was important to examine their behavior in a biochemical setting. Thus, the [Leu]enkephalin pentapeptide was synthesized on an aminated 340-base ssDNA support, which was subsequently converted to duplex form. The conjugate exhibited a peptide-dependent electrophoretic mobility gel shift when incubated with the 3-E7 antibody (Halpin and Harbury 2004b), demonstrating its biochemical activity. Discussion DNA-directed synthesis requires “chemical translation” (Gartner and Liu 2001; Halpin and Harbury 2004b). Rather than simply acting as a tag to report a synthetic history (Brenner and Lerner 1992), the DNA must actively determine the series of reactions that construct the molecule. No matter how this is achieved, it involves associated steps of DNA “reading” and synthetic execution. The synthetic steps must not damage the DNA and consequently compromise the reading process. Once each product is covalently attached to an amplifiable support that carries the information necessary for its synthesis, the product molecules are amenable to evolution and genetic manipulation. The proximity approach to chemical translation uses hybridization to induce proximity-driven chemical transformations. Because the DNA “reading” and chemical execution steps are simultaneous, reactions are necessarily performed in aqueous solutions with solute, pH, and temperature conditions that promote DNA–oligonucleotide hybridization. These conditions limit the generality, efficiency, and speed of possible organic transformations. The partitioning strategy separates the DNA reading and the chemical step, and also introduces a solid-phase format (Halpin and Harbury 2004b). The separation overcomes incompatibility between hybridization conditions and optimal reaction conditions. Synthetic transformations are carried out using standard solvents and elevated temperature. This advantage cannot be fully appreciated with solution-phase reaction formats, given the insolubility of DNA in organic solvents. Solid-phase formats, however, can take full advantage of flexibility in reaction conditions. Once DNA is bound to the solid phase, the chemical transformation, rather than DNA solubility, dictates solvent choice. We carry out transformations in H2O, MeOH, ethanol, isopropanol, DMF, dimethyl sulfoxide, dichloromethane, and ethyl acetate (data not shown). We also carry out reactions at high temperatures. For example, difficult peptide couplings are facilitated with elevated temperature, and the BME-mediated deprotection of lysine, arginine, histidine, and cysteine is carried out at 60 °C. We have recently used microwave-assisted methods (Lew et al. 2002) to accelerate by 100-fold alkylation reactions on DNA-linked substrates (data not shown). Standard, commercially available reagents are used to perform chemistry, and we employ them in large excess (1,000- to 1,000,000-fold) over the DNA support, facilitating rapid reactions with high yields. The peptide coupling detailed here proceeds quantitatively in less than an hour, on par with the fastest standard solid-phase peptide synthesis coupling times. With the possible exception of tert-butyl esters, which are slowly removed during the 72 °C step of hybridization-mediated library splitting, the peptide chemistry and protecting groups presented here are suitable for use with DNA display (Halpin and Harbury 2004b). Another important aspect of the solid-phase method is reversible immobilization. This is an essential component of the DNA display chemical-translation cycle, where the DNA moves on and off a series of hybridization and chemistry columns (Halpin and Harbury 2004a, 2004b). In a simplistic view, we have essentially taken advantage of the polyanionic “handle” covalently attached to our synthetic substrates. The handle acts as a phase label (Curran 1998) for solid-phase extractions with anion exchange resins. Previously, soluble polymer-supported synthesis has been accomplished with PEG and fluorinated hydrocarbon purification handles (Han et al. 1995; Curran 1998). The polyanionic handle here uniquely accommodates both liquid- and solid-phase chemical steps. Our approach offers a general tool for derivatization of DNA. Unprotected peptide–DNA conjugates have been recognized as biochemically useful reagents for over 15 years (Corey and Schultz 1987; Zuckermann and Schultz 1988; Allinquant et al. 1995; Tong et al. 1995; Troy et al. 1996). The methods described here are efficient, rapid, and inexpensive, and they utilize DNA of synthetic or enzymatic origin, offering advantages over previously reported techniques (Robles et al. 1999; Stetsenko and Gait 2000; Debethune et al. 2002). Importantly, the protocols are not inherently limited to peptides. We have designed a set of tools for the adaptation of new chemistries. Reaction conditions are rapidly examined and optimized with oligonucleotides using HPLC mobility assays and MALDI-MS analysis. Nuclease P1 digestion facilitates the characterization of reactions on long DNA fragments and improves chromatographic and mass spectral resolution of synthetic products. MALDI-MS, primer extension analysis, and DNA sequencing reveal the presence of chemistry-induced DNA damage. We have used these tools to develop highly efficient protocols for solid-phase N-substituted polyglycine (“peptoid”) submonomer synthesis on unprotected DNA (data not shown). The chemistry used for peptoid synthesis is entirely different from peptide chemistry, illustrating the generality of the strategy. The potential for adapting other chemistries is essentially limitless. Wittig reactions, azide reductions, 1,3 dipolar cycloadditions, reductive aminations, Heck couplings, and a wide variety of other useful chemical transformations have been carried out in the presence of unprotected DNA without modification of DNA (Bruick et al. 1996; Xu et al. 2001; Gartner et al. 2002a; Li et al. 2002). Each could be used to synthesize and evolve interesting and potentially useful small molecule–DNA conjugate libraries. Our results demonstrate a robust method for solid-phase organic synthesis on unprotected DNA supports. Taken with the chemical-translation and DNA-manipulation strategies detailed elsewhere (Halpin and Harbury 2004a, 2004b), they facilitate a physical linkage between “genes” and synthetic “gene products” that is generalizable with respect to chemistry. The establishment of such a genetic underpinning to synthetic chemistry makes possible in vitro selection-based molecular discovery strategies for wholly abiotic molecular populations. Materials and Methods Materials. Fmoc amino acids were purchased from Novabiochem (La Jolla, California, United States), Chem-Impex International (Wood Dale, Illinois, United States), or Fluka (Basel, Switzerland). EDC was purchased from Omega Chemical (Levis, Quebec, Canada). N-hydroxysuccinimide was purchased from Chem-Impex. HOAt was purchased from Millipore (Billerica, Massachusetts, United States). Nuclease P1 (#27–0852-01), DEAE Sepharose Fast Flow (#17–0709-01), and Medium Grade G-25 Sephadex (#17–0033-01) were purchased from Pharmacia-LKB Technology (Uppsala, Sweden). Xba1 was purchased from New England Biolabs (Beverly, Massachusetts, United States). DEAE Sepharose columns were poured in Empty TWIST synthesis columns (#20–0030, Glen Research, Sterling, Virginia, United States). Kendall Monoject syringes (#1180100555, Kendall, Walpole, Massachusetts, United States) and a Promega (Madison, Wisconsin, United States) manifold with chemically resistant PFTE stopcocks (#121–0009, Argonaut, Foster City, California, United States) were used. All other chemical reagents or solvents were purchased from either Sigma-Aldrich (St. Louis, Missouri, United States) or Fisher Scientific International (Hampton, New Hampshire, United States). The internal control 10-base oligonucleotide had the sequence CGGACTAGAG. The reactive 20-base oligonucleotides had the sequence H2N-X-AGCAGGCGAATTCGTAAGCC, where X represents a C12 linker (NC20) or a longer PEG linker (NP20). NC20 was synthesized using the Glen Research 5′-Amino-Modifier C12 (#10–1922). NP20 was synthesized using the Glen Research Spacer Phosphoramidite 18 (#10–1918) followed by the 5′-Amino-Modifier 5 (# 10–1905). Reverse-phase HPLC assay. Coupling reactions were monitored by HPLC mobility shift using a C18 analytical column (Microsorb, Varian, Palo Alto, California, United States) and UV detection at 260 nm and 280 nm (Spectra FOCUS, Thermo Separation Products, San Jose, California, United States). Linear gradients from 0%–90% acetonitrile in 100 mM triethylammonium acetate (pH 5.5) were employed. Coupling efficiencies (recovered product DNA/ total recovered DNA) and yields (recovered product DNA/total starting material DNA) were determined by integration of elution peaks from the 260-nm channel. Chromophores added or removed during reactions cause changes in extinction coefficients less than the sensitivity (5%) of our HPLC assay (for NC20/NP20 ɛ260nm ≈ 224.5 mM−1cm−1) and were not considered in efficiency and yield determination. Reaction products were collected, concentrated to approximately 50 μM using centrifugal evaporation, and desalted over G-25 Sephadex. A mixture of 1 μl of desalted oligonucleotide and 1 μl of a freshly prepared saturated matrix solution was spotted on a matrix-assisted laser desorption/ionization target and allowed to air dry before mass spectrometry analysis. The matrix solution was made from 250 μl of H2O, 250 μl of acetonitrile, 25 mg of THAP, and 10 mg of ammonium tartrate. Peptide sequences (five or more amino acids) were verified by Edman degradation peptide sequencing. Nuclease P1 assay. DEAE elute buffer containing the peptide–DNA conjugates was neutralized and brought to 100 mM sodium acetate (pH 5.2) and 400 μM ZnSO4. Then 1 μg of nuclease P1 was added, and the mixture was incubated at 37 °C for 30 min. The entire reaction mixture was directly injected onto a C18 reverse-phase HPLC column and analyzed using linear gradients from 0%–90% acetonitrile in 10 mM ammonium acetate (pH 5.2). Yields were determined by integration of elution peaks from the 260-nm channel, using the P1 digestion product of unreacted starting material as a reference. Approximately 1 nmol of material was required for accurate UV detection. Products were collected, concentrated by centrifugal evaporation, and applied to a C18 SepPak cartridge (#WAT023590, Waters, Milford, Massachusetts, United States) in 25 mM triethylammonium acetate (pH 5.5). The cartridge was washed with 3 ml of 25 mM triethylammonium acetate (pH 5.5) and 1 ml of H2O. The products were eluted with 1 ml of 50/50 MeCN/H2O, concentrated to 100 μl by centrifugal evaporation, and analyzed by electrospray ionization mass spectrometry. For coumarin-labeled products, fluorescence was monitored (320 nm excitation/380 nm emission) with a scanning fluorescence detector (Thermo Separation Products, FL2000), and less than 50 pmol of material was necessary for accurate fluorescence detection. Primer extension assay. A 5′-aminated 340-base ssDNA support was generated as described (Halpin and Harbury 2004a). After loading the support onto DEAE Sepharose, the pentapeptide [Leu]enkephalin (Tyr-Gly-Gly-Phe-Leu) was synthesized using EDC chemistry. The resulting peptide–DNA conjugate was desalted over reverse-phase cartridge (Halpin and Harbury 2004a) and used as a template for primer extension (Halpin and Harbury 2004b). The radiolabeled duplex product was digested with Xba1, subjected to denaturing polyacrylamide gel electrophoresis, exposed to a phosphorimager cassette, and imaged on a Typhoon 8600 (dynamic range 105/pixel). The intensity of full-length control and [Leu]enkephalin bands were similar to within 1% (S/N approximately 600). Upon peak integration along the entire lane, the full-length band represented a similar percentage of total intensity in the control (83%) and [Leu]enkephalin (81%) samples. The data suggest that, in the worst case, 3% of the DNA could have been modified during the course of peptide synthesis. Resin loading and eluting. Approximately 250 μl of DEAE Sepharose suspension was pipetted into an empty Glen Research column housing and washed with 20 ml of H2O followed by 12 ml of DEAE bind buffer (10 mM acetic acid and 0.005% Triton X-100) using a syringe or a syringe barrel, a male-male luer adapter, and a vacuum manifold (see Figure 1). The DNA was loaded onto the washed chemistry column in 1 ml of DEAE bind buffer at approximately 1 ml/min. The column was then washed with 3 ml of DEAE bind buffer, followed by 500 μl of H2O and 3–5 ml of the solvent required for the subsequent reaction. At least 50 nmol of oligonucleotide can be loaded onto one 250-μl DEAE Sepharose column. After the desired chemical transformations were carried out, the column was washed with 3–5 ml of the reaction solvent followed by 3–5 ml of DEAE bind buffer. The DNA was then eluted with 2 ml of DEAE elute buffer (1.5 M NaCl, 50 mM Tris-HCl [pH 8.0], and 0.005% Triton X-100) using a syringe. Long DNA molecules (340mers) were eluted with 4 ml of 1.5 M NaCl, 10 mM NaOH, and 0.005% Triton X-100 heated to 80 °C. Peptide coupling: succinimidyl esters. The following process was carried out twice. The column, with DNA bound, was washed with 3 ml of DMF. Using two syringes (see Figure 1), the column was incubated for 5 min at room temperature with a freshly prepared solution containing 225 μl of DMF, approximately 19 mg of Fmoc–amino acid-OSu, 67.5 μl of H2O, and 7.5 μl of diisopropylethylamine. Fmoc-Asn-OSu required four couplings rather than two to achieve quantitative yields. After the second amino acid incubation, the column was washed with 3 ml of DMF. Fmoc deprotection was carried out as follows: 3 ml of 20% piperidine in DMF was applied to a 3-ml syringe barrel attached to the top of the column. 1.5 ml was pushed through the column, followed by a 3-min incubation. An additional 1 ml was pushed through the column, followed by a 17-min incubation. The procedure was completed with a final 3-ml DMF wash. Peptide coupling: in situ activation. The column was washed with 500 μl of H2O and 3 ml of MeOH and then incubated for 30 min at room temperature with a freshly prepared 500-μl solution of 50 mM Fmoc–amino acid-OH, 50 mM EDC, and 5 mM HOAt in MeOH. These conditions were derived directly from Nozaki (1997). The column was then washed with 3 ml of MeOH. Fmoc-Asn-OH and Fmoc-His(Trt)-OH required 50 mM HOAt for efficient coupling. The following amino acids couple optimally in DMF/MeOH mixtures: Fmoc-Arg(Ns)-OH, Fmoc-Asn-OH ,and Fmoc-Gln-OH (20% DMF); Fmoc-Phe-OH and Fmoc-Val-OH (40% DMF); Fmoc-Ala-OH (80% DMF); and Fmoc-His(Trt)-OH (100% DMF). These mixtures were determined primarily by solubility. Typical reactions were carried out with 100 pmol of aminated oligonucleotide (see Table 1). Peptide–DNA conjugates were synthesized on small (0.1–2 nmol) or preparative scales (greater than 10 nmol). For particularly difficult sequences or preparative-scale reactions, the coupling procedure was repeated multiple times to achieve high yields. In all cases, absolute yields for peptide–DNA products with n amino acids exceeded (0.9)n. Fmoc deprotection was carried out as described for succinimidyl ester coupling. See Supporting Information for a more detailed description of peptide coupling. Side chain deprotection. For Lys(Ns), Arg(Ns), Cys(StBu), and His(CNP), the column was washed with 3 ml of DMF and subsequently incubated for 30 min with 700 μl of DMF containing 500 mM BME and 250 mM DBU while submerged in a 60 °C H2O bath. The column was then washed with 3 ml of DMF and 12 ml of DEAE bind buffer. Lys(Ns) can also be deprotected quantitatively with a DMF solution containing 5% thiophenol and saturated K2CO3 at 37 °C for 90 min. These conditions deprotect Arg(Ns) inefficiently, and have not been tested for Cys(StBu) or His(CNP). For Asp(tBu), Glu(tBu), and His(Trt), after HPLC purification, the tert-butyl ester and/or trityl containing oligonucleotide–peptide hybrid was incubated in a 20-mM MgCl2 solution at 70 °C, yielding quantitative deprotection in 3 h (Asp, His) or 12 h (Glu). Deprotection can alternatively be carried out before HPLC purification: after eluting from the DEAE Sepharose column, NaOAc (pH 5.2) and MgCl2 were added to final concentrations of 30 mM and 200 mM, respectively, and the solution was then incubated at 70 °C for the appropriate time. In contrast, acid deprotection on solid support was inefficient. Supporting Information Figure S1 MALDI-MS Analysis of Conjugates All reported conjugates were verified by MALDI-MS analysis. Example mass spectra of a conjugate before (A; Leu-NC20) and after (B; Arg-Leu-NC20) peptide coupling. Calculated masses are noted to the left of the mass peaks. (149 KB PDF). Click here for additional data file. Figure S2 Sequential Coupling Efficiencies and Yields of Peptide–DNA Synthesis (A) Reaction scheme for synthesis of GLFYG-NC20. Coupling efficiencies for individual steps are noted in black, and absolute yields from NC20 are noted in red. MALDI-MS results for all species are denoted under each species as “Observed (Calculated).” See Protocol S1 for precise coupling procedures. (B) HPLC analysis of sequential couplings during peptide synthesis monitored at 260 nm. Load and elutes from columns 1–5 are indicated. Sequential coupling efficiencies (black) were calculated by integration of recovered aminated DNA peaks. Absolute yields (red) were calculated by integration of intended product peak relative to load. A nonaminated 10-base oligonucleotide (10mer) was included as a control for nonspecific DNA loss and modification. Percent recovery of 10mer is noted in red. The HPLC analysis employed a 60-min gradient of 0%–45% MeCN in100 mM TEAA (pH 5.5). (368 KB PDF). Click here for additional data file. Protocol S1 Synthetic Methods (1.0 MB PDF). Click here for additional data file. We thank Al Smith for experimental advice, Peter Walker for oligonucleotide synthesis, Dick Winant for peptide sequencing of conjugates, and Elizabeth Zuo for MALDI-MS analysis. DRH is a Howard Hughes Medical Institute Predoctoral Fellow. SJW is supported by the Medical Scientist Training Program. This work was funded by a Stanford Office of Technology Licensing Research Incentive Award (#127P304 to PBH) and a Burroughs-Wellcome Fund New Investigator in the Pharmacological Sciences Award (#1001162 to PBH). Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. DRH, JAL, SJW, and PBH conceived and designed the experiments. DRH, JAL, and SJW performed the experiments. DRH, JAL, SJW, and PBH analyzed the data. SJW and PBH wrote the paper. Academic Editor: Gerald Joyce, Scripps Research Institute Abbreviations BMEβ-mercaptoethanol CNP4-cyano-2-nitrophenyl DBU1,8-diazabicyclo[5.4.0]undec-7-ene DEAEdiethylaminoethyl DMF N,N-dimethylformamide EDC1-(3-[dimethylamino]propyl)-3-ethylcarbodiimide hydrochloride Fmoc9-fluorenylmethyoxycarbonyl HOAt1-hydroxy-7-azabenzotriazole HPLChigh performance liquid chromatography MALDI-MSmatrix-assisted laser desorption/ionization mass spectrometry MeOHmethanol Nsnosyl PEGpolyethylene glycol ssDNAsingle-stranded DNA tBu tert-butyl Trttrityl ==== Refs References Allinquant B Hantraye P Mailleux P Moya K Bouillot C Down-regulation of amyloid precursor protein inhibits neurite outgrowth in vitro J Cell Biol 1995 128 919 927 7876315 Anderson GW Zimmerman JE Callahan FM N -hydroxysuccinimide esters in peptide synthesis J Am Chem Soc 1963 85 3039 Brenner S Lerner RA Encoded combinatorial chemistry Proc Natl Acad Sci U S A 1992 89 5381 5383 1608946 Bruick RK Dawson PE Kent SB Usman N Joyce GF Template-directed ligation of peptides to oligonucleotides Chem Biol 1996 3 49 56 8807828 Corey DR Schultz PG Generation of a hybrid sequence-specific single-stranded deoxyribonuclease Science 1987 238 1401 1403 3685986 Curran DP Strategy-level separations in organic synthesis: From planning to practice Angew Chem Int Ed Engl 1998 37 1175 1196 Debethune L Marchan V Fabregas G Pedroso E Towards nucleopeptides containing any trifunctional amino acid (II) Tetrahedron 2002 58 6965 6978 Fukuyama T Jow CK Cheung M 2-nitrobenzenesulfonamides and 4-nitrobenzenesulfonamides: Exceptionally versatile means for preparation of secondary amines and protection of amines Tetrahedron Lett 1995 36 6373 6374 Garay HE Gonzalez LJ Cruz LJ Estrada RC Reyes O Cleavage of dinitrophenyl side chain protecting group of histidine under Fmoc-deprotection conditions during the synthesis of the peptide Gly-His-Ala-Leu-Gly Biotecnol Apl 1997 14 193 195 Gartner ZJ Liu DR The generality of DNA-templated synthesis as a basis for evolving non-natural small molecules J Am Chem Soc 2001 123 6961 6963 11448217 Gartner ZJ Kanan MW Liu DR Expanding the reaction scope of DNA-templated synthesis Angew Chem Int Ed Engl 2002a 41 1796 1800 19750721 Gartner ZJ Kanan MW Liu DR Multistep small-molecule synthesis programmed by DNA templates J Am Chem Soc 2002b 124 10304 10306 12197733 Halpin DR Harbury PB DNA display I. Sequence-encoded routing of DNA populations PLoS Biol 2004a 2 e173 10.1371/journal.pbio.0020173 15221027 Halpin DR Harbury PB DNA Display II. Genetic manipulation of combinatorial chemistry libraries for small-molecule evolution PLoS Biol 2004b 2 e174 10.1371/journal.pbio.0020174 15221028 Han HS Wolfe MM Brenner S Janda KD Liquid-phase combinatorial synthesis Proc Natl Acad Sci U S A 1995 92 6419 6423 7541541 Harbury PB Halpin DR Board of Trustees of the Leland Stanford Junior University DNA-templated combinatorial library chemistry United States patent application WO 99-US24494 2000 Lew A Krutzik PO Hart ME Chamberlin AR Increasing rates of reaction: Microwave-assisted organic synthesis for combinatorial chemistry J Comb Chem 2002 4 95 105 11886281 Li X Zhan ZJ Knipe R Lynn DG DNA-catalyzed polymerization J Am Chem Soc 2002 124 746 747 11817938 Nakaie CR Ianzer DA Malavolta L Cilli EM Rodrigues MM Use of commercial anion-exchange resins as solid support for peptide synthesis and affinity chromatography Anal Biochem 2003 318 39 46 12782029 Nozaki S Efficient amounts of additives for peptide coupling mediated by a water-soluble carbodiimide in aqueous media Chem Lett 1997 26 1 2 Orgel LE Unnatural selection in chemical systems Acc Chem Res 1995 28 109 118 11542502 Robles J Beltran M Marchan V Perez Y Travesset I Towards nucleopeptides containing any trifunctional amino acid Tetrahedron 1999 55 13251 13264 Stetsenko DA Gait MJ Efficient conjugation of peptides to oligonucleotides by “native ligation.” J Org Chem 2000 65 4900 4908 10956469 Tegge W Frank R Peptide synthesis on sepharose beads J Pept Res 1997 49 355 362 9176820 Tong G Lawlor JM Tregear GW Haralambidis J Oligonucleotide–polyamide hybrid molecules containing multiple pyrene residues exhibit significant excimer fluorescence J Am Chem Soc 1995 117 12151 12158 Troy CM Derossi D Prochiantz A Greene LA Shelanski ML Downregulation of Cu/Zn superoxide dismutase leads to cell death via the nitric oxide-peroxynitrite pathway J Neurosci 1996 16 253 261 8613791 Xu Y Karalkar NB Kool ET Nonenzymatic autoligation in direct three-color detection of RNA and DNA point mutations Nat Biotechnol 2001 19 148 152 11175729 Zuckermann RN Schultz PG A hybrid sequence-selective ribonuclease S J Am Chem Soc 1988 110 6592 6594

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020203Research ArticleCell BiologyGenetics/Genomics/Gene TherapyImmunologyMolecular Biology/Structural BiologyDrosophilaFunctional Dissection of an Innate Immune Response by a Genome-Wide RNAi Screen RNAi Screen of Innate ImmunityFoley Edan 1 O'Farrell Patrick H [email protected] 1 1Department of Biochemistry and Biophysics, University of California, San FranciscoSan Francisco, CaliforniaUnited States of America8 2004 22 6 2004 22 6 2004 2 8 e2036 2 2004 4 5 2004 Copyright: © 2004 Foley and O'Farrell.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Identifying Genes Involved in Innate Immunity through RNAi The innate immune system is ancient and highly conserved. It is the first line of defense and the only recognizable immune system in the vast majority of metazoans. Signaling events that convert pathogen detection into a defense response are central to innate immunity. Drosophila has emerged as an invaluable model organism for studying this regulation. Activation of the NF-κB family member Relish by the caspase-8 homolog Dredd is a central, but still poorly understood, signaling module in the response to gram-negative bacteria. To identify the genes contributing to this regulation, we produced double-stranded RNAs corresponding to the conserved genes in the Drosophila genome and used this resource in genome-wide RNA interference screens. We identified numerous inhibitors and activators of immune reporters in a cell culture model. Epistatic interactions and phenotypes defined a hierarchy of gene action and demonstrated that the conserved gene sickie is required for activation of Relish. We also showed that a second gene, defense repressor 1, encodes a product with characteristics of an inhibitor of apoptosis protein that inhibits the Dredd caspase to maintain quiescence of the signaling pathway. Molecular analysis revealed that Defense repressor 1 is upregulated by Dredd in a feedback loop. We propose that interruption of this feedback loop contributes to signal transduction. By silencing all conserved genes in Drosphila, Foley and O'Farrell have identified numerous new regulators of the innate signaling response to gram-negative bacteria ==== Body Introduction As a typical metazoan suffers numerous microbial assaults during its lifespan, survival depends on robust defense strategies. Metazoan defenses are classified as either innate or adaptive. Adaptive immunity is characterized by elaborate genetic rearrangements and clonal selection events that produce an extraordinary diversity of antibodies and T-cell receptors that recognize invaders as nonself. While of profound importance, the adaptive responses are slow and limited to higher vertebrates. In contrast, the machinery of innate immunity is germ-line encoded and includes phylogenetically conserved signaling modules that rapidly detect and destroy invading pathogens (Medzhitov and Janeway 2000; Janeway and Medzhitov 2002). Model organisms, particularly insects, have played an important role in uncovering the wiring of innate immune pathways (Hoffmann 2003). Importantly, these organisms have provided powerful genetic approaches for identifying molecules that sense pathogens, elucidating steps that trigger innate defenses, and uncovering the weaponry used to kill or divert potential pathogens (Hoffmann et al. 1999). We have further refined the experimental approaches for rapid functional dissection of immune responses and describe new steps in an important pathway of the innate immune response. Signaling in innate immunity consists of three steps: detection of pathogens, activation of signal transduction pathways, and mounting of appropriate defenses. The first step is triggered by the detection of pathogen-associated molecular patterns by host pattern recognition receptors (Akira et al. 2001). Typical pathogen-associated molecular patterns are β-1,3-glucan of fungi, peptidoglycan and lipopolysaccharides (LPS) of bacteria, and phosphoglycan of parasites. Signaling engages several pathways, including Toll, tumor necrosis factor, mitogen-activated protein kinase (MAPK), and Jun kinase pathways. NF-κB–type transcription factors form an important downstream nexus of the signaling pathways, and their activation promotes important defense responses. Although the defense responses are diverse and often tailored to the type of pathogen, some of the defense strategies, such as production of a panel of antimicrobial peptides, activation of phagocytic cells, and production of toxic metabolites, are evolutionarily conserved. Interest in Drosophila as a model for analyzing innate immune signal transduction had a serendipitous origin. The Toll signaling pathway was discovered and characterized in Drosophila because of its role in specification of the embryonic dorsal ventral axis (Anderson et al. 1985). Similarities of pathway components to genes involved in mammalian immunity stimulated a hallmark study showing that the Toll pathway is a central mediator of antifungal and gram-positive bacterial defenses in Drosophila (Ip et al. 1993; Lemaitre et al. 1996). It is now recognized that Toll signaling is a conserved mediator of innate immune responses. A combination of classical genetics and molecular approaches has since identified numerous components of Toll signaling in Drosophila immunity, and it has highlighted similarities to mammals at the level of signal transduction and differences at the stage of pathogen detection (Ip et al. 1993; Rosetto et al. 1995; Nicolas et al. 1998; Drier et al. 1999; Manfruelli et al. 1999; Meng et al. 1999; Rutschmann et al. 2000a, 2002; Tauszig et al. 2000; Horng and Medzhitov 2001; Michel et al. 2001; De Gregorio et al. 2002; Ligoxygakis et al. 2002; Tauszig-Delamasure et al. 2002; Gobert et al. 2003; Weber et al. 2003). A second pathway, the Immune deficiency (Imd) pathway, mediates responses to gram-negative bacterial infection in Drosophila (Lemaitre et al. 1995). Although similar to the mammalian tumor necrosis factor pathway, there are several differences between the two signaling cassettes, particularly at the level of activation. As it is presently understood, the Imd pathway is headed by an apparent pattern recognition receptor, the transmembrane peptidoglycan recognition protein LC (PGRP-LC; Choe et al. 2002; Gottar et al. 2002; Ramet et al. 2002). Although the mechanisms are largely unknown, signaling proceeds through Imd (homolog of mammalian receptor interacting protein), dTAK1 (MAP3K homolog), and a complex of Ird5/Kenny (homologous to the IKKβ/IKKγ kinase). The active IKK complex phosphorylates the p105 homolog Relish, and Dredd (caspase-8 homolog) cleaves Relish, separating an N-terminal NF-κB domain of Relish from a C-terminal ankyrin domain (Lemaitre et al. 1995; Dushay et al. 1996; Wu and Anderson 1998; Hedengren et al. 1999; Hu and Yang 2000; Leulier et al. 2000, 2002; Rutschmann et al. 2000b; Silverman et al. 2000; Stoven et al. 2000; Georgel et al. 2001; Lu et al. 2001; Vidal et al. 2001; De Gregorio et al. 2002; Gottar et al. 2002; Khush et al. 2002; Naitza et al. 2002; Silverman et al. 2003; Stoven et al. 2003; Ryu et al. 2004). The N-terminal domain enters the nucleus and promotes transcription of genes encoding proteins with defense functions such as the antimicrobial peptide Diptericin (Dipt), whose expression provides a signature for activation of the pathway. Unlike the Toll pathway, which was thoroughly studied in its developmental capacities, analysis of the Imd pathway is relatively recent. Its more complete genetic dissection may well define another conserved and fundamental pathway of immune signaling. Of particular interest, a pivotal step in the Imd pathway—the regulation of Dredd-mediated cleavage of Relish—is not understood. To begin to address this, we developed a powerful RNA interference (RNAi)–based approach to functionally dissect the Imd pathway. In collaboration with others at the University of California, San Francisco, we produced a library of 7,216 double-stranded RNAs (dsRNAs) representing most of the phylogenetically conserved genes of Drosophila. We developed a cell culture assay that allowed application of this library to a high-throughput RNAi evaluation of Imd pathway activity. This screen identified numerous components of signal transduction (including negative and positive regulators of innate immune signaling), defined a hierarchy of gene action, and identified a novel gene, sickie (sick), required for activation of Relish. Focusing on regulation of the Dredd caspase, we identified a novel inhibitor of Dredd, Defense repressor 1 (Dnr1), which is upregulated by Dredd in a feedback loop that maintains quiescence. We propose that interruption of this feedback loop contributes to signal transduction. Results A Drosophila Reporter Cell Line of Imd Pathway Activity To facilitate rapid dissection of Imd pathway signaling, we established an S2 reporter cell line that expresses β-galactosidase under control of the promoter from a gene, Dipt, that encodes an antimicrobial peptide, Dipt-lacZ. Commercial preparations of LPS contain bacterial cell wall material capable of activating the receptor PGRP-LC and act as gratuitous inducers of antimicrobial peptide genes in Drosophila tissue culture cells (Samakovlis et al. 1992; Engstrom et al. 1993; Dimarcq et al. 1997). Consistent with previous studies, 20-hydroxyecdysone enhanced Dipt-lacZ induction by LPS (Figure 1A; Silverman et al. 2000, Silverman et al. 2003). Inactivation of critical Imd pathway members (PGRP-LC, Imd, Ird5, and Dredd) by RNAi virtually eliminated Dipt-lacZ induction by LPS (Figure 1B). In contrast, inactivation of the Toll pathway members Spaetzle, Tube, or Dif by RNAi had no effect on LPS-dependent induction of Dipt-lacZ. We conclude that LPS-dependent induction of Dipt-lacZ requires an intact Imd signaling pathway. Figure 1 A Cell Culture Screen Identifies Novel Regulators of the Innate Immune Response (A) LPS induces an increase of about 10-fold in the number of Dipt-lacZ cells that stain positively for β-galactosidase. Ecdysone sensitizes the cells and promotes the response. (B) Dipt-lacZ induction by LPS requires known Imd signaling components, but not Tl pathway members. The fraction of β-galactosidase-positive cells was normalized to the induced control (normalized %), and influence of RNAi of Tl pathway members (dif, spz, and tub) or Imd pathway members (PGRP-LC, Imd, Ird5, and Dredd) is shown. (C–H) Activity stain (X-Gal) for β-galactosidase. (C) Untreated cells. (D) Cells treated with ecdysone alone. (E) Cells treated with ecdysone and LPS. About 10% of cells express detectable β-galactosidase. (F) RNAi against the DDRi sick reduces Dipt-lacZ expression in response to LPS. (G) RNAi of a representative EDRi, the Ras signaling pathway component Cnk, enhances Dipt-lacZ induction by LPS. (H) RNAi of a representative CDRi, the actin regulator SCAR induces Dipt-lacZ in the absence of LPS. (I–J) Immunofluorescence of S2 cells with actin in red, tubulin in green, and DNA in blue. Scale bars in (I) and (J) indicate 10 μm. (I) Wild-type cells have a characteristic rounded morphology. (J) RNAi against many CDRi genes disrupts morphological features of wild-type S2 cells. S2 cells are shown treated with MESR4 dsRNA. Cells are significantly larger in appearance and less round, with irregular tubulin and actin networks. To identify additional modulators of Dipt-lacZ expression, we prepared a library of 7,216 dsRNAs representing most of the phylogenetically conserved genes of Drosophila. Using the Dipt-lacZ cell line, we performed a high-throughput RNAi screen for genes whose inactivation impinges on Dipt-lacZ induction. In one screen, we identified dsRNAs that altered Dipt-lacZ induction by LPS, either enhancing or suppressing activation. In a second screen performed without addition of LPS, we identified genes whose inactivation spontaneously activated the reporter. The phenotypes defined three categories of genes, which we named—based on the phenotype of their inactivation—decreased defense by RNAi (DDRi) genes, enhanced defense by RNAi (EDRi) genes, and constitutive defense by RNAi (CDRi) genes (Figure 1). Identification of DDRi, EDRi, and CDRi Genes In an initial visual screen, dsRNAs that altered the induced or constitutive expression of β-galactosidase were selected as candidate innate immunity genes. We subjected all the initial positives to a more stringent retest where we resynthesized the candidate dsRNAs, retested these under identical conditions, and counted the number of β-galactosidase-positive cells. We defined DDRi dsRNAs as reducing the frequency of Dipt-lacZ-expressing cells to below 40% of LPS-treated controls, EDRi dsRNAs as increasing the frequency of Dipt-lacZ-expressing cells more than 2-fold, and CDRi dsRNAs as inducing Dipt-lacZ-expressing cells to a level equal to or higher than that induced by LPS. About 50% of the initial positives met these criteria, yielding 49 DDRi dsRNAs, 46 EDRi dsRNAs, and 26 CDRi dsRNAs (Figure 2A–Figure 2C; Table 1). The entire process of screening and retesting was performed without knowing the identity of the dsRNAs. Nonetheless, we successfully identified all of the known Imd pathway components in the library (PGRP-LC, Dredd, and Relish) as DDRi genes, supporting the validity of this approach for identifying genes that affect Imd pathway signaling. Figure 2 List of Modulators of the Immune Response and a False Color Display of Their Influence on Dipt-lacZ Induction The genes identified as DDRi (A), EDRi (B), and CDRi (C) are listed, and the colored bars show the influence of the corresponding dsRNA on Dipt-lacZ expression. The top two entries in (A), (B), and (C) show control cells (no dsRNA) without and with LPS, respectively. The scales for the false colors are given at the bottom left. Dipt-lacZ levels are given in terms of percent positive cells. For exact Dipt-lacZ expression values for each dsRNA refer to accompanying supplemental tables. In (B), the color scale (right) is compressed and extended compared to (A), and an asterisk indicates genes that also caused a CDRi phenotype. In (C), the pound sign indicates morphological defects and an asterisk indicates genes that also caused an EDRi phenotype, and the division of the genes into epistatic groups is shown. To the immediate left a false-color bar (coded as in [B]) indicates the effect of the dsRNAs on Dipt-lacZ expression without LPS addition. The block of colored columns shows the results of epistasis tests. Here, we set the undisturbed level of CDRi activation to 100% (as indicated in the left column in this group and the color code below), and to the right we represent reduction of this activation by prior RNAi of different Imd pathway genes. Five epistatic clusters (I–V) were identified (indicated by the lines to the left). Table 1 Measurement of the Percent of LacZ-Positive Cells After Treatment with dsRNA Against Individual EDRi, DDRi, and CDRi Genes TF, transcription factor Cell culture conditions were as described above, with the exception that CDRi genes were not treated with LPS. For each sample, 350–550 cells were counted. Controls (+ LPS and −LPS) are the average of five independent experiments. EDRi genes were defined as having greater than or equal to twice +LPS control induction levels. DDRi genes were defined as having less than or equal to 40% of +LPS control induction levels. CDRi genes were defined as having greater than or equal to four times −LPS control levels. Results were reproducible for all genes subjected to further analysis The dsRNAs that enhance, and those that constitutively activate, the immune reporter are both expected to target inhibitors of the immune response. Nonetheless, there was only a small overlap between the EDRi genes and CDRi genes. Of the 46 confirmed EDRi dsRNAs, only five caused a CDRi phenotype, suggesting that the mechanisms that silence Imd pathway activity in the absence of infection are largely distinct from those moderating or downregulating the response to infection. We distinguish the five EDRi genes capable of constitutive activation and designate them EDRiC. EDRiC genes are listed as both EDRi and CDRi (Figure 2B and Figure 2C, indicated with an asterisk). Approximately half of the CDRi dsRNAs also caused morphological defects (Figure 2C, indicated with a pound sign), i.e., enlarged cells with irregular cytoskeletal structures (see Figure 1J). While we do not know the basis for the altered morphology, gene expression profiling showed that LPS induces numerous cytoskeletal regulators, suggesting that cytoskeletal rearrangement is a component of the innate immune response (Boutros et al. 2002). We also observed EDRi and CDRi phenotypes upon inactivation of Act5C and Act42A. Due to extensive sequence homology, RNAi against either actin triggers destruction of both transcripts (A. Echard, G. R. X. Hickson, E. Foley, and P. H. O'Farrell, unpublished data). Inactivation of either actin with dsRNA directed to the actin UTRs demonstrated that both actin transcripts must be inactivated for an observable EDRi or CDRi phenotype (Figure 2B and Figure 2C). Epistatic Evaluation of CDRi Genes As RNAi of CDRi genes leads to ectopic Dipt-lacZ induction, we reasoned that CDRi genes are required to maintain quiescence in the absence of LPS and that induction by a CDRi dsRNA corresponds to release of inhibition of the Imd pathway. The large number of CDRi genes makes it likely that individual CDRi genes inhibit distinct steps in the Imd pathway. We sought to determine the position at which the individual CDRi genes impact the Imd pathway. In contrast to Caenorhabditis elegans, several genes can be inactivated by RNAi in Drosophila without an obvious drop in the efficiency of gene inactivation (Li et al. 2002; Schmid et al. 2002). The ability to inactivate two different gene products in sequence by RNAi provides a powerful tool to position CDRi genes relative to known Imd pathway components. In a first step, we inactivated one of three known Imd signaling components—either Imd, Dredd, or Relish. In a second step, we inactivated individual CDRi genes and monitored Dipt-lacZ induction. We reasoned that inactivation of Imd, Dredd, or Relish would not block pathway derepression by a CDRi dsRNA if the cognate CDRi impinged on the pathway at a step beyond the actions of Imd, Dredd, or Relish. Using this approach, we subdivided 20 CDRi genes into five epistatic groups (Figure 2C; Table 2). Group I contained four CDRi dsRNAs whose action was independent of Imd, Dredd, and Relish. Group II contained 12 dsRNAs whose CDRi phenotype was independent of Imd and Dredd, but depended on Relish. Group III contained two dsRNAs whose CDRi phenotype was Dredd-independent, but was reduced in the absence of Imd and Relish. Group IV contained a single dsRNA whose phenotype was independent of Imd, but dependent on Dredd and Relish. Finally, Group V contained three dsRNAs whose ability to activate the immune reporter depended on Imd, Dredd, and Relish. The epistatic relationships demonstrate that genes in Groups II–V have inputs into the known Imd pathway, while Group I might have inputs in independent pathways required for effective Dipt-lacZ expression. Table 2 Measurement of the Percent of LacZ-Positive Cells after Treatment with dsRNA against Imd, Dredd, or Relish Followed by RNAi for the Individual CDRi Cell culture conditions were as described in the body of the manuscript, with the exception that CDRi genes were not treated with LPS. For each sample, 350–550 cells were counted Sick Is a Conserved Gene Required for Relish Activation We are particularly interested in regulators contributing to activation of the Relish transcription factor by the caspase Dredd, because this is such a pivotal step in the Imd pathway and its regulation is not understood. To identify regulators that affect Relish processing, we developed an assay that more directly monitored Relish activation. We produced an S2 cell line that expresses a copper-inducible N-terminal green fluorescent protein (GFP)–tagged Relish (GFP-Relish; Figure 3). GFP-Relish is predominantly cytoplasmic in untreated cells (Figure 3A) and rapidly translocates to the nucleus upon treatment of cells with LPS or exposure to Escherichia coli (Figure 3B and Figure 3D). Western blot analysis with a monoclonal anti-GFP antibody showed that GFP-Relish is rapidly processed from a full-length form to a shorter form after exposure to LPS (Figure 3C). These findings indicate that the GFP-Relish cell line is a reliable reporter for Relish activation. Additionally, inactivation of PGRP-LC by RNAi prevented nuclear translocation of GFP-Relish in response to bacterial exposure (Figure 3E), indicating that the reporter can be used to assay function of Imd pathway genes. Figure 3 A GFP-Relish Reporter Cell Line Subdivides DDRi dsRNA into Three Categories (A–B) Immunofluorescence of GFP-Relish cells with GFP-Relish in green, DNA in blue, and actin in red. Relish is predominantly cytoplasmic in untreated control cells and rapidly translocates to the nucleus of cells incubated with LPS. (C) An anti-GFP Western blot of lysates harvested from GFP-Relish cells treated with LPS for different periods. GFP-Relish rapidly shifts from a full-length form to a shorter processed form after exposure to LPS, and full-length Relish gradually reaccumulates. (D–E) Immunohistochemistry of GFP-Relish cells incubated with GFP-expressing E. coli (arrowheads) and treated with (E) or without (D) dsRNA against PGRP-LC. Imd pathway inactivation prevents bacterial-induced Relish nuclear translocation. (F) Shows effects of treatment of GFP-Relish cells with DDRi dsRNAs for 4 d prior to LPS treatment. GFP-Relish was scored as cytoplasmic (uninduced), nuclear (induced), or reduced in amount (abnormal). (G) Shows an epistatic analysis of the DDRi, sick. Suppression of sick interferes with Dipt-lacZ induction by Group III, IV, and V CDRi dsRNAs, but not those of Groups I and II, suggesting that Sick acts downstream of Imd and Dredd, but upstream of Relish in signal transduction. We tested all DDRi dsRNAs for their effects on the response of GFP-Relish to LPS (Figure 3F). Most DDRi dsRNAs did not affect GFP-Relish levels or its LPS-stimulated nuclear concentration, suggesting that their effects on Dipt-lacZ are independent of this step of Relish activation. Four DDRi dsRNAs (Relish, ubiquitin, CG8129, and Asph) severely reduced GFP-Relish levels, indicating that these dsRNAs directly or indirectly interfered with Relish expression or stability. The ability of these dsRNAs to block Dipt-lacZ induction suggests that Dipt-lacZ induction requires substantial levels of Relish. We identified four DDRi dsRNAs that prevented LPS-stimulated nuclear translocation of GFP-Relish: PGRP-LC, Dredd, Dox-A2, and CG10662. We named CG10662 sick. While prolonged Dox-A2 RNAi caused cell lethality, cell viability appeared unaffected by sick RNAi for up to 8 d. As sick RNAi prevents nuclear translocation of GFP-Relish and decreases Dipt-lacZ induction after LPS treatment, we propose that the Imd pathway requires sick activity for Relish-dependent Dipt-lacZ induction. Epistasis provides a second approach for positioning a DDRi gene in the hierarchy of gene action. To this end we assessed the relationship of sick to the five CDRi epistatic groups that we defined (above). We inactivated sick by RNAi and subsequently tested dsRNAs representing the five CDRi epistatic subgroupings for their ability to activate Dipt-lacZ expression in the absence of Sick (Figure 3G; Table 3). Group I and II CDRi do not require Sick, indicating that Sick acts upstream of, or in parallel to, their action, which is at the level of Relish or downstream of Relish. Induction of Dipt-lacZ by Group III and IV CDRi dsRNAs requires Sick, suggesting that Sick is required for the effective induction of Dipt-lacZ by Dredd and Imd. Combined with the observed Sick requirement for Dipt-lacZ induction and the nuclear translocation of Relish by LPS, these data imply that Sick either mediates or supports Relish activation by Dredd and Imd. Table 3 Measurement of the Percent of LacZ-Positive Cells after Treatment With dsRNA against Sick Followed by RNAi for the Individual CDRi Cell culture conditions were as described in the body of the manuscript, with the exception that cells were not treated with LPS. For each sample, 350–550 cells were counted Dnr1 Is a Novel Inhibitor of Dredd Negative regulators are likely to participate in the circuitry that controls Dredd activation of Relish. The key candidate for action at this level was the single Group IV CDRi gene, CG12489, which showed epistatic relationships consistent with a role in inhibiting Dredd. RNAi of CG12489 induced Dipt-lacZ expression without immune stimulus, indicating that CG12489 normally prevents Dipt expression. As CG12489 inactivation fails to induce Dipt-lacZ in the absence of Dredd or Relish (see Figure 2C), we reasoned that CG12489 normally suppresses Dredd-dependent induction of Dipt-lacZ. We named this gene dnr1 and discuss its actions more fully below. Dnr1 is a conserved protein with an N-terminal ezrin/radixin/moesin domain and a C-terminal RING finger (Figure 4A). To confirm that Dnr1 inactivation stimulated Dipt-lacZ production, we measured the β-galactosidase activity of lysates from Dipt-lacZ cells treated with dnr1 dsRNA. Exposure of Dipt-lacZ cells to LPS reproducibly increased Dipt-lacZ production 4- to 5-fold (Figure 4B). Importantly, in three independent experiments, Dnr1 RNAi stimulated Dipt-lacZ production to a similar degree in the absence of LPS. Furthermore, Dipt-lacZ activation in response to LPS was essentially reduced to background levels upon inactivation of sick. These findings provide additional support for negative and positive regulation of Relish by Dnr1 and Sick, respectively. While we detected genes with similarity to dnr1 in many higher eukaryotes, we failed to find a homolog of Dnr1 in C. elegans. Interestingly, C. elegans does not rely on an Imd pathway for innate defenses (Kurz and Ewbank 2003). Other RING finger proteins are E3 ubiquitin ligases that target a variety of substrates for proteolytic destruction. The RING finger motif in Dnr1 has greatest sequence homology to the RING fingers found in inhibitor of apoptosis proteins (IAPs; Figure 4C). IAPs are critical inhibitors of caspase activity that ubiquitinate their targets and promote autoubiquitination (Bergmann et al. 2003). Previous reports demonstrated that caspase inhibitors activate their own destruction and that this activity is RING finger mediated (Yang et al. 2000). Consistent with these reports, we observed surprisingly low levels of accumulation of a hemagluttanin (HA)–tagged Dnr1 in transfected cells. A point mutation in a residue critical for RING finger function resulted in increased accumulation of transfected HA-Dnr1 (Figure 4D). We also detected a protein processing event that appears to depend on the RING finger. Upon expression of C-terminally HA-tagged Dnr1, we observed a slightly lower molecular weight isoform, suggesting N-terminal processing of Dnr1 (Figure 4E). The absence of this lower molecular weight isoform in cells transfected with the N-terminally HA-tagged Dnr1 (Figure 4D) is consistent with processing near the N-terminus. This processed isoform was absent in cells transfected with constructs containing the RING finger mutation (Figure 4E). The presence of the RING finger motif, and its apparent role in destabilizing Dnr1, argues that Dnr1 is a caspase inhibitor and that, given its functional role and epistatic position as an inhibitor of Dredd, it is likely to act directly to inhibit this caspase. Figure 4 Dnr1 Is a Conserved Inhibitor of Dredd Activity (A) A comparison of the amino acid sequence of Dnr1 with XP_32149 from Anopheles gambiae and human MIR. Shaded regions indicate the N-terminal ezrin/radixin/moesin domain and C-terminal RING finger. Asterisks indicate conserved residues. (B) Measurements of β-galactosidase activity in lysates prepared from Dipt-lacZ in control cells, LPS-treated cells, dnr1 dsRNA –treated cells, and sick dsRNA–treated cells exposed to LPS, respectively. Each experiment was performed in triplicate. (C) Similarity between the RING finger in Dnr1 and other IAPs. Critical residues are shaded. Asterisks indicate conserved residues. (D) Lysates from S2 cells transfected with equal amounts of N- and C-terminally HA-tagged wild-type Dnr1 (lanes 1 and 3, respectively), or N- and C-terminally HA-tagged C563Y Dnr1 (lanes 2 and 4, respectively), and analyzed by an anti-HA Western blot. Residue C563 is critical for RING finger function and is indicated with an arrowhead in (C). (E) Higher resolution of lysates from C-terminally HA-tagged wild-type or C563Y Dnr1. Mutation of the RING finger prevents accumulation of a lower isoform of Dnr1. (F and G) Subcellular localization of HA-Dnr1 transiently expressed in S2 cells treated without (F) or with (G) LPS, with HA in green, DNA in blue, and actin in red. Dnr1 Protein Levels Are Regulated by Dredd Activity While LPS had no dramatic effect on the subcellular localization of HA-Dnr1 (Figure 4F and Figure 4G), exposure to LPS had a transient effect on the levels of Dnr1 protein. Addition of LPS caused an increase in HA-Dnr1 levels (Figure 5A), which rose 4- to 5-fold 2 h after treatment with LPS and then gradually declined. Since LPS-dependent processing of Relish by Dredd proceeded in a similar manner (see Figure 3C), we tested whether Dredd inactivation affected Dnr1 protein levels. Cotransfection of the caspase inhibitor p35 along with HA-Dnr1 blocked HA-Dnr1 accumulation (Figure 5B). Similarly, even transient treatment with the caspase inhibitor z-VAD-FMK at concentrations sufficient to prevent Relish processing (Figure 5C) reduced LPS-dependent HA-Dnr1 accumulation (Figure 5D). As these data implicated caspase function in Dnr1 accumulation, we tested the five Drosophila caspases represented in our library for their influence on Dnr1 stability. Only Dredd RNAi reproducibly reduced HA-Dnr1 levels (Figure 5E and Figure 5F). Consistent with a role for Dredd as the critical caspase in LPS-dependent Relish activation, of all caspases tested only Dredd inactivation blocked LPS-dependent Dipt-lacZ induction (Figure 5G). Figure 5 Dnr1 Protein Levels Are Regulated by Dredd Activity (A) Amounts of HA-Dnr1 transiently increase in S2 cells treated with LPS. Anti-HA Western blot of lysates from HA-Dnr1-transfected S2 cells that were incubated with LPS for indicated periods. (B) Anti-HA Western blot of lysates from S2 cells transfected with HA-Dnr1. Coexpression of the caspase inhibitor p35 dramatically inhibits HA-Dnr1 accumulation in the absence (lanes 1 vs. 3) or presence (lanes 2 vs. 4) of LPS. Actin levels are shown as a loading control. (C) Upper panel shows the percentage of cells with nuclear GFP-Relish after the indicated treatments. The lower panel is an anti-GFP Western blot of lysates from S2 cells treated in the identical manner. z-VAD-FMK prevents nuclear accumulation of GFP-Relish and GFP-Relish processing in response to LPS. (D) Anti-HA Western blot of lysates from S2 cells transiently transfected with HA-Dnr1. While 2 h incubation with LPS normally leads to a 4-fold increase (quantified by titration) in HA-Dnr1 (lanes 1 vs. 2), incubation with z-VAD-FMK prevents the accumulation (lanes 3 vs. 4). (E) Anti-HA Western blot of lysates from S2 cells transfected with HA-Dnr1. Cells had been previously incubated with (lanes 3 and 4) or without (lanes 1 and 2) Dredd dsRNA. Results are shown for two independent experiments. Actin levels are shown as a loading control. (F) Anti-HA Western Blot of lysates from S2 cells transfected with HA-Dnr1 shows that prior RNAi against the caspases Dcp-1, Ice, Nc, and Decay does not substantially affect HA-Dnr1 levels (compare with control without LPS). (G) The number of Dipt-lacZ-expressing cells after LPS treatment is greatly reduced after Dredd RNAi, while RNAi against Dcp-1, Ice, Nc, or Decay has no effect. In summary, addition of LPS to S2 cells activates Dredd and stabilizes Dnr1, while inactivation of Dredd by RNAi or caspase inhibitors reduces Dnr1 protein levels. We conclude that Dnr1 protein levels are regulated by Dredd activity. While it is not presently known how Dredd caspase function might influence Dnr1 accumulation, we note that the data are consistent with a negative feedback loop in which Dredd activity promotes accumulation of its own inhibitor, Dnr1. Discussion It was previously recognized that the Drosophila macrophage-like S2 cell line responds to bacterial cell wall components with the induction of antimicrobial peptide expression. This model lacks the complexities of communication between tissues that drive the spread of the immune response in larvae (Foley and O'Farrell 2003), but it offers an exceedingly powerful system for identification of mediators of antimicrobial peptide induction. To develop a genetic approach to identify novel signal transduction components, we produced reporter cell lines to follow innate immune signaling and a library of 7,216 dsRNAs representing the conserved genes of Drosophila to inactivate genes by RNAi. We focused on a screen for immune response genes in the Imd pathway because it is the less thoroughly understood of the two immune response pathways in Drosophila. A central aspect of our strategy for dissection of the pathway was to identify negative regulators as well as positively acting genes. In addition to modulating signal transduction pathways, negative regulators participate directly in signaling when downregulated by the inducing signal. Beyond the inherent importance of this relatively unexplored group of regulators, we were interested in their potential utility as an experimental lever: Identification of inhibitors acting at numerous levels of the pathway provides tools for ordering the action of the positively acting genes in the pathway and vice versa. The experimental approach and strategy proved highly efficient, yielding numerous regulators and defining a cascade of gene action by epistasis. A secondary test for the influence of positively acting genes on the nuclear translocation of Relish and the epistasis order allowed us to narrow our focus to genes that are centrally involved in the immune response. Focusing on the unresolved issue of Dredd regulation, we characterized a negative regulator, Dnr1, that provides a critical check on unwarranted Dredd activity. Our results suggest that Dredd controls Dnr1 stability in a negative feedback loop that restricts Dredd function. Normal activation of the Imd pathway may include release or bypass of this negative feedback loop. Categories of Innate Immune Inhibitors A priori, we considered two roles for inhibitors of the Imd pathway: either suppression of spontaneous activation of immune responses in the absence of infection, or downmodulation of a response to limit or terminate it. We designed screens for both these types of activities. In conjunction with the RNAi screen for dsRNAs that blocked response to LPS, we identified dsRNAs that enhanced the response—EDRis. This phenotype represents a failure to downmodulate the response. In an independent screen without LPS, we identified dsRNAs that resulted in constitutive activation of the pathway—CDRis. Surprisingly, there was remarkably little overlap in the genes identified in these two screens: Of 26 CDRis and 46 EDRis only five were in common. At present, we do not understand the functional underpinnings of the distinctions between inhibitors that sustain quiescence (CDRis) and those that downregulate an ongoing response (EDRis). Interestingly, groups of inhibitors implicate distinct pathways in immune regulation. For example, of the 17 genes that had the strongest EDRi phenotype, four encode splicing factors and four encode products that appear to interact with RNA. This functional cluster suggests that disruptions to some aspect of RNA processing/metabolism can substantially increase the number of S2 cells that activate expression of the Dipt-lacZ reporter in response to LPS exposure. While we do not know how RNA metabolism contributes to this phenotype, the repeated independent isolation of genes lying in a functional cluster reinforces a conclusion that the process is involved. Several other functional clusters were picked up in our screens. Three genes involved in Ras signaling (MESR4, Ras, and Cnk) were identified as EDRi genes. In addition, we noted weak EDRi phenotypes with three additional Ras signaling components (rolled/MAPK, Dsor1, and Pointed). These findings argue that Ras signaling downregulates responses to LPS. This might represent a negative feedback circuit. However, the finding that MESR4 also has a CDRi phenotype suggests that the Ras/MAPK pathway may also impinge on the maintenance of quiescence. Several genes involved in cytoskeletal structure or regulation were identified among the inhibitors. Genes encoding tubulin (α-Tub84D), a kinesin motor (Klp10A), and microtubule-severing function (CG4448/katanin) were isolated as EDRi genes. Perhaps an event involving microtubule structures helps limit immune responses. The two cellular actin genes (Act5C and Act42A) were individually dispensable, but their joint inactivation produced both EDRi and CDRi phenotypes. A regulator of actin function, SCAR, was also identified as a CDRi, and both actin and SCAR CDRi phenotypes fell into epistasis Group II. This suggests that disruption of the actin cytoskeleton in quiescent cells can activate the immune response in a Relish-dependent fashion. Since S2 cells are induced to phagocytose bacteria, and changes in cell shape are induced in response to LPS, it would not be surprising if cytoskeletal functions contribute to immune responses. Indeed, microarray studies showed induction of numerous cytoskeletal components in S2 cells upon incubation with LPS (Boutros et al. 2002). Our findings, however, suggest a different involvement of the cytoskeleton in which it functions to constrain S2 cells, preventing or limiting their innate immune responses. A previous conventional genetic screen for mutations leading to constitutive action of the Imd pathway in Drosophila larvae demonstrated that Relish basal signaling is maintained at a low level by proteosomal destruction of processed Relish (Khush et al. 2002). A Skp1/Cullin/F-box (SCF) component was identified as involved in ubiquitination of the N-terminal Relish domain. We did not include any genes in the category of ubiquitination and proteasome function in our CDRi group. This might mean that this pathway does not influence the cellular responses in the S2 tissue culture system. However, our first round of screening suggested that RNAi to a Drosophila F-box resulted in increased basal signaling (unpublished data). This and other tentative indications of involvement of this pathway were either not reproduced or fell below the threshold in retesting. We are left uncertain about SCF contributions to immune induction in our system. A GFP-Relish Reporter Line Subdivides DDRi Genes As in the case of the CDRi and EDRi phenotypes, our screen for DDRi phenotypes identified numerous genes falling into functional categories. One potential limitation of our approach for identification of DDRi is that some genes required for ecdysone maturation may be selected as immune deficient. Additionally, one of the largest functional categories was genes involved in translation and included four ribosomal proteins, three initiation factors, two amino acyl-t-RNA synthases, and an elongation factor. It seems likely that RNAi of genes in this category affects translation of the Dipt-LacZ reporter, as opposed to affecting modulation of signaling events. To cull our collection of DDRis of such indirect modulators of the response, we developed a secondary screen that does not rely on de novo gene expression. Based on the previously described phenotypes of Imd pathway members, we reasoned that inactivation of the core components transducing the signal would compromise activation of the Relish transcription factor. To identify DDRi dsRNAs that prevented Relish activation, we prepared a GFP-Relish reporter cell line and rescreened DDRi dsRNAs for loss of GFP nuclear translocation in response to LPS. In addition to confirming a requirement for Dredd and PGRP-LC in Relish activation, we implicated a proteosomal regulatory subunit Dox-A2 and identified a novel gene sick as involved in Relish nuclear translocation in response to LPS. Although cells treated with sick dsRNA failed to mount an immune response, the cells were otherwise healthy through the course of the experiment. Dox-A2 RNAi reduced the survival of cells and was effectively lethal within a few days of the scoring of the immune response. We conclude from this that Sick and Dox-A2 contribute to the central signal transduction process, but it is presently unclear whether Dox-A2 has a significant specific input or if its effects are secondary to a global effect on cell viability. It is notable that only two DDRi genes passed our secondary screen based on GFP-Relish localization. Does this mean that all the other DDRis are not really involved? While we have not yet analyzed all these genes, we suspect that many of them will modify the Imd pathway, either impinging on the pathway at a point beyond Relish translocation, or quantitatively or kinetically modifying Relish translocation in a manner that we did not detect in our screens. Insight into this issue is likely to be derived from further epistasis tests that might place some of these DDRis in the signaling pathway. An Epistatic Network to Position CDRi and DDRi Genes We identified an unprecedented large number of immune response inhibitors (CDRi genes) in our screens. As there are diverse steps within and potentially outside the Imd signaling pathway at which the CDRi inhibitors might act, we sought to position their actions with respect to known Imd pathway functions by RNAi epistasis tests. By sequential inactivation of known Imd pathway components and CDRi gene products, we tested whether constitutive activation of immune reporters by CDRi dsRNAs depends on steps in the signal transduction pathway. In this way, we defined five distinct epistatic categories of CDRi gene products. The four CDRi genes that continue to activate immune responses despite inactivation of Imd, Dredd, or Relish are likely to act on signal-transduction-independent factors that maintain transcriptional quiescence of Dipt. The largest group of CDRis (12) depends on Relish function but not on upstream activators of Relish. These are likely to include two types of regulators: one type that sets the threshold of response so that basal activity of Relish does not trigger pathway activity, and a second type that contributes to suppression of Relish activity. The latter type of regulator might include inhibitors that impinge on the late steps in the signal transduction cascade. For example, genes whose normal function inhibits the activity of the full-length Relish transcription factor might be required to make the pathway activator dependent, and these would be found in this category. The remaining upstream epistasis groups that rely on additional signal transduction components are strongly implicated as significant contributors to the immune induction pathway. As all of the CDRis induced robust immune responses in the absence of ecdysone (unpublished data), we propose that the CDRis have their input into the Imd pathway at a level that is the same or lower than the level of the input from ecdysone. Given that this is true for all five epistatic groups of CDRis, the result suggests that ecdysone has its input at an early level of the Imd pathway. The identification of five epistasis groups of inhibitors also provides reference points for a second round of epistasis tests that position novel DDRi genes within the Imd pathway. We used this approach to show that the novel DDRi sick is required for constitutive activation of the responses by inactivation of CDRi genes in Groups III, IV, and V genes but not for the action of CDRi Group II or Group I genes. If we assume a simple linear pathway, this would indicate that Sick functions upstream of Relish and downstream of Imd and Dredd. It is noteworthy that the epistatic data are consistent with molecular data indicating that Sick is required for Dipt-lacZ induction and the nuclear translocation of Relish in response to LPS. This combination of phenotypic, epistatic, and molecular data argues for participation of Sick in the regulated activation of the Relish transcription factor. Dnr1 Prevents Ectopic Dredd-Dependent Relish Activation One epistatic group struck us as particularly interesting. While Dnr1 inactivation caused ectopic Dipt-lacZ expression, simultaneous loss of Dredd or Relish restored cells to their resting state. These data indicate that the wild-type function of Dnr1 is to prevent Dredd-dependent activation of Relish. Consistent with this hypothesis, we identified a C-terminal RING finger in Dnr1 with greatest similarity to the RING finger motifs observed in the C-terminus of IAP proteins. In addition to regulating caspase activity, IAPs also regulate their own stability through ubiquitin-mediated proteolysis. Similarly, we observed that mutation of a critical RING finger residue greatly stabilized Dnr1. These features suggest that Dnr1 is a caspase inhibitor, suggesting that it might act directly to inhibit Dredd activity. We observed that exposure of cells to LPS transiently stabilized Dnr1 and that this stabilization directly paralleled the period of Dredd-dependent Relish processing. This suggested to us that Dnr1 stability and accumulation might be regulated by its target, Dredd, a regulatory connection that could establish a negative feedback loop. We confirmed that Dredd activity is required for accumulation of Dnr1. These results suggest that Dredd modulates a RING-finger-dependent Dnr1 destruction pathway (Figure 6). Figure 6 A Schematic of the Proposed Relationships of the Novel Immune Regulators, Sick and Dnr1, to Dredd and Rel Pointed and blunt arrows indicate activation and inhibition, respectively. Both Sick and Dredd are required for translocation of Rel to the nucleus and for activation of Dipt expression and they are consequently positioned upstream of Rel as activators. In the absence of Sick or Dredd, Dnr1 function is not needed to maintain pathway quiescence. Thus, Dnr1 is ordinarily required to either inhibit Sick and Dredd functions or to negate their actions, and we have indicated these regulators as being downstream of Dnr1 (A). Although we have no epistatic data that separates the action of Sick and Dredd, Dredd appears to directly cleave Rel and is hence likely to be immediately upstream of Rel. Sick might function in conjunction with Dredd or as an activator of Dredd. Since dnr1 RNAi does not enhance the response to LPS, we suggest that its inhibitor activity is either repressed or bypassed upon exposure to LPS. Consequently, we have shown that treatment with LPS counteracts Dnr1-dependent Dredd inhibition (B). We do not mean to preclude other actions of LPS that might contribute to induction, but it is notable that inactivation of Dnr1 is sufficient to activate signaling. Finally, we have shown that Dnr1 levels are affected by Dredd, and we have indicated this with a positive feedback arrow. Our results are consistent with a feedback inhibitory loop where Dredd activity promotes accumulation of its own inhibitor (Figure 6); however, it is not clear under what circumstance this loop functions. Since Dnr1 inactivation did not enhance Dipt-lacZ production by LPS, we propose that Dnr1 inhibition of Dredd is suppressed or bypassed by LPS treatment and that Dnr1 is not essential for downregulation of an ongoing response. Further, as suppression of Dnr1 by RNAi is sufficient to activate immune responses, Dnr1 functions in the absence of induction and this function is required for quiescence. Thus, LPS inactivation of Dnr1 function ought to be sufficient to trigger Dredd-dependent cleavage of Relish in the Imd pathway, and it could make a significant contribution to pathway activation. In summary, a new and powerful screening approach has provided many candidate regulators of the Imd pathway of the innate immune response, and we suggest that the newly identified contributors Dnr1 and Sick will govern central steps in the regulatory cascade that activates the Relish transcription factor. While our analysis has led to a focus on these two regulators, we suspect that other genes among those isolated will also make important direct contributions to the Imd pathway. Furthermore, some of the groups of genes falling into functional clusters are likely to define physiologically relevant inputs into the induction pathway. Materials and Methods Generation of dsRNA library A library of DNA templates bearing the T7 RNA polymerase promoter at each 5′ end was prepared from genomic DNA in a two-step PCR protocol. In the first round of PCR, targeted regions of DNA were amplified using gene-specific primers (18–22 nucleotides) with a 5′ GC-rich anchor (GGGCGGGT). Primers were designed to amplify a region of nonintronic genomic DNA between 250 and 800 bp with minimal sequence overlap to all other amplimers. Templates from the first step were amplified in a second round using a universal primer containing the T7 RNA polymerase promoter sequence followed by the GC-rich anchor TAATACGACTCACTATAGGGAGACCACGGGCGGGT. dsRNA was generated from templates in in-vitro transcription reactions for 6 h at 37 °C. In vitro transcription products were annealed by heating to 65 °C and slowly cooling to room temperature. All products were tested for yield and size by gel electrophoresis, with 97% giving satisfactory results. Generation of stable cell lines Dipt-lacZ cell and copper-inducible GFP-Relish stable S2 cell lines were generated according to the Invitrogen Drosophila Expression System Protocol using hygromycin B (Invitrogen, Carlsbad, California, United States) as a selection marker. The Dipt-lacZ plasmid has been described previously (Dimarcq et al. 1997). To prepare N-terminally GFP-tagged full-length Relish, the stop codon in enhanced GFP (EGFP) was replaced with a Not site, and EGFP was cloned into pUAST as an EcoRI/NotI fragment. Full-length Relish cDNA was fused in frame to the EGFP coding sequence as a NotI/XbaI fragment, and the entire sequence was confirmed by sequencing. GFP-Relish was cloned into pMT/V5-HisB as an EcoRI/XbaI fragment to allow copper-inducible expression of GFP-Relish under control of the metallothionine promoter. Cell culture S2 cells were plated into glass-bottomed 96-well microplates (BD Biosciences Pharmingen, San Diego, California, United States) with 40,000–50,000 cells in 200 μl of Schneider's Drosophila medium (GIBCO, San Diego, California, United States) supplemented with 10% heat-inactivated fetal calf serum, penicillin, streptomycin, and hygromycin per well. dsRNA was added to each well at a final concentration of 10 μg/ml. Cells were cultured for 4 d at 25 °C and incubated an additional 24 h in 1 μM 20-hydroxyecdysone (Sigma, St. Louis, Missouri, United States). LPS (Calbiochem, San Diego, California, United States) was added at a final concentration of 50 μg/ml for 12 h. RNAi protocols were as previously described (Clemens et al. 2000). β-galactosidase assays To measure β-galactosidase in S2 cells, medium was aspirated from the wells, and cells were fixed in 0.5% glutaraldehyde in PBS for 30 s. Cells were then incubated in X-Gal staining buffer overnight at 37 °C (10 mM phosphate buffer [pH 7.2], 150 mM NaCl, 1 mM MgCl2, 3.5 mM K3Fe(CN)6, 3.5 mM K4Fe(CN)6, and 0.2% X-Gal in DMF). β-galactosidase activity assays were performed as described previously (Dimarcq et al. 1997). Microscopy, immunofluorescence, and image processing β-galactosidase induction in S2 cells was observed with a Leica (Wetzlar, Germany) IMRB microscope. Immunofluorescent images were taken on an Olympus (Tokyo, Japan) IX70 driven with DeltaVision software (Applied Precision, Issaquah, Washington, United States). S2 cells were deposited on Superfrost Plus Gold slides (Fisher Scientific, Hampton, New Hampshire, United States) for immunofluorescence. Cells were fixed for 10 min in 4% formaldehyde (Sigma). Tubulin was detected with mouse anti-α-tubulin (Sigma). DNA was visualized with Hoechst 33258, and actin was visualized with rhodamine-coupled phalloidin (both from Molecular Probes, Eugene, Oregon, United States). Images were processed with Adobe Photoshop 5.5, and figures were assembled with Adobe Illustrator 9.0. Western blotting Dnr1-expressing vectors were prepared by cloning Dnr1 cDNA into pAc5/V5HisA (Invitrogen). The C365Y mutant form of Dnr1 was prepared with the Stratagene point mutation protocol using a TTCAATCCGTACTGTCACGTC sense primer and a GACGTGACAGTACGGATTGAA antisense primer. The mutation was confirmed by sequencing. For experiments with z-VAD-FMK, S2 cells were incubated in 100 μM z-VAD-FMK for 4 h at room temperature. Cells were harvested by centrifugation at 1,000 g for 3 min, washed in PBS and lysed on ice for 10 min in lysis buffer (0.5 M HEPES [pH 7.5], 150 mM NaCl, 5 mM EDTA, 0.2% NP40, PMSF, leupeptin, pepstatin, NaF, and microcystine LR). Lysate was spun for 10 min at maximum speed, and the supernatant was added to sample loading buffer. Samples were separated by SDS-PAGE and analyzed by Western blotting. Anti-GFP antibody was purchased from BabCO (Richmond, California, United States), and HA and actin antibodies were purchased from Sigma. Table 1 Continued Table 1 Continued Table 1 Continued The dsRNA library used in this screen was produced in collaboration with Ben Eaton, Nico Stuurman, Steve Rogers, Graeme Davis, and Ron Vale at the University of California, San Francisco. Dipt-lacZ, Relish, and p35 DNA plasmids were provided by Jean-Luc Imler, Dan Hultmark, and Pascal Meijer, respectively. We are grateful to Pascale Dijkers, Ben Eaton, Arnaud Echard, Gilles Hickson, Sandy Johnson, Bruno Lemaitre, Maria Carla Saleh, and Shannon Stroschein for comments on the manuscript. This work was supported by a gift from the Sandler Family, and National Instititutes of Health Research Grant GM 60988 to PHO. Supported in part by Fellowship DRG-1713–02 from the Damon Runyon Cancer Research Foundation. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. EF and PHO'F conceived and designed the experiments. EF performed the experiments. EF and PHO'F analyzed the data and wrote the paper. Academic Editor: Michael Levine, University of California, Berkeley Abbreviations CDRiconstitutive defense by RNAi DDRidecreased defense by RNAi DiptDiptericin Dnr1Defense repressor 1 dsRNAdouble-stranded RNA EDRienhanced defense by RNAi EGFPenhanced green fluorescent protein GFPgreen fluorescent protein HAhemagluttanin IAPinhibitor of apoptosis protein ImdImmune deficiency LPSlipopolysaccharides MAPKmitogen-activated protein kinase PGRP-LCpeptidoglycan recognition protein LC RNAiRNA interference SCFSkp1/Cullin/F-box sick sickie ==== Refs References Akira S Takeda K Kaisho T Toll-like receptors: Critical proteins linking innate and acquired immunity Nat Immunol 2001 2 675 680 11477402 Anderson KV Jurgens G Nusslein-Volhard C Establishment of dorsal-ventral polarity in the Drosophila embryo: Genetic studies on the role of the Toll gene product Cell 1985 42 779 789 3931918 Bergmann A Yang AY Srivastava M Regulators of IAP function: Coming to grips with the grim reaper Curr Opin Cell Biol 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gram-negative bacterial infection EMBO Rep 2000 1 353 358 11269502 Leulier F Vidal S Saigo K Ueda R Lemaitre B Inducible expression of double-stranded RNA reveals a role for dFADD in the regulation of the antibacterial response in Drosophila adults Curr Biol 2002 12 996 1000 12123572 Li X Scuderi A Letsou A Virshup DM B56-associated protein phosphatase 2A is required for survival and protects from apoptosis in Drosophila melanogaster Mol Cell Biol 2002 22 3674 3684 11997504 Ligoxygakis P Pelte N Hoffmann JA Reichhart JM Activation of Drosophila Toll during fungal infection by a blood serine protease Science 2002 297 114 116 12098703 Lu Y Wu LP Anderson KV The antibacterial arm of the Drosophila innate immune response requires an IkappaB kinase Genes Dev 2001 15 104 110 11156609 Manfruelli P Reichhart JM Steward R Hoffmann JA Lemaitre B A mosaic analysis in Drosophila fat body cells of the control of antimicrobial peptide genes by the Rel proteins Dorsal and DIF EMBO J 1999 18 3380 3391 10369678 Medzhitov R Janeway C Innate immunity N Engl J Med 2000 343 338 344 10922424 Meng X Khanuja BS Ip YT Toll receptor-mediated Drosophila immune response requires Dif, an NF-kappaB factor Genes Dev 1999 13 792 797 10197979 Michel T Reichhart JM Hoffmann JA Royet J Drosophila Toll is activated by gram-positive bacteria through a circulating peptidoglycan recognition protein Nature 2001 414 756 759 11742401 Naitza S Rosse C Kappler C Georgel P Belvin M The Drosophila immune defense against gram-negative infection requires the death protein dFADD Immunity 2002 17 575 581 12433364 Nicolas E Reichhart JM Hoffmann JA Lemaitre B In vivo regulation of the IkappaB homologue cactus during the immune response of Drosophila J Biol Chem 1998 273 10463 10469 9553105 Ramet M Manfruelli P Pearson A Mathey-Prevot B Ezekowitz RA Functional genomic analysis of phagocytosis and identification of a Drosophila receptor for E. coli Nature 2002 416 644 648 11912489 Rosetto M Engstrom Y Baldari CT 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genes—An immune response in a Drosophila blood cell line Biochem Biophys Res Commun 1992 188 1169 1175 1445351 Schmid A Schindelholz B Zinn K Combinatorial RNAi: A method for evaluating the functions of gene families in Drosophila Trends Neurosci 2002 25 71 74 11814553 Silverman N Zhou R Stoven S Pandey N Hultmark D A Drosophila IkappaB kinase complex required for Relish cleavage and antibacterial immunity Genes Dev 2000 14 2461 2471 11018014 Silverman N Zhou R Erlich RL Hunter M Bernstein E Immune activation of NF-kappaB and JNK requires Drosophila TAK1 J Biol Chem 2003 278 48928 48934 14519762 Stoven S Ando I Kadalayil L Engstrom Y Hultmark D Activation of the Drosophila NF-kappaB factor Relish by rapid endoproteolytic cleavage EMBO Rep 2000 1 347 352 11269501 Stoven S Silverman N Junell A Hedengren-Olcott M Erturk D Caspase-mediated processing of the Drosophila NF-kappaB factor Relish Proc Natl Acad Sci U S A 2003 100 5991 5996 12732719 Tauszig S Jouanguy E Hoffmann JA Imler JL Toll-related receptors and the control of antimicrobial peptide expression in Drosophila Proc Natl Acad Sci U S A 2000 97 10520 10525 10973475 Tauszig-Delamasure S Bilak H Capovilla M Hoffmann JA Imler JL Drosophila MyD88 is required for the response to fungal and gram-positive bacterial infections Nat Immunol 2002 3 91 97 11743586 Vidal S Khush RS Leulier F Tzou P Nakamura M Mutations in the Drosophila dTAK1 gene reveal a conserved function for MAPKKKs in the control of rel/NF-kappaB-dependent innate immune responses Genes Dev 2001 15 1900 1912 11485985 Weber AN Tauszig-Delamasure S Hoffmann JA Lelievre E Gascan H Binding of the Drosophila cytokine Spatzle to Toll is direct and establishes signaling Nat Immunol 2003 4 794 800 12872120 Wu LP Anderson KV Regulated nuclear import of Rel proteins in the Drosophila immune response Nature 1998 392 93 97 9510254 Yang Y Fang S Jensen JP Weissman AM Ashwell JD Ubiquitin protein ligase activity of IAPs and their degradation in proteasomes in response to apoptotic stimuli Science 2000 288 874 877 10797013

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PLoS Biol. 2004 Aug 22; 2(8):e203

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020222SynopsisBiotechnologyIn VitroHarnessing DNA-Based Technology for Drug Discovery Synopsis7 2004 22 6 2004 22 6 2004 2 7 e222Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. DNA Display II. Genetic Manipulation of Combinatorial Chemistry Libraries for Small-Molecule Evolution DNA Display III. Solid-Phase Organic Synthesis on Unprotected DNA DNA Display I. Sequence-Encoded Routing of DNA Populations Translating DNA into Synthetic Molecules ==== Body Traditionally, developing small molecules for research or drug treatments has been a painstaking enterprise. Drugs work largely by binding to a target protein and modifying or inhibiting its activity, but discovering the rare compound that hits a particular protein is like, well, finding a needle in a haystack. With a specific protein target identified, scientists typically either gather compounds from nature or synthesize artificial compounds, then test them to see whether they act on the target. Small-molecule evolution The birth of combinatorial chemistry in the early nineties promised to revolutionize this laborious process by offering a way to synthesize trillions of compounds at a time. Though molecules still had to be evaluated one by one, high-throughput screening technology could manage up to a million molecules a day. Despite these technological advances, few drugs have emerged from combinatorial chemistry approaches, leaving the promise largely unfulfilled. Another strategy looks to nature as a model. The immune system fights disease and infection by generating billions of antibodies, each primed to recognize a specific pathogen. Antibodies recognize antigens (protein fragments of pathogens) with an exacting specificity that develops through an iterative process. The body first produces a diverse, random collection of antibodies. Every antibody is encoded with a unique DNA blueprint in a B-cell. Antibodies that happen to bind to a pathogen are “selected” to pass on their blueprints: successful binding stimulates cell division, during which blueprints are copied, varied by mutation, and used to create a new generation of antibodies. Specificity is refined over multiple generations. Over the past fifteen years, biologists have developed techniques to recreate this process in a test tube. Today, it's common practice to “evolve” collections of as many as a quadrillion different proteins or nucleic acids to bind a molecular target. These techniques are called molecular breeding, because like traditional livestock and crop breeding techniques, they combine sets of genotypes over generations to produce a desired phenotype. Molecular breeding has so far been restricted only to applications that involve materials encoded by DNA. Drugs produced by conventional synthetic organic chemistry, for example, cannot be bred. In a series of three articles in this issue of PLoS Biology, David Halpin et al. describe a strategy that addresses this limitation. By inventing a genetic code that acts as a blueprint for synthetic molecules, the authors show how chemical collections of nonbiological origin can be evolved. In the first article, Halpin et al. present a method for overcoming the technical challenge of using DNA to direct the chemical assembly of molecules. In the second, they demonstrate how the method works and test its efficacy by creating a synthetic library of peptides (protein fragments) and then showing that they can find the “peptide in a haystack” by identifying a molecule known to bind a particular antibody. The third paper shows how the method can support a variety of chemistry applications that could potentially synthesize all sorts of nonbiological “species.” Such compounds, the authors point out, can be used for drug discovery or as molecular tools that offer researchers novel ways to disrupt cellular processes and open new windows into cell biology. While medicine has long had to cope with the evolution of drug-resistant pathogens, it may now be possible to fight fire with fire. To learn more about the DNA display method described here, see the primer “Translating DNA into Synthetic Molecules,” also in this issue.

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PLoS Biol. 2004 Jul 22; 2(7):e222

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10.1371/journal.pbio.0020222

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020230Research ArticleEvolutionImmunologyInfectious DiseasesMicrobiologyZoologyPlasmodiumMus (Mouse)Immunity Promotes Virulence Evolution in a Malaria Model Immunity Promotes Virulence EvolutionMackinnon Margaret J [email protected] 1 Read Andrew F 1 1School of Biological Sciences, University of EdinburghEdinburghUnited Kingdom9 2004 22 6 2004 22 6 2004 2 9 e23028 7 2003 25 5 2004 Copyright: © 2004 Mackinnon and Read.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Host Immunity Escalates the Evolution of Parasite Virulence Evolutionary models predict that host immunity will shape the evolution of parasite virulence. While some assumptions of these models have been tested, the actual evolutionary outcome of immune selection on virulence has not. Using the mouse malaria model, Plasmodium chabaudi, we experimentally tested whether immune pressure promotes the evolution of more virulent pathogens by evolving parasite lines in immunized and nonimmunized (“naïve”) mice using serial passage. We found that parasite lines evolved in immunized mice became more virulent to both naïve and immune mice than lines evolved in naïve mice. When these evolved lines were transmitted through mosquitoes, there was a general reduction in virulence across all lines. However, the immune-selected lines remained more virulent to naïve mice than the naïve-selected lines, though not to immunized mice. Thus, immune selection accelerated the rate of virulence evolution, rendering parasites more dangerous to naïve hosts. These results argue for further consideration of the evolutionary consequences for pathogen virulence of vaccination. Immunisation against malaria can promote the evolution of more virulent pathogens in mice, making non-immunised mice more vulnerable -- a result with important implications for the development of malaria vaccines ==== Body Introduction Genetic variation in pathogen virulence (harm to the host) has been found whenever it has been looked for. A considerable body of theory, based on the transmission consequences of virulence, has been developed to predict how natural selection will act on this genetic variation and how it will shape virulence levels in natural populations of disease-causing organisms (Frank 1996; Dieckmann et al. 2002). For instance, natural or vaccine-acquired host immunity protects hosts from dying, thereby relieving the parasite of the potential fitness costs of prematurely shortened infections. Thus, host populations with high levels of immunity can maintain more virulent pathogens than can naïve host populations (Gandon et al. 2001). To date, the best example of virulence evolving upwards in response to enhanced levels of host defense comes from an uncontrolled “experiment” in the field: upon release into a highly susceptible host population, the myxomatosis virus evolved lower virulence (Fenner and Ratcliffe 1965) but then later increased in virulence once the host population had evolved resistance (Best and Kerr 2000). As well as altering between-host selection pressures on virulence, host immunity can alter the nature of inhost selection. Different directions of virulence evolution are expected depending on the details of inhost competition among parasites (e.g., Nowak and May 1994; Van Baalen and Sabelis 1995; Chao et al. 2000; Brown et al. 2002). Unfortunately, these details are not well understood for any pathogen (Read and Taylor 2001). The only generality is that serial passage of pathogens almost always increases virulence (Ebert 1998), implying that virulent variants have a fitness advantage within hosts. However, all serial passage experiments of which we are aware were conducted in immunologically naïve hosts, so the effects of immunity on virulence evolution are unknown. In theory, immunity could impose selection in several ways. For instance, lower parasite loads should reduce resource competition (e.g., for red blood cells) among parasites occupying the same host, but increase the competition for enemy-free space (e.g., by immune evasion). This could lead to more aggressive parasites racing to stay ahead of proliferating immune responses (Antia et al. 1994); it could also lead to the evolution of novel antigenic variants that have a selective advantage only in immunized hosts. Immunization will also alter the timing of immune selection, thus potentially selecting for changes in parasite life history parameters that affect virulence, such as an earlier or higher rate of production of transmission stages (Koella and Antia 1995). Finally, the rate at which virulence evolution occurs may be limited by the size of the parasite population inside the host, and therefore may be retarded by host immunity. Thus, at least in theory, there are many potential consequences for virulence evolution of prior host immunity, both long-term and short-term in nature. One barrier to testing theoretical models of virulence evolution is that the models typically predict the outcome at evolutionary and epidemiological equilibrium. New equi-libria may or may not take a long time to reach, but will in any case depend on the dynamics of the host population and the environmental conditions under which transmission occurs: this means that experimental evolution to new equilibria will be hard to study in the laboratory for medically relevant pathogens. However, the short-term consequences for virulence evolution, which are at least as important to public health policy as the long-term consequences, may be more tractable. This is especially true for diseases for which animal models are available. In this study, we begin the empirical effort to determine the likely direction of immune-mediated virulence evolution by performing experimental evolution of the rodent malaria parasite, Plasmodium chabaudi, in laboratory mice. We evolved multiple lines of P. chabaudi in immunized and naïve mice by repeated serial passage of blood-stage parasites (i.e., bypassing the normally obligate mosquito vector) starting from two different starting populations. After 20 passages, the lines had evolved sufficiently to make comparisons between the immune-selected lines (I-lines) and naïve-selected lines (N-lines) for virulence and transmissibility. Results/Discussion We found that both the I-lines and N-lines evolved to become more virulent than their ancestral populations, but the I-lines became even more virulent than the N-lines (Figure 1A). This higher virulence was manifest in both naïve and immunized mice. When the lines were transmitted through mosquitoes, there was generally a reduction in virulence across all the lines, but the I-lines remained more virulent than the N-lines to naïve mice, though not to immunized mice (Figure 1B). We discuss these two principal findings separately below. Figure 1 Virulence Evolution in Mouse Malaria during Serial Passage in Immunized Versus Naïve Mice Virulence was measured by minimum red blood cell density (y-axis) in lines of P. chabaudi before (“ancestral lines;”black and gray symbols) and after serial passage through immunized (“I-lines;” red lines and triangles) or naïve (“N-lines,” green lines and circles) mice before (A) and after (B) mosquito transmission. Evolved and ancestral lines were compared in both naïve (solid lines) and immunized mice (broken lines). Filled symbols, before mosquito transmission; open symbols, after mosquito transmission. Lines were selected from an avirulent, “unadapted” clone (CW-0; left set of lines) and a virulent, “preadapted” ancestral population (CW-A; right): the latter was derived from the former by 12 serial passages in a previous experiment (Mackinnon and Read 1999b). Each symbol (with ± 1 standard error based on the variance between subline means) represents the mean of mice infected with an ancestral line or a set of passaged lines (i.e., five sublines, two mice per subline). Prior to mosquito transmission (A), differences between the I-lines and N-lines were significant in three out of the four cases (p < 0.05 for lines from the unadapted line infecting naïve mice, p < 0.01 for unadapted infecting immunized, and p < 0.001 for preadapted infecting immunized): in the fourth case (p > 0.1 for preadapted infecting naïve), virulence of the ancestral line was already apparently near-maximal. After mosquito transmission (B), the differences between the I-lines and N-lines remained the same in naïve mice as before transmission (interaction between the mosquito transmission effect and the I-line-versus-N-line difference was p > 0.7 in both the unadapted and preadapted cases). However, these line differences were eliminated in immunized mice (interaction term: p = 0.02 for the unadapted case, p = 0.08 for the preadapted case). Mosquito transmission significantly reduced the virulence of the preadapted ancestral line in immunized mice (p = 0.03) but not in the other ancestral-line-by-immune-treatment combinations (p > 0.2 in these cases). In the selection lines, mosquito transmission significantly reduced the virulence in five out of the eight comparisons (p = 0.009 and p = 0.13 in the N-lines in naïve mice derived from CW-0 and CW-A, respectively, with values of p = 0.55 and p = 0.005 in N-lines in immunized mice, p = 0.022 and p = 0.26 in I-lines in naïve mice, and p = 0.006 and p < 0.0001 in I-lines in immunized mice). Ancestral pretransmission lines had similar levels of virulence in the separate pretransmission and posttransmission experiments, with the exception of the preadapted ancestral line in immunized mice, which had higher virulence in the latter than the former (p = 0.002). Similar results to the above were obtained when virulence was measured by maximum weight loss (unpublished data). No deaths occurred during the pretransmission experiments, but in addition to the one death that occurred early in the infection prior to the occurrence of any weight loss or anemia (excluded from analyses), five occurred in the posttransmission experiment, four of these in naïve mice (two in the N-lines, one in the I-lines, and one in the nontransmitted ancestral line, all derived from the preadapted line) and one in an immunized mouse (preadapted, nontransmitted ancestral line). Immunity Selects for Higher Virulence The results suggest that immune selection on blood-stage parasites is more efficient at selecting virulent variants than is selection in naïve mice. Response to selection is a function of the amount of variation in the population and the proportion of the population that survives to produce offspring, i.e., the selection intensity. The higher selection response in the I-lines is unlikely to be due to greater variation on which selection could act because the parasite population size on the day of transfer in immunized mice was on average 2-fold smaller than in naïve mice (Figure 2). It is also unlikely to be due to lower host death in the I-lines as there were no line differences in mortality in naïve mice over the entire course of the experiment (10/223 naïve mice infected with N-lines versus 2/40 naïve mice infected with I-lines, p > 0.10 by 2-tailed Fisher's Exact test, zero mortality in immunized mice), and all but one of the deaths occurred after the day of transfer. The most likely explanation is that immunity generated more intense selection by killing a greater proportion of the parasite population up until the point of transfer (Figure 2). Winners of the race into the syringe on day 7 were those parasite variants that survived immune selection, and these parasites proceeded to cause more damage to their host later in the infection. Figure 2 Effect of Immunization on Asexual Parasitemia and Gametocytemia Each curve represents the mean asexual parasitemia (dark blue) and gametocytemia (light blue) over all parasite lines (ancestral and selected) in naïve (solid lines; n = 47) and immunized mice (broken lines; n = 50) during the pretransmission evaluation phase. Immunization reduced asexual parasitemia and gametocytemia throughout the infection (p < 0.001 based on the log10 daily average taken over all days). The arrow indicates the day of transfer during the selection phase of the experiment. But why would selection favor more virulent parasites? Our previous studies have consistently shown that peak parasite densities in the acute phase are positively correlated to the level of virulence that they generate (Mackinnon and Read 1999a, 1999b, 2003; Mackinnon et al. 2002; Ferguson et al. 2004). We therefore expected to find that the higher virulence in I-lines was accompanied by higher parasite densities, in which case we would deduce that immune selection had favored variants that were better able to outgrow immune defenses. While we found positive relationships between asexual multiplication and virulence across all the lines including the ancestral ones (Figure 3A), the I-lines and N-lines were statistically indistinguishable (p > 0.05) for (i) parasitemia on day 4, (ii) parasitemia on day 6 or 7, (iii) the increase in parasitemia from day 4 to day 6 or 7, and (iv) maximum parasitemia, with one exception: maximum parasitemia was significantly higher in I-lines than N-lines derived from unadapted ancestors when measured in immunized mice, and this only in one of the two replicate experiments (23% versus 6.9% parasitemia, p < 0.001). Thus, there is little evidence to suggest that the increased virulence was due to a higher asexual multiplication rate (or a lower death rate of asexuals) in those parasites that successfully made it into the syringe. Our data demonstrate that immunity acts as a powerful and upward inhost selective force on virulence, but the precise mechanism awaits further study. Figure 3 Relationships between Virulence, Asexual Multiplication, and Transmission Potential across Ancestral and Selection Lines Virulence, as measured by minimum red blood cell density, is plotted against maximum parasitemia in (A), and average daily gametocyte production (a measure of lifetime transmission potential) is plotted against virulence in (B). Data are all from pretransmission lines—ancestral and selected—measured in naïve (closed symbols; solid line) and immunized mice (open symbols; broken line). Regression analysis for both traits showed significant (p < 0.001) and similar (p > 0.05) slopes within both naïve and immunized mice, and significantly lower (p < 0.001) maximum parasitemia and gametocyte production in immunized than in naïve mice. When the two data points from naïve mice with values of above 3 × 109 rbc/ml were excluded from the analyses, the slopes remained statistically similar (p > 0.05). Unselected ancestral populations, black squares; N-lines, green circles; I-lines, red triangles; avirulent unadapted ancestral population, small symbols; virulent preadapted ancestral population, large symbols. There were positive relationships between virulence and lifetime transmission potential across all the lines (Figure 3B), consistent with our previous studies (reviewed in Mackinnon and Read 2004), but the differences between the I-lines and N-lines were not statistically significant (p > 0.05). Gametocyte densities are a good predictor of transmission probability in P. chabaudi and other Plasmodium species (Mackinnon and Read 2004), so these results demonstrate that the more virulent parasites evolved in semi-immune mice would transmit as successfully as the less virulent parasites evolved in naïve hosts. Thus, in the absence of a cost, virulent variants favored by within-host immune selection are expected to spread throughout an immunized host population. The Effects of Mosquito Transmission Malaria parasites, like many microbes (Ebert 1998), are remarkable in their ability to rapidly adapt to changes in their host environment, and some of this is known to be due to phenotypic switching mechanisms in virulence-related phenotypes such as binding to host cells (Barnwell et al. 1983), red cell surface antigen expression (Brown and Brown 1965; Barnwell et al. 1983; David et al. 1983; Handunetti et al. 1987; Gilks et al. 1990), and red cell invasion pathways (Dolan et al. 1990). Some of these phenotype-based changes are transient, while others appear to be stable, i.e., maintained over sequential blood-stage passages. In our experiment, it is possible that the increases in virulence we observed following serial passage were at least partly due to altered gene expression rather than changes at the genome level. The public health consequences of this sort of change depend on whether the higher virulence is maintained during mosquito transmission, and upon transfer to hosts with different levels of immunity from those in which selection took place. We found that the I-lines were more virulent than the N-lines in both naïve and immunized hosts (see Figure 1A). However, after mosquito transmission, the I-lines remained more virulent than the N-lines, only in naïve hosts: the difference in immune hosts was negated by mosquito transmission (see Figure 1B). Possible reasons for this are discussed further below. For now, we note that the data are consistent with (though do not directly test) the prediction (Gandon et al. 2001) that enhancement of host immunity by anti-blood-stage vaccination will render malaria populations more dangerous to naïve hosts, at least in the short- to medium-term. Whether or not our long-term prediction (Gandon et al. 2001) that immunized populations will drive virulence to a higher level at evolutionary equilibrium proves true can be established only by monitoring vaccine-covered parasite populations in the field. We observed a general reduction in virulence across all lines following mosquito transmission (see Figure 1), particularly when measured in immunized mice, and particularly in lines that had been selected under immune pressure, i.e., the I-lines, and in the CW-A ancestral line, which had been serially passaged on day 12 postinfection (PI). Many laboratory studies in malaria have shown that high or low virulence phenotypes accrued through serial passage can be maintained upon transmission through mosquitoes (James et al. 1936; Coatney et al. 1961; Alger et al. 1971; Walliker et al. 1976; Knowles and Walliker 1980; Walliker 1981; Barnwell et al. 1983), although occasional major losses (or gains) of virulence do occur (Alger et al. 1971; Walliker et al 1976; Knowles and Walliker 1980; Gilks et al. 1990). Mosquito transmission could play a significant role in virulence evolution that is driven by inhost selective processes (as distinct from the between-host selective processes underlying the vaccination hypothesis in Gandon et al. [2001]). The mechanistic basis for the reduction in virulence following mosquito transmission remains to be determined. We offer the following speculations. It may be that the virulence reductions we and others have observed are due to stochastic loss of virulent variants during the population bottlenecking that occurs during mosquito transmission (the variability between lines in virulence loss during mosquito transmission favors this hypothesis). Alternatively, virulence reduction may be due to the deterministic forces of selection against virulent variants that have lost or reduced the ability to transmit through mosquitoes (Ebert 1998): the potential trade-off between virulence in the vertebrate host and production and infectivity of sporozoites in the mosquito has not yet been explored. A further possibility is that the virulence reductions observed following mosquito transmission are due to the systematic resetting during meiosis of the expression of genes that have been switched on or up-regulated during asexual serial passage. For example, it is known that mosquito transmission induces the expression of a different set of the clonally variant (i.e., phenotypically switching) surface antigens from those expressed at the time of ingestion by the mosquito (McLean et al. 1987; Peters et al. 2002). It is possible that the variants that appear early in the infection, either because of some genetically programmed ordering of expression or because of higher intrinsic switching rates, are recognized by the immune system in a preimmunized host, thus giving the late-appearing variants a selective advantage. Our data are consistent with this idea, since mosquito transmission eliminated the difference between the I-lines and N-lines in immunized mice but not in naïve mice, suggesting that part of the virulence advantage in immunized hosts was due to novelty in the clonally variant surface antigens. Finally, an interesting possibility is that it is loss of diversity per se during mosquito transmission (either at the genetic level or at the phenotypic expression level) that causes a reduction in virulence by limiting the invading parasites' ability to evade immune defenses: our data are also consistent with this hypothesis. Any of these mechanisms could explain the loss of virulence during mosquito transmission, but none are sufficient to explain why the I-lines were more virulent than the N-lines in naïve mice both before and after mosquito transmission. Thus, more than one distinct underlying mechanism probably explains the virulence differences observed here, such as differences in intrinsic virulence properties and differences in levels of antigenic diversity within the lines. Identifying the mechanisms, any links between them, and their relative roles in determining parasite survival in naïve versus immunized hosts are of key importance in understanding virulence evolution and immunoepidemiology of malaria in the field. Other Serial Passage Studies in Malaria To what extent do our observations accord with previous work on serial passage of malaria in immune-modified environments? Results from other studies are difficult to interpret as none maintained control lines for selection (i.e., lines that were passaged in the nonmanipulated immune environment), most had no replication of lines within selection treatment, and some used just a single selection step. Nevertheless, some tentative conclusions may be drawn. Comparisons of selected and ancestral parasites have been made after three different forms of immune manipulation: (i) down-regulation of immunity by removal of the spleen prior to infection, (ii) up-regulation of immunity by transfer of immune serum at the beginning of infection, and (iii) up-regulation of immunity by infection, sometimes with subcurative drug treatment in order to establish a chronic infection. In the first two, parasites were selected from the primary wave of parasitemia, as in our experiment, whereas in the third, selected parasites were isolated from relapses much later in the infection (40–150 d PI). Parasite lines passaged through splenectomized hosts often lose the ability to bind to host endothelial cells (cytoadherence) in the microvasculature of the deep tissues and therefore the ability to avoid being passaged through the spleen (Garnham 1970), the primary site of immune-mediated clearance (Wyler 1983). This loss of binding is often accompanied by a loss of ability to express (Barnwell et al. 1983; Handunetti et al. 1987; Gilks et al. 1990)—or a major alteration in the level of expression of (David et al. 1983; Fandeur et al. 1995)—the highly variable and clonally variant switching parasite antigens on the surface of the red cell known to be important for the maintenance of long-term chronic infections (Brown and Brown 1965). In P. falciparum at least (David et al. 1983; Hommel et al. 1983), this coincident change in the two properties is because both phenotypes are mediated by the same parasite molecule, denoted PfEMP1 (Baruch et al. 1995; Smith et al. 1995; Su et al. 1995). Importantly, in two of three studies, the line of parasites that lost cytoadherence and/or surface antigen expression had much-reduced virulence to spleen-intact naïve hosts compared to their ancestral lines (Barnwell et al. 1983; Langreth and Peterson 1985; Gilks et al. 1990). If our immunization procedure was priming the spleen for effective parasite clearance, our results are consistent with these findings. However, the second form of immune selection—passage of acute-phase parasites from hosts injected with antiserum at the beginning of the infection—yielded parasites with lower virulence to naïve mice than their ancestors in one study (Wellde and Diggs 1978), although it had no impact on virulence in two other studies (see Briggs and Wellde 1969). The third type of immune selection—isolation of parasites from relapses late in the infection—has generated parasites with virulence to naïve hosts that is lower than (Cox 1962), higher than (Sergent and Poncet 1955), or similar to (Cox 1959) that of their ancestors. In all these studies, which involved only single passages, selected parasites were more virulent than their ancestors to immunized hosts, suggesting that the selected parasites were predominantly of a novel antigenic type (a fact that has sometimes been demonstrated; Voller and Rossan 1969). Whether antigenic novelty is traded off against multiplication rate or virulence among the repertoire of variants expressed during a single infection—as has also been suggested from field population studies (Bull et al. 1999)—is an interesting question that deserves more attention. However, in our study, in which we focused on the longer-term and more natural environment of hosts preimmunized with a heterogeneous parasite population, the higher virulence of the I-lines compared to the N-lines in both naïve and immunized mice leads us to deduce that selection associated with virulence overrides selection for immune evasion alone. Conclusion Our data demonstrate that host immunity can increase the potency of inhost selection for higher virulence in malaria. Whether our results generalize to other immunization protocols, parasite clones, parasite species, host genotypes, repeated mosquito passage, and so on requires extensive further experimentation. But, coupled with the malaria parasite's famous ability to rapidly adapt to novel conditions in the laboratory (see above) and to variant-specific vaccine pressure (Genton et al. 2002) and drugs (Peters 1987) in the field, these results urge the continuous monitoring of virulence of parasite populations if asexual-stage malaria vaccines become widely used. And for other microparasites (bacteria, viruses, and protozoa) that rely on rapid multiplication within the host for successful transmission, similar concerns might apply. Materials and Methods Selection phase. Starting from two separate ancestral lines derived from clone CW (see below), five parasite lines (“sublines”) from each ancestral line were repeatedly passaged in mice (female C57Bl/6J, 7–10 wk old) that were naïve to malaria infection (N-lines), and five from each ancestral line were passaged in immunized mice (I-lines, see below), forming 20 lines (“sublines”) in total. Passages involved the syringe transfer to a fresh mouse of 0.1 ml of diluted blood containing 5 × 105 parasites from a donor mouse that had been infected 7 d previously. Day 7 PI is during the period of rapid population growth, and is about 2 d prior to peak parasitemia, after which population size rapidly declines (see Figure 2). Parasite lines under the same selection regime (i.e., passage in immune versus naïve mice) were not mixed at each transfer, thus yielding five independent replicate sublines in each of the four selection treatment–ancestral line groups. Immunization was by infection with 104 parasites of a different clone (denoted ER), followed by drug cure with 10 mg/kg of mefloquine for 4 d starting on day 5 PI. Naïve mice were injected with parasite-free media but were not drug treated. Re-infection took place on average 3 wk after the end of drug treatment (range 1.5–5 wk): as the half-life of mefloquine in mice is reported to be 18 h (Peters 1987), the residual amount in the blood by this stage was expected to be very low. The same deep-frozen stock of ER was used each generation. ER is genetically distinct from CW at marker loci (data not shown) and was originally isolated from different hosts. Before use in this experiment, ER had undergone two passages since mosquito transmission and more than 20 passages prior to that. No recrudescent infections in immunized mice were detected prior to challenge. In generations 10 and 11, all lines were passaged through naïve mice. The serial passage experiments in this study were replicated using two different starting populations (ancestral lines)—one avirulent (CW-0) and one virulent (CW-A). CW-0 had been cloned by serial dilution from an isolate obtained from its natural host, the thicket rat, Thamnomys rutilans, and then blood passaged every 12 d for a total of 12 passages to produce the CW-A line. During these passages, CW-A was subjected to selection for low virulence on the basis of how much weight loss it caused to mice. Despite this selection, however, CW-A increased in virulence relative to CW-0 during these passages (Mackinnon and Read 1999b). Prior to use in the current experiments, both CW-0 and CW-A underwent four further serial passages in naïve mice, and were not recloned. All the lines, including the ancestral lines, were transmitted once through Anopheles stephensi mosquitoes by allowing 50–100 mosquitoes aged 2–5 d to take a blood meal for 20–30 min on an anaesthetized gametocytemic mouse that had been inoculated 6–10 d previously, i.e., prior to the peak of infection. Then, 11–12 d later these mosquitoes—typically 10–20 of them infected as assessed by random surveys of oocyst prevalence—were allowed to feed back onto anaesthetized naïve mice. After 7–10 d, the blood from these sporozoite-infected mice was harvested and stored in liquid nitrogen. These aliquots were used to initiate blood infections in naïve mice that were then used as donors of asexual parasites to mice involved in the posttransmission experiments. As the lines were transmitted through mosquitoes noncontemporaneously, and involved typically one mouse per subline, comparisons among the lines for infectivity to mosquitoes were not made during these transmission exercises. Evaluation phase. After 18 passages, the pretransmission lines were evaluated in two replicate experimental blocks in naïve (generations 19 and 21) and immunized mice (generations 20 and 22). Ancestral lines were only evaluated in generations 21 and 22. This set of trials was denoted the “pretransmission experiments.” In a separate set of experiments, the “posttransmission experiments,” the mosquito-transmitted lines were compared with each other, as well as with the nontransmitted ancestral lines in two replicate experimental blocks in both naïve (generations 23 and 24) and immunized mice (generations 25 and 26). In both these experiments, across both blocks, ten mice were used for each of the four selection groups (two per subline), and five mice were used per ancestral line. Red blood cell density was measured every 1 or 2 d until day 18 PI by flow cytometry (Coulter Electronics, Luton, United Kingdom), and the minimum density reached was taken as a measure of virulence. Liveweight of the mouse was also recorded every 1–2 d. During the pretransmission experiments (generations 19–22), parasitemia and gametocytemia (proportions of red blood cells infected with asexual parasites and gametocytes, respectively) were evaluated from Giemsa-stained thin blood smears every 2 d from day 4 PI until day 18 PI, and then four more times until day 43 PI. Total lifetime transmission potential was measured as the average gametocytemia throughout the infection from day 4 to day 18 PI. Analysis. Statistical analyses were performed separately for the pretransmission and posttransmission experiments as these were carried out at different times. The virulence measure used for the final analysis was minimum red blood cell density, though other measures of virulence were also analyzed (unpublished data). Since selection treatment was replicated on sublines, thus making subline the independent experimental unit, the means of mice within sublines were first calculated. These were then analyzed for the effects of immune environment on selection response by fitting a linear model to these data with factors for selection line (with three levels for nontransmitted ancestral lines, N-lines, and I-lines in the case of the pretransmission experiments, and four levels for the transmitted versions of these three lines plus the nontransmitted ancestral lines in the case of the posttransmission experiments), ancestral population (CW-0, CW-A), and an interaction between these two factors. Thus, statistical tests of differences between the selection lines and other factors in the model were made using t-tests, with the variance for subline means as the residual. An alternative model fitted to data on individual mice (rather than means of sublines) that incorporated subline as a random effect was found to be unsatisfactory because in some treatment groups, the model did not converge and estimates of the subline variance were highly variable between groups. To determine the effects of mosquito transmission on the line differences in virulence, a further analysis was performed on the combined data from the pretransmission and posttransmission experiments fitting a fixed effect factor of line-within-experiment in the statistical model (seven levels—three lines for the pretransmission experiment and four for the posttransmission experiment). These analyses were carried out separately for each of the four immune-treatment-by-ancestral-line groups. Since the pretransmission ancestral line was included in both the pretransmission and posttransmission experiments, the effect of mosquito transmission (and its standard error) on the N-lines and I-lines, which was not measured directly (i.e., in a single experiment), could be estimated by reference to this line. For example, the effect of mosquito transmission in the N-lines was estimated from the difference between the N-lines and their pretransmission ancestral line in the pretransmission experiment minus the analogous contrast in the posttransmission experiment. This was done using the method of linear contrasts provided for in the SAS GLM procedure (SAS 1990). The effect of mosquito transmission on the difference between the I-lines and N-lines was similarly calculated but without reference to the pretransmission ancestral line. The effect of mosquito transmission on the ancestral lines was estimated from the direct comparison available from only the posttransmission experiment data. This work was supported by the Leverhulme Trust, the University of Edinburgh, the Royal Society, and the Wellcome Trust. We thank J. Allen, H. Ferguson, S. Gandon, A. Graham, S. Nee, A. Rowe, A. Saul, and two anonymous reviewers for useful comments on the manuscript. B. Chan and K. Watt are thanked for technical assistance. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. MJM and AFR jointly contributed to all aspects of this work. Academic Editor: Bryan Grenfell, University of Cambridge Abbreviations I-lineimmune-selected line N-linenaïve-selected line ==== Refs References Alger NE Branton M Harant J Silverman PH Plasmodium berghei NK65 in the inbred A/J mouse: Variations in virulence in P. berghei demes J Protozool 1971 18 598 601 5133123 Antia R Levin BR May RM Within-host population dynamics and the evolution and maintenance of microparasite virulence Am Nat 1994 144 457 472 Barnwell JW Howard RJ Miller LH Evered D Whelan J Influence of the spleen on the expression of surface antigens on parasitized erythrocytes CIBA Foundation symposium on malaria and the red cell 1983 London Pitman 117 136 Baruch DI Pasloske BL Singh HB Bi X Ma XC Cloning the P. falciparum gene encoding pfEMP1, a malarial variant antigen and adherence receptor on the surface of parasitized human erythrocytes Cell 1995 82 77 87 7541722 Best SM Kerr PJ Coevolution of host and virus: The pathogenesis of virulent and attenuated strains of myxoma virus in resistant and susceptible European rabbits Virology 2000 267 36 48 10648181 Briggs NT Wellde BT Some characteristics of Plasmodium berghei “relapsing” in immunized mice Mil Med 1969 134 1243 1248 4987043 Brown KN Brown IN Immunity to malaria: Antigenic variation in chronic infections of Plasmodium knowlesi Nature 1965 208 1286 1288 4958335 Brown SP Hochberg ME Grenfell BT Does multiple infection select for raised virulence? Trends Microbiol 2002 10 401 405 12217504 Bull PC Lowe BS Kortok M Marsh K Antibody recognition of Plasmodium falciparum erythrocyte surface antigens in Kenya: Evidence for rare and prevalent variants Infect Immun 1999 67 733 739 9916084 Chao L Hanley KA Burch CL Dahlberg C Turner PE Kin selection and parasite evolution: Higher and lower virulence with hard and soft selection Q Rev Biol 2000 75 261 275 11008699 Coatney GR Elder HA Contacos PG Getz ME Greenland R Transmission of the M strain of Plasmodium cynomolgi to man Am J Trop Med Hyg 1961 10 673 678 13694174 Cox HW A study of the relapse Plasmodium berghei infections isolated from white mice J Immunol 1959 82 209 214 13631252 Cox HW The behavior of Plasmodium berghei strains isolated from relapsed infections of white mice J Protozool 1962 9 114 118 David PH Hommel M Miller LH Udeinya IJ Oligino LD Parasite sequestration in Plasmodium falciparum malaria: Spleen and antibody modulation of cytoadherence of infected erythrocytes Proc Natl Acad Sci U S A 1983 80 5075 5079 6348780 Dieckmann U Metz H Sabelis MW Sigmund K Virulence management: The adaptive dynamics of pathogen-host interactions 2002 Cambridge University Press Cambridge 532 Dolan SA Miller LH Wellems TE Evidence for a switching mechanism in the invasion of erythrocytes by Plasmodium falciparum J Clin Invest 1990 86 618 624 2200806 Ebert D Experimental evolution of parasites Science 1998 282 1432 1435 9822369 Fandeur T Le Scanf C Bonnemains B Slomianny C Mercereau-Puijalon O Immune pressure selects for Plasmodium falciparum parasites presenting distinct red blood cell surface antigens and inducing strain-specific protection in Saimiri sciureus monkeys J Exp Med 1995 181 283 295 7807008 Fenner F Ratcliffe RN Myxomatosis 1965 Cambridge University Press Cambridge 394 Ferguson HM Mackinnon MJ Chan BHK Read AF Mosquito mortality and the evolution of malaria virulence Evolution 2004 57 2792 2804 Frank SA Models of parasite virulence Q Rev Biol 1996 71 37 78 8919665 Gandon S Mackinnon MJ Nee S Read AF Imperfect vaccines and the evolution of parasite virulence Nature 2001 414 751 755 11742400 Garnham PCC The role of the spleen in protozoal infections with special reference to splenectomy Acta Trop 1970 27 1 13 4393028 Genton B Betuela I Felger I Al-Yaman F Anders RF A recombinant blood-stage malaria vaccine reduces Plasmodium falciparum density and exerts selective pressure on parasite populations in a phase 1–2b trial in Papua New Guinea J Infect Dis 2002 185 820 827 11920300 Gilks CF Walliker D Newbold CI Relationships between sequestration, antigenic variation and chronic parasitism in Plasmodium chabaudi chabaudi A rodent malaria model Parasite Immunol 1990 12 45 64 2314922 Handunetti SM Mendis KN David PH Antigenic variation of cloned Plasmodium fragile in its natural host Macaca sinica J Exp Med 1987 165 1269 1283 3553414 Hommel M David PH Oligino LD Surface alterations of erythrocytes in Plasmodium falciparum malaria: Antigenic variation, antigenic diversity and the role of the spleen J Exp Med 1983 157 1137 1148 6187885 James SP Nicol WD Shute PG Clinical and parasitological observations on induced malaria Proc R Soc Med 1936 29 879 894 19990731 Knowles G Walliker D Variable expression of virulence in the rodent malaria parasite Plasmodium yoelii yoelii Parasitology 1980 81 211 219 7422362 Koella JC Antia R Optimal pattern of replication and transmission for parasites with two stages in their life-cycle Theor Pop Biol 1995 47 277 291 Langreth SG Peterson E Pathogenicity, stability and immunogenicity of a knobless clone of Plasmodium falciparum in Colombian owl monkeys Infect Immun 1985 47 760 766 3882566 Mackinnon MJ Read AF Genetic relationships between parasite virulence and transmission in the rodent malaria Plasmodium chabaudi Evolution 1999a 53 689 703 Mackinnon MJ Read AF Selection for high and low virulence in the malaria parasite Plasmodium chabaudi Proc R Soc Lond B Biol Sci 1999b 266 741 748 Mackinnon MJ Read AF Effects of immunity on relationships between growth rate, virulence and transmission in semi-immune hosts Parasitology 2003 126 103 112 12636347 Mackinnon MJ Read AF Virulence in malaria: An evolutionary viewpoint Philos Trans R Soc Lond B Biol Sci 2004 359 965 986 15306410 Mackinnon MJ Gaffney DJ Read AF Virulence in malaria parasites: Host genotype by parasite genotype interactions Infect Genet Evol 2002 1 287 296 12798007 McLean SA Phillips RS Pearson CD Walliker D The effect of mosquito transmission of antigenic variants of Plasmodium chabaudi Parasitology 1987 94 443 449 3614987 Nowak MA May RM Superinfection and the evolution of parasite virulence Proc R Soc Lond B Biol Sci 1994 255 81 89 Peters W Chemotherapy and drug resistance in malaria 1987 Volume 1 Academic Press London 542 Peters J Fowler E Gatton M Chen N Saul A High diversity and rapid changeover of expressed var genes during the acute phase of Plasmodium falciparum infections in human volunteers Proc Natl Acad Sci U S A 2002 99 10689 10694 12142467 Read AF Taylor LH The ecology of genetically diverse infections Science 2001 292 1099 1102 11352063 [SAS] SAS Institute SAS/STAT user's guide, version 6.0 1990 Volume 2 Cary (North Carolina) SAS Institute 795 Sergent E Poncet A Étude expérimentale du paludisme des rongeurs à Plasmodium berghei . II. Stade d'infection latente métacritique Arch Inst Pasteur Alger 1955 33 195 222 13283613 Smith JD Chitnis CE Craig AG Roberts DJ Hudson-Taylor DE Switches in expression of Plasmodium falciparum var genes correlate with changes in antigenic and cytoadherent phenotypes of infected erythrocytes Cell 1995 82 101 110 7606775 Su X Heatwole VM Wertheimer SP Guinet F Herrfeldt JA The large diverse gene family var encodes proteins involved in cytoadherence and antigenic variation of Plasmodium falciparum– infected erythrocytes Cell 1995 82 89 100 7606788 Van Baalen M Sabelis MW The dynamics of multiple infection and the evolution of virulence Am Nat 1995 146 881 910 Voller A Rossan RN Immunological studies with simian malarias. I. Antigenic variants of Plasmodium cynomolgi bastianellii Trans R Soc Trop Med Hyg 1969 63 46 56 4978046 Walliker D Canning EU The genetics of virulence in Plasmodium yoelii Parasitological topics 1981 Lawrence (Kansas) Society of Protozoologists 260 265 Walliker D Sanderson A Yoeli M Harant J Hargreaves B A genetic investigation of virulence in a rodent malaria parasite Parasitology 1976 72 183 194 1264490 Wellde BT Diggs CL Plasmodium berghei : Biological variation in immune serum–treated mice Exp Parasitol 1978 44 197 201 350601 Wyler DJ Evered D Whelan J The spleen in malaria Ciba Foundation symposium on malaria and the red cell 1983 London Pitman 98 116

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PMC434153

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2021-01-05 08:21:11

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PLoS Biol. 2004 Sep 22; 2(9):e230

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PLoS Biol

10.1371/journal.pbio.0020230

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020249SynopsisCell BiologyGenetics/Genomics/Gene TherapyImmunologyMolecular Biology/Structural BiologyDrosophilaIdentifying Genes Involved in Innate Immunity through RNAi Synopsis8 2004 22 6 2004 22 6 2004 2 8 e249Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Functional Dissection of an Innate Immune Response by a Genome-Wide RNAi Screen ==== Body An organism's ability to sense and respond to potentially harmful pathogens is key to its survival. To fight off disease and infection, organisms must detect pathogens, activate immune cell signaling pathways, and produce molecules able to thwart a pathogenic attack. So fundamental is this need that molecules and protein domains related to innate immunity are evident in organisms as diverse as plants, flies, and humans, highlighting the ancient origins of defense mechanisms. Once detected, the pathogen's presence triggers a cascade of signaling events that generate a rapid response tailored to specific classes of pathogens. When pathogens attack the Drosophila fruitfly, they elicit a range of defensive reactions in the fly, including the production of antimicrobial proteins, phagocytes (which engulf the pathogen), and toxic metabolites. A microbe sets off one of these responses by interacting with a receptor, triggering a pathway that activates a special class of transcription factors, which in turn activate genes needed to make antimicrobial peptides, say, or toxins. Drosophila attacked by fungi and a certain class of bacteria activate these transcription factors through a pathway (the Toll pathway) that also operates in mammals. A second pathway is activated when a different class of bacteria attack. While the general steps of this pathway, called the Immune deficiency (Imd) pathway, are known—Dredd-mediated activation of the Relish transcription factor, for example, is central to this antibacterial response—the details and mechanisms remain unclear. Signaling pathways are notoriously complex and the Imd pathway is no different. In the current model, bacterial pathogens stimulate a transmembrane receptor, which activates the Imd protein, which then transmits the signal through intermediary proteins, which ultimately activate Dredd, sending Relish into the nucleus to activate genes required for an immune response. In this issue of PLoS Biology, Edan Foley and Patrick O'Farrell use a genome-wide approach to characterize the Imd pathway, with an eye toward understanding what regulates the Dredd-Relish interaction. To identify pertinent genes and their roles, Foley and O'Farrell took advantage of a technique, called RNA interference (RNAi), that can selectively target and “silence,” or inhibit, nearly any gene. After silencing a gene, researchers can then track how a cell responds and infer the gene's function. The technique uses double-stranded RNA (dsRNA) molecules with nucleic acid sequences that match a gene of interest (RNA can bind to DNA through complementary base pairing). The dsRNAs precipitate a series of steps that ultimately degrade the messenger RNA associated with the gene, preventing messenger RNA translation, thereby silencing the gene. The authors produced over 7,000 dsRNAs corresponding to most of the Drosophila genes with counterparts in mammals or the worm C. elegans (that is, conserved genes), then developed a cell culture model to analyze the components of the Imd pathway. Since the cells in these cultures respond to the presence of bacterial proteins by generating antimicrobial peptides, they provide a good testbed for identifying genes involved in the pathway. Foley and O'Farrell's RNAi screen identified many molecules involved in signaling, including both signal inhibitors and activators. Among these signaling components, they discovered two new genes: one, which they named sickie, is required to activate Relish; the second, called defense repressor 1 (dnr1), appears to inhibit Dredd activity and thus inhibit the pathway. Based on molecular analysis of these genes—which involved exposing cells to bacterial proteins—the authors propose a model of Imd signaling in which Dredd and the protein produced by dnr1, called Dnr1, operate through a negative feedback loop: Dredd activity appears to promote the accumulation of its own inhibitor, Dnr1. Since suppression of Dnr1 through RNAi can trigger an immune response, the authors explain, it appears that interruption of this feedback loop activates the signaling pathway. Without the inhibitory action of Dnr1, Dredd can activate Relish, which dissociates from Dredd, enters the nucleus, and activates the transcription of antimicrobial genes. It's likely that the two genes described here play a key role in activating Relish, the authors conclude, and that others identified in this screen will also prove significant. Since many of these components are conserved in mammals, the pathway may likewise operate in humans. Future experiments will be the test of these assumptions, but until then, Foley and O'Farrell have demonstrated the soundness of using RNAi screens on a large scale to dissect the elements of a complex signaling pathway.

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PLoS Biol. 2004 Aug 22; 2(8):e249

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PLoS Biol

10.1371/journal.pbio.0020249

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020251SynopsisEvolutionImmunologyInfectious DiseasesMicrobiologyZoologyPlasmodiumMus (Mouse)Host Immunity Escalates the Evolution of Parasite Virulence Synopsis9 2004 22 6 2004 22 6 2004 2 9 e251Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Immunity Promotes Virulence Evolution in a Malaria Model ==== Body Strictly defined, evolution is a change in the gene pool, or total set of genes, of a given population over time. Genetic changes that increase the fitness of an organism—that is, increase survival or fertility—are more likely to be retained, through natural selection, and passed on to succeeding generations. In the classic case of Darwin's finches, different ecological niches exerted different selective pressures on an original population, and resulted in 14 different species, each sporting a beak uniquely adapted to harvesting particular available food sources. When it comes to microbial evolution, an ecological niche often takes the form of a host. If the microbe is a pathogen, its presence might trigger strong selective pressure from the host's immune system, precipitating an evolutionary two-step between microbe and host. Hosts with strong immune defenses can typically tolerate relatively virulent pests: conversely, ill-defended hosts die, which is bad news for the parasite. When the myxoma virus first infected a population of European rabbits in Australia in 1950, the virus was particularly lethal. Over time, less virulent strains were selected for—killing off your habitat would be an unsustainable fitness cost by most standards—and the rabbits developed resistance. In keeping with evolutionary theory, host immunity should affect the evolution of parasite virulence. Though theory predicts that immunity could potentially heighten virulence, there's no evidence that this is true. Being able to predict how natural selection will act on, and thus shape, virulence is vital for developing effective public health policies—and desperately needed vaccines—to deal with the ever growing roster of rapidly evolving pathogenic threats. Malaria infecting red blood cells To investigate whether immune system defenses escalate pathogen virulence, Margaret Mackinnon and Andrew Read studied the malarial parasite Plasmodium in the mouse. Mackinnon and Read first directly injected two groups of mice with infectious parasites: “immunized” mice, which had been exposed to Plasmodium and then treated with the antimalarial drug mefloquine, and “naïve” mice, which had not. Parasites were serially transferred twenty times via a syringe from one mouse host to another. The virulence and infectiousness of the respective strains were evaluated by introducing the strains into another set of immunized and naïve mice. As theoretically predicted, parasites evolved in the immunized mice were indeed more virulent than parasites evolved in the naïve mice. But what if the parasites were first transmitted through their natural vector, the mosquito, rather than through a syringe? Would they be as virulent? Interestingly, infection was not as severe after mosquito transmission. But parasites evolved in the immunized mice retained a higher level of virulence than those evolved in the naïve mice. This means that immunity accelerates the evolution of virulence in malaria, even after mosquito transmission, making them more dangerous to nonimmunized hosts. How does immune selection create more virulent pathogens? One possibility is that even though many parasites die in immunized hosts, those that “win the race to the syringe”—or the mosquito—are likely genetically equipped to stay ahead of advancing immune system defenses. It's not entirely clear why selection would favor more virulent parasites, but since the virulent strains showed no problems transmitting infection to new hosts, it's likely that such strains would spread throughout an immunized population. While mosquito transmission likely plays a significant role in virulence evolution—it clearly reduced virulence here—the molecular mechanics of this effect are also mostly speculative at this point. Many questions remain, but these results make a strong case that vaccine development aimed at protecting individuals against infectious pathogens would do well to consider the evolutionary implications, or increased pathogen virulence could be an unintended consequence.

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PLoS Biol. 2004 Sep 22; 2(9):e251

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PLoS Biol

10.1371/journal.pbio.0020251

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020253SynopsisCell BiologyMus (Mouse)A New Role for a Synaptotagmin Protein in Calcium-Dependent Exocytosis Synopsis8 2004 29 6 2004 29 6 2004 2 8 e253Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Synaptotagmin VII Restricts Fusion Pore Expansion during Lysosomal Exocytosis ==== Body The hardest working molecules in cell biology, proteins abound in a dazzling variety of shapes and sizes to carry out an equally impressive array of tasks. Many proteins function within the cell, while others get shipped out to locations both near and far. Specialized organelles within the cell package the traveling proteins into cargo containers called vesicles. Vesicles move through a highly regulated transportation system until they reach their ultimate destination, either inside or outside the cell, and release their cargo. When vesicles fuse to the plasma membrane and release their cargo outside the cell the process is called exocytosis. Exocytosis allows macromolecules to leave the cell without compromising the structural integrity of its membrane. “Professional” secretory cells specialize in producing copious quantities of their protein product and sending vesicles packed with their customized issue to the plasma membrane. Once there, the vesicles wait for a signal to fuse with the membrane. The signal most often comes in the form of a transient and localized increase in calcium ion levels. Over a decade ago, researchers discovered that calcium-triggered exocytosis also occurs in “nonprofessional” secretory cells. In these cells, the process was thought to be important for healing ruptured plasma membrane, though the identity of the vesicles responsible remained unknown. It has been suggested that these vesicles are lysosomes, enzyme-filled organelles that break down waste and extracellular debris ingested by the cells. Sanford Simon, with his colleagues Jyoti Jaiswal and Norma Andrews, previously confirmed that calcium does specifically trigger exocytosis of lysosomes in the “nonprofessional” secretory cells. Calcium-triggered exocytosis is thought to require the services of a family of proteins called synaptotagmins. But the fifteen members of the synaptotagmin family diverge from this job description in various ways, calling its role into question. Synaptotagmin VII (Syt VII)—the synaptotagmin member expressed on the lysosomes—is present in most tissues in organisms ranging from worms to humans. This protein functions in processes requiring lysosomal exocytosis and during invasion by trypanosome parasites, such as the one that causes Chagas disease. In this issue of PLoS Biology, Jaiswal, Simon, and colleagues investigate the molecular mechanisms underlying calcium-triggered lysosomal exocytosis, focusing on the role of Syt VII. The researchers took cells from two lines of mice: one lacked the functional Syt VII protein and the other produced normal levels of Syt VII. First they labeled the surface and interior cavity of lysosomes in these cells with fluorescent tags and triggered an increase in cells' calcium level; then they watched the behavior of single lysosomes releasing their contents in real time. Most of the lysosomes from normal cells released only a portion of their contents and had very small fusion pores that remained open to the outside of the cell for only a short time. Interestingly, while proteins in secretory vesicle membranes typically diffuse into the plasma membrane during exocytosis, proteins in the lysosomal membrane stayed near the site of fusion. In the cells from Syt VII–deficient mice, Simon and colleagues discovered, to their surprise, that this protein isn't necessary for calcium-triggering of lysosomal exocytosis. Calcium-triggered exocytosis not only occurred in these cells, it happened more rapidly than in normal cells. Plus most ofthese deficient lysosomes fused completely and their membrane proteins fully diffused into the plasma membrane. Simon and colleagues argue that these results show that Syt VII restricts rather than facilitates lysosomal exocytosis. It does so by limiting the formation and size of fusion pores and by preventing lysosomal membrane proteins from integrating into the plasma membrane.

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PMC439008

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PLoS Biol. 2004 Aug 29; 2(8):e253

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PLoS Biol

10.1371/journal.pbio.0020253

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020265SynopsisDevelopmentGenetics/Genomics/Gene TherapyNeuroscienceMus (Mouse)Genomic Analysis of Retinal Development in the Mouse Synopsis9 2004 29 6 2004 29 6 2004 2 9 e265Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Genomic Analysis of Mouse Retinal Development ==== Body The eyes may be the window to the soul for poets, but for neuroscientists, they serve a more practical purpose. Of the 100 trillion or so cells that make up the human body, over 100 billion are dedicated to the structure and operation of the brain alone. Given the molecular and functional complexity inherent in such numbers, neuroscientists have historically focused on a more tractable system, the vertebrate retina, to study central nervous system development and physiology. Cells in the retina are packaged into highly ordered anatomical layers, based on their specialized functions. This organizational structure is characteristic of other regions of the central nervous system, and allows the brain to take in and integrate sensory information simultaneously, using discrete computational units. Creating such functional microprocessors depends on making the right cell at the right place and time. During development, cells undergo periods of proliferation and increasing specialization (differentiation), generating seven types of retinal cells (six types of neurons and one glial cell type) in a precise order at specific times. Mature, specialized cells arise from a pool of proliferating progenitors—cells that have already committed to becoming a retinal cell but haven't yet settled on a particular cell type. But progenitors are not all alike; they display intrinsic differences in their “competence” to produce a particular subset of retinal cells at a particular stage of development. These differences may help ensure that ganglion cells, for example, are established before photoreceptors, since photoreceptors rely on ganglion cells to transmit their signals to the brain. Classic drawing of the retina by Ramón y Cajal Which path a cell ultimately chooses stems from a combination of both intrinsic competence factors—likely determined by a cell's gene expression program—and external signals from the cell's environment. Progenitors give rise to “postmitotic” cells (cells that have exited the cell cycle and ceased proliferating), which go on to express characteristics associated with a specific cell type. Beyond this framework, the molecular underpinnings of retinal development remain obscure. Differentiated cells exhibit a gene expression program unique to their cell type, but it's not clear what accounts for underlying differences among progenitors, for example, or what factors usher retinal cells into their respective specialties. To map the genetic landscape of retinal development, Constance Cepko and colleagues looked for genes expressed in retinal cells passing through various competence levels and making cell fate choices. They determined gene expression profiles by collecting bits of gene transcripts from the retinal tissue of developing mice at two-day intervals, starting with mice entering neurogenesis and ending with mice about six and a half days old. They also collected gene expression data from postnatal day 10 and from adult mice. The authors then examined the cellular expression patterns of 1,051 of the genes that showed dynamic patterns by genomic expression profiling. Cepko and colleagues then pegged these genes to specific cell types to create a “molecular atlas of gene expression in the developing retina.” (Though the retina has many millions of cells, different cell types can be easily identified based on their telltale shape and position in the retina.) Nearly every gene known to direct retinal cell differentiation was detected in this analysis and showed high levels of expression. Genes required for cell fate choices showed peak expression near or after cells exited the cell cycle, supporting the idea that similar controls operate to put the brakes on cell proliferation and to determine cell fate. Many uncharacterized genes were expressed only in certain progenitor subsets, making them good candidates as cell fate determinants for different subtypes of retinal cells. A promising list of candidate genes for retinal development and function appear in this molecular atlas, along with candidates for retinal disease. Since many degenerative retinal diseases stem from defects in development, these genes will help researchers focus their search for therapies. And if the eye truly is the window of the nervous system, these findings may suggest general principles of cell fate determination for the developing brain, spinal cord, and other regions of the vertebrate nervous system.

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PLoS Biol. 2004 Sep 29; 2(9):e265

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020233Research ArticleCell BiologyMus (Mouse)Synaptotagmin VII Restricts Fusion Pore Expansion during Lysosomal Exocytosis Synaptotagmin VII and Lysosomal ExocytosisJaiswal Jyoti K 1 Chakrabarti Sabyasachi 2 Andrews Norma W 2 Simon Sanford M [email protected] 1 1Department of Cellular Biophysics, Rockefeller UniversityNew York, New YorkUnited States of America2Section of Microbial Pathogenesis and Department of Cell Biology, Yale University School of MedicineNew Haven, ConnecticutUnited States of America8 2004 29 6 2004 29 6 2004 2 8 e23323 1 2004 24 5 2004 Copyright: © 2004 Jaiswal et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A New Role for a Synaptotagmin Protein in Calcium-Dependent Exocytosis Synaptotagmin is considered a calcium-dependent trigger for regulated exocytosis. We examined the role of synaptotagmin VII (Syt VII) in the calcium-dependent exocytosis of individual lysosomes in wild-type (WT) and Syt VII knockout (KO) mouse embryonic fibroblasts (MEFs) using total internal reflection fluorescence microscopy. In WT MEFs, most lysosomes only partially released their contents, their membrane proteins did not diffuse into the plasma membrane, and inner diameters of their fusion pores were smaller than 30 nm. In Syt VII KO MEFs, not only was lysosomal exocytosis triggered by calcium, but all of these restrictions on fusion were also removed. These observations indicate that Syt VII does not function as the calcium-dependent trigger for lysosomal exocytosis. Instead, it restricts the kinetics and extent of calcium-dependent lysosomal fusion. Lysosomes only partially release their contents in wild-type mouse embryonic fibroblasts (MEFs); but in MEFs in which synaptotagmin VII has been deleted, lysosomes fuse completely ==== Body Introduction Exocytosis allows cells to transport membrane-impermeable macromolecules outside without compromising the integrity of the plasma membrane. The proteins that form the conserved machinery for constitutive and regulated exocytosis have been identified (Sollner and Rothman 1996), and calcium has been identified as the most common trigger for regulated exocytosis (Burgoyne and Morgan 1998; Jaiswal 2001). However, there is not yet a consensus on the calcium-responsive components involved in this process. It has been suggested that multiple Ca2+-binding proteins with distinct properties could act as the trigger for membrane fusion (Burgoyne and Morgan 1998). Evidence supporting the role of synaptotagmin I (Syt I) as the Ca2+-dependent trigger for synaptic vesicle fusion in several organisms has led to the belief that the members of the synaptotagmin family act as ubiquitous calcium-dependent triggers for exocytosis (Brose et al. 1992; Geppert et al. 1994; Littleton and Bellen 1995). While Syt I is the most well-studied member of this family, there are at least 15 different synaptotagmin isoforms with differing affinities for calcium and phospholipid and different cellular localization (Chapman 2002; Fukuda 2003). Some members of synaptotagmin family (including Syt I) have also been found to regulate endocytosis and even negatively regulate Ca2+-dependent exocytosis (Jorgensen et al. 1995; Martin et al. 1995; Morimoto et al. 1995; Baram et al. 1999; Tucker and Chapman 2002). Thus, the role of synaptotagmin family members as Ca2+-dependent triggers for exocytosis is still an open question. We have previously identified that in nonprofessional secretory cells calcium preferentially triggers exocytosis of lysosomes (Jaiswal et al. 2002). A variety of agents that result in calcium increase, including membrane damage, trypanosome invasion, calcium ionophores, or the IP3 agonists thrombin or bombesin, trigger lysosomal exocytosis (Rodriguez et al. 1997; Caler et al. 2000, 2001; Ayala et al. 2001; Reddy et al. 2001; Jaiswal et al. 2002). However, the molecular machinery that regulates this calcium-triggered lysosomal exocytosis has remained elusive. Syt VII is the synaptotagmin isoform present on lysosomes (Martinez et al. 2000). It is expressed in most tissues and is present in organisms ranging from nematodes to humans (Fukuda et al. 2002). Syt VII is involved in processes requiring lysosomal exocytosis, namely, release of lysosomal enzymes, repair of membrane rupture, and trypanosome invasion (Martinez et al. 2000; Caler et al. 2001; Reddy et al. 2001). Further, the recent demonstration that cells from Syt VII knockout (KO) mice are compromised in these functions supports a role of Syt VII in regulating lysosomal exocytosis (Chakrabarti et al. 2003). To understand how Syt VII regulates lysosomal fusion, we used total internal reflection fluorescence microscopy (TIR-FM) and studied the behavior of individual lysosomes following calcium increase in mouse embryonic fibroblasts (MEFs) from wild-type (WT) and Syt VII KO mice. Results To monitor the fate of exocytic lysosomes in MEFs, we labeled their lumen using fluorescent dextran (FITC–dextran). Treating MEFs with calcium ionophore A23187 or the IP3 agonist bombesin or thrombin caused lysosomal exocytosis (Figure 1A and 1B). Fusion of a FITC–dextran-loaded lysosome was indicated by a transient increase followed by a decrease in its fluorescence (Figure 1A–1C). The increase in fluorescence was due to a combination of two factors: (a) movement of the lysosome closer to the coverslip, which results in better excitation of its cargo by the evanescent wave; (b) opening of the fusion pore, which results in dissipation of the acidic pH of the lysosomes, resulting in dequenching of the fluorescence of FITC–dextran. The rapid decrease in fluorescence was due to the diffusion of lumenal cargo away from the site of fusion (Figure 1A–1C). In some of the exocytosing lysosomes, the lumenal fluorescence decreased down to baseline, indicating that they completely released their lumenal cargo (Figure 1A). The fluorescence of other lysosomes did not decrease down to baseline at the site of fusion (Figure 1B). Thus, these lysosomes only partially released their contents upon fusion. To resolve whether partial release represented a very slow diffusion of lumenal content or an opening of the fusion pore that was transient, we observed the lysosomes for longer periods. During partial release, the lumenal fluorescence decreased rapidly within the first second (Figure 1B), but remained relatively constant afterwards, decreasing only at the rate of photobleaching (t 1/2 for FITC in our setup is 28.5 s). Absence of any subsequent decrease in its fluorescence, even over longer periods, indicated that cessation of release was the result of closure of the fusion pore prior to complete release of the lumenal cargo. Quantitation of the lumenal contents retained in all exocytosed lysosomes analyzed in the WT MEFs revealed that only 21% completely released their lumenal content (Figure 1D, gray bar). The percentage of lysosomes in individual WT MEFs that only partially released their lumenal cargo of 70 kDa dextran in response to A23187-induced increase in cellular Ca2+ was 65.3% (n = 7 cells) (Figure 1E, black bars; Table 1; Video S1). A comparable fraction of lysosomes, respectively, underwent partial release when calcium increase was triggered by the IP3 agonists thrombin (66.3) or bombesin (69.5), (Figure 1E; Table 1). Figure 1 Fate of Lumenal Content during Lysosomal Fusion MEFs were incubated for 2 h with 70 kDa FITC–dextran followed by more than 3 h in dextran-free media to chase the dextrans into the lysosomes. These cells were then treated with calcium ionophore (A23187) to trigger exocytosis of lysosomes. (A and B) The middle panels are images of lyosomes undergoing complete (A) and partial (B) exocytosis. Intensity plots for the regions in these images marked by dotted circles are shown in the lower panel. The top panel shows a schematic representation of these different stages. (C) Schematic fluorescence intensity plots for lysosomes undergoing partial (red) or complete (green) fusion. Owing to the exponential decay of the evanescent field (blue; top panels in [A] and [B]) away from the coverslip, a lysosome that is more than 150 nm from the cell membrane (black line in top panels in [A] and [B]) is not fluorescent. As this lysosome moves closer (labeled as “entry into evanescent field”), its fluorescence intensity increases. Since the lumen of lysosome is acidic, it quenches FITC fluorescence. As soon as the fusion pore is formed, the lysosomal lumen is rapidly alkalinized resulting in an increase of FITC–dextran fluorescence (“pore opening”). Following the pore opening, the dextran is released and it diffuses away from the site of the fusion, causing the lumenal fluorescence to decrease (“release”). (D) A histogram of the fraction of lumenal contents released by exocytosing lysosomes. Upon ionophore-triggered fusion, 21% of all lysosomes analyzed in WT MEFs (n = 47; gray bars) and 45% of all in Syt VII KO MEFs (n = 51; white bars) completely released their lumenal content. (E and F) To monitor the nature of lysosomal fusion in individual WT MEFs (E) and Syt VII KO MEFs (F), calcium was increased using ionophore (WT, n = 7 cells; KO, n = 9 cells) as well as the IP3 agonists bombesin (WT, n = 6 cells; KO, n = 9 cells) and thrombin (WT, n = 5 cells; KO, n = 7 cells). Irrespective of the means, increase in calcium led to most lysosomes to fuse partially in WT MEFs (E) and completely in Syt VII KO MEFs (F). The error bars represent SEM. Table 1 Nature of Release of Lysosomal Lumenal Content Lysosomes in MEFs were labeled with 70 kDa FITC–dextran as described in Figure 1. Increase in cytosolic calcium in response to treatment with calcium ionophore or with the IP3 agonist bombesin or thrombin led to either partial or complete release of 70 kDa FITC–dextran. The table describes the fraction of lysosomes that showed these in a few WT and SytVII KO MEFs Found at In order to determine the fate of the membrane proteins during Ca2+-triggered lysosomal exocytosis, we simultaneously imaged the lysosomal lumenal (TRITC–dextran) and membrane (CD63–GFP) cargoes in WT MEFs. We observed that the membrane proteins were delivered to the plasma membrane during complete release of lumenal content (Figure 2A), but not when the lumenal contents were released partially (Figure 2B). Even when the lysosomal membrane proteins were delivered to the plasma membrane, their diffusion into plasma membrane was restricted (Figure 2A and 2C; Table 2). This is unlike the fate of membrane proteins during exocytosis of biosynthetic vesicles (Schmoranzer et al. 2000; Kreitzer et al. 2003; Schmoranzer and Simon 2003) or recycling endosomes (Lampson et al. 2001), where following its delivery to plasma membrane, the vesicular membrane protein diffuses away completely from the site of fusion. Thus, exocytosis of lysosomes in the WT MEF is different from other exocytic events in two manners. First, the majority of exocytic lysosomes only partially release their lumenal cargo with no release of membrane proteins. Second, even when the lumenal cargo is completely released, the membrane proteins delivered to the plasma membrane do not diffuse freely, but are retained into punctae at the site of fusion. Figure 2 Fate of Membrane Protein during Lysosomal Fusion Lysosomal membranes in MEFs were labeled by transfecting cells with a vector encoding a CD63–GFP fusion protein, and expression was allowed for 48 h. For simultaneous labeling of lysosomal membrane and lumen, the CD63–GFP transfected cells were labeled with 70 kDa TRITC–dextran as described in Figure 1. (A) Following ionophore-induced calcium increase in WT MEFs, when the TRITC–dextran was released completely (left), CD63–GFP (right) was delivered to the plasma membrane, but it remained in multiple puncta near the site of fusion rather than diffuse away. The panels are pseudocolor surface plots, with the x and y axis representing the coordinates and the z axis representing the fluorescence intensity of individual pixels. (B) In the event of partial release of TRITC–dextran (top row), the CD63–GFP (bottom row) did not appear to be delivered to the plasma membrane. The lower panel shows the plot of fluorescent intensity of lumenal and membrane label (within the dotted circle) of the lysosome shown in (B). (C and D) Analysis of CD63–GFP-labeled lysosomes in WT MEFs (C) and in Syt VII KO MEFs (D) indicates that while CD63–GFP is retained in puncta in the WT MEFs, it diffuses freely in the plasma membrane in the SytVII KO MEFs. The lower panel shows the total and peak intensity plots of CD63–GFP-labeled lysosome in (D). Table 2 Nature of Release of Lysosomal Membrane Protein Lysosomal membranes in MEFs were labeled with CD63-GFP as described in Figure 2. When ionophore-induced lysosomal exocytosis resulted in delivery of CD63-GFP to plasma membrane, it either diffused away from the site of fusion or remained trapped into punctae near the site of fusion (see Figure 4B and 4D). The table describes the fraction of lysosomes that showed these behaviors in individual cells in WT and SytVII KO MEFs Found at To test the role of Syt VII in regulating these processes and acting as a calcium-dependent trigger for lysosomal exocytosis, we carried out a similar analysis using embryonic fibroblasts from mice deficient in Syt VII (Syt VII KO MEF) (Chakrabarti et al. 2003). Absence of Syt VII did not abolish calcium-dependent triggering of lysosomal fusion in response to ionophore, thrombin, or bombesin (see Figure 1D and 1F; Video S2). Upon examining individual exocytic lysosomes, we observed two significant differences between the WT and Syt VII KO MEFs. First, in individual Syt VII KO MEFs, a significantly greater (2-fold) fraction of lysosomes completely released their lumenal contents when the cellular calcium was raised using ionophore (p = 0.001) (Video S2), thrombin (p = 0.001), or bombesin (p = 0.003) (see Figure 1F; see Table 1). Second, upon complete fusion, the membrane protein of most exocytic lysosomes in Syt VII KO MEFs diffused freely in the plane of plasma membrane (see Figure 2D; see Table 2). These phenotypes indicated that the presence of Syt VII restricts complete fusion of lysosomes. To identify whether Syt VII restricts lysosomal fusion by preventing its flattening into the plasma membrane or by regulating the size of the fusion pore, we quantified the simultaneous release of dextrans of different sizes in individual lysosomes (10 kDa [Stokes radius = 2.4 nm], 70 kDa [5.8 nm], 145 kDa [8 nm], 250 kDa [10.5 nm], and 500 kDa [14.7 nm]). Lysosomes were loaded with dextrans of two different sizes, each tagged with a different fluorophore (FITC or TRITC). In WT MEFs, all the lysosomes that released the TRITC–dextran of 10 or 70 kDa also released similarly sized FITC–dextran (Figure 3A and 3B). Thus, the fluorophore did not appear to affect release of the dextran. The fluorophore also had no affect on the lysosomal uptake of the dextran, as every lysosome that had the TRITC-labeled dextran also had the FITC-labeled dextran (data not shown). Further, just prior to fusion, the fluorescence of the TRITC cargo and the FITC cargo in each lysosome started to increase at the same moment (Figure 3). This increase is the result of the movement of the lysosome to the plasma membrane just prior to fusion, which increases the excitation of the fluorophores (see Figure 1A–1C). Thus, the TRITC and FITC cargos were not only spatially and temporally coincident in the plane of the plasma membrane, but also coincident in the plane perpendicular to the plasma membrane even in a motile lysosome. These observations rule out the possibility that the two fluorophores are present in separate lysosomes. Figure 3 Presence of Syt VII Restricts the Size of the Fusion Pore The lumen of lysosomes was loaded simultaneously using different-sized FITC- and TRITC-labeled dextran, using the approach described in Figures 1 and 2. Representative plots shown here demonstrate the fate of both dextran populations in individual lysosomes following the increase in intracellular calcium by addition of calcium ionophore. In WT MEFs, exocytosing lysosomes that released 70 kDa TRITC–dextran also simultaneously released 70 kDa FITC–dextran (A and B), 250 kDa FITC–dextran (C and D), but not 500 kDa FITC–dextran (E and F). In Syt VII KO MEFs, lysosomes that released 70 kDa TRITC–dextran also released 500 kDa FITC–dextran (G and H). In all cases the plots represent the normalized fluorescence intensity of the region marked in the images by dotted circles. In WT MEFs, each lysosome that partially released the 10 or 70 kDa TRITC–dextran at the same time also partially released the 145 kDa (data not shown) and 250 kDa FITC–dextran (Figure 3C and 3D). However, when the lysosome was simultaneously loaded with 500 kDa FITC–dextran and with 70 kDa TRITC–dextran, the smaller cargo was released and the larger cargo was not (Figure 3E and 3F). This suggests the fusion pore opened large enough to release the smaller but not the larger cargo (Figure 4). Furthermore, at the moment of release of the TRITC cargo, the fluorescence from the 500 kDa FITC–dextran increased to a significantly greater amount. As the FITC fluorescence is partially quenched by the acidic lumen of the lysosomes, this provided additional evidence in favor of the conclusion that the lysosome formed a fusion pore, without releasing the 500 kDa dextran. The lack of subsequent decrease in the fluorescence of the 500 kDa FITC–dextran, when the 70 kDa TRITC–dextran fluorescence decreases, indicates that the radius of the fusion pore is smaller than the size of the 500 kDa dextran (Stokes radius = 14.7 nm; diameter, 29.4 nm). We repeated these studies in Syt VII KO MEFs and found that in these cells all exocytosing lysosomes were able to fully release dextrans of all sizes, including the 500 kDa dextran (see Figure 3G and 3H). This suggests that the presence of Syt VII blocks complete release of lysosomal contents during exocytosis, potentially by restricting the size of the exocytic pore. Figure 4 Schematic Representation of the Fate of Lumenal and Membrane Cargo during Lysosomal Exocytosis (A) Partial fusion of the lysosomes from WT MEFs result in the release of a fraction of the smaller (70–250 kDa; red circles) dextran, but no release of the larger (500 kDa; blue circles) dextran. None of the lysosomal membrane protein (green bars) is delivered to the plasma membrane. (B) Complete fusion leads to release of both the large and the small dextrans and delivery of the membrane proteins to the plasma membrane, but the proteins aggregate in small puncta near the site of fusion. (C) Knockout of Syt VII causes larger-sized dextran to be released even during partial fusion, and the membrane protein is still not delivered to the plasma membrane. (D) During complete fusion, both sized dextrans are released completely and the membrane proteins delivered to plasma membrane are free to diffuse away from the site of fusion. Since cargo of any size would be released more rapidly through a larger pore, we independently assayed for the size of the fusion pore by measuring the time taken by exocytosing lysosomes to release their lumenal cargo. Increase in FITC–dextran fluorescence was taken as the indicator for the time of opening of the fusion pore (see Figure 1C). We measured the time taken for the lumenal fluorescence of individual exocytic lysosomes (fusing partially or completely) to decrease to the post-fusion resting value in WT and Syt VII KO MEF (see Figure 1C). For most lysosomes (greater than 81%) in WT MEFs, it took longer than 0.75 s for the fluorescence of the lumenal cargo to reach their post-fusion resting value (gray bars in Figure 5A). In contrast, in Syt VII KO MEFs for most lysosomes (greater than 75%), this occurred in less than 0.75 s (white bars in Figure 5A). The increased propensity of lysosomes to rapidly release their lumenal content was also observed when Syt VII KO cells were treated with bombesin or thrombin (Figure 5B). This suggests that in the absence of Syt VII, the fusion pore either opens faster, opens to a larger size, or both. While we cannot distinguish among these possibilities, they are all consistent with Syt VII restricting the expansion of the fusion pore. Figure 5 Temporal Analysis of Lysosomal Exocytosis and Fusion Pore Opening Using 70 kDa FITC–Dextran as Lumenal Marker (A) Histogram of the release time (time taken for the vesicular fluorescence to drop from peak to postfusion resting value). Release time was less than 0.75 s for more than two-thirds of lysosomes in Syt VII KO MEF (n = 62 lysosomes), while most lysosomes (81%) in WT MEFs (n = 56 lysosomes) released their lumenal content for more than 0.75 s. (B) Analysis of release time of lysosomes using ionophore, bombesin, and thrombin to trigger lysosomal exocytosis. Irrespective of the means of calcium increase, lysosomes in Syt VII KO MEFs released their lumenal content significantly faster (p = 0.002, 0.01, and 0.03, respectively). (C) Histogram of the number of lysosomes exocytosing as a function of the time following calcium ionophore addition. Fluorescent dextran was used as a lumenal marker; the time axis indicates seconds elapsed following the addition of ionophore. Exocytosis initiated earlier in the Syt VII KO MEFs (white bars; n = 8 cells) compared to WT MEFs (gray bars; n = 6 cells). (D) No change was observed in the total number of lysosomes that exocytosed at the basal surface of WT or Syt VII KO MEFs when calcium was raised using ionophore or thrombin; however, compared to WT MEFs, bombesin triggered exocytosis of half as many lysosomes in the Syt VII KO MEFs (asterix represents p value < 0.02). The error bars represent SEM. To further explore potential consequences of the presence of Syt VII on lysosomal secretion, we analyzed the effect of Syt VII knockout on the time course of initiation of lysosomal exocytosis. Similar to what we observed previously with CHO cells (Jaiswal et al. 2002), in WT MEFs, lysosomal exocytosis was initiated approximately 35 s after the addition of ionophore and peaked by 110 s (see Video S1; gray bars in Figure 5C). In contrast, in Syt VII KO MEFs, the earliest lysosomal fusion event was observed within 7 s after the addition of ionophore, and it peaked within 40 s (see Video S2; white bars in Figure 5C). As the time delay for IP3 agonist-induced calcium release varied significantly among the cells within a dish, we could not determine whether these agents have a similar affect on the latency of calcium-triggered lysosomal fusion in Syt VII KO MEFs. While all the behaviors of individual lysosomes described above were independent of the agent used to trigger calcium increase, the bulk cellular behavior, such as the total number of lysosomal fusion events that occur at the basal surface of the cell, was dependent on the agent used to increase the cellular calcium level (Figure 5D). While ionophore or thrombin triggered exocytosis of a similar number of lysosomes (p = 0.46 and 0.51, respectively) in both WT and KO MEFs, there was a 2-fold decrease (p = 0.02) when bombesin was used to raise cellular calcium in KO MEFs. Discussion It has been shown that biosynthetic vesicles, secretory granules, and synaptic vesicles can undergo partial release (Holroyd et al. 2002; Aravanis et al. 2003; Gandhi and Stevens 2003; Schmoranzer and Simon 2003; Taraska et al. 2003). However, our analysis of calcium-triggered fusion of individual lysosomes has revealed several unique features of this process. In WT MEFs, partial fusion is the predominant mode of lysosomal exocytosis. Unlike partial fusion of secretory granules in PC12 cells, which results in incomplete release of the large lumenal cargo (Holroyd et al. 2002; Taraska et al. 2003), partially exocytosing lysosomes do not release any of the large (500 kDa) lumenal cargo. Similarly, during partial release of lumenal contents, none of the lysosomal membrane protein diffuses into the plasma membrane. The complete release of lysosomal cargo is also unlike the complete release of biosynthetic vesicles, where the membrane proteins of secretory vesicles fully diffuse into the plasma membrane (Schmoranzer and Simon 2003). In constrast, the lysosomal membrane proteins remain as puncta near the site of fusion. These unique features associated with lysosomal exocytosis are dependent on the presence of Syt VII. Absence of Syt VII causes most lysosomes to fuse completely and allows the membrane proteins of completely fusing lysosomes to diffuse freely into the plasma membrane. Moreover, in the presence of a functional Syt VII, opening of the fusion pore is restricted both temporally (the pores open more slowly and remain open for a short period of time) and geometrically (large lumenal cargo cannot leave and membrane proteins cannot diffuse out into the plasma membrane). Thus, our analysis reveals that Syt VII is not critical for calcium-dependent triggering of lysosomal exocytosis. This is consistent with what has been proposed earlier and observed both in vitro and in vivo for Syt I (Popov and Poo 1993; Martin et al. 1995; Morimoto et al. 1995; Mahal et al. 2002). The effects of Syt VII on the kinetics, size, and extent of calcium-triggered exocytosis of lysosomes in WT MEF are also consistent with observations reported for Syt I: the slower dilation of fusion pores caused by overexpression of Syt I (Wang et al. 2001), and a 10-fold increased frequency of asynchronous release in Syt I null cells (Yoshihara and Littleton 2002). Interestingly, unlike the knockout of Syt I, which blocks the fast component of calcium-triggered exocytosis in neuronal cells (Yoshihara and Littleton 2002), knockout of Syt VII did not abolish calcium-triggered exocytosis in the MEFs. This could reflect different roles played by Syt VII (inhibitor for lysosome fusion) and Syt I (calcium-dependent trigger) in calcium-dependent exocytosis. Alternatively, each synaptotagmin may play multiple roles, including being a trigger (Geppert et al. 1994), an inhibitor of asynchronous release (Yoshihara and Littleton 2002), or an inhibitor of fusion pore dilation (Wang et al. 2001). While it remains to be determined whether other members of the synaptotagmin family can act as inhibitors of fusion, this possibility is supported by the recent observation that overexpression of Syt IV causes increased partial release of secretory granules in PC12 cells (Wang et al. 2003). Involvement of Syt VII in premature closure of the fusion pore (leading to partial release) is also consistent with the proposed role of Syt I in facilitating the rapid retrieval of vesicular membrane following exocytosis (Jorgensen et al. 1995). Syt VII function has been shown to be crucial for membrane repair and trypanosome invasion (Martinez et al. 2000; Caler et al. 2001; Chakrabarti et al. 2003). Cells lacking a functional Syt VII show reduced membrane repair and Trypanosoma cruzi invasion (Chakrabarti et al. 2003). Our observations suggest a few mechanisms by which Syt VII may contribute. Syt VII KO MEFs lose the restricted fusion of lysosomes: unlike the lysosomes of WT MEFs, the membrane-proximal lysosomes in Syt VII KO MEFs fully release their contents and deliver their membrane proteins to the surface. Since membrane of lysosomal origin has been shown to be required for healing membrane rupture and forming parasitophorous vacuoles during trypanosome invasion, it is possible that retaining the lysosomal membrane components at the site of fusion aids in both these processes. Additionally, upon treatment with bombesin, which may recapitulate the calcium signaling occurring during T. cruzi invasion (Tardieux et al. 1994), total lysosomal exocytosis is decreased 2-fold in the Syt VII KO MEF. This effect is reminiscent of the approximately 2-fold decrease in lysosomal enzyme secretion observed by collagen matrix-mediated wounding of Syt VII KO MEFs (Chakrabarti et al. 2003). We have previously observed that calcium predominantly triggers the exocytosis of membrane-proximal lysosomes (Jaiswal et al. 2002). In the Syt VII KO MEFs, these lysosomes also showed decreased propensity for partial release. Thus, it is possible that the membrane-proximal lysosomes play a role in decreased membrane repair and trypanosome invasion observed in the Syt VII KO MEFs (Chakrabarti et al. 2003). Our analysis of Syt VII function not only adds to the roles of synaptotagmin in regulating calcium-triggered exocytosis, but also provides mechanistic clues regarding how lysosomal exocytosis might regulate membrane repair and pathogen invasion. Materials and Methods Cell growth and treatments MEFs were prepared from day 13.5 embryos of WT and Syt VII-deficient mice, expressing functional (WT MEF) or truncated Syt VII (KO MEF), as described elsewhere (Chakrabarti et al. 2003). Cells were cultured in DMEM (Cellgro, Mediatech, Washington, District of Columbia, United States) supplemented with 10% FBS (GIBCO Technologies, Carlsbad, California, United States). All experiments were done with cells between passages 1 and 3. For imaging, cells were plated for more than 24 h on sterile glass coverslips (Fisher Scientific, Hampton, New Hampshire, United States). Just before imaging, the medium was replaced with cell imaging medium (CIM) (HBSS plus 10 mM HEPES plus 1% FBS [pH 7.4]). Transient transfection of cells with CD63–GFP (Blott et al. 2001) was carried out using Lipofectamine 2000 (Invitrogen Corporation, Carlsbad, California, United States) 48 h prior to imaging. For calcium ionophore and calcium agonist treatments, growth medium was replaced with CIM, the coverslip was mounted on the microscope stage maintained at 37 °C, and while the cells were being imaged using TIR-FM, agents were added to a final concentration of 10 μM A23187 ionophore, 0.2 U/ml thrombin, or 20 nM bombesin. Calcium ionophore-A23187, thrombin, bombesin, 70 kDa FITC–dextran, and 65 kDa TRITC–dextran were obtained from Sigma (Sigma Chemicals, St. Louis, Missouri, United States). All other fluorescent dextrans were obtained from Molecular Probes (Eugene, Oregon, United States) and used to load lysosomes as previously described (Jaiswal et al. 2002). TIR-FM The illumination and image acquisition using TIR-FM was done as previously described (Jaiswal et al. 2002). For simultaneous dual-color imaging of GFP/TRITC or FITC/TRITC, we used an emission splitter (Dual-View, Optical Insights, Santa Fe, New Mexico, United States). Cells were excited using the 488 nm line of an argon laser, containing the emission band pass filters (GFP/FITC, HQ525/50M; TRITC, HQ580LP). All filters were obtained from Chroma Technologies Corporation (Brattleboro, Vermont, United States). Images were acquired with a 12-bit cooled CCD ORCA-ER (Hamamatsu Photonics, Hamamatsu, Japan) with a resolution of 1280 × 1024 pixels (pixel size = (6.45 μm)2). The camera and mechanical shutters (Uniblitz, Vincent Associates, Rochester, New York, United States) were controlled using MetaMorph (Universal Imaging, Downingtown, Pennsylvania, United States). Images were acquired at 5–10 frames/s. Images containing a region of interest of the cell were streamed to memory on a PC during acquisition and then saved to hard disk. The depth of the evanescent filed for the Apo 60× N.A. 1.45 lens (Olympus Scientific, Melville, New York, United States) was typically approximately 70–120 nm (Schmoranzer et al. 2000). Image processing and quantitative analysis For dual-color video sequences, the images acquired through the emission splitter were separated, subtracted for background fluorescence, aligned within accuracy of a single pixel, and analyzed using MetaMorph. For measuring fluorescence intensity, a region was drawn around the vesicle and the peak and average intensity was measured in this region. The minimum and maximum average intensities were normalized on a scale of 0 to 1. For measurement of total number of exocytic lysosomes in a cell, fusion events were counted starting from the addition of ionophore until the cells start to lift off the coverslip (1–3 min in WT and fewer than 2 min in Syt VII KO MEFs). Supporting Information Video S1 Lysosomal exocytosis in WT MEFs Lysosomes of WT MEFs were loaded with 70 kDa FITC–dextran and the cells were treated with 10 μM calcium ionophore. The video shows a single cell 30 s after ionophore addition. The images were acquired at five frames per second, and alternate frames are displayed in the video at five frames per second. The time stamp indicates the time (mm:ss) elapsed since the start of the video. (6.8 MB AVI). Click here for additional data file. Video S2 Lysosomal exocytosis in Syt VII KO MEFs Lysosomes in Syt VII KO MEFs were loaded with 70 kDa FITC–dextran and the cells were treated with 10 μM calcium ionophore. The video shows a single cell immediately following ionophore addition. The images were acquired at five frames per second, and alternate frames are displayed in the video at five frames per second. The time stamp indicates the time (mm:ss) elapsed since the start of the video. (4.9 MB AVI). Click here for additional data file. We would like to thank Gillian Griffiths (Oxford University) for the CD63–GFP construct. This work was supported by National Science Foundation grants BES-0110070 and BES-0119468 to SMS and National Institutes of Health grants RO1-AI34867 and RO1-GM064625 to NWA. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. The work presented here was conceived and jointly written by JKJ, SMS, and NAW. JKJ carried out the experiments and analyzed data together with SMS. SC generated the Syt VII KO mice and prepared the cells used in this study. Academic Editor: Peter Walter, University of California, San Francisco Citation: Jaiswal JK, Chakrabarti S, Andrews NW, Simon SM (2004) Synaptotagmin VII restricts fusion pore expansion during lysosomal exocytosis. PLoS 2(8): e233. Abbreviations CIMcell imagine medium KOknockout MEFmouse embryonic fibroblast Syt Isynaptotagmin I Syt VIIsynaptotagmin VII TIR-FMtotal internal reflection fluorescence microscopy WTwild-type ==== Refs References Aravanis AM Pyle JL Tsien RW Single synaptic vesicles fusing transiently and successively without loss of identity Nature 2003 423 643 647 12789339 Ayala BP Vasquez B Clary S Tainer JA Rodland K The pilus-induced Ca2+ flux triggers lysosome exocytosis and increases the amount of Lamp1 accessible to Neisseria IgA1 protease Cell Microbiol 2001 3 265 275 11298650 Baram D Adachi R Medalia O Tuvim M Dickey BF Synaptotagmin II negatively regulates Ca2+ -triggered exocytosis of lysosomes in mast cells J Exp Med 1999 189 1649 1658 10330444 Blott EJ Bossi G Clark R Zvelebil M Griffiths GM Fas ligand is targeted to secretory lysosomes via a proline-rich domain in its cytoplasmic tail J Cell Sci 2001 114 2405 2416 11559749 Brose N Petrenko AG Sudhof TC Jahn R Synaptotagmin: A calcium sensor on the synaptic vesicle surface Science 1992 256 1021 1025 1589771 Burgoyne RD Morgan A Calcium sensors in regulated exocytosis Cell Calcium 1998 24 367 376 10091006 Caler EV Morty RE Burleigh BA Andrews NW Dual role of signaling pathways leading to Ca(2+) and cyclic AMP elevation in host cell invasion by Trypanosoma cruzi Infect Immunol 2000 68 6602 6610 11083771 Caler EV Chakrabarti S Fowler KT Rao S Andrews NW The exocytosis-regulatory protein synaptotagmin VII mediates cell invasion by Trypanosoma cruzi J Exp Med 2001 193 1097 1104 11342594 Chakrabarti S Kobayashi KS Flavell RA Marks CB Miyake K Impaired membrane resealing and autoimmune myositis in synaptotagmin VII-deficient mice J Cell Biol 2003 162 543 549 12925704 Chapman ER Synaptotagmin: A Ca(2+) sensor that triggers exocytosis? Nat Rev Mol Cell Biol 2002 3 498 508 12094216 Fukuda M Synaptotagmins, Ca2+ - and phospholipid-binding proteins that control Ca2+ -regulated membrane trafficking Recent Res Dev Chem Physics Lipids 2003 1 15 51 Fukuda M Ogata Y Saegusa C Kanno E Mikoshiba K Alternative splicing isoforms of synaptotagmin VII in the mouse, rat and human Biochem J 2002 365 173 180 12071850 Gandhi SP Stevens CF Three modes of synaptic vesicular recycling revealed by single-vesicle imaging Nature 2003 423 607 613 12789331 Geppert M Goda Y Hammer RE Li C Rosahl TW Synaptotagmin I: A major Ca2+ sensor for transmitter release at a central synapse Cell 1994 79 717 727 7954835 Holroyd P Lang T Wenzel D De Camilli P Jahn R Imaging direct, dynamin-dependent recapture of fusing secretory granules on plasma membrane lawns from PC12 cells Proc Natl Acad Sci U S A 2002 99 16806 16811 12486251 Jaiswal JK Calcium: How and why? J Biosci 2001 26 357 363 11568481 Jaiswal JK Andrews NW Simon SM Membrane proximal lysosomes are the major vesicles responsible for calcium-dependent exocytosis in nonsecretory cells J Cell Biol 2002 159 625 635 12438417 Jorgensen EM Hartwieg E Schuske K Nonet ML Jin Y Defective recycling of synaptic vesicles in synaptotagmin mutants of Caenorhabditis elegans Nature 1995 378 196 199 7477324 Kreitzer G Schmoranzer J Low SH Li X Gan Y Three-dimensional analysis of post-Golgi carrier exocytosis in epithelial cells Nat Cell Biol 2003 5 126 136 12545172 Lampson MA Schmoranzer J Zeigerer A Simon SM McGraw TE Insulin-regulated release from the endosomal recycling compartment is regulated by budding of specialized vesicles Mol Biol Cell 2001 12 3489 3501 11694583 Littleton JT Bellen HJ Synaptotagmin controls and modulates synaptic-vesicle fusion in a Ca(2+)-dependent manner Trends Neurosci 1995 18 177 183 7778189 Mahal LK Sequeira SM Gureasko JM Sollner TH Calcium-independent stimulation of membrane fusion and SNAREpin formation by synaptotagmin I J Cell Biol 2002 158 273 282 12119360 Martin KC Hu YH Armitage BA Siegelbaum SA Kandel ER Evidence for synaptotagmin as an inhibitory clamp on synaptic vesicle release in aplysia neurons Proc Natl Acad Sci U S A 1995 92 11307 11311 7479985 Martinez I Chakrabarti S Hellevik T Morehead J Fowler K Synaptotagmin VII regulates Ca(2+)-dependent exocytosis of lysosomes in fibroblasts J Cell Biol 2000 148 1141 1149 10725327 Morimoto T Popov S Buckley KM Poo MM Calcium-dependent transmitter secretion from fibroblasts: Modulation by synaptotagmin I Neuron 1995 15 689 696 7546747 Popov SV Poo MM Synaptotagmin: A calcium-sensitive inhibitor of exocytosis? Cell 1993 73 1247 1249 8100739 Reddy A Caler EV Andrews NW Plasma membrane repair is mediated by Ca(2+)-regulated exocytosis of lysosomes Cell 2001 106 157 169 11511344 Rodriguez A Webster P Ortego J Andrews NW Lysosomes behave as Ca2+ -regulated exocytic vesicles in fibroblasts and epithelial cells J Cell Biol 1997 137 93 104 9105039 Schmoranzer J Simon SM Role of microtubules in fusion of post-Golgi vesicles to the plasma membrane Mol Biol Cell 2003 14 1558 1569 12686609 Schmoranzer J Goulian M Axelrod D Simon SM Imaging constitutive exocytosis with total internal reflection fluorescence microscopy J Cell Biol 2000 149 23 32 10747084 Sollner TH Rothman JE Molecular machinery mediating vesicle budding, docking and fusion Experientia 1996 52 1021 1025 8988241 Taraska JW Perrais D Ohara-Imaizumi M Nagamatsu S Almers W Secretory granules are recaptured largely intact after stimulated exocytosis in cultured endocrine cells Proc Natl Acad Sci U S A 2003 100 2070 2075 12538853 Tardieux I Nathanson MH Andrews NW Role in host cell invasion of Trypanosoma cruzi -induced cytosolic-free Ca2+ transients J Exp Med 1994 179 1017 1022 8113670 Tucker WC Chapman ER Role of synaptotagmin in Ca2+ -triggered exocytosis Biochem J 2002 366 1 13 12047220 Wang CT Grishanin R Earles CA Chang PY Martin TF Synaptotagmin modulation of fusion pore kinetics in regulated exocytosis of dense-core vesicles Science 2001 294 1111 1115 11691996 Wang CT Lu JC Bai J Chang PY Martin TF Different domains of synaptotagmin control the choice between kiss-and-run and full fusion Nature 2003 424 943 947 12931189 Yoshihara M Littleton JT Synaptotagmin I functions as a calcium sensor to synchronize neurotransmitter release Neuron 2002 36 897 908 12467593

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020247Research ArticleDevelopmentGenetics/Genomics/Gene TherapyNeuroscienceMus (Mouse)Genomic Analysis of Mouse Retinal Development Genomic Analysis of Retinal DevelopmentBlackshaw Seth 1 Harpavat Sanjiv 1 Trimarchi Jeff 1 Cai Li 2 Huang Haiyan 3 Kuo Winston P 1 4 Weber Griffin 5 Lee Kyungjoon 4 Fraioli Rebecca E 1 Cho Seo-Hee 1 Yung Rachel 1 Asch Elizabeth 1 Ohno-Machado Lucila 5 Wong Wing H 6 Cepko Constance L [email protected] 1 1Department of Genetics and Howard Hughes Medical Institute, Harvard Medical SchoolBoston, Massachusetts, United States of America2Dana-Farber Cancer Institute, Harvard Medical SchoolBoston, MassachusettsUnited States of America3Department of Statistics, University of CaliforniaBerkeley, CaliforniaUnited States of America4Children's Hospital Informatics Program, BostonMassachusettsUnited States of America5Decision Systems Group, Brigham and Women's HospitalBoston, MassachusettsUnited States of America6Department of Biostatistics, Harvard School of Public HealthBoston, MassachusettsUnited States of America9 2004 29 6 2004 29 6 2004 2 9 e24716 12 2003 26 5 2004 Copyright: © 2004 Blackshaw et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Genomic Analysis of Retinal Development in the Mouse The vertebrate retina is comprised of seven major cell types that are generated in overlapping but well-defined intervals. To identify genes that might regulate retinal development, gene expression in the developing retina was profiled at multiple time points using serial analysis of gene expression (SAGE). The expression patterns of 1,051 genes that showed developmentally dynamic expression by SAGE were investigated using in situ hybridization. A molecular atlas of gene expression in the developing and mature retina was thereby constructed, along with a taxonomic classification of developmental gene expression patterns. Genes were identified that label both temporal and spatial subsets of mitotic progenitor cells. For each developing and mature major retinal cell type, genes selectively expressed in that cell type were identified. The gene expression profiles of retinal Müller glia and mitotic progenitor cells were found to be highly similar, suggesting that Müller glia might serve to produce multiple retinal cell types under the right conditions. In addition, multiple transcripts that were evolutionarily conserved that did not appear to encode open reading frames of more than 100 amino acids in length (“noncoding RNAs”) were found to be dynamically and specifically expressed in developing and mature retinal cell types. Finally, many photoreceptor-enriched genes that mapped to chromosomal intervals containing retinal disease genes were identified. These data serve as a starting point for functional investigations of the roles of these genes in retinal development and physiology. Spatial and temporal patterns of expression for over 1000 genes in identified retinal cells invites functional investigations into the role of these genes in development and physiology ==== Body Introduction The vertebrate retina is a model system for studying both the development and function of the central nervous system (CNS). Only six major types of neurons develop within the retina, along with a single type of glial cell (Rodieck 1998). These cells are readily distinguished from one another by morphology and laminar position within the retina. Birthdating studies have shown that retinal cell types are generated in overlapping intervals, with ganglion cells, cone photoreceptors, amacrine cells, and horizontal cells generated prior to birth, and bipolar neurons and Müller glia generated after birth in mice (Sidman 1961; Young 1985a, 1985b). Rod photoreceptors, the most abundant retinal cell type in the retina, are born both pre- and postnatally, with a peak of genesis coincident with the day of birth in the mouse. These birthdating studies, together with heterochronic coculture experiments (Belliveau and Cepko 1999; Belliveau et al. 2000; Rappaport et al. 2001), heterochronic transplantation (Rappaport et al. 2001), and lineage analysis (Turner and Cepko 1987; Holt et al. 1988; Wetts and Fraser 1988; Turner et al. 1990), have given rise to the competence model of retinal cell fate specification (Cepko et al. 1996). The competence model states that the intrinsic ability of mitotic retinal progenitor cells to produce a particular cell fate changes continually through development. A cell produces only a single fate, or a subset of fates, at any one time even though lineage analysis has shown that most retinal progenitors have the potential to produce many or all fates over the entire period of retinal development. Interestingly, even at one time in development, retinal progenitor cells show heterogeneity in their developmental competence (Alexiades and Cepko 1997; Belliveau and Cepko 1999; Belliveau et al. 2000; Rapaport et al. 2001). In addition to the contribution of intrinsic determinants of cell fate specification, the fates chosen by the daughters of a retinal progenitor may be influenced by extrinsic factors (Watanabe and Raff 1990; Altschuler et al. 1993; Kelley et al. 1994; Levine et al. 1997, 2000; Belliveau and Cepko 1999; Young and Cepko 2004). Finally, certain aspects of retinal cell fate choice, such as the specification of at least some rod and bipolar cells, appear to occur in postmitotic cells (Ezzeddine et al. 1997). Although the competence model was formulated to explain cell fate choice in the retina, it is clear that cell specification in many other regions of the developing nervous system—including neural crest (Selleck and Bronner-Fraser 1996), spinal cord (Ericson et al. 1996), and cerebral cortex (McConnell 1988; Qian et al. 2000)—involve changes in progenitor competence over time, frequently resulting in altered sensitivity to extrinsic factors. The model of temporal changes in competence is strongly supported by recent elegant studies of Drosophila CNS development (Isshiki et al. 2001; Pearson and Doe 2003), where a temporal order of transcription factor expression was found to set the context of cell fate determination. The fundamental similarity among these systems nonetheless accommodates mechanistic differences. The situation in the retina, where early progenitor cells cannot be induced to adopt late fates and vice versa (although see James et al. [2003] for a possible exception to this rule), is distinct from the progressive developmental restriction that is seen in the cerebral cortex, where early cortical progenitor cells are competent to generate cells of upper (late-born) and lower (early-born) layers of the cortex, but become restricted to generating only late-born fates as development proceeds (Desai and McConnell 2000). It is not known what genes mediate changes in progenitor competence during retinal development. Likewise, it is not known to what extent individual retinal progenitor cells from a single time point differ in their developmental competence from one another, although a few genes that are expressed in distinct subsets of progenitor cells have been found (Austin et al. 1995; Matter et al. 1995; Alexiades and Cepko 1997; Dyer and Cepko 2000a; Brown et al. 2001; Wang et al. 2001). Moreover, the genes that regulate the differentiation of any retinal cell type following commitment to a specific fate are generally poorly understood, although a number of transcription factors such as Crx, Nrl, and NR2E3 (Chen et al. 1997; Furukawa et al. 1997a; Haider et al. 2001; Mears et al. 2001) are clearly important in rod development. Unbiased, comprehensive expression profiling studies offer the possibility of identifying the molecular components and networks underlying these processes, as well as revealing target genes involved in intermediate and terminal differentiation of individual retinal cell types. We have used serial analysis of gene expression (SAGE) to profile gene expression during the development of the mouse retina (Blackshaw et al. 2001). SAGE, which provides an unbiased and nearly comprehensive readout of gene expression, is conceptually very much like expressed sequence tag (EST) sequencing, with the difference being that concatenated libraries of short sequence tags derived from each cDNA found in the sample of interest are sequenced (Velculescu et al. 1995). By identifying genes that show dynamic expression via SAGE and testing the cellular expression of these genes via in situ hybridization (ISH), we can identify genes that potentially regulate proliferation, cell fate determination, and cell differentiation. Furthermore, by examining SAGE libraries made from adult tissue, genes that are specifically expressed in mature cell types can be identified. By employing both SAGE-based expression profiling and large-scale ISH analysis to determine cellular expression of developmentally dynamic transcripts, we aim to combine the strengths of these two approaches and obtain a detailed picture of molecular events taking place during development of the retina. The laminar structure of the retina, which allows identification of the major cell types expressing a transcript under examination, makes large-scale ISH particularly informative relative to many other regions of the nervous system. Results/Discussion Summary of SAGE Data SAGE was conducted on mouse retinal tissue taken at 2-d intervals from near the start of neurogenesis at embryonic day 12.5 (E12.5) to nearly the end of neurogenesis at postnatal day 6.5 (P6.5). In addition, libraries were made from P10 wild-type mice and the adult retina. Previously generated SAGE data from the microdissected outer nuclear layer (ONL) of the retina, which comprises roughly 97% rod photoreceptors, from retinal tissue from mice that were deficient for Crx (littermates of the wild-type P10 mice), and from adult hypothalamus were also incorporated into the analysis (Blackshaw et al. 2001). All of these libraries were sequenced to a depth of 50,000–60,000 SAGE tags each 14 bp long. Table S1 lists the number of distinct tags found in the 12 retina1 libraries and their abundance levels, along with the number of tags that do not match any known transcript. While 10% of all unique tags found twice or more in the 12 libraries did not correspond to an identified transcript, only 3% of the tags found five times or more did not match a known transcript (Table S1). Table S2 lists all individual tag levels in each of these retinal libraries, along with data from a number of other publicly available nonretinal mouse libraries. We have also created a database, accessible at http://134.174.53.82/Cepko/, that is searchable by gene name, SAGE tag sequence, accession number, genome location, or UniGene number. It displays all SAGE tags and their levels, as well as ISH images (see below). The accuracy of the SAGE data was assessed by comparing the 15,268 SAGE tags from E14.5 retina to an unnormalized and unsubtracted set of 15,268 ESTs generated by another research group from E14.5 mouse retina of a different strain (Mu et al. 2001). An r-value of 0.65 (see Figure S1) was obtained that compares well with SAGE expression profiles obtained in similar tissues but from different individuals that were not strain-matched (Blackshaw et al. 2003). Analysis of SAGE Tag Expression Patterns in Developing Retina Using Cluster Analysis In order to determine whether the temporal pattern of a gene's expression during retinal development might predict its cellular site of expression or its molecular function, clusters of coexpressed genes were assembled. The ten libraries obtained from wild-type total retina were analyzed by cluster analysis using a new Poisson model–based k-means algorithm designed specifically for SAGE data (Cai et al. 2004) (see Materials and Methods for a full description of the algorithm and the protocols used). The results for a 24-cluster analysis are shown graphically in Figure 1. Table 1 provides a list of previously characterized genes corresponding to tags within these clusters, the number of genes associated with tags within each cluster that were tested via ISH, and select functional categories of genes that were enriched in specific clusters. Table S3 lists all SAGE tags used in the analysis and their corresponding cluster assignments. Figure 1 Median Plot of SAGE Tag K-Means Cluster Analysis Using 24 Clusters Tags present at greater than 0.1% in one or more of the ten wild-type total retina libraries are considered. SAGE libraries are plotted on the x-axis, and tag abundance, plotted as a fraction of the total tags for a gene in the library in question, is shown on the y-axis. A full list of tags and their abundance levels used for the analysis is detailed in Table S3. Table 1 Summary of SAGE Tag K-Means Cluster Data Tags present at greater than 0.1% in one or more of the ten wild-type total retina libraries were considered. The number of SAGE tags in each cluster is shown, along with the number and percentage of SAGE tags in each cluster that match genes whose expression was examined by ISH in developing retina. Selected genes that were previously examined in the context of retinal development are indicated. P-values for GO categories that are overrepresented in individual clusters were calculated using EASE (Hosack et al. 2003) and represent raw EASE scores for the categories in question Virtually every gene previously reported to regulate retinal development was detected in this analysis and showed dynamic expression during development. Several of these transcripts were found at high levels during their period of peak expression. For instance, NeuroD1—which regulates rod photoreceptor survival, as well as possibly rod differentiation (Morrow et al. 1999; Wang et al. 2001)—makes up 0.34% of all retinal mRNA at P4.5. In the case of genes previously shown to be required for production of certain cell types in the developing retina, such as Ath5 and Chx10—which are required for ganglion cell and bipolar neurons , respectively (Burmeister et al. 1996; Morrow et al. 1999; Brown et al. 2001; Wang et al. 2001)—peak expression typically occurred around or just after the peak time of exit from mitosis for that cell type. Certain functional categories of genes were highly overrepresented in a number of SAGE tag clusters. Ribosomal proteins, which typically showed higher expression early in development, were highly enriched in clusters 5, 9, 10, 15, and 23 (Table 1)—clusters that also were enriched for cell cycle regulators (particularly clusters 10 and 23). Mitochondrial proteins, by contrast, were concentrated in clusters 4 and 5. Cluster 2 consisted entirely of crystallins, which may be due to contamination by lens tissue in the E12.5 and P0.5 libraries. Phototransduction genes, on the other hand, were found to be concentrated in the late-onset clusters 1, 21, 22, and 24. Genes representing a number of other functional categories also were enriched in specific clusters, although the reasons in these cases are not clear. Examples of this include the concentration of genes involved in RNA processing in clusters 6 and 7, genes coding membrane transporters in cluster 10, and genes that are involved in vesicle-mediated transport in cluster 20. Large-Scale ISH of Dynamically Expressed Genes Genes identified by SAGE were chosen for analysis via ISH by focusing on genes that showed dynamic expression by k-means cluster analysis using Euclidean distance, and some degree of retinal enrichment (i.e., genes were expressed at lower levels in nonretinal SAGE libraries—see Table S2). Within this data set, genes whose presumptive function suggested that they might regulate cell fate choice (e.g., transcription factors, growth factors and their receptors, etc.) received highest priority for testing, although many genes of unknown function with developmentally dynamic expression also were tested. See Table S4 for the Gene Ontology Consortium (GO) classification of each probe tested. The analysis was restricted to genes represented by at least 0.1% of total SAGE TAGS in at least one of the retinal libraries, so as to control for sampling variability and to allow for ready detection via ISH. (Exceptions were made for a number of transcription factors and other genes of potentially major functional interest.) This abundance threshold was met by 4,133 tags. Probes corresponding to 1,051 of these tags were tested via ISH. This total included the 346 candidate photoreceptor-enriched genes tested in our previous work (Blackshaw et al. 2001), as well as 37 previously characterized retinal genes that served as positive controls for ISH and to allow clarification of cellular expression patterns. Retinal expression was examined at every time point used for SAGE (see Materials and Methods for details). See Table S5 for a full list of the cellular expression data for each probe in the retina, along with the accession number of the cDNA used to generate each probe used for ISH. See also http://134.174.53.82/cepko/ for images of all of the ISH data. Classification of Cellular Gene Expression Patterns in the Developing Retina The laminar structure of the retina makes it relatively straightforward to assign a tentative identity to cells expressing a given gene. During early stages of retinal development, the outer neuroblastic layer (ONBL) consists almost entirely of mitotic progenitor cells, while newborn neurons (mostly consisting of amacrine and ganglion cells) reside in the inner neuroblastic layer (INBL). The position of mitotic progenitors within the ONBL varies depending upon their progress through the cell cycle, with S phase cells being found on the vitreal side of the ONBL near the border with the INBL and M-phase cells being found on the scleral side of the ONBL abutting the retinal pigment epithelium (Young 1985a, 1985b). Around the time of birth, immature photoreceptors occupy the outer portion of the ONBL. They are comingled with mitotic cells of the G2, M, and G1 phases of the cell cycle, while the S phase mitotic progenitors are in the vitreal side of the ONBL. Finally, by P6, most retinal cells occupy their final positions within the retina. Rod and cone photoreceptors occupy the ONL. Bipolar neuron cell bodies occupy the scleral portion of the inner nuclear layer (INL); the cell bodies of Müller glia occupy a strip in the center of the INL; and amacrine cell bodies are found in the vitreal portion of the INL. The ganglion cell layer (GCL) contains both ganglion cells and a displaced amacrine cells. In the developing retina, expression in the scleral and vitreal portions of both the ONBL and INBL were scored separately, along with whether the gene in question was expressed in all or only a subset of cells in the layer in question. In the case of the adult retina, cell identity in wild-type animals could be scored readily by laminar position of the cells expressing the gene of interest (Rodiek 1998), and thus the identity of expressing cells was scored directly. Extracting order from the diversity of gene expression patterns observed in the developing nervous system can be a daunting task. It is not obvious how best to generate a useful taxonomy of these expression patterns. In tackling this problem, we found it useful to classify cellular expression patterns of genes both by eye and by clustering software. Both methods have specific advantages—user classification more readily identifies rare but distinct patterns, while machine-based clustering allows more flexibility with respect to cluster number and appears to better accommodate classification of intermediate patterns. All classifications were based on the location of the ISH signal within the retinal layers over time during development. Table S6 contains the full list of expression patterns generated by visual inspection, and Table S7 has the full list of cellular expression clusters generated by clustering software. See Materials and Methods for more details on how these data were generated. Comparison of the user-annotated and machine-generated clusters demonstrated fairly strong similarities between the two sets of clusters (Table S8), although genes placed in a single category by user annotation were invariably grouped into larger clusters by clustering software. On the other hand, genes in certain large clusters generated by user annotation—such as panretinal, TRAP2-like, and Nlk-like (see Table S6)—were dispersed among many clusters in the machine-generated data sets, with placement within particular clusters varying with replicate program runs. Genes in these categories were expressed at some level in most cells of the developing and mature retina. This variability likely reflects the relative lack of specificity of the expression pattern in these clusters. The finding that most of the highly cell-specific clusters identified by user annotation were readily distinguished by the clustering software supports this hypothesis (Table S8). Using SAGE Data to Predict Cellular Expression Patterns in Developing Retina Temporal changes in gene expression as measured by SAGE turn out to be a useful but inexact method of predicting cellular expression patterns of genes within the retina. While no SAGE cluster was invariably associated with a given cellular expression pattern, genes in certain late-onset SAGE clusters (e.g., clusters 1 and 22) were highly likely to be expressed in developing rods. In the case of early-onset gene expression patterns, which would likely be expressed in retinal progenitor cells, comparison to a microarray-based study could be made. Microarray profiling data of 4N progenitor enriched versus 2N cells has led to the identification of a number of these genes as being enriched in 4N progenitor cells (Livesey et al. 2004). These genes were concentrated in a limited number of SAGE tag clusters (particularly clusters 5, 15, and 23), but were largely absent from clusters that showed a perinatal peak in expression (such as cluster 6), which were enriched for genes expressed in developing rods, bipolars, and amacrine cells (see Table S9 for a full breakdown of 4N-enriched genes by SAGE tag cluster). In general, the temporal expression pattern observed in a given SAGE tag cluster was accurately reflected by the ISH data, although precise prediction of cellular expression patterns based on cluster data were not achieved. Clusters that showed postnatal peaks in expression, such as cluster 6, could contain a great diversity of cellular expression patterns, yet still be enriched for genes that showed strong expression in specific cell types that were differentiating. Table S10, which details the percentage of tags in a given cluster that represent each specific user-annotated expression pattern, can serve as a starting point for predicting the probability that a gene matching a given SAGE tag will show a given cellular expression pattern in the developing retina. The expression clusters—whether generated by user annotation or clustering software—at best represent a lower limit to the number of distinct expression patterns within the developing retina. Although the number of distinct types of cells in the developing retina is not known, it is undoubtedly high (MacNeil and Masland 1998). Particularly when considering genes expressed in subsets of cells in the ONBL, or subsets of developing amacrine cells, the level of resolution of our ISH-based screen does not allow one to distinguish many of the more complex patterns. Techniques such as multiple-probe fluorescence-based ISH (Levsky et al. 2002) and single-cell microarray analysis (Tietjen et al. 2003) will be required to resolve such questions as whether individual cells coexpress genes that display complex expression patterns. One interesting and potentially useful finding from the SAGE cluster data is that genes known to have highly selective cell-specific expression within a single retinal cell type could show different times of onset of expression. For instance, there is heterogeneity in the time of onset of expression among the genes that mediate rod phototransduction, a feature that has previously been reported in ferret retina (Johnson et al. 2001). Phototransduction genes were found in four different clusters (see Table 1), with genes such as RPGRIP showing comparatively early onset of expression, followed by the progressively later onset timesof rod arrestin, rhodopsin, and, finally, Gα1 and GCAP1 (see Table S11 for a full list of tags corresponding to these genes). ISH confirmed the accuracy of the SAGE data for these onset times (see Figure S2). This heterogeneity of the time of onset of expression is observed for terminal differentiation markers of every cell type studied in the retina, as well as for markers of subsets of mitotic progenitor cells (see http://134.174.53.82/cepko/ for the full set of ISH data). Such profiles could be explored for the possibility of control by cascades of transcription factors. Gene Expression Patterns Define Subsets of Retinal Progenitor Cells Recent studies in systems as diverse as Drosophila neuroblast specification and the specification of neural-crest-derived cells (Anderson 1999; Isshiki et al. 2001; Pearson and Doe 2003) have demonstrated the role of temporal changes in gene expression in the specification of neural cells. With respect to the retina, the competence model as originally proposed predicted that mitotic progenitor cells would show both temporal changes in gene expression across broad sets of retinal progenitors, and expression of selected genes in specific subsets of progenitor cells at a given time (Cepko et al. 1996). We have identified a number of genes that show temporally restricted expression in early ONBL. By analyzing the expression of a large number of genes that were highly expressed early in development (particularly in SAGE tag clusters 5, 11, and 15), a number of genes that are expressed in broad but temporally restricted subsets of mitotic progenitor cells were identified (Figure 2A). sFrp2 RNA was found to be broadly expressed in the ONBL until E16, after which it rapidly decreased, a pattern that corresponded well with its SAGE tag levels. Expression of Fgf15 and Edr RNA was seen to persist longer, but neither was easily detected after P0, at which time both cyclin D1 mRNA—a recognized marker of mitotic progenitor cells in the retina (Sicinski et al. 1995; Ma et al. 1998)—and BrdU labeling were still readily detectable in the central retina. Edr RNA showed an unusual patchy distribution in the ONBL at P0—a pattern that was not detected for any other gene tested and has not been previously reported. Lhx2, by contrast, was weakly expressed in subsets of cells in the ONBL until P0, when it was dramatically and transiently upregulated throughout the ONBL. Microarray analysis of 4N versus 2N retinal cells at E16 indicates that both sFrp2 and Lhx2 are enriched in 4N mitotic progenitor cells (Livesey et al. 2004). Figure 2 Genes Expressed in Subsets of Mitotic Progenitors (A) Genes expressed in temporally distinct subsets of progenitors. The first column shows relative SAGE tag levels for each gene under consideration. The UniGene identities and common names of the genes in question are Mm.19155/sFrp2, Mm.3904/Fgf15, Mm.142856/Lhx2, Mm.35829/Edr, and Mm.22288/cyclin D1. The sections for ISH and BrdU shown here were taken from near the center of the retina at the developmental times shown. Mice were albino Swiss Websters except in the case of the adults, which were pigmented C57B/6. See Table S5 for a full list of probes used. Cellular laminae of both the developing and mature retina are indicated with colored bars. All pictures were taken at 200x. The graph plotting the fraction of mitotic cells in the retina adjacent to the BrdU staining is an estimate based on data from both rat and mouse (Young 1985a, 1985b; Alexiades and Cepko. 1996). (B) Spatially heterogeneous ONBL. Genes that were expressed in spatial subsets of cells in the prenatal ONBL are shown. The genes shown are Mm.4541/Sox2, Mm.18789/Sox4, Mm.4605/Tbx2, Mm.29067/Mbtd1, Mm.2229/Eya2, Mm.34701/Pum1, Mm.29924/Arl6ip1, Mm.11738/Ark-1, Mm.40321/Pgrmc2, and Mm.22288/cyclin D1. Sections were from central retina. Cellular laminae of both the developing and mature retina are indicated with colored bars. All pictures were taken at 200x. See Table S5 for a full list of probes used. To further investigate the expression of these genes in mitotic progenitor cells, ISH was performed on dissociated retinal cells in conjunction with 3H thymidine labeling at E14, E16, and P0 (Table 2). A substantially lower fraction of double-labeled cells for Fgf15 at P0 relative to earlier time points was observed, while sFrp2 labeling was absent at birth and substantially lower at E16 than at E14. Table 2 Fraction of Progenitor Cells Expressing sFRP2 and FGF15 Decreased as Development Proceeded Retinal explants were labeled with 3H-thymidine for 1 h, and then disociated and placed on slides. ISH was performed and the fraction of cells expressing sFRP2 and FGF15 is indicated, along with the fraction of cells labeled with 3H-thymidine, and the fraction of 3H-thymidine-positive cells that were labeled with probe A limited number of genes have previously been reported as expressed in subsets of mitotic retinal progenitor cells, including genes such as Ath5, and have been shown to be required for retinal ganglion cell development (Brown et al. 2001; Wang et al. 2001). We identified a large number of genes that showed selective expression at certain times during development in relatively small subsets of cells in the ONBL (Figure 2B). These include a large number of known and putative transcription factors, such as Sox2, Sox4, Tbx2, Eya2 and Mbtd1 (a novel polycomb family member), along with many genes of other functional classes. Particularly intriguing is the early and transient expression of Pum1, a mammalian homolog of the pumilio gene, which has been shown to mediate asymmetric mRNA distribution in Drosophila (Micklem 1995). Many of these genes showed highly dynamic expression during development—rapidly shifting their cellular expression patterns in the course of a few days, as in the case of Pum1 and Sox2, or being expressed for only a few days, as in the case of Eya2 and Pgrmc2. In some cases, these subsets were scattered throughout the ONBL, such as Eya2 at E14, while for other genes, such as Pum1 and Pgrmc2, expression was in only the scleral portion of the ONBL, suggesting that these genes may show strongest expression near M phase in retinal progenitor cells. From these data, it is difficult to determine whether most of these genes were expressed in cycling progenitor cells or cells that have newly exited from mitosis, as these two populations are intermingled in the ONBL. However, microarray analysis of 4N versus 2N cells of the early retina (Livesey et al. 2004) has indicated that a number of these genes, such as Sox2, are enriched in 4N progenitor cells. See Figure S3 for more examples of genes expressed in subsets of ONBL cells and contrast with Figure S4, which shows genes with broad but selective expression in the ONBL. The genes that are expressed in subsets of presumptive retinal progenitors include a large number of transcription factors (e.g., Sox2, Lhx2, and Eya2) as well as signal transduction components. These intrinsically acting factors represent potential candidates for regulating developmental competence and, by analogy with the Drosophila retina, may act combinatorially to help specify cell fate (Flores et al. 2000). Furthermore, a number of genes that are expressed in temporal subsets of progenitor cells encode secreted differentiation factors such as FGF15 and sFRP2. Since cell fate choice is determined by the interaction of intrinsic properties and extrinsic factors, these genes are good candidate regulators of cell fate determination. Strikingly, the temporal expression profile of very few progenitor-enriched cell cycle genes tracked precisely with the fraction of mitotic cells in the retina. Even many well-established markers of mitotic progenitor cells, such as cyclinD1 and cdk4 were highly expressed until P2.5 and detectably expressed as late as P6.5—long after the fraction of mitotic cells in the retina had decreased drastically (Figure 2A). These data imply that expression of these genes frequently persists after the end of mitosis. In addition, one might have predicted that the levels of cell cycle regulators would be highest at the earliest time point analyzed (E12.5), when the percentage of mitotic cells was highest. However, we found that progenitor-enriched genes such as cyclinD1 and cdk4 often had RNA levels that peaked around P0.5. This observation suggests that the number of mRNA molecules per cell for many of the genes that mediate mitotic activity increases as development proceeds. The functional significance of these findings is unclear, although a number of features of retinal progenitor cells change over the course of development, including the length of the cell cycle (Young 1985a; Alexiades and Cepko 1996) and the probability of producing progeny that are no longer mitotic (Livesey and Cepko 2001). Genes Expressed in Immature Differentiating Retinal Cell Subtypes One characteristic expression pattern of genes likely to be involved in cell fate specification and/or the early steps of the differentiation process is restriction to newly postmitotic cells and cells actively undergoing differentiation. Many of the genes demonstrated to show such expression in developing retina, such as Crx, Nrl, and NR2E3 (Furukawa et al. 1997a, 1997b; Chen et al. 1997; Haider et al. 2001; Mears et al. 2001) have been shown to play an active role in regulating cell differentiation. We have identified genes that are selectively expressed in immature postmitotic retinal cells of every major class, with the exception of cone photoreceptors, greatly expanding the set of genes known to be selectively expressed in immature retinal precursor cells (Figure 3). KIAA0013, an uncharacterized RhoGAP, was found to be expressed exclusively in immature ganglion cells, and only expressed detectably outside in limited subsets of developing neurons, such as Cajal-Retzius cells of the developing cerebral cortex, and the developing thymus. Cdc42GAP was found to be strongly and transiently expressed in newly postmitotic rods, while the leucine zipper transcription factor Zf-1 was expressed in presumptive bipolar cells. Septin 4 was found to be selectively and persistently expressed in developing horizontal cells, while Mm.23916, a novel dual-specificity protein phosphatase, was found to be expressed selectively in immature amacrine cells. Finally Tweety1, an unconventional chloride channel (Suzuki and Mizuno 2004) was strongly expressed in newly postmitotic Müller glia. Along with genes whose cellular expression could be clearly identified visually, a number of genes with strong but transient expression in undefined subsets of cells of the neonatal retina were observed. Expression of these genes persisted after the end of mitosis in the central retina (see Figure 2A), so at least some of the cells that express them must be postmitotic. Genes in this category include inhibin βB, brain fatty acid binding protein 7, BMP7, the transcription factor Sal3, and the orphan neurotransmitter transporter NTT7(see Figure S5). Figure 3 Precursor Patterns for Major Retinal Cell Types Genes that are selectively expressed in immature subtypes of retinal cells. From the top, the differentiating cell types that express the genes in question are ganglion cells (Mm.45753/KIAA0013), rod photoreceptors (Mm.103742/Cdc42GAP), bipolar cells (Mm.29496/Zf-1), horizontal cells (Mm.2214 /septin 4), amacrine cells (Mm.23916), and Müller glia (Mm.29729/Tweety1). Sections were from central retina. Cellular laminae of both the developing and mature retina are indicated with colored bars. All pictures were taken at 200x. See Table S5 for a full list of probes used. Genes Expressed in Developing Photoreceptor Cells Rod photoreceptors make up 70% of cells in the retina (Young et al. 1985b; Jeon et al. 1998). The SAGE-derived expression profile of genes selectively expressed in developing rods is thus more comprehensive than that of other cell types. Based on the ISH data and aided by our SAGE study of mature tissue (Blackshaw et al. 2001), as well as previous reports of mutant mice lacking transcription factors known to be important for rod development, a model of a temporal order of transcription factor expression during rod development was made (Figure 4). Transcription factors known to be involved in cell fate specification sometimes show broad expression in mitotic progenitor cells and persistent expression in mature cell types (e.g., Liu et al. 1994; Belecky-Adams et al. 1997; Livesey and Cepko 2001). We observed a number of genes that were expressed in early ONBL from E16 on, with expression persisting in mature photoreceptors, such as Yboxbp4. A similar pattern were seen for the mouse ortholog of the Drosophila castor gene, though this gene was observed in a more restricted subset of cells in the ONBL at E16, and for the orphan nuclear receptor ERRβ, although this gene had relatively lower expression prenatally and had pronounced expression in an undefined subset of cells in the immature photoreceptor layer during the first postnatal week. Figure 4 Transcription Factor Cascade in Photoreceptor Development Transcription factors that are selectively expressed in developing rods (and possibly cones as well) are shown. The schematic diagram integrates gene expression data from previously identified photoreceptor-enriched transcription factors and from genes explored in this study. The genes shown are Mm.193526/Yboxbp4, Mm.3499/Rax, Mm. 89623/mCas, Mm.1635/PIAS3, and Mm.235550/ERRβ. See Figure S6 for images of the developmental expression patterns of previously characterized transcription factors. Sections were from central retina. Cellular laminae of both the developing and mature retina are indicated with colored bars. All pictures were taken at 200x. See Table S5 for a full list of probes used. In contrast to being expressed in mitotic cells as well as differentiating photoreceptor cells, a number of transcription factors were selectively expressed in postmitotic but immature photoreceptors. The Rax homeodomain factor showed, as has been previously reported (Furukawa et al. 1997a; Mathers et al. 1997), strong expression in mitotic progenitor cells in the ONBL that vanished with the end of mitosis. However, expression transiently reappeared in immature photoreceptors at P8. This situation is analogous to that seen in a number of other vertebrates, in which a duplication of the ancestral Rax gene has resulted in Rax genes with distinct expression in photoreceptor and progenitor cells (Chuang et al. 1999; Chen and Cepko 2002). PIAS3, which encodes a SUMO lyase that directly regulates the activity of a broad subset of transcription factors (Kotaja et al. 2002; Haider et al. 2001), was strongly and selectively expressed only in developing photoreceptors, with expression beginning at E18, peaking at P8, and largely fading away in the adult, a pattern that in many respects is reminiscent of Crx (see Figure S6). In contrast to these patterns, Nrl and NR2E3 showed no detectable expression prenatally, and showed peak expression around P6. Somewhat surprisingly, the RNAs for many of these transcription factors is enriched in the inner segments of photoreceptors, as are a large fraction of the other photoreceptor-enriched genes characterized in this study, a finding that is in line with our earlier work (Blackshaw, et al. 2001). The functional significance of this remains unclear. In addition to transcription factors, other functional classes of genes, including genes of unknown function, were expressed in developing photoreceptors, with strongest expression typically found in the first postnatal week (Figure S7). In some cases, these genes fall into pathways known to regulate rod differentiation. Both PIAS3 and the multifunctional protein Hrs (Chung et al. 1997; Scoles et al. 2002) selectively inhibit STAT3, and thus possibly inhibit the action of ciliary-derived neurotrophic factor, a factor that has been shown to inhibit rod differentiation in rodents (Ezzeddine et al. 1997; Kirsch et al. 1998; Schulz-Key et al. 2002). Cdc42GAP expression (see Figure 2) may mediate the polarization and initiation of outer segment formation taking place in photoreceptors at this time (Nobes and Hall 1999). In other cases, genes newly identified as selectively expressed in developing photoreceptors imply the existence of novel facets of photoreceptor development. The expression of synaptic vesicle protein Cpx2 suggests that developing photoreceptors may be actively secreting some developmentally relevant signal, while the expression of Hrs also potentially suggests high levels of regulated endocytosis and destruction of unknown extracellular proteins (Lu et al. 2003). The expression of the previously uncharacterized tumor necrosis factor family member Tnfsf13 and A20-like signal transduction components such as TRABID and Fln29 suggest an unexplored role for this pathway in normal photoreceptor development. Genes Expressed in Developing Interneurons of the INL Many genes were selectively expressed in the other, nonphotoreceptor retinal cell types during development. A temporal sequence of transcription factors was observed in bipolar cells as they differentiated (Figure S8). The homeodomain factor Lhx4, and the uncharacterized leucine-zipper protein Zf-1 (see Figure 2), showed expression at E16 in the ONBL, with expression continuing postnatally and persisting in adult bipolar cells. Zfh4 was expressed in developing amacrine cells and in subsets of cells in the ONBL prior to P4, and was robustly and transiently expressed in bipolar cells, with peak expression at P6. The relatively late-onset Dbp was first seen in the second postnatal week across the INL. Chx10, as has been previously reported (Liu et al. 1994), and Gli5 were broadly expressed across the ONBL prior to P4, at which point they both showed elevated expression in developing bipolar cells. Microarray analysis confirmed that both of these genes are expressed in mitotic progenitor cells (Livesey et al. 2004). Possible downstream targets of these transcription factors include previously uncharacterized cell adhesion molecules such as the Ig-superfamily member Mm.41284, kinases such as Prkcl, and the putative growth factor receptor SEZ-6. Furthermore, despite the fact that they comprise only 0.3% of the cells in the adult retina, genes that are highly enriched in both developing and mature horizontal cells (Figure S9), such as the GTPase regulator Borg4, were found. Many genes tested by ISH were selectively expressed in developing amacrine cells (Figure S10). The expression patterns were tremendously diverse, a fact that may reflect the reported extensive heterogeneity among amacrine cell subtypes (MacNeil and Masland 1998). Certain genes, such as the kinase Unc51-like-1, ArfGAP, and the orphan G-protein-coupled receptor Mm.6393, were found to be expressed both in immature amacrine cells and in subsets of cells in the ONBL, particularly in the region of the ONBL that comprises the outer or scleral surface, where M phase mitotic progenitor cells are localized. Cytoskeletal-associated kinases such as Unc51-like-1, and small GTPases such as ArfGAP, may play a role in neurite extension or process formation. Additionally, the expression of neuropeptide receptors such as Mm.6393 in the ONBL before mature neural circuits have formed fits with data from other parts of the developing CNS showing early expression of neurotransmitter receptors and suggesting that neurotransmitters may act on mitotic progenitor cells to regulate cell cycle or cell fate specification (Rueda et al. 2002; Ohtani et al. 2003). Similarly, recent work from our laboratory on the role of glycine receptors in the formation of rod photoreceptors (Young and Cepko 2004) confirms such predictions for at least one such receptor. Other genes, such as syntrophin-associated kinase and the novel dual-specificity phosphatase Mm.23916, were confined to immature amacrines only. Syntrophin-associated kinase, in particular, may regulate maturation of synaptic connections (Lumeng et al. 1999). Others genes, such as necdin, the basic helix-loop-helix transcription factor Nhlh2, and the novel PLC isoform Mm.215653, showed complex and often biphasic patterns. The Slit receptor robo3 was strongly and transiently expressed in the first postnatal week in a single sublamina within the INBL, perhaps corresponding to a single subtype of developing amacrine cells. A role for Slit-Robo signaling in regulating cortical dendrite maturation has been demonstrated (Whitford et al. 2002), and these data suggest such a mechanism may be at work in regulating subtype-specific amacrine cell laminae formation in the retina. Neuropeptide Y was strongly and transiently expressed in a subset of amacrine and horizontal cells towards the end of the first postnatal week, with expression dropping dramatically in the adult—suggesting a possible role for this factor in the formation of mature retinal circuitry. Finally Mm.41638, which is weakly homologous to a lysosomal membrane protein, was expressed solely in postnatal amacrine cells, though expression remained in a more restricted subset of amacrine cells in the adult. Müller Glia Are Highly Similar to Retinal Progenitor Cells Genes selectively expressed in Müller glia share a number of defining features. Mitotic retinal progenitor cells and Müller glia showed a great degree of transcriptional overlap—far more so than other retinal cells that differentiate postnatally. Of the genes identified as being specifically expressed in Müller glia after the first postnatal week, 68% were found to be enriched in mitotic progenitor cells based on their ISH pattern, in contrast to only 14% of photoreceptor-specific genes (Figure 5A). Of the genes identified as enriched in 4N progenitor cells by micorarray analysis (Livesey et al. 2004) that were tested by ISH in adult retina, 43% were enriched in Müller glia, compared to 11% that were enriched in photoreceptors. Figure 5 Müller-Glia-Enriched Genes (A) Müller-glia-enriched genes show stronger expression in retinal progenitors than do genes enriched in other postnatally born cell types. See Materials and Methods for details of how progenitor-enriched and cell-specific expression patterns were determined, and p-values for progenitor-enrichment of genes that are cell type–specific in the mature retina were calculated. Data on 4N-enriched transcripts were obtained from Livesey et al. (2004). Numbers for each value are as follows. For N, the number of cell-enriched genes, N MG = 86, N Pr = 112, N BC = 21, and N AC = 57. For I, the number of genes that show retinal progenitor-enriched patterns by ISH, I total = 180, I MG = 66, I PR = 15, I BC = 4, and I AC = 8. For M, the number of genes enriched in 4N retinal progenitor cells that were tested by ISH in adult retina, M total = 28, M MG = 12, M PR = 3, M BC = 3, and M AC = 1. *, p < 10−13; **, p < 0.0001. (B) Müller-glia-enriched genes show strong expression in mitotic progenitors. The genes shown are: Mm.26062/ADO24, Mm.55143/Dkk3, Mm.5021/DDR1, Mm.35817, Mm.20465/GPCR37, Mm.200608/clusterin, and Mm.22288/cyclin D1. Sections were from central retina. Cellular laminae of both the developing and mature retina are indicated with colored bars. All pictures were taken at 200x. See Table S5 for a full list of probes used. (C) Dynamic expression of metabolic genes in developing retina. Metabolic enzymes are often selectively expressed in mitotic progenitors and developing Müller glia. The genes shown are Mm.27953/glycine decarboxylase, Mm.9114/mu-crystallin, and Mm.213213/HK-R. Cellular laminae of both the developing and mature retina are indicated with colored bars. Sections were from central retina. All pictures were taken at 200x. See Table S5 for a full list of probes used. Typical expression patterns for Müller-glia-enriched genes are shown Figure 5B. Genes in this category, such as the negative regulator of Wnt signaling Dkk3, the collagen receptor DDR1, and the endosomal protein AD024, were observed to be strongly and broadly expressed across the ONBL throughout development, though expression in the adult was restricted to Müller glia. Microarray analysis suggests that a number of these genes, including Dkk3 and DDR1, are enriched in 4N mitotic progenitor cells (Livesey et al. 2004). A smaller set of genes, such as Mm.35817, GPCR37, and Tweety1 (see Figure 2) were found to be expressed across the ONBL early in development, but showed dramatically and transiently upregulated expression at the end of the first postnatal week as Müller glia began to differentiate. While over two-thirds of Müller-glia-enriched genes showed enriched expression in retinal progenitors relative to other cell types in the developing retina, virtually all Müller-glia-enriched genes were expressed at detectable levels in retinal progenitors (without necessarily being enriched in progenitors). In fact, only two genes that are Müller-specific in the adult—clusterin and carbonic anhydrase 2—were expressed in mature Müller glia but not detected in mitotic progenitors. However, previous work suggests that carbonic anhydrase 2 may be expressed in retinal progenitors at levels below our ability to detect (Vardimon et al. 1986), and this may be the case for clusterin as well. Additional Müller-glia-enriched genes are shown in Figure S11. The extensive overlap in gene expression between Müller glia and mitotic progenitor cells raises the question of how closely these two cell types resemble each other at the functional level. Müller glia morphologically resemble mitotic progenitor cells in having apical and basal processes that span the radial dimension of the retina (Rodiek 1998)—a feature that is shared with retinal progenitor cells as well as radial glia of the developing brain, a cell type known to be the cortical progenitor cell (Doetsch 2003). Müller glia are one of the last cell types to exit mitosis (Young 1985b; Reh and Levine 1998), and they are the only cell type in the mature retina that can reenter mitosis following retinal injury (Dyer and Cepko 2000b; Vetter and Moore 2001). Finally, data from chicken suggest that, at least in some birds, Müller glia can be induced to divide and give rise to some types of retinal neurons for a short period of time near the end of retinal development (Fischer and Reh 2001). The question arises, then, as to whether Müller glia are fundamentally multipotent progenitor cells that are quiescent regarding cell division and the production of neurons (Morest and Silver 2003; Walcott and Provis 2003). If they are progenitor cells, they are progenitor cells that have acquired the specialized properties needed for a support role in the mature retina, e.g., neurotransmitter reuptake and structural roles. The few genes that are specifically expressed in mature Müller glia, such as clusterin, may be emblematic of such roles. Misexpression in mature Müller glia of genes that are candidates for regulating neuronal production in the postnatal retina, followed by injury-induced division, offers a potential approach for future therapies that might lead to photoreceptor or ganglion cell replacement in diseased retinas by cells derived from Müller glia. Prominent Expression of Metabolic Enzymes in Developing Müller Glia A second notable feature of genes expressed nearly specifically in developing Müller glia is the highly dynamic and cell-specific expression of a number of metabolic enzymes (Figure 5). The novel hexokinase-related gene HK-R was selectively expressed in developing Müller glia cells, but not in any other cell in the body examined. Mu-crystallin, which does not encode a crystallin in placental mammals but rather an uncharacterized homolog of the bacterial enzyme ornithine cyclodeaminase (Segovia et al. 1997), showed a similar expression pattern in the retina but also was expressed in other developing sensory organs. Glycine decarboxylase was strongly and selectively expressed in retinal progenitor cells, differentiating Müller glia, and to a lesser extent, developing photoreceptors. The reasons for such high enzymatic activity in development is unclear, although some of these genes may have regulatory functions unconnected to their metabolic roles. For instance, mu-crystallin is also a thyroid hormone binding protein (Vie et al. 1997). Such proteins also may regulate the abundance of small molecules that can act as signals that may be relevant for development. For example, glycine levels may be kept low by glycine decarboxylase so that taurine can bind to and activate the glycine receptor to promote rod differentiation (Young and Cepko 2004). These data point to future directions of research examining the intersection of metabolism and development and suggest the usefulness of supplementing gene expression profiling with metabolomic analysis (Watkins and German 2002). Dynamic Expression of Putative Noncoding RNAs in Developing Retina A number of RNA transcripts that do not appear to encode proteins were strongly expressed in the developing retina (Figure 6). These transcripts are typically spliced and polyadenylated, but do not encode evolutionarily conserved open reading frames (ORFs), or any ORFs encoding proteins longer than 100 amino acids, while often showing high similarity at the nucleotide level between mouse and human (Numata et al. 2003). Table S12 provides a list of these transcripts. Putative noncoding transcripts that showed developmentally dynamic expression include retinal noncoding RNA 1 (RNCR1), which was expressed throughout the ONBL during early development and which was later restricted to Müller glia. It was transcribed in a head-to-head fashion, and largely coexpressed, with Six3. This transcript showed extensive alternative splicing, and while one splice form contained a potential ORF of greater than 100 amino acids, no mouse/human conservation of this putative protein was observed, while high similarity was observed at the nucleotide level in other regions of the transcript. RNCR2 , on the other hand, was expressed in a large subset of cells in both the ONBL and INBL prenatally, with expression restricted to the INL and GCL postnatally. ISH signal for RNCR2 was strongly concentrated in what appeared to be nuclear or perinuclear regions of expressing cells. RNCR3 was expressed in a steadily increasing subset of cells in the ONBL from E14 and gradually resolved to an adult pattern that was photoreceptor-enriched but present in the inner retina at lower levels. Figure 6 Noncoding RNAs in Retinal Development A number of presumptive noncoding RNAs are strongly expressed in dynamic subsets of retinal progenitor and precursor cells. The transcripts shown are Mm.150838/ RNCR1, Mm.44854/ RNCR2, and Mm.194050/RNCR3. Sections were from central retina. Cellular laminae of both the developing and mature retina are indicated with colored bars. All pictures were taken at 200x. See Table S5 for a full list of probes used. Although additional assays are required to conclusively demonstrate that these RNAs do not encode functional proteins, there is precedent for this conclusion from recent genomic work. Large-scale EST sequencing efforts from mouse have uncovered up to several thousand putative spliced transcripts that do not appear to encode for proteins (Numata et al. 2003). Likewise, oligonucleotide array experiments using probes that tile individual human chromosomes at high density report substantial transcription from many regions not predicted to have protein-coding genes (Kapranov et al. 2002; Cawley et al. 2004), and suggest that microarray-based expression profiling that uses probes designed only against predicted protein-coding genes may miss a significant fraction of the transcriptome. The functional role of these transcripts is obscure, although noncoding spliced RNAs such as Xist and H19 in mammals and Rox1 and Rox2 in Drosophila have been implicated in a variety of epigenetic processes (Mattick 2003). The possibility that RNCR1 might somehow regulate expression of Six3 or other progenitor-specific transcripts awaits further investigation. Both Xist and Tsix, noncoding RNAs that play a crucial role in X-inactivation, were expressed in subsets of cells in the ONBL and INBL early in development, but were expressed strongly and selectively in the INL around the end of the first postnatal week (Figure S12). This finding is quite surprising, given that photoreceptors and ganglion cells do not express these transcripts and would thus appear to escape X-inactivation. Since genetic evidence suggests that this is not the case for either cell type (Reese et al. 1999), our findings implicate the existence of possibilities such as alternate cell-specific pathways of X-inactivation or dramatic cell-specific variations in Xist levels required to mediate X-inactivation. Expression Profiling and Candidate Gene Analysis Although we have identified a plethora of transcription factors, growth factors, and signal transduction components, the data do not clearly implicate a known signaling pathway as selectively involved in the differentiation of a given cell type within the retina. For example, negative regulators of Wnt signaling were identified, but these genes display a diversity of cellular expression patterns that cloud a simple model for their action. Dkk3 and Nkd1 are expressed broadly in progenitor cells and Müller glia, together with beta-catenin, while sFRP-2 is expressed exclusively in early progenitor cells, and Nlk is expressed strongly in postmitotic but immature cells of the postnatal retina. Another approach to the creation of models of pathways that control retinal development is to combine the ISH analysis of genes identified via SAGE with a candidate gene approach, even for genes not identified by SAGE. For example, we examined the expression of all known regulators of Wnt signaling, all fibroblast growth factor receptors, and all Slit and Robo genes whether or not SAGE tags corresponding to these genes were identified. See Table S5 and http://134.174.53.82/cepko/ for a full list of genes and their expression patterns. Cell-Specific Gene Expression in the Mature Retina Identifies Candidate Retinal Disease Genes A molecular catalog of gene expression in the adult retina was assembled with molecular markers for every major class of retinal cell (Figure 7). The catalog of photoreceptor-enriched genes reported in previous work (Blackshaw et al. 2001) was expanded, and a large number of genes expressed in the inner retina were identified. Some of these include genes that mark subsets of amacrine and ganglion cells. Knowledge of which genes show cell-specific expression in the retina can aid in identifying retinal disease genes. The expression of nearly half of all cloned photoreceptor dystrophy genes is selectively enriched in photoreceptors (Blackshaw et al. 2001), while hereditary optic neuropathies have been suggested to be partially mediated by mutations in ganglion-cell-enriched genes (Votruba et al. 1998). Furthermore, a number of other retinal and anterior segment abnormalities result from mutations in genes that are broadly expressed in retinal progenitor cells (Hanson et al. 1999; Ferda Percin et al. 2000). See Table S13 for a full list of the chromosomal locations of the human orthologs of genes examined in this work. This list also contains a full list of mapped but unidentified Mendelian human retinal disease genes and orthologs of photoreceptor-enriched genes identified in this work that lie within those chromosomal intervals. A total of 164 photoreceptor-enriched genes not previously linked to retinal disease were found in chromosomal intervals containing retinal disease loci, representing a total of 42 distinct loci. While photoreceptor-enriched transcripts make up roughly half of all cloned retinal disease genes (Blackshaw et al. 2001), roughly one-third of retinal disease genes are expressed in all cells of the retina, suggesting that it is fruitful to consider such genes when screening candidate disease genes. We find that 22 panretinally expressed genes map within intervals containing unidentified disease genes, representing 16 distinct loci. Figure 7 Catalog of Gene Expression in Adult Retina The most commonly observed patterns of gene expression in the adult retina are indicated. Data are taken from Table S5 and cover all genes examined in the adult retina. Genes are placed in a category corresponding to a single cell type if expression is substantially greater in that cell type than in any of the other cell types examined. Genes are placed in categories corresponding to multiple cell types if expression is approximately equal in more than one cell type. The number of genes expressed in photoreceptors and Müller glia differs somewhat from those used in the analysis shown in Figure 5A, since the expression of a large number of photoreceptor-enriched genes was not examined prenatally, and a number of Müller-enriched genes were detectable in Müller glia through the end of the second postnatal week, but not in adult retina. AC, amacrine cells; BC, bipolar cells; GC,ganglion cells; HC, horizontal cells; MG, Müller glia; sAC, subset of amacrine cells; sBC, subset of bipolar cells; sGC, subset of ganglion cells Genomic Approaches to Development The retina consists of a number of distinct cell types that are relatively well defined morphologically, as well as molecularly. They undergo differentiation in defined intervals and are found in stereotypical locations within the retina. These characteristics allow a fairly straightforward evaluation of the cell-specific expression of genes within the retina. We have coupled SAGE-based expression profiling with large-scale ISH analysis to obtain an atlas of gene expression for the developing and mature retina. This atlas is useful for many purposes—in particular, providing many candidate genes for studies of retinal development and function. SAGE analysis can be nearly comprehensive (Velculescu et al. 1995), but its sensitivity is limited by the number of tags sequenced, the level of expression of a transcript within a given cell, and the abundance of given cell subtypes within a tissue sample. Thus this analysis detected relatively rare cell-specific transcripts primarily for the abundant rod photoreceptors and their precursors, and for genes broadly expressed in retinal progenitor cells. Nonetheless, the catalog does include some genes selectively expressed even in the rarest cell types, such as the horizontal cells (0.3% of all retinal cells; Jeon et al. 1998) and subtypes of ganglion cells, as well as genes expressed selectively in small subsets of cells in the early ONBL. A recent microarray-based study in developing neural crest screened over 90 candidate genes via ISH (Gammill and Bronner-Fraser 2002), and a recent study using serial stages of embryonic Drosophila has analyzed hundreds of genes by such methods (Tomancak et al. 2002). However, while a number of recent studies have used microarray analysis to profile developing neural tissue, large-scale ISH-based validation of genes identified as being expressed in developing CNS by such expression profiling has not yet been conducted. Large-scale ISH studies enhance our ability to interpret expression profiling data, as the precise cellular expression of a gene in heterogeneous tissues of the developing nervous system cannot be inferred reliably from the profiling of bulk tissue. Other considerations underscore the benefits of verifying primary expression data from expression profiling methods by using other approaches. For instance, several studies describing microarray-based expression profiling of similar starting material have obtained contrasting results for sets of differentially regulated genes (Claridge-Chang et al. 2001; McDonald and Rosbash 2001; Lin et al. 2002; Ivanova et al. 2002; Ramalho-Santos et al. 2002). These may result from either experimental variation among labs or biological variation in gene expression among the samples and individuals tested (Pritchard et al. 2001; Blackshaw et al. 2003), but nonetheless suggest that large-scale verification of expression differences by techniques such as quantitative RT-PCR or ISH would aid interpretation of such differences. Studies that rely on large-scale ISH as an initial screen generate vast amounts of data, but typically have been conducted using sets of identified or random cDNAs without using expression screening to preselect genes that show high or dynamic expression in the tissue of interest (Gawantka et al. 1998; Neidhardt et al. 2000; Kudoh et al. 2001; Thut et al. 2001). Using expression profiling to generate a set of candidate genes for large-scale ISH analysis will increase the probability of testing genes that show enriched or dynamic expression in a tissue of interest. Towards a Functional Genomics of Neural Development The data presented here provide the starting point for medium-throughput functional analysis of the role of many genes in retinal development. The use of in vivo electroporation (Matsuda and Cepko 2004) and plasmid constructs encoding small inhibitory RNAs delivered by electroporation or retroviruses will make possible medium-throughput gain- and loss-of-function studies of gene function in the retina. The identification of a variety of progenitor subtypes and stage-specific precursor markers will enable a deeper interpretation of such studies. Construction of appropriate Cre lines will allow lineage analysis to determine with precision the mature cell types to which subsets of mitotic progenitor cells or posmitotic precursors give rise. Combining the knowledge of cell-specific transcription factors and cell-specific target genes, together with bioinformatic approaches that take advantage of mammalian genome sequence information in a manner like recent efforts in Drosophila (Stathopoulos et al. 2002), may allow the characterization of the combinatorial code of cis- and trans-acting elements that specify mature neuronal identity. We anticipate that similar approaches are likely to be useful in any region of a developing tissue where birthdating studies have been conducted and cell subtypes can be readily identified based on their spatial localization. Materials and Methods Generation of SAGE libraries Isolation of mouse brain and retinal tissue, as well as construction of all SAGE libraries derived from retinal and hypothalamic tissue, was conducted as previously described (Blackshaw et al. 2001). Publicly available mouse libraries used in the analysis include 3T3 fibroblasts (obtained from http://www.sagenet.org), P8 cerebellar granule precursor cells maintained in culture for 24 h (GCPcntr; obtained from http://www.ncbi.nlm.nih.gov/SAGE), P8 cerebellar granule precursor cells maintained in culture and treated with Shh for 24 h (GCP+SHH; obtained from http://www.ncbi.nlm.nih.gov/SAGE), freshly harvested P8 cerebellar granule precursor cells (GC_P8; obtained from http://www.ncbi.nlm.nih.gov/SAGE). Libraries from E15 and P1 cerebral cortex were obtained from Gunnersen, et al. 2002. ). All retinal and hypothalamic SAGE data have been submitted to NCBI, and will be available for download at http://www.ncbi.nlm.nih.gov/SAGE. SAGE data analysis The SAGE 3.0.1 program (courtesy of Victor Velculescu and Ken Kinzler, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States) was used to extract SAGE tags and eliminate duplicate ditags. Identity of SAGE tags was obtained from the National Center for Biotechnology Information (NCBI) “reliable” tag map set for UniGene (available at http://www.ncbi.nlm.nih.gov/SAGE). UniGene Build 131 of Mus musculus (http://www.ncbi.nlm.nih.gov/UniGene) was used for the mappings. In cases where ISH results for genes matching a “reliable” tag did not match the temporal expression profile for the tag in question, along with all cases of unknown tags (i.e., tags which had no “reliable” tag to gene assignment) that were present at greater than 0.1% of total tags in any one SAGE library, the genes were tested via NCBI BLASTN searching (http://www.ncbi.nlm.nih.gov/BLAST/) against the nr and dbest databases, with Expect threshold set to 100 (Karlin and Altschul 1990). A tag was considered to match a specific transcript if it corresponded to the 3′-most NlaIII site in a given polyadenylated transcript (Velculescu et al. 1995). If no such match was found, tags matching the 3′-most NlaII sites in 5′ reads of retinal-derived MGC cDNAs (Strausberg et al. 2002) were considered to match those transcripts, in cases where no further 3′ sequence information was available for those ESTs. Each tag representing a gene tested by ISH, moreover, was checked by BLASTN using these parameters to verify the accuracy of the NCBI tag-to-gene matches. Human orthologs of mouse genes were identified through the use of the Homologene data set and verified by BLASTN and/or BLASTX analysis using the NCBI server, or BLAT analysis using the University of California at Santa Cruz genome server (http://genome.ucsc.edu). In cases where no curated ortholog was present in the database, BLASTN analysis against nr, dbest, and htgs databases was used to identify transcripts that showed over 85% sequence conservation over 100 bp and did not match any repeat sequence. The University of California at Santa Cruz genome browser using the October 2003 freeze (http://genome.ucsc.edu/cgi-bin/hgGateway) was used to determine if any transcripts with no obvious coding sequence mapped within 5 kb of the 3′ end of an identified gene and were transcribed in the sense orientation relative to that gene. If so, these were considered to represent novel 3′ ends of that gene. All other data analysis and curation was conducted with Microsoft Excel and Microsoft Access. Tissue section, ISH, and BrdU staining ISH was conducted as previously described (Blackshaw et al. 2001). For BrdU staining, mice were given a single interperitoneal injection of 37.5 mg/kg BrdU and killed 1 h later. Fresh-frozen sections were used following 15 min fixation in 4% paraformaldehyde. The protocol of BrdU staining was carried out using an anti-BrdU monoclonal antibody (Roche, Basel, Switzerland) and detected using an AP-conjugated secondary antibody, using recommended blocking and washing conditions. Dissociated cell ISH Retinas were dissected from E14.5, E16.5, and P0 mice and cultured for 1 h in DMEM/10% fetal calf serum containing 5 μCi/ml 3H-thymidine. The labeled retinas were dissociated into single cells by incubating for 30 min at 37 °C in 100 units/ml of papain (Worthington Biochemical, Lakewood, New Jersey, United States) in Hank's balanced salt solution (HBSS) containing 10 mM HEPES (pH 7.6), 2.5 mM cysteine, and 0.5 mM EDTA. The suspensions were then gently triturated and incubated with 0.1 mg/ml DNase I for 10 min at 37 °C. The cells were pelleted, washed twice in HBSS, and plated on polyD-lysine-coated glass slides for 15 min at room temperature. Cells were fixed to the slides in 4% paraformaldehyde for 5 min at room temperature, washed twice in PBS, and dehydrated in 100% methanol. For acetylation, probe incubation, and subsequent washings, the in situ protocol detailed herein for tissue sections was used. A tyramide signal amplification system (TSA Plus, PerkinElmer, Wellesey, Massachusetts, United States) combined with an anti-digoxigenin-HRP antibody (Roche) was used according to the manufacturer's instructions to detect the signal. Autoradiographic processing was performed in emulsion (NTB2, Eastman Kodak, Rochester, New York, United States) exactly as previously described (Alexiades and Cepko 1996). Classification of cellular expression data in retina by user-based classification and cluster analysis Two classification schemes of the patterns of expression over time were developed: human and machine-aided. In the first case, a single observer (S.B.) generated a presumptive minimal classification of expression patterns following visual inspection of each hybridization pattern (see Table S6 for a full list). This subjective classification took into account a relatively informal assessment of signal intensity. This approach yielded a total of 72 distinct patterns, of which 19 contained only a single member. In the second case, laminar expression within the retina was scored on a 0–5 point scale based upon visual inspection for each defined cell type in the prenatal, perinatal, and mature retina, and cluster analysis software was used to perform k-means clustering (using Euclidean distance) of cellular expression patterns (see Table S7 for the full data set). As with the cluster analysis of the SAGE data, in order to determine an optimal minimal number of clusters, the total distance among data points within the clusters of cellular expression data (within cluster dispersion) were plotted for cluster sizes from 10 to 65 over 100 simulations (Table S14) using Euclidean distance measure (De Hoon et al. 2004). Algorithms used for this analysis are available at http://bonsai.ims.u-tokyo.ac.jp/mdehoon/software/cluster/index.html. It was found that at approximately 45 clusters there was a pronounced discontinuity in the rate of change in the distance among points within the cluster, and this was adopted as a tentative minimal number of clusters. Determination of cell-enriched expression in adult retina and retinal progenitor cells For the data presented in Figure 5A, numerical cellular expression data from Table S7 was used. Transcripts were assayed as enriched in a specific cell type if they showed highest (but not necessarily exclusive) expression in the cell type in question after the first postnatal week of life. Genes enriched in subsets of bipolars or amacrines were treated as bipolar- and amacrine-enriched, respectively. Whether or not a gene showed retinal-progenitor-enriched expression was determined from Table S7 by the following empirical set of criteria, which were found to cover virtually all known retinal-progenitor-enriched genes: early vO/svO or scO/sscO greater than 1, early (scO + sscO + vO + svO) greater than early (scI + sscI + vI + svI), early (vO + svO) greater than or equal to early (scO + sscO), and mid (vO + svO) greater than mid (scO + sscO). (See legend of Table S5 for a key to these abbreviations.) To determine whether genes that are cell type–specific in the adult retina are disproportionately enriched in retinal progenitors (see Figure 5A), we have used the hypergeometric distribution statistical analysis to compute the probability that a subset of genes of a given size will have a given number of occurrences of the pattern we examine, when chosen randomly from the group of all known genes (Johnson et al. 1992). Cluster analysis of SAGE data Considering the numerous types of transcripts present in a cell or tissue and the small probability of sampling a particular type of transcript at each draw, the number of sampled transcripts of each type is assumed to be approximately Poisson distributed. Statistically, when this actual sampling process is random enough, Poisson would be the most practical and reasonable assumption compared to other probability models. This assumption, with the assumption that each tag is uniquely mapped to a transcript, leads to the probability model used for clustering analysis of SAGE data (below). First, all SAGE tags were assigned at random to k groups. Second, a cluster center, which led to the expected expression pattern of each tag, was calculated for each cluster. Chi-square test statistics were used to measure the distance between the observed expression pattern and the expected expression pattern of a tag in a cluster. Third, using an iterative method, tags were moved between clusters, and intra- and intercluster distances were measured with each move. Tags were allowed to remain in the new cluster only if they were closer to it than to their previous cluster. Fourth, after each move, the expression vectors for each cluster were recalculated. Last, the shuffling proceeded until moving any more tags made the clusters more variable, increasing intracluster distances and decreasing intercluster dissimilarity (see Protocol S1 for full details of the algorithms used, as well as Cai, et al. 2004 for a more detailed discussion of applications of the protocol). To compute optimal values for the number of clusters k, the within-cluster dispersion was computed for increasing values of k. This within-cluster dispersion declined as new clusters were added. We thus looked for the reduction at each step, and observed the rate of change. Discontinuities in the rate of change were taken to indicate that a meaningful cluster number had been obtained, with the lowest number of clusters that showed such a discontinuity being used for analysis (Hartigan 1975; Yeung et al. 2001). In order to determine the optimal number of clusters to use in the analysis of the SAGE data, the within-cluster dispersion was determined for a range of ten to 65 clusters over 100 iterations. If certain numbers of clusters gave a better fit to the data, they should show discontinuities in the rate of decrease (Hartigan 1975). It was found that setting the number of k-means clusters at around 25, 40, and 55 showed these features (see Table S15) Database construction Data from 21 SAGE libraries and ISH images were gathered and stored in a MySQL relational database (http://www.mysql.com). Information on the measurement values for the SAGE libraries and ISH images can be accessed at http://134.174.53.82/cepko/. The database was developed to provide up-to-date mapping of SAGE tags to UniGene clusters. Since a single sequence tag can represent different genes and, conversely, an individual UniGene cluster can be represented by more than one tag, both “full” and “reliable” tag-to-UniGene mappings (Lash et al. 2000) have been created and can be selected by the user. The cluster assignments and their reliability were obtained from NCBI SAGEmap (http://www.ncbi.nlm.nih.gov/SAGE). For the database reported herein, UniGene Build 131 of Mus musculus and Build 164 of Homo sapiens (http://www.ncbi.nlm.nih.gov/UniGene) were used for the mappings. However, the database at http://134.174.53.82/cepko/ includes up-to-date mapping data. For each UniGene cluster, all measurement values and ISH images of associated tags are provided. Measurement values can also be segregated and summed up for each library if more than one SAGE tag is mapped to a given UniGene cluster. A plot of measurement values was also created to visualize patterns across the SAGE libraries. Additionally, for each UniGene cluster, links to gene functions using GO, accession numbers for annotated human orthologs, and LocusLink IDs have been provided. Supporting Information Figures S2–S12 show ISH data for genes that show dynamic expression in developing retina. All pictures were obtained from central retina. Cellular laminae of both the developing and mature retina are indicated with colored bars. All pictures were taken at 200x. See Table S5 for a full list of probes used. Figure S1 Comparison of E14.5 EST Versus E14.5 SAGE Data The number of times a gene was observed in a set of 15,268 individual ESTs obtained from E14.5 mouse retina (data obtained from Mu et al. [2001]) compared to a set of 15,268 individual E14.5 retinal SAGE tags generated in this study. Only genes present at least ten times in the EST data set were considered. (1.7 MB TIF). Click here for additional data file. Figure S2 Heterogeneous Developmental Onset of Phototransduction Gene Expression The genes shown are rod arrestin, PrCdh, Gγ1, rod PDEγ, rhodopsin, peripherin 2, Gα1, and GCAP1. (26.9 MB TIF). Click here for additional data file. Figure S3 Genes Expressed in Subsets of Cells in Developing ONBL Sections were from central retina. The genes shown are Otx2, RORβ, Yboxbp1, Mm.38347, Mm.11660, BTF3, H2Ax, Ppp1r14b, Grb10, Mm.158631, HMG-AT1, Mm.24141, KIAA1411, Mm.25018, IAP5, and Chaf1b. (25.7 MB TIF). Click here for additional data file. Figure S4 Genes Expressed Broadly in Mitotic Progenitors The genes shown are PDK3, Giα2, β-catenin, LRC8, Nrarp, Foxn4, and HMG-17. (27.4 MB TIF). Click here for additional data file. Figure S5 Genes Expressed in Undefined Subsets of Progenitors/Precursors The genes shown are FABP7, BMP7, NTT7, Inhibin βB, and Sal3. (20.8 MB TIF). Click here for additional data file. Figure S6 Known Transcription Factors Expressed in Developing Rods These data are shown to allow direct comparison with the data in Figures 4 and S7. The genes shown are NeuroD1, Crx, Nrl, and NR2E3. (24.9 MB TIF). Click here for additional data file. Figure S7 Genes Expressed in Developing Rods The genes shown are Cpx2, TRABID, Fln29, Mak, Mm.24642, Nlk, Hrs, Tnfsf13, and Arip2. (18.3 MB TIF). Click here for additional data file. Figure S8 Genes Expressed in Developing Bipolar Cells The genes shown are Chx10, Gli5, Dbp, Lhx4, Mm.41284, Prkcl, SEZ-6, and Zfh4. (21.4 MB TIF). Click here for additional data file. Figure S9 Genes Expressed in Developing Horizontal Cells The gene shown is Borg4. (11.0 MB TIF). Click here for additional data file. Figure S10 Genes Expressed in Developing Amacrine Cells The genes shown are Unc-51-like-1, ArfGAP, robo3, necdin, SAK, Mm.6393, Mm.34130, Nhlh2, NPY, Mm.21657, Mm.215653, and Mm.41638. (19.1 MB TIF). Click here for additional data file. Figure S11 Genes Expressed in Developing Müller Glia The genes shown are KIAA0937, Mm.157502, Slc38a3, Nkd1, Dsp8, carbonic anhydrase 2, and cyclin D1. (40.1 MB TIF). Click here for additional data file. Figure S12 Additional Noncoding RNAs Expressed in Developing Retina The genes shown are MEG3, Xist, and Tsix. (13.6 MB TIF). Click here for additional data file. Protocol S1 Description of Methodology Used for Cluster Analysis of SAGE Tags (52 KB DOC). Click here for additional data file. Table S1 Summary of SAGE Tag Distribution The total cumulative number of tags found at each abundance level in all 12 retinal libraries (i.e., the ten libraries from total retinal of wild-type animals, the library from P10.5 crx−/− animals, and the library from microdissected ONL of adult animals) is shown. The number of tags, and the fraction of total tags, that do not show any reliable match for any gene (data from NCBI) are also shown. (14 KB XLS). Click here for additional data file. Table S2 Full List of Tag Counts in All SAGE Libraries Considered This list includes not only all libraries made from retinal tissue, but also nonretinal SAGE libraries made by this group, and other mouse libraries that are publicly available. Raw, unnormalized tag counts are shown. See Materials and Methods for more details on the SAGE libraries analyzed. (17.9 MB TXT). Click here for additional data file. Table S3 Twenty-Four-Cluster Analysis for SAGE Tags All tag abundance levels were normalized to 100,000. Tags present at greater than 0.1% in one or more of the ten wild-type total retina libraries were considered. The single most probable “reliable” tag-to-gene match (http://www.ncbi.nlm.nih.gov/SAGE) is shown, along with the confidence level of that assignment. Mouse UniGene number is shown for each tag-to-gene match, along with LocusLink ID, where available. In each case where a gene was analyzed by ISH in developing retina, that fact is indicated in the final column. In some cases, a gene that matched the tag with a lower confidence level was tested. In these cases, the UniGene number of the gene tested by ISH differs from that of the most probable tag match. (1.0 MB XLS). Click here for additional data file. Table S4 Molecular Function, Biological Process, and Subcellular Compartment GO Data Are Shown for Each Gene Analyzed by ISH in the Retina Gene names and LocusLink IDs for these genes are also shown (225 KB XLS). Click here for additional data file. Table S5 Complete List of Cellular Expression Patterns for Each Probe Tested The SAGE tag matching each gene tested is given, as well as the accession number of the cDNA used to generate each probe used for ISH. Cellular expression is scored on a 0–5 point scale for each time point tested, as well as for E16 embryo and P6 head cut in horizontal section. A, amacrine cells; Ast, astrocytes; B, bipolar cells; Bv, blood vessels; Cb, cerebellum; CM, ciliary margin; CP, cortical plate; Ctx, cerebral cortex; DG, dentate gyrus of hippocampus; DRG, dorsal root ganglia; EGL, external granule layer of developing cerebellum; EOM, extraocular muscles; G, ganglion cells; H, horizontal cells; Hippo, hippocampus; I, inner neuroblastic layer; In, inner nuclear layer; MG, Müller glia; MGE, medial ganglionic eminence; ND, not determined; O, outer neuroblastic layer; OB, olfactory bulb; OE, olfactory epithelium; ORN, olfactory receptor neurons; P, panretinal; PC, Purkinje cells; PNS, peripheral nervous system; Pr, photoreceptors; Pr(is), inner segments of photoreceptors; sA, subset of amacrine cells; sB, subset of bipolar cells; SC, spinal cord; sG, subset of ganglion cells; sI, subset of cells in INBL; sIn, subset of cells in INL; scI, scleral INBL; sscI, subset of cells in scleral INBL; svI, subset of cells in vitreal INBL; sO, subset of cells in outer neuroblastic layer; scO, scleral ONBL; sscO, subsets of cells in scleral ONBL; svO, subset of cells in vitreal ONBL; sPr, subset of photoreceptors; SVZ, subventricular zone; vI, vitreal INBL; vO, vitreal ONBL; VRN, vomeronasal receptor neurons; VZ, ventricular zone. (381 KB XLS). Click here for additional data file. Table S6 User-Curated Cellular Expression Clusters for Genes Tested by ISH in Retina Here, data from Table S5 are summarized such that the predominant cellular expression pattern from early (E12–E18), mid (P0–P4), and late (P6–adult) developing retina is recorded, and genes are grouped into coexpressed clusters by user annotation. The main cell types expressing the gene in the retina over the interval in question are listed, with weaker expression in other cell types being noted in parentheses. Clusters are given a name (after a representative gene) and a unique cluster number, and the presumptive cell types that show greatest expression are listed. Genes for which the full developmental expression profile was not determined are tentatively assigned to clusters that showed the best fit based on two out of three criteria, with tentative assignments being indicated as such (261 KB XLS). Click here for additional data file. Table S7 Numerical Cellular Expression Data Used for Machine-Aided Cluster Analysis of Cellular Expression Patterns of Genes Tested by ISH in Retina To obtain these numbers, data from Table S5 were modified. As in Figure S6, expression data were summarized for early (E12–E18), mid (P0–P4), and late (P6–adult) developing retina. In cases where cellular expression changed dramatically within one of these three intervals (e.g., expression shifted from INBL to ONBL), these cellular expressions were both entered in the category in question. Genes that were not examined in all three of these time intervals were not considered in this analysis. Cellular expression data, scored on a 0–5 point scale, were then entered for each time point separately in each of the categories used to score retinal cellular expression in Table S5. (266 KB XLS). Click here for additional data file. Table S8 Comparison of User-Curated Cellular Expression Clusters from Table S6 and a 45-Cluster Machine-Aided Analysis of the Cellular Expression Data from Table S7 The fraction listed notes the fraction of genes in the machine-generated cluster that were found in a given user-curated cellular expression cluster. The presumptive cellular expression pattern of each user-curated cellular expression cluster is also listed (following Table S6). (86 KB XLS). Click here for additional data file. Table S9 Comparison of 4N-Enriched Genes from Livesey et al. (2004) and SAGE Cluster Data from Table S3 Shown is the percentage of tags that matched genes enriched in 4N retinal progenitor cells found in a given SAGE tag cluster. (14 KB XLS). Click here for additional data file. Table S10 Comparison of the SAGE Tag Cluster Data from Table S3 and the 72-Cluster Analysis of the User-Curated Cellular Expression Data from Table S6 Values indicate the fraction of all tags found in a given SAGE tag cluster that were found in a specific user-curated cellular expression cluster. The presumptive cellular expression pattern of each cellular expression cluster is also listed (following Table S6). (209 KB XLS). Click here for additional data file. Table S11 SAGE Tags Representing the Known Photoreceptor-Specific Genes Analyzed in Figure S2 Tags in each library are expressed as the fraction of all tags that match the gene in question that were found in the ten libraries considered. (15 KB XLS). Click here for additional data file. Table S12 Candidate Noncoding RNAs Analyzed by ISH in This Study The SAGE tag corresponding to the transcript in question is listed, along with UniGene numbers, and accession numbers of the probes used for ISH for each candidate noncoding RNA. P-values for BLASTN and BLASTX mouse/human comparisons are shown. Transcripts that show high BLASTN, but low BLASTX, matches to human may represent the best candidates for noncoding mRNAs of functional importance and are indicated as likely to be genuine noncoding RNAs. NS, not significant. (17 KB XLS). Click here for additional data file. Table S13 Accession Numbers for Full-Length Transcripts for Genes Tested by ISH in This Study, Along with Their Human Orthologs Chromosomal localizations are shown for both the mouse genes and their human orthologs. Genes located within chromosomal intervals containing mapped but uncloned retinal disease genes are indicated by the name of the disease (terminology from Retnet; http://www.sph.uth.tmc.edu/Retnet/disease.htm). User-curated cellular expression data of the genes in question (derived from Table S6) are shown to aid in prioritizing candidate disease genes for further investigation. ND, not determined. (291 KB XLS). Click here for additional data file. Table S14 Average Distance Analysis of Cellular Expression Data from Table S7 The values shown here are the average sum-of-squares within k-means clusters over all variables. Euclidian mean distance–directed clustering is used (Hartigan 1975). The proportional reduction of error (PRE) for each number of clusters is also shown. This measures the ratio of reduction in within-cluster dispersion to the previous within-cluster dispersion (Hartigan 1975). For this analysis, PRE is given by (Ni − N(i – 5))/Ni, where N is the average within-cluster distance and i is cluster number. (14 KB XLS). Click here for additional data file. Table S15 Average Distance Analysis of SAGE Tag Clusters Tags present at greater than 0.1% in one or more of the ten wild-type total retina libraries were considered and were normalized to 100,000 for this analysis. The average sum-of-squares within k-means clusters for each number of clusters is shown. The PRE, given by (Ni − N(i – 5))/Ni, is also shown. (14 KB XLS). Click here for additional data file. Accession Numbers The GenBank (www.ncbi.nlm.nih.gov) accession numbers for the genes discussed in this paper are β-catenin (NM_007614), ArfGAP (BC030682), Arip2 (NM_025292), BMP7 (NM_007557), Borg4 (NM_012121), brain fatty acid binding protein 7 (NM_021272), BTF3 (NM_145455), carbonic anhydrase 2 (NM_009801), cdk4 (NM_009870), Chaf1b (NM_028083), Chx10 (NM_007701), Cpx2 (NM_007756), Crx (NM_007770), Dbp (NM_016974), Drosophila castor gene (BC035954), Dsp8 (XM_181424), FABP7 (NM_021272), Fln29 (NM_172275), Foxn4 (NM_148935), GCAP1 (NM_008189), Giα2 (NM_008138), Gli5 (NM_031184), Grb10 (NM_010345), Gα1 (NM_008140), Gγ1 (NM_010314), H2Ax (NM_010436), HMG-17 (NM_016957), HMG-AT1 (NM_016660), Hrs (NM_008244), IAP5 (NM_009689), inhibin βB (BC048845), KIAA0937 (NM_172442), KIAA1411 (NM_026604), Lhx4 (NM_010712), LRC8 (NM_172736), Mak (NM_008547), MEG3 (NM_144513), Mm.103742/Cdc42GAP (NM_020260), Mm.11660 (AK034313), Mm.11738/Ark-1 (BC005425), Mm.142856/Lhx2 (NM_010710), Mm.150838/RNCR1 (AK044330), Mm.157502 (NM_026592), Mm.158631 (XM_132295), Mm.1635/PIAS3 (NM_018812), Mm.18789/Sox4 (NM_009238), Mm.19155/sFrp2 (NM_009144), Mm.193526/Yboxbp4 (NM_007705), Mm.194050/RNCR3 (AK044422), Mm.200608/clusterin (NM_013492), Mm.20465/GPCR37 (NM_010338), Mm.213213/HK-R (NM_145419), Mm.215653 (NM_183191), Mm.21657 (BC038057), Mm.2214/septin 4 (NM_011129), Mm.22288/cyclin D1 (NM_007631), Mm.2229/Eya2 (NM_010165), Mm.235550/ERRβ (NM_011934), Mm.23916 (AK009781), Mm.24141 (NM_025615), Mm.24642 (NM_146168), Mm.25018 (BC010304), Mm.26062/AD024 (NM_025565), Mm.27953/glycine decarboxylase (NM_138595), Mm.29067/Mbtd1 (NM_134012), Mm.29496/Zf-1 (AK004085), Mm.29729/Tweety1 (NM_021324), Mm.29924/Arl6ip1 (BC010196), Mm.34130 (AK012601), Mm.34701/Pum1 (NM_030722), Mm.3499/Rax homeodomain factor (NM_013833), Mm.35817 (NM_145940), Mm.35829/Edr (NM_133362), Mm.38347 (XM_126644), Mm.3904/Fgf15 (NM_008003), Mm.40321/Pgrmc2 (XM_130859), Mm.41284 (NM_153137), Mm.41638 (NM_029530), Mm.44854/RNCR2 (AK028326), Mm.4541/Sox2 (NM_011443), Mm.45753/KIAA0013 (NM_181416), Mm.4605/Tbx2 (NM_009324), Mm.5021/DDR1 (NM_007584), Mm.55143/Dkk3 (NM_015814), Mm.6393 (NM_010045), Mm.89623/mCas ( BC035954), Mm.9114/mu-crystallin (NM_016669), necdin (NM_010882), NeuroD1 (NM_010894 , Neuropeptide Y (NM_023456), Nhlh2 (NM_178777), Nkd1 (NM_027280), Nlk (NM_008702), NPY (NM_023456), NR2E3 (NM_013708), Nrarp (NM_025980), Nrl (NM_008736), NTT7 (NM_175328), Otx2 (NM_144841), PDK3 (NM_005391), peripherin 2 (NM_008938), Ppp1r14b (NM_008889), PrCdh (NM_130878), Prkcl (NM_008857), RGPRIP (NM_023879), rhodopsin (NM_145383), robo3 (NM_011248), rod arrestin (NM_009118), rod PDEγ (NM_012065), RORβ (NM_146095), SAK (NM_019945), Sal3 (NM_026528), SEZ-6 (NM_021286), Slc38a3 (NM_023805), syntrophin-associated kinase (NM_019945), Tnfsf13 (NM_023517), TRABID (AK005926), Tsix (AF138745), Unc-51-like-1 (NM_009469), Xist (AK011511), Yboxbp1 (NM_011732), and Zfh4 (NM_030708). We thank Mary Maresca-Jay, Sean Limberis, and Sarah Levy for assistance with generating photomicrographs of the ISH data. We thank Kornelia Polyak for advice regarding SAGE library construction, and Frederik Vannberg for assistance with SAGE library sequencing. SB was a Howard Hughes Medical Institute Fellow of the Life Sciences Research Foundation; JT is a Howard Hughes Medical Institute postdoctoral fellow; and REF was a Howard Hughes Medical Student Fellow. This work was supported by the Howard Hughes Medical Institute, National Institutes of Health grant EY08064, and a grant from the Foundation for Retinal Research to CLC. Much gratitude is extended to the Schwartz and Brint families for their generosity. WHW acknowledges the support of National Institutes of Health grant P20 CA96470. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. SB and CLC conceived and designed the experiments. SB, SH, JT, REF, RY, and EA performed the experiments. SB, LC, HH, and WHW analyzed the data. S-HC edited photomicrographs. SB, LC, HH, WPK, KL, LM-O, GW, and WHW contributed reagents/materials/analysis tools. SB and CLC wrote the paper. Academic Editor: William Harris, Cambridge University Citation: Blackshaw S, Harpavat S, Trimarchi J, Cai L, Huang H, et al. (2004) Genomic analysis of mouse retinal development. PLoS Biol 2(9):e247. Abbreviations CNScentral nervous system E[number]embryonic day [number] ESTexpressed sequence tag GCLganglion cell layer GOGene Ontology Consortium INLinner nuclear layer INBLinner neuroblastic layer ISHin situ hybridization NCBINational Center for Biotechnology Information ONLouter nuclear layer ONBLouter neuroblastic layer ORFopen reading frame P[number]postnatal day [number] RNCRretinal noncoding RNA SAGEserial analysis of gene expression ==== Refs References Alexiades MR Cepko CL Quantitative analysis of proliferation and cell cycle length during development of the rat retina Dev Dyn 1996 205 293 307 8850565 Alexiades MR Cepko CL Subsets of retinal progenitors display temporally regulated and distinct biases in the fates of their progeny Development 1997 124 1119 1131 9102299 Altshuler D Lo Turco JJ Rush J Cepko C Taurine promotes the differentiation of a vertebrate retinal cell type in vitro Development 1993 119 1317 1328 8306890 Anderson DJ Lineages and transcription factors in 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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020158Research ArticleCell BiologyDevelopmentDrosophilaSubcellular Localization of Frizzled Receptors, Mediated by Their Cytoplasmic Tails, Regulates Signaling Pathway Specificity Fz Localization and Pathway SpecificityWu Jun 1 Klein Thomas J 1 Mlodzik Marek [email protected] 1 1Brookdale Department of Molecular, Cell, and Developmental Biology, Mount Sinai School of MedicineNew York, New YorkUnited States of America7 2004 13 7 2004 13 7 2004 2 7 e15820 12 2003 24 3 2004 Copyright: © 2004 Wu et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. When It Comes to Frizzled-Mediated Development, Location Matters The Frizzled (Fz; called here Fz1) and Fz2 receptors have distinct signaling specificities activating either the canonical Wnt/β-catenin pathway or Fz/planar cell polarity (PCP) signaling in Drosophila. The regulation of signaling specificity remains largely obscure. We show that Fz1 and Fz2 have different subcellular localizations in imaginal disc epithelia, with Fz1 localizing preferentially to apical junctional complexes, and Fz2 being evenly distributed basolaterally. The subcellular localization difference directly contributes to the signaling specificity outcome. Whereas apical localization favors Fz/PCP signaling, it interferes with canonical Wnt/β-catenin signaling. Receptor localization is mediated by sequences in the cytoplasmic tail of Fz2 that appear to block apical accumulation. Based on these data, we propose that subcellular Fz localization, through the association with other membrane proteins, is a critical aspect in regulating the signaling specificity within the Wnt/Fz signaling pathways. Differential subcellular localization of Fz1 and Fz2 receptors contributes to signaling specificity within the Wnt/Fz signaling pathways ==== Body Introduction Pattern formation in multicellular organisms relies on inductive signaling events. Several evolutionarily conserved ligand–receptor combinations and associated signal transduction pathways are used again and again during development to induce tissue- and cell-type-specific responses. Thus, context-dependent signaling specificity is an important prerequisite for patterning and differentiation. Although for many signaling pathways the flow of information is largely established, the underlying signaling specificity mechanisms remain unclear. Members of the Frizzled (Fz) family of seven-pass transmembrane proteins act as receptors for the Wnt family of secreted ligands (Bhanot et al. 1996). In most cases, Wnt/Fz signal transduction leads to posttranslational stabilization of the intracellular protein β-catenin (β-cat) (β-cat or Armadillo [Arm] in Drosophila; reviewed in Polakis 1999, 2000). However, recent work has established that some Wnt ligands and Fz receptors can also signal through pathways independent of the Wnt/β-cat (Wg/Arm) cascade in certain contexts in vertebrates and invertebrates (reviewed in Mlodzik 2002; Veeman et al. 2003). In particular, the Fz/planar cell polarity (PCP) pathway has been studied extensively in both Drosophila and vertebrates (Adler 2002; Keller 2002; Mlodzik 2002; Tada et al. 2002; Strutt 2003). PCP is easy to study and evident in all adult tissues in Drosophila. For example, in wing cells the PCP response is the formation of an actin spike (the wing “hair”) that points distally, and in the eye PCP is manifest in the regular ommatidial arrangement in the anteroposterior and dorsoventral axes (reviewed in Adler 2002; Mlodzik 2002). These distinct PCP manifestations are regulated by the same set of genes, the so-called primary polarity genes, of which Fz is the most prominent and best studied. Similarly, this noncanonical Fz/PCP pathway has been implicated in PCP establishment in vertebrates, with prominent examples including the polarization of the sensory epithelium in the inner ear (Curtin et al. 2003; Dabdoub et al. 2003; Montcouquiol et al. 2003) and aspects of cell polarization in the convergent extension process during gastrulation (for a description of the similarities, pathway conservation, and specific readouts see reviews (Keller 2002; Mlodzik 2002; Veeman et al. 2003). Despite the increasing knowledge about the distinct pathways mediated by Wnt/Fz signaling, the regulation of Fz signaling specificity remains largely obscure. Both pathways, Wnt/β-cat and Fz/PCP, signal via Disheveled (Dsh) (reviewed in Boutros and Mlodzik 1999). This raises the intriguing question of how structurally very similar receptors can signal through a common protein into distinct downstream effector pathways. In Drosophila, Fz (for clarity we will refer to it as Fz1) and Fz2 are functionally redundant receptors for Wg, activating the canonical Wg/Arm cascade (Bhat 1998; Kennerdell and Carthew 1998; Bhanot et al. 1999; Chen and Struhl 1999). In addition to this redundant role in canonical signaling, Fz1 has a specific nonredundant role in the Fz/PCP pathway (Vinson and Adler 1987; Vinson et al. 1989). Subdomains of Fz1 and Fz2 have been analyzed with respect to the functional similarities and differences of the two receptors (Boutros et al. 2000; Rulifson et al. 2000; Strapps and Tomlinson 2001). These studies have suggested that signaling differences between Fz1 and Fz2 could lie in their different affinities for ligands (e.g., Wg has a 10-fold higher affinity for Fz2; Rulifson et al. 2000) and in additional cytoplasmic sequences which govern distinct intrinsic signaling preferences between Fz1 and Fz2 for the canonical and Fz/PCP pathways (Boutros et al. 2000; Strapps and Tomlinson 2001). Signaling specificity could be regulated by distinct Wnt-Fz combinations that would activate either the canonical or noncanonical pathway. Although a PCP-specific Wnt ligand for Fz1 has not yet been identified in flies, in vertebrates specific Wnt(s)-Fz(s) combinations are associated with either canonical or Fz/PCP signaling. However, the specificity is not simple. For example, although Wnt5a and Wnt11 cause embryonic phenotypes associated with the Fz/PCP-like pathway (Heisenberg et al. 2000; Tada and Smith 2000), coexpression of Wnt5a with Fz5 causes axis duplications, a canonical Wnt/β-cat phenotype (He et al. 1997). Similarly, vertebrate Fz7 receptors have been shown to affect both noncanonical (Djiane et al. 2000; Medina et al. 2000) and β-cat signaling (Kuhl et al. 2000). These data suggest that signaling specificity is not necessarily associated with a particular Wnt ligand or Fz receptor. Wnt/Fz signaling specificity may be determined, in part, by the presence of distinct coreceptors. For example, the Arrow-LRP5/6 protein acts as a Wnt/Wg coreceptor and is only required for Wnt/β-cat signaling (Tamai et al. 2000; Wehrli et al. 2000). No coreceptor of Fz1 has been reported for Fz/PCP signaling. Clearly this is a complicated issue and is likely to be context and cell-type dependent. Endogenous Fz2 is difficult to detect, but in the wing hinge region it is localized evenly in membranes along the apical–basal axis (M. Strigini, unpublished data). Similarly, overexpressed Fz2 (under dppGal4 control) is localized throughout the apical–basal axis of larval imaginal disc epithelia, and extracellular Wg binds to Fz2 predominantly at the basolateral membrane (Strigini and Cohen 2000), suggesting indirectly that canonical Wg/β-cat signaling is initiated at the basolateral cell surface. The existing anti-Fz antibodies are, similarly, not sensitive enough to detect endogenous levels of Fz protein (Krasnow and Adler 1994), but green fluorescent protein (GFP)–tagged Fz (Fz1-GFP) expressed under the control of a ubiquitous promoter shows apical localization in pupal wings and larval eye discs during PCP signaling (Strutt 2001; Strutt et al. 2002). All PCP molecules analyzed (Dsh, Flamingo [Fmi; a.k.a. Starry Night], Strabismus [a.k.a. Van Gogh], Prickle, and Diego ) are also localized in the apical region of pupal wings and eye epithelia (reviewed in Strutt 2003). Importantly, the apical localization of many PCP genes is lost in mutants of Fz1/PCP signaling components, suggesting that Fz/PCP signaling regulates apical localization (Axelrod 2001; Feiguin et al. 2001; Shimada et al. 2001; Strutt 2001; Bastock et al. 2003; Jenny et al. 2003). Thus, as Fz1 and Fz2 show different subcellular membrane localization within the apical–basal axis, we have here extended our analysis of Fz1 and Fz2 to determine whether the specific subcellular localization is important for signaling readout and to identify the molecular aspects responsible for the localization differences. Our data indicate that localization to apical junction complexes promotes Fz/PCP signaling and inhibits canonical Wg/β-cat signaling, and that the subcellular localization of Fz receptors is mediated through sequences in the cytoplasmic tail (C-tail). In addition, we show that the seven-pass transmembrane region contains elements that are critical for PCP signaling. Based on our data, we propose a model in which subcellular localization, possibly through the association of Fz with other membrane proteins such as coreceptors, is a critical aspect in regulating the signaling readout and specificity within the Wnt/Fz signaling pathways. Results Different Subcellular Localization of Fz and Fz2 in Imaginal Disc Epithelia To confirm that Fz1 is localized apically, we analyzed Fz1 distribution in third instar larval discs (Figure 1). Similar to previous reports (Strutt 2001), we found that a ubiquitously expressed Fz1-GFP is always enriched at apical junctions (although the expression in third instar larval discs is weaker than in pupal wings; Figure 1A). Expression of a Myc-tagged Fz1 under dpp-Gal4 control also displays a strong enrichment in the apical region of the disc epithelium (Figure 1C–1F) and in some punctae that appear to be intracellular vesicles (Figure 1F). There are only low levels of Fz1 detected basolaterally (Figure 1F; unpublished data). Apical Fz1 largely colocalizes with DE-Cadherin (DE-Cad; a marker for adherens junctions; Figure 1D), whereas it only slightly overlaps with Discs large (Dlg) staining (Figure 1E; Dlg is localized to septate junctions just basally to adherens junctions [reviewed in Tepass et al. 2001]). This Fz1 localization pattern is very similar to the PCP factor Strabismus/Vang, which also largely colocalizes with DE-Cad, and only slightly with Dlg (Bellaiche et al. 2004). Figure 1 Subcellular Localization of the Fz1 Protein (A) Anti-GFP staining of Fz-GFP in arm-fz-GFP third instar wing disc (arm drives ubiquitous expression); xz-section is shown. (B) Illustration of a cross section of a third instar wing disc. Wing epithelium forms several folds in the hinge region, where apical–basal localization can be visualized in a horizontal xy-section. The purple line in (B) indicates the position of the xy-optical section in such folds shown in (C–F). (C) Staining of a dpp-Gal4/UAS-fz1–1-1(myc) third instar wing disc. Localization of DE-Cad (in red), Dlg (green), and Fz1–1-1 (anti-Myc, blue) is shown. Apical region of the epithelium faces the lumen in the fold, and the basolateral regions are away from the lumen. (D) same staining as in (C) with two channels shown: DE-Cad and Fz1–1-1. DE-Cad (red) and Fz1–1-1 (blue) largely overlap. (E) Dlg (green) and Fz1–1-1 (blue) from (C) are shown. Fz1–1-1 localizes generally more apical than Dlg (with only a very slight overlap). (F) Fz1–1-1 single-channel staining. In summary, Fz1–1-1 is mainly localized in the apical adherens junctions and strong punctae inside cells (probably intracellular vesicles). Low levels of Fz1–1-1 also exist more ubiquitously in the basolateral region. (G) Schematic illustration of relative positions of DE-Cad, Dlg, and Fz1–1-1 along the apical–basal axis epithelial cells. DE-Cad marks the adherens junctions, whereas Dlg localization correlates with septate junctions. Taken together, these data suggest that Fz1 is mostly localized at adherens junctions. This is in contrast to Fz2, which is distributed throughout the cellular membrane along the apical–basal axis in the wing imaginal disc epithelium (Strigini and Cohen 2000). As only Fz1 can signal effectively in the Fz/PCP pathway and other PCP proteins also show apical localization (Strutt 2003), we speculate that apical Fz1 localization is an important feature of signaling specificity. The C-Tail of Fz Family Receptors Controls Subcellular Localization As Fz1 and Fz2 show different subcellular localization, we wished to determine which domains or sequences within the receptors are responsible for the specific localization. To address this question, we examined the localization of Fz1/2 chimeric receptor proteins (expressed under the control of dpp-Gal4) in wing imaginal discs. Fz1 and Fz2 were subdivided into three parts: (1) the N-terminal Wnt-interacting cysteine-rich domain (CRD), (2) the remaining proximal extracellular domain and 7 transmembrane region and loop region (collectively referred to as 7-TM), and (3) the intracellular C-tail. All chimeric proteins were Myc-tagged between the CRD and 7-TM region (see Materials and Methods) and labeled with three digits (separated by dashes) corresponding to the three domains of Fz1/2, with “1” and “2” reflecting Fz1 and Fz2 origin, respectively. In all cases tested, the hybrid Fz1/2 proteins carrying the C-tail of Fz1 were enriched apically (Figure 2), comparable to wild-type Fz1, and colocalized with apical junctional markers (see Figure 1; unpublished data). In contrast, chimeric Fz receptors carrying the Fz2 C-tail, including Fz2–2-2, were localized evenly along the apical–basal axis (Figure 2B, 2C, and 2G), comparable to wild-type Fz2 (e.g., endogenous Fz2 [M. Strigini, personal communication] or overexpressed Fz2 under dpp-Gal4 control [Strigini and Cohen 2000]). In summary, these data indicate that the C-tails of Fz receptors are responsible for their specific subcellular localization. Figure 2 The Cytoplasmic Region of Fz Regulates Subcellular Localization All Fz1/2 chimeras shown are Myc-tagged (the tag being inserted right after the CRD of Fz1 or Fz2; see Materials and Methods; Boutros et al. 2000). The respective Fz1/2 chimeras, with their schematic structure shown under each photomicrograph, were expressed under dpp-Gal4 (expression domain marked with UAS-EGFP in example in [A]) and analyzed by confocal microscopy xz-sections (perpendicular to the stripe of expression in the wing pouch region). (A) Subcellular localization of wild-type Fz-Myc (Fz1–1-1, in green; red channel shows coexpressed GFP to mark expressing cells). Single-channel black-and-white staining of Fz-Myc is shown on right. (B–F) Anti-Myc staining of different Fz1/2 chimeras: (B) Fz1–2-2, (C) Fz1–1-2, (D) Fz2–1-1, (E) Fz1–2-1, and (F) Fz2–2-1. (G) Fz2–2-2. Note the correlation of apical Fz localization with the presence of the Fz1 C-tail. To address whether subcellular localization correlates with specific Fz signaling events, we tested the signaling preferences of the respective chimeric receptors. This was analyzed in adult wings by scoring for either a PCP or canonical Wg-signaling gain-of-function (GOF) phenotype (Figure 3; Table 1). Expression of Fz1–1-1 under dpp-Gal4 control in wing imaginal discs caused wing cell hairs to point away from the expression domain (Figure 3B). This is consistent with the notion that hairs point away from regions of higher Fz signaling levels in the PCP context (Adler et al. 1997). Expression of the chimeric Fz receptors showed that the presence of the Fz1 C-tail is necessary for a strong PCP GOF phenotype (Figure 3; Table 1), suggesting that the apical localization of Fz is important for normal PCP signaling. These experiments also indicated that, in addition to apical localization, the 7-TM region of Fz1 is necessary for effective PCP signaling (Figure 3; Table 1). Similar results were obtained in GOF PCP assays during eye development (Table 1; Boutros et al. 2000; unpublished data). Figure 3 GOF Planar Polarity Wing Phenotype of Fz1/2 Chimeras dpp-Gal4 was used to express the respective Fz1/2 chimeras in the wing (same as described in Figure 2). (A) Wild-type wing. The dpp-Gal4 expression domain is highlighted by a thick orange line. In wild-type, all wing hairs are pointing distally. (B) dpp-Gal4; UAS-EGFP/UAS-fz1–1-1 wing (dpp>fz1–1-1; the expression domain is again highlighted with light orange). Wing hairs flanking the expression domain point away from it, consistent with previous observations that hair point away from higher levels of Fz1 activity (Adler et al. 1997). (C) dpp>fz1–2-2 wing. Wing hairs are not pointing away from expression domain, suggesting that Fz1–2-2 is not active for PCP signaling. (D) dpp>fz1–1-2 wing. Hairs point away only very slightly (less than 45 o; compare with Fz1–1-1, showing a 90 o reorientation next to expression domain). Several different lines of UAS-fz1–1-1 and UAS-fz1–1-2 were compared, showing identical behavior (Fz1–1-1 having a much stronger phenotype), suggesting that the C-tail is required for full PCP Fz activity. (E) dpp>fz2–1-1 wing. Most wing hairs point away from expression domain. The phenotype is weaker than Fz1–1-1. (F) dpp>fz1–2-1 wing. Wing hair orientation is hardly affected. Since Fz1–2-1 is apically localized (see Figure 2E), this result indicates that the presence of the Fz1 7-TM region is important for PCP activity. Table 1 Behavior of Chimeric Fz Receptors aRescue that resembles wild-type bFor schematic presentation of these mutants see Figure 4A wt, wild-type; NA, not available; ND, not determined Behavior of chimeric Fz receptors was assayed in four different ways as indicated. The eye rescue phenotype was quantified by analyzing 3–6 independent eyes for each genotype. Wild-type represents 99.5%–100% correctly oriented ommatidia, whereas in the fz−/− background only about 30% show the correct orientation. Note that for rescue both the CRD and 7-TM region of Fz1 are required in addition to apical localization Taken together, these experiments demonstrate that (1) apical Fz1 localization correlates with higher levels of Fz/PCP signaling activities and (2) the 7-TM region of Fz1 is critical for effective PCP signaling. Sequence Requirement for Apical Localization within the C-Tail Next we wished to determine which part of the C-tail of Fz1 or Fz2 is responsible for the difference in subcellular localization. The protein sequences of the Fz1 and Fz2 C-tails are homologous over the first 29 amino acids (45% identity), but Fz2 is longer by an additional 61 amino acids (Figure 4). The apical localization sequence could thus be located either in the nonconserved stretches within the common 29 residues, or within the Fz2 C-tail extension. We addressed both possibilities systematically and analyzed the localization of the respective mutants and their effects in the functional GOF assay in the wing (see above). Figure 4 Effects of Fz1/2 C-Tail Mutations on Subcellular Localization and PCP Activity (A) Sequence alignment of Fz1 and Fz2 C-tails. Note high degree of conservation within the membrane proximal shared portion of the Fz1 and Fz2 C-tails. The respective mutations generated and analyzed are indicated above the sequence (see also Table 1 for complete data set). As in Figures 2 and 3, dpp-Gal4 was used to drive expression of the respective mutants, and these were detected by anti-Myc staining in third instar wing discs. Examples for Fz1–1-1V559E (V to E substitution) are shown in (B) (localization) and (F) (function). All other mutants analyzed as shown in (A) are listed in Table 1. (C–E, G, and H) show the effects of the Fz2 C-tail-specific sequences. The Fz2 C-tail was truncated at the position of the Fz1 stop codon (amino acid L633), yielding a short Fz2 C-tail (2S). The localization (C and D) and GOF PCP function (G and H) of the respective chimeras, Fz1–2-2S and Fz1–1-2S, is shown. Note that both chimeras localize apically (C and D), and Fz1–1-2S shows a strong PCP GOF phenotype (H), very similar to Fz1–1-1 (see Figure 3B). Fz1–2-2S shows only a very weak PCP phenotype (G), mainly occurring at an anterior distal region of the wing (marked by arrow; the rest of the wing is wild-type). (E) Subcellular localization of Fz1–1-1C2. Fz1–1-1C2 is Fz1 with the addition of the Fz2-specific tail extension (see Materials and Methods). Note ubiquitous protein localization within the apical–basal axis (E) and a much reduced PCP activity, as compared to wild-type Fz1–1-1, in the functional assay (I). The phenotype is much weaker than in wild-type Fz1 (compare with [F] and [H] and Figure 3B). First, we mutated several Fz1–1-1–specific residues to those of Fz2, or deleted conserved amino acid stretches within the Fz1–1-1 C-tail (see Figure 4A and Table 1 for specific mutations analyzed). All mutated Fz1–1-1 receptor proteins showed normal localization to apical junctions (Figure 4B; Table 1), and when analyzed for their function also showed a typical Fz GOF PCP phenotype in the wing in that the wing hairs were directed away from the source of expression (Figure 4F; Table 1). Second, we tested whether sequences within the extended Fz2 C-tail have an effect on localization or PCP signaling. We introduced a stop codon after the L633 residue of Fz2 (corresponding to the position of the stop codon in Fz1) in Fz1–1-2 and Fz1–2-2 chimeras (Figure 4A, blue arrowhead), thus truncating the Fz2 C-tail and generating chimeras Fz1–1-2S (“S” for “short”) and Fz1–2-2S. Whereas Fz1–1-2 and Fz1–2-2 are ubiquitously localized, both Fz1–1-2S and Fz1–2-2S localize apically to adherens junctions, in a manner indistinguishable from that of Fz1–1-1 and Fz1–2-1 (compare Figure 4C and 4D to Figure 2A and 2E). These data suggest that the Fz2 C-tail extension interferes with apical localization. These same chimeras were tested in the functional assay for PCP signaling activity. Strikingly, expression of Fz1–1-2S caused a phenotype very similar to that of Fz1–1-1 (Figure 4H), but different from that caused by Fz1–1-2 (see Figure 3D). Expression of Fz1–2-2S resembled that of Fz1–2-2 or Fz1–2-1, with very weak PCP effects (compare Figure 4G to 3C and 3F). In summary, these results confirm that both apical localization and sequences located within the 7-TM region are functionally important for PCP signaling. To test whether the extension within the Fz2 C-tail can more generally block apical localization, we added the Fz2 extension on to Fz1–1-1 (Figure 4; see Materials and Methods for details). This Fz1–1-1C2 receptor isoform was not apically enriched (Figure 4E), resembling the localization of Fz1–1-2. Consistently, in the functional PCP readout assay, expression of Fz1–1-1C2 showed only very weak GOF PCP effects (Figure 4I). Based on the results with Fz1–1-1C2 and Fz1–1-2S, we conclude that the Fz2 C-tail extension causes Fz receptors to acquire a ubiquitous membrane distribution, preventing them from accumulating at the apical junctions and thereby affecting their ability to signal via the Fz/ PCP pathway. Apical Localization Affects Rescue Capability of the Fz Chimeras The chimeric Fz1/2 receptors (driven directly by the ubiquitous tubulin promoter [tub] ) were also tested for their ability to rescue the fz− eye and wing PCP phenotype. tub-Fz1–1-1 and tub-Fz1–1-2S (which are both apically localized) fully rescue the fz− loss-of-function (fzP21/fzR52) phenotype in both the eye and wing (Figure 5; Table 1; unpublished data), suggesting that the shortened Fz2 C-tail is functionally equivalent to the Fz1 C-tail. In contrast, tub-Fz1–2-2S and tub-Fz1–2-1 did not rescue the fz− mutant phenotype (Figure 5F; Table 1), confirming again that the Fz1 7-TM region is important for Fz/PCP signaling. Although Fz2–1-1 has activity in GOF studies (Figure 3E; Table 1), tub-Fz2–1-1 did not rescue the fz− phenotype, suggesting that the specific extracellular CRD is required for normal receptor regulation (Table 1; unpublished data). This could be due to a Fz1 requirement to interact with a ligand (or extracellular domain of another transmembrane protein) to provide regulation to Fz/PCP signaling. Figure 5 Rescue of the fz− Eye Phenotype with tub-Promoter-Driven Fz Chimeras Tangential eye sections with corresponding schematic in lower part of panel reflecting ommatidial polarity (respective genotypes are also marked below each panel). Black arrows, dorsal chiral form; red arrows, ventral chiral form; green arrows, symmetric ommatidia; black circles, ommatidia with missing photoreceptors. Anterior is to the left, dorsal is up, and an area around the equator is shown for each genotype. (A) Section of a wild-type eye (equator is indicated by yellow line). (B) fzP21/fzR52 (fz null). Note random orientation of ommatidia. (C) fzP21/fzR52; tub-fz1–1-1. The fz− phenotype is fully rescued (100% with respect to chirality; only a minor rotation wobble is rarely seen). (D) fzP21/fzR52; tub-fz1–1-2. Note partial rescue with respect to polarity (approximately 83%) and occasional photoreceptor loss representative of Wg/β-cat signaling. (E) fzP21/fzR52; tub-fz1–1-2S. Note 100% rescue, identical to wild-type Fz1 (compare with [C]). (F) fzP21/fzR52; tub-fz1–2-1. No rescue due to the presence of the Fz2 7-TM region. This chimera actually shows a mild dominant negative behavior as apparent by the increased percentage of symmetric clusters (approximately 50% as compared to fz− [approximately 15%]). The tub-Fz1–1-2 receptor, which contains the Fz1 7-TM region, but is localized throughout the cellular membrane, is also able to rescue the fz− eye and wing phenotype. However, it does so less efficiently (Figure 5D; Table 1), and it also causes eye phenotypes reflecting the activation of Wg/β-cat signaling, such as photoreceptor loss (Wg/β-cat signaling during photoreceptor induction and differentiation blocks the development of these cells as photoreceptors; Wehrli and Tomlinson 1998). These effects of Fz1–1-2 suggest that proper apical enrichment is critical for a clean PCP readout, but that a ubiquitously distributed Fz1 chimera might be sufficiently present at apical adherens junctions to allow for partial rescue. In summary, our data are consistent with the notion that the C-tail provides the information for correct localization required for full and clean PCP signaling specificity, and that sequences within the Fz1 7-TM region and extracellular domain are required for PCP signaling activity or regulation (see also Discussion). Flamingo Is Not Required for the Initial Apical Fz Localization Previous work has shown that Fz1 is not localized to apical junctions in the wings of fmi mutants 30–32 h after puparium formation (APF) (Strutt 2001). Similar observations were made in the late third instar eye imaginal disc (Strutt et al. 2002). These data suggest that Fmi is required for apical localization of Fz1 during PCP signaling. Similarly, Fmi depends on Fz1/PCP signaling to maintain its apical junctional localization in wings 30–36 h APF (Usui et al. 1999), suggesting that Fmi and Fz1 localization are interdependent when PCP signaling is active. However, this might not reflect initial requirements for apical localization. To test whether Fmi is required for the initial apical localization of Fz1, which happens prior to the initiation of PCP signaling, we examined Fz1–1-1 localization in fmiE59 clones in larval wing imaginal discs. Fz1–1-1 is localized apically in fmiE59 mutant cells in third instar imaginal discs, indistinguishable from its localization in wild-type tissue (Figure 6). These data suggest that Fmi is not required for the initial apical localization of Fz1–1-1. The difference between the early stage (larval discs) and late stage (pupal wings, late eye discs posterior to morphogenetic furrow during PCP signaling) suggests that initial apical localization is independent of the later maintenance evens regulated by PCP signaling (see also Discussion). Figure 6 Subcellular Localization of Fz1–1-1 in fmi− Mutant Clones Fz1–1-1 (Myc-tagged; shown in green) is expressed with omb-Gal4 (in large parts of the third instar wing pouch). fmiE59 clones were labeled by the absence of anti-βGal staining (red). A projection of several horizontal sections in the apical region (A) and the corresponding xz-section (B) across the clone (as indicated by a white line in A) are shown. Fz1–1-1 is localized apically inside and outside the clone, indicating that initial apical Fz recruitment is independent of Fmi. Apically Localized Fz1/2 Chimeras Act As Dominant Negatives for Wnt/β-Cat Signaling During imaginal disc development and patterning, Wg binds to the Fz2 receptor at basolateral membranes of the wing epithelium (Strigini and Cohen 2000). This result suggests that canonical Wnt signaling occurs mainly at the basolateral side of the epithelium in imaginal discs. In contrast, apically localized Fz appears to have high PCP signaling activity (as described above). These results suggest that PCP signaling and canonical Wnt/βcat signaling occur in different subcellular locations or membrane compartments. Previous work has suggested that Fz2–1-1 and Fz2–2-1, which are shown here as localized to apical junction complexes (see Figure 2D and 2F), act as dominant negative isoforms for canonical Wg signaling (Boutros et al. 2000). We have noticed that expression of the Fz chimeras (with en-Gal4 in the posterior wing compartment) often causes wing notching and loss of wing margin bristles (in the posterior wing region; Figure 7), indicative of reduced Wnt/βcat signaling (Couso et al. 1994). To gain insight into why chimeric Fz receptors can behave as dominant negatives, we analyzed ubiquitous Dsh-GFP localization (expressed from the endogenous promoter; Axelrod 2001) in en-Gal4- and dpp-Gal4-driven UAS-fz wing discs (Figure 7; unpublished data). In wild-type, Dsh-GFP is mainly cytoplasmic with a mild, slightly stronger apical enrichment at membranes (Figure 7E–7H, anterior compartments). In wing epithelia with overexpressed Fz1–1-1 or Fz2–1-1, much more Dsh-GFP is recruited apically in cells expressing the Fz chimeras (Figure 7E–7H, posterior compartments). At the same time, Dsh-GFP levels are reduced in basolateral regions of these cells. These data suggest that Fz in adherens junctions (apical) is trapping Dsh there, depleting it away from Wnt/βcat signaling components located possibly more basally and thus reducing canonical Wnt signaling. Figure 7 Overexpression of Apically Localizing Fz1/2 Chimeras Has an Inhibitory Effect on Canonical Wnt Signaling (A–D) show adult wings of the respective genotypes. Anterior is up and distal to the right. (A) Adult wing of an en-Gal4/+; UAS-fz1–1-1/+ fly (en>fz1–1-1). en-Gal4 drives UAS reporter genes only in the posterior compartment. Inset shows high magnification of region marked by arrowhead. Some wing margin bristles are missing (arrow) in the posterior compartment. The border between anterior (“a”) and posterior (“p”) compartments is marked with black line. (B) dshV26/+; en>fz1–1-1 adult wing. Note enhancement of the margin bristle phenotype: all margin bristles are missing from the area between the arrows in the posterior compartment. (C) en>fz2–1-1 wing. Most of the wing margin bristles are missing in the posterior compartment. Note also that the posterior compartment is smaller. (D) en>fz2–2-1 wing. Again the posterior compartment is smaller and most of the margin is missing. (E–G) show that Fz1–1-1 expression increases apical localization of Dsh-GFP and reduces Dsh-GFP in more basolateral areas of wing cells. (E) and (F) are xy-horizontal optical sections, and (G) is an xz-cross section. The positions of (E) and (F) sections are indicated in (G). (E) Apical xy-optical section of a third instar wing disc. Fz1–1-1 (red) is overexpressed by en-Gal4 in the posterior compartment (anterior–posterior border is labeled by white line, and the corresponding compartments are labeled “a” and “p,” respectively). Dsh-GFP (green) accumulates at higher levels apically in the posterior compartment. Single-channel Dsh-GFP staining is shown at right. In wild-type disc, Dsh-GFP is evenly distributed with no anterior–posterior bias (not shown). (F) A more basal xy-section of the same disc as in (E). Note reduction of Dsh-GFP staining in the posterior compartment, except at the apical junctions as seen in folds (arrowhead). In the anterior compartment, where Fz1–1-1 is not overexpressed, Dsh-GFP is only slightly enriched in the apical folds (arrow). (G) xz-section of the same wing disc shown in (E) and (F), with top panel showing double labeling for anti-Myc (red) and anti-Dsh-GFP (green) and bottom panel showing single channel of Dsh-GFP staining. (H) xz-section of a comparable disc expressing Fz2–1-1 in the posterior compartment. Fz2–1-1 overexpression (red) also causes accumulation of Dsh-GFP in apical junctions and reduction of Dsh-GFP along the basolateral region. To test this hypothesis, we analyzed the effect of reducing dsh gene dosage in en-Gal4/UAS-fz1–1-1 flies, where wing notching and loss of marginal hairs is mild (21% of wings have large areas of margin bristles missing; Figure 7A; Table 2). Strikingly, the en-Gal4/UAS-fz1–1-1 effect is enhanced in dsh heterozygous flies (dshV26/+), with 65% of wings showing large areas of margin bristles missing and severe wing notching (Table 2; see Figure 7B for example). To corroborate the dsh dosage sensitivity in this context, we generated flies with three copies of dsh (by introducing an additional dsh copy as a dsh-GFP transgene expressed under its endogenous promoter; Axelrod 2001). In this genetic background with three dsh copies, only 4% of the en-Gal4/UAS-fz1–1-1 wings displayed a large area of missing wing margin bristles (Table 2), suggesting that the presence of extra Dsh suppresses the en-Gal4/UAS-fz1–1-1 wing phenotype. Taken together, these Dsh dosage effects support the idea that trapping Dsh into apical junctional complexes reduces its availability for Wnt/βcat signaling, and thus reduces the strength of canonical signaling. Table 2 Wing Margin Phenotypes of en-Gal4; UAS-fz1/2 Chimeras Note dsh dosage sensitivity of the Fz1–1-1-induced wing nick frequency and size by removal of one copy of dsh, and the correlation of a dominant negative effect with apical localization (e.g., presence of Fz1 or Fz2S C-tails; compare also to Table 1). “Large nick” is defined as an area of the wing lacking more than 20 margin bristles In further support of this explanation, en-Gal4/+; UAS-fz1–1-2/+ flies show only a very mild effect on wing margin bristles (Table 2). As Fz1–1-2 is ubiquitously localized along the apical–basal axis, recruiting of Dsh by such chimeras should not have an adverse effect on canonical Wg signaling. In contrast, when Fz2–1-1 and Fz2–2-1 are expressed (with en-Gal4) we observe very strong wing notching effects and a general reduction of the posterior wing compartment (Figure 7C and 7D; Table 2). This can be explained as follows. As the Fz2 ligand-binding CRD has a much higher affinity for Wg than the Fz1 CRD (Rulifson et al. 2000), the strong dominant negative behavior of Fz2–1-1 and Fz2–2-1 can be explained by adverse effects on both Dsh and Wg: Fz2–1-1 and Fz2–2-1 have a high-affinity Wg-binding CRD (sequestering Wg efficiently) and can trap Dsh at junctional complexes as well (Figure 7E–7H), making large pools of Wg and Dsh unavailable for canonical signaling, and thus causing a strong dominant negative effect. In summary, the dominant negative effect of the overexpression of Fz1–1-1, Fz2–1-1, and Fz2–2-1 is caused by trapping Dsh into apical junctions, making it unavailable for canonical Wnt/βcat signaling, and, when present, the Fz2 CRD enhances this effect by also sequestering Wg to these complexes. These results suggest that a Fz-Dsh complex in the apical junctions is largely incapable of canonical β-cat signaling, suggesting that the subcellular localization of Fz receptors contributes significantly to the signaling outcome and specificity (see Discussion). Discussion We have shown that Fz1 and Fz2 have different subcellular localizations within the wing imaginal epithelium. This difference is mediated by sequences in the cytoplasmic tail of Fz2 that appear to block apical accumulation. The subcellular localization difference directly contributes to the signaling specificity outcome. Whereas apical localization favors Fz/PCP signaling, it interferes with canonical Wnt/β-cat signaling. The Relationship between Apical Localization of Fz1 and Its PCP Signaling Activity Is the apical localization of Fz required for PCP signaling? The Fz1–1-2 chimera, which is distributed ubiquitously within the apical–basolateral membrane, only partially rescues the fz− eye phenotype, and it can also cause defects related to canonical Wg/Arm signaling (see Figure 5D). In contrast, apically localized Fz1–1-2S fully rescues the fz− phenotype and has no additional effects. The Fz1–1-2 chimera also shows much weaker PCP phenotypes in the GOF assay (see Figure 3 and Boutros et al. [2000]). Taken together, these results suggest that a reduction in the apical localization of Fz leads to a reduction in PCP signaling activity. However, about 80% of the chirality defects in fz− eyes are rescued by tub-fz1–1-2, and in the wing tub-fz1–1-2 rescues the fz− mutant to a similar extent as tub-fz1–1-1 and tub-fz1–1-2S (unpublished data), suggesting that Fz1–1-2 contains substantial PCP signaling activity. Because both GOF and loss-of-function studies indicate that the Fz1 7-TM region is critical for Fz1 function, Fz1–1-2 is expected to have Fz/PCP signaling activity, although with altered subcellular distribution. Thus, the remaining PCP signaling activity of Fz1–1-2 seen is probably due to the presence of some of this protein in apical regions. It is difficult to determine how much of Fz1–1-2 is actually localized to this membrane region. Since the immunohistochemical staining indicates that it is not excluded apically, we assume that Fz1–1-2 has enough apical localization to participate when PCP signaling is initiated. It has been suggested that wing cell orientation does not depend on absolute Fz levels, but instead depends on relative Fz/PCP activity differences in a Fz activity gradient across a field (Adler et al. 1997). Thus, although the absolute activity of Fz1–1-2 is reduced (based on weaker GOF phenotypes and weaker rescue of fz− in the eye), the relative difference might be sufficient for the partial rescue. In this context, it is worth noting that tub-fz1–1-2 rescues the fz− phenotype better in the wing than in the eye, whereas there is no apparent difference in rescue activity between the eye and the wing for tub-fz1–1-1 or tub-fz1–1-2S. The difference could be due to the observed nonautonomous PCP signaling effects in the wing (Vinson and Adler 1987), where neighboring cells affect each other's planar polarization. Fz1–1-2 may allow some wing cells to adopt the correct orientation, which then in turn influences many of the remaining wing cells to also orient themselves correctly through nonautonomous interactions. Regulation of Fz Apical Localization It has been shown that Fz1 localization is affected in fmi mutant clones at about 30 h APF (Strutt 2001), leading to the proposal that Fmi recruits Fz1 into apical junctions (Strutt 2001; Bastock et al. 2003). However, we find that Fz1 is localized normally in fmi null mutant clones earlier in the third instar wing disc. What causes the difference between these two observations? PCP signaling in the wing is thought to act in two phases (one 6–24 h APF and the second 24–32 h APF [Strutt and Strutt 2002]), and it results in the distal enrichment and maintenance of Fz1 (Strutt 2001). As Fz1/PCP signaling is modulated by Fmi (Usui et al. 1999), Fmi-dependent changes in Fz1 localization likely result from effects on PCP signaling activity. At the same time, Fmi localization is also dependent on Fz1 activity and becomes also less apically localized in fz− tissue at 30–36 h APF (Usui et al. 1999), suggesting that the regulation of apical localization between Fz1 and Fmi is complicated and mutual at these late stages. We showed here that initial apical localization of Fz1, preceding both stages of PCP signaling, is not fmi dependent. This result suggests that Fmi and Fz1 get recruited to apical junctions independently. During later stages, Fmi and Fz1 then affect each other's localization through PCP signaling. At this point, it remains unclear which molecules initially recruit Fz1 into the apical junctional region. Fz Receptor Localization and Canonical Wnt Signaling Secreted Wg mainly binds to Fz2 at basolateral membrane regions of the wing epithelium (Strigini and Cohen 2000), indirectly suggesting that canonical signaling occurs in the basolateral membrane compartment. Our experiments show that overexpression of Fz1–1-1 or Fz2–1-1 leads to a cell-autonomous loss of wing margin bristles and associated tissue, suggesting that these molecules act like dominant negatives, inhibiting Wnt/β-cat signaling. As these molecules are enriched apically and sequester Dsh there, Fz-Dsh complexes at apical junctions may be largely inactive for canonical Wnt signaling. This result suggests that canonical Wnt signaling and PCP signaling occur in different subcellular compartments. Basolateral Wnt/β-cat signaling is also suggested by the fact that (1) secreted Wg binds to Fz2 at the basolateral membrane and that (2) apical Wg secretion and signaling could lead to mis-specification in disc folds and cells in the peripodial membrane (Strigini and Cohen 2000). Both Fz1 and Fz2 are capable of canonical Wnt/β-cat signaling (Bhat 1998; Kennerdell and Carthew 1998; Bhanot et al. 1999; Chen and Struhl 1999). Consistently, different Fz1/2 chimeras, including related versions of Fz2–1-1 and Fz2–2-1, are capable of rescuing the fz, fz2 double mutant phenotype (Strapps and Tomlinson 2001). However, when Fz1–1-1, Fz2–1-1, or Fz2–2-1 is expressed at high levels, Dsh accumulates at apical junctions, thus decreasing cytosolic Dsh levels. As the chimeric receptors can rescue the fz, fz2 double mutant when expressed at low levels (under the control of the tub promoter; Strapps and Tomlinson 2001), the relative level of each receptor together with its subcellular localization appear critical for the signaling outcome. In conclusion, we have shown that subcellular localization contributes to Fz signaling specificity. Our data indicate that the localization of Fz1 at apical junctions promotes Fz/PCP signaling, whereas this localization can inhibit canonical Wnt/β-cat signaling. The localization is mediated through sequences in the C-tail. Materials and Methods Flies and constructs The flies carrying the chimeric receptor constructs UAS-Fz1–1-1, UAS-Fz1–1-2, UAS-Fz1–2-2, UAS-Fz2–1-1, and UAS-Fz2–2-1 are described in Boutros et al. (2002). UAS-Fz1–2-1 was constructed by combining the Fz1 CRD (Fz1 residues 1–166) with the Myc tag, the Fz2 7-TM region (amino acids 220–617), and the Fz1 C-tail (amino acids 558–585). A HindIII site (generated in vitro) was used to combine Fz1 CRD with the Fz27-TM region. An XhoI site was used to link the Fz2 7-TM region with the Fz1 C-tail. C-tail mutation constructs of Fz1 were generated through PCR-based site-directed mutagenesis (Quikchange kit, Stratagene, La Jolla, California, United States). Fz1–1-2S and Fz1–2-2S were generated by introducing a stop codon after residue L633 of Fz2. Fz1–1-1C2 was generated by introducing a BsiWI site at the residues R574–T575 of Fz1 and R626–T627 of Fz2 (the RT residues remain the same by this mutagenesis). This added the Fz2 amino acids 627–694 to the Fz1 C-tail at the RT residues. The respective UAS transgenic flies were generated by standard procedures. The Gal4/UAS system was used to express the chimeric UAS-Fz1/2 transgenes (Brand and Perrimon 1993) with dpp-Gal4, en-Gal4, or omb-Gal4 (Brand and Perrimon 1993; Yoffe et al. 1995; Lecuit et al. 1996; Morimura et al. 1996). tub-promoter-driven Fz chimeric constructs were generated by cloning the respective Fz1/2 constructs into the Casper4-tub vector (containing a 2.4-kb tub promoter fragment in Casper4—a kind gift from Stephen Cohen). fzP21 and fzR52 are null alleles of fz (Jones et al. 1996). dshV26 is a null allele of dsh (Perrimon and Mahowald 1987). Immunohistochemistry Rat anti-DE-Cad was used at 1:200 (Oda et al. 1994). Mouse anti-Myc (9E10) was used at 1:250–500 (Santa Cruz Biotechnology, Santa Cruz, California, United States). Rabbit anti-Dlg was used at 1:3500 (Lee et al. 2003). Rabbit anti-GFP (Molecular Probes, Eugene, Oregon, United States) was used at 1:4000 to detect Dsh-GFP and Fz1-GFP. fmiE59 clones were induced in first instar larvae via the Flp/FRT system in the w, hs-flp; FRT42B fmiE59/FRT42 arm-lacZ genotype. Larvae were dissected 4 d after clone induction during late third instar. fmiE59 is a null allele of fmi (Usui et al. 1999). Adult wing and eye preparation Wings were soaked (with agitation) in 0.1% Triton X-100 PBS for about 30 min or longer, and then mounted in 80% Glycerol PBS. Eye embedding and sectioning was performed as described by Tomlinson (1987). We thank Paul Adler, Jeffrey Axelrod, Kyung-ok Cho, Steve Cohen, and David Strutt for flies, antibodies, and plasmids, and Maura Strigini for sharing unpublished results. We are grateful to Zhongfong Du for generating transgenic flies and technical support. Jennifer Curtiss, Joe Delaney, Alex Djiane, Andreas Jenny, and Ursula Weber provided helpful suggestions and comments on the manuscript. This work is supported by a National Institutes of Health (NIH) grant (R01 EY013256) to MM. JW was partially supported by a postdoctoral training grant from the National Cancer Institute (NCI). Confocal laser scanning microscopy was performed at the MSSM-Microscopy Shared Resource Facility, supported with funding from an NIH–NCI shared resources grant (1 R24 CA095823 ) and a National Science Foundation Major Research Instrumentation grant (DBI-9724504). Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. JW and MM conceived and designed the experiments. JW, TJK, and MM performed the experiments and analyzed data. JW, TJK, and MM discussed the results and preparation for the paper. JW and MM wrote the paper. Academic Editor: Mark Peifer, University of North Carolina at Chapel Hill Abbreviations 7-TMproximal extracellular domain and 7 transmembrane region and loop region β-catβ-catenin APFafter puparium formation ArmArmadillo CRDcysteine-rich domain C-tailcytoplasmic tail DE-CadDE-Cadherin DlgDisks large DshDisheveled FmiFlamingo FzFrizzled GFPgreen fluorescent protein GOFgain-of-function PCPplanar cell polarity tub tubulin ==== Refs References Adler PN Planar signaling and morphogenesis in Drosophila Dev Cell 2002 2 525 535 12015961 Adler PN Krasnow RE Liu J Tissue polarity points from cells that have higher Frizzled levels towards cells that have lower Frizzled levels Curr Biol 1997 7 940 949 9382848 Axelrod JD Unipolar membrane association of Dishevelled mediates Frizzled planar cell polarity signaling Genes Dev 2001 15 1182 1187 11358862 Bastock R Strutt H Strutt D Strabismus is asymmetrically localised and binds to Prickle and Dishevelled during Drosophila planar polarity patterning Development 2003 130 3007 3014 12756182 Bellaiche Y Beaudoin-Massiani O Stuttem I Schweisguth F The planar cell polarity protein Strabismus promotes Pins anterior localization during asymmetric division of sensory organ precursor cells in Drosophila Development 2004 131 469 478 14701683 Bhanot P Brink M Samos CH Hsieh JC Wang Y A new member of the frizzled family from Drosophila functions as a Wingless receptor Nature 1996 382 225 230 8717036 Bhanot P Fish M Jemison JA Nusse R Nathans J Frizzled and Dfrizzled-2 function as redundant receptors for Wingless during Drosophila embryonic development Development 1999 126 4175 4186 10457026 Bhat KM frizzled and frizzled 2 play a partially redundant role in wingless signaling and have similar requirements to wingless in neurogenesis Cell 1998 95 1027 1036 9875856 Boutros M Mlodzik M Dishevelled: At the crossroads of divergent intracellular signaling pathways Mech Dev 1999 83 27 37 10507837 Boutros M Mihaly J Bouwmeester T Mlodzik M Signaling specificity by Frizzled receptors in Drosophila Science 2000 288 1825 1828 10846164 Brand AH Perrimon N Targeted gene expression as a means of altering cell fates and generating dominant phenotypes Development 1993 118 401 415 8223268 Chen CM Struhl G Wingless transduction by the Frizzled and Frizzled2 proteins of Drosophila Development 1999 126 5441 5452 10556068 Couso JP Bishop SA Martinez Arias A The wingless signalling pathway and the patterning of the wing margin in Drosophila Development 1994 120 621 636 8162860 Curtin JA Quint E Tsipouri V Arkell RM Cattanach B Mutation of Celsr1 disrupts planar polarity of inner ear hair cells and causes severe neural tube defects in the mouse Curr Biol 2003 13 1129 1133 12842012 Dabdoub A Donohue MJ Brennan A Wolf V Montcouquiol M Wnt signaling mediates reorientation of outer hair cell stereociliary bundles in the mammalian cochlea Development 2003 130 2375 2384 12702652 Djiane A Riou J Umbhauer M Boucaut J Shi D Role of frizzled 7 in the regulation of convergent extension movements during gastrulation in Xenopus laevis Development 2000 127 3091 3100 10862746 Feiguin F Hannus M Mlodzik M Eaton S The ankyrin repeat protein Diego mediates Frizzled-dependent planar polarization Dev Cell 2001 1 93 101 11703927 He X Saint-Jeannet JP Wang Y Nathans J Dawid I A member of the Frizzled protein family mediating axis induction by Wnt-5A Science 1997 275 1652 1654 9054360 Heisenberg CP Tada M Rauch GJ Saude L Concha ML Silberblick/Wnt11 mediates convergent extension movements during zebrafish gastrulation Nature 2000 405 76 81 10811221 Jenny A Darken RS Wilson PA Mlodzik M Prickle and Strabismus form a functional complex to generate a correct axis during planar cell polarity signaling Embo J 2003 22 4409 4420 12941693 Jones KH Liu J Adler PN Molecular analysis of EMS-induced frizzled mutations in Drosophila melanogaster Genetics 1996 142 205 215 8770598 Keller R Shaping the vertebrate body plan by polarized embryonic cell movements Science 2002 298 1950 1954 12471247 Kennerdell JR Carthew RW Use of dsRNA-mediated genetic interference to demonstrate that frizzled and frizzled 2 act in the wingless pathway Cell 1998 95 1017 1026 9875855 Krasnow RE Adler PN A single frizzled protein has a dual function in tissue polarity Development 1994 120 1883 1893 7924994 Kuhl M Sheldahl LC Malbon CC Moon RT Ca(2+)/calmodulin-dependent protein kinase II is stimulated by Wnt and Frizzled homologs and promotes ventral cell fates in Xenopus J Biol Chem 2000 275 12701 12711 10777564 Lecuit T Brook WJ Ng M Calleja M Sun H Two distinct mechanisms for long-range patterning by Decapentaplegic in the Drosophila wing Nature 1996 381 387 393 8632795 Lee OK Frese KK James JS Chadda D Chen ZH Discs-Large and Strabismus are functionally linked to plasma membrane formation Nat Cell Biol 2003 5 987 993 14562058 Medina A Reintsch W Steinbeisser H Xenopus frizzled 7 can act in canonical and noncanonical Wnt signaling pathways: Implications on early patterning and morphogenesis Mech Dev 2000 92 227 237 10727861 Mlodzik M Planar cell polarization: Do the same mechanisms regulate Drosophila tissue polarity and vertebrate gastrulation? Trends Genet 2002 18 564 571 12414186 Montcouquiol M Rachel RA Lanford PJ Copeland NG Jenkins NA Identification of Vangl2 and Scrb1 as planar polarity genes in mammals Nature 2003 423 173 177 12724779 Morimura S Maves L Chen Y Hoffmann FM decapentaplegic overexpression affects Drosophila wing and leg imaginal disc development and wingless expression Dev Biol 1996 177 136 151 8660883 Oda H Uemura T Harada Y Iwai Y Takeichi M A Drosophila homolog of cadherin associated with armadillo and essential for embryonic cell–cell adhesion Dev Biol 1994 165 716 726 7958432 Perrimon N Mahowald AP Multiple functions of segment polarity genes in Drosophila Dev Biol 1987 119 587 600 3803719 Polakis P The oncogenic activation of beta-catenin Curr Opin Genet Dev 1999 9 15 21 10072352 Polakis P Wnt signaling and cancer Genes Dev 2000 14 1837 1851 10921899 Rulifson EJ Wu CH Nusse R Pathway specificity by the bifunctional receptor frizzled is determined by affinity for wingless Mol Cell 2000 6 117 126 10949033 Shimada Y Usui T Yanagawa S Takeichi M Uemura T Asymmetric colocalization of Flamingo, a seven-pass transmembrane cadherin, and Dishevelled in planar cell polarization Curr Biol 2001 11 859 863 11516647 Strapps WR Tomlinson A Transducing properties of Drosophila Frizzled proteins Development 2001 128 4829 4835 11731462 Strigini M Cohen SM Wingless gradient formation in the Drosophila wing Curr Biol 2000 10 293 300 10744972 Strutt DI Asymmetric localization of frizzled and the establishment of cell polarity in the Drosophila wing Mol Cell 2001 7 367 375 11239465 Strutt D Frizzled signalling and cell polarisation in Drosophila and vertebrates Development 2003 130 4501 4513 12925579 Strutt D Johnson R Cooper K Bray S Asymmetric localization of frizzled and the determination of notch-dependent cell fate in the Drosophila eye Curr Biol 2002 12 813 824 12015117 Strutt H Strutt D Nonautonomous planar polarity patterning in Drosophila : dishevelled -independent functions of frizzled Dev Cell 2002 3 851 863 12479810 Tada M Smith JC Xwnt11 is a target of Xenopus Brachyury: Regulation of gastrulation movements via Dishevelled, but not through the canonical Wnt pathway Development 2000 127 2227 2238 10769246 Tada M Concha ML Heisenberg CP Non-canonical Wnt signalling and regulation of gastrulation movements Semin Cell Dev Biol 2002 13 251 260 12137734 Tamai K Semenov M Kato Y Spokony R Liu C LDL-receptor-related proteins in Wnt signal transduction Nature 2000 407 530 535 11029007 Tepass U Tanentzapf G Ward R Fehon R Epithelial cell polarity and cell junctions in Drosophila Annu Rev Genet 2001 35 747 784 11700298 Tomlinson A Ready DF Cell fate in the Drosophila ommatidium Dev Biol 1987 123 264–275 17985474 Usui T Shima Y Shimada Y Hirano S Burgess RW Flamingo, a seven-pass transmembrane cadherin, regulates planar cell polarity under the control of Frizzled Cell 1999 98 585 595 10490098 Veeman MT Axelrod JD Moon RT A second canon: Functions and mechanisms of beta-catenin-independent Wnt signaling Dev Cell 2003 5 367 377 12967557 Vinson CR Adler PN Directional noncell autonomy and the transmission of polarity information by the frizzled gene of Drosophila Nature 1987 329 549 551 3116434 Vinson CR Conover S Adler PN A Drosophila tissue polarity locus encodes a protein containing seven potential transmembrane domains Nature 1989 338 263 264 2493583 Wehrli M Tomlinson A Independent regulation of anterior/posterior and equatorial/polar polarity in the Drosophila eye: Evidence for the involvement of Wnt signaling in the equatorial/polar axis Development 1998 125 1421 1432 9502723 Wehrli M Dougan ST Caldwell K O'Keefe L Schwartz S arrow encodes an LDL-receptor-related protein essential for Wingless signalling Nature 2000 407 527 530 11029006 Yoffe KB Manoukian AS Wilder EL Brand AH Perrimon N Evidence for engrailed -independent wingless autoregulation in Drosophila Dev Biol 1995 170 636 650 7649390

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020171Research ArticleCell BiologyDevelopmentMolecular Biology/Structural BiologyMus (Mouse)MammalsA Chromosomal Memory Triggered by Xist Regulates Histone Methylation in X Inactivation Histone H3-K27 Trimethylation by XistKohlmaier Alexander 1 Savarese Fabio 1 Lachner Monika 1 Martens Joost 1 Jenuwein Thomas 1 Wutz Anton [email protected] 1 1Research Institute of Molecular PathologyViennaAustria7 2004 13 7 2004 13 7 2004 2 7 e17115 1 2004 9 4 2004 Copyright: © 2004 Kohlmaier et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Remembering Which X Chromosome to Use We have elucidated the kinetics of histone methylation during X inactivation using an inducible Xist expression system in mouse embryonic stem (ES) cells. Previous reports showed that the ability of Xist to trigger silencing is restricted to an early window in ES cell differentiation. Here we show that this window is also important for establishing methylation patterns on the potential inactive X chromosome. By immunofluorescence and chromatin immunoprecipitation experiments we show that histone H3 lysine 27 trimethylation (H3K27m3) and H4 lysine 20 monomethylation (H4K20m1) are associated with Xist expression in undifferentiated ES cells and mark the initiation of X inactivation. Both marks depend on Xist RNA localisation but are independent of silencing. Induction of Xist expression after the initiation window leads to a markedly reduced ability to induce H3K27m3, whereas expression before the restrictive time point allows efficient H3K27m3 establishment. Our data show that Xist expression early in ES cell differentiation establishes a chromosomal memory, which is maintained in the absence of silencing. One consequence of this memory is the ability to introduce H3K27m3 efficiently after the restrictive time point on the chromosome that has expressed Xist early. Our results suggest that this silencing-independent chromosomal memory has important implications for the maintenance of X inactivation, where previously self-perpetuating heterochromatin structures were viewed as the principal form of memory. In female mammals, one X chromosome is inactivated. Anton Wutz and colleagues find evidence for a very early event that 'marks' the chromosome for future stable inactivation ==== Body Introduction In mammals, dosage differences of X-linked genes between XX female and XY male cells are adjusted by transcriptional inactivation of one of the two female X chromosomes. X inactivation is a multistep process, in which the cell counts the number of X chromosomes, chooses one to be active, and silences all others. Initiation of silencing is triggered by accumulation of the 17-kb noncoding Xist RNA (Borsani et al. 1991; Brockdorff et al. 1991; Brown et al. 1991). Remarkably, Xist RNA attaches to chromatin and spreads from its site of transcription in cis over the entire inactive X chromosome (Xi), mediating transcriptional repression. Xist is essential for initiation of silencing, but not for the maintenance of transcriptional repression on the Xi at later stages of cellular differentiation (Penny et al. 1996; Marahrens et al. 1998; Csankovszki et al. 2001). Presently, the molecular nature of the silencing mechanism is not known. Previous studies have shown that X-chromosome inactivation involves the progressive recruitment of a variety of different factors and posttranslational modifications of lysine residues in the amino termini of histones (reviewed in Brockdorff 2002). The current view is that Xist expression initiates the formation of heterochromatin on the Xi, which can be perpetuated by redundant silencing mechanisms at later stages. Consistent with this view, it has been shown that the Xi in mouse embryonic fibroblasts is kept inactive in the absence of Xist by redundant mechanisms, including DNA methylation and histone H4 hypoacetylation (Csankovszki et al. 2001). The Polycomb group proteins Ezh2 and Eed localise to the Xi in embryonic and extraembryonic tissues early in mouse development (Wang et al. 2001; Mak et al. 2002; Plath et al. 2003; Silva et al. 2003). The human EZH2/EED and its homologous E(z)/ESC complex in Drosophila melanogaster show intrinsic histone H3 lysine 9 (H3-K9) and lysine 27 (H3-K27) methyltransferase activity (Cao et al. 2002; Czermin et al. 2002; Kuzmichev et al. 2002; Muller et al. 2002). Interestingly, H3-K27 methylation is one of the earliest chromosomal modifications on the Xi (Plath et al. 2003), and the requirement of Eed for histone methylation on the Xi has been demonstrated (Silva et al. 2003). However, analysis of Eed mutant embryos suggests that Eed is not required for initiation of silencing in trophoblast cells but is required for the maintenance of the Xi at later stages (Wang et al. 2001). Although data are consistent with the interpretation that Xist RNA recruits the Ezh2/Eed complex, thereby introducing histone H3 methylation, the significance of H3-K27 methylation for chromosomal inactivation is unclear. In flies, methylation on H3-K27 facilitates the binding of Polycomb to amino-terminal fragments of histone H3 (Cao et al. 2002; Min et al. 2003). Polycomb recruitment to the Xi has not been observed, and current models suggest that H3-K27 methylation in X-chromosome inactivation is indepen-dent of classical Polycomb silencing (Mak et al. 2002; Silva et al. 2003). We have previously shown that chromosomal silencing can be recapitulated in embryonic stem (ES) cells by expressing Xist RNA from cDNA transgenes integrated into autosomes and the X chromosome (Wutz and Jaenisch 2000), and this allowed for an uncoupling of Xist regulation from cellular differentiation. In this transgenic system, Xist expression is under the control of a tetracycline-responsive promoter, which can be induced by the addition of doxycycline to the culture medium. We showed that Xist RNA localisation and silencing can be separated by introducing specific mutations in Xist RNA (Wutz et al. 2002). Initiation of silencing depends on the repeat A sequence at the 5′ end of Xist. Deletion of this element results in an RNA that localises to chromatin and spreads over the chromosome, but does not trigger transcriptional repression. Initial silencing in ES cells is reversible and dependent on Xist expression. At a later stage in differentiation this silent state becomes irreversible and independent of Xist, corresponding to the maintenance phase of X inactivation. We also showed that Xist expression must be induced early in ES cell differentiation to cause transcriptional repression (Wutz and Jaenisch 2000). Therefore, establishment of silencing is restricted to an initiation window in ES cell differentiation, and induction of Xist expression at a time point later than 24 h in differentiation no longer causes silencing. We found that Xist RNA loses its potential to initiate transcriptional repression roughly 24 h earlier in differentiation than the point at which silencing becomes irreversible. Notably, this left a gap of approximately one cell cycle in length between the initiation and maintenance phases. How silencing is maintained during this period and how the silent state becomes irreversible remained previously unexplained. In this report we perform kinetic measurements and quantification of histone H3 lysine 27 trimethylation (H3K27m3), revealing a novel chromosomal memory that is established by Xist expression at an early time point in ES cell differentiation independent of transcriptional repression. Our analysis suggests that this chromosomal memory might have an important role in the transition from the initiation phase to the maintenance phase of X inactivation. Results Profiling Histone Modification States at the Initiation of X Inactivation We have previously reported that the initial steps of chromosomal silencing in mammalian X inactivation can be recapitulated in transgenic undifferentiated male ES cells (Wutz and Jaenisch 2000). Such ES cells are useful for studying the function of Xist RNA in the initiation of chromosomal silencing and for analysing the kinetics and relevance of chromosomal modifications. We aimed to delineate a pattern of histone methylation states that define the initial decision for facultative heterochromatin. To achieve this we performed immunofluorescence staining against the various modification states on histone H3 and H4 lysine residues in clone 36 ES cells, in which Xist expression can be induced from a transgene integrated on Chromosome 11 by addition of doxycycline to the culture medium (Wutz and Jaenisch 2000). We used highly specific antisera for a defined methylation state (mono-, di-, or tri-) at a particular lysine residue in the amino terminus of histone H3 and H4 (Peters et al. 2003; Perez-Burgos et al. 2004). Some cross reactivity of the H3K27m2 antiserum with H3K27m1 and H3K27m3, of the H3K4m3 antiserum with H3K4m2, and of the H4K20m2 antiserum with H4K20m1 and H4K20m3 was detected on peptide blots (Figure S1), but does not affect the conclusions drawn in this study. Our cytological experiments show a focal signal for H3K27m3 in the interphase nuclei of clone 36 ES cells upon Xist expression, which colocalises with Chromosome 11 in metaphase spreads and Xist RNA in interphase nuclei (Figure 1). In cells grown in the absence of doxycycline, a diffuse nuclear signal was observed. H3-K27 mono- and dimethylation were equally present on the inactivated chromosome and other autosomes (Table 1). Notably, we did not observe any specific enrichment for the H3K9m1, H3K9m2, or H3K9m3 signal on Chromosome 11 upon Xist induction (Figures 1C, 1G, and S2). H3K9m3 and H3K27m1 colocalised strictly with constitutive heterochromatin at pericentric regions and the Y chromosome (Figure 1G and 1H). H3K4m2 and H3K4m3 gave banded signals on chromosome arms that were reduced but not entirely erased on the transgenic chromosome, when Xist expression was induced (Figures 1E and S2J). The heterochromatic Y chromosome completely lacked both H3K4m2 and H3K4m3 in the same metaphase spread. Thus, we conclude that the reduction of H3K4m2 and H3K4m2 on the Xist-expressing chromosome is consistent with a state of transcriptional repression (Santos-Rosa et al. 2002) and with earlier reports that implicate H3-K4 hypomethylation early in X inactivation (Heard et al. 2001; O'Neill et al. 2003). H3K4m1 was equally present on the Xist-expressing chromosome and other autosomes. Using antisera specific for methylation states of H4K20, we observed that H4K20m1 decorated Chromosome 11 upon Xist induction in undifferentiated clone 36 ES cells (in 46% of interphase nuclei; Figure 1B). H4K20m2 and H4K20m3 were not enriched on the Xist-expressing chromosome (Figure S2H and S2I; G. Schotta and M. Lachner, unpublished data). We also investigated the acetylation state of histone H4 in these cells using a sheep polyclonal antiserum that preferentially recognises multiply acetylated H4 (Morrison and Jeppesen 2002). Using this antiserum, we detected partial hypoacetylation of Chromosome 11 in metaphase spreads of clone 36 ES cells that were induced to express Xist (Figures 1F, S2K, and S2L). This observation is different from the global chromosome-wide hypoacetylation of H4 that was reported on the Xi later in differentiation (Keohane et al. 1996) and might reflect the absence of active promoters. We also detected a degree of hypoacetylation when a silencing-defective Xist RNA was expressed (Figure S3), making it likely to be the consequence of cross talk with H4-K20 methylation, which is mutually exclusive at least with H4-K16 acetylation (Nishioka et al. 2002). In conclusion, H3K27m3, H4K20m1, reduction of H3K4m2 and H3K4m3, and reduced multiple-lysine acetylation of histone H4 correlate with the inactive state of the chromosome in undifferentiated ES cells (Table 1). Figure 1 Epigenetic Imprints at the Initiation of X Inactivation (A–H) Indirect immunofluorescence and subsequent DNA FISH analysis on mitotic chromosomes prepared from undifferentiated clone 36 ES cells after 3 d of Xist induction. H3K27m3 (A), H4K20m1 (B), and Ezh2 (D) are enriched on the arms of Chromosome 11 upon ectopic Xist expression. H3K9m2 (C) is not enhanced upon Xist expression. H3K4m2 (E) is reduced on Chromosome 11 upon Xist induction (green box) and absent from pericentric heterochromatin and the Y chromosome (orange arrow). (F) Histone H4 multiple-lysine acetylation is partially reduced (green box, left panel). Hypoacetylation (red) is restricted to chromosomal regions which show high levels of H3-K27 trimethylation (green, right panel). H3K9m3 (G) and H3K27m1 (H) are enriched at constitutive heterochromatin of pericentric regions and the Y (orange arrows). (I–K) Indirect immunofluorescence (upper panels) and subsequent Xist RNA FISH (red, Xist RNA; blue, DAPI) analysis of H3K27m3 (I), H4K20m1 (J), and Ezh2 (K) in interphase nuclei of undifferentiated clone 36 ES cells expressing Xist for 3 d. Table 1 Histone Lysine Methylation States as Epigenetic Imprints during X Inactivation +, chromosome-wide mitotically stable methylation marks recruited by Xist RNA; −, decreased levels due to initiation of X inactivation; 0, abundance and distribution independent of Xist (equal on all chromosomes); 0a, small regional increase during differentiation revealed by ChIP (see text); 0b, identified as epigenetic imprints of constitutive heterochromatin Further confirmation of the cytological findings comes from chromatin immunoprecipitation (ChIP) experiments using antibodies specific for H3K27m3, H4K20m1, H3K4m3, H3K4m2, and H3K9m2 in both undifferentiated and differentiated clone 36 ES cells in the presence or absence of doxycycline (Figure 2). We observed enhanced H3K27m3 and H4K20m1 in the cells expressing Xist regardless of the differentiation state on three microsatellite sequences on Chromosome 11 (Figure 2). A control microsatellite on Chromosome 15 did not show this effect (Figure 2F and 2L). Upon Xist expression, we also observed H3K27m3 on the puromycin marker gene cointegrated with the Xist transgene on Chromosome 11, compared to nearly undetectable levels in the uninduced sample (Figure 2B). This increase in H3K27m3 was paralleled by a marked decrease in H3K4m2 and H3K4m3, but no increase in H4K20m1 could be observed at this locus in undifferentiated ES cells. Upon differentiation, an increase in the H4K20m1 signal was observed when Xist was expressed on all sequences on Chromosome 11. A control tubulin gene located on Chromosome 15 showed no significant change upon Xist induction (Figure 2E and 2K). These data show that H3K27m3 and H4K20m1 are elevated by Xist RNA expression on the transgenic chromosome, in agreement with our cytological analysis. However, regional differences are revealed by the higher resolution of the ChIP experiment, showing that the two modifications do not display a completely overlapping distribution on the chromosome. Differentiation of the ES cells resulted in increased H4K20m1 signals dependent on Xist expression. H3K9m2 was also elevated on two loci on Chromosome 11. Figure 2 ChIP Mapping of H3K27m3, H4K20m1, H3K9m2, H3K4m3, and H3K4m2 on the Xist-Expressing Chromosome 11 during Differentiation of Clone 36 ES Cells A genetic map of Chromosome 11 indicating the loci analysed is given on the left (Xist-TG, approximate integration site of Xist transgene; puro, PGKpuromycin marker). (A to F) Chromatin was prepared from undifferentiated clone 36 ES cells grown for 3 d in the presence (light bars) or absence (dark bars) of doxycycline. H3K27m3 and H4K20m1 were enriched at three intergenic microsatellite sequences at 18.0 (A), 45.5 (C), and 75.2 (D) cM. (B) H3K27m3 was established over the coding sequence of PGKpuromycin in doxycycline-induced cells, which was accompanied by a loss of H3K4m2 and H3K4m3. (E) Tubulin control. (F) Control microsatellite located on Chromosome 15. (G–L) Analysis of H3K27m3, H4K20m1, and H3K9m2 in clone 36 ES cells differentiated for 9 d with (light bars) or without (dark bars) doxycycline. Histone methylation marks were monitored. Experiments were performed in duplicate, and the standard error is indicated in the graphs. H3K27m3 and H4K20m1 Are Triggered by Xist RNA Localisation and Are Independent of Silencing In agreement with earlier studies (Plath et al. 2003; Silva et al. 2003), our results indicate that chromosome-wide histone H3K27m3 is efficiently triggered in undifferentiated ES cells and therefore is an early mark of X inactivation. We measured the kinetics of H3K27m3 following induction of Xist RNA expression in undifferentiated clone 36 ES cells (Figure S3A). At 6, 12, and 24 h after induction 0%, 12%, and 37% of the cells, respectively, showed a signal, and by 48 h a maximum of 70% was reached. Furthermore, the recruitment of Ezh2 protein to the transgenic Chromosome 11 upon Xist expression (see Figure 1D and 1K) is consistent with the idea that the Ezh2/Eed complex contains the enzymatic activity causing H3K27m3 in X inactivation (Mak et al. 2002; Plath et al. 2003; Silva et al. 2003). To identify the Xist sequences that are required for the binding of the Ezh2/Eed complex and to trigger H3K27m3, we examined a panel of Xist RNA mutations (Figure 3A). In an earlier study we inserted Xist cDNA transgenes containing defined deletions into the Hprt gene locus on the single X chromosome in male mouse T20 ES cells and measured their ability to cause silencing (Wutz et al. 2002). We used deletions spanning the entire RNA that eliminate relatively large parts of Xist to analyse H3K27m3 by immunofluorescence in ES cells after induction of transgenic Xist expression (Figure 3). H3K27m3 staining was observed for all Xist mutations tested, with the exception of the ΔXSa deletion, where sequences required for localisation are deleted. The resulting XistΔXSa RNA did not localise well to chromatin and showed consequently greatly diminished potential to silence (Figure S4). We interpret the absence of detectable H3K27m3 in this case as a consequence of the failure of the RNA to localise. All other mutants analysed, including that containing a ΔXN deletion spanning a similar region, gave rise to RNA that localised well to chromatin and caused H3K27m3. A mutant with a deletion of repeat A (T20:ΔSX ES cells; Figure 3), which localises to chromatin but does not cause silencing, was able to induce H3K27m3, suggesting that methylation can be established independent of silencing, a finding consistent with the results of an earlier study (Plath et al. 2003). The expression of the silencing-deficient Xist RNA led to a significantly lower percentage of cells with H3K27m3 foci in interphase nuclei (3- to 4-fold reduction compared to wild-type Xist RNA; Figure 3D). Moreover, on metaphase chromosomes methylation appeared mostly as a single band (only 5% showed a wild-type pattern; Figure 3C). Since the transgene is integrated in the Hprt locus on the X chromosome and the endogenous Xist gene is still present in this cell line, the possibility exists that the transgenic RNA might have stabilised the endogenous Xist RNA or vice versa to effect H3K27m3. To address this point we made use of another cell line in which repeat A was deleted from the endogenous Xist gene and an inducible promoter was inserted by homologous recombination (J1:XistΔSX-tetOP; Wutz et al. 2002). Induction of Xist RNA expression caused H3K27m3 on the single X chromosome in these cells, confirming that H3K27m3 can be established by Xist expression in complete absence of repeat A sequences. However, in undifferentiated ES cells, expression of the silencing-deficient Xist RNA led consistently to lower numbers of cells (30%–35%) showing H3K27m3 staining compared to the wild-type Xist RNA (80%; Figure 3B and 3C). Mono- and dimethylation of H3-K27 were not visibly elevated in J1:XistΔSX-tetOP cells at the expense of the H3K27m3 signal (data not shown), suggesting that recruitment of the Ezh2/Eed complex was impaired in the absence of repeat A, and ruling out the possibility that repeat A would change the specificity of the complex to induce trimethylation activity. Consistent with this interpretation, Ezh2 was observed in only 9% of the J1:XistΔSX-tetOP ES cells compared to 76% of the clone 36 ES cells (see Figures 1K and S3D). We note that the lower methylation potential of Xist RNA lacking repeat A sequences was only observed in undifferentiated ES cells. When the cells were differentiated, methylation levels were elevated (see Figure S3C). We further determined the role of H4K20m1 in silencing. We detected H4K20m1 upon induction of Xist expression in 14% of the interphase nuclei in undifferentiated J1:XistΔSX-tetOP ES cells, showing that H4K20m1 can be established in the absence of repeat A (see Figure S3D). We conclude that H3K27m3 and H4K20m1 are independent of and not sufficient for silencing. Figure 3 Sequences of Xist RNA Required for H3K27m3 Establishment (A) Schematic representation of the Xist cDNA (top) indicating repeats A to E, restriction sites, and the locations of deletions (coloured bars) relative to the location of sequences required for localisation (black and hatched boxes; Wutz et al. 2002). (B) Analysis of H3K27m3 on metaphase chromosome spreads from undifferentiated ES cells after 3 d of Xist induction (see text). The staining patterns (n > 100) were scored as chromosome-wide dense methylation (black), reduced methylation (grey), and a single band (open). (C) Pattern of H3K27m3 triggered by different Xist mutants on metaphase chromosomes after 3 d of induction. Enlarged view of Chromosome 11 (clone 36) or the X chromosome (T20 lines, J1 knock-in line). (D) Focal H3K27m3 staining in interphase nuclei (percentage given; n > 100) of undifferentiated ES cells expressing Xist constructs. Efficient H3K27m3 Is Restricted to Early Stages of Differentiation Xist-mediated transcriptional silencing is restricted in ES cell differentiation in that the potential of Xist to initiate repression diminishes 48 h after differentiation (Wutz and Jaenisch 2000). We investigated whether the ability to establish H3K27m3 would be restricted to this initiation window in clone 36 ES cells. These cells carry a puromycin resistance gene (puro), which is cointegrated with the Xist cDNA transgene on Chromosome 11 and can be silenced by transgenic Xist expression. Xist expression was induced either from the beginning or at 24, 48, 72, 96, or 120 h after the onset of differentiation. The ability of Xist to initiate silencing at various time points was monitored by measuring puro expression, and H3K27m3 was analysed in parallel in all cultures at 12 d after differentiation (Figure 4A). When Xist was induced within 24 h of differentiation, H3K27m3 was observed in a large fraction of the cells. Induction of Xist after 24 h led to significantly lower methylation levels (10%–15% of cells; Figure 4A). The efficiency in H3K27m3 pattern establishment correlated at all time points with the potential of Xist to initiate silencing and Ezh2 recruitment (Figure 4B). Hence, an efficient H3K27m3 pattern was established in a time window that overlapped with the window for the initiation of Xist-mediated repression. We also determined the levels of Eed and Ezh2 protein during ES cell differentiation (Figure 4C). Our analysis shows that Eed levels are significantly reduced at day 3 of differentiation and Ezh2 levels diminish more gradually towards even later time points. This demonstrates that the ability of Xist to induce efficient H3K27m3 is restricted at a time when both Eed and Ezh2 proteins are detected in similar amounts, as in undifferentiated ES cells, suggesting that the efficiency of methylation is not a function of the protein levels. Figure 4 Restriction of H3K27m3 Establishment and Transcriptional Silencing in Differentiation (A) Initiation of H3K27m3 during clone 36 ES cell differentiation. Xist expression was induced at the beginning (+) or at various time points (24 to 120 h) after the start of differentiation, or not induced (−). The percentages of interphase cells showing H3K27m3 (black bars; n > 700) and Ezh2 (grey bars; n > 200) staining were determined at day 12 of differentiation. (B) Initiation of transcriptional silencing during differentiation was assessed by Northern blot analysis of PGKpuromycin (puro) and Gapd as a loading control in parallel cultures as described for (A). (C) Western analysis of Ezh2 and Eed protein levels during differentiation of clone 36 ES cells after induction with retinoic acid. Histones H3 and H4 were used as a loading control. (D) Establishment of H3K27m3 during embryonic development. Xist expression was induced from the single X chromosome of male Xist-tetOP embryos (see text) for 3 d (E9.5–12.5 and E13.5–16.5). The percentage of cells with H3K27m3 staining in interphase (left) and clusters of Xist RNA (right, open bars) are given (n > 300). Grey areas indicate the proportion of H3K27m3-positive cells to Xist-positive cells. (E) Xist RNA FISH (top) and H3K27m3 (bottom) staining of histological sections prepared from neck connective tissue of embryos described in (C). To confirm this finding, we assayed the effect of induction of Xist expression on H3K27m3 in embryonic fibroblasts. Fibroblasts were isolated from male, day 13.5 embryos carrying an insertion of the doxycycline-inducible promoter in the endogenous Xist locus (Xist-tetOP allele) and a homozygous insertion of the tetracycline-responsive transactivator in the ROSA26 locus (ROSA26-nlsrtTA allele; Wutz et al. 2002; F. Savarese, unpublished data). In these fibroblasts, expression of the endogenous Xist RNA from the single male X chromosome could be induced in 80% of the cells by addition of doxycycline (data not shown). In uninduced cultures and control male fibroblasts no H3K27m3 foci were detected by immunofluorescence in interphase nuclei. However, upon Xist induction 5% (after 48 h of Xist induction) or 15% (after 72 h) of the cells showed focal H3K27m3 staining (H4K20m1 was established, as well; see Figure S3G). In control female fibroblasts H3K27m3 staining was detected in 85% of the cells. This shows that Xist induction in embryonic fibroblasts leads to H3-K27 methylation in a low percentage of cells. We further examined histological sections of male embryos carrying the inducible Xist-tetOP allele and the ROSA26-nlsrtTA allele. Xist expression was induced by feeding doxycycline in drinking water to the mothers for 3 d starting either from day 9.5 or day 13.5 of gestation. Embryos were dissected 3 d later, on day 12.5 and 16.5, respectively. In the sections, 74% (day 12.5 embryos) and 52% (day 16.5 embryos) of the cells expressed Xist, as determined by RNA fluorescent in situ hybridization (FISH) analysis (Figure 4D and 4E). Focal H3K27m3 staining was detected in 61% of the cells in sections of the day 12.5 embryos but in only 18% of the day 16.5 embryos (Figure 4D and 4E), demonstrating a clear reduction in the number of cells showing H3K27m3 staining in response to Xist expression in the later-stage embryos. In summary, our data demonstrate that Xist has been able to effect H3K27m3 in all cell types tested. However, the efficiency of methylation is regulated in cellular differentiation and development. Our experiments show that Xist is not sufficient for efficient establishment of the H3K27m3 pattern in differentiated cells. Reversibility of H3K27m3 Once efficient H3K27m3 is established by Xist expression in early ES cell differentiation, it can be maintained throughout differentiation. This would be consistent with the view that lysine methylation is a permanent epigenetic mark. To test whether H3K27m3 is stably maintained in the absence of continuous Xist expression, we tested H3K27m3 reversibility in undifferentiated clone 36 ES cells. Xist expression was induced from the transgenic Chromosome 11 in these cells for 3 days, and then the cells were washed and split into medium without doxycycline to shut off Xist expression. H3K27m3 levels and Xist RNA were determined by combined immunofluorescence RNA FISH at consecutive time points at 6, 12, 24, and 48 h. High levels of H3K27m3 persisted until 24 h after Xist was turned off, but H3K27m3 disappeared by 48 h (Figures 5A and S3B). Our data show that the Xist RNA signal disappeared by 12 h after the withdrawal of doxycycline, demonstrating that H3-K27 methylation is reversible in undifferentiated ES cells and is removed after a period of approximately two cell divisions following the turning off of Xist expression. We also analysed the reversibility of H4K20m1 and Ezh2 in undifferentiated clone 36 ES cells. The percentage of cells showing a signal went from 46% and 70% initially to 5% and 11% at 48 h after withdrawal of doxycycline for H4K20m1 and Ezh2, respectively. Figure 5 Kinetic Study of H3K27m3 Stability (A) The percentage of interphase nuclei (n > 100) showing H3K27m3 staining and Xist RNA was analysed for undifferentiated clone 36 ES cells, which expressed Xist for 3 d (+) or were further grown without inducer for 6, 12, 24, or 48 h. (B) Representative images of the time points analysed in (A) are shown. (C) Reversibility of H3K27m3 in differentiating clone 36 ES cells. The percentage of interphase cells showing H3K27m3 staining (n > 100) was determined for cells differentiated for 4 d in the presence of doxycycline (+) or further differentiated for 48, 72, or 96 h in the absence of inducer. To test whether H3K27m3 would become irreversible during ES cell differentiation, we turned off Xist expression in clone 36 ES cells at progressively later time points up to 6 d after initiation of differentiation. The H3K27m3 pattern was analysed in all cultures at day 12 of differentiation. In cells continuously expressing Xist during differentiation, methylation was detected in 60% of the cells at day 12. If Xist expression was turned off at any time points in the course of differentiation, the percentage of cells showing H3K27m3 was reduced to less than 10%, suggesting that methylation was reversible throughout differentiation and not stabilised (data not shown). We then analysed the kinetics of loss of methylation in differentiated ES cells. Clone 36 ES cells differentiated for 4 d in the presence of doxycycline were differentiated for 24, 48, and 72 more hours in the absence of doxycycline, and H3K27m3 was measured (Figure 5C). Focal H3K27m3 staining was initially observed in 97% of interphase nuclei and was reduced to 50% and 25% at 3 and 4 d, respectively, after Xist had been turned off. This shows that the decay of focal H3K27m3 was slower than in undifferentiated ES cells, possibly reflecting the slower cell cycle of differentiating cells. Early Xist Expression Triggers a Chromosomal Memory Independent of Silencing Detection of focal H3K27m3 staining persisted throughout ES cell differentiation when Xist was continuously expressed. Yet the methylation mark was reversible throughout ES cell differentiation, and Xist RNA could only establish an efficient methylation pattern during the initiation window early in ES cell differentiation. These observations could indicate that silencing enhances histone methylation in ES cell differentiation. To address this interpretation, we analysed the H3K27m3 pattern caused by expression of a mutant Xist RNA lacking repeat A, which cannot initiate silencing, in differentiating J1:XistΔSX-tetOP ES cells. When these cells were differentiated in the presence of doxycycline, focal H3K27m3 staining was observed in 78% of the cells at day 12 (Figure 6). This clearly indicated that methylation was maintained in a high number of these cells. Silencing is therefore dispensable for methylation in ES cell differentiation. Notably, we observed H3K27m3 staining in a high percentage of differentiated ES cells but in a significantly reduced percentage of undifferentiated ES cells expressing a silencing-defective Xist RNA (see Figure 3B, 3C, and S3C). Silencing or repeat A sequences are therefore required to sustain high H3K27m3 levels specifically in undifferentiated ES cells but are dispensable upon differentiation. Figure 6 Early Xist Expression Imparts a Chromosomal Memory Independent of Silencing Transgenic Xist expression was induced from Chromosome 11 in clone 36 ES cells (black bars) or a silencing-deficient Xist RNA from the X in J1:XistΔSX-tetOP ES cells (open bars) at time points during differentiation (see text). The percentage of cells showing H3K27m3 staining is plotted (n > 250). Below, a scheme of Xist induction is given for all cultures, with arrows representing time of analysis. To test whether continuous Xist expression was required for maintenance of efficient H3K27m3, we induced Xist expression from the transgenic Chromosome 11 in undifferentiated clone 36 cells and from the X chromosome in J1:XistΔSX-tetOP ES cells for 3 d. The cells were then differentiated for 5 d in the presence of doxycycline followed by 5 and 7 d, respectively, without the inducer. At the end of this period H3K27m3 was analysed and could be detected in less than 20% and 10% of the cells, respectively (Figure 6). Parallel cultures were differentiated for 5 d in the presence of doxycycline followed by 4 d in the absence of doxycycline, and then doxycycline was added back for 1 or 3 more days. In these cells, in which Xist had been induced early, H3K27m3 was restored and detectable in 50%–55% of all cells. This level is significantly higher than the level in control cultures that had been induced de novo at day 6 or day 9 of differentiation (10% of all cells). In cells that had been continuously differentiated in the presence of doxycycline, methylation was detected in 73%–78% of the nuclei. Our data show that efficient methylation at late time points in differentiation did not require continuous Xist expression. Efficient remethylation occurred on a chromosome that had been exposed to Xist in early ES cell differentiation, consistent with the idea that Xist triggers a chromosomal change in early differentiation that is remembered until later time points to enhance H3K27m3 reestablishment. Importantly, the silencing-deficient Xist mutant RNA in J1:XistΔSX-tetOP ES cells gave identical results, showing that this memory is established independent of silencing. We further determined at which time point in differentiation the chromosomal memory is established. For this, clone 36 ES cells were differentiated for 0, 12, 24, 36, 48, 60, or 72 h in the presence of doxycycline. Then Xist was turned off until day 8 of differentiation, when doxycycline was added back, and remethylation was assayed by immunofluorescence at day 13 in differentiation (Figure 7). In this experiment a transition occurred in a 24-h interval around 60 h if Xist was expressed for more than 48 h early in differentiation, allowing for efficient remethylation, a result consistent with the establishment of the memory in this interval. When Xist was turned off earlier than 60 h, remethylation was observed in only 10%–30% of the cells, demonstrating that the memory was not established. Turning off Xist at 72 h or later allowed remethylation in 85% of the cells. We also analysed the transition from Xist-dependent reversible to irreversible silencing in this experiment by Northern analysis of puro expression from the transgenic chromosome in differentiated clone 36 ES cells (Figure 7B). These data show that irreversible silencing was established in an interval between 48 and 72 h in ES cell differentiation, with puro expression levels dropping from 60% to 15% of the level in uninduced samples (Figure 7C), in agreement with our initial report (Wutz and Jaenisch 2000). The 24-h intervals for the transition can be explained by the asynchronous cell cycle states in the ES cell culture (doubling time, 21.4 h) at the time when differentiation was induced. We conclude that the establishment of the chromosomal memory is silencing independent and occurs at the time when X inactivation becomes irreversible and Xist independent. Figure 7 Establishment of Chromosomal Memory during ES Cell Differentiation (A) Clone 36 ES cells were differentiated for 13 d in the presence of doxycycline (lane 1) or in the absence of inducer (lane 2) and the percentage of cells with H3K27m3 staining was determined (n > 800). At the beginning of differentiation, parallel cultures received either no Xist induction (lane 3) or a pulse of doxycycline for 24 h (lane 4), 36 h (lane 5), 48 h (lane 6), 60 h (lane7), or 72 h (lane 8) followed by withdrawal of inducer and concerted late induction from day 8 to day 13. A dashed red line indicates the 24-h interval of the transition when the chromosomal memory is recruited. (B and C) Establishment of irreversible transcriptional silencing during differentiation. (B) Ectopic inactivation of Chromosome 11 caused by Xist induction in differentiating clone 36 ES cells was assessed by Northern blot analysis of PGKpuromycin (puro) and Gapd as a loading control. Lanes were aligned electronically for better readability. ES cells were differentiated for 13 d in the presence of doxycycline (lane 1) or in the absence of inducer (lane 2). At the start of differentiation, parallel cultures received a Xist pulse for 24, 36, 48, or 60 h followed by withdrawal of inducer for the rest of the time (lanes 3 to 7) or followed by reinduction of Xist at day 8 of differentiation (lanes 8 to 11). All cells were analysed at day 13 of differentiation. (C) A quantitation of the puro expression relative to Gapd was derived from two independent Northern blots using tnimage software. A dashed red line indicates the 24-h interval in which the transition from reversible to irreversible silencing occurs. Discussion Our results identify H3K27m3 and H4K20m1 as specific modifications that mark the Xist-expressing chromosome in undifferentiated ES cells and contribute to the epigenetic histone code of the Xi (Table 1). We did not observe an enrichment of H3K9m2 or H3K9m3 signals on the Xist-expressing chromosome, which has been reported by other studies. This could be a shortcoming of our transgenic system, but we also did not detect the H3K9m2 or H3K9m3 signals in female mouse primary embryonic fibroblasts (less than 2% of the cells). We attribute the different observations in other studies to the various antisera used. We supply peptide blot analysis for our antisera that suggest that the antibodies are highly specific (see Figure S1). This is also supported by the specific staining patterns in immunofluorescence experiments. The lysine 9 methylation signal observed in other studies could potentially be a result of cross reactivity with H3K27m3, a fact we can exclude for our H3K9 antibodies based on the staining pattern and peptide blots. Alternatively, our antibody might not recognise the H3K9m2 modification in the context of the chromosome. However, this is unlikely since the H3K9m3 signals for the pericentric regions and the Y chromosome are clearly identified. The H3K9m2 antiserum has been successfully used in ChIP analysis of the minor centromeric repeats (Yan et al. 2003) and reacts with these repeats in immunofluorescence, but does not show cross reactivity to H3K27m3. This suggests that our reagent is able to detect the modification in both ChIP and immunofluo-rescence experiments. Using highly specific antisera, we failed to see a strong signal for H3K9m2 in either ChIP or immunofluorescence experiments (see Figures 2 and S2E). In our ChIP analysis two chromosomal loci showed an increase for H3K9m2 upon Xist expression in differentiated ES cells, suggesting some enrichment for H3K9m2. We take these data to indicate that H3K9m2 is not a prominent mark of X inactivation but might be enriched locally to some degree upon differentiation. Using Xist alleles that express a mutated version of Xist, which has a deletion of repeat A sequences and is unable to cause silencing, we showed that both H3K27m3 and H4K20m1 were established in the absence of transcriptional repression. This demonstrates that neither modification is sufficient to trigger silencing. Xist expression led to rapid H3K27m3, which was complete after 1 to 2 d of Xist expression in both ES cells and differentiated cells (see Figure S3A and Figure 6, columns 1 and 2). This kinetics follows the localisation of Xist RNA, which accumulates between 4 and 12 h after doxycycline addition in ES cells (Wutz and Jaenisch 2000), suggesting that H3K27m3 is an immediate effect. We have further shown that in undifferentiated ES cells no progressive accumulation of the histone modifications occurs over time by comparing the percentage of cells showing H3K27m3, H4K20m1, and Ezh2 staining after 3 and 10 d expressing either full-length Xist RNA or a silencing-deficient mutant lacking repeat A (see Figure S3C). We have shown that H3K27m3 is a reversible modification throughout ES cell differentiation and depends at all stages on Xist expression. In undifferentiated ES cells H3K27m3 disappeared 48 h after Xist expression was turned off, corresponding to about two cell divisions. The kinetics would be consistent with the idea that replication is involved in the replacement of methylated histones, albeit our data do not rule out an active enzymatic process of demethylation. Importantly, we have observed nearly unchanged methylation levels 24 h after Xist expression has been turned off (see Figure S3B). This could reflect the intrinsic stability of the trimethylation mark or the persistence of the Eed/Ezh2 complex, which can stably associate with metaphase chromosomes from which Xist RNA is displaced (see Figure 1C; Mak et al. 2002). The transient maintenance of H3K27m3 might be significant for the mechanism of X inactivation. It could explain our observation that the inactive state will be “locked in” roughly 24 h after Xist loses its ability to initiate silencing, it will be locked in at 72 h of ES cell differentiation (Wutz and Jaenisch 2000). Efficient methylation is established only when Xist expression is induced early in ES cell differentiation. The window in which Xist causes efficient methylation overlaps precisely with the initiation window, in which transcriptional silencing can be initiated. Yet methylation is independent of initiation of silencing. This would be consistent with the notion that H3K27m3 is necessary but not sufficient for silencing. However, this is unlikely, as a previous report has shown that in Eed mutant embryos, initiation of silencing is normal, but a defect in the maintenance of the inactive state leads to reactivation at later stages (Wang et al. 2001). Lower levels of Ezh2 and Eed could explain the restriction on the ability of Xist to induce H3K27m3 efficiently in differentiated ES cells (Silva et al. 2003). We do not favour this interpretation, as this restriction is observed at day 2 in differentiation, when Ezh2 and Eed protein levels are still high (see Figure 4C). Our data further show that the ability to efficiently methylate a chromosome late in ES cell differentiation is a feature of the chromosome and not a function of the protein levels of Eed and Ezh2. This is also in line with our observation that chromosome-wide H3K27m3 in clone 36 ES cells, in which Eed messenger RNA was reduced to 10%–15% of wild-type levels by stable RNAi, was still detected in 45%–60% of cells compared to 80% in control clone 36 cells (data not shown). Therefore, less abundant levels of Eed are sufficient to achieve efficient methylation. Xist induction later in ES cell differentiation or in cells of embryonic origin establishes H3K27m3 in only a small percentage of cells. The significance of H3K27m3 in this small number of cells is unclear at present. The restriction of efficient methylation to early ES cell differentiation and the finding that methylation is reversible logically require that a chromosomal memory exists that enables H3K27m3 maintenance during differentiation. Previous models have suggested that a lock-in of X inactivation is based on chromosomal silencing, arguing that self-maintaining heterochromatin structures establish the principal form of memory. Our data clearly demonstrate that H3K27m3 is maintained in the absence of transcriptional repression, suggesting a chromosomal memory independent of silencing on the Xi. Using the inducible Xist expression system we have directly demonstrated the chromosomal memory (see Figure 6). A chromosome that had been exposed to Xist and been H3-K27 trimethylated early could be remethylated later in differentiation, after a period where Xist was turned off and methylation decayed, with significantly greater efficiency than a chromosome that had not expressed Xist early (see Figure 6). We have further determined the time point in ES cell differentiation when the chromosomal memory is established and found that it overlaps with the transition from Xist-dependent and reversible silencing to irreversible silencing. These data place the establishment of the memory in a critical phase of X inactivation. We note that the establishment of efficient H3K27m3 in the initiation window and the implementation of the memory are separated by a gap of approximately one cell division in ES cell differentiation. This parallels the gap between initiation of silencing and the maintenance of the silenced state independent of Xist. Our kinetic measurements indicate that H3K27m3 would decay from the Xist-expressing chromosome after two cell divisions; therefore, H3K27m3 could bridge the gap (critical window). We suggest that Xist expression and H3K27m3 might be the signal to recruit a chromosomal memory mediating the lock-in of X inactivation (Figure 8). In this model, silencing would be specified by separate signals depending on repeat A of Xist, which we predict would interact with the memory at the transition from reversible to irreversible and Xist-independent repression. In this regard we note that silencing or repeat A sequences enhance the efficiency of H3K27m3 in undifferentiated ES cells (see Figure 3B). However, there is no requirement for repeat A when ES cells are induced to differentiate (see Figures 6 and S3C). This could point to interactions between the silencing machinery and the Ezh2/Eed methylation complex specifically in ES cells. Figure 8 Model for the Transition from Initiation to Maintenance of X Inactivation Phases of X inactivation are given relative to days of ES cell differentiation (bottom). (A) In undifferentiated ES cells, efficient chromosome-wide H3K27m3 depends on both Xist RNA localisation to the chromosome in cis and initiation of transcriptional silencing via the A repeat (black triangles). (B) Early in differentiation, silencing becomes dispensable for high-level H3K27m3 (dotted arrow). (C) The beginning of the critical window is specified in that Xist loses its potential to trigger H3K27m3 (dotted arrow) and transcriptional silencing. The critical window is negotiated by sustaining high levels of H3K27m3, which is thought to constitute—together with Xist RNA—the signal for the recruitment of the chromosomal memory (black oval). The memory is established on the Xi exactly when silencing becomes irreversible and Xist independent. (D) During the maintenance phase of X inactivation the chromosomal memory allows Xist RNA to establish H3K27m3 efficiently. The molecular basis for the chromosomal memory is presently unknown. Our data rule out the possibility that continuous Xist RNA expression or silencing is required for maintenance of the chromosomal memory and suggest that H3K27m3 is also not involved. The latter interpretation has to be treated cautiously, as it depends on the sensitivity of our assay to detect H3K27m3. Formally it is conceivable that low levels of H3K27m3 undetected by our assay could remain on the chromosome. Presently, it is also unclear what the role of H4K20m1 is and to what extent it interacts with H3K27m3. A H4-K20–specific histone methyltransferase has been identified (Fang et al. 2002; Nishioka et al. 2002; Rice et al. 2002), and we have performed in vitro functional analysis of the mouse Pr-Set7 protein (Figure S5; Protocol S1). Our results indicate that Pr-Set7 is a monomethylase for H4-K20. Its involvement in X inactivation and the function of H4K20m1 remain unclear at present. Future work is needed to identify the components of the memory configuration and to determine its precise function in X inactivation. Materials and Methods Cell lines, culture conditions, and histological sections. Clone 36 ES cells (Wutz and Jaenisch 2000) and J1:XistΔSX-tetOP, T20:Xist, and ES cells expressing Xist deletions (Wutz et al. 2002) were cultured in DMEM (Biochrome, Berlin, Germany), 15% fetal calf serum (Euroclone, Milan, Italy), and 250 U of LIF/ml as described in those references. ES cells were induced to differentiate in ES medium without LIF by addition of all-trans-retinoic acid to 100 nM as described previously (Wutz and Jaenisch 2000). Primary mouse embryonic fibroblasts were derived from day 13.5 embryos and grown in DMEM (Biochrome) and 10% fetal calf serum as described previously (Wutz and Jaenisch 2000). Xist expression was induced by the addition of 1 μg/ml of doxycycline to the culture medium or was administered in drinking water (100 mg and 100 g of sucrose per liter). For sections, embryos were sexed (Lambert et al. 2000) and fixed, and 10-μm-thick frozen sections were prepared. Mice were handled according to institutional guidelines. Immunostaining and Western blot. For metaphase chromosome spreads, cells were incubated for 15 min at 37 °C in RBS solution (10 mM Tris-HCl [pH 7.5], 10 mM NaCl, 5 mM MgCl2), centrifuged for 10 min at 1,200 rpm onto Menzel SuperFrost slides (Roth, Karlsruhe, Germany) using a Cytospin 3 centrifuge (Thermo Shandon, Pittsburgh, Pennsylvania, United States). Staining was performed as described previously (Peters et al. 2003). Briefly, slides were extracted for 10 min at room temperature (RT) in KCM (10 mM Tris [pH 8.0], 120 mM KCl, 20 mM NaCl, 0.5 mM EDTA, 0.1% [vol/vol] Tween-20) containing 0.1% (vol/vol) Triton-X100, fixed for 10 min at RT in 2% PFA/PBS, washed in KCM/0.1% Tween-20, and blocked for 30 min at RT in KCM containing 2.5% (wt/vol) BSA, 0.1% Tween-20, and 10% normal goat serum (Jackson ImmunoResearch, West Grove, Pennsylvania, United States). Primary antibodies were diluted in blocking solution and incubated overnight at 4 °C. After washes in KCM/0.1% Tween-20, slides were incubated with secondary antibodies for 1 h at RT, washed, and mounted (Vectashield; Vector Laboratories, Burlingame, California, United States). For analysis of interphase nuclei, differentiated ES cells were grown on Roboz slides (CellPoint Scientific, Gaithersburg, Maryland, United States) and undifferentiated cells were attached to poly-l-lysine coated coverslips or cytospun as described above. Immunostaining was performed as described previously (Peters et al. 2003). Briefly, cells were fixed for 10 min at RT in 2% PFA in PBS, permeabilized for 5 min at RT in 0.1% Na Citrate/0.1% Triton-X100, blocked for 30 min at RT in PBS containing 2.5% (wt/vol) BSA, 0.1% Tween-20, and 10% normal goat serum, and processed as described above. Antibodies for histone lysine methylation states are described elsewhere (Peters et al. 2003) and were used as follows (metaphase spreads/interphase): α-H3-K9m1 (IgG fraction of α-2x-monomethH3-K9, #4858, 1.7 mg/ml), 1:200/1:500; α-H3-K9m2 (IgG fraction of α-2x-dimeth H3-K9, #4679, 1.7 mg/ml), 1:100/1:200; α-H3-K9m3 (IgG fraction of α-2x-trimeth H3-K9, #4861, 1.3 mg/ml), 1:300/1:500; α-H3-K27m1 (IgG fraction of α-2x-monometh H3-K27, #8835, 0.7 mg/ml), 1:500/1:1,000; α-H3-K27m2 (IgG fraction of α-2x-dimeth H3-K27, #8841, 0.6 mg/ml), 1:500/1:1,000; α-H3-K27m3 (IgG fraction of α-2x-trimeth H3-K27, #6523, 1.1 mg/ml), 1:300/1:500. Additional antibodies were as follows: α-H3-K4m1 (α-monomethyl-Histone H3 [Lys4], #1799; Upstate Biotechnology, Lake Placid, New York, United States), 1:400/1:1,000; α-H3-K4m2 (α-dimethyl-Histone H3 [Lys4], #07-030; Upstate), 1:400/1:1,000; α-H3-K4m3 (α-trimethyl-Histone H3 [Lys4], #1819; Upstate), 1:700/1:1,000; α-H4-K20m1 (α-monomethyl-Histone H4 [Lys20], #07-440; Upstate), 1:100/1:200; α-H4-K20m2 (α-dimethyl-Histone H4 [Lys20], #07-367; Upstate), 1:200/1:200; α-H4-K20m3 (α-trimethyl-Histone H4 [Lys20], #07-463; Upstate), 1:350/1:500; polyclonal sheep α-H4Ac (Morrison and Jeppesen 2002), 1:500/1:1,000; polyclonal rabbit α-Ezh2 (Sewalt et al. 1998), 1:100/1:200. Secondary antibodies (Molecular Probes, Eugene, Oregon, United States) were as follows: Alexa A-11034 Fluor 488 goat antirabbit IgG (H+L), Alexa A-11036 Fluor 568 goat antirabbit IgG (H+L), and Alexa A-21099 Fluor 568 donkey antisheep IgG (H+L), all at 1:500. For Western blots, total nuclear extract was separated by SDS PAGE, blotted onto a PVDF membrane (Immobilon-P; Millipore, Bedford, Massachusetts, United States), blocked in blocking solution (PBS, 3% [wt/vol] BSA), and incubated with primary antibodies for 3 h. After washing three times for 10 min in TBST (50 mM Tris-HCl [pH 8.0], 100 mM NaCl, 0.1% Tween 20) and incubation with secondary antibodies (HRP; Jackson Laboratory, Bar Harbor, Maine, United States), detection was performed using ECL reagent (Amersham Pharmacia Biotech, Little Chalfont, United Kingdom). Rabbit polyclonal α-Eed (1:3,500), rabbit polyclonal α-Ezh2 (1:1,000), goat polyclonal α-histone H3 (1:800, #sc-8654; Santa Cruz Biotechnology, Santa Cruz, California, United States), and rabbit polyclonal α-histone H4 (1:300, #07–108; Upstate) were used. DNA FISH and RNA analysis. For DNA FISH analysis, biotin-labelled STAR*FISH mouse whole chromosome-specific probes (1187-YMB-02, 1187–11MB-01; Cambio, Cambridge, United Kingdom) were detected with streptavidin, Alexa Fluor 633 conjugateS-21375 (Molecular Probes). RNA FISH probes were generated by random priming (Stratagene, La Jolla, California, United States) using Cy3-dCTP (Amersham). Hybridisation and washing were carried out as described previously (Wutz and Jaenisch 2000). Specimens were analysed using a fluorescence microscope (Zeiss Axioplan, Oberkochen, Germany) equipped with a CCD camera and the MetaMorph image analysis software (Universal Imaging, Downingtown, Pennsylvania, United States). Northern analysis was performed using 20 μg of RNA (Trizol; Invitrogen, Carlsbad, California, United States) as described previously (Wutz et al. 2002). ChIPs. Cells were cross-linked with 1% formaldehyde for 10 min at RT and quenched with 125 mM glycine, and whole-cell extracts were prepared. ChIPs were performed in duplicates as described previously (Martens et al. 2003). Briefly, 400 μg of fragmented chromatin (between 400 and 1000 base pairs) was used for immunoprecipitation, and DNA was extracted from the precipitates and analysed by real-time PCR using a Lightcycler (Roche Diagnostics, Basel, Switzerland). Results were corrected for nonspecific binding to the beads and presented as a percentage of the input DNA (4 μg of fragmented chromatin, 100%). Primers sequences were as follows: tubulin, CCTGCTGGGAGCTCTACT and GGGTTCCAGGTCTACGAA; puromycin, GCTGCAAGAACTCTTCCTC and GCCTTCCATCTGTTGCTG; d11mit117, AAAAGACCCTATTTACAATACAACTGA and TGTCATTTTTGATTAATCGCTCC; d11mit108, GGCACAAGAAAGACACAGCA and AAAGAGAAACCCCAGAGGGA; d11mit102, CCAGGAGAGCAGGAAGGTC and TCCTTCTGGGTGCTGCAT; d15mit15, AGCATACACTCTTGTTCCTGCT and AATAAATACCAGAGAAGCACCGTG. Supporting Information Figure S1 Specificities of H3-K9, H3-K27, H4-K20, and H3-K4 Mono-, Di-, and Trimethyl Antibodies Immunodotblot analysis (Peters et al. 2003) of the antisera used to detect specific methylation states of histone H3 on Lysine 9 (A), H3 on Lysine 27 (B), H4 on Lysine 20 (C), and H3 on Lysine 4 (D). IgG fractions of the methyl-lysine histone antibodies were tested at various dilutions, with the most optimal dilution being displayed. Dotblots contain 0.4, 2, 10, and 50 pmol of linear H3 (amino acids 1–20; amino acids 19–34; amino acids 25–45; amino acids 72–91) and peptides, either unmodified or mono-, di-, or trimethylated at the K4, K9, K27, K36, or K79 positions. In addition, a linear H4 (amino acids 12–31) peptide, mono-, di-, or trimethylated at the K20 position, was also used. (611 KB PDF). Click here for additional data file. Figure S2 Histone Modification Pattern of the Inactive X Chromosome Immunofluorescence staining of metaphase spreads of clone 36 ES cells induced to express Xist for 3 d using H3K27m1 (A), H3K27m2 (B), H3K27m3 (C), H3K9m1 (D), H3K9m2 (E), H3K9m3 (F), H4K20m1 (G), H4K20m2 (H), H4K20m3 (I), H3K4m3 (J), and H4Ac (K) antisera. Chromosome 11 was identified by a DNA FISH probe (red; blue, DAPI) in (J) and (K). Clone 36 ES cells grown in the absence of doxycycline are used as a control for the H4Ac staining without Xist expression (L). (4.8 MB TIF). Click here for additional data file. Figure S3 Initiation and Maintenance of Histone Methylation during Differentiation (A) The kinetics of H3K27m3 was measured in undifferentiated clone 36 ES cells. The number of cells showing H3K27m3 staining 6, 12, 24, and 48 h after induction of Xist expression is shown. (B) The stability of H3K27m3 was determined in undifferentiated ES cells. The percentage of metaphase chromosome spreads (n > 150) showing H3K27m3 staining was analysed in undifferentiated clone 36 ES cells, which expressed Xist for 3 d (lane 1) or were further grown without inducer for 24 h (lane 2) or 48 h (lane 3). This experiment complements data presented in Figure 5A and 5B providing a ‘cell cycle synchronous' view of the H3K27m3 decay kinetics. (C) Levels of H3K27m3 were measured in undifferentiated ES cells after 3 and 10 d of Xist expression. No progressive accumulation over time was observed, indicating that the steady state of H3K27m3 has been reached at 3 d Xist expression. However, a marked increase in methylation is observed in J1:XistΔSX-tetOP ES cells upon differentiation for 2 d (hatched bar). (D) Combined Xist RNA FISH (red) immunofluorescence analysis of Ezh2 and H4K20m1 in undifferentiated J1:XistΔSX-tetOP cells expressing Xist for 3 and 10 d (percentage of nuclei showing a staining is given). Analysis of H3K27m3 and H4 acetylation using an antiserum specific for multiply acetylated forms of H4 in clone 36 and J1:XistΔSX-tetOP ES cells that were grown for 4 d in the presence of doxycycline and then shifted to differentiation conditions for 2 d more in the presence of doxycylcine. (E) Male primary mouse fibroblasts (PMEFs) hemizygous for the inducible Xist-tetOP allele and homozygous for the tetracycline-inducible transactivator were induced with doxycycline for 2 d (lane 1) or 3 d (lane 2), and the number of cells showing H3K27m3 staining in interphase was analysed. Control female PMEFs showed a methylation signal in the large majority of cells (lane 3); uninduced male PMEFs were always negative. (F) Representative indirect immunofluorescence of uninduced (top) and induced (bottom) male Xist-tetOP PMEFs. The inducible Xist RNA triggers less pronounced and less dense foci of H3-K27 trimethylation (green) compared to the female wild-type control. (G) Upon Xist expression, H4-K20 monomethylation (green) is observed in interphase Xist-tetOP PMEFs (left). Focal enrichment colocalises with the site or Xist RNA clusters (red) on the X chromosome. Female wild-type PMEFs (right). (3.0 MF TIF). Click here for additional data file. Figure S4 Analysis of the XistΔXSa Mutation The XistΔXSa transgene was integrated by Cre-mediated recombination into the Hprt locus on the single X chromosome in T20 ES cells (Wutz et al. 2002). A schematic representation of the Xist cDNA in given (top): repeats A to E are indicated by arrays of triangles, sequences mediating localisation to chromatin are indicated by boxes underneath (degree of hatching represents importance), and the location of the deletion is indicated by a coloured box. RNA localisation was analysed by FISH (lower left), showing that the RNA localises in small clusters in some cells. The ability of the RNA to induce silencing was measured by cell survival of differentiating cultures under induced versus uninduced conditions (lower right). Controls are cells either having a fully functional Xist cDNA transgene (Xist) or a cDNA lacking repeat A that is incompetent to induce silencing (ΔSX). The ΔXSa RNA shows poor silencing activity, presumably as a consequence of its failure to localise well to the chromosome. (1.5 MB TIF). Click here for additional data file. Figure S5 Selective H4-K20 Monomethylation Activity of Mouse Pr-Set7 In Vitro (A) Schematic presentation of full-length mouse PR/SET domain-containing protein 07 (Pr-Set7), indicating SET domain in black (gi:38080595). Below, region tested for histone methyltransferase (HMTase) activity. (B) Coomassie stain (left) shows purified recombinant GST-tagged Pr-Set7 (arrow), H4 peptides (arrowhead), and histones used for in vitro reactions with S-adenosyl-[methyl-14C]-L-methionine as methyl donor. Fluorography (right) indicates HMTase activity on the unmodified H4 peptide comprising residues 12–31 of the histone H4 N-terminus. Notably, no further methyl groups could be transferred to the same peptide if it had been synthetically monomethylated at residue H4 lysine 20 (K20m1) before usage in the in vitro reaction. Free histones are not accepted as substrate. (C) Fluorography indicates histone H4 HMTase activity of GST–Pr-Set7 selective for the unmodified histone H4 peptide (12–31). H4-K20 monomethylation obviously is the terminal state for Pr-Set7, because synthetically mono- (K20m1), di- (K20m2), and trimethylated H4 peptides (K20m3) could not be significantly methylated. (948 KB TIF). Click here for additional data file. Protocol S1 Supplementary Methods (22 KB DOC). Click here for additional data file. We thank Antoine Peters and Karen Ng for comments on the manuscript, Arie Otte for the Ezh2 antiserum, Andreas Bichl and Mijo Dezic for maintenance of the mouse colony, Karl Mechtler for peptide synthesis, and Peter Steinlein and Gotthold Schaffner for technical help. This research was supported by grants from the Austrian GEN-AU initiative financed by the Ministry of Education, Science, and Culture. JM is the recipient of a long-term European Molecular Biology Organization fellowship. The Research Institute of Molecular Pathology is sponsored by Boehringer Ingelheim. Conflicts of Interest. The authors have declared that no conflicts of interest exist. Author Contributions. AK, FS, ML, JM, TJ, and AW conceived and designed the experiments. AK, FS, ML, JM, and AW performed the experiments. AK, FS, ML, JM, and AW analyzed the data. AK, TJ, and AW contributed reagents/materials/analysis tools. AK, TJ, and AW wrote the paper. Academic Editor: Peter Becker, Adolf Butenandt Institute Abbreviations ChIPchromatin immunoprecipitation ES cellembryonic stem cell FISHfluorescent in situ hybridization H3-K9histone H3 lysine 9 H3K27m3histone H3 lysine 27 trimethylation HMTasehistone methyltransferase PMEFprimary mouse fibroblast RTroom temperature Xiinactive X chromosome ==== Refs References Borsani G Tonlorenzi R Simmler MC Dandolo L Arnaud D Characterization of a murine gene expressed from the inactive X chromosome Nature 1991 351 325 329 2034278 Brockdorff N X-chromosome inactivation: Closing in on proteins that bind Xist RNA Trends Genet 2002 18 352 358 12127775 Brockdorff N Ashworth A Kay GF Cooper P Smith S Conservation of position and exclusive expression of mouse Xist from the inactive X chromosome Nature 1991 351 329 331 2034279 Brown CJ Ballabio A Rupert JL Lafreniere RG Grompe M A gene from the region of the human X inactivation centre is expressed exclusively from the inactive X chromosome Nature 1991 349 38 44 1985261 Cao R Wang L Wang H Xia L Erdjument-Bromage H Role of histone H3 lysine 27 methylation in Polycomb-group silencing Science 2002 298 1039 1043 12351676 Csankovszki G Nagy A Jaenisch R Synergism of Xist RNA, DNA methylation, and histone hypoacetylation in maintaining X chromosome inactivation J Cell Biol 2001 153 773 784 11352938 Czermin B Melfi R McCabe D Seitz V Imhof A Drosophila enhancer of Zeste/ESC complexes have a histone H3 methyltransferase activity that marks chromosomal Polycomb sites Cell 2002 111 185 196 12408863 Fang J Feng Q Ketel CS Wang H Cao R Purification and functional characterization of SET8, a nucleosomal histone H4-lysine 20-specific methyltransferase Curr Biol 2002 12 1086 1099 12121615 Heard E Rougeulle C Arnaud D Avner P Allis CD Methylation of histone H3 at Lys-9 is an early mark on the X chromosome during X inactivation Cell 2001 107 727 738 11747809 Keohane AM O'Neill LP Belyaev ND Lavender JS Turner BM X-Inactivation and histone H4 acetylation in embryonic stem cells Dev Biol 1996 180 618 630 8954732 Kuzmichev A Nishioka K Erdjument-Bromage H Tempst P Reinberg D Histone methyltransferase activity associated with a human multiprotein complex containing the Enhancer of Zeste protein Genes Dev 2002 16 2893 2905 12435631 Lambert JF Benoit BO Colvin GA Carlson J Delville Y Quick sex determination of mouse fetuses J Neurosci Methods 2000 95 127 132 10752483 Mak W Baxter J Silva J Newall AE Otte AP Mitotically stable association of polycomb group proteins Eed and Enx1 with the inactive X chromosome in trophoblast stem cells Curr Biol 2002 12 1016 1020 12123576 Marahrens Y Loring J Jaenisch R Role of the Xist gene in X chromosome choosing Cell 1998 92 657 664 9506520 Martens JH Verlaan M Kalkhoven E Zantema A Cascade of distinct histone modifications during collagenase gene activation Mol Cell Biol 2003 23 1808 1816 12588998 Min J Zhang Y Xu RM Structural basis for specific binding of Polycomb chromodomain to histone H3 methylated at Lys 27 Genes Dev 2003 17 1823 1828 12897052 Morrison H Jeppesen P Allele-specific underacetylation of histone H4 downstream from promoters is associated with X-inactivation in human cells Chromosome Res 2002 10 579 595 12498347 Muller J Hart CM Francis NJ Vargas ML Sengupta A Histone methyltransferase activity of a Drosophila Polycomb group repressor complex Cell 2002 111 197 208 12408864 Nishioka K Rice JC Sarma K Erdjument-Bromage H Werner J PR-Set7 is a nucleosome-specific methyltransferase that modifies lysine 20 of histone H4 and is associated with silent chromatin Mol Cell 2002 9 1201 1213 12086618 O'Neill LP Randall TE Lavender J Spotswood HT Lee JT X-linked genes in female embryonic stem cells carry an epigenetic mark prior to the onset of X inactivation Hum Mol Genet 2003 12 1783 1790 12874099 Penny GD Kay GF Sheardown SA Rastan S Brockdorff N Requirement for Xist in X chromosome inactivation Nature 1996 379 131 137 8538762 Perez-Burgos L Peters AH Opravil S Kauer M Mechtler K Generation and characterization of methyl-lysine histone antibodies Methods Enzymol 2004 376 234 254 14975310 Peters AH Mermoud JE O'Carroll D Pagani M Schweizer D Histone H3 lysine 9 methylation is an epigenetic imprint of facultative heterochromatin Nat Genet 2002 30 77 80 11740497 Peters AH Kubicek S Mechtler K O'Sullivan RJ Derijck AA Partitioning and plasticity of repressive histone methylation states in mammalian chromatin Mol Cell 2003 12 1577 1589 14690609 Plath K Fang J Mlynarczyk-Evans SK Cao R Worringer KA Role of histone H3 lysine 27 methylation in X inactivation Science 2003 300 131 135 12649488 Rice JC Nishioka K Sarma K Steward R Reinberg D Mitotic-specific methylation of histone H4 Lys 20 follows increased PR-Set7 expression and its localization to mitotic chromosomes Genes Dev 2002 16 2225 2230 12208845 Santos-Rosa H Schneider R Bannister AJ Sherriff J Bernstein BE Active genes are tri-methylated at K4 of histone H3 Nature 2002 419 407 411 12353038 Sewalt RG van der Vlag J Gunster MJ Hamer KM den Blaauwen JL Characterization of interactions between the mammalian polycomb-group proteins Enx1/EZH2 and EED suggests the existence of different mammalian polycomb-group protein complexes Mol Cell Biol 1998 18 3586 3595 9584199 Silva J Mak W Zvetkova I Appanah R Nesterova TB Establishment of histone h3 methylation on the inactive X chromosome requires transient recruitment of Eed-Enx1 polycomb group complexes Dev Cell 2003 4 481 495 12689588 Wang J Mager J Chen Y Schneider E Cross JC Imprinted X inactivation maintained by a mouse Polycomb group gene Nat Genet 2001 28 371 375 11479595 Wutz A Jaenisch R A shift from reversible to irreversible X inactivation is triggered during ES cell differentiation Mol Cell 2000 5 695 705 10882105 Wutz A Rasmussen TP Jaenisch R Chromosomal silencing and localization are mediated by different domains of Xist RNA Nat Genet 2002 30 167 174 11780141 Yan Q Huang J Fan T Zhu H Muegge K Lsh, a modulator of CpG methylation, is crucial for normal histone methylation EMBO J 2003 22 5154 5162 14517253

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020178Research ArticleCell BiologyDevelopmentGenetics/Genomics/Gene TherapyDrosophilaHairy Transcriptional Repression Targets and Cofactor Recruitment in Drosophila Hairy Targets and Cofactor RecruitmentBianchi-Frias Daniella 1 Orian Amir 1 Delrow Jeffrey J 2 Vazquez Julio 3 Rosales-Nieves Alicia E 1 Parkhurst Susan M [email protected] 1 1Division of Basic Sciences, Fred Hutchinson Cancer Research CenterSeattle, Washington, United States of America2Genomics Resource, Fred Hutchinson Cancer Research CenterSeattle, Washington, United States of America3Scientific Imaging, Fred Hutchinson Cancer Research CenterSeattle, WashingtonUnited States of America7 2004 13 7 2004 13 7 2004 2 7 e17813 10 2003 14 4 2004 Copyright: © 2004 Bianchi-Frias et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Calling the Steps in Development's Genetic Square Dance Members of the widely conserved Hairy/Enhancer of split family of basic Helix-Loop-Helix repressors are essential for proper Drosophila and vertebrate development and are misregulated in many cancers. While a major step forward in understanding the molecular mechanism(s) surrounding Hairy-mediated repression was made with the identification of Groucho, Drosophila C-terminal binding protein (dCtBP), and Drosophila silent information regulator 2 (dSir2) as Hairy transcriptional cofactors, the identity of Hairy target genes and the rules governing cofactor recruitment are relatively unknown. We have used the chromatin profiling method DamID to perform a global and systematic search for direct transcriptional targets for Drosophila Hairy and the genomic recruitment sites for three of its cofactors: Groucho, dCtBP, and dSir2. Each of the proteins was tethered to Escherichia coli DNA adenine methyltransferase, permitting methylation proximal to in vivo binding sites in both Drosophila Kc cells and early embryos. This approach identified 40 novel genomic targets for Hairy in Kc cells, as well as 155 loci recruiting Groucho, 107 loci recruiting dSir2, and wide genomic binding of dCtBP to 496 loci. We also adapted DamID profiling such that we could use tightly gated collections of embryos (2–6 h) and found 20 Hairy targets related to early embryogenesis. As expected of direct targets, all of the putative Hairy target genes tested show Hairy-dependent expression and have conserved consensus C-box–containing sequences that are directly bound by Hairy in vitro. The distribution of Hairy targets in both the Kc cell and embryo DamID experiments corresponds to Hairy binding sites in vivo on polytene chromosomes. Similarly, the distributions of loci recruiting each of Hairy's cofactors are detected as cofactor binding sites in vivo on polytene chromosomes. We have identified 59 putative transcriptional targets of Hairy. In addition to finding putative targets for Hairy in segmentation, we find groups of targets suggesting roles for Hairy in cell cycle, cell growth, and morphogenesis, processes that must be coordinately regulated with pattern formation. Examining the recruitment of Hairy's three characterized cofactors to their putative target genes revealed that cofactor recruitment is context-dependent. While Groucho is frequently considered to be the primary Hairy cofactor, we find here that it is associated with only a minority of Hairy targets. The majority of Hairy targets are associated with the presence of a combination of dCtBP and dSir2. Thus, the DamID chromatin profiling technique provides a systematic means of identifying transcriptional target genes and of obtaining a global view of cofactor recruitment requirements during development. A new chromatin profiling technique identifies targets of the transcription factor Hairy -- as well as targets of three co- repressors -- which implicate Hairy in several developmental processes ==== Body Introduction Transcriptional repression is an important feature of developmental processes, where it is necessary for establishing intricate patterns of gene expression (reviewed in Herschbach and Johnson 1993; Gray and Levine 1996; Hanna-Rose and Hansen 1996; Courey and Jia 2001; Gaston and Jayaraman 2003). Drosophila embryogenesis is marked by the subdivision of the embryo into progressively more precise spatial domains, achieved through the coordinated functions of both transcriptional activators and repressors (maternal→gap→pair-rule→segment polarity; for review, see Lawrence 1992). One such developmental repressor is the pair-rule gene hairy, which sits at a key position in the segmentation gene hierarchy: it is one of the first genes to show the reiterated periodicity that is central to the establishment of proper embryonic body plan throughout metazoa (Ingham et al. 1985). During segmentation, hairy behaves genetically as a negative regulator of a downstream (secondary) pair-rule gene, fushi tarazu (ftz; Carroll and Scott 1986; Howard and Ingham 1986). In addition to embryonic segmentation, Hairy also regulates several other developmental processes (cf. Brown et al. 1995; Davis and Turner 2001; Myat and Andrew 2002). For example, during larval development, Hairy is required for proper peripheral nervous system development, where it is a negative regulator of the proneural basic Helix-Loop-Helix (bHLH) activator gene achaete (ac; Botas et al. 1982; Ohsako et al. 1994; Van Doren et al. 1994). Hairy belongs to the evolutionarily conserved Hairy/Enhancer of split/Deadpan (HES) subclass of repressor bHLH proteins (Rushlow et al. 1989). These proteins function throughout development as dedicated transcriptional repressors of genes necessary for cell fate decisions in processes including segmentation, myogenesis, somitogenesis, sex determination, vasculogenesis, mesoderm formation, and neurogenesis (reviewed in Fisher and Caudy 1998a; Davis and Turner 2001). Misregulation of HES family members has been linked to developmental defects and oncogenesis. In Drosophila, the HES family consists of Hairy and twelve other structurally related proteins, including Deadpan and seven members of the Enhancer of split complex . All members of this repressor family possess a highly conserved bHLH domain, required for DNA binding and protein dimerization; an adjacent Orange domain, which confers specificity among family members; and a C-terminal tetrapeptide motif, WRPW, which has been shown to be necessary and sufficient for the recruitment of the corepressor Groucho. HES proteins have been shown to bind preferentially to Class C sites (CACNNG; C-box) as homodimers in vitro (Sasai et al. 1992; Tietze et al. 1992; Oellers et al. 1994; Ohsako et al. 1994; Van Doren et al. 1994). The prevailing view is that Hairy functions as a promoter-bound repressor: an intact bHLH region is required for Hairy to bind to specific DNA sites, where it then recruits cofactors to mediate its activities. Indeed, ac has been shown to be a direct transcriptional target of Hairy during peripheral nervous system development (Ohsako et al. 1994; Van Doren et al. 1994; Fisher et al. 1996). However, while ftz was identified as a genetic target of Hairy during segmentation, there is currently no evidence for Hairy binding directly to the ftz promoter to regulate its transcription (despite the efforts of several labs to find such an association). A common theme among DNA-bound transcriptional regulators is the recruitment of coactivators or corepressors to carry out their functions (reviewed in Mannervik et al. 1999; Bone and Roth 2001; Urnov et al. 2001; Jepsen and Rosenfeld 2002). Three such cofactors have been identified as Hairy-interacting proteins that are required for Hairy-mediated transcriptional repression: Groucho, Drosophila C-terminal binding protein (dCtBP), and Drosophila silent information regulator 2 (dSir2) (Paroush et al. 1994; Poortinga et al. 1998, Phippen et al. 2000, Rosenberg and Parkhurst 2002). None of these cofactors bind DNA themselves, but they are brought to the DNA through their interaction with sequence-specific DNA binding repressors, such as Hairy. Groucho was the first cofactor shown to be required for Hairy-mediated repression, where it was shown to enhance the hairy mutant phenotype (Paroush et al. 1994). Groucho, as well as its mammalian homologs collectively called TLEs (TLE1–4), share a similar overall domain structure (reviewed in Parkhurst 1998; Fisher and Caudy 1998b). Groucho has been proposed to utilize a chromatin remodeling mechanism through its recruitment of Rpd3 (Drosophila histone deacetylase 1 homolog), but the evidence for the significance of this interaction is somewhat mixed (Chen et al. 1999; Mannervik and Levine 1999; Courey and Jia 2001). C-terminal binding protein (CtBP) family members are an interesting new class of transcriptional coregulators that encode nicotinamide adenine dinucleotide+–dependent (NAD+-dependent) acid dehydrogenases (reviewed in Turner and Crossley 2001; Chinnadurai 2002a, 2002b; Kumar et al. 2002). CtBP proteins function as context-dependent cofactors: they act as either coactivators or corepressors of transcription, with distinct regions of the CtBP protein being required for activation or repression (Nibu et al. 1998a, 1998b; Poortinga et al. 1998; Phippen et al. 2000; Chinnadurai 2002a). The mechanism of CtBP coactivation is not known. CtBP proteins, however, have also been postulated to use a chromatin-based mechanism when functioning as a corepressor for transcription: they can bind to histone deacetylases and have been shown to modify histones (Sundqvist et al. 1998; Shi et al. 2003). Like its yeast homolog, dSir2 encodes NAD+-dependent histone deacetylase activity that is required for heterochromatic silencing (Rosenberg and Parkhurst 2002; Newman et al. 2002; reviewed in Gottschling 2000; Imai et al. 2000; Denu 2003). While yeast silent information regulator 2 (Sir2) has been thought to function as a dedicated heterochromatic silencing factor, dSir2, and more recently the human Sir2-related protein SIRT1, have been shown to play a role in euchromatic repression by interacting with Hairy and other HES family members (Rosenberg and Parkhurst 2002; Takata and Ishikawa 2003). dSir2 mutants are viable (Newman et al. 2002) and exhibit a dominant genetic interaction with hairy, resulting in derepression of Ftz expression (Rosenberg and Parkhurst 2002), suggesting that Sir2 in higher organisms plays a role in both euchromatic repression and heterochromatic silencing. The choice of cofactor recruited by a particular DNA-bound repressor has been proposed to help distinguish among the mechanisms of repression employed. Despite the importance of Hairy and other HES family proteins in many developmental regulatory processes, little is known about the number and kinds of target genes they regulate. Understanding the spectrum of direct targets will be essential to addressing mechanistic questions such as how or when different cofactors are recruited. To this end, we have used the chromatin profiling technique DamID to systematically identify direct Hairy transcriptional target genes and to obtain a global view of the cofactors Hairy recruits to the various loci at which it acts. Results Identification of Direct Hairy Transcriptional Targets in Drosophila Kc Cells Using the Chromatin Profiling Technique DamID To identify direct transcriptional targets for Hairy in vivo, we employed a powerful new chromatin profiling technique, DamID, in which E. coli DNA adenine methyltransferase (Dam) tethered to a chromatin binding protein leads to specific methylation of DNA adjacent to the protein binding/recruitment sites (van Steensel and Henikoff 2000; van Steensel et al. 2001). We generated a functional Dam–Hairy fusion construct under the control of the heat shock promoter to use in Drosophila Kc cells (see Materials and Methods). Since overexpression of Dam fusion constructs leads to a high level of nonspecific methylation (van Steensel and Henikoff 2000), only low-level leaky expression from the uninduced heat shock promoter was used: the cells were not heat shocked. Genomic DNA was isolated from the Kc cells 24 h post transfection, and methylated DNA fragments were recovered on a sucrose gradient following digestion of the genomic DNA with the methylation-sensitive enzyme DpnI. These methylated fragments were labeled with the Cy5 (Dam–Hairy fusion protein) and Cy3 (Dam alone, a control for nonspecific binding/accessibility; van Steensel and Henikoff 2000) fluorochromes, then cohybridized to a Drosophila microarray chip containing approximately 6200 full-length Drosophila Gene Collection (DGC) cDNAs and ESTs (DGC Release 1; Rubin et al. 2000) representing roughly half of the fly cDNAs. Putative targets were identified based on the Cy5:Cy3 fluoresence ratio (van Steensel and Henikoff 2000; van Steensel et al. 2001). The DamID chromatin profiles were generated as previously described (van Steensel et al. 2001; Orian et al. 2003) and subjected to a series of statistical analyses to determine the statistically significant targets (see Materials and Methods; Datasets S1 and S2). We identified 40 statistically significant putative direct Hairy transcriptional targets in Kc cells (Table 1). For just over half of these putative Hairy targets, some genetic, molecular, or functional information exists, allowing us to divide them roughly into three functional categories: those affecting morphogenesis (e.g., egghead [egh], kayak, pointed, mae), those affecting cell cycle or cell growth (e.g., string (stg), ImpL2, Idgf2), and those with unknown/unlinked functions. Unfortunately, the two previously identified Hairy targets, ftz and ac, are not present in the DGC Release 1 cDNA set used to generate our microarray chips. Table 1 Hairy Targets Identified in Kc Cells DamID was recently used to identify targets for the Drosophila Myc/Max/Mad-Mnt network of bHLH leucine zipper proteins (Orian et al. 2003), which shares many structural and functional similarities with the HES network of bHLH proteins (Gallant et al. 1996). Using the same Drosophila cDNA microarray chips, Orian et al. (2003) found that hundreds of binding sites are occupied by dMyc (287 targets) or dMnt (429 targets), and that their expression is modulated by dMyc in the Drosophila larva. Their study is consistent with a global role for Myc family proteins in modulating chromatin responsiveness of targets, and identified most of the transcriptional targets that had been found previously utilizing other approaches. As our current knowledge of direct Hairy transcriptional targets for comparison is minimal, we applied a higher stringency than Orian et al. (2003) when analyzing our Hairy DamID datasets so that we would reduce the likelihood of getting false positives. However, at this stringency we may be missing some bona fide Hairy targets. We compared the Hairy targets we identified with those identified for dMyc and dMnt using datasets analyzed at the higher statistical stringency (Figure 1). As might be expected, there was minimal overlap of Hairy targets with those identified for the transcriptional activator dMyc (three of 40 Hairy targets) (Figure 1A). There was also little overlap of Hairy targets with those identified for the transcriptional repressor dMnt (nine of 40 Hairy targets) (Figure 1B). Even when the less stringent statistics were applied to the datasets, we did not see additional overlap (data not shown). Thus, sequence-specific DNA binding factors are exhibiting binding specificity in the DamID assay, and the 40 statistically significant putative direct Hairy transcriptional targets we identified are what might be expected for a nonglobally acting sequence-specific DNA binding developmental repressor. Figure 1 Hairy Binds to a Specific Set of Transcriptional Targets (A and B) Comparison of DamID-identified targets for Hairy with the Drosophila Myc and Mad/Mnt family proteins. Venn diagram comparing DamID-identified Hairy downstream targets in Kc cells compared to the transcriptional activator dMyc (A) and the transcriptional repressor dMnt (B). (C) Venn diagram comparing DamID-identified Hairy targets from Kc cells and embryos. Identification of Direct Hairy Transcriptional Targets in Early Embryos Using DamID Since Hairy is part of the segmentation gene transcriptional regulatory cascade, we expected to find segmentation-related transcription factors as downstream targets of Hairy. The putative Hairy targets we identified in Kc cells do not fulfill this expectation, but rather suggest roles for Hairy in cell cycle, cell growth, and morphogenesis; these putative targets are likely targets for Hairy during its other developmental roles. This could be because of cellular context (i.e., Kc cells are thought to be embryonic neuronal stem cell in origin and may reflect Hairy's later role in neurogenesis rather than segmentation), or because only half of the Drosophila cDNAs are present on the chip (and the ones responding to Hairy during segmentation are not in this subset), or because the mechanism by which Hairy acts during segmentation is different than expected. To begin distinguishing among these possibilities, we used the DamID approach to identify Hairy targets in Drosophila embryos during segmentation. Towards this aim, we generated functional transgenic flies carrying a UAS–Dam or UAS–Dam–Hairy fusion gene construct (see Materials and Methods). As with the Kc cells, we did not drive overexpression of these Dam fusion constructs, but rather relied on the leaky expression from the minimal promoter of the pUASp vector. Genomic DNA was harvested from 2–6-h embryos (at and just after peak Hairy expression during segmentation), then used to generate probes for the microarray chips, similar to the procedure used for the Kc cells (see Materials and Methods; Datasets S1 and S3). We identified 20 putative direct Hairy targets from the 2–6-h embryos, which fell into four broad functional categories: transcription factors, cell cycle or cell growth, morphogenesis, and unknown/unlinked functions (Table 2). When compared to the 40 Hairy targets identified in Kc cells, we found that only one target, egh, overlapped between the datasets (Figure 1C). This result suggests, perhaps not surprisingly, that transcriptional targets exhibit context dependence/tissue specificity, and that the DamID approach is sensitive to developmental context/tissue specificity. Table 2 Hairy Targets Identified in Embryos (2–6 h) Taken together, the DamID profiles for Hairy targets from Kc cells and embryos identified 59 potential new direct targets of Hairy regulation. Importantly, one of the putative Hairy targets in embryos, paired (prd), is a homeobox-encoding transcription factor known to function in segmentation (cf. Baumgartner and Noll 1990). The Expression of Potential Target Genes Depends on Hairy Regulation In Vivo Direct Hairy targets would be expected to exhibit altered expression in a hairy mutant background compared to wild-type. For a subset of targets from both the Kc cell and embryo DamID experiments, we performed whole mount RNA in situ hybridization on wild-type and hairy mutant embryos (hairy7H; Figure 2 and data not shown). For embryo targets, we examined early embryos representing the same stages used for the DamID analysis. In keeping with our primary focus on Hairy's role in segmentation, we chose as the subset of Kc target genes to examine genes known to be expressed in the embryo (but not necessarily as early as the embryo targets), since we would not expect all of the Kc cell targets to be expressed during embryogenesis. In all cases examined, the alterations in the levels, as well as spatial and temporal patterns, of putative target gene expression were consistent with derepression in a hairy mutant background (Figure 2). For example, as previously described (Lehman et al. 1999), segmental expression of stg is altered (expanded) in a hairy mutant background (Figure 2C and 2D). Similarly, for prd, there is a failure of stripe sharpening consistent with a role for Hairy in prd repression and stripe maintenance (Figure 2A and 2B; Gutjahr et al. 1993). Figure 2 Expression of Hairy Target Genes Is Disrupted in hairy Mutant Embryos Whole mount in situ hybridization on wild-type (A, C, E, G, I, K, and M) or hairy7H mutant (B, D, F, H, J, L, and N) embryos with probes recognizing prd (A and B), stg (C and D), ImpL2 (E and F), mae (G and H), egh (I and J), kayak (K and L), or Idgf2 (M and N). Anterior is to the left. Dorsal is up, except in (M) and (N), which are dorsal views. hairy Exhibits Dominant Genetic Interactions withMutants Encoding Target Genes and Affects stg-lacZ Reporter Expression If Hairy is a direct regulator of a particular target gene, genetic interaction might be expected between hairy and a mutant corresponding to this putative target. Reduction of hairy dose might be expected to deregulate the expression of its target gene, resulting in increased or spatially aberrant expression of its target gene. We examined seven of the 15 Hairy targets for which mutant alleles are available for genetic interaction with hairy (Table 3). In all seven cases, we observed dominant genetic interactions where a reduced number of transheterozygous progeny survive (i.e., synthetic lethality). Embryos from mothers heterozygous for either hairy (hairy/+) or its target gene (i.e., prd/+) alone were viable. The reduction of Hairy in this target-gene-sensitized background allows inappropriate target gene regulation (i.e., target gene expression in spatial domains where it should not be expressed, with subsequent embryo lethality). Table 3 Dominant Genetic Interactions between hairy and Mutants Corresponding to Its Putative Downstream Targets aHomozygous egh7 exhibits pupal (not embryonic) lethality For one Hairy target identified in Kc cells, stg, a series of transgenic lines have been generated in which lacZ expression is driven from different promoter fragments (Lehman et al. 1999). For stg to be a direct transcriptional target of Hairy, we would expect Hairy to bind to the stg promoter. To narrow down regions of the stg promoter sensitive to Hairy, we examined the expression of four of these stg-lacZ reporter genes in hairy mutant and wild-type backgrounds. Sequence analysis of the promoter fragments for each of the four reporter genes revealed the presence of canonical Hairy binding sites in two of them (pstg β-E4.9 and pstg β-E6.4), but not the other two (pstg β-E2.2 and pstg β-E6.7). Consistent with the presence of Hairy binding sites, the lacZ expression from pstg β-E4.9 and pstg β-E6.4, but not from pstg β-E2.2 or pstg β-E6.7, was derepressed (expanded) in a hairy mutant background compared to wild-type (Figure 3; data not shown). We mutated the C-box (Hairy binding site) in the pstg β-E4.9 reporter construct (CACGCG→CTCGCA) to generate pstg β-E4.9Δhairy. This mutation abolishes Hairy binding in vitro (see next section and Materials and Methods). Wild-type flies carrying this pstg β-E4.9Δhairy reporter exhibit the same lacZ derepression as observed for the original pstg β-E4.9 reporter when in a hairy mutant background, indicating that the derepression is due to Hairy binding (Figure 3G). Figure 3 hairy Affects stg-lacZ Reporter Expression (A–F) β-galactosidase expression from the stg-lacZ reporter lines pstg β-E4.9 (A and B), pstg β-E6.4 (C and D), and pstg β-E6.7 (E and F) in wild-type (A, C, and E) and hairy mutant (B, D, and F) embryos. Note the expanded (de-repressed) lacZ expression in the hairy mutant background compared to wild-type for the E4.9 and E6.4 lines (compare [B] to [A] and [D] to [C], respectively). (G) β-galactosidase expression from the stg-lacZ reporter line pstg β-E4.9ΔHairy (same as the reporter construct shown in [A], but with a Hairy binding site mutation) in a wild-type background. Note the expanded (de-repressed) lacZ expression (compare with [A]). Anterior is to the left. Dorsal is up in (A–D) and (G), whereas the ventral surface is shown in (E–F). Hairy Binds Directly to Target Genes Hairy has been shown to bind at Class C sequences (ggCACGCGA/CC) that contain the canonical core Hairy site (CACGCG). We searched for this consensus site within the promoter and transcribed regions of three Hairy targets: stg, egh, and prd. We identified one site in prd, three in egh, and four in the stg genomic region (Figure 4A). In the latter case, we focused on the site within the 4.9-kb promoter fragment, as its segmental expression was derepressed in a hairy mutant background (see above). We tested whether the identified sites are direct Hairy binding sites in electromobility shift assays (EMSAs), utilizing bacterially purified full-length Hairy protein and 32P-labeled oligos containing the appropriate Hairy binding sites (see Materials and Methods). The C-box within the ac promoter, a bona fide Hairy target (Ohsako et al. 1994; Van Doren et al. 1994), served as our positive control. A slow migrating complex was observed when the ac probe was incubated with GST–Hairy protein, but not with GST alone (Figure 4B, compare lanes 2 and 3). This binding is specific: the complex is competed by excess unlabeled wild-type ac oligo, but not by excess mutated ac oligo (Figure 4B, lanes 4 and 5, respectively). Similar assays showed direct and specific binding to the sole C-box site within the prd promoter, as well as to the site within the stg 4.9-promoter region (Figure 4C). While an oligo containing the wild-type Hairy binding site efficiently competes with Hairy binding to the stg 4.9-promoter region in EMSAs, an oligo encoding the mutated Hairy site used in the pstg β-E4.9Δhairy reporter is unable to compete (Figure 4D). Three putative sites were identified within the egh promoter. Hairy binding to these sites was differential, and can be summarized as egh1 > egh3 > egh2 (Figure 4E; compare lanes 3, 7, and 11). This preferential binding may reflect sequences flanking the core C-box (CACGCG; see Figure 4A). Indeed, experiments with the related fly Enhancer of split proteins have shown that even subtle sequence changes within the core C-box or flanking sequences have dramatic consequences for the overall range of proteins that can bind in vivo (Jennings et al. 1999). We have used several bioinformatics approaches to analyze Hairy target gene promoters, to determine if there are conserved sequences flanking the core Hairy binding sites or association of the Hairy binding sites with other transcription factor binding sites as defined by the TRANSFAC database that correlate with the context dependence of Hairy binding. However, we have been unable to uncover any common features of regulation, perhaps because of the relatively small sample size of Hairy targets for these types of approaches (see Materials and Methods; data not shown). Figure 4 Binding of Hairy to Class C (C-Box) Sites in Putative Targets In Vitro (A) Schematic diagram (not to scale) of C-boxes within putative Hairy targets. C-boxes (Hairy binding sites) are denoted by white boxes, black arrows indicate transcription start sites (Ra, Rb, and Rc), ATG denotes the initiating methionine, and capital letters indicate bases matching with the Hairy consensus C-box. The distances in kilobases of the C-boxes from transcription start sites are noted in gray. (B) EMSA with either GST or GST–Hairy and the ac h/E-1 oligonucleotide. Lane 1, probe alone; lane 2, binding to probe by GST; lanes 3–5, binding to probe by GST–Hairy. In lanes 4 and 5, binding to probe by GST–Hairy was in the presence of competitor unlabeled oligos. An arrow indicates the Hairy–DNA complex; compwt and compmut indicate wild-type and mutated cold probes, respectively. (C) EMSA with either GST or GST–Hairy to the C-boxes within the stg and prd genes. Lanes 1–5, GST and GST–Hairy binding to the stg C-box (location: 25072658); lanes 6–10, GST and GST–Hairy binding to the prd C-box. (location: 12074032). Lane order and annotations are as in Figure 4B. (D) EMSA with GST–Hairy to the same C-box within the stg 4.9-kb genomic fragment is not competed by the presence of mutant competitor unlabeled oligo. Lane 1, probe alone; lane 2, binding to GST; lane 3, binding to probe by GST–Hairy; lanes 4 and 5, binding to probe by GST–Hairy in the presence of wild-type and mutant competitor unlabeled oligos, respectively. (E) Differential binding to C-boxes within the egh gene. EMSA with either GST or GST–Hairy to C-boxes within the egh promoter and transcribed region. Binding to three putative C-box sites is shown: egh1 (location: 2341609), egh2 (location: 2350367), and egh3 (location: 2352168). Lanes 1, 5, and 9: probe alone; lanes 2, 6, and 10: binding to probes by GST; lanes 3, 7, and 11: binding to probes with GST–Hairy. Lanes 4, 8, and 12: binding with GST–Hairy in the presence of unlabeled wild-type competitor. C-box locations and promoter information generated using Apollo (Berkeley Drosophila Genome Project). Hairy Binds to Specific Sites on Polytene Chromosomes To confirm the genomic loci associated with Hairy in vivo, we examined binding of endogenous Hairy to third instar larval salivary gland polytene chromosomes using antibodies to Hairy (Figure 5). We identified approximately 120 strongly staining sites for Hairy (Figure 5). This is likely an underestimate as some bands stain more intensely than others and likely represent more than one closely spaced binding site. Hairy binding sites are, for the most part, distributed evenly along all chromosome arms (Figures 5 and 9A). Figure 5 Hairy Binds to Specific Loci on Polytene Chromosomes (A and B) Hairy staining (green) on third instar larval salivary gland polytene chromosome sets counterstained with DAPI (blue) to visualize the chromosomes. (C and D) Higher magnification of chromosome arms X, 3R (C) and 2L, 2R (D). Since there are a relatively small number of Hairy binding sites on the polytene chromosomes, the location of the bands can be determined cytologically with relatively high resolution. While we have not been able to unambiguously assign all of the approximately 120 binding sites cytologically, we examined whether Hairy staining corresponds to the targets identified in the Kc cells and embryo DamID experiments. There are 39 out of 40 Kc cell and 20 out of 20 embryo targets that map cytologically to regions that correspond to Hairy binding sites (e.g., Figure 6A–6F). Thus, while tissue or developmental specificity appear to be lost, polytene chromosomes provide a reliable indicator for Hairy DNA binding targets. Note the presence of Hairy binding at the tip of the X chromosome, the cytological location of the direct Hairy transcriptional target ac (Figure 6A). Interestingly, we were unable to detect Hairy binding at position 84A, the cytological location for ftz (Figure 6B). Hairy binding was also detected at the cytological location for stg (Figure 6C) and egh (Figure 6D), as well as at 33C, the cytological location of prd (Figure 6E). Recent work established a role for Hairy in regulating salivary gland tube morphology that genetically depends, in part, on repression of huckebein (hkb), a zinc-finger-encoding transcription factor (Myat and Andrew 2002). It is not yet known if Hairy's repression of hkb is direct or not. hkb is not in the DGC Release1 cDNA set used to generate our microarray chips, but we do find that one of the strong Hairy binding sites maps to 82A on polytene chromosomes, the cytological location of hkb (see Figure 6F). Consistent with our identification of stg as a Hairy target, derepression of C-box-containing stg-lacZ reporter lines, and gel shift assays, we detect a new band of Hairy staining in chromosomes from larvae carrying the stg-lacZ (pstg β-E4.9) reporter at cytological location 1F, the transgene insertion site (Figure 6G–6J; see Materials and Methods). Figure 6 Hairy Binds to Putative Target Loci on Polytene Chromosomes (A) Hairy binds to polytene region 1A, the location of the Hairy target, ac. (B) Hairy is not found at 84A, the cytological location for ftz. (C–F) Hairy also binds to polytene region 99A, the location of stg (C); polytene region 3A, the location of egh (D); polytene region 33C, the location of prd (E); and polytene region 82A, the location of hkb (F). (G–I) Hairy is recruited to the insertion site for the pstg βE-4.9 reporter construct (arrow in [H] and [I]). Compare to the equivalent region of wild-type X chromosomes marked by brackets in (A), (D), and (G). (J) In situ hybridization to polytene chromosomes from pstg βE-4.9 larvae showing that this line has two insertions on the X chromosome at 1F and 6C. The probe also recognizes sequences to the endogenous white locus (asterisk). Identification of Targets for Recruitment of the Transcriptional Cofactors Groucho, dCtBP, and dSir2 As with other sequence-specific DNA binding transcription factors, Hairy recruits cofactors to carry out its functions. One of the major questions in the field concerns how and when particular cofactors are recruited. It has been technically challenging to address this question with current methods such as ChIP assays, since cofactor association may be transient, unstable, or far removed from the DNA binding protein. Utilizing expression-based microarray analysis is also not easy, because of the difficulty in sorting direct from indirect interactions with such widely recruited cofactors. To circumvent these technical issues and as a first step towards understanding the rules governing Hairy cofactor recruitment, we used the DamID approach to determine if the three known Hairy cofactors, Groucho, dCtBP, and dSir2, are recruited to all or a subset of Hairy targets. We generated Dam fusions to Groucho and dCtBP (see Materials and Methods). The Dam–dSir2 fusion construct was described previously (van Steensel et al. 2001). While none of these cofactors binds DNA on its own, they are recruited to the DNA through their interaction with sequence-specific DNA binding proteins such as Hairy. Using the same procedure and statistical analyses used for the identification of Hairy targets in Kc cells (see Material and Methods; Datasets S1 and S4–S6), we identified 155 loci that recruit Groucho, 496 loci that recruit dCtBP, and 107 loci that recruit dSir2 in Kc cells (Figure 7; Datasets S7–S9). Comparison for overlap between these cofactor datasets and that of Hairy from Kc cells showed that, surprisingly, only one of the putative Hairy targets we identified overlaps with Groucho recruitment (Figure 7A and 7D). The majority of Hairy targets, however, overlap with dCtBP (38/40; Figure 7B and 7D), and most of these also overlap with dSir2 (34/40; Figure 7C and 7D). At present, we cannot rule out the possibility that a protein unrelated to Hairy is recruiting these cofactors to a given putative Hairy target. Interestingly, dCtBP and dSir2 appear to colocalize at loci outside the subset of putative Hairy targets (90% of dSir2 targets overlap with those of dCtBP; Figure 7D). Figure 7 Hairy Overlaps with Cofactors Differentially (A–C) Venn diagram showing the overlap between Hairy targets and those loci also binding to the cofactors Groucho (A), dCtBP (B), and dSir2 (C). (D) Venn diagram showing combined overlaps of Hairy with its three known cofactors. Hairy Target Gene Expression Depends on Hairy Cofactor Regulation In Vivo If particular Hairy targets require specific cofactors to be appropriately regulated, we would expect their expression to be altered (deregulated) in a cofactor mutant background. We performed RNA in situ hybridization for two Hairy Kc cell targets that differentially recruit Groucho, dCtBP, and dSir2. We chose to examine the expression of two hairy targets that are expressed relatively early in the embryo since these cofactors are used in a number of developmental systems and exhibit severe morphological phenotypes when their activity is removed maternally (cf. Phippen et al. 2000). Consistent with a requirement for dCtBP and dSir2, stg expression is derepressed in dCtBP and dSir2, but not groucho mutant backgrounds (Figure 8A–8D). Similarly, consistent with a requirement for dCtBP alone, kayak expression is expanded in dCtBP, but not in groucho or dSir2 mutant backgrounds (Figure 8E–8H). While we cannot extrapolate the cofactor recruitment requirements from Kc cells to embryos, we used in situ hybridization as a prediction for cofactor recruitment for the embryo target, prd. We examined the expression of prd in cofactor mutant backgrounds and found that prd expression is altered in groucho and dCtBP, but not dSir2, mutant backgrounds (Figure 8I–8L), suggesting that prd may represent a minority of Hairy targets that could recruit both Groucho and dCtBP. Consistent with this finding, we find both Groucho and dCtBP staining on polytene chromosomes at the cytological location for prd (data not shown). Figure 8 Hairy Target Gene Expression Is Disrupted in the Mutant Background of the Cofactors Associated with a Particular Target Whole mount in situ hybridization on wild-type (A, E, and I), groucho germline clone (B, F, and J), dCtBP germline clone (C, G, and K), and dSir2 mutant (D, H, and L) embryos with probes recognizing stg (A–D), kayak (E–H), or prd (I–L). Anterior is to the left. Dorsal is up. The Transcriptional Cofactors Groucho, dCtBP, and dSir2 Are Recruited to Specific Sites on Polytene Chromosomes When DamID data for the three Hairy cofactors and Hairy itself are graphically projected onto chromosomes, several interesting features come to light (Figure 9A). For example, while Groucho and dCtBP are distributed along all the chromosomes, dSir2 shows region- and chromosome-specific binding (e.g., there are more dSir2 sites on Chromosome 2R than on Chromosome 3L). To confirm loci associated with recruitment of the different cofactors in vivo, we examined the localization of endogenous Groucho, dCtBP, and dSir2 on wild-type third instar larval salivary gland polytene chromosomes using antibodies to Groucho, dCtBP, and dSir2, respectively (Figure 9B–9D). Consistent with the relative numbers of targets identified for each of the cofactors by the DamID approach, we find many more sites for dCtBP than either Groucho or dSir2. Also consistent with our DamID findings, Groucho overlaps with Hairy at only a small number of the Hairy binding sites (Figure 9E), whereas dCtBP overlaps with the majority of Hairy binding sites (Figure 9F). Differences in distribution for the cofactors observed by DamID are reflected on the polytene staining patterns. For example, our DamID data suggest that the distal portion of Chromosome 2L has more sites for dCtBP than the proximal half of the chromosome. This observation is reflected in dCtBP recruitment on the polytene chromosomes as well (Figure 9F). Likewise, as predicted from the DamID data, dSir2 staining on the polytene chromosomes exhibits region-specific association in which some chromosomes and chromosomal regions exhibit a high degree of staining, while other whole chromosomes exhibit very little staining (Figure 9D and 9G–9I; Rosenberg and Parkhurst 2002). Figure 9 Hairy Shows Context-Dependent Association with Its Cofactors (A) Sites of Hairy binding and Hairy cofactor recruitment based on DamID. The gray lines depict the relative position on the chromosomes of the approximately 6200 cDNAs on the microarray chip. The blue dots below the line represent Hairy binding sites while the green (Groucho), red (dCtBP), and yellow (dSir2) dots represent the positions of cofactor recruitment. (B–D) Cofactor recruitment visualized on third instar larval salivary gland chromosomes. Polytene chromosome sets stained (green) with antibodies to Groucho (B), dCtBP (C), and dSir2 (D). All chromosomes were counterstained with DAPI (blue) to visualize the DNA. (E) Higher magnification view of chromosome arms 2L and 2R costained with Groucho (red) and Hairy (green), and the merged image. (F) Higher magnification view of chromosome arm 2L costained with dCtBP (red) and Hairy (green), and the merged image, compared to the predicted DamID map. Note that both the DamID projected map and polytene chromosomes have more dCtBP recruitment sites to the left of the dashed line than to the right of the dashed line. (G) Chromosome arm 3R stained with dSir2 (green), highlighting regional specificity of dSir2 recruitment. (H and I) Higher magnification view of the distal ends of chromosome arms 2R (H) and 3L (I) from (D), stained with dSir2 (green), showing regional specificity and lack of dSir2 recruitment, respectively. Discussion We have known for almost two decades that Hairy plays a pivotal role in the segmentation hierarchy, as well as other developmental processes, but the details of Hairy action have not been easy to tease apart. An important step in understanding the molecular mechanisms surrounding Hairy-mediated repression was made with the identification of Groucho as a Hairy binding protein (Paroush et al. 1994). One of the key remaining questions regarding the mechanism(s) of repression employed by Hairy concerns the identities of its direct transcriptional targets. We have employed a novel chromatin profiling approach, DamID, to effectively identify a total of 59 potentially direct Hairy targets from 2–6-h embryos and Kc cells. As expected of direct targets, these genes show Hairy-dependent expression, are detected as Hairy binding sites in vivo on polytene chromosomes, and have consensus C-box-containing sequences that are directly bound by Hairy in vitro. While the DamID approach had previously been used only in Kc cells, we found that this technique is also powerful when utilizing transgenic embryos that carry fusions of the protein of interest to the Dam methylase. As target genes are likely context dependent, the use of embryos makes it possible to choose the precise time or place of development to be examined, as well as allowing the analysis to take place in an organismal context. The 59 putative Hairy targets we identified in the embryo and Kc cell DamID experiments correspond to bands of Hairy immunostaining on polytene chromosomes, suggesting that the polytene chromosome staining faithfully represents Hairy binding. Polytene chromosomes are functionally similar in transcriptional activity and display factor/cofactor binding properties similar to chromatin of diploid interphase cells, despite their DNA endoreplication (Hill et al. 1987; Andrew and Scott 1994; Hill and Mott 2000; Pile and Wassarman 2000, 2002). Since the microarray chips we used contain roughly half of Drosophila cDNAs, we estimate the actual number of Hairy targets to be approximately twice that number (i.e., 118 targets). This predicted number of Hairy targets is close to the approximately 120 strongly staining sites we observe on polytene chromosomes. Of the 59 putative Hairy targets we identified in both the Kc cell and embryo DamID experiments, 58 correspond to bands of Hairy staining on the polytene chromosomes, suggesting that polytene chromosome staining is representing Hairy binding sites without regard to tissue specificity. It is not yet clear what is limiting Hairy accessibility in different tissues or why Hairy's access does not appear to be limited in salivary glands. It may be that polytene chromosome organization necessitates a looser chromatin structure or that the large number of factors that seem to be endogenously expressed in salivary glands affects accessibility. Ultimately, additional confirmation of the DamID and polytene staining correspondence will require microarray tiling chips containing overlapping genomic DNA fragments; however, such genomic DNA tiling chips are currently unavailable. Van Steensel and Henikoff (2000) showed that DNA methylation by tethered Dam spreads up to a few kilobases from the point where it is brought to the DNA. We were concerned in the beginning that we might miss Hairy targets if the DNA fragments of 2.5 kb or less that we recovered for probes were far away from the start of the transcribed region, especially since the Drosophila microarray chip we used was generated using full-length cDNAs. Indeed, as Hairy has been described as a long-range repressor (Barolo and Levine 1997), it is likely to bind at a distance from the transcription start site. However, the targets we identified by DamID in both Kc cells and in embryos correspond closely to the Hairy staining pattern on polytene chromosomes. As is the case for Hairy, the distribution of DamID-identified loci that recruit the long-range repression-mediating Groucho corepressor (Zhang and Levine 1999) corresponds well with the distribution of Groucho binding sites on polytene chromosomes. Our results suggest that there is a higher-order structure to the promoter that is allowing factors that bind far upstream of the transcription start site to have physical access to the transcribed region (i.e., DNA looping; reviewed in Ogata et al. 2003) or that Hairy does not bind as far away from the transcription start site as it has been proposed to do. Hairy Targets Hairy is needed at multiple times during development, where it has primarily been associated with the regulation of cell fate decisions. During embryonic segmentation, ftz has long been thought to be a direct Hairy target. However, the order of appearance of ftz stripes is not inversely correlated with those of Hairy, as would be expected if ftz stripes are generated by Hairy repression (Yu and Pick 1995). While we were unable to assess ftz as a direct Hairy target using DamID, we did not find evidence for ftz being a direct Hairy target based on the association of Hairy with polytene chromosomes. Indeed, the evidence suggesting that ftz is a direct target of Hairy is based on timing, i.e., that there is not enough time for another factor to be involved (cf. Ish-Horowicz and Pinchin 1987). As the half-life of the pair-rule gene products is very short (less than 5 min; Edgar et al. 1986), it is possible that additional factors could be acting and that the interaction between Hairy and ftz is indirect. Interestingly, one of the Hairy targets we identified in embryos is the homeobox-containing transcriptional regulator, prd. Pair-rule genes have been split into two groups: primary pair-rule genes mediate the transition from nonperiodic to reiterated patterns via positional cues received directly from the gap genes, whereas secondary pair-rule genes take their patterning cues from the primary pair-rule genes and in turn regulate the segment polarity and homeotic gene expression. The transcriptional regulator prd was originally categorized as a secondary pair-rule gene since its expression is affected by mutations in all other known pair-rule genes. However, prd stripes were subsequently shown to require gap gene products for their establishment, and the prd locus has the modular promoter structure associated with primary pair-rule genes (Baumgartner and Noll 1990; Gutjahr et al. 1993). Thus, prd has properties of both primary and secondary pair-rule genes and is a good candidate to directly mediate Hairy's effects on segmentation. We found that Hairy can specifically bind to C-box sequences in the prd promoter and interacts genetically with prd. Further experiments will be required to determine if Paired in turn binds to the ftz promoter, such that the order of regulation would be Hairy > prd > ftz. In addition to identifying potential targets for Hairy in segmentation, we identified targets that implicate Hairy in other processes including cell cycle, cell growth, and morphogenesis. The group of targets implicating Hairy in the regulation of morphogenesis includes: concertina, a G-alpha protein involved in regulating cell shape changes during gastrulation (Parks and Wieschaus 1991); kayak, the Drosophila Fos homolog involved in morphogenetic processes such as follicle cell migration, dorsal closure, and wound healing (Riesgo-Escovar and Hafen 1997; Dequier et al. 2001; Dobens et al. 2001; Ramet et al. 2002); pointed and mae, both of which function in the ras signaling pathway to control aspects of epithelial morphogenesis (cf. Beitel and Krasnow 2000; Baker et al. 2001; James et al. 2002); egh, a novel, putative secreted or transmembrane protein proposed to play a role in epithelial morphogenesis (Goode et al. 1996); and Mipp1, a phosphatase required for proper tracheal development (Ebner et al. 2002). Hairy has been thought to be involved mostly in the regulation of cell fate decisions. However, mosaic experiments in the eye imaginal disc have suggested that Hairy may also play a role in the regulation of cell cycle or cell growth (Brown et al. 1995). Consistent with this, another group of Hairy targets implicates Hairy in the regulation of cell cycle or cell growth; this group includes stg, the Drosophila Cdc25 homolog (cf. Lehman et al. 1999); dacapo, a cyclin-dependent kinase inhibitor related to mammalian p27kip1/p21waf1 (Lane et al. 1996; Meyer et al. 2002); IDGF2, a member of a newly identified family of growth-promoting glycoproteins (Kawamura et al. 1999); and ImpL2, a steroid-responsive gene of the secreted immunoglobulin superfamily that functions as a negative regulator of insulin signaling (Garbe et al. 1993; Andersen et al. 2000; Montagne et al. 2001; Tapon et al. 2001; Johnston and Gallant 2002). Consistent with a role for Hairy in growth signaling, mammalian HES family proteins have been linked to insulin signaling (Yamada et al. 2003). Since cells that are dividing or proliferating cannot simultaneously undergo the cell shape changes and cell migrations required for morphogenetic movements, Hairy may be required to transiently pause the cell cycle in a spatially and temporally defined manner, thereby allowing the cell fate decisions regulated by the transcription cascade to be completed. As Hairy is itself spatially and temporally expressed, Hairy must be only one of several genes necessary to orchestrate these processes. While much progress has been made in understanding the regulatory networks governing pattern formation, cell proliferation, and morphogenesis, and while it is clear that they must be integrated, the details surrounding their coordination have not yet been elucidated. Thus, the putative Hairy targets we identified are consistent with known processes involving Hairy and suggest that in addition to regulating pattern formation, Hairy plays a role in transiently repressing other events, perhaps in order to coordinate cell cycle events with the segmentation cascade. Further experiments will be needed to determine how these different roles for Hairy fit together. Cofactor Recruitment Corepressor recruitment is an important aspect of transcriptional repression (reviewed in Mannervik et al. 1999; Bone and Roth 2001; Mannervik 2001; Urnov et al. 2001; Jepsen and Rosenfeld 2002). While the sequence-specific DNA-bound repressors contribute to target specificity, the corepressors are thought to help distinguish among particular repression mechanisms to be used via alteration of their recruitment or function. For example, the Drosophila developmental factors Dorsal and T-cell factor (TCF) have been shown to function as either positive or negative regulators of transcription depending on promoter context and cofactor recruitment (Dubnicoff et al. 1997; Cavallo et al. 1998). As each of Hairy's cofactors appears to act differently with Hairy, thereby conferring different developmental consequences, we used the DamID approach, along with polytene chromosome staining, to get our first look at the patterns of Hairy's cofactor recruitment. The numbers of loci that recruit Groucho, dCtBP, and dSir2 cofactors are consistent with the breadth of interaction they have been shown to exhibit. We identified by DamID profiling 155 loci that recruit Groucho and, as expected, found roughly twice as many sites on polytene chromosomes. Groucho was one of the first corepressors identified and shown to affect a variety of different developmental processes, suggesting that it is a widely used corepressor (Parkhurst 1998; Chen and Courey 2000). In addition to its interaction with Hairy, Groucho was subsequently shown to mediate repression through several other classes of DNA-binding transcriptional regulators including Engrailed, Dorsal, T-cell factor, and Runt (Aronson et al. 1997; Dubnicoff et al. 1997; Jiménez et al. 1997; Cavallo et al. 1998; Roose et al. 1998). Although Groucho was the first Hairy cofactor identified (Paroush et al. 1994) and its interaction site is often described as Hairy's “major” repression motif (Mannervik 2001), we find that it is associated with only a minority of Hairy targets in Kc cells. Groucho's dominance as a cofactor during segmentation may reflect a preference for Groucho in the reporter assays used previously to assess corepressor activity, or it may be more heavily recruited to Hairy's targets during segmentation. In the future it will be interesting to determine the loci that recruit Groucho in early embryos and, as Groucho binds a number of other repressors, which, if any, of these factors recruits Groucho as its major cofactor. CtBP was identified more recently, first on the basis of its binding to the C-terminal region of E1A, and in Drosophila by its association with the developmental repressors Hairy and Knirps (reviewed in Turner and Crossley 2001; Chinnadurai 2002a). CtBP is an integral component in a variety of multiprotein transcriptional complexes. It has been shown to function as a context-dependent cofactor, having both positive and negative effects on transcriptional repression depending upon the repressor to which it is recruited. More than 40 different repressors have been shown to recruit CtBP. Consistent with this wide recruitment of CtBP, we identified 496 loci that recruit dCtBP by DamID profiling and roughly twice that many sites on polytene chromosomes. A recently reported global protein–protein interaction study showed that the binding partners for Groucho and dCtBP are largely nonoverlapping (Giot et al. 2003). This, along with the near exclusivity of Groucho and dCtBP binding as assayed by DamID and polytene chromosome staining, makes it unlikely that both cofactors work together as a general rule and strengthens the possibility that the binding of each of these factors assembles different protein complexes that are, for the most part, mutually exclusive. dSir2 was only very recently identified as a corepressor for Hairy and other HES family members (Rosenberg and Parkhurst 2002; Takata and Ishikawa 2003). We identified 107 loci that recruit dSir2 by DamID profiling and roughly twice that many sites on polytene chromosomes. Surprisingly, the distribution of loci recruiting dSir2 identified by DamID profiling, as well as dSir2′s staining on polytene chromosomes, shows regional binding specificity (see Figure 9D and 9G). This binding specificity may be a reflection of the different nuclear compartments that these regions of the chromosomes find themselves in (cf. Francastel et al. 2000; Leitch 2000). Sir2 has been described mostly as a protein involved in heterochromatic silencing rather than in euchromatic repression. The number of dSir2 euchromatic sites we observe is similar to that of Groucho, suggesting that euchromatic repressors (in addition to HES family members) are likely to recruit Sir2. Consistent with this, a recent report has described a role for mammalian Sir2 in repressing the muscle cell differentiation program (Fulco et al. 2003). The region-specific binding of dSir2 might reflect a difference in the types of factors it can associate with, or the association of dSir2 with particular chromosomal regions or nuclear domains (cf. Spellman and Rubin 2002). Interestingly, dCtBP and dSir2 recruitment are largely overlapping, and this association continues outside of those loci where Hairy binds: 90% of dSir2-recruiting loci also recruit dCtBP. dCtBP and dSir2 are unique among transcriptional coregulators in that they both encode NAD+-dependent enzymatic activities. As NAD and NADH levels within the cell exist in closely regulated equilibrium (for review see Dang et al. 1997; Ziegler 2000), it is possible that dCtBP and dSir2 function as NAD/NADH redox sensors (cf. Denu 2003; Fjeld et al. 2003). In this way, the cell could use coenzyme metabolites to coordinate the transcriptional activity of differentiation-specific genes with the cellular redox state. The success of the combination of DamID profiling and polytene chromosome staining results provides a global systematic way in which to address a number of mechanistic questions concerning the rules governing cofactor recruitment. For example, it will be possible to address whether target gene location or promoter structure determines the accessibility of cofactors to specifically bound repressors or whether, conversely, the association of repressors with cofactors influences target gene choice by altering DNA binding specificity. We now have a number of direct Hairy targets and in vivo assay systems to use in future experiments addressing questions surrounding Hairy's biological functions and the precise molecular mechanisms it employs to carry out its functions. Materials and Methods DamID. To generate Dam–Hairy or Dam–dCtBP, a full-length hairy or dCtBP cDNA fragment was generated by standard PCR using primers containing a BglII 5′ site and a XbaI 3′ site, cut with BglII and XbaI, and subcloned into the BglII and XbaI sites of pNMycDam plasmid, as described previously (van Steensel and Henikoff 2000). To generate Dam–Groucho, a full-length groucho cDNA fragment (minus the stop codon) was generated by standard PCR using primers containing a BamHI 5′ site and a NotI 3′ site, cut with BamHI and NotI, and subcloned into the BglII and NotI sites of pCMycDam plasmid, as described previously (van Steensel and Henikoff 2000). Dam–dSir2 was described previously (van Steensel et al. 2001). All four of these constructs are expressed in Kc167 cells (data not shown). Kc cell culture and transfections were performed as described previously (Henikoff et al. 2000). The Kc cells were harvested 24 h posttransfection, then genomic DNA was isolated and processed for microarray hybridizations as described previously (van Steensel et al. 2001). The UAS–Dam and UAS–Dam–Hairy expression constructs were made by first amplifying the Dam or Dam–Hairy open reading frames by PCR from the appropriate fusion construct described above, then cloning them into the pUASp vector (Rørth 1998) as 5′KpnI-3′XbaI fragments. The resulting UAS–Dam and UAS–Dam–Hairy plasmids (500 μg/ml) were injected along with the pTURBO helper plasmid (100 μg/ml) (Mullins et al. 1989) into isogenic w1118 flies as described by Spradling (1986). Transgenics were scored by eye color, and the insertions were mapped and balanced using standard genetic methods. These chimeric genes are properly expressed when induced with various Gal4 driver lines (e.g., Engrailed–Gal4; Brand and Perrimon 1993; data not shown). The Dam–Hairy fusion protein is functional because presence of the UAS–Dam–Hairy transgene, but not the UAS–Dam transgene, partially rescues the segmentation phenotype of hairy mutant embryos when induced with an actin–Gal4 driver (rescue is similar to UAS–Hairy; data not shown). As in Kc cells, induced expression of these Dam fusion constructs leads to high levels of nonspecific methylation. Therefore we utilized low-level leaky expression from the minimal promoter of the pUASp vector for these experiments. 2–6-h embryos were collected and dechorionated with 100% bleach. Approximately 500 μl of embryos were crushed in 1 ml of lysis buffer (100 mM Tris [pH 9.0], 100 mM NaCl, 100 mM EDTA, and 5% sucrose). SDS (to 0.5%) and proteinase K (to 100 μg/ml) were added immediately after homogenization, followed by incubation at 55 °C for 2 h. SDS was increased to 1.5%, followed by incubation for an additional 2–3 h. The genomic DNA was isolated and processed for microarray hybridizations essentially as described previously (van Steensel et al. 2001). Drosophila microarray chips were produced in house (Genomics Shared Resource; Fred Hutchinson Cancer Research Center, Seattle, Washington, United States) for the Northwest Fly Consortium and contain approximately 6200 full-length DGC cDNAs (DGC Release 1; Rubin et al. 2000), as well as approximately 300 clones added by members of the Consortium. Arrays were scanned using a GenePix 4000 scanner (Axon Instruments, Union City, California, United States), and image analysis was performed using GenePix Pro 3.0. For each array, spot intensity signals were filtered and removed if the values did not exceed 3 standard deviations above the background signal in at least one channel or if the spot was flagged as questionable by the GenePix Pro software. For each spot, background-corrected ratios were natural log transformed and a median-centered normalization strategy was applied across each array. Dam–protein and Dam transfections were independently replicated three times, and the subsequent array comparisons (i.e., Dam–protein/Dam) were analyzed using CyberT (Baldi and Long 2001), a Bayesian t-statistic derived for microarray analysis (http://genomics.biochem.uci.edu/genex/cybert/). We employed the default window size of 101 and used a confidence value of ten in our CyberT analysis. The null hypothesis was rejected and a spot ratio was called significantly changed if p Bon ≤ 0.05, where p Bon is the Bayesian p-value adjusted for multiple hypothesis tests using the conservative Bonferroni correction. Based on prior “self versus self” DamID comparisons, we empirically determined a lower-bound ln(ratio) threshold = |0.405| as an additional significance criterion to discriminate spot intensity signals from the inherent noise in the hybridization process. For each protein analyzed, a fluor-reversed array comparison was performed and used to screen all significant calls for fluor-specific artifacts. For our analyses, we treated the small subset of replicated spots on the array independently. For those cases, both spots were required to be called significant. Reported ratio values were retransformed to log2 as a matter of convention. The complete raw and processed datasets can be accessed at http://www.fhcrc.org/labs/parkhurst/supplementary-data/. Flies and genetics. Flies were cultured and crossed on yeast-cornmeal-molasses-malt extract medium at 25 °C. The alleles used in this study were the following: h7H rucuca/ TM3, h12C st e/ TM3, Df(3 l)hi22 Ki roe pp/TM3, and prd2.45.17/CyO (D. Ish-Horowicz); FRT82B- P{ry+t7.2=PZ}CtBP03463 ry506/TM3 (N. Perrimon); FRT 82B- groE47/TM3 (Phippen et al. 2000); dSir25.26/SM6 and dSir24.5/SM6 (Newman et al. 2002); FRT82B-ovoD1/TM3, y w hs-FLP22, TM3/CxD, egh7/FM7a (#3902), ImpL2KG02223 (#14083), maek06602/CyO (#10633), pntΔ88/TM3 (#861), and rgrKG03110 (#13770) (Bloomington Drosophila Stock Center, Indiana University, Bloomington, Indiana, United States). Details of these strains are found on FlyBase (http://flybase.bio.indiana.edu:82/). stgAR2 and the stg-lacZ reporter lines (pstg β-E2.2, pstg β-E4.9, pstg β-E6.4, pstg β-E6.7) were described previously (Lehman et al. 1999). The genomic locations of the Hairy binding sites in pstg β-E4.9 and pstg β-E6.4 are 25072653 and 25080219, respectively. Germline clones for dCtBP and groucho were generated as previously described (Poortinga et al. 1998, Phippen et al. 2000). The pstg-βE4.9Δhairy transgenic flies were generated by injecting vector (500 μg/ml) along with the pTURBO helper plasmid (100 μg/ml) (Mullins et al. 1989) into isogenic w1118 flies as described by Spradling (1986). Transgenics were scored by eye color, and the insertions were mapped and balanced using standard genetic methods. Embryo analysis. Larval cuticle preparations were prepared and analyzed as described by Wieschaus and Nüsslein-Volhard (1986). Immunohistochemical detection of proteins in embryos was performed as described previously (Parkhurst et al. 1990) using Alkaline Phosphatase–coupled secondary antibodies (Jackson Laboratory, Bar Harbor, Maine, United States) visualized with Substrate Kit II reagents (Vector Laboratories, Burlingame, California, United States). Antisera used were as follows: antiMyc (9e10, 1:100 dilution; Santa Cruz Biotechnology, Santa Cruz, California, United States). Immunohistochemical whole mount RNA in situ hybridization was performed according to the protocol of Tautz and Pfeifle (1989). Digoxygenin-substituted probes were obtained by PCR amplification with primers to the vector just 3′ of the cDNA insert. EMSA. EMSA was carried out using either bacterially expressed GST or GST–Hairy ( full-length) proteins, similar to the procedure described by Van Doren et al. (1994) and Rosenberg and Parkhurst (2002). Briefly, 40 fmol of 32P-end-labeled probe of each oligo was incubated with either GST– or GST–Hairy–purified proteins (200 ng each), in a 25-μl reaction supplemented with binding buffer (5% glycerol, 20 mM HEPES [pH 7.6], 50 mM KCl, 1 mM EDTA, 1 mM DTT, and 10 ng/μl poly dI-dC) at room temperature. Where indicated, the binding was preformed in the presence of 15-fold excess of unlabeled wild-type or mutated ac competitor oligos. Following incubation, the complexes were resolved using 0.5% TBE-PAGE gels and visualized by autoradiograms. The following oligos were used (forward primers are shown): ac 5′-TAAACCGGTTGGCAGCCGGCACGCGACAGGGCCAGGTTTT-3′; egh egh1 5′-TGCGCGTCACGCGCCGTTC-3′, egh2 5′-TCATTCGCACGCGGAATCT-3′, and egh 3 5′-GCCGGACACGCGATGATGG-3′; mutated ac oligo 5′-TAAACCGGTTGGCAGCCGGGACGCGACAGGGCCAGGTTTT-3′; mutated stg oligo 5′-TCTACCACACACAAACACTCGCACGCGAAAACTGGG -3′; prd 5′-AAGTGACACGCGCTCCGCT-3′; and stg 5′-AAACACACGCGCGCGAAAA-3′. Hairy binding site bioinformatics analysis. Several bioinformatics approaches were employed to analyze Hairy target gene promoters. In particular, Drosophila promoter sequences were captured using Apollo Genome Sequence and Annotation Tool (Lewis et al. 2002). Match v1.0-public (BIOBASE Biological Databases, Wolfenbüttel, Germany) was used to search promoter sequences for known transcription factor binding sites using a library of mononucleotide-weighted matrices from TRANSFAC v6.0. Match v1.0-public employs the core- and matrices-matching algorithms published by Quandt et al. (1995). Sequences were interrogated using only high-quality Drosophila transcription factor binding sites found in TRANSFAC v6.0, and the software parameters were adjusted to minimize the sum of false positives and false negatives. The number of Hairy binding sites found in target gene promoters was tallied (excluding “hits” to AT-rich regions [assigned to CF2-II, BRC-Z1, and BRC-Z4] that were ubiquitous in both the target and nontarget sequence under analysis). Using the Hairy site closest to transcription start site, the composition of transcription factor binding sites adjacent to (within 500 bp of) the Hairy site was assessed. This was also performed for non-Hairy targets selected because they contained one or more core C-box sequences. Matrices were compared that matched percentages of known Hairy targets (egh2, egh3, prd1, ac1, and stg1) to C-box–containing nontargets. Chromosomes. Wild-type or pstg βE4.9 third instar larval salivary gland polytene chromosomes were prepared and stained for endogenous proteins essentially as described by Andrew and Scott (1994). Antisera used were as follows: rat anti-Hairy polyclonal (1:50 dilution; gift of J. Reinitz; Kosman et al. 1998), mouse anti-Groucho monoclonal (1:40 dilution; gift of C. Delidakis; Delidakis et al. 1991), mouse anti-dCtBP polyclonal (1:100; Poortinga et al. 1998); mouse anti-dSir2 polyclonal (1:20 dilution; Rosenberg and Parkhurst 2002); rabbit anti-β-galactosidase polyclonal (1:1000); donkey antirat Alexa 488 (1:1000 dilution; Molecular Probes, Eugene, Oregon, United States); and goat antimouse Texas Red (1:200; Jackson ImmunoResearch Laboratories, West Grove, Pennsylvania, United States). Chromosomes were viewed on an Olympus (Tokyo, Japan) IX-70 inverted microscope equipped with a 40×/N.A. 1.35 oil immersion objective. Three-dimensional stacks were collected using the DeltaVision softWoRx acquisition software (Applied Precision, Issaquah, Washington, United States), and out-of-focus information was removed using a constrained iterative deconvolution algorithm (Agard et al. 1989). The insertion site for the pstg β-E4.9 reporter line was performed as described by Pardue and Gall (1975) using DIG-substituted probes according to the protocol of Tautz and Pfeifle (1989). Supporting Information Dataset S1 Complete List of Binding Loci for Hairy in Kc Cells and Embryos As Well As the Cofactors Groucho (Kc Cells), dCtBP (Kc Cells), and dSir2 (Kc Cells) (2.9 MB XLS). Click here for additional data file. Dataset S2 DamID Primary Binding Data for Hairy in Kc Cells (11.8 MB XLS). Click here for additional data file. Dataset S3 DamID Primary Binding Data for Hairy in Embryos (11.8 MB XLS). Click here for additional data file. Dataset S4 DamID Primary Binding Data for Groucho in Kc Cells (11.8 MB XLS). Click here for additional data file. Dataset S5 DamID Primary Binding Data for dCtBP in Kc Cells (11.8 MB XLS) Click here for additional data file. Dataset S6 DamID Primary Binding Data for dSir2 in Kc Cells (11.8 MB XLS) Click here for additional data file. Dataset S7 List of the 155 Target Loci That Recruit Groucho (Duplicates Removed) (230 KB XLS). Click here for additional data file. Dataset S8 List of the 496 Target Loci That Recruit dCtBP (Duplicates Removed) (276 KB XLS). Click here for additional data file. Dataset S9 List of the 107 Target Loci That Recruit dSir2 (Duplicates Removed) (44 KB XLS). Click here for additional data file. We thank Steve Henikoff, Bas van Steensel, Bob Eisenman, Christos Delidakis, Suki Parks, and members of the Parkhurst lab for their advice and interest during the course of this work. We thank Eleanor Williams for generating the map in Figure 8A, Ed Giniger for assembling the Northwest Fly Consortium, and the Berkeley Drosophila Genome Project/FlyBase for the fly genome sequence Apollo and other excellent genomics tools. We are very grateful to Sue Biggins, Christos Delidakis, Bob Eisenman, Dan Gottschling, Toshi Tsukiyama, and members of the lab for their comments on the manuscript. We also thank C. Alonso, B. Edgar, L. Cherbas, P. Cherbas, C. Delidakis, T. Furuyama, S. Henikoff, K. Jordan, H. Ruohola-Baker, S. Smolik, B. van Steensel, the Bloomington Drosophila Stock Center, and especially Jon Reinitz for cell lines, antibodies, DNAs, flies, and other reagents used in this study. This work was supported by a Human Frontiers Postdoctoral Fellowship (to AO) and by NIH grant GM47852 (to SMP). Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. DB, AO, and SMP conceived and designed the experiments. DB, AO, JJD, JV, AER, and SMP performed the experiments. DB, AO, JJD, JV, AER, and SMP analyzed the data. SMP wrote the paper. Academic Editor: Michael Levine, University of California, Berkeley Abbreviations ac achaete bHLHbasic Helix-Loop-Helix CtBPC-terminal binding protein DamDNA adenine methyltransferase dCtBP Drosophila C-terminal binding protein DGC Drosophila Gene Collection dSir2 Drosophila silent information regulator 2 egh egghead EMSAelectromobility shift assay ftz fushi tarazu HESHairy/Enhancer of split/Deadpan hkb huckebein NADnicotinamide adenine dinucleotide prd paired Sir2silent information regulator 2 stg string ==== Refs References Agard DA Hiraoka Y Shaw P Sedat JW Fluorescence microscopy in three dimensions Methods Cell Biol 1989 30 353 377 2494418 Andersen AS Hansen PH Schäffer L Kristensen C A new secreted insect protein belonging to the immunoglobulin superfamily binds insulin and related peptides and inhibits their activities J Biol Chem 2000 275 16948 16953 10748036 Andrew DJ Scott M Immunological methods for mapping protein distribution on polytene chromosomes. 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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020179Research ArticleCell BiologyImmunologyMammalsActivation-Induced Cytidine Deaminase Initiates Immunoglobulin Gene Conversion and Hypermutation by a Common Intermediate Ig Hypermutation in ψV Gene-Deficient DT40Arakawa Hiroshi 1 Saribasak Huseyin 1 Buerstedde Jean-Marie [email protected] 1 1GSF–National Research Center for Environment and Health, Institute for Molecular Radiobiology, Neuherberg-MunichGermany7 2004 13 7 2004 13 7 2004 2 7 e17926 2 2004 14 4 2004 Copyright: © 2004 Arakawa et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Training the Immune Response: B Cells' Master Regulator Depending on the species and the lymphoid organ, activation-induced cytidine deaminase (AID) expression triggers diversification of the rearranged immunoglobulin (Ig) genes by pseudo V (ψV) gene- templated gene conversion or somatic hypermutation. To investigate how AID can alternatively induce recombination or hypermutation, ψV gene deletions were introduced into the rearranged light chain locus of the DT40 B-cell line. We show that the stepwise removal of the ψV donors not only reduces and eventually abolishes Ig gene conversion, but also activates AID-dependent Ig hypermutation. This strongly supports a model in which AID induces a common modification in the rearranged V(D)J segment, leading to a conversion tract in the presence of nearby donor sequences and to a point mutation in their absence. Activation induced cytidine deaminase (AID) is required for antibody diversification, a process that varies in different species. By removing one diversification pathway in chickens, AID induces an alternative method of diversification ==== Body Introduction Immunoglobulin (Ig) genes are further diversified after V(D)J rearrangement by gene conversion, hypermutation, or a combination of the two. Surprisingly, even closely related species employ different strategies: mice and humans use exclusively hypermutation (Milstein and Rada 1995), whereas rabbits, cows, and pigs use mainly gene conversion (Butler 1998). The balance between the two phenomena can also shift during differentiation: for example, chicken B-cells first develop their Ig repertoire by gene conversion in the bursa (Reynaud et al. 1987; Arakawa and Buerstedde 2004) and later fine tune it by hypermutation in splenic germinal centers (Arakawa et al. 1996). All three B-cell specific activities of Ig repertoire formation—gene conversion (Arakawa et al. 2002), hypermutation, and isotype switch recombination (Muramatsu et al. 2000; Revy et al. 2000)—require expression of the activation-induced cytidine deaminase (AID) gene. Whereas it was initially proposed that AID is an mRNA editing enzyme (Muramatsu et al. 1999), more recent studies indicate that AID directly modifies DNA by deamination of cytosine to uracil (Di Noia and Neuberger 2002). However, the cytosine deamination activity must be further regulated, because only differences in the type, the location, or the processing of the AID-induced DNA modification can explain the selective occurrence of recombination or hypermutation in different species and B-cell environments. Based on the finding that certain AID mutations affect switch recombination but not somatic hypermutation, it was suggested that AID needs the binding of a cofactor to start switch recombination (Barreto et al. 2003; Ta et al. 2003). Analysis of knockout mutants of the chicken B-cell line DT40 indicate that the RAD54 gene (Bezzubova et al. 1997) and other members of the RAD52 recombination repair pathway are needed for efficient Ig gene conversion (Sale et al. 2001). Most interestingly, disruption of RAD51 paralogs reduces Ig gene conversion and induces hypermutation in the rearranged light chain gene (Sale et al. 2001), suggesting that a defect in DNA repair by homologous recombination can shift Ig gene conversion to hypermutation. Valuable insight into complex recombination processes has been gained by the genetic and biochemical analysis of reaction intermediates (Haber 1998). Since sequence information needs to be copied from the donor to the target at some stage of Ig gene conversion, we reasoned that the deletion of the donor sequences might arrest the reaction and allow the recovery of an intermediate. Here we report that ablation of pseudo V (ψV) donors activates AID-dependent Ig hypermutation in DT40 cells. This shows that Ig gene conversion and hypermutation are competing pathways derived from the same AID-initiated intermediate. Furthermore we propose ψV knockout DT40 as an ideal model system to approach the molecular mechanism of Ig hypermutation and as a new tool for in situ mutagenesis. Results Targeted Deletion of ψV Donor Sequences in the Rearranged Light Chain Locus Two ψV knockout constructs were made by cloning genomic sequences that flank the intended deletion end points, upstream and downstream of a floxed guanine phosphoribosyl transferase (gpt) cassette (Arakawa et al. 2001). Upon targeted integration, the first construct, pψVDel1-25, deletes all pseudogenes (ψV25 to ψV1), whereas the second construct, pψVDel3-25, deletes most pseudogenes (ψV25 to ψV3) (Figure 1A). A surface IgM–positive (sIgM[+]) clone, derived from DT40Cre1AID–/– cells (Arakawa et al. 2002) by transfection and stable integration of a floxed AID–internal ribosome entry site (IRES)-green fluorescent protein (GFP) transgene, was chosen for the transfection of the ψV knockout constructs. This AID-reconstituted clone, named AIDR, has the advantage that the appearance of deleterious Ig light chain mutations can be easily detected by the loss of sIgM expression, and that GFP-marked AID expression can be shut down after tamoxifen induction of the Cre recombinase transgene inherited from DT40Cre1 (Arakawa et al. 2002). Figure 1 ψV Gene Deletion (A) Physical map of the rearranged Ig light chain locus in the chicken B-cell line DT40 and the ψV knockout constructs. The locus contains a total of 25 ψV genes upstream of the functional V segment. The strategy of knocking out ψV genes by the targeted integration of the pψVDel1-25 and the pψVDel3-25 constructs is shown. Only the relevant EcoRI sites are indicated. (B) Southern blot analysis of wild-type and knockout clones using the probe shown in (A) after EcoRI digestion. The wild-type locus hybridizes as a 12-kb fragment, whereas ψVpartial and ψV– loci hybridize as 7.4-kb and 6.3-kb fragments, respectively. (C) AID status. The AID gene was amplified by PCR to verify the presence or absence of the AID cDNA expression cassette. Following transfection of the ψV knockout constructs into the AIDR clone, mycophenolic acid–resistant clones containing targeted deletions of the rearranged light chain locus were identified. These primary ψV knockout clones contain two floxed transgenes, the inserted gpt marker gene in the rearranged light chain locus and the AID-IRES-GFP gene of the AIDR progenitor clone. Since the gpt gene might perturb the adjacent transcription or chromatin configuration, the primary ψV knockouts were exposed to a low concentration of tamoxifen and then subcloned by limited dilution. In this way, secondary ψV knockout clones could be isolated that lacked either only the gpt gene (AIDRψV– and AIDRψVpartial) or the gpt gene together with the AID-IRES-GFP gene (AID–/–ψV– and AID–/–ψVpartial). The disruption of ψV genes in the rearranged light chain locus and the excision of the AID overexpression cassette were confirmed by Southern blot analysis (Figure 1B) and PCR (Figure 1C), respectively. Increased Loss of sIgM Expression after Deletion of ψV Genes in AID-Positive Clones To estimate the rates of deleterious Ig mutations, sIg expression was measured by fluorescence-activated cell sorting (FACS) after 2 weeks' culture for 24 subclones each of the DT40Cre1, AIDR, DT40Cre1AID–/–, and ψV knockout clones (Figure 2). Analysis of the controls with the intact ψV locus revealed an average of 0.52% and 2.27% sIgM(–) cells for the DT40Cre1 and AIDR subclones respectively, but only 0.08% for the DT40Cre1AID–/–. Previous analysis of spontaneously arising sIgM(–) DT40 variants demonstrated that about a third contained frameshift mutations in the rearranged light chain V segment that were regarded as byproducts of the Ig gene conversion activity (Buerstedde et al. 1990). This view is now supported by the finding that the AID-negative DT40Cre1AID–/– clone, which should have lost the Ig gene conversion activity, stably remains sIgM(+). Most interestingly, subclones of the AID-positive ψV knockout clones (AIDRψVpartial and AIDRψV–) rapidly accumulate sIgM(–) populations, whereas subclones of the AID-negative ψV knockout clones (AID–/–ψVpartial and AID–/–ψV–) remain sIgM(+) (Figure 2). This suggests that the deletion of the pseudogenes dramatically increases the rate of deleterious light chain mutations in AID-expressing cells. Figure 2 sIgM Expression Analysis of Control and ψV Knockout Clones (A) FACS anti-IgM staining profiles of representative subclones derived from initially sIgM(+) clones. (B) Average percentages of events falling into sIgM(–) gates based on the measurement of 24 subclones. Replacement of Ig Gene Conversion by Hypermutation in the Absence of ψV Donors To analyze the newly identified mutation activity, the rearranged light chain VJ segments of the ψV knockout clones were sequenced 5–6 weeks after subcloning. A total of 135 nucleotide changes were found in the 0.5-kb region between the V leader and the 5′ end of the J-C intron within 95 sequences from the AIDRψV– clone (Figure 3, upper reference sequence). In contrast to the conversion tracts seen in wild-type DT40 cells, almost all changes are single base substitutions, and, apart from a few short deletions and dinucleotide changes, mutation clusters were not observed. The lack of conversion events in AIDRψV–, which still contains the ψV genes of the unrearranged light chain locus, confirms that Ig gene conversion recruits only the ψV genes on the same chromosome for the diversification of the rearranged light chain gene (Carlson et al. 1990). No sequence diversity was found in a collection of 95 light chain gene sequences from the AID–/–ψV– clone (Figure 4A; Table 1), indicating that AID is required for the mutation activity. Figure 3 Ig Light Chain Sequence Analysis of the ψV Knockout Clones Mutation profiles of the AIDRψV– and AIDRψVpartial clones. All differences identified in different sequences in the region from the leader sequence to the J-C intron are mapped onto the rearranged light chain sequence present in the AIDR precursor clone. Mutations of the AIDRψV– and AIDRψVpartial clones are shown above and below the reference sequence, respectively. Deletions, insertions, and gene conversion events are also indicated. Hotspot motifs (RGYW and its complement WRCY) are highlighted by bold letters. Changes displayed in the same horizontal line are not necessarily derived from the same sequence. Figure 4 Mutation Profiles of Hypermutating Cell Lines (A) Percentages of sequences carrying a certain number of mutations. Each untemplated nucleotide substitution is counted, but gene conversions, deletions, and insertions involving multiple nucleotides are counted as single events. PM, point mutation; GC, gene conversion; D, deletion; I, insertion. (B) Hotspot preferences of untemplated nucleotide substitution mutations. Mutations occurring within a hotspot motif (RGYW or its complement WRCY) are shown by percentages. The hotspot preference was statistically significant (p < 0.05) by the standard difference test. (C) Patterns of nucleotide substitutions within sequences from ψV and the XRCC3 knockout clones. Nucleotide substitutions as part of gene conversion events are excluded. The ratios of transitions (trs) to transversions (trv) are also shown. Table 1 Mutation Profile aPoint mutations that are not templated by cis-pseudogene donors bNumber of gene conversion tracts cFrom sorted IgM(–) dTemplated point mutation in ψV-positive clones cannot be distinguished from short conversion tracts Sequences derived from the AIDRψVpartial clone occasionally display stretches of mutations that can be accounted for by the remaining ψV1 and ψV2 (Figure 3, lower reference sequence). Nevertheless, the majority of AIDRψVpartial mutations are single untemplated base substitutions as seen with the AIDRψV– cells (Figure 4A; Table 1). Only three base substitutions, which are possibly PCR artifacts, were found in 92 sequences of the AID–/–ψVpartial clone, confirming that both the gene conversion and the mutation activities of AIDRψVpartial are AID-dependent. The New Mutation Activity of the ψV Knockout Clones Closely Resembles Somatic Hypermutation The Ig mutation activity discovered in the ψV knockout clones with a predominance of single nucleotide substitutions suggests that somatic hypermutation had replaced Ig gene conversion. There is, however, a difference between the nucleotide substitutions in the AIDRψVpartial and AIDRψV– clones and Ig hypermutations in germinal center B-cells: The clones show very few mutations in A/T bases and a preference for transversion mutations, and among transversions, a preference for G-to-C and C-to-G changes (Figure 4). Ig hypermutations are typically localized within 1 kb of the transcribed gene sequence, with preferences for the complementary determining regions (CDRs) of the V(D)J segments, whereas no or few mutations are present in the downstream C region (Lebecque and Gearhart 1990). To investigate whether the mutations in the AIDRψV– clone follow a similar distribution, sequence analysis was extended to the promoter region and the J-C intron of the rearranged light chain gene (Figure 5). Although mutations are found close to the promoter and in the intron downstream of the J segments, the peak incidence clearly coincides with the CDR1 and CDR3, which are also preferred sites of gene conversion in DT40 (unpublished data). Approximately half of all point mutations fall within the RGYW (R = A/G; Y = C/T; W = A/T) sequence motif or its complement WRCY (see Figure 4B), known as hotspots of Ig hypermutation in humans and mice. Figure 5 Mutations within Unsorted and Sorted sIgM(–) cells Distribution of nucleotide substitutions within genomic sequences from unsorted AIDRψV– cells and within cDNA sequences from sorted IgM(–) AIDRψV– cells. The numbers of mutations are counted for every 50 bp, and are shown together with the corresponding physical maps of the light chain genomic locus or the cDNA sequence. It was previously reported that the deletion of RAD51 paralogs induces Ig hypermutation in DT40 cells (Sale et al. 2001). To compare the hypermutation activity in the ψV gene-negative and RAD51 paralog-negative backgrounds, the XRCC3 gene was disrupted in the DT40Cre1 clone, and the rearranged VJ genes were sequenced 6 weeks after subcloning. The mutation spectrum of the XRCC3-deficient clone was similar to that of the AIDRψV– clone (see Figure 4C) and to what was previously reported for the XRCC3 knockout (Sale et al. 2001). Nevertheless, the mutation rate in the new XRCC3 mutant was about 2.5-fold lower than in the AIDRψV– clone, and there was a clear slow-growth phenotype of the XRCC3 mutant compared to wild-type DT40 and the AIDRψV– clone (unpublished data). To identify the mutations responsible for the loss of sIgM expression in the AIDRψV– clone, 94 light chain cDNAs from sorted sIgM(–) cells were amplified and sequenced. Although one short insertion and five deletions were detected in this collection (Table 1), 89% of the 245 total mutations are single-nucleotide substitutions within the VJ segments and only few mutations were observed in the C segment (Figure 5). Surprisingly, only about 10% of the sequences contained a stop codon or a frameshift, suggesting that the lack of sIgM expression is mainly caused by amino acid substitutions that affect the pairing of the Ig light and heavy chain proteins. Ig Locus Specificity of Hypermutation It has been reported that high AID expression in fibroblasts (Yoshikawa et al. 2002) and B-cell hybridomas (Martin and Scharff 2002) leads to frequent mutations in transfected transgenes. To rule out the possibility that the pseudogene deletions had induced a global hypermutator phenotype, the 5′ ends of the genes encoding the B-cell -specific marker Bu-1 and the translation elongation factor EF1α were sequenced for the AIDRψV– clone. Only a single 1-bp deletion was found within 95 sequences of the Bu-1 gene, and only two single nucleotide substitutions within 89 sequences of EF1α (Table 1). As these changes most likely represent PCR artifacts, this further supports the view that the hypermutations induced by the ψV deletions are Ig-locus-specific. Discussion These results demonstrate that the deletion of the nearby pseudogene donors abolishes Ig gene conversion in DT40 and activates a mutation activity that closely resembles Ig hypermutation. The features shared between this new mutation activity and somatic hypermutation include (1) AID dependence, (2) a predominance of single nucleotide substitutions, (3) distribution of the mutations within the 5′ transcribed region, (4) a preference for hotspots, and (5) Ig gene specificity. The only differences between the mutation activity induced by loss of ψV and Ig hypermutation in vivo are the relative lack of mutations in A/T bases and a predominance of transversion mutations in the ψV knockout clones. However, these differences are also seen in hypermutating Epstein Barr virus–transformed B-cell lines (Bachl and Wabl 1996; Faili et al. 2002) and DT40 mutants of RAD51 paralogs (Sale et al. 2001), indicating that part of the Ig hypermutator activity is missing in transformed B-cell lines. Interestingly, the rate of Ig hypermutation in the AIDRψV– clone seems higher than the rate of Ig gene conversion in the DT40Cre1 progenitor. An explanation for this could be that some conversion tracts are limited to stretches of identical donor and target sequences and thus leave no trace. The ratio of transversion to transition was lower for the AIDRψVpartial clone (see Figure 4). Although we can only speculate about the cause of this difference, it might be due to the correction of point mutations by mismatch correction of one or more sites in gene conversion tracts. The induction of Ig hypermutation by the blockage of Ig gene conversions supports a simple model explaining how hypermutation and recombination are initiated and regulated (Figure 6). Initiating the events is a modification of the rearranged V(D)J segment that is either directly or indirectly induced by AID. The default processing of this lesion in the absence of nearby donors or of high homologous recombination activity leads to Ig hypermutation in the form of a single nucleotide substitution (Figure 6, right). However, if donor sequences are available, processing of the AID-induced lesion can be divided into a stage before strand exchange, when a shift to Ig hypermutation is still possible, and a stage after strand exchange, when the commitment toward Ig gene conversion has been made (Figure 6, left). Whereas completion of the first stage requires the participation of the RAD51 paralogs, the second stage involves other recombination factors, such as RAD54. Figure 6 A Model of the Regulation of Ig Gene Conversion and Hypermutation This difference in commitment explains why disruptions of the RAD51 paralogs not only decrease Ig gene conversion, but also induce Ig hypermutation (Sale et al. 2001), whereas disruption of the RAD54 gene only decreases Ig gene conversion (Bezzubova et al. 1997). The model also predicts that low cellular homologous recombination activity prevents Ig gene conversion even in the presence of conversion donors. Such a low homologous recombination activity might be the reason why human and murine B-cells never use Ig gene conversion despite the presence of nearby candidate donors in the form of unrearranged V segments and why chicken germinal center B-cells have shifted the balance from Ig gene conversion to Ig hypermutation (Arakawa et al. 1998). The AIDR and the ψV knockout DT40 clones are a powerful experimental system to address the role of trans-acting factors and cis-acting regulatory sequences for Ig gene conversion and hypermutation. Compared to alternative animal or cell culture systems, it offers the advantages of (1) parallel analysis of Ig gene conversion and hypermutation, (2) conditional AID expression, (3) easy genome modifications by gene targeting, (4) normal cell proliferation and repair proficiency, and (5) Ig locus specificity of hypermutation. The ability to induce gene-specific hypermutation in the DT40 cell line might also find applications in biotechnology. One possibility is to replace the chicken antibody coding regions with their human counterparts and then to simulate antibody affinity maturation from a repertoire that continuously evolves by Ig hypermutation. Materials and Methods Cell lines DT40Cre1, which displays increased Ig gene conversion due to a v-myb transgene and contains a tamoxifen-inducible Cre recombinase, has been described previously (Arakawa et al. 2001). DT40Cre1AID–/– was generated by the targeted disruption of both AID alleles of DT40Cre1 (Arakawa et al. 2002). AIDR was derived from DT40Cre1AID–/– after stable integration of a floxed AID-IRES-GFP bicistronic cassette, in which both AID and GFP are expressed from the same β-actin promoter. AIDRψV– was derived from AIDR by transfection of pψVDel1-25 (see Figure 1A). Stable transfectants that had integrated the construct into the rearranged light chain locus were then identified by locus-specific PCR. Targeted integration of pψVDel1-25 results in the deletion of the entire ψV gene loci starting 0.4 kb upstream of ψV25 and ending 1 bp downstream of ψV1. AIDRψVpartial was produced in a similar way as was AIDRψV–, by transfection of pψVDel3-25, which, upon targeted integration, leads to a partial deletion of the ψV loci starting 0.4 kb upstream of ψV25 and ending 1 bp downstream of ψV3. Cell culture and electroporation were performed as previously described (Arakawa et al. 2002). XRCC3–/– was derived from DT40Cre1 by deleting amino acids 72–170 of the XRCC3 gene following transfection of XRCC3 knockout constructs. Clones that underwent targeted integration were initially identified by long-range PCR, and the XRCC3 deletion was then confirmed by Southern blot analysis. Ig reversion assay Subcloning, antibody staining, flow cytometry, and quantification of sIgM expression has been described previously (Arakawa et al. 2002). All clones used in the study were sIgM(+) because of the repair of the light chain frameshift of the original Cl18(–) variant (Buerstedde et al. 1990) by a gene conversion event. PCR To minimize PCR-introduced artificial mutations, PfuUltra hotstart polymerase (Stratagene, La Jolla, California, United States) was used for amplification prior to sequencing. Long-range PCR, RT-PCR, and Ig light chain sequencing were performed as previously described (Arakawa et al. 2002). The promoter and J-C intron region of Ig light chain plasmid clones were sequenced using the M13 forward and reverse primers. Bu-1 and EF1α genes were amplified using BU1/BU2 (BU1, 5′-GGGAAGCTTGATCATTTCCTGAATGCTATATTCA-3′; BU2, 5′-GGGTCTAGAAACTCCTAGGGGAAACTTTGCTGAG-3′) and EF6/EF8 (EF6, 5′-GGGAAGCTTCGGAAGAAAGAAGCTAAAGACCATC-3′; EF8, 5′-GGGGCTAGCAGAAGAGCGTGCTCACGGGTCTGCC-3′) primer pairs, respectively. The PCR products of these genes were cloned into the pBluescript plasmid vector (Stratagene) and were sequenced using the M13 reverse primer. Supporting Information Accession Numbers The GenBank (http://www.ncbi.nlm.nih.gov/) accession numbers of the genes discussed in this paper are as follows. AID (NM_009645; NM_020661), RAD54 (GGU92461), RAD52 (U01047), Bu-1 (X92865), and EF1α (NM_204157). We are grateful to Claire Brellinger and Andrea Steiner-Mezzadri for excellent technical assistance, to Wolfgang Beisker for cell sorting, and to Olga Bezzubova, Anna Friedl, Randy Caldwell, Kenji Imai, and Matthias Wahl for critically reading the manuscript. This work was supported by the EU Framework V programs “Chicken Image” and “Genetics in a Cell Line.” Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. HA and J-MB conceived and designed the experiments. HA and HS performed the experiments. HA, HS, and J-MB analyzed the data. HA and HS contributed reagents/materials/analysis tools. HA and J-MB wrote the paper. Academic Editor: James Haber, Brandeis University Abbreviations ψVpseudo V AIDactivation-induced cytidine deaminase CDRcomplementary determining region FACSfluorescence-activated cell sorting GFPgreen fluorescent protein gptguanine phosphoribosyl transferase Igimmunoglobulin IRESinternal ribosome entry site sIgsurface Ig ==== Refs References Arakawa H Buerstedde J-M Immunoglobulin gene conversion: Insights from bursal B cells and the DT40 cell line Dev Dynamics 2004 229 458 464 Arakawa H Furusawa S Ekino S Yamagishi H Immunoglobulin gene hyperconversion ongoing in chicken splenic germinal centers EMBO J 1996 15 2540 2546 8665861 Arakawa H Kuma K Yasuda M Furusawa S Ekino S Oligoclonal development of B cells bearing discrete Ig chains in chicken single germinal centers J Immunol 1998 160 4232 4241 9574524 Arakawa H Lodygin D Buerstedde J-M Mutant loxP vectors for selectable marker recycle and conditional knock-outs BMC Biotechnol 2001 1 7 11591226 Arakawa H Hauschild J Buerstedde J-M Requirement of the activation-induced deaminase (AID) gene for immunoglobulin gene conversion Science 2002 295 1301 1306 11847344 Bachl J Wabl M An immunoglobulin mutator that targets GñC base pairs Proc Natl Acad Sci U S A 1996 93 851 855 8570647 Barreto V Reina-San-Martin B Ramiro AR McBride KM Nussenzweig MC C-terminal deletion of AID uncouples class switch recombination from somatic hypermutation and gene conversion Mol Cell 2003 12 501 508 14536088 Bezzubova O Silbergleit A Yamaguchi-Iwai Y Takeda S Buerstedde J-M Reduced X-ray resistance and homologous recombination frequencies in a RAD54–/– mutant of the chicken DT40 cell line Cell 1997 89 185 193 9108474 Buerstedde J-M Reynaud CA Humphries EH Olson W Ewert DL Light chain gene conversion continues at high rate in an ALV-induced cell line EMBO J 1990 9 921 927 2155784 Butler JE Immunoglobulin diversity, B-cell and antibody repertoire development in large farm animals Rev Sci Tech 1998 17 43 70 9638800 Carlson LM McCormack WT Postema CE Humphries EH Thompson CB Templated insertions in the rearranged chicken IgL V gene segment arise by intrachromosomal gene conversion Genes Dev 1990 4 536 547 2113878 Di Noia J Neuberger MS Altering the pathway of immunoglobulin hypermutation by inhibiting uracil-DNA glycosylase Nature 2002 419 43 48 12214226 Faili A Aoufouchi S Gueranger Q Zober C Leon A AID-dependent somatic hypermutation occurs as a DNA single-strand event in the BL2 cell line Nat Immunol 2002 3 815 821 12145648 Haber JE Mating-type gene switching in Saccharomyces cerevisiae Annu Rev Genet 1998 32 561 599 9928492 Lebecque SG Gearhart PJ Boundaries of somatic mutation in rearranged immunoglobulin genes: 5′ boundary is near the promoter, and 3′ boundary is approximately 1 kb from V(D)J gene J Exp Med 1990 172 1717 1727 2258702 Martin A Scharff MD Somatic hypermutation of the AID transgene in B and non-B cells Proc Natl Acad Sci U S A 2002 99 12304 12308 12202747 Milstein C Rada C The maturation of the antibody response. In: Honjo T, Alt FW, editors. Immunoglobulin genes, 2nd ed 1995 London Academic Press 57 81 Muramatsu M Sankaranand VS Anant S Sugai M Kinoshita K Specific expression of activation-induced cytidine deaminase (AID), a novel member of the RNA-editing deaminase family in germinal center B cells J Biol Chem 1999 274 18470 18476 10373455 Muramatsu M Kinoshita K Fagarasan S Yamada S Shinkai Y Class switch recombination and hypermutation require activation-induced cytidine deaminase (AID), a potential RNA editing enzyme Cell 2000 102 553 563 11007474 Revy P Muto T Levy Y Geissmann F Plebani A Activation-induced cytidine deaminase (AID) deficiency causes the autosomal recessive form of the Hyper-IgM syndrome (HIGM2) Cell 2000 102 565 575 11007475 Reynaud C-A Anquez V Grimal H Weill J-C A hyperconversion mechanism generates the chicken light chain preimmune repertoire Cell 1987 48 379 388 3100050 Sale JE Calandrini DM Takata M Takeda S Neuberger MS Ablation of XRCC2/3 transforms immunoglobulin V gene conversion into somatic hypermutation Nature 2001 412 921 926 11528482 Ta VT Nagaoka H Catalan N Durandy A Fischer A AID mutant analyses indicate requirement for class-switch-specific cofactors Nat Immunol 2003 4 843 848 12910268 Yoshikawa K Okazaki IM Eto T Kinoshita K Muramatsu M AID enzyme-induced hypermutation in an actively transcribed gene in fibroblasts Science 2002 296 2033 2036 12065838

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020197Research ArticleEcologyEvolutionZoologyMammalsHuman Population Density and Extinction Risk in the World's Carnivores Predicting Carnivore Extinction RiskCardillo Marcel [email protected] 1 2 Purvis Andy 1 Sechrest Wes 3 Gittleman John L 4 Bielby Jon 2 Mace Georgina M 2 1Department of Biological Sciences, Imperial College LondonAscot, United Kingdom2Institute of Zoology, Zoological Society of LondonLondon, United Kingdom3IUCN Global Mammal Assessment, Species Survival Commission of IUCN and Conservation International Center for Applied Biodiversity Science Biodiversity Assessment Unit, Center for Applied Biodiversity, Conservation InternationalWashington, District of Columbia, United States of America4Department of Biology, University of VirginiaCharlottesville, VirginiaUnited States of America7 2004 13 7 2004 13 7 2004 2 7 e19714 1 2004 22 4 2004 Copyright: © 2004 Cardillo et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. On the Brink: How Biology and Humans Affect Extinction Risk Understanding why some species are at high risk of extinction, while others remain relatively safe, is central to the development of a predictive conservation science. Recent studies have shown that a species' extinction risk may be determined by two types of factors: intrinsic biological traits and exposure to external anthropogenic threats. However, little is known about the relative and interacting effects of intrinsic and external variables on extinction risk. Using phylogenetic comparative methods, we show that extinction risk in the mammal order Carnivora is predicted more strongly by biology than exposure to high-density human populations. However, biology interacts with human population density to determine extinction risk: biological traits explain 80% of variation in risk for carnivore species with high levels of exposure to human populations, compared to 45% for carnivores generally. The results suggest that biology will become a more critical determinant of risk as human populations expand. We demonstrate how a model predicting extinction risk from biology can be combined with projected human population density to identify species likely to move most rapidly towards extinction by the year 2030. African viverrid species are particularly likely to become threatened, even though most are currently considered relatively safe. We suggest that a preemptive approach to species conservation is needed to identify and protect species that may not be threatened at present but may become so in the near future. As human populations increase, the importance of intrinsic biological factors in determining which carnivores become extinct will also rise - an interaction that makes some species more threatened than previously thought ==== Body Introduction Mammals have been severely affected by the current extinction crisis: around a quarter of extant species are considered to be threatened with extinction (Hilton-Taylor 2000). Understanding the ecological processes that cause some species to decline, while others remain relatively safe, may help to predict future declines and focus conservation efforts on species in urgent need. The underlying cause of virtually all recent and ongoing declines of mammal species is the growth of human populations and associated impacts such as habitat loss, hunting, and the spread of invasive species. Threatening processes such as these vary in intensity across the surface of the Earth, and species that inhabit more heavily impacted regions are expected to have a higher risk of extinction (Forester and Machlis 1996; Woodroffe 2000; Brashares et al. 2001; Harcourt et al. 2001; McKinney 2001; Ceballos and Ehrlich 2002; Harcourt and Parks 2002; Parks and Harcourt 2002). Although exposure to threatening processes is the ultimate cause of extinction, a species' biology determines how well it is able to withstand the threats to which it is exposed. Biological traits that confer ecological flexibility and allow populations to recover rapidly from depletion may offer a degree of protection from external threats. A number of recent studies have linked variation in extinction risk or decline among species to biological traits (Gaston and Blackburn 1995; Bennett and Owens 1997; Owens and Bennett 2000; Purvis et al. 2000; Cardillo and Bromham 2001; Cardillo 2003; Fisher et al. 2003; Jones et al. 2003), and, indeed, biology accounts for over a third of the variation in extinction risk among carnivore and primate species (Purvis et al. 2000). However, only one study to date, using Australian marsupials (Fisher et al. 2003), has explicitly examined the relative importance of biological versus external, anthropogenic predictors of extinction risk. Hence, we know little about the extent to which adding external predictors might increase the explanatory power of models of extinction risk based on biology alone. Furthermore, we do not know whether the combined effects of biological and external predictors are simply additive, or whether interactions exist: does the influence of biology vary depending on the degree of external threat a species faces? Here we present a global-scale analysis of biological and external predictors of extinction risk in the mammal order Carnivora. As well as including many symbols of conservation such as the giant panda, tiger, and sea otter, carnivores in general are a good model taxon for the development of a predictive science of conservation: their biology and phylogeny are well studied, they are near-global in distribution, they represent a wide range of biological strategies, and they include species at all levels of extinction risk. Our analysis emphasizes those threatened species that have suffered measurable declines, rather than those simply with small populations or ranges that may be considered “naturally” rare. We use human population density (HPD) as a summary measure of anthropogenic impact. Although not all types of impact are necessarily associated with high-density human populations, on a global scale HPD is more reliably quantified than direct threatening processes such as habitat loss or hunting, which are difficult to measure accurately in ways that are consistent across regions and biomes. Therefore, HPD represents one of the best available means of summarizing the impact faced by mammal species on a global scale. At local or regional scales, high HPD is often associated with some measure of mammal decline (Forester and Machlis 1996; Woodroffe 2000; Brashares et al. 2001; Harcourt et al. 2001; McKinney 2001; Ceballos and Ehrlich 2002; Harcourt and Parks 2002; Parks and Harcourt 2002). Here we ask whether HPD influences carnivore extinction risk at the species level, whether it is more or less important than species biology, and how biology interacts with HPD to determine risk. Results We followed the international standard for species-level extinction risk classification, the IUCN Red List (Hilton-Taylor 2000), which has also been used in previous studies of species-level extinction risk (Purvis et al. 2000; Harcourt and Parks 2002; Jones et al. 2003). We used multiple linear regression to find minimum adequate models (MAMs) predicting extinction risk from HPD and a set of biological traits. Confounding effects of phylogeny were controlled for by calculating phylogenetically independent contrasts in all variables before analysis. Using a Geographic Information System, we derived seven summary measures of HPD for each species: mean HPD across the species' geographic range and the proportion of the range with HPD of at least 2, 5, 10, 20, 50, and 100 people/km2. With the exception of the last two of these, all showed significant nonlinear effects on extinction risk as separate predictors (Table 1). In the multiple regression, however, biological variables were of overriding importance compared to HPD as predictors of extinction risk (Table 2). A MAM based on main effects alone explained 45.1% of variation in risk, with four biological variables independently associated with high extinction risk: small geographic range size, long gestation, low species population density and high trophic level. No HPD variables added significant explanatory power to this model. However, when interactions between HPD and biological variables were added to the model, a HPD–gestation length interaction was significant, and the variance explained by the model increased to 51.4% (Table 2). Using the Akaike information criterion (AIC), the model with the interaction term provided a better fit to the data (AIC = 57.38) than the model based on main effects only (AIC = 61.98). However, the partial variance explained by HPD (0.5%) in this model was very small compared to that explained by the combined biological variables (44%). So, although HPD variables were significant separate predictors of extinction risk, the independent effect of HPD virtually disappeared once the effects of biology were accounted for. Table 1 Regressions of Extinction Risk against HPD Using Phylogenetically Independent Contrasts *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001 Results are shown for linear and nonlinear (quadratic and cubic) terms for each variable. The results presented are for a reduced dataset (n = 143 contrasts) in which four datapoints with studentized residuals greater than or equal to 3 have been removed. For all variables, the effect of removing these outliers was to reduce slightly the slope of the relationship Table 2 MAMs from Multiple Regression of HPD and Biological Predictors of Extinction Risk in Carnivores *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001 Model with main effects only: n = 67 contrasts, model r2 = 0.451, AIC = 61.98. Model with interactions: n = 67 contrasts, model r2 = 0.514, AIC = 57.38. The HPD variable is the percent of a species' geographic range in which HPD is 10/km2 or greater (other HPD variables were tested but had lower predictive power) The importance of interactions between HPD and biology was confirmed by separate analyses of the subsets of carnivore species with relatively low and high exposure to human populations (Table 3). For “low-exposure” species the final MAM included only two predictors, species population density and geographic range size, and explained 37.9% of the variation in extinction risk. However, for “high-exposure” species the model included geographic range size, species population density, and gestation length, and the explanatory power increased sharply, to 80.1%. Therefore, despite the fact that independent main effects of HPD were relatively unimportant, HPD did appear to be a significant modifier of the effects of biology on extinction risk. Table 3 MAMs for Carnivore Species with Low and High Exposure to Human Populations *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001 See Materials and Methods for definitions of “low exposure” and “high exposure.” Low-exposure species: n = 48 contrasts, model r2 = 0.379. High-exposure species: n = 19 contrasts, model r2 = 0.801 Discussion While the ultimate sources of current threats to species are virtually all anthropogenic, intrinsic ecological and life-history traits determine how well populations are able to withstand exposure to threatening processes. In our models, four biological traits accounted for nearly half of the variation in extinction risk among carnivore species. Small geographic ranges and low population densities determine the maximum population size a species can attain; gestation length is an important indicator of life-history speed (Gittleman 1993), which determines how quickly populations can recover from low levels; and extinction risk for species at high trophic levels may be compounded by their need for large hunting areas and their dependence on prey species that may themselves be threatened (Carbone and Gittleman 2002). Of these traits, geographic range size is of particular importance. As an example, the Ethiopian wolf (Canis simensis) is the most threatened species of the Canidae family despite the fact that its population density is not especially low, nor its gestation especially long, compared to other Canis species. However, it has a geographic distribution only a fraction of the size of its congenerics, and within that distribution is limited to afroalpine habitat (Sillero-Zubiri and Macdonald 1997). In contrast, independent, main effects of HPD on extinction risk were weak once the effects of biology were accounted for: HPD explained only 0.5% of variation in extinction risk in the final model, compared to 44% for biological traits. That extinction risk in carnivores should be so strongly determined by biology rather than HPD is surprising, given that carnivores inhabit regions as disparate in human impact as western Europe and the Canadian Arctic, and that their requirements often conflict with human interests (Gittleman et al. 2001). Based on previous studies, HPD is expected to be a good proxy for threats to mammal populations (Forester and Machlis 1996; Woodroffe 2000; Brashares et al. 2001; Harcourt et al. 2001; McKinney 2001; Ceballos and Ehrlich 2002; Harcourt and Parks 2002; Parks and Harcourt 2002), and, indeed, it has been suggested that HPD be incorporated into a scheme for quantifying extinction risk for primate species (Harcourt and Parks 2002). However, no previous study has examined the effects of HPD on extinction risk after controlling for the effects of a wide range of biological traits. The strength of HPD as a predictor of risk may also be compromised by the fact that the relationship between HPD and threat intensity may be a complex one (McKinney 2001): for example, habitat loss, the most important threat to mammals (Hilton-Taylor 2000), is often not associated with high HPD. Recent work suggests that number of households may be a better demographic indicator of threat intensity than number of people (Liu et al. 2003), although this is based on coarse-scale, country-level data that cannot easily be incorporated into phylogenetically explicit analyses. Furthermore, differences in the degree of technological development of different human societies may contribute to differences in the effect of HPD. For example, a small human population with access to highly mechanized means of habitat destruction may have a level of impact on carnivores equal to that of a far larger or denser population without such means. Another possibility is that a species' current extinction risk status may reflect patterns of human impact in the past more closely than it does current impact. Extinction filter effects (Balmford 1996) may mean that the most vulnerable species have already disappeared or contracted away from regions of highest HPD, obscuring any underlying positive association between HPD and extinction risk. Unfortunately, the difficulty of reliably reconstructing historical ranges of species from available evidence probably precludes thorough testing of this idea. Clearly, the importance of HPD on current extinction risk of carnivores is less as an independent main effect than as a modifier of the effects of biology. For “low-exposure” species (for which HPD was 10 people/km2 or higher in less than half of the range), life history did not predict extinction risk, and the only variables included in the model were those that determine the maximum population size a species can achieve (geographic range size and species population density). For “high-exposure” species, life history (gestation length) became additionally significant, and the explanatory power of the model as a whole was very high (80%). Evidently, a small species population contributes to high extinction risk no matter what the threat level, but where exposure to human populations is high, the disadvantage of a small species population is compounded by the disadvantage of a slow life history. This might suggest a difference in the major threat types experienced by carnivore species with different levels of exposure to human populations. For example, habitat loss, the predominant threat type for the great majority of mammals (Hilton-Taylor 2000), can be severe even far from centers of human population. Hence, species with restricted distributions and small populations may be susceptible to habitat loss even in regions of relatively low HPD. Where people are more numerous, species may be threatened by direct persecution and exploitation as well as habitat loss. In such regions, species with slow life histories and low population growth rates would become additionally susceptible. This could explain our finding that biology becomes a more powerful predictor of risk status as exposure to human populations increases. If species in regions of high HPD are threatened by direct persecution and exploitation as well as habitat loss, we should expect top-level predators to be particularly threatened in these regions (Woodroffe 2000). Why, then, was trophic level not a predictor of extinction risk for “high-exposure” species? One possibility is an extinction filter effect, whereby species at the highest trophic levels, which live at low densities and are relatively rare, have already disappeared from regions of high HPD (Diamond 1984; Woodroffe 2001). This explanation appears to be supported by a negative correlation across species between trophic level and the proportion of a species' range with a HPD of 10 people/km2 or higher (r = −0.23, p = 0.003, d.f. = 166). The strong effect of biological traits on extinction risk status for “high-exposure” species suggests that, as human populations increase globally over coming decades, the importance of biology in determining which species persist and which decline will also increase. This is worrying for species that possess traits making them more vulnerable to external threats, as their extinction risk can be expected to increase more sharply. Of particular concern are species that not only have unfavorable biology, but also live in regions of rapid human population growth. We have identified the carnivore species with the greatest expected increase in extinction risk over the next few decades, given HPD projected to the year 2030 based on recent growth rates. We acknowledge the problems with converting the ordinal categories of the Red List to an interval scale, and these have been discussed previously (Purvis et al. 2000). However, we emphasize that we are not attempting to make accurate quantitative predictions about the future status of species. We are simply illustrating a way of identifying those species likely to move most rapidly towards extinction in coming decades based on projected growth in human populations, all else being equal. Figure 1 shows those species with the greatest discrepancy between current and predicted risk status. These species are from a wide range of carnivore families, but the Viverridae (civets and genets) are particularly well represented: five of the top seven species on the list are viverrids. Most of the species in Figure 1 are from Africa, much of which has rates of human population growth far higher than the global average. It is particularly worrying that most are currently rated as “least concern” in the Red List, so they are unlikely to be receiving as much conservation attention as species currently rated as threatened. Furthermore, our estimates are conservative in that they treat species' geographic range sizes and population densities as static—they do not account for ongoing declines. Figure 1 Carnivore Species Predicted to Move Most Rapidly towards Extinction by the Year 2030 Species listed are those expected to move from the “low-exposure” into the “high-exposure” group (see Materials and Methods for definitions), and for which the extinction risk rating is predicted to increase by at least one index value. Bars indicate the discrepancy between current Red List rating at the left, and the predicted rating at the right. General distributions of each species are shown on the far right. Abbreviations for Red List categories: LC, least concern; NT, near threatened; CD, conservation dependent; VU, vulnerable; EN, endangered; CR, critically endangered; EW, extinct in the wild; EX, extinct. We conclude that there is no room for complacency about the security of species simply because they are not currently considered globally threatened. There is a strong case to be made for preemptive conservation of species, such as the African viverrids, that live in regions of rapid human population growth and have a biology predisposing them to decline. Preemptive action could include, for example, establishing population-monitoring programs, or listing species under national species protection laws on the basis of potential future susceptibility. Arguably, maintaining the stability of particularly susceptible species before they become threatened could be more cost-effective in the long term than postdecline attempts to rescue them from the brink of extinction. Materials and Methods Data As the response variable in our analyses, we followed Purvis et al. (2000) in converting the IUCN Red List categories (Hilton-Taylor 2000) to a continuous linear index as follows: least concern = 0, near threatened = 1, conservation dependent/vulnerable = 2, endangered = 3, critically endangered = 4, extinct in the wild/extinct = 5. Among our predictor variables were geographic range size and species population density, both of which may contribute to the criteria for determining the Red List category (Hilton-Taylor 2000). To avoid potential circularity, our analyses excluded the 15% of carnivore species listed based on these criteria, and included threatened species only when they were listed under criterion A (a measured recent decline in geographic range or population size), which is independent of absolute geographic range or population density. We used the database of biological variables used by Purvis et al. (2000), updated to include more recently published information. This database consists of information compiled from the published literature on species' geographic range size, body size, interbirth interval, age at sexual maturity, litter size, gestation length, home range size, population density, group size, trophic level, activity timing, sociality, and island endemicity. Continuous variables were log-transformed before analysis. For our measures of HPD, we used the Gridded Population of the World (CIESIN 2000), a spatially explicit global database of HPD for 1995, coarsened to a resolution of 0.5 ° × 0.5 ° to speed analyses. We used two methods to summarize the spatial variation in HPD within the geographic range of each species, each of which captures different aspects of HPD variation. Firstly, we used the log-transformed mean HPD across the geographic range of each species: this measure is sensitive to relatively small areas of very high HPD (e.g., around major cities). Secondly, we calculated the logit-transformed proportion of each species' range in which HPD exceeded a given threshold value. This measures a more explicitly spatial aspect of HPD variation and is less sensitive to small areas of very high HPD. Because it is difficult to know a priori the HPD threshold that is most critical to carnivore extinction risk, we repeated all analyses using threshold values of 2, 5, 10, 20, 50, and 100 people/km2. Geographic variation in both HPD and the distribution of threatened species may be confounded with net primary productivity (Balmford et al. 2001), so we included in the models a measure of actual evapotranspiration (AET) (UNEP 2003), as a proxy for primary productivity. The above calculations were all done with the Spatial Analyst extension in the program ArcGIS, using equal-area projections of the HPD map and each carnivore species' estimated current geographic distribution (compiled as part of the IUCN Global Mammal Assessment). The datasets used in the analyses are provided in Supporting Information (Datasets S1 and S2). Analyses To test the predictors of extinction risk we used linear regression through the origin (Garland et al. 1992) on phylogenetically independent contrasts generated using the program Comparative Analysis by Independent Contrasts (Purvis and Rambaut 1995). Although the extinction risk index itself does not evolve along phylogenies, it is closely associated with biological variables that do, making it necessary to use analyses that control for phylogeny to ensure statistical independence of data points (Jones et al. 2004; Purvis et al. 2000, 2004). The carnivore phylogeny of Bininda-Emonds et al. (1999) was used to define the contrasts, with branch lengths set to equal. The decision to use equal branch lengths was based on previous analyses (Purvis et al. 2000) using the same phylogeny and essentially the same biological dataset that showed that equal branch lengths gave contrasts with more homogeneous variances than those based on divergence times. We first carried out univariate regressions of each HPD predictor against extinction risk (this had already been done for biological predictors by Purvis et al. [2000] using essentially the same dataset). We then combined external and biological predictors in multiple regressions. To find MAMs, we used backwards elimination of predictor variables from a full model (Crawley 2002). The large number of missing values in the dataset, and the need to recalculate contrasts at each step, meant that this process could not be automated without discarding most of the information in the dataset. We therefore used the following manual procedure to find MAMs, following Purvis et al. (2000). We began by fitting a model with all predictors included, then identifying the predictor that contributed the smallest amount of marginal variance to the model. This predictor was then dropped, a new set of contrasts calculated, and the process repeated. In some cases dropping a predictor with many missing values resulted in a substantial increase in the number of contrasts at the next step; when this happened, other predictors previously dropped were reintroduced in turn and the model retested for each. A MAM was found when all remaining predictors contributed a significant (p ≤ 0.05) amount of variance to the model. Previously dropped predictors were then once again reintroduced in turn and the model retested each time. It should be noted that this method cannot guarantee to find the best-fitting model: it is essentially a heuristic search for the best model, and simulations on a dataset in which associations between variables are known would be needed to fully test the accuracy of the method. To avoid potential problems of colinearity among the seven variables derived from HPD, the variables were included one at a time in the multiple regression models in the process of finding MAMs. At each step we substituted each of the seven HPD variables into the model in turn, retesting the model each time. Once the final MAM was found, we added terms describing the interactions between HPD and biological variables, again testing the model for significance each time. Finally, we compared the predictive power of biological variables for subsets of species with low and high levels of exposure to human impact. “Low-exposure” and “high-exposure” species were defined, respectively, as species with less than or greater than 50% of their geographic range with HPD of at least 10 people/km2. The procedure for finding MAMs, using biological variables only, was then repeated for each of these two subsets of species. Predictions of future risk increases From global-scale spatial HPD data for 1990 and 1995 (CIESIN 2000) we calculated a mean annual rate of change, which we used to project HPD to the year 2030. For each species we then recalculated the proportion of the geographic range with HPD of at least 10 people/km2. Those species which moved from the “low-exposure” into the “high-exposure” group were identified, and the MAM for “high-exposure” species (Table 3) was used to predict extinction risk for these species. This method is more rigorous than simply identifying currently stable members of higher taxa that have declined in response to human population pressure, because it accounts for the unique biology and geographic distribution of each species. Supporting Information Dataset S1 Definitions of Variable Names in the Dataset (1 KB TDS). Click here for additional data file. Dataset S2 External and Biological Data for Carnivores Used for Analyses (38 KB TDS). Click here for additional data file. We thank Justine Klass and Jonathan Davies for help with ArcGIS. This work was funded by grants from the Natural Environment Research Council (NER/A/S/2001/00581), Conservation International's Center for Applied Biodiversity Science, and the National Science Foundation (DEB/0129009). Conflicts of Interest.The authors have declared that no conflicts of interest exist. Author Contributions. MC, AP, JLG, JB, and GMM conceived and designed the study. MC, WS, JLG, and JB contributed data. MC analyzed the data. JLG and JB contributed reagents/materials/analysis tools. MC, AP, WS, and JLG wrote the paper. Academic Editor: Craig Moritz, University of California at Berkeley Abbreviations HPDhuman population density MAMminimum adequate model AICAkaike information criterion ==== Refs References Balmford A Extinction filters and current resilience: The significance of past selection pressures for conservation biology Trends Ecol Evol 1996 11 193 196 21237807 Balmford A Moore JL Brooks T Burgess N Hansen LA Conservation conflicts across Africa Science 2001 291 2616 2619 11283376 Bennett PM Owens IPF Variation in extinction risk among birds: Chance or evolutionary predisposition? Proc R Soc Lond B Biol Sci 1997 264 401 408 Bininda-Emonds ORP Gittleman JL Purvis A Building large trees by combining phylogenetic information: A complete phylogeny of the extant Carnivora (Mammalia) Biol Rev Camb Philos Soc 1999 74 143 175 10396181 Brashares JS Arcese P Sam MK Human demography and reserve size predict wildlife extinction in West Africa Proc R Soc Lond B Biol Sci 2001 268 2473 2478 Carbone C Gittleman JL A common rule for the scaling of carnivore density Science 2002 295 2273 2276 11910114 Cardillo M Biological determinants of extinction risk: Why are smaller species less vulnerable? Anim Conserv 2003 6 63 69 Cardillo M Bromham L Body size and risk of extinction in Australian mammals Conserv Biol 2001 15 1435 1440 Ceballos G Ehrlich PR Mammal population losses and the extinction crisis Science 2002 296 904 907 11988573 [CIESIN] Center for International Earth Science Information Network Gridded population of the world, version 2. Available: http://sedac.ciesin.columbia.edu/plue/gpw via the Internet 2000 Accessed 5 May 2004 Crawley MJ Statistical computing: An introduction to data analysis using S-Plus 2002 Hoboken (New Jersey) John Wiley and Sons 772 Diamond JM Historic extinctions: A Rosetta stone for understanding prehistoric extinctions. In: Martin PS, Klein RG, editors. Quaternary extinctions: A prehistoric revolution 1984 Tucson University of Arizona Press 824 862 Fisher DO Blomberg SP Owens IPF Extrinsic versus intrinsic factors in the decline and extinction of Australian marsupials Proc R Soc Lond B Biol Sci 2003 270 1801 1808 Forester DJ Machlis GE Modeling human factors that affect the loss of biodiversity Conserv Biol 1996 10 1253 1263 Garland T Harvey PH Ives AR Procedures for the analysis of comparative data using phylogenetically independent contrasts Syst Biol 1992 41 18 32 Gaston KJ Blackburn TM Birds, body size and the threat of extinction Philos Trans R Soc Lond B Biol Sci 1995 347 205 212 Gittleman JL Carnivore life histories: A reanalysis in the light of new models. In: Dunstone N, Gorman ML, editors. Mammals as predators 1993 Oxford Oxford University Press 65 86 Gittleman JL Funk SM Macdonald DW Wayne RK Carnivore conservation 2001 Cambridge Cambridge University Press 690 Harcourt AH Parks SA Threatened primates experience high human densities: Adding an index of threat to the IUCN Red List criteria Biol Conserv 2002 109 137 149 Harcourt AH Parks SA Woodroffe R Human density as an influence on species/area relationships: Double jeopardy for small African reserves? Biodivers Conserv 2001 10 1011 1026 Hilton-Taylor C 2000 IUCN Red List of Threatened Species 2000 Gland (Switzerland) IUCN Species Survival Commission 61 compiler Jones KE Purvis A Gittleman JL Biological correlates of extinction risk in bats Am Nat 2003 161 601 614 12776887 Jones KE Sechrest W Gittleman JL Age and area revisited: Identifying global patterns and implications for conservation. In: Purvis A, Gittleman JL, Brooks TM, editors. Phylogeny and conservation 2004 Cambridge Cambridge University Press In press Liu JG Daily GC Ehrlich PR Luck GW Effects of household dynamics on resource consumption and biodiversity Nature 2003 421 530 533 12540852 McKinney ML Role of human population size in raising bird and mammal threat among nations Anim Conserv 2001 4 45 57 Owens IPF Bennett PM Ecological basis of extinction risk in birds: Habitat loss versus human persecution and introduced predators Proc Natl Acad Sci U S A 2000 97 12144 12148 11005835 Parks SA Harcourt AH Reserve size, local human density, and mammalian extinctions in U.S. protected areas Conserv Biol 2002 16 800 808 Purvis A Rambaut A Comparative analysis by independent contrasts (CAIC): An Apple Macintosh application for analyzing comparative data Comput Appl Biosci 1995 11 247 251 7583692 Purvis A Gittleman JL Cowlishaw G Mace GM Predicting extinction risk in declining species Proc R Soc Lond B Biol Sci 2000 267 1947 1952 Purvis A Cardillo M Grenyer R Collen B Correlates of extinction risk: Phylogeny, biology, threat and scale. In: Purvis A, Gittleman JL, Brooks TM, editors. Phylogeny and conservation 2004 Cambridge Cambridge University Press In press Sillero-Zubiri C Macdonald DW The Ethiopian wolf: Status survey and conservation action plan 1997 Gland (Switzerland) IUCN The World Conservation Union 123 [UNEP] United Nations Environment Programme GRID data. Available: http://www.grid.unep.ch/data/index.php via the Internet 2003 Accessed 5 May 2004 Woodroffe R Predators and people: Using human densities to interpret declines of large carnivores Anim Conserv 2000 3 165 173 Woodroffe R Strategies for carnivore conservation: Lessons from contemporary extinctions. In: Gittleman JL, Funk SM, Macdonald DW, Wayne RK, editors. Carnivore conservation 2001 Cambridge Cambridge University Press 61 92

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020198Research ArticleCell BiologyImmunologyInfectious DiseasesVirologyVirusesHomo (Human)HIV Infection of Naturally Occurring and Genetically Reprogrammed Human Regulatory T-cells HIV Infection of Treg CellsOswald-Richter Kyra 1 Grill Stacy M 1 Shariat Nikki 1 Leelawong Mindy 1 Sundrud Mark S 1 Haas David W 1 2 Unutmaz Derya [email protected] 1 1Department of Microbiology and Immunology, Vanderbilt University Medical SchoolNashville, Tennessee, United States of America2Department of Medicine, Vanderbilt University Medical SchoolNashville, TennesseeUnited States of America7 2004 13 7 2004 13 7 2004 2 7 e19819 12 2003 4 4 2004 Copyright: © 2004 Oswald-Richter et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Regulating the Regulators: Immune System Regulators Are Highly Susceptible to HIV Infection A T-cell subset, defined as CD4+CD25hi (regulatory T-cells [Treg cells]), was recently shown to suppress T-cell activation. We demonstrate that human Treg cells isolated from healthy donors express the HIV-coreceptor CCR5 and are highly susceptible to HIV infection and replication. Because Treg cells are present in very few numbers and are difficult to expand in vitro, we genetically modified conventional human T-cells to generate Treg cells in vitro by ectopic expression of FoxP3, a transcription factor associated with reprogramming T-cells into a Treg subset. Overexpression of FoxP3 in naïve human CD4+ T-cells recapitulated the hyporesponsiveness and suppressive function of naturally occurring Treg cells. However, FoxP3 was less efficient in reprogramming memory T-cell subset into regulatory cells. In addition, FoxP3-transduced T-cells also became more susceptible to HIV infection. Remarkably, a portion of HIV-positive individuals with a low percentage of CD4+ and higher levels of activated T-cells have greatly reduced levels of FoxP3+CD4+CD25hi T-cells, suggesting disruption of the Treg cells during HIV infection. Targeting and disruption of the T-cell regulatory system by HIV may contribute to hyperactivation of conventional T-cells, a characteristic of HIV disease progression. Moreover, the ability to reprogram human T-cells into Treg cells in vitro will greatly aid in decoding their mechanism of suppression, their enhanced susceptibility to HIV infection, and the unique markers expressed by this subset. Human regulatory T-cells cells (Treg) -- or conventional T cells that have been genetically modified to generate Tregs -- express the HIV-coreceptor CCR5 and are susceptible to HIV infection ==== Body Introduction There is now compelling evidence that a subset of T-cells with regulatory activity suppresses T-cell activation in both mice and humans (Sakaguchi et al. 1995; Asano et al. 1996; Suri-Payer et al. 1998; Takahashi et al. 1998; Thornton and Shevach 1998; Baecher-Allan et al. 2001; Dieckmann et al. 2001; Jonuleit et al. 2001, 2002; Levings et al. 2001; Ng et al. 2001; Taams et al. 2001). Regulatory T-cells (Treg cells) have been shown to inhibit various autoimmune and allergic diseases (Shevach 2000; Furtado et al. 2001; Curotto de Lafaille and Lafaille 2002; Green et al. 2002, 2003; McHugh and Shevach 2002), mediate transplantation and self-tolerance (Sakaguchi et al. 1995; Hara et al. 2001; Taylor et al. 2001, 2002; Sanchez-Fueyo et al. 2002), and block the activation and proliferation of T-cells both in vitro and in vivo (Takahashi et al. 1998; Thornton and Shevach 1998; Annacker et al. 2000, 2001). These findings strongly suggest that Treg cells play a key role in immune regulation. Human and murine Treg cells are functionally characterized by a decrease in both proliferation and IL-2 secretion in response to T-cell receptor (TCR) stimulation and by their ability to suppress activation of conventional T-cells (Asano et al. 1996; Takahashi et al. 1998; Thornton and Shevach 1998; Baecher-Allan et al. 2001; Dieckmann et al. 2001; Jonuleit et al. 2001; Levings et al. 2001; Ng et al. 2001; Taams et al. 2001, 2002). Treg cells mediate their suppressive effects only when stimulated via their TCRs (Takahashi et al. 1998; Thornton and Shevach 1998), although their suppressive effector function is antigen nonspecific (Thornton and Shevach 2000). Treg cells are clearly enriched within peripheral CD4+ T-cells that also express the α subunit of the IL-2 receptor (CD25), which is currently the best marker for identifying these cells (Shevach 2002). However, CD25 is also expressed on activated effector T-cells, and not all CD4+ Treg cells express CD25 (Annacker et al. 2001; Stephens et al. 2001). In adults, Treg cells are exclusively found in the CD45RO+ memory subset, and a sizable portion of these cells express the activation marker HLA-DR and the recently identified molecule glucocorticoid-induced tumor necrosis factor receptor (GITR, also known as TNFRSF18) (Gumperz et al. 2002; Lee et al. 2002). Upon activation, Treg cells express the inhibitory receptor CTLA-4 at a higher level and for a longer period of time than conventional T-cells (Read et al. 2000; Salomon et al. 2000; Takahashi et al. 2000). Interestingly, Treg cells have also been shown to express high levels of certain chemokine receptors such as CCR4 and CCR8 (Iellem et al. 2001). The forkhead transcription factor FOXP3 was recently shown to be specifically expressed in mouse Treg cells and is required for their development (O'Garra and Vieira 2003; Ramsdell 2003). A mutation in the FOXP3 gene carried by the scurfy mouse strain or a knockout of this gene causes a CD4+ T-cell-mediated lymphoproliferative disease characterized by cachexia and multiorgan lymphocytic infiltrates (Lyon et al. 1990; Brunkow et al. 2001). The human genetic disease immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome (also called X-linked autoimmunity-allergic disregulation syndrome) is caused by mutations in the human homolog of FoxP3 and is characterized by hyperactivation of T-cells with autoimmune endocrinopathy, early-onset type 1 diabetes and thyroiditis, and in some cases manifestations of severe atopy (Chatila et al. 2000; Bennett and Ochs 2001; Bennett et al. 2001; Wildin et al. 2001; Gambineri et al. 2003). In addition, expression of FOXP3 in conventional T-cells either in transgenic mice or by retroviral transduction is sufficient to confer a Treg cell phenotype (Fontenot et al. 2003; Hori et al. 2003; Khattri et al. 2003). However, the role of FoxP3 in the development of human Treg cells has not been examined. The role of Treg cells in controlling T-cell activation during immune responses to pathogens such as chronic viral infections is currently a subject of great interest. Recently it was shown that Treg cells can regulate virus-specific or memory CD8+ T-cell responses, thus diminishing the magnitude of the immune response (Kursar et al. 2002, 2004; Murakami et al. 2002; Suvas et al. 2003; Aandahl et al. 2004). Because Treg cells express CD4, they are also potential targets of HIV in vivo. HIV entry into target cells also requires cellular expression of the chemokine receptors CCR5 or CXCR4 in conjunction with CD4 (Boshoff et al. 1997). However, the ability of HIV to establish a persistent infection is also critically dependent on activation signals that regulate HIV replication within target T-cells. Quiescent T-cells are resistant to infection unless TCR or cytokine activation signals are provided (Unutmaz 2001). Indeed, chronic states of T-cell hyperactivation, viral persistence, and T-cell depletion are all hallmarks of HIV infection (Grossman et al. 2002). Consequently, this state of chronic immune activation combined with the direct destruction of CD4+ T-cells by HIV leads to a profound immunodeficiency characterized by progressive deterioration of immune function (Fauci 1993). If Treg cells are lost because of HIV infection, this could potentially result in hyperactivation of conventional T-cells due to lack of immunoregulation. In contrast, if Treg cells are activated to expand during certain stages of the infection, this could have a suppressive effect on protective immune responses against the virus. Thus, in both scenarios dysregulation of Treg subset during HIV infection could have a profound impact on anti-HIV immune responses and pathogenesis of the infection. We tested the susceptibility of both naturally occurring and in vitro genetically reprogrammed Treg cells to HIV infection. We found that Treg cells isolated from healthy donors express CCR5 and are highly susceptible to HIV infection. Ectopic expression of FoxP3 in conventional human T-cells genetically reprogrammed them into a Treg phenotype and enhanced their susceptibility to HIV infection. Remarkably, we also found a profound defect of FoxP3+CD4+CD25hi T-cells in HIV-infected patients with low CD4+ and a high percentage of activated T-cells. Our findings have important implications in understanding the role of Treg cells and the chronic activated state of T-cells during HIV infection. Furthermore, reprogramming of T-cells in vitro into Treg cells establishes a novel system to understand the mechanism of T-cell suppression and enhanced susceptibility of this subset to HIV infection. Results Isolation and Characterization of Human Treg Cells To analyze susceptibility of Treg cells to HIV infection, we first developed a method to isolate these cells from peripheral blood. A sizable portion of human CD4+ T-cells (between 10%–20%) express CD25 (Figure 1A). However, approximately 1%–2% of CD4+ T-cells within the memory subset (CD45RO+) express high levels of CD25 (CD25hi) (Figure 1A). Previous studies suggested that human Treg cells resided within the CD45RO+CD25hi subset (Baecher-Allan et al. 2001; Taams et al. 2001). We first performed a phenotypic analysis of CD45RO+CD25hi (referred to as Treg), CD45RO+CD25low/neg (memory T) and CD45RO−CD25neg (naïve T) cells. Treg cells expressed higher levels of GITR and HLA-DR (Figure 1B), consistent with previous reports (Baecher-Allan et al. 2001; McHugh et al. 2002; Shimizu et al. 2002). Treg cells also expressed high levels of CCR5 and CCR4 compared to memory and naïve T-cells, while expression of CXCR4 and CCR7 was lower and CXCR3 expression was similar as compared to memory T-cells (Figure 1B). Figure 1 Identification and Phenotype of Treg Cells (A) Purified CD4+ T-cells were stained with anti-CD45RO-FITC and anti-CD25-PE antibodies. The naïve, memory, and Treg subsets were identified as shown in boxes. (B) Purified CD4+ T-cells were first stained with a pure antibody against the cell surface molecule shown in the figure, followed by antimouse IgG conjugated with allophycocyanin, followed by CD25-PE and CD45RO-FITC. Gates were set for Treg, memory, and naïve T-cells as shown in (A). These results are representative of one out of five donors analyzed. A low proliferative response and reduced IL-2 secretion are characteristics of Treg cells (Asano et al. 1996; Takahashi et al. 1998; Thornton and Shevach 1998; Baecher-Allan et al. 2001; Dieckmann et al. 2001; Jonuleit et al. 2001; Levings et al. 2001; Ng et al. 2001; Taams et al. 2001). To analyze their capacity to proliferate and secrete IL-2 upon TCR triggering, Treg and memory T-cells were sorted into highly purified populations by flow cytometry. Purified cells were then labeled with carboxy-fluorescein diacetate succinimidyl ester (CFSE) to monitor cell division in a quantitative manner and stimulated through the TCR using plate-bound anti-CD3 and soluble anti-CD28 antibodies. The secretion of IL-2 by TCR-stimulated Treg cells was about 10-fold lower as compared to memory T-cells (Figure 2). Treg cells also secreted lower levels of IL-4, IL-5, and IFNγ as compared to memory T-cells (Figure 2). The CFSE-labeled cells were analyzed 6 d after stimulation. Treg cells exhibited little proliferation, whereas most of the memory T-cells had divided four to five times (Figure 3A). In order to demonstrate that purified Treg cells also displayed suppressive activity, both naïve and CD25low/neg memory CD4+ T-cells were labeled with CFSE and stimulated under suboptimal T-cell activation conditions in the presence of unlabeled purified autologous Treg, naïve, or memory T-cells. Coculture with Treg cells significantly slowed the proliferation of TCR-stimulated resting naïve and memory CD4+ T-cells as compared to the coculture with either unlabeled naïve or memory T-cells (Figure 3B and Figure3C). Taken together, these results confirm that human Treg cells are part of the CD4+CD25hi subset of T-cells. Figure 2 Cytokine Secretion by Treg T-cells Sorted Treg and memory T-cells were activated using plate-bound anti-CD3 (3 μg/ml) and soluble anti-CD28 (1 μg/ml) antibodies. Supernatants were collected 18–24 h postactivation and analyzed for cytokines using the CBA assay. Results are representative of cytokine secretion from Treg and memory T-cells from three different donors. Figure 3 Proliferation and Suppressive Capacity of Treg Cells (A) Sorted Treg and memory T-cells were labeled with CFSE and then activated through suboptimal anti-CD3 (100 ng/ml) and anti-CD28 (1 μg/ml) antibodies. Day 6 postactivation, cells were fixed and CFSE expression was analyzed by flow cytometry. (B) Resting naïve or memory CD4+ T-cells (1.5 × 105 T-cells) were labeled with CFSE and cocultured with either unlabeled purified Treg, naïve, or memory T-cells at 1:1 ratio in 96-well plates coated with suboptimal anti-CD3 (100 ng/ml) and anti-CD28 (1 μg/ml) antibodies. At day 4 postactivation, cells were fixed and analyzed for CFSE expression and cell size by flow cytometry. (C) Regions were set based on 2-fold reduction in CFSE mean intensity of naïve or memory T-cells as gated on (B), and plotted as number of cell divisions. Results represent three separate experiments from three different donors. Treg Cells Are Highly Susceptible to HIV Infection The ability to obtain a pure population of functional human Treg cells provides an excellent model to study their role in HIV pathogenesis. To determine whether Treg cells were susceptible to HIV infection, purified Treg cells were first activated through the TCR and were infected with either replication-competent HIV, which uses CCR5 as a coreceptor (R5.HIV), or replication-defective viruses pseudotyped with vesicular stomatitis virus glycoprotein (VSV-G.HIV) that encode green fluorescent protein (GFP) as a marker of infection (Motsinger et al. 2002). Treg and memory T-cells challenged with VSV-G.HIV resulted in an equivalent infection rate, while in some experiments R5.HIV resulted in about a 2-fold higher infection rate of the Treg cells as compared to the memory T-cells (Figure 4A). To determine the level of HIV replication in the Treg cells as compared to activated memory T-cells, both subsets were infected with R5.HIV for 2 d and washed to remove input virus. Supernatants were collected from the infected cultures daily and p24 levels were quantified by enzyme-linked immunosorbent assay (ELISA). HIV replicated in Treg cells as efficiently as in memory T-cells (Figure 4B). To assess whether the virus produced by Treg cells was infectious, supernatants from infected cells were added to Hut78/CCR5 cells, which are highly susceptible to HIV infection, and the titer of infectious virus was determined by GFP expression. Treg cells produced levels of infectious virus similar to those of the memory T subset (data not shown). The viability of infected cultures was also determined at days 3 and 7 to determine if HIV infection killed Treg cells. Indeed, infection with replication-competent HIV was highly cytotoxic to both Treg and memory T-cells (Figure 4C). We conclude that Treg cells are highly susceptible to HIV infection and are killed by viral replication. Figure 4 HIV Infection of Treg Cells (A) Sorted Treg and memory T-cells were activated using plate-bound anti-CD3 (3 μg/ml) and soluble anti-CD28 (1 μg/ml) antibodies and concurrently infected with R5.HIV or VSV-G.HIV at a MOI of 5 (based on prior virus titration using Hut78/CCR5 cells). The percentage of infected cells was determined by GFP expression at 3 d postinfection by flow cytometry. (B) Supernatants from Treg and memory T-cells infected with R5.HIV cultures were collected at different time points and HIV p24 levels were measured by ELISA. (C) Treg cell death was assessed by analyzing infected cells at days 3 and 7 postinfection based on forward- and side-scatter analysis and in some experiments using propidium iodine staining analysis with flow cytometry. All of the infection results are representative of one out of five separate experiments with reproducible results. Statistical significance was determined using the Student's two-tailed t test. * p < 0.05. Genetic Reprogramming of Conventional Human T-cells into Treg Cells by Ectopic Expression of FoxP3 Treg cells constitute less than 1%–2% of total human T-cells (see Baecher-Allan et al. 2001; see Figure 1). Although we could purify several hundred thousand Treg cells from 300 ml of blood as described below, isolation of sufficient numbers of Treg cells is clearly an obstacle to studying their function and susceptibility to HIV infection. Recently, a transcription factor called FOXP3 was shown to program murine T-cells into a Treg subset (Fontenot et al. 2003; Hori et al. 2003; Khattri et al. 2003). Therefore we hypothesized that ectopic expression of FoxP3 in naïve T-cells could facilitate the generation of large numbers of human Treg cells. Accordingly, we subcloned FoxP3 cDNA into a HIV-derived vector (HDV) that encodes murine CD24 (mCD24) as a marker (Sundrud et al. 2003). CD4+ T-cells were purified from both neonatal cord blood (CB) and adult blood (AB), activated through the TCR and transduced with FoxP3-expressing HDV (HDV.FoxP3) or control HDV as described previously (Sundrud et al. 2003). Expression of FoxP3 mRNA in transduced cells was confirmed by real-time PCR analysis and was found to be about 50- to 100-fold higher in FoxP3 transduced primary CD4+ T-cells as compared to control HDV-transduced cells (data not shown). FoxP3-transduced and control cells were expanded for 14 d in IL-2-containing medium. Cells were then stained for mCD24 and also for CD25, GITR, and CCR4 markers that are expressed at higher levels on naturally occurring Treg cells (see Figure 1). Naïve T-cells ectopically expressing FoxP3 displayed higher levels of CD25, GITR (Figure 5A), and CCR4 (Figure 5B) as compared to control transduced T-cells. FoxP3-transduced memory T-cells displayed much less upregulation of these markers (data not shown). However, while the majority of these transduced T-cells also expressed CCR5, its expression levels on FoxP3-transduced and control T-cells were similar (Figure 5C). To determine whether FoxP3-transduced T-cells display functional properties such as hyporesponsiveness to TCR triggering, similar to freshly isolated Treg cells, transduced cells were purified by sorting mCD24+ cells as described previously (Sundrud et al. 2003). Equal numbers of purified cells were then stimulated using plate-bound anti-CD3 and soluble anti-CD28 antibodies, and cytokine secretion was monitored. Secretion of IL-2 from both CB and AB naïve T-cells was reduced between 8- and 10-fold in FoxP3-expressing cells as compared to control cultures (Figure 6A). Secretion of IL-4, IL-5, and IFNγ was also reduced in FoxP3-expressing naïve T-cells (Figure 6B). In contrast, FoxP3-transduced memory T-cells secreted similar levels of IL-2 as compared to cells transduced with HDV alone (Figure 6A). To assess the proliferative capacity of FoxP3-expressing cells, transduced cells were labeled with CFSE and stimulated through the TCR. After 4 d of activation, very few FoxP3-transduced naïve T-cells had divided as compared to control lines (Figure 7A). Although, FoxP3-expressing memory T-cells also divided fewer times as compared to HDV-transduced cells, the effect of FoxP3 was greatly diminished in this subset (Figure 7A). Figure 5 Phenotype of FoxP3 Transduced T-cells Purified CB naïve CD4+ T-cells were activated through the TCR and transduced with either HDV.FoxP3 or HDV. Cells were expanded for 14 d in IL-2-containing medium and stained with (A) anti-mCD24, anti-GITR, and anti-CD25, (B) anti-mCD24 and anti-CCR4, or (C) anti-mCD24 and anti-CCR5 antibodies. Gates were set on the mCD24-positive population (transduced), and expression of surface molecule was analyzed. The expression of these markers in the CD24-negative portion of both cultures was identical (data not shown). These results are representative of T-cells isolated from five different donors and transduced independently. Figure 6 Cytokine Production by FoxP3-Transduced T-cells CD4+ naïve T-cells isolated from CB (CB-naïve) and AB (AB-naïve) and memory T-cells from AB (AB-memory) were transduced with HDV or HDV.FoxP3 as described in Figure 5. Transduced T-cells were purified through magnetic sorting of mCD24+ cells and activated using plate-bound anti-CD3 and soluble anti-CD28 antibodies. Supernatants were collected at 18–24 h postactivation and analyzed for (A) IL-2 production or (B) IFNγ, IL-4, and IL-5 production from HDV or HDV.FoxP3-tranduced naïve T-cells, using CBA assay. The results represent five separate experiments from different donors with similar relative differences in cytokine production. Figure 7 Proliferation and Suppression by FoxP3-Expressing Cells (A) Purified CD4+ naïve and memory T-cells were transduced with either HDV.FoxP3 or HDV as described. The transduced T-cells were labeled with CFSE and activated with anti-CD3 (100 ng/ml) and anti-CD28 (1 μg/ml) antibodies. Day 6 postactivation, cells were fixed and analyzed for CFSE expression by flow cytometry. (B) Resting CD4+ T-cells (1.5 × 105) were labeled with CFSE and cocultured at 1:1 with either unlabeled sorted HDV.FoxP3-expressing or HDV-transduced CB naïve T-cells and activated with anti-CD3 (100 ng/ml) and anti-CD28 (1 μg/ml ) antibodies. At 4 d postactivation, cells were stained with mCD24-PE as a marker for infection. (C) Naïve and memory T-cells isolated from adult blood were transduced with HDV.FoxP3 or HDV. A coculture suppression experiment was set up with resting purified autologous CD4+ T-cells as described above. Region was set on mCD24 negative CFSE+ cells (target resting CD4+ T-cells) as shown in (B), and CFSE expression was analyzed 6 d poststimulation by flow cytometry. The results are representative of three separate experiments. The key characteristic of Treg cells is suppression of conventional T-cells activated through the TCR (Takahashi et al. 1998; Thornton and Shevach 1998). Thus, purified resting CD4+ T-cells were labeled with CFSE and cocultured with unlabeled naïve or memory T-cells that were transduced either with HDV.FoxP3 or HDV and then stimulated through the TCR as described for freshly isolated Treg cells (see Figure 3). FoxP3-expressing naïve T-cells, both from CB or AB, completely suppressed proliferation of target resting CD4+ T-cells (Figure 7B and Figure7C). A significant but lower level of suppression was apparent with memory T-cells transduced with FoxP3 (Figure 7C). We conclude that ectopic expression of FoxP3 in naïve human T-cells recapitulates key phenotypic and functional features of naturally occurring Treg cells. T-cells Ectopically Expressing FoxP3 Are More Susceptible to HIV Infection We next determined the susceptibility of FoxP3-expressing cells to HIV infection. FoxP3-transduced cells were purified by flow cytometry sorting based on mCD24 expression and activated through their TCR or cultured in IL-2-containing medium. Subsequently, activated cells were challenged with either VSV-G.HIV or R5.HIV. Remarkably, FoxP3-expressing cells were infected at a level about 2- to 3-fold higher than control cells at different concentrations of the virus, in both activated and nonactivated conditions (Figure 8A). FoxP3-expressing cells stimulated at suboptimal levels of anti-CD3 antibody also displayed a very similar enhancement of infection compared to HDV-transduced cells (data not shown). Figure 8 HIV Infection of FoxP3-Expressing T-cells (A) HDV.FoxP3 and HDV-transduced T-cells were activated using plate-bound anti-CD3 (100 ng/ml) and soluble anti-CD28 (1 μg/ml) antibodies or maintained in IL-2-containing medium. Cells were concurrently infected at different MOI of VSV-G.HIV, and infection was determined by GFP expression at 72 h postinfection by flow cytometry. (B) Supernatants were collected at different time points from R5.HIV-infected HDV.FoxP3-expressing or HDV-transduced cell cultures, and HIV p24 levels were measured by ELISA. The percentages of infected cells at days 3, 9, and 16 for HDV.FoxP3 were 2, 10, and 26, and for HDV were 0.8, 10, and 18, respectively. We next analyzed the level of HIV replication and cell death in FoxP3-expressing cells as compared to HDV-transduced T-cells. Activated FoxP3-expressing and control cells were infected with R5.HIV, and culture supernatants were collected daily from day 3 postinfection. FoxP3-expressing cells showed increased HIV-infection and replication (Figure 8B). Infectivity of virus produced by FoxP3-expressing cells, as assessed on Hut78/CCR5 cells, as well as cell death in these cultures, was also proportionately higher (data not shown). These findings demonstrate that the expression of FoxP3 renders CD4+ primary T-cells more susceptible to HIV infection. HIV-Infected Patients Have Greatly Decreased Levels of FoxP3-Expressing CD4+CD25hi T-cells Our findings that Treg cells are highly susceptible to HIV infection prompted us to determine if this subset was disturbed within HIV-infected individuals. However, a major difficulty in such analysis is that many of the cell surface markers that define Treg cells are also expressed on activated T-cells (CD25, HLA-DR, GITR). Because a portion of HIV-positive individuals contain high levels of activated T-cells, simple cell surface analysis would not be sufficiently reliable to quantify Treg cells in these donors. Therefore, we utilized FoxP3 expression as the most reliable marker that defines Treg cells. To accomplish this, we sorted CD4+CD25hi, naïve, and memory T-cells from 11 HIV-negative, healthy donors (median age, 31; 64% male) and 24 HIV-infected individuals (median age, 38; 85% male; 88% receiving antiretroviral therapy). Total RNA was then isolated from each subset and FoxP3 mRNA expression was quantified using real-time PCR analysis. In order to normalize for experimental variability, FoxP3 expression of the CD4+CD25hi cells was normalized to GAPDH levels from the same samples and compared to the naïve T-cell subset isolated from the same donor. We found that within HIV-negative subjects there was on average a 49-fold higher level of expression of FoxP3 in CD4+CD25hi cells as compared to naïve T-cells (Figure 9A). The lowest FoxP3-expressor in the healthy subject group had 16-fold higher FoxP3 expression as compared to naïve T-cells from the same donor (Figure 9A). There was a similar increase in FoxP3 expression as compared to memory T-cells (data not shown). In HIV-positive subjects FoxP3 expression was only increased a mean of 25-fold in CD4+CD25hi cells. In contrast to healthy donors, we also observed that in about half of the HIV-positive subjects, CD4+CD25hi cells expressed very low to undetectable levels of FoxP3 (Figure 9A). FoxP3 expression in memory T-cells was similar in HIV-positive and HIV-negative subjects (Figure 9A). Figure 9 FoxP3 Expression in Purified CD4+CD25hi (Treg), Naïve, and Memory T-cells from HIV-Infected and Healthy Individuals (A) RNA was isolated from sorted Treg, naïve, and memory T-cells from HIV-positive (n = 24) and HIV-negative (n = 11) subjects, followed by cDNA synthesis. FoxP3 expression was quantified by TaqMan real-time PCR. The FoxP3-fold difference expression was calculated for CD4+CD25hi (Treg) versus naïve (N), and memory (Mem) versus naïve (N) T-cells. Treg cells sorted from HIV-positive subjects were further subdivided into two groups based on FoxP3 expression of Treg compared to naïve T-cells (FoxP3-high, n = 13, FoxP3 difference >10-fold; FoxP3-low, n = 11, FoxP3 difference <10-fold; HIV-negative, n = 9). These groups were stained with anti-CD3, anti-CD4, anti-CD45RO, anti-CD25, and anti-HLA-DR and analyzed by flow cytometry for (B) CD4+ T-cell percentage, (C) activated T-cell percentage (CD4+HLA-DR+), and (D) CD4+CD25hi percentage. Horizontal lines identify means. Statistical significance between groups was determined by Mann–Whitney U test and shown on top of each figure. Progressive HIV disease is associated with decreased CD4+ T-cell percentages and increased levels of activated T-cells. We hypothesized that this hyperactivation may be due to a loss of Treg cells. Therefore, to further evaluate relationships between FoxP3 expression and these parameters in HIV-infected individuals, samples were divided into low FoxP3 expressors (less than 10-fold higher expression in CD4+CD25hi T-cells compared to naïve T-cells, designated FoxP3-low) versus high FoxP3 expressors (greater than 10-fold higher expression compared to naïve T-cells, designated FoxP3-high). HIV-positive subjects with a FoxP3-low profile had significantly lower CD4+ T-cell percentages, while FoxP3-high HIV-positive subjects had CD4+ T-cell percentages comparable to HIV-seronegative subjects (Figure 9B). Similarly, HIV-positive FoxP3-low subjects had significantly greater activated CD4+T-cells (CD4+HLA-DR+) than either HIV-positive subjects with FoxP3-high profiles or HIV-seronegative subjects (Figure 9C). Interestingly, the CD4+CD25hi T-cells are also significantly increased in FoxP3-low expressors as compared to HIV-negative or HIV-positive FoxP3-high expressors (Figure 9D). These differences are most likely due to recently activated T-cells that also express high levels of CD25, as shown by higher HLA-DR expression on T-cells from the same subset of subjects (Figure 9C). Among HIV-positive subjects there was no significant association between FoxP3 expression and plasma HIV-1 RNA concentration, age, race, sex, or whether the subject was receiving antiretroviral therapy (P > 0.05 for each comparison). These findings demonstrate that a decrease in Treg cells is associated with HIV disease progression and suggest that loss of Treg cells may contribute to increased T-cell hyperactivation. Discussion In this study we demonstrated that human Treg cells are highly susceptible to HIV infection and that ectopic expression of FoxP3 genetically reprograms conventional naïve T-cells, phenotypically and functionally, into Treg cells. Remarkably, overexpression of FoxP3 also greatly enhances the susceptibility of activated T-cells to HIV infection. Although Treg cells constitute a small subset of the total T-cells in humans (less than 1%–2%) and thus may not be a significant target population for HIV, they appear to have very potent suppressive activity against activation of T-cells (Takahashi et al. 1998; Thornton and Shevach 1998; Baecher-Allan et al. 2001; Curotto de Lafaille and Lafaille 2002). Here we demonstrate that FoxP3-expressing CD4+CD25hi T-cells are greatly decreased in a portion of HIV-infected individuals with low CD4 and high activated T-cells, suggesting a loss of Treg cells. We therefore propose that infection and disruption of Treg cells during HIV infection could have a major influence on T-cell homeostasis and immune regulation. Similar to previous reports, our findings demonstrate that human Treg cells appear to be enriched within the CD25hi subset of CD4+ T-cells. However, it is not clear if Treg cells are the only population represented in the CD25hi subset since they share this phenotype with recently activated T-cells. Indeed, a portion of purified CD25hi cells proliferated and their suppressive function was less efficient as compared to FoxP3-expressing cells (see Figure 3). Because the purification of Treg cells is rather arbitrary (brightest 1%–2% of antibody-stained CD25+ memory T-cells), it is conceivable that there is sizable contamination of non-Treg cells in these sorted preparations. In addition, the differences seen in the infection susceptibility of Tregs as compared to FoxP3-expressing cells may also be partly due to presence of non-Treg activated T-cells within the purified cells. Identification of the Treg subset in disease conditions with chronic T-cell activation, such as HIV, is even more problematic because a large proportion of CD4+CD25hi cells possibly represent recently activated T-cells. Availability of large numbers of genetically reprogrammed Treg cells should facilitate the identification of novel markers that can reliably detect human Treg cells. Our results clearly demonstrate that, similar to the mouse system, ectopic expression of FoxP3 is sufficient to recapitulate all of the characteristics of Treg cells, including lower cytokine secretion, higher expression of CD25 and GITR, and their suppressive functions. This system has allowed us to generate large numbers of Treg cells, which will be invaluable in characterizing their suppressive function as well as mechanisms of enhanced HIV susceptibility. The ability to genetically manipulate primary T-cells to reprogram them into the Treg phenotype also could have profound implications for preventing graft-versus-host disease, a serious clinical condition that can be manifested following hematopoietic cell transplantation (Hoffmann et al. 2002; Taylor et al. 2002). It is interesting to note that naïve T-cells are more prone to reprogramming into a Treg phenotype than memory T-cells are. This loss in flexibility of reprogramming with a master transcription factor is reminiscent of Th1- and Th2-type T-cell reprogramming with ectopic expression of lineage-specific transcription factors T-bet and GATA-3, respectively (Sundrud et al. 2003). The loss of flexibility in genetic modification of effector/memory T-cells could reflect heritable epigenetic changes at effector gene loci that might otherwise be responsive to FoxP3-mediated transcription. A recent study supports this hypothesis by demonstrating that lineage-committed human memory cells failed to modify their histone acetylation patterns of cytokine genes, unlike naïve T-cells, to differentiate into Th1- or Th2-type cells (Messi et al. 2003). The cause of progressive depletion of CD4+ cells and the reason for high T-cell activation or turnover during HIV infection remains controversial (Hazenberg et al. 2000; Grossman et al. 2002). It is thought that HIV-mediated destruction of CD4+ T-cells results in decline of this subset and that to maintain homeostasis the immune system attempts to replenish this subset, resulting in a massive turnover of T-cells (Ho et al. 1995; Wei et al. 1995; Perelson et al. 1996; Mohri et al. 1998, 2001). This excessive turnover rate eventually compromises proper function of homeostatic responses. Alternatively, T-cell depletion could result from disrupted thymic and peripheral homeostatic mechanisms by virus-induced generalized T-cell activation and gradual wasting of T-cell supplies, eventually leading to T-cell depletion (Hazenberg et al. 2000; Grossman et al. 2002). Indeed, several mechanisms control unwanted activation of T-cells, including thymic deletion of autoreactive T-cells and induction of anergy in the periphery. In addition to these passive mechanisms, recent evidence clearly demonstrates that Treg cells exert an active suppression of T-cell activation. We postulate that the high susceptibility of Treg cells to HIV in vivo, as demonstrated by our in vitro studies, could result in gradual elimination of this subset. This Treg cell decline during HIV infection would in turn reduce active suppression of conventional T-cells and, hence, contribute to hyperactivation of T-cells. Our analyses of peripheral blood T-cells from HIV-infected subjects support this hypothesis that loss of Treg cells during HIV infection contributes to HIV disease progression. Indeed, we found that in a subset of HIV-infected subjects, the CD4+CD25hi T-cell subset had greatly reduced FoxP3 expression, suggesting that these cells represent recently activated T-cells rather then Treg cells. The presence of a higher percent of activated T-cells in this FoxP3-low profile supports this conjecture. The HIV-infected subjects with lower levels of FoxP3+ T-cells also contained a lower percentage of CD4+ T-cells. It is conceivable that the loss of Treg cells may be a correlative factor for disease progression; however, more detailed prospective studies will be required to address this important implication of our findings. Our findings show a 2- to 3-fold enhancement of HIV infection in FoxP3-expressing T-cells. While freshly purified Treg and memory T-cells are similar in their susceptibility to HIV infection, activated effector T-cells become gradually more resistant to infection (unpublished results). Indeed, preactivated T-cells require reactivation to render them susceptible to infection after about 1–2 weeks in culture (unpublished results). We do not yet know the mechanisms by which FoxP3 renders activated T-cells more susceptible to infection; however, two possibilities can be considered: (1) FoxP3 may be overcoming innate resistance factor(s) that accumulate in activated T-cells that block HIV infection, or (2) FoxP3 expression may be inducing critical host factors that are required for efficient completion of the HIV life cycle in primary T-cells. It is important to note that since FoxP3 expression enhances VSV-G.HIV single-round infections, it likely affects an early, postentry step in viral replication. Determining the mechanisms by which FoxP3 enhances HIV infection could reveal host factors involved in this process. In summary, our results indicate that both naturally occurring and genetically reprogrammed Treg cells are susceptible to HIV infection and that ectopic FoxP3 expression greatly increases the susceptibility of T-cells to HIV infection. Our finding that FoxP3-expressing CD4+CD25+ T-cells are greatly reduced in HIV patients with low CD4+ T-cell percentages and increased T-cell activation suggests that loss of Treg cells may contribute to HIV disease progression. Further prospective studies will be required to unravel the role of this important subset in HIV infection. The ability to genetically reprogram conventional human T-cells to generate Treg cells will also lead the way to identifying unique markers expressed on this population in order to further investigate their status in HIV-infected individuals. Moreover, understanding how FoxP3 enhances HIV infection and programs T-cells into the Treg subset could help in the identification of novel host factors that mediate HIV infection in primary T-cells and decoding the molecular mechanisms by which Treg cells mediate their suppressive function. Materials and Methods Study subjects and statistical analysis Healthy subjects (n = 11) were adults who were HIV-negative and with no history of chronic viral infections such as Hepatitis B or C. Blood samples from adults with HIV infection (n = 24) were obtained during routine primary care visits at the Comprehensive Care Center, Vanderbilt University Medical School, Nashville, Tennessee, United States. There were no selection criteria based on race or sex. All subjects provided written consent, and the study was approved by the Vanderbilt Institutional Review Board. Continuous variables were compared by a Mann–Whitney U test, and categorical variables by an χ2 test. All significance levels were based on two-tailed tests. Statistical analyses were performed using SPSS, version 12.0 (SPSS, Chicago, Illinois, United States). Cell isolation and culture Peripheral blood mononuclear cells (PBMCs) were separated from buffy coats of healthy and HIV-positive donors through Ficoll–Hypaque separation (Pharmacia-LKB Technology, Uppsala, Sweden). Resting CD4+ T-cells were purified as previously described (Unutmaz et al. 1999). This purification protocol typically resulted in 99.5% purity of positively selected cells, as determined by postpurification fluorescence-activated cell sorting (FACS) analysis. To isolate Treg cells, PBMCs, or purified CD4+ cells, were stained with CD45RO, CD25, CD4, and HLA-DR antibodies (BD Biosciences Pharmingen, San Diego, California, United States), and CD4+CD45RO+CD25hi and CD4+CD45RO−CD25low/neg cells were sorted using flow cytometry (FACS Aria; BD Biosciences Pharmingen). For some experiments, adult CD4+ T-cells were sorted into CD45RO+ (memory) and CD45RO− (naïve) T-cells with anti-CD45RO conjugated magnetic beads (Miltenyi Biotec, Bergisch Gladbach, Germany) using AutoMACS (Miltenyi Biotec). Purified resting T-cells were activated by cross-linking with plate-bound anti-CD3 antibody (OKT-3; American Type Culture Collection, Manassas, Virginia, United States) and soluble anti-CD28 antibody (1 μg/ml, BD Biosciences Pharmingen). The plates were first coated with goat antimouse IgG (10 μg/ml, CalTag Laboratories, Burlingame, California, United States) followed by either 3 μg/ml anti-CD3 for optimal TCR stimulation or 100 ng/ml anti-CD3 for suboptimal stimulation. The culture medium used in all experiments was RPMI (Life Technologies, Carlsbad, California, United States) and was prepared as previously described (Motsinger et al. 2002). All cytokines were purchased from R & D Systems (Minneapolis, Minnesota, United States). Monocyte-derived dendritic cells were generated as previously described (Motsinger et al. 2002). Superantigen, staphylococcal enterotoxin B (Sigma, St. Louis, Missouri, United States) was used to stimulate resting T-cells in the presence of dendritic cells (Motsinger et al. 2002). Virus production and infections VSV-G.HIV particles were generated as previously described (Unutmaz et al. 1999). R5-HIV was prepared similarly by transfecting 293T-cells with HIV that encodes R5-tropic (BAL) envelope and enhanced GFP (Clontech, Palo Alto, California, United States) in place of the nef gene as previously described (Unutmaz et al. 1999). Typically viral titers ranged from 1–5 × 106 ifu/ml for replication-competent viruses and 10–30 × 106 for VSV-G.HIV. T-cells were infected at varying multiplicities of infection (MOI), and infection was quantified by GFP expression using flow cytometry. In some experiments, cells inoculated with virus were centrifuged for 1 h at 2,000 rpm to enhance infectivity, as described by O'Doherty et al. (2000). Viral replication in T-cell cultures was determined by measuring p24 levels within supernatants by an ELISA (Motsinger et al. 2003), and infectious virus production by infected T-cells was determined by culturing Hut78 cells expressing CCR5 (Hut78/CCR5) (Wu et al. 2002) in infected T-cell supernatants. CFSE labeling Cell division was measured by labeling the T-cells with CFSE (Molecular Probes, Eugene, Oregon, United States). Purified cells were first washed and resuspended in PBS. While vortexing the cells, CFSE was added at a final concentration of 5 μM. The mixture was vortexed for an additional 15 s and incubated at 37 °C for 3 min. Labeling was quenched by addition of 50% fetal calf serum in PBS. Cells were washed one more time with 50% serum PBS, followed by two washes with RPMI-supplemented medium. All CFSE labeling and culturing were performed under dark conditions. FACS analysis and cytokine assay T-cells were stained with the relevant antibody on ice for 30 min in PBS buffer containing 2% fetal calf serum and 0.1% sodium azide. Cells were then washed twice, fixed with 1% paraformaldehyde, and analyzed with a FACSCalibur four-color cytometer. Live cells were gated based on forward- and side-scatter properties, and analysis was performed using FlowJo software (Tree Star, Ashland, Oregon, United States). The following antihuman antibodies were used for staining: CD3, CD4, CD45RO, CD45RA, CD25, GITR, HLA-DR, CCR5, CCR4, CCR7, CXCR4, CXCR3, and antimouse CD24, all obtained from PharMingen (San Diego, California, United States). Cytokines (IL-2, IL-4, IL-5, and IFNγ) in the supernatants were assayed using a commercially available cytometric bead array (CBA) (BD Biosciences Pharmingen) (Cook et al. 2001) and analyzed using CBA 6-bead analysis software (BD Biosciences Pharmingen). Cloning of human FoxP3 RNA was isolated from activated human T-cells using an RNeasy kit (Qiagen, Valencia, California, United States). To synthesize cDNA 100 ng RNA was used (Superscript II Reverse Transcriptase; Invitrogen, Carlsbad, California, United States). FoxP3 was PCR amplified from T-cell cDNA with the following primers: FoxP3 forward, 5′-AGATATCCCAGCCATGCCCAACCCCAGGCCTGGCAAG-3′; FoxP3 reverse, 5′-TCAGGGGCCAGGTGTAGGGTTGGAACACCT-3′. The forward primer included an EcoRV restriction site to facilitate cloning. The FoxP3 PCR product was subcloned into a TOPO shuttle vector (pcDNA3.1/CT-GFP-TOPO; Invitrogen). Following an EcoRV digest, FoxP3 was ligated into an HDV-encoding mCD24 down stream of an internal ribosome entry site (Sundrud et al. 2003). The FoxP3 coding sequence was confirmed by DNA sequencing. Real-time PCR protocol RNA was extracted, as described above, from cells transduced with HDV or HDV.FoxP3 or sorted Treg, naïve, and memory T-cells from HIV-positive and HIV-negative subjects. RNA (100 ng) was used to synthesize cDNA (as described above). Taqman Assays-on-Demand Gene Expression Primers (Applied Biosystems, Foster City, California, United States) were used in real-time PCR analyses: GAPDH primer mix assay ID Hs99999905_m1; FoxP3 primer mix assay ID Hs00203958_m1. Real-time PCR was performed using the ABI 7700 apparatus (PE Applied Biosystems, Weiterstadt, Germany). The reaction mixtures (20-μl total volume) contained 2 μl of serially diluted cDNA, 10 μl of Taqman Universal PCR Master Mix (PE Applied Biosystems), and 1 μl of either FoxP3 or GAPDH primer mix. The reactions were amplified as follows: 50 °C for 2 min and 95 °C for 10 min, followed by 40 cycles of 95 °C for 1 min and 65 °C for 1 min. Expression of FoxP3 was normalized to GAPDH expression in each sample. Supporting Information Accession Number The GenBank (http://www.ncbi.nlm.nih.gov/) accession number for the gene FoxP3 discussed in this paper is AF277993. We thank Vineet KewalRamani, Luc Van Kaer, Sebastian Joyce, Karla Eger, and Wasif Khan for critical reading of the manuscript and helpful comments. We are grateful to Janet Nicotera, Therese Remus, Husamettin Erdem, and Melissa Rueff for providing clinical data and samples from HIV-positive subjects. This work was supported by a National Institutes of Health grant (A1055349) and the Vanderbilt Meharry Center for AIDS Research (P30AI054999). Conflicts of Interest. The authors have declared that no conflicts of interest exist. Author Contributions. DU conceived and designed the experiments. KO-R, SG and ML performed the experiments. KO-R, SG, and DU analyzed the data. NS, MS, and DWH contributed reagents/materials/analysis tools. KO-R and DU wrote the paper. Academic Editor: Sarah Rowland-Jones, Weatherall Institute of Molecular Medicine Abbreviations ABadult blood CBcord blood CBAcytometric bead array CFSEcarboxy-fluorescein diacetate succinimidyl ester ELISAenzyme-linked immunosorbent assay FACSfluorescence-activated cell sorting GFPgreen fluorescent protein GITRglucocorticoid-induced tumor necrosis factor receptor HDVHIV-derived vector HDV.FoxP3FoxP3-expressing HIV-derived vector MOImultiplicity of infection R5.HIVCCR5-tropic HIV TCRT-cell receptor Treg cellregulatory T-cell VSV-G.HIVHIV pseudotyped with vesicular stomatitis virus glycoprotein envelope ==== Refs References Aandahl EM Michaelsson J Moretto WJ Hecht FM Nixon DF Human CD4+ CD25+ regulatory T cells control T-cell responses to human immunodeficiency virus and cytomegalovirus antigens J Virol 2004 78 2454 2459 14963140 Annacker O Burlen-Defranoux O Pimenta-Araujo R Cumano A Bandeira A Regulatory CD4 T cells control the size of the peripheral activated/memory CD4 T cell compartment J Immunol 2000 164 3573 3580 10725712 Annacker O Pimenta-Araujo R Burlen-Defranoux O Barbosa TC Cumano A CD25+ CD4+ T cells regulate the expansion of peripheral CD4 T cells through the production of IL-10 J Immunol 2001 166 3008 3018 11207250 Asano M Toda M Sakaguchi N Sakaguchi S Autoimmune disease as a consequence of developmental abnormality of a T cell subpopulation J Exp Med 1996 184 387 396 8760792 Baecher-Allan C Brown JA Freeman GJ Hafler DA CD4+CD25 high regulatory cells in human peripheral blood J Immunol 2001 167 1245 1253 11466340 Bennett CL Ochs HD IPEX is a unique X-linked syndrome characterized by immune dysfunction, polyendocrinopathy, enteropathy, and a variety of autoimmune phenomena Curr Opin Pediatr 2001 13 533 538 11753102 Bennett CL Christie J Ramsdell F Brunkow ME Ferguson PJ The immune dysregulation, polyendocrinopathy, enteropathy, X-linked syndrome (IPEX) is caused by mutations of FOXP3 Nat Genet 2001 27 20 21 11137993 Boshoff C Endo Y Collins PD Takeuchi Y Reeves JD Angiogenic and HIV-inhibitory functions of KSHV-encoded chemokines Science 1997 278 290 294 9323208 Brunkow ME Jeffery EW Hjerrild KA Paeper B Clark LB Disruption of a new forkhead/winged-helix protein, scurfin, results in the fatal lymphoproliferative disorder of the scurfy mouse Nat Genet 2001 27 68 73 11138001 Chatila TA Blaeser F Ho N Lederman HM Voulgaropoulos C JM2, encoding a fork head-related protein, is mutated in X-linked autoimmunity-allergic disregulation syndrome J Clin Invest 2000 106 R75 R81 11120765 Cook EB Stahl JL Lowe L Chen R Morgan E Simultaneous measurement of six cytokines in a single sample of human tears using microparticle-based flow cytometry: Allergics vs. nonallergics J Immunol Methods 2001 254 109 118 11406157 Curotto de Lafaille MA Lafaille JJ CD4+ regulatory T cells in autoimmunity and allergy Curr Opin Immunol 2002 14 771 778 12413528 Dieckmann D Plottner H Berchtold S Berger T Schuler G Ex vivo isolation and characterization of CD4+CD25+ T cells with regulatory properties from human blood J Exp Med 2001 193 1303 1310 11390437 Fauci AS Multifactorial nature of human immunodeficiency virus disease: Implications for therapy Science 1993 262 1011 1018 8235617 Fontenot JD Gavin MA Rudensky AY Foxp3 programs the development and function of CD4+CD25+ regulatory T cells Nat Immunol 2003 4 330 336 12612578 Furtado GC Olivares-Villagomez D Curotto de Lafaille MA Wensky AK Latkowski JA Regulatory T cells in spontaneous autoimmune encephalomyelitis Immunol Rev 2001 182 122 134 11722629 Gambineri E Torgerson TR Ochs HD Immune dysregulation, polyendocrinopathy, enteropathy, and X-linked inheritance (IPEX), a syndrome of systemic autoimmunity caused by mutations of FOXP3 a critical regulator of T-cell homeostasis Curr Opin Rheumatol 2003 15 430 435 12819471 Green EA Choi Y Flavell RA Pancreatic lymph node–derived CD4+CD25+ Treg cells: Highly potent regulators of diabetes that require TRANCE-RANK signals Immunity 2002 16 183 191 11869680 Green EA Gorelik L McGregor CM Tran EH Flavell RA CD4+CD25+ T regulatory cells control anti-islet CD8+ T cells through TGF-beta-TGF-beta receptor interactions in type 1 diabetes Proc Natl Acad Sci U S A 2003 100 10878 10883 12949259 Grossman Z Meier-Schellersheim M Sousa AE Victorino RM Paul WE CD4+ T-cell depletion in HIV infection: Are we closer to understanding the cause? Nat Med 2002 8 319 323 11927927 Gumperz JE Miyake S Yamamura T Brenner MB Functionally distinct subsets of CD1d-restricted natural killer T cells revealed by CD1d tetramer staining J Exp Med 2002 195 625 636 11877485 Hara M Kingsley CI Niimi M Read S Turvey SE IL-10 is required for regulatory T cells to mediate tolerance to alloantigens in vivo J Immunol 2001 166 3789 3796 11238621 Hazenberg MD Hamann D Schuitemaker H Miedema F T cell depletion in HIV-1 infection: How CD4+ T cells go out of stock Nat Immunol 2000 1 285 289 11017098 Ho DD Neumann AU Perelson AS Chen W Leonard JM Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection Nature 1995 373 123 126 7816094 Hoffmann P Ermann J Edinger M Fathman CG Strober S Donor-type CD4+CD25+ regulatory T cells suppress lethal acute graft-versus-host disease after allogeneic bone marrow transplantation J Exp Med 2002 196 389 399 12163567 Hori S Nomura T Sakaguchi S Control of regulatory T cell development by the transcription factor Foxp3 Science 2003 299 1057 1061 12522256 Iellem A Mariani M Lang R Recalde H Panina-Bordignon P Unique chemotactic response profile and specific expression of chemokine receptors CCR4 and CCR8 by CD4+CD25+ regulatory T cells J Exp Med 2001 194 847 853 11560999 Jonuleit H Schmitt E Stassen M Tuettenberg A Knop J Identification and functional characterization of human CD4+CD2+ T cells with regulatory properties isolated from peripheral blood J Exp Med 2001 193 1285 1294 11390435 Jonuleit H Schmitt E Kakirman H Stassen M Knop J Infectious tolerance: Human CD25+ regulatory T cells convey suppressor activity to conventional CD4+ T helper cells J Exp Med 2002 196 255 260 12119350 Khattri R Cox T Yasayko SA Ramsdell F An essential role for Scurfin in CD4+CD25+ T regulatory cells Nat Immunol 2003 4 337 342 12612581 Kursar M Bonhagen K Fensterle J Kohler A Hurwitz R Regulatory CD4+CD25+ T cells restrict memory CD8+ T cell responses J Exp Med 2002 196 1585 1592 12486101 Kursar M Kohler A Kaufmann SH Mittrucker HW Depletion of CD4+ T cells during immunization with nonviable Listeria monocytogenes causes enhanced CD8+ T cell–mediated protection against listeriosis J Immunol 2004 172 3167 3172 14978123 Lee PT Benlagha K Teyton L Bendelac A Distinct functional lineages of human valpha24 natural killer T cells J Exp Med 2002 195 637 641 11877486 Levings MK Sangregorio R Roncarolo MG Human CD25+CD4+ T regulatory cells suppress naive and memory T cell proliferation and can be expanded in vitro without loss of function J Exp Med 2001 193 1295 1302 11390436 Lyon MF Peters J Glenister PH Ball S Wright E The scurfy mouse mutant has previously unrecognized hematological abnormalities and resembles Wiskott-Aldrich syndrome Proc Natl Acad Sci U S A 1990 87 2433 2437 2320565 McHugh RS Shevach EM Cutting edge: Depletion of CD4+CD25+ regulatory T cells is necessary, but not sufficient, for induction of organ-specific autoimmune disease J Immunol 2002 168 5979 5983 12055202 McHugh RS Whitters MJ Piccirillo CA Young DA Shevach EM CD4+CD25+ immunoregulatory T cells: Gene expression analysis reveals a functional role for the glucocorticoid-induced TNF Receptor Immunity 2002 16 311 323 11869690 Messi M Giacchetto I Nagata K Lanzavecchia A Natoli G Memory and flexibility of cytokine gene expression as separable properties of human T(H)1 and T(H)2 lymphocytes Nat Immunol 2003 4 78 86 12447360 Mohri H Bonhoeffer S Monard S Perelson AS Ho DD Rapid turnover of T lymphocytes in SIV-infected rhesus macaques Science 1998 279 1223 1227 9469816 Mohri H Perelson AS Tung K Ribeiro RM Ramratnam B Increased turnover of T lymphocytes in HIV-1 infection and its reduction by antiretroviral therapy J Exp Med 2001 194 1277 1287 11696593 Motsinger A Haas DW Stanic AK Van Kaer L Joyce S CD1d-restricted human natural killer T cells are highly susceptible to human immunodeficiency virus 1 infection J Exp Med 2002 195 869 879 11927631 Motsinger A Azimzadeh A Stanic AK Johnson RP Van Kaer L Identification and simian immunodeficiency virus infection of CD1d-restricted macaque natural killer T cells J Virol 2003 77 8153 8158 12829854 Murakami M Sakamoto A Bender J Kappler J Marrack P CD25+CD4+ T cells contribute to the control of memory CD8+ T cells Proc Natl Acad Sci U S A 2002 99 8832 8837 12084927 Ng WF Duggan PJ Ponchel F Matarese G Lombardi G Human CD4+CD25+ cells: A naturally occurring population of regulatory T cells Blood 2001 98 2736 2744 11675346 O'Doherty U Swiggard WJ Malim MH Human immunodeficiency virus type 1 spinoculation enhances infection through virus binding J Virol 2000 74 10074 10080 11024136 O'Garra A Vieira P Twenty-first century Foxp3 Nat Immunol 2003 4 304 306 12660726 Perelson AS Neumann AU Markowitz M Leonard JM Ho DD HIV-1 dynamics in vivo: Virion clearance rate, infected cell life-span, and viral generation time Science 1996 271 1582 1586 8599114 Ramsdell F Foxp3 and natural regulatory T cells: Key to a cell lineage? Immunity 2003 19 165 168 12932350 Read S Malmstrom V Powrie F Cytotoxic T lymphocyte-associated antigen 4 plays an essential role in the function of CD25+CD4+ regulatory cells that control intestinal inflammation J Exp Med 2000 192 295 302 10899916 Sakaguchi S Sakaguchi N Asano M Itoh M Toda M Immunologic self-tolerance maintained by activated T cells expressing IL-2 receptor alpha-chains (CD25): Breakdown of a single mechanism of self-tolerance causes various autoimmune diseases J Immunol 1995 155 1151 1164 7636184 Salomon B Lenschow DJ Rhee L Ashourian N Singh B B7/CD28 costimulation is essential for the homeostasis of the CD4+CD25+ immuno-regulatory T cells that control autoimmune diabetes Immunity 2000 12 431 440 10795741 Sanchez-Fueyo A Weber M Domenig C Strom TB Zheng XX Tracking the immunoregulatory mechanisms active during allograft tolerance J Immunol 2002 168 2274 2281 11859115 Shevach EM Regulatory T cells in autoimmmunity* Annu Rev Immunol 2000 18 423 449 10837065 Shevach EM CD4+CD25+ suppressor T cells: More questions than answers Nat Rev Immunol 2002 2 389 400 12093005 Shimizu J Yamazaki S Takahashi T Ishida Y Sakaguchi S Stimulation of CD25+CD4+ regulatory T cells through GITR breaks immunological self-tolerance Nat Immunol 2002 3 135 142 11812990 Stephens LA Mottet C Mason D Powrie F Human CD4+CD25+ thymocytes and peripheral T cells have immune suppressive activity in vitro Eur J Immunol 2001 31 1247 1254 11298351 Sundrud MS Grill SM Ni D Nagata K Alkan SS Genetic reprogramming of primary human T cells reveals functional plasticity in Th cell differentiation J Immunol 2003 171 3542 3549 14500650 Suri-Payer E Amar AZ Thornton AM Shevach EM CD4+CD25+ T cells inhibit both the induction and effector function of autoreactive T cells and represent a unique lineage of immunoregulatory cells J Immunol 1998 160 1212 1218 9570536 Suvas S Kumaraguru U Pack CD Lee S Rouse BT CD4+CD25+ T cells regulate virus-specific primary and memory CD8+ T cell responses J Exp Med 2003 198 889 901 12975455 Taams LS Smith J Rustin MH Salmon M Poulter LW Human anergic/suppressive CD4+CD25+ T cells: A highly differentiated and apoptosis-prone population Eur J Immunol 2001 31 1122 1131 11298337 Taams LS Vukmanovic-Stejic M Smith J Dunne PJ Fletcher JM Antigen-specific T cell suppression by human CD4+CD25+ regulatory T cells Eur J Immunol 2002 32 1621 1630 12115645 Takahashi T Kuniyasu Y Toda M Sakaguchi N Itoh M Immunologic self-tolerance maintained by CD25+CD4+ naturally anergic and suppressive T cells: Induction of autoimmune disease by breaking their anergic/suppressive state Int Immunol 1998 10 1969 1980 9885918 Takahashi T Tagami T Yamazaki S Uede T Shimizu J Immunologic self-tolerance maintained by CD25+CD4+ regulatory T cells constitutively expressing cytotoxic T lymphocyte-associated antigen 4 J Exp Med 2000 192 303 310 10899917 Taylor PA Noelle RJ Blazar BR CD4+CD25+ immune regulatory cells are required for induction of tolerance to alloantigen via costimulatory blockade J Exp Med 2001 193 1311 1318 11390438 Taylor PA Lees CJ Blazar BR The infusion of ex vivo activated and expanded CD4+CD25+ immune regulatory cells inhibits graft-versus-host disease lethality Blood 2002 99 3493 3499 11986199 Thornton AM Shevach EM CD4+CD25+ immunoregulatory T cells suppress polyclonal T cell activation in vitro by inhibiting interleukin 2 production J Exp Med 1998 188 287 296 9670041 Thornton AM Shevach EM Suppressor effector function of CD4+CD25+ immunoregulatory T cells is antigen nonspecific J Immunol 2000 164 183 190 10605010 Unutmaz D T cell signaling mechanisms that regulate HIV-1 infection Immunol Res 2001 23 167 177 11444382 Unutmaz D KewalRamani VN Marmon S Littman DR Cytokine signals are sufficient for HIV-1 infection of resting human T lymphocytes J Exp Med 1999 189 1735 1746 10359577 Wei X Ghosh SK Taylor ME Johnson VA Emini EA Viral dynamics in human immunodeficiency virus type 1 infection Nature 1995 373 117 122 7529365 Wildin RS Ramsdell F Peake J Faravelli F Casanova JL X-linked neonatal diabetes mellitus, enteropathy and endocrinopathy syndrome is the human equivalent of mouse scurfy Nat Genet 2001 27 18 20 11137992 Wu L Bashirova AA Martin TD Villamide L Mehlhop E Rhesus macaque dendritic cells efficiently transmit primate lentiviruses indepen-dently of DC-SIGN Proc Natl Acad Sci U S A 2002 99 1568 1573 11818554

15252446

PMC449855

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2021-01-05 08:26:26

no

PLoS Biol. 2004 Jul 13; 2(7):e198

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PLoS Biol

10.1371/journal.pbio.0020198

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020204SynopsisCell BiologyDevelopmentGenetics/Genomics/Gene TherapyDrosophilaCalling the Steps in Development's Genetic Square Dance Synopsis7 2004 13 7 2004 13 7 2004 2 7 e204Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Hairy Transcriptional Repression Targets and Cofactor Recruitment in Drosophila ==== Body A single, fertilized egg divides into apparently identical daughter cells. As these twins divide again and again, differences emerge among their progeny, establishing segments that will distinguish back from front and head from tail in the growing embryo. Development of segments—and, later, distinct tissues—requires a carefully coordinated square dance of gene expression machinery. Proteins coded by special genes called transcription factors call out the steps by binding to DNA to block or encourage expression of specific genes. With to-the-minute timing, transcription factors call other genes into action to produce the proteins that will determine cell fate. Developing organisms express different transcription factors at specific times and locations to coordinate the changes that make some cells head, others tail; one, a neuron, another, muscle. Polytene chromosomes (blue) stained for Hairy (green) and Groucho (red) binding A transcription factor called Hairy is one of the first activated during segment development in the fruitfly Drosophila melanogaster. Misregulation of Hairy and related factors is associated with cancer and developmental defects across species. Mutations in the gene that codes Hairy lead to overexpression of genes involved in development, but it is not clear whether Hairy normally represses those genes directly, or through intermediaries. To find the answer, Susan Parkhurst and colleagues at the Fred Hutchinson Cancer Research Center in Seattle, Washington, set out to identify Hairy's direct targets. The researchers found a total of 59 genes bound directly by the Hairy protein in cultured Drosophila cells called Kc cells and in embryos collected at the peak of Hairy expression during Drosophila segmentation. Because they searched approximately half of the expected Drosophila genome, the researchers estimate that they identified roughly half of all Hairy target genes. The list included genes known to act during segmentation, as expected, as well as many others with roles in cell division, growth, and shape. Of the 59 Hairy targets identified, only one appeared in both Kc cells and embryos. The lack of overlap may reflect a difference in developmental stage between the Kc cells, which are thought to be precursors to neurons, and the relatively undifferentiated embryos, and suggests that Hairy's role changes with context, such as the stage of development or tissue type. But Hairy doesn't act alone. Like most transcription factors, Hairy requires assistant proteins, called cofactors, to do its job. The availability of Hairy's known cofactors—Groucho, Drosophila C-terminal binding protein (dCtBP), and Drosophila Sir2 (dSir2)—may help to set the tempo and timing of Hairy's repression of genes. Groucho was thought to be Hairy's main assistant, but Parkhurst and colleagues found that only one of the identified Hairy target genes bound Groucho in Kc cells. The majority of Hairy targets overlapped with those of dCtBP, most often in combination with dSir2. All of the cofactors also bound to non-Hairy targets, suggesting that they assist other transcription factors as well. Next, Parkhurst's lab plans to explore the biological functions of the 59 Hairy target genes, to see how they help Hairy coordinate development. Meanwhile, the list of known Drosophila genes has grown 2-fold to include almost all of the expected genome. Repetition of these experiments with the expanded gene set could identify most or all of Hairy's target genes. The current results suggest that Hairy plays roles in segmentation, cell division, and tissue formation that evolve as an organism develops. Differences in cofactor involvement could help regulate Hairy's repression of genes. This paper demonstrates a powerful technique to explore how developing embryos keep gene expression in step.

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PMC449864

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2021-01-05 08:26:25

no

PLoS Biol. 2004 Jul 13; 2(7):e204

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PLoS Biol

10.1371/journal.pbio.0020204

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020205SynopsisCell BiologyDevelopmentDrosophilaWhen It Comes to Frizzled-Mediated Developmental Pathways, Location Matters Synopsis7 2004 13 7 2004 13 7 2004 2 7 e205Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Subcellular Localization of Frizzled Receptors, Mediated by Their Cytoplasmic Tails, Regulates Signaling Pathway Specificity ==== Body The process of morphogenesis has long inspired the wonder and imagination of those who study it. And until the advent of adequate microscopy and lab techniques in the early 19th century, theories based more on imagination—like preformation, which held that sperm harbored fully formed, tiny beings—than observation persisted. But observationally based embryology, it turned out, revealed a notion even more fantastic: the complex higher-order architecture of tissues and organs emerges from a single cell. Patterns and structures arise largely through cell-to-cell signaling, directed by signaling molecules (ligands) and their receptor targets. These signaling pathways control key developmental processes like cell proliferation and orientation (also called polarity). A relatively small cadre of molecules is enlisted over and over again to initiate an equally limited number of pathways to shape a developing embryo. Though the mechanics and effects of many of these pathways are understood, far less is known about the mechanisms that regulate which pathway is activated. One well-studied family of proteins, called Frizzled (Fz), regulates body symmetry and cell polarity, which, among other things, makes sure the bristles on a fly's wing all point in the same direction. In the fruitfly Drosophila, Fz can activate two distinct developmental pathways: the Wnt/β-catenin pathway and the Fz/planar cell polarity (Fz/PCP) pathway. In the Wnt pathway, a Wnt ligand activates the transmembrane Frizzled receptor, which in turn activates the subcellular Disheveled (Dsh) protein, setting off a signaling cascade that ultimately activates genes involved in cell division. The Fz/PCP pathway affects the orientation of wing bristles and the symmetry of the repeating units (ommatidia) in the fly's compound eye. Previous studies suggest no clear association between a particular ligand–receptor combination and the downstream pathway, begging the question of how similarly structured receptors can signal through a common protein (Dsh) to activate different signaling pathways. As Jun Wu, Thomas Klein, and Marek Mlodzik report in this issue, it's all a matter of being in the right place at the right time. Since the same Wnt ligand–Fz receptor combinations can produce different results, the researchers reasoned that signaling specificity might depend on the context and cell type. This notion is supported by evidence that Wnt ligands bind at Fz mainly along the basolateral membrane of developing epithelial cells and that Fz hews to the apical membrane of developing wing epithelia during PCP signaling. (The plasma membrane of epithelial cells contains distinct polar domains—the apical and basolateral domains—with distinct properties.) The researchers investigated whether this location bias affects which pathway is activated by focusing on two members of the Fz family: Fz1 and Fz2. Either can activate the Wnt pathway, but only Fz1 is involved in the Fz/PCP pathway. Wu et al. first confirmed that the proteins congregated in distinct subcellular regions of developing wing epithelial cells. Then they looked for sequences or domains in the proteins that might account for their location preferences by creating Fz1/Fz2 hybrids made of various combinations of three different Fz domains. (One was the ligand-binding domain, the second the transmembrane domain, and the third the cytoplasmic “tail.”) All the hybrids with a Fz1 tail localized along the apical membrane while those with a Fz2 tail preferred the basolateral membrane, indicating that the tail domain of a receptor controls its location. The team went on to correlate apical Fz with higher levels of Fz/PCP signaling, based in part on observations that wing hairs point away from areas of Fz expression, a result associated with PCP signaling. They also showed that increased Fz activity in apical regions results in wing notches and missing bristles—traits associated with reduced Wnt signaling—indicating that apical Fz expression interferes with Wnt/β-catenin signaling. That Fz receptors can elicit distinct responses depending on their subcellular location helps explain how so few molecules can juggle so many tasks, including the miraculous feat of building an organism.

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PMC449866

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2021-01-05 08:21:11

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PLoS Biol. 2004 Jul 13; 2(7):e205

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PLoS Biol

10.1371/journal.pbio.0020205

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020206PrimerEvolutionGenetics/Genomics/Gene TherapyEukaryotesGene Duplication: The Genomic Trade in Spare Parts PrimerHurles Matthew 7 2004 13 7 2004 13 7 2004 2 7 e206Copyright: © 2004 Matthew Hurles.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Lineage-Specific Gene Duplication and Loss in Human and Great Ape Evolution The duplication of genes and their subsequent diversification has had a key role in evolution. A range of fates can befall a duplicated gene ==== Body If necessity is the mother of invention, then its father is an inveterate tinkerer, with a large garage full of spare parts. Innovation (like homicide) requires motive and opportunity. Clearly, the predominant ‘motive’ during the evolution of a novel gene function is to gain a selective advantage. To understand why gene duplications represent the major ‘opportunities’ from which new genes evolve, we must first consider what constrains genic evolution. The vast majority of genes in every genome are selectively constrained, in that most nucleotide changes that alter the fitness of the organism are deleterious. How do we know this? Comparisons between genomes clearly demonstrate that coding sequences diverge at slower rates than non-coding regions, largely due to a deficit of mutations at positions where a base change would cause an amino-acid change. Gene duplication provides opportunities to explore this forbidden evolutionary space more widely by generating duplicates of a gene that can ‘wander’ more freely, on condition that between them they continue to supply the original function. Susumu Ohno was the first to comprehensively elucidate the potential of gene duplication, in his book Evolution by Gene Duplication, published more than 30 years ago (Ohno 1970). The prescience of Ohno's book is highlighted by the fact that his book has almost certainly been cited more times in the past five years than in the first five years after its publication. What Is the Evidence for the Importance of Gene Duplication? The primary evidence that duplication has played a vital role in the evolution of new gene functions is the widespread existence of gene families. Members of a gene family that share a common ancestor as a result of a duplication event are denoted as being paralogous, distinguishing them from orthologous genes in different genomes, which share a common ancestor as a result of a speciation event. Paralogous genes can often be found clustered within a genome, although dispersed paralogues, often with more diverse functions, are also common. Whole genome sequences of closely related organisms have allowed us to identify changes in the gene complements of species over relatively short evolutionary distances. These comparisons typically reveal dramatic expansions and contractions of gene families that can be related to underlying biological differences. For example, humans and mice differ in their sensory reliance on sight and smell respectively; colour vision in humans has been significantly enhanced by the duplication of an Opsin gene that allows us to distinguish light at three different wavelengths, while mice can distinguish only two. By contrast, a much higher proportion of the large gene family of olfactory receptors have retained their functionality in mice, as compared to humans. Given the apparent importance of gene duplication for the evolution of new biological functions over all evolutionary timescales, it is of great interest to be able to comprehensively document the duplicative differences that exist between our own species and our closest relatives, the great apes. The study by Fortna et al. (2004) in this issue of PLoS Biology identifies over 3% of around 30,000 genes as having undergone lineage-specific copy number changes among five hominoid (humans plus the great apes) species. This is the first time that copy number changes among apes have been assayed for the vast majority of human genes, and we can expect that the biological consequences of the 140 human-specific copy number changes identified in this study will be heavily investigated over the coming years. How Do Duplications Arise? The various mechanisms by which genes become duplicated are often classified on the basis of the size of duplication generated, and whether they involve an RNA intermediate (Figure 1). Figure 1 Mechanism of Gene Duplication A two-exon gene is flanked by two Alu elements and a neighbouring replication termination site. Recombination between the two Alu elements leads to a tandem duplication event, as does a replication error instigated by the replication termination site. Retrotransposition of the mRNA of the gene leads to the random integration of an intron-less paralogue at a distinct genomic location. ‘Retrotransposition’ describes the integration of reverse transcribed mature RNAs at random sites in a genome. The resultant duplicated genes (retrogenes) lack introns and have poly-A tails. Separated from their regulatory elements, these integrated sequences rarely give rise to expressed full-length coding sequences, although functional retrogenes have been identified in most genomes. Tandem duplication of a genomic segment (segmental duplication) is one of the possible outcomes of ‘unequal crossing over’, which results from homologous recombination between paralogous sequences. These recombination events can also give rise to the deletion or inversion of intervening sequences. Recent evidence suggests that the explosion of segmental duplications in recent primate evolution has been caused in part by the rapid proliferation of Alu elements about 40 MYA. Alu elements are derived from the 7SL RNA gene and represent the most frequent dispersed repeat in the human genome, with the approximately 1 million copies of the 300-bp Alu element representing around 10% of the entire genome. The striking enrichment of Alu elements at the junctions between duplicated and single copy sequences implicates unequal crossing over between these repeats in the generation of segmental duplications (Bailey et al. 2003). The observation of segmental duplication events with no evidence for homology-driven unequal crossing over suggests that segmental duplications can also arise through non-homologous mechanisms. A recent screen for spontaneous duplications in yeast suggests that replication-dependent chromosome breakages also play a significant role in generating tandem duplications, because duplication breakpoints are enriched at replication termination sites (Koszul et al. 2004). Genome duplication events generate a duplicate for every gene in the genome, representing a huge opportunity for a step-change in organismal complexity. However, genome duplication presents significant problems for the faithful transmission of a genome from one generation to the next, and is consequently a rare event, at least in Metazoa. In principle, genome duplications should be easily identified through the coincident emergence within a phylogeny of many gene families. Unfortunately, this signal is complicated by subsequent piecemeal loss and gain of gene family members. Consequently, there is heated debate over possible ancient genome duplication events in early vertebrate evolution and more recently in teleost fish, both of which must have occurred hundreds of millions of years ago (McLysaght et al. 2002; Van de Peer et al. 2003). So what are the relative contributions of these different mechanisms? Not all interspersed duplicate genes are generated by retrotransposition. The initially tandem arrangement of segmental duplications can be broken up by subsequent rearrangements. In keeping with this hypothesis, duplicated genes in a tandem arrangement typically represent more recent duplication events (Friedman and Hughes 2003). Recent analyses suggest that 70% of non-functional duplicated genes (pseudogenes) in the human genome result from retrotransposition rather than any DNA-based process (Torrents et al. 2003). What Fates Befall a Recently Duplicated Gene? A duplicated gene newly arisen in a single genome must overcome substantial hurdles before it can be observed in evolutionary comparisons. First, it must become fixed in the population, and second, it must be preserved over time. Population genetics tells us that for new alleles, fixation is a rare event, even for new mutations that confer an immediate selective advantage. Nevertheless, it has been estimated that one in a hundred genes is duplicated and fixed every million years (Lynch and Conery 2000), although it should be clear from the duplication mechanisms described above that it is highly unlikely that duplication rates are constant over time. However, once fixed, three possible fates are typically envisaged for our gene duplication. Despite the slackened selective constraints, mutations can still destroy the incipient functionality of a duplicated gene: for example, by introducing a premature stop codon or a mutation that destroys the structure of a major protein domain. These degenerative mutations result in the creation of a pseudogene (nonfunctionalization). Over time, the likelihood of such a mutation being introduced increases. Recent studies suggest that there is a relatively narrow time window for evolutionary exploration before degradation becomes the most likely outcome, typically of the order of 4 million years (Lynch and Conery 2000). During the relatively brief period of relaxed selection following gene duplication, a new, advantageous allele may arise as a result of one of the gene copies gaining a new function (neofunctionalization). This can be revealed by an accelerated rate of amino-acid change after duplication in one of the gene copies. This burst of selection is necessarily episodic—once a new function is attained by one of the duplicates, selective constraints on this gene are reasserted. These patterns of selection can be observed in real data: most recently duplicated gene pairs in the human genome have diverged at different rates from their ancestral amino-acid sequence (Zhang et al. 2003). A convincing instance of neofunctionalization is the evolution of antibacterial activity in the ECP gene in Old World Monkeys and hominoids after a burst of amino-acid changes following the tandem duplication of the progenitor gene EDN (a ribonuclease) some 30 MYA (Zhang et al. 1998). The divergence of duplicated genes over time can be also monitored in genome-wide functional studies. In both yeast and nematodes, the ability of a gene to buffer the loss of its duplicate declines over time as their functional overlap decreases. Rather than one gene duplicate retaining the original function, while the other either degrades or evolves a new function, the original functions of the single-copy gene may be partitioned between the duplicates (subfunctionalization). Many genes perform a multiplicity of subtly distinct functions, and selective pressures have resulted in a compromise between optimal sequences for each role. Partitioning these functions between the duplicates may increase the fitness of the organism by removing the conflict between two or more functions. This outcome has become associated with a population genetic model known as the Duplication–Degeneration–Complementation (DDC) model, which focuses attention on the regulatory changes after duplication (Force et al. 1999). In this model, degenerative changes occur in regulatory sequences of both duplicates, such that these changes complement each other, and the union of the expression patterns of the two duplicates reconstitutes the expression pattern of the original (Figure 2). Figure 2 Fates of Duplicate Genes A new duplication in a gene (blue) with two tissue-specific promoters (arrows) arises in a population of single copy genes. Fixation within the population results in a minority of cases. After fixation, one gene is inactivated (degradation) or assumes a new function (neofunctionalization), or the expression pattern of the original gene is partitioned between the two duplicates as one promoter is silenced in each duplicate in a complementary manner (subfunctionalization). A recent study by Dorus and colleagues (Dorus et al. 2003) investigated the retrotransposition (since the existence of a human–mouse common ancestor) of one of the two autosomal copies of the CDYL gene to Y chromosome (forming CDY). In the mouse, both Cdyl genes produce two distinct transcripts, one of which is expressed ubiquitously while the other is testis-specific. By contrast, in humans both CDYL genes produce a single ubiquitously expressed transcript, and CDY exhibits testis-specific expression. As CDY is a retrogene (see above) that has not been duplicated together with its ancestral regulatory sequences, it is clear that the DDC model is not the only route by which to achieve spatial partitioning of ancestral expression patterns. Subfunctionalization can also lead to the partitioning of temporal as well as spatial expression patterns. In humans, the β-globin cluster of duplicated genes contains three genes with coordinated but distinct developmental expression patterns. One gene is expressed in embryos, another in foetuses, and the third from neonates onwards. In addition, coding sequence changes have co-evolved with the regulatory changes so that the O2 binding affinity of haemoglobin is optimised for each developmental stage. This coupling between coding and regulatory change is similarly noted at a genomic level when expression differences between many duplicated genes pairs are correlated with their coding sequence divergence (Makova and Li 2003). Other Evolutionary Consequences of Gene Duplication If duplication results in the formation of a novel function as a result of interaction between the two diverged duplicates, which of the above categories of evolutionary outcome does this innovation fall into? Not all new biological functions resulting from gene duplications can be ascribed to individual genes. Protein–protein interactions often occur between diverged gene duplicates. This is especially true for ligand–receptor pairs, which are often supposed to coevolve after a gene duplication event, and thus progress from homophilic to heterophilic interactions. This emergent function of the new gene pair does not fit comfortably into any of the scenarios outlined above: both genes are functional yet neither retains the original function, nor has the original function been partitioned. This mode of ‘duplicate co-evolution’ is likely to be especially prevalent in signalling pathways. Earlier, we saw that homologous recombination between paralogous sequences can result in rearrangements, including tandem duplications. Such recombination events need not cause rearrangements, but can also result in the nonreciprocal transfer of sequence from one paralogue to the other—a process known as gene conversion. Gene conversion homogenizes paralogous sequences, retarding their divergence, and consequently obscuring their antiquity. This leads to the observation of ‘concerted evolution’ whereby duplicates within a species can be highly similar and yet continue to diverge between species (Figure 3). Once gene duplicates have diverged sufficiently so that they differ in their functionality (or non-functionality), gene conversion events can become deleterious—for example, by introducing disrupting mutations from a pseudogene into its functional duplicate. A substantial proportion of disease alleles in Gaucher disease result from the introduction of mutations into the glucocerebrosidase gene from a tandemly repeated pseudogene (Tayebi et al. 2003). These kinds of recombinatorial interactions only occur between paralogues that are minimally diverged. Thus, while selective interactions and functional overlap between duplicates declines relatively slowly over evolutionary time, the potential for recombinatorial interactions between paralogues is relatively short-lived. Figure 3 Concerted Evolution Different gene conversion events homogenize minimally diverged duplicate genes in each daughter species (A and B), with the result that while paralogues are highly similar, orthologues diverge over time. For some genes, duplication confers an immediate selective advantage by facilitating elevated expression, or as Ohno put it, ‘duplication for the sake of producing more of the same’. This has clearly been the case for histones and ribosomal RNA genes. In this scenario, gene conversion is of potential benefit in maintaining homogeneity between copies. Certainly both histone and rDNA genes are commonly found in arrays of duplicates: structures that facilitate array homogenization by both gene conversion and repeated unequal crossing over. Mechanisms of segmental duplication are oblivious to where genes begin and end, and so are additionally capable of duplicating parts of genes or several contiguous genes. The intragenic duplication of individual exons or enhancer elements also presents new opportunities for the evolution of new functions or greater regulatory complexity. Conclusions The likelihood that newly duplicated genes will both remain functional clearly relates to their inherent potential to undergo subfunctionalization or neofunctionalization. Under the DDC model, greater regulatory complexity bestows greater potential for subfunctionalization (Force et al. 1999), whereas neofunctionalization is more likely to occur in genes that are necessarily rapidly evolving, such as those involved in reproduction, immunity, and host defence (Emes et al. 2003). This is not to say that these biases are deterministic, there are plenty of ‘successful’ gene family clusters that contain associated pseudogenes. Duplicate gene evolution has most likely played a substantial role in both the rapid changes in organismal complexity apparent in deep evolutionary splits and the diversification of more closely related species. The rapid growth in the number of available genome sequences presents diverse opportunities to address important outstanding questions in duplicate gene evolution. For those interested in patterns of selection following duplication, the transient nature of the evolutionary window of opportunity following duplication will focus attention on recently duplicated genes. In this regard it will be important to document copy number variation not only among species, as Fortna et al. have, but within species as well. In addition, it has been, and will continue to be, a lot easier to identify copy number changes between genomes than it is to identify their biological consequences (if any). Extensive functional studies targeted at duplicated genes are required if we are to more fully understand the range of evolutionary outcomes. Moreover, collaborations between the proteomics and evolutionary genetics communities would facilitate investigation of the potential role of gene duplication during the evolution of the protein–protein and cell–cell interactions that are fundamental to the biology of multicellular organisms. Matthew Hurles is at the Wellcome Trust Sanger Institute near Cambridge in the United Kingdom. E-mail: [email protected] ==== Refs References Bailey JA Liu G Eichler EE An Alu transposition model for the origin and expansion of human segmental duplications Am J Hum Genet 2003 73 823 834 14505274 Dorus S Gilbert SL Forster ML Barndt RJ Lahn BT The CDY-related gene family: Coordinated evolution in copy number, expression profile and protein sequence Hum Mol Genet 2003 12 1643 1650 12837688 Emes RD Goodstadt L Winter EE Ponting CP Comparison of the genomes of human and mouse lays the foundation of genome zoology Hum Mol Genet 2003 12 701 709 12651866 Force A Lynch M Pickett FB Amores A Yan YL Preservation of duplicate genes by complementary, degenerative mutations Genetics 1999 151 1531 1545 10101175 Fortna A Young K MacLaren E Marshall K Hahn G Genome-wide identification of great ape and human lineage-specific genes PLoS Biol 2004 2 e207 10.1371/journal.pbio.0020207 15252450 Friedman R Hughes AL The temporal distribution of gene duplication events in a set of highly conserved human gene families Mol Biol Evol 2003 20 154 161 12519918 Koszul R Caburet S Dujon B Fischer G Eucaryotic genome evolution through the spontaneous duplication of large chromosomal segments EMBO J 2004 23 234 243 14685272 Lynch M Conery JS The evolutionary fate and consequences of duplicate genes Science 2000 290 1151 1155 11073452 Makova KD Li WH Divergence in the spatial pattern of gene expression between human duplicate genes Genome Res 2003 13 1638 1645 12840042 McLysaght A Hokamp K Wolfe KH Extensive genomic duplication during early chordate evolution Nat Genet 2002 31 200 204 12032567 Ohno S Evolution by gene duplication 1970 London George Allen and Unwin 160 p Tayebi N Stubblefield BK Park JK Orvisky E Walker JM Reciprocal and nonreciprocal recombination at the glucocerebrosidase gene region: Implications for complexity in Gaucher disease Am J Hum Genet 2003 72 519 534 12587096 Torrents D Suyama M Zdobnov E Bork P A genome-wide survey of human pseudogenes Genome Res 2003 13 2559 2567 14656963 Van de Peer Y Taylor JS Meyer A Are all fishes ancient polyploids? J Struct Funct Genomics 2003 3 65 73 12836686 Zhang J Rosenberg HF Nei M Positive Darwinian selection after gene duplication in primate ribonuclease genes Proc Natl Acad Sci U S A 1998 95 3708 3713 9520431 Zhang P Gu Z Li WH Different evolutionary patterns between young duplicate genes in the human genome Genome Biol 2003 4 R56 12952535

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020207Research ArticleEvolutionGenetics/Genomics/Gene TherapyHomo (Human)PrimatesLineage-Specific Gene Duplication and Loss in Human and Great Ape Evolution Human and Great Ape EvolutionFortna Andrew 1 Kim Young 2 MacLaren Erik 1 Marshall Kriste 1 Hahn Gretchen 3 Meltesen Lynne 3 Brenton Matthew 1 Hink Raquel 1 Burgers Sonya 1 Hernandez-Boussard Tina 4 Karimpour-Fard Anis 5 Glueck Deborah 5 McGavran Loris 3 Berry Rebecca 3 Pollack Jonathan [email protected] 2 Sikela James M [email protected] 1 1Department of Pharmacology and Human Medical Genetics Program, University of Colorado Health Sciences CenterDenver, Colorado, United States of America2Department of Pathology, Stanford UniversityStanford, California, United States of America3Colorado Genetics Laboratory, University of Colorado Health Sciences CenterDenver, Colorado, United States of America4Department of Genetics, Stanford UniversityStanford, California, United States of America5Department of Preventive Medicine and Biometrics, University of Colorado Health Sciences CenterDenver, ColoradoUnited States of America7 2004 13 7 2004 13 7 2004 2 7 e20712 2 2004 6 5 2004 Copyright: © 2004 Fortna et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Gene Duplication: The Genomic Trade in Spare Parts Great Ape Genomes Offer Insight into Human Evolution Given that gene duplication is a major driving force of evolutionary change and the key mechanism underlying the emergence of new genes and biological processes, this study sought to use a novel genome-wide approach to identify genes that have undergone lineage-specific duplications or contractions among several hominoid lineages. Interspecies cDNA array-based comparative genomic hybridization was used to individually compare copy number variation for 39,711 cDNAs, representing 29,619 human genes, across five hominoid species, including human. We identified 1,005 genes, either as isolated genes or in clusters positionally biased toward rearrangement-prone genomic regions, that produced relative hybridization signals unique to one or more of the hominoid lineages. Measured as a function of the evolutionary age of each lineage, genes showing copy number expansions were most pronounced in human (134) and include a number of genes thought to be involved in the structure and function of the brain. This work represents, to our knowledge, the first genome-wide gene-based survey of gene duplication across hominoid species. The genes identified here likely represent a significant majority of the major gene copy number changes that have occurred over the past 15 million years of human and great ape evolution and are likely to underlie some of the key phenotypic characteristics that distinguish these species. This genome-wide analysis reports the major lineage-specific gene copy number changes that have occurred over the past 15 million years of human and great ape evolution ==== Body Introduction Gene and Genome Evolution The evolution of genomes has been primarily driven by single basepair mutation, chromosomal rearrangement, and gene duplication (Ohno 1970; Samonte and Eichler 2002), with the latter being the key mechanism for generating new genes and biological processes that facilitated the evolution of complex organisms from primitive ones (Li 1997). These factors are thought to also be important in hominoid evolution and speciation, although a systematic assessment of the relative contribution of each has not yet been possible. Over the past few years, as the human genome sequence has become available, it has become apparent that recent segmental duplications in the human genome are far more frequent than originally believed, comprising approximately 5% of the available sequence (Bailey et al. 2001). Duplicated regions can range from one to several hundred kilobases in size and show very high sequence similarity (90%–100%) (Bailey et al. 2001; Stankiewicz and Lupski 2002). While such regions can pose unusually difficult challenges for accurate genome assembly (Cheung et al. 2003), they are also likely to be among the most evolutionarily recent duplications and thus are among the most important to human speciation and evolution. Interspecies cDNA Array-Based Comparative Genomic Hybridization The assessment of DNA copy number changes between different human genomes has been aided by the development of comparative genomic hybridization (CGH), which originally involved cohybridizing differentially labeled test and reference genomic DNAs to normal metaphase chromosomes (Kallioniemi et al. 1992). A cytogenetic representation of copy number variation was obtained by scoring the resulting fluorescence ratios along the length of the chromosome. Increased resolution was obtained through the subsequent use of arrayed sets of either large genomic DNA clones or cDNA clones (array CGH [aCGH]) (Pinkel et al. 1998; Pollack et al. 1999), with the latter having the advantage of permitting the analysis of individual genes. While cDNA microarrays, containing sequences derived from tens of thousands of genes, have been used extensively to profile mRNA expression levels (Schena et al. 1995), their use in aCGH is technically more challenging. Human genomic DNA represents at least a 20-fold increase in complexity compared to human cellular mRNA, and the cDNA array elements represent a smaller (e.g., less than 2 kb), generally more discontinuous hybridization target for a genomic DNA sample. These technical issues notwithstanding, highly reproducible aCGH signals can be obtained using human genomic DNA against high-density human cDNA microarrays, and gene changes as small as an increase or decrease of a single copy can be detected (Pollack et al. 1999). Until now, cDNA aCGH studies have been limited to only within-species comparisons, partly due to concerns related to the increased sequence divergence that would come into play with interspecies applications. Such sequence divergence may produce differential hybridization signals that would be difficult to distinguish from those that arose from copy number changes. Fortunately, despite their significant anatomical and physical differences, hominoid species show a strikingly high degree of similarity at the genome sequence level, with the average sequence divergence values estimated as 1.24%, 1.62%, and 1.63% for human–chimp, human–gorilla, and chimp–gorilla, respectively, and orangutan showing approximately 3.1% sequence divergence when compared to human, chimp, or gorilla (Chen and Li 2001). Because of this close sequence conservation, we reasoned that it may be possible to use cDNA aCGH to directly compare the cross-species hybridization signatures of human genes to those of the great apes and to identify genes that have alterations in copy number and/or significant changes in exonic sequence between human and other hominoid species. After we initiated such a cDNA aCGH study, two interhominoid aCGH reports appeared that used arrays containing either cloned or amplified genomic DNAs (Frazer et al. 2003; Locke et al. 2003). While these studies provided useful insights into hominoid DNA copy variations, they afforded little direct knowledge of changes in individual gene copy number and covered only limited sections of the genome. In contrast, interhominoid aCGH using human cDNA microarrays, representing more than 29,000 different genes, would allow a level of genomic resolution not previously obtainable and also provide direct data regarding the recent evolutionary history of a significant majority of human and great ape genes. Results/Discussion Identification of Lineage-Specific Gene Duplication and Contraction Interhominoid cDNA aCGH was carried out in a series of pairwise comparisons using microarrays containing 39,711 human cDNAs, representing the majority of all human genes (Table S1). The pairwise comparisons involved using a great ape (or human control) as the test genomic DNA sample (Cy5 red dye) and a sex-matched human as the reference genomic DNA sample (Cy3 green dye) in all comparisons. In each experiment, a test and a reference genomic DNA were simultaneously hybridized to a human cDNA microarray under standard cDNA aCGH conditions (Pollack et al. 1999, 2002). Specific test/reference DNAs were bonobo/human, chimp/human, gorilla/human, orangutan/human, and, as a control, human/human. After background was subtracted and data normalized, hybridization signals were scored and fluorescence ratios of the test/reference genomic DNAs determined. Using relatively conservative cutoff values (see Materials and Methods), cDNAs were identified that gave aCGH signatures unique to one or more of the hominoid lineages, permitting such gene changes to be placed within specific evolutionary time frames (Figure 1). The TreeView program (http://rana.lbl.gov/EisenSoftware.htm) was used for visualization of aCGH data for each gene as it occurred in the genome, permitting a “gene-by-gene” survey of the data and allowing for easy detection of interspecies copy number variations, whether they occur as single isolated genes or as multigene blocks. Figure 1 TreeView Images of Examples of Great Ape and HLS Gene Copy Number Increases and Decreases Interhominoid cDNA aCGH was carried out as described in the text and Materials and Methods. Specific test DNAs were, left to right, human (H) (n = 5), bonobo (B) (n = 3), chimpanzee (C) (n = 4), gorilla (G) (n = 3), and orangutan (O) (n = 3). Each horizontal row represents aCGH data for one cDNA clone on the microarray, while each vertical column represents data from one microarray experiment. Regions shown contain LS genes (vertical black lines) and adjacent flanking genes ordered by chromosome map position using the UCSC Golden Path genome assembly (http://genome.ucsc.edu), November 2002 sequence freeze. Arrows denote for which hominoid lineage the copy number change is unique. Note that fluorescence ratios (pseudocolor scale indicated) reflect copy number changes relative to the human genome. For great ape LS changes, red signal is interpreted according to parsimony as increased gene copy number, and green signal as decreased gene copy number in the specific ape lineage, while increased or decreased gene copy number specific to the human lineage is represented by green or red signal, respectively, in all the great ape lineages. Gray signal indicates cDNA comparisons scored as absent. Estimates of the time at which indicated branch points occurred during hominoid evolution are derived from Chen and Li (2001). Results of the distribution of lineage-specific (LS) aCGH signatures for different individual hominoid species are presented in Figure 2A. Several lines of evidence indicate that the aCGH signature variations that were obtained are primarily due to gene copy number changes and not to interspecies sequence divergence or highly repetitive sequences (Figure S1; see also Materials and Methods). Because bonobos and chimpanzees diverged relatively recently and show a striking degree of sequence similarity (Kaessmann et al. 1999; Wildman et al. 2003), they were dealt with both as individual lineages as well as a single clade. After collapsing the LS dataset by UniGene cluster to remove redundant cDNAs corresponding to the same gene, 815 different genes were identified that gave aCGH signatures unique to a specific hominoid lineage. Each respective lineage and the numbers of genes identified that showed LS copy number change (increases/decreases) are as follows: human: 134/6; bonobo: 23/17; chimpanzee: 11/4; bonobo/chimpanzee pre-split: 26/11; gorilla: 121/52; and orangutan: 222/188. Figure 2 Number of LS Genes for Indicated Hominoid Lineages Totals of aCGH-identified LS genes are indicated for single lineages (A) and multiple (B) lineages, showing both increases (+) and decreases (–) for each. The numbers reflect totals after collapsing the dataset by UniGene cluster to remove redundant cDNAs corresponding to the same gene. Bonobo represents genes unique to this species; likewise with chimpanzee. “Bonobo and chimpanzee (pre-split)” refers to genes that were changed in both species and therefore likely occurred before these species diverged, and “bonobo and chimpanzee (total)” refers to the sum of the previous three categories, which was chosen to represent the period since the Homo/Pan split. Estimated evolutionary age of each lineage is also plotted for comparison. Letters denoting different great ape species are as in Figure 1. For (B), bonobo and chimpanzee were grouped together as one lineage (C), but selection criteria had to first be met by both species independently. In (B), no LS genes were identified for the following cases: C(+)G(–); CG(–)O(+); C(–)GO(+); and CO(+)G(–). Several interesting features were evident from these data. First, when increases and decreases were scored separately or combined, the number of LS signatures was generally in proportion to the evolutionary age of that lineage, although not in all cases. Bonobo and chimpanzee, from the time since the Homo/Pan split, showed fewer LS signatures (92) than did human (140), even though they represent the same evolutionary age. As mentioned below, this is due in large part to the significant number of LS gene copy number increases found in human. Second, while all lineages showed more gene copy number increases than decreases, this was most pronounced in humans, with 134 cDNAs representing increases and only six representing decreases. This increase-to-decrease ratio (22.3:1) was significantly greater than that of any of the great apes, which showed ratios ranging from 2.75:1 (chimpanzee) to 1.18:1 (orangutan). It is worth noting that only genes found in the human genome are represented on the cDNA arrays, and if there are genes that are absent in human but present in the great apes, e.g., genes that were lost as the human lineage emerged, those genes would not be part of this analysis. So, while it is likely that the complete loss of both copies of a gene in an LS manner is a rare event, the number of genes identified here as having a reduced copy number specifically in the human lineage may be an underestimate of the true total. Third, as mentioned above, for all lineages tested, the number of genes showing LS increases was greater than those showing LS decreases. Determination as to whether this is due to some, as yet unknown, ascertainment bias of the method or whether this is a real evolutionary tendency favoring gene duplication over gene loss will require further investigation. The favoring of gains over losses is even more striking when two additional factors are considered. (1) The fact that the cDNAs were only from human, while likely to be important to the low number of genes showing human lineage-specific (HLS) losses previously mentioned, does not help explain why, for all lineages tested, the number of LS genes showing increases was greater than the number showing decreases. To the contrary, if there were genes not on the microarray because they were only found in one or more of the great ape lineages, inclusion of such genes would be expected to add to the total number of LS increases, making the disparity between increased and decreased LS genes even greater. (2) If human/great ape sequence divergence was responsible for some of the LS aCGH signals that were obtained, it would, if anything, produce a falsely elevated number of LS decreases. Fourth, while only orangutan had more LS gene copy number increases (222) than did human (134), when the number of genes showing copy number increases was measured as a function of the evolutionary age of the lineage, human showed the greatest number of expansions of any hominoid. When measured as copy number increases per million years of age, the following values were obtained: human, 26.8; bonobo and chimpanzee since the Homo/Pan split, 12; gorilla, 17.3; and orangutan, 17.1. We also identified genes that gave aCGH signatures indicative of great ape gene copy number changes, relative to human, that were present in more than one great ape lineage (Figure 2B). For situations in which two great ape lineages showed copy number losses relative to human, there was a general trend that correlated with evolutionary age of the represented species: Pan/gorilla, 16 genes; Pan/orangutan, 27, and gorilla/orangutan, 45. For gene increases, this trend continued, with gorilla/orangutan (17) showing more changes than Pan/orangutan (nine). Interestingly, Pan/gorilla showed a departure from this trend with 28 increased genes, suggesting that gene expansion may have been particularly active in the African great apes as a group. There were also a number of more complex gene copy number changes in the five hominoid lineages, with some species showing an increase relative to human for a particular gene and others showing a decrease. These changes are likely due to more than one event, which may be indicative of a genomic region that is relatively unstable and/or of genes whose copy numbers have been influenced by different selection pressures. We identified 190 genes that showed copy number changes in multiple lineages, bringing the total number of LS genes identified to 1,005, which represents 3.4% of the total number of genes tested on the microarrays. Given the relatively conservative selection criteria used (see Materials and Methods), this likely reflects an underestimate of the true total. To visualize the effects of relaxing the selection criteria below a log2 fluorescence ratio of 0.5, a series of HLS datasets were generated using progressively reduced thresholds. Using values of 0.45, 0.4, 0.35, and 0.3 added 27, 31, 31, and 22 cDNAs, respectively, as the cutoff was progressively lowered. As seen in the TreeView image of these data (Figure S2), while some of the additional cDNAs could plausibly be scored HLS, several appeared to give marginal HLS signals. Independent Confirmation of Interspecies cDNA aCGH Data: Fluorescence In Situ Hybridization Analysis A cluster of several genes located around map position 70 Mb in human Chromosome 5q13.3 showed one of the stronger HLS aCGH signatures. Several of these genes (test probe), as well as a set of flanking genes not shown to be increased in human (control probe), were evaluated by interphase and metaphase fluorescence in situ hybridization (FISH) using bacterial artificial chromosome (BAC) probes (see Materials and Methods). The FISH studies confirmed a duplication of the gene region in human, while the control probe containing a flanking region showed no duplication (Figure 3A). Two separate probe signals (and sometimes multiple probe signals) for the test probe could be seen in interphase nuclei with only one signal for the flanking probe; metaphase chromosomes showed a larger signal for the test probe than for the flanking probe. In all of the four great ape species, on the other hand, the FISH analyses showed no duplication of the gene region; all of these experiments showed a single signal for the test probe and a single signal of comparable size for the flanking probe (Figure 3B–3E). The Golden Path (http://genome.ucsc.edu) genome assembly lists multiple Chromosome 5 locations for some of the HLS cDNAs contained on the positive BAC (e.g., BIRC1) and therefore it is likely that the multiple, closely spaced signals seen in some of the human interphase spreads (Figure 3A) reflect additional copies of these genes. Figure 3 FISH Confirmation of a Human-Specific Duplication of a Gene Cluster on Chromosome 5q13.3 Detected by Interspecies cDNA aCGH (A) Human duplication of a cluster of genes at Chromosome 5q13.3. is shown by two separate, and sometimes multiple, red BAC probe (CTD-2288G5) signals in interphase cells, with only one green BAC probe signal (RP11-1077O1) for a flanking region. Metaphase FISH shows both probes at band 5q13. The third nucleus in (A) shows four signals of the control probe (green) and eight copies of the BAC probe duplicated in the aCGH assay, consistent with the pattern expected in an S/G2 nucleus. (B–E) Bonobo (B), chimpanzee (C), gorilla (D), and orangutan (E) interphase FISH studies all show no increased signal for the human duplicated gene cluster, with signals of comparable size for the CTD-2288G5 (red) and the flanking RP11-107701 (green) probes. Metaphase FISH analyses show the gene cluster to be in the p arm of Chromosomes 4 (corresponding to the human Chromosome 5) in both the bonobo and chimpanzee, in the q arm of Chromosome 4 (corresponding to the human Chromosome 5) in the orangutan, and in the p arm of the gorilla Chromosome 19 (syntenic regions to human Chromosomes 5 and 17). Metaphase FISH showed both the test probe and the flanking probe to be located in the human 5q13 band. Both probes were located in the proximal q arm of the orangutan (PPY) Chromosome 4 and in the p arms of the bonobo (PPA) and chimpanzee (PTR) Chromosomes 4. In the gorilla (GGO), both probes were located on the gorilla Chromosome 19. All of these primate locations are consistent with described evolutionary chromosomal rearrangements, with the orangutan Chromosome 4 considered to be the ancestral Chromosome V (Stanyon et al. 1992). These rearrangements include a pericentric inversion of the ancestral Chromosome V (Chromosome 5 in human, Chromosome 4 in the great apes), in the bonobo and chimpanzee, and a translocation between the ancestral chromosome for human Chromosome 5 and the ancestral chromosome for human Chromosome 17 to form the gorilla Chromosomes 4 and 19. It is of interest that, considering the orangutan Chromosome 4 as the ancestral Chromosome V, rearrangements at this site have occurred in all of the other three great ape species (pericentric inversion in bonobo and chimpanzee, translocation in gorilla) and in the human (gene duplication). This region is also involved in spinal muscular atrophy (SMA), which is characterized by deletions of one or more genes in this region (Lefebvre et al. 1995). Taken together these data suggest this region is one of high genomic instability that is relevant to both disease and evolutionary processes. Independent Confirmation of Interspecies cDNA aCGH Data: Literature-Based Validation FGF7-like genes. Some genes we identified as having LS aCGH signatures have been previously studied by others using different methods, which provides a means of independently checking the accuracy of the cDNA aCGH data presented here. One such gene, the FGF7 gene on Chromosome 15, was studied by Zimonjic et al. (1997) using FISH analysis of the same hominoids used in this study. The FISH analysis showed an interhominoid variation in gene copy number with eight copies in human, five in chimp, four in gorilla, and two in orangutan. Interspecies aCGH data presented here mirrored these results (correlation = 0.97), showing an elevation of the human gene number with respect to the chimp, gorilla, and orangutan, with the most pronounced difference being between human and orangutan (Figure 4A). Figure 4 Independent Confirmation of Interspecies cDNA aCGH Data for Three Gene Families with Known Species Differences in Copy Number The chromosomal location, IMAGE clone ID, and GenBank accession are provided for each cDNA. The species average log2 ratios for each cDNA clone and the previously published estimate of gene copy number are shown for the indicated species. Also shown are TreeView images of interhominoid aCGH results for the indicated cDNAs, and a graphical depiction of the correlation between aCGH signal and published estimate of gene copy number (PECN). (A) FGF7 cDNA clone located on human Chromosome 15 was identified using the UCSC November 2002 human genome assembly and FGF7-like cDNA clones located on human Chromosome 9 were identified based on UniGene cluster sequence similarity to FGF7 reference sequence NM_002009. The correlation between published and aCGH-based copy number estimates is 0.97. (B) morpheus family cDNA clones were identified based on sequence similarity to one morpheus family member (Johnson et al. 2001). As in (A), except data relate to the morpheus genes and published data are from Johnson et al. (2001). Correlation = 0.97. (C) As in (A), except data relate to the CXYorf1 genes and published data are from Ciccodicola et al. (2000). Correlation = 0.99. Morpheus genes Recently the identification of a multimember gene family named morpheus on Chromosome 16 was reported and shown to exhibit gene copy number variation between several hominoid species (Johnson et al. 2001). Using a combination of approaches, the investigators estimated copy numbers for the morpheus genes to be 15, 25–30, 17, and nine for human, chimp, gorilla, and orangutan, respectively. In order to provide an independent test of the accuracy of the interspecies cDNA aCGH data we generated, the aCGH signatures of morpheus-like cDNAs were assembled for the same hominoids (Figure 4B). The average test/reference log2 ratios for these cDNAs indicated that chimpanzee had the most copies, gorilla was slightly higher than human, and orangutan clearly had the fewest, results that are in very good agreement (correlation = 0.96) with the copy number estimates reported independently by Johnson et al. (2001). CXYorf1 genes Ciccodicola et al. (2000) used cross-species FISH to estimate the hominoid gene copy numbers for the CXYorf1 gene family. They found values of seven, two, three, and one for human, chimpanzee, gorilla, and orangutan, respectively. These values closely mirrored the aCGH values that were obtained (Figure 4C) (correlation = 0.99). Based on aCGH data, the FLJ22004 gene shows the greatest gorilla-specific copy number increase (average log2 ratio = 3.94). This gene resides near the fusion region on Chromosome 2q14.1 (see below) and is contained within BAC RP11-432G15. Consistent with the aCGH data, two independent interhominoid FISH studies, by our lab (Figure S3) and by Fan et al. (2002), using this BAC showed that the copy number was highly elevated (more than 30 signals) in gorilla relative to all other hominoids tested (fewer than or equal to three signals). Further independent support for the accuracy of the aCGH data comes from a comparison of the HLS gene dataset to the segmental duplication dataset generated by Bailey et al. (2002a), who used whole genome shotgun data to generate a genome-wide database (the Whole Genome Shotgun Segmental Duplication [WSSD] database) of recent (less than 40 million years ago [MYA]) segmental duplications for the human genome (see Table S2). The majority of changes in copy number of the HLS gene set we identified are likely to have occurred since the Homo/Pan split (less than 5–6 MYA) and therefore should represent a subset of the segmental duplications found in the WSSD dataset. Results of this analysis confirmed this expectation (Table 1): 80% of HLS genes gave significant basic local alignment search tool (BLAST) scores with the WSSD dataset (as a control, only 13% of a randomly selected set of cDNAs were positive for the WSSD dataset), and 57% (5414/9461) of the segments in the WSSD were positive with the HLS gene list. Table 1 Comparison of HLS Gene and WSSD Datasets The complete HLS clone-by-clone comparison to the WSSD dataset can be found in Table S1 Non-Random Distribution of LS Genes Genes identified as having a variation in copy number specific for one or more hominoid lineages occurred either as single isolated genes or as clusters of genes. This latter category likely reflects LS copy number changes that involved blocks of contiguous genes. In addition, certain specific regions of the genome, while not necessarily composed of contiguously positioned LS genes, showed a marked enrichment for LS genes. Surveying the genome for regions containing contiguous gene clusters of LS genes or for regions highly enriched in LS genes (greater than or equal to eight contiguous or nearly contiguous LS cDNAs) identified 23 prominent sites (Figures 5 and 6; Table 2). Most (18) of these are not randomly distributed in the genome, but instead are found near regions thought to be more genomically and evolutionarily dynamic. Among these are heterochromatic C-band regions, pericentromeric and subtelomeric regions, breakpoints of recent pericentromeric inversions, and sites of recent chromosomal fusions. For example, the two cytogenetic regions with the most LS genes represented were 1p13.2–1q21.2 (66 cDNAs) and 9p13.3–9q21.12 (77 cDNAs) (see insets in Figure 5, regions C and M). Interestingly, these regions are also known to contain C-band regions of heterochromatin which, along with C-band regions at pericentromeric 16 and at the distal end of Yq, are found at these chromosomal locations only in human and are known to be highly polymorphic. (While C-band chromosomal regions contain the alphoid class of repetitive DNA, there are several reasons that argue that the LS signals in these regions are not due to human-specific repetitive DNA. First, several HLS cDNAs were checked and found to contain no repetitive sequences in them. Second, Cot-1 analyses, described earlier, indicated that HLS signals did not correspond to repetitive DNA regions. Third, the genes in these regions showed LS signals for other hominoid lineages in addition to human.). The regions near the C-band regions on 16 (15 cDNAs) and Y (14 cDNAs) also showed an enrichment of LS genes, although to a lesser extent. These regions, as well as the pericentromeric regions of the acrocentric chromosomes, which showed enrichment for LS genes, are known to contain highly repetitive DNA, which may make them especially prone to recombination and duplication. Figure 5 Whole Genome TreeView Representation of Interhominoid cDNA aCGH Data for Five Hominoid Species for Human Chromosomes 1–9 Hominoid species are identified by color bar (see key). Genes along each chromosome are ordered by map position. cDNAs mapping to multiple genome locations (more than 1 Mb apart) are shown at each of the multiple genomic locations. Fluorescence ratios are depicted using a pseudocolor scale (indicated). Megabase positions, cytobands, centromeres (black vertical triangles), and selected genes are indicated. Boxed and lettered regions (A–M) identify clusters of LS genes (greater than or equal to eight per cluster); insets show detailed views of clusters C, F, I, and M. The complete annotated interhomioid aCGH dataset depicted here is available in Table S1 and can be viewed either as a TreeView image (see Protocol S1) or as a tab-delimited text file that can be opened in Excel. Figure 6 Whole Genome TreeView Representation of Interhominoid cDNA aCGH Data for Five Hominoid Species for Human Chromosomes 10–22, X, and Y Data are as described for Figure 5, except boxed and lettered regions denoting clusters of LS genes are N–W. The complete annotated interhomioid aCGH dataset depicted here is available in Table S1 and can be viewed either as a TreeView image (see Protocol S1) or as a tab-delimited text file that can be opened in Excel. Table 2 Genome Distribution and Repeat Content of Clusters of LS Genes Inspection of the whole genome aCGH dataset identified clusters of genes that showed LS signatures. While a number of smaller (e.g., at Chromosome 13p11.1) or more diffuse (e.g., at 16p13.12–16p11.2) clusters were also present, 23 of the most prominent clusters (A–W) were selected by visual inspection. In general, selection required that at least half of the cDNAs in the cluster be lineage-specific (i.e., changed in one or more hominoid lineage) and that at least eight LS cDNAs be present. Cytogenetic and nucleotide positions were obtained from the UCSC Golden Path genome assembly November 2002 sequence freeze. cDNA totals reflect estimated numbers of LS cDNAs within the indicated interval. Repeat content associated with LS gene clusters and HLS genes was assessed and compared to average repeat content of the genome Previous reports have shown that recent (less than 40 MYA) segmental duplications in the human genome are positionally biased and found more frequently in pericentromeric and subtelomeric regions (Bailey et al. 2001; Mefford and Trask 2002; Samonte and Eichler 2002). Consistent with this, most of the LS clusters we identified mapped to either pericentromeric (10/23) or subtelomeric (4/23) regions (Table 2). Also, a recent report by Bailey et al. (2002b) showed that a 400 kb HLS duplication transposed from Chromosome 14 to the most proximal pericentromeric region of Chromosome 22 (at approximately 13–14 Mb) and suggested that a pericentromeric gradient of duplications exists in which the most recent duplications transpose nearest to the centromere. Data presented here, showing a cluster of LS genes in this same region with HLS changes occurring nearer to the centromere, are consistent with this view. Additional clusters were also identified at other sites known to be particularly unstable and prone to rearrangement and duplication. For example, the 5q13 region (see inset to Figure 5, region I) is known to be involved in SMA, and deletions in the BIRC1 gene, which we show is amplified uniquely in humans, are sometimes found in SMA patients. This region and another at 5p14.3–5p13.3 that also contains a cluster of LS genes are near the breakpoint sites of a pericentric inversion that occurred during hominoid speciation (Yunis and Prakash 1982). Another unstable region, the 2q14.1 region (see inset to Figure 5, region F), is known to be the site at which two ancestral ape chromosomes fused telomere-to-telomere to form human Chromosome 2 (IJdo et al. 1991; Fan et al. 2002). This region shows a complex pattern of LS genes, with aCGH gene signatures specific for at least four different hominoid lineage combinations represented within a genomic region of only 400 kb. Enrichment of LS genes was also found in regions associated with other genetic disorders, including Di George syndrome, Williams–Beuren syndrome, and Angelman and Prader–Willi syndromes. Taken together, these data support the view that regions of the genome that are particularly unstable are enriched for LS gene copy number changes and are often disease-associated hotspots of evolutionary change. To assess the frequency and type of repeated sequences associated with the HLS gene and LS gene cluster datasets, the repeat content near these genes was determined. Of known repeat classes surveyed, only the Satellite class showed a major deviation from the overall genome frequency (Table 2). Satellite repeats associated with LS gene clusters and HLS genes were 10-fold and 4-fold enriched, respectively, over the genome average frequency. This may not be unexpected given the known pericentromeric and subtelomeric positional bias of Satellite sequences and their known involvement in interchromosomal duplication processes (Horvath et al. 2000). Relative frequencies of the subclasses of Satellite sequences associated with each cluster can be found in Table S3. Genes Showing HLS Variation in Copy Number Of the 140 genes showing HLS variation in copy number, 134 represented human gene increases and six represented decreases (Figure 7; Table S4). While roughly half of these genes were represented as expressed sequence tags (ESTs) or uncharacterized genes with little or no information as to possible biological function, the remaining cDNAs corresponded to known genes. Among this latter category were a number with interesting predicted functional characteristics. For example, the gene encoding the neuronal apoptosis inhibitory protein (NAIP or BIRC1) maps to Chromosome 5q13 and was elevated specifically in the human lineage. NAIP has been implicated in delaying neuronal programmed cell death (Liston et al. 1996) and is known to have at least one duplicated copy in the genome that appears to be functional (Xu et al. 2002). If an increase in gene dosage results in an elevated functional effect, the possibility exists that such an LS increase in NAIP gene copy number may contribute to an increase in neuronal proliferation and/or brain size (either globally or regionally) in humans. Figure 7 TreeView Images of LS Genes for Different Hominoid Lineages and Lineage Combinations Ranked as a Function of aCGH Ratio TreeView representation of cDNAs that exhibit great ape or human LS aCGH signatures are presented. Order of genes within each lineage is based on the average log2 fluorescence ratios (ordered highest to lowest) of the respective species. The dataset used for this figure was not collapsed by UniGene cluster to minimize the chance that significant LS cDNAs would be missed. Fluorescence ratios are depicted using a pseudocolor scale (indicated). The complete annotated LS dataset depicted here is available as Table S4 and can be viewed either as a TreeView image (see Protocol S1) or as a tab-delimited text file that can be opened in Microsoft Excel. Several other genes implicated in neuronal function showed HLS changes in copy number: a neurotransmitter transporter for γ-aminobutyric acid (GABA) (SLC6A13), a leucine zipper-containing gene highly expressed in brain (KIAAA0738), α7 cholinergic receptor/Fam7 fusion gene (CHRFAM7A), a p21-activated kinase (PAK2), a Rho GTPase-activating protein (SRGAP2), a Rho guanine nucleotide exchange factor (ARHGEF5) that is a member of the rhodopsin-like G protein-coupled receptor family, and Rho-dependent protein kinase (ROCK1). Inhibition of ROCK1 has been shown to prevent long-term memory, and ROCK1, together with a RhoGEF and RhoGAP, have been recently implicated in a model of long-term memory based on fear conditioning (Lamprecht et al. 2002). Also, members of the ARHGEF, PAK, and RhoGAP gene families comprise a disproportionately high fraction of the genes known to produce syndromic or nonsyndromic forms of mental retardation (Ramakers 2000). Another gene showing an HLS copy number increase, USP10, encodes a ubiquitin-specific protease, an enzymatic class implicated in learning and memory and in synaptic growth (DiAntonio et al. 2001). Overexpression of the USP10 homologue in Drosophila leads to uncontrolled synaptic overgrowth and elaboration of the synaptic branching pattern (DiAntonio et al. 2001), raising the possibility that the human-specific copy number increase for USP10 could be relevant to expanded synaptic growth in humans. Interestingly, the USP10 gene at Chromosome 16q24 and an unknown gene (integrated molecular analysis of genomes and their expression [IMAGE] 854706) at Chromosome 19q13 that is significantly elevated in human relative to most hominoids map to the two chromosomal regions giving the highest LOD scores in a recent genome-wide scan related to specific language impairment (SLI Consortium 2002). The aquaporin 7 gene (AQP7), which is thought to be involved in water transport across membranes, shows an HLS increase in copy number, while the genes immediately flanking it (NFX1 and AQP3) do not show HLS aCGH signals. Similarly, Bailey et al. (2002a) predict that a 22 kb region containing the AQP7 gene has been recently (less than 40 MYA) duplicated several times while flanking regions show no recent duplication. These data suggest that a series of HLS segmental duplications occurred that focused primarily on the AQP7 gene, which spans 17 kb of the 22 kb duplication. This observation, together with the fact that several of the additional AQP7 copies appear to be potentially functional (see below), raises the possibility that significant selection pressure may have been exerted on AQP7-like genes specifically in the human lineage. Genes Showing Copy Number Variation Specific to One or More Great Ape Lineages In addition to identifying HLS gene changes, interhominoid cDNA aCGH allows genes to be identified that have changed during other branch points within the past 15 MY of hominoid evolutionary history. In the present study, 865 great ape LS genes were identified (Figure 7; Table S4), several of which are mentioned below. Chimpanzees are known to be the original reservoir for HIV and show genetic resistance to progression to AIDS (Novembre et al. 1997; Gao et al. 1999), a process likely to be immunologically mediated. Among genes elevated in copy number in chimpanzees are several with possible relevance to immune function, including the BMI1 gene (B-cell lymphoma Mo-MLV insertion region) and, in bonobos and chimps, the FCER2 gene, encoding a lymphocyte IgE receptor, and the IL1RL1 gene encoding an interleukin receptor 1-like protein. Also, it has been shown that chimpanzees can synthesize a form of sialic acid while humans cannot, owing to the loss of function in humans of a specific sialic acid hydroxylase (Muchmore et al. 1998). Interestingly, one of the genes elevated in chimpanzees and bonobos encodes a CMP-sialic acid transporter (SLC35A1). As mentioned previously, of genes specifically amplified in the gorilla lineage, the FLJ22004 gene showed the largest gorilla-specific aCGH signal increase. While the function of this gene is unknown, the encoded protein contains a DUF6 domain, which is found in the Erwinia PecM protein involved in cellulase and pectinase regulation (Rieder et al. 1998). Interestingly, gorillas more than any other hominoid are folivorous. They eat leaves primarily, but also (like other hominoids) fruit, foods that contain energy-rich cellulose and pectin. This fact, together with the observation that FLJ22004 is highly amplified only in the gorilla lineage, raises the possibility that amplification of this gene provides enhanced cellulase and pectinase capabilities, which in turn would facilitate utilization of the two key dietary staples of this species. Another gene specifically increased in gorilla (average log2 ratio = 2.02) encodes the fibroblast growth factor receptor 3 (FGFR3), which when disrupted in humans causes achondroplasia, the most frequent form of short-limb dwarfism. The SET8 gene is also significantly elevated in copy number only in gorilla (average log2 ratio = 2.65) and also related to development. The gene encodes a transcription factor and appears to be homologous (protein similarity of 43% over 110 amino acids) to the Drosophila trithorax gene, which functions in segmentation determination through interaction with bithorax and antennapedia complex genes, suggesting that it may serve a role in gorilla-specific development. There were a significant number of genes (28) showing increased copy numbers specifically in the African great apes (bonobo, chimpanzee, and gorilla). Among these were the MSTP028 gene, encoding a voltage gated potassium channel; the PLA2G4B gene, encoding phospholipase A2β, which shows high brain and (in particular) cerebellar expression; and the SPTBN5 gene, which encodes a nonerythroid spectrin. SPTBN5 is immediately adjacent to PLA2G4B at Chromosome 15q15.1 in the genome and, like PLA2G4B, shows high cerebellar expression, raising the possibility that their function(s) in the African great apes may be linked. Finally, while the HLS and LS genes mentioned above have interesting biological implications related to human and great ape differences, each should be viewed as tentatively HLS or LS until the interhominoid copy number differences for these genes are confirmed by independent methods. Functional Classification of HLS and LS Genes Classification of HLS and LS genes according to predicted molecular function was carried out by Gene Ontology (GO) analysis. For the great majority of functional categories, both HLS and LS gene groups gave GO distributions similar to that found with all known genes (UniGene collapsed set), with ligand binding, catalytic activity, signal transducer activity, and transporter activity being the four most highly represented functional categories (Figure S4; Table S5). This analysis should be tempered somewhat by the fact that almost half of all HLS and LS genes are unclassified or lack functional information and that some human genes are not present on the microarrays used (e.g., only 20–30 olfactory receptor-related cDNAs were on the microarrays while, in hominoids, this family is thought to be comprised of several hundred functional members [Gilad et al. 2003]). It can be expected that copies arising from gene duplications will be a mix of functional genes and pseudogenes, the exact ratio of which will vary depending on the gene involved. Although definitive assessment of the functional status of the copies of HLS genes identified here requires additional study, a preliminary analysis of several HLS genes, including those mentioned above, found this general trend to be evident (Table S6). For example, analysis of BLAST-like alignment tool (BLAT) hits for the AQP7 gene predicts that of seven closely related (greater than 90%) copies in the genome, at least four appear to be potentially functional. In contrast, the FLJ13263 gene had four closely related sequences, and these all appear to be pseudogene-like. Finally, the fact that it has been shown that pseudogenes can play important functional roles (Hirotsune et al. 2003) implies that one cannot assume that even bonafide pseudogene copies will necessarily be functionally silent or unimportant to evolutionary differences between species. Human and Chimpanzee Genome Sequences A human versus chimpanzee genome comparison is now publicly available, through the University of California, Santa Cruz (UCSC) database's best reciprocal alignment of the July 2003 human genome and the November 2003 Arachne 4X chimpanzee draft genome (http://genome.ucsc.edu/goldenPath/hg16/versusPt0/). Using this comparison, we have determined that genes that gave aCGH signatures indicative of copy number increase specifically in the human lineage, showed a 7-fold increase in the frequency of gaps and absent sequence homology in the chimpanzee draft compared to a randomly selected gene (EST) set (Table S7). Such a pronounced bias would be expected for genes with significant copy number increases in human relative to chimpanzee, independently supporting the accuracy of the HLS gene dataset we have defined. However, a limitation of only comparing the human and chimpanzee genomes is that no out-group analysis is provided, preventing discrimination of ancestral and derived forms and limiting the ability to identify gene copy number changes unique to a specific hominoid lineage. In contrast, the interhominoid aCGH studies described here provide reliable genome-wide data for out-group analysis across five primate species, allowing easy identification of LS copy number differences. In order to provide some perspective on the importance of out-group data when trying to identify LS gene changes, a comparison was carried out between two aCGH clone sets. One set contained 153 genes we identified by cDNA aCGH that were specifically increased in copy number in the human lineage when compared to each of the four great ape lineages (i.e., HLS). The other clone set, while derived from the same aCGH experiments using the same cutoff values, contained 353 genes that showed aCGH signals in which the human copy number was greater than the chimpanzee (i.e., “human > chimp”). Comparison of these two datasets allows one to determine how frequently a “human > chimp” gene is also HLS (i.e., human copy number is greater than each of the four great apes studied). Of the 353 genes that were “human > chimp,” 200 were not found in the HLS set, indicating that over half (57%) of the “human > chimp” genes were not HLS. It has been pointed out that the human genome is a mosaic composed of some regions more closely related to chimpanzee and, less frequently, others more closely related to gorilla (Pääbo 2003). Data presented here contain a number of examples of genes showing such evolutionary histories, but also contains examples of other more complex phylogenetic patterns (Figure 7; see Table S4). For example, the significant number of genes showing copy number increases or decreases specifically in the African great apes, in which human and orangutan copy numbers were equivalent to one another, suggests that either more than one event occurred to produce this distribution or the genomic mosaicism found in the human genome extends back to include sequences present at the time the orangutan lineage split. Because of this unusual phylogenetic profile, we tested several such cDNAs by interhominoid real-time PCR (RT-PCR) and FISH as an independent verification of our aCGH results. In all cases, copy number estimates based on RT-PCR analysis showed high correlation (0.94–0.97) to estimates based on our aCGH data (Figure S5). Interestingly, FISH analysis using a BAC probe containing two genes (PLA2G4B and SPTBN5) specifically elevated in the African great apes, showed that, in chimpanzee, signals were widely distributed among many chromosomes, while in gorilla the signals were restricted to two sites, one single copy and the other multicopy (Figure S6). These results indicate that the increase in gene copy number in gorilla and chimp occurred independently of each other and therefore support the view that multiple separate events are likely responsible for the African great ape-specific aCGH signals we obtained. In summary, the dataset presented here, containing over 714,000 aCGH datapoints, represents to our knowledge the first genome-wide survey of gene duplication and loss across five hominoid species. The changes identified likely represent most of the major LS gene-associated copy number changes that have occurred over the past 15 MY of human and great ape evolution. Further analyses of this dataset, of which only a fraction has been highlighted here, should provide additional insights into gene duplication and genome evolution, the relationship of genome instability, evolutionary adaptation, and disease, and the genes that underlie the phenotypic differences among human and great ape species. Materials and Methods Copy Number Variation, Sequence Divergence, and Repetitive Sequences Though discussed above as copy number alterations, changes in cross-species cDNA aCGH signals could be due to changes in gene copy number between species, to pronounced exonic sequence divergence of the gene between species, or to a combination of both. To attempt to distinguish among these possibilities, we took advantage of the fact that, while cDNAs are randomly positioned on the microarrays, for analysis purposes they had previously been computationally grouped into two categories: cDNAs with single known genome locations (i.e., unique location) and cDNAs that mapped to multiple genomic locations (multiple locations). In this latter category, we also included a minority of cDNAs that had no assignable location in the genome assembly. We identified HLS cDNAs that showed stronger hybridization with human DNA (green signals in all great ape/human comparisons) and determined how many of these occurred in each of the two mapping categories. HLS signatures were found for 0.185% of unique location cDNAs (66/35,680) and 2.88% of multiple location cDNAs (116/4,031), a frequency difference of more than an order of magnitude (approximately 1:16). Such a strong enrichment, in the multiple location category, of genes showing increased human aCGH signals specific to the human lineage would be expected if such genes were present as multiple closely related copies with distinct genome locations and, as a result, were placed in the multiple location group. No such gene distribution bias would be expected if the LS signatures were mainly due to sequence divergence. Additionally, we estimated what fraction of LS cDNAs in each species were cDNAs with multiple human map positions. Values of 59%, 10%, 13%, 14%, 10%, and 20% were obtained for human, bonobo, chimp, bonobo/chimp total, gorilla, and orangutan, respectively, providing further support that the increased (i.e., green in all great ape:human comparisons) HLS aCGH signatures that were obtained are likely due to gene copy number increases specific to the human lineage. We also carried out interhominoid FISH using a BAC probe (RP11-93K3) containing a gene (IMAGE 1882505) that gave a reduced signal specifically in the orangutan lineage, which is the lineage where sequence divergence might have its greatest artifactual contribution. Resulting FISH data (see Figure S1) showed 10–15 signals in human, bonobo, chimpanzee, and gorilla, while for orangutan only two signals were evident. Finally, further evidence of aCGH data reflecting copy number change comes from the three examples of literature-based validation of aCGH-predicted copy number changes (see Figure 4). In all three cases, the orangutan signals were reduced relative to the human signals, and each of these genes were shown in published reports to have fewer copies in orangutan relative to human. Lastly, to address the possibility that such signals might be due to highly repetitive sequences associated with LS genes that were not effectively blocked during hybridization, we examined the cDNA sequences of five cDNAs that showed stronger hybridization with human DNA. In all cases no repeats were found that would account for the HLS aCGH data. In addition, hybridization using labeled Cot-1 DNA (human Cot-1 versus total human DNA) indicated that there was no correspondence between genes hybridizing more strongly to Cot-1 and genes that are LS. DNAs DNAs that were used for this study were derived from human (two females, two males), bonobo (three males), chimpanzee (one male, three females), gorilla (one male, two females), and orangutan (three females). Human and chimpanzee genomic DNA samples were isolated from blood cells using Super Quick-Gene kits from the Analytical Genetic Testing Center (Denver, Colorado, United States). One gorilla and two bonobo samples were isolated from cell lines using DNeasy Tissue kits from Qiagen (Valencia, California, United States). An orangutan sample and a gorilla sample were isolated from blood by other laboratories. Remaining DNAs (one bonobo, one gorilla, and two orangutan) were obtained from the Coriell Institute (Camden, New Jersey, United States) and originally derived from primary fibroblast cell lines. aCGH DNA microarrays used in this study were fabricated by PCR-amplifying IMAGE clones (http://image.llnl.gov) and spotting them onto Corning GAPSII aminosilane slides using a custom-built robotic arrayer (http://cmgm.stanford.edu/pbrown/mguide/index.html). The labeling of genomic DNA and hybridization to cDNA microarrays were performed as previously described (Pollack et al. 1999). In brief, 4 μg of genomic DNA from test (hominoid DNA) and sex-matched reference (normal human DNA) were DpnII-digested (New England Biolabs, Beverly, Massachusetts, United States) and subsequently purified using Qiaquick PCR purification kit (Qiagen). Purified samples were random-primer labeled according to manufacturer's directions in a 50 μl reaction volume using BioPrime Labeling Kit (Invitrogen, Carlsbad, California, United States), with the exception of substituting the provided dNTP mix with dATP, dGTP, dTTP (120 μM), dCTP (60 μM), and Cy3-dCTP (reference) or Cy5-dCTP (test) at 60 μM. Labeled Cy3-dCTP and Cy5-dCTP products were copurified and concentrated using Microcon YM-30 filters (Millipore, Billerica, Massachusetts, United States) along with 50 μg of human Cot-1 DNA (Invitrogen), 100 μg of yeast tRNA (Invitrogen), and 20 μg of poly(dA-dT) (Sigma, St. Louis, Missouri, United States) to block hybridization to nonspecific and repetitive elements in genomic DNA. We adjusted the final hybridization volume (40 μl) to contain 3.5× SSC and 0.3% SDS. Following sample denaturation (2 min at 100 °C) and a Cot-1 preannealing step (20 min at 37 °C), we cohybridized test and reference samples to a cDNA microarray containing 39,711 nonredundant cDNA clones, representing 29,619 different human genes. Samples were hybridized at 65 °C for 16 h. Following hybridization, arrays were washed in 2× SSC, 0.03% SDS for 5 min at 65 °C, followed by successive washes in 1× and 0.2× SSC for 5 min each at room temperature. aCGH Data Analysis Individual microarrays were imaged with a GenePix 4000B scanner (Axon Instruments, Union City, California, United States) and fluorescence intensities were extracted using GenePix Pro 3.0 software and uploaded into the Stanford Microarray Database (SMD) (http://genome-www5.stanford.edu) for analysis. For each experiment, fluorescence ratios were normalized by setting the average log2 fluorescence ratio for all array elements equal to 0. We included for analysis only those genes that were reliably measured (i.e., fluorescence intensity/background of greater than 1.4 in the reference channel) in greater than or equal to 50% of samples. Genes not meeting these criteria were viewed as absent. Map positions for cDNA clones on the array were assigned using the UCSC GoldenPath assembly (http://genome.ucsc.edu/), November 2002 freeze. Gene copy number ratios were visualized in log2 colorimetric scale with the genes ordered by chromosomal position using TreeView version 1.6 (http://rana.lbl.gov/EisenSoftware.htm). To provide the most accurate depiction of chromosomal gene distribution, cDNAs with multiple genome map positions (more than 1 Mb apart) were represented in TreeView at each assigned map location. Selection Criteria Applied to cDNA aCGH Data Genes showing copy number variation specific to a single hominoid lineage For selection of LS cDNAs, the values considered were the log2 of the aCGH fluorescence ratio of the test and reference genomic DNAs. Selection of LS cDNAs was based on the following criteria: First, for a given cDNA and a given species, no more than one value out of the species versus human comparisons for that species could be absent (see aCGH methods regarding absent signals). Second, for a gene copy number change to be considered unique to a particular species, at least half of the absolute values of comparisons within that species had to meet or exceed a threshold of 0.5 with all such values in the same direction, i.e., either all positive or all negative, and at least half of the absolute values of comparisons within each of the remaining species had to be below a threshold of 0.5. For example, for a gorilla LS gene, at least half of the gorilla comparisons had to meet or exceed the 0.5 threshold, while at least half of the comparisons within each of the remaining species had to be below the threshold. Third, in order to compensate for missing (i.e., “absent”) values for a given cDNA of all “present” values within each species, no more than one could fall below the threshold (0.5) for each species. Fourth, to ensure sufficiently high signal-to-noise in the identification of altered ratios, for a given cDNA and given great ape species, each absolute value of the average of the species versus human comparison for that species had to be at least 2.5-fold greater than the absolute value of each remaining species average, including human versus human comparisons. For HLS genes, the absolute value of each species average of the great ape versus human comparisons had to be at least 2.5-fold greater than the average of the absolute value of the human versus human comparisons. Genes showing copy number variation unique to more than one hominoid lineage For cDNAs in which the copy number was either increased or decreased in two or more hominoids relative to all the other hominoids, the same criteria were used as before, except the cDNA would have to meet or exceed the 0.5 threshold selection criteria for more than one species. Relationship of aCGH signal to gene copy number It is difficult to establish a precise relationship between gene copy number and interhominoid aCGH ratio because sequence divergence can influence hybridization signal strength and the sequences of additional gene copies are, in almost all cases, not known. However, prior studies by Pollack et al. (1999) showed that, using cell lines containing increasing numbers of X chromosomes, copy number, and aCGH signal exhibited a linear relationship over the copy number range tested, with an increase of a single gene copy corresponding to a ratio of 1.31 (log2 value = 0.39). In a similar manner, we took advantage of the fact the one of the human-to-human comparisons used in our experiments was between a male and female. In this context, X chromosome genes in the female should be present as two copies while in the male will exist as one copy. Calculation of the average aCGH ratios of 957 such genes in the male/female comparison yielded a log2 value of 0.21. The different values obtained in these two tests may reflect the fact that in the male/female comparison a Y chromosome was present, while this was not true in the other study, which used XO cell lines. The presence of sequences on the Y that are shared with the X could have produced a compression of aCGH fluorescence ratio values, accounting for the difference in X chromosome-related log2 ratios described above. Similar compression effects on X chromosome ratios have recently been reported (Snijders et al. 2001). While both the 0.39 and 0.21 values fall below the 0.5 threshold we employed for the selection of LS genes, 0.5 was used to insure that selection of false positives was minimized. In an interhominoid aCGH study, Locke et al. (2003) also determined a threshold of 0.5 to be most appropriate. Finally, the use of this relatively conservative threshold implies that the numbers presented here are likely to be underestimates of the actual number of genes that exhibit LS copy number differences between these hominoids. FISH Analysis Using standard procedures, metaphase spreads and interphase nuclei were prepared from human lymphocytes (Homo sapiens [HSA]) and from great ape fibroblast cell lines, obtained from Coriell. The four great ape species studied were bonobo (Pan paniscus [PPA], Coriell #AG05253A), chimpanzee (Pan troglodytes [PTR], Coriell #AG06939A), lowland gorilla (Gorilla gorilla [GGO], Coriell #AG05251B), and Sumatran orangutan (Pongo pygmaeus [PPY], Coriell #AG12256). One BAC clone (CTD-2288G6) containing all or portions of the coding regions for OCLN, GTF2H2, and BIRC1 was selected as a probe for the region with increased copy number in human. A second BAC clone (RP11-1077O1) flanking the region amplified in human and containing portions of the RAD17 gene was selected as a control probe. BAC clones were obtained from BACPAC Resources at the Children's Hospital Oakland Research Institute and from Research Genetics. Whole-cell PCR was done to verify that the OCLN, GTF2H2, and BIRC1 genes were on BAC CTD-2288G5 and that the RAD17 gene was on BAC RP11-1077O1. BAC DNAs were prepared using Large Construct Kits (Qiagen). BAC probes were directly labeled with Spectrum Green (Vysis, Downers Grove, Illinois, United States) and Spectrum Orange (Vysis) using the Vysis Nick Translation Kit and protocol. FISH analyses with the BAC probes were performed using standard techniques. Cot-1 DNA was used to block cross-hybridization of high-copy repeat sequences. In each experiment, dual-color hybridization was performed using a probe carrying genes with a predicted increase in copy number specifically in the human lineage (CTD-2288G6 or CTC-790E5) and a flanking probe (RP11-1077O1 or RP11-1113N2) containing a gene not predicted to show an HLS increase in copy number. For each species, two separate hybridizations were performed: one with the probe containing the genes showing increased human copy number labeled with Spectrum Green and the flanking probe with Spectrum Orange, and the other in which the dyes were reversed. For each probe combination for each species, a minimum of 200 interphase nuclei and ten metaphase spreads were examined. A whole chromosome painting probe for human Chromosome 5 (wcp5; Vysis) was used to confirm the gorilla Chromosome 19 to be syntenic with the human Chromosome 5 for the region of interest. The hominoid cell lines used for FISH analysis were grown asynchronously in monolayer culture. Metaphase spreads and nuclei were obtained from a shake-off preparation and thus were somewhat selected for proliferative activity. Similarly, human lymphocyte cultures stimulated with the mitogen phytohemagglutinin contain cells in various stages of the cell cycle. In order to judge the replication state of the nuclei scored, dual-color FISH assays included probes both for DNA sequences that by aCGH showed copy number difference between test and reference DNA and for sequences on the same human chromosome that had the same (diploid) number of copies. Nuclei that showed diploid copy number of this control probe were assessed to be in G0. Nuclei that were in S/G2 demonstrated four copies of the control probe and the test probes were proportionately in multiple copies of the number established in the nonproliferating cells. Similar experimental conditions were used for the additional BAC FISH analyses described. Comparison of HLS Gene and WSSD Datasets Sequences of IMAGE clones for each HLS gene were obtained using NCBI's Entrez (http://www.ncbi.nlm.nih.gov/Entrez) sequence retrieval tool and saved locally in FASTA format. Likewise, the random IMAGE clone sequences were obtained by first downloading GI numbers for all human IMAGE clones and then using a random number generator to pick approximately 200 random IMAGE clones from the list of GI numbers. These random IMAGE clone sequences were then downloaded from Entrez in a similar fashion. The April 2002 WSSD dataset was downloaded from the Segmental Duplication Database website (http://humanparalogy.gene.cwru.edu/SDD/). The two IMAGE clone sequence datasets were formatted and “BLASTed” against the WSSD sequences locally using NCBI's stand-alone BLAST executables for Windows. BLASTs were limited to an expect value of e−20 and then the best match was reported by a Perl (http://activestate.com/) script for each query. No restrictions on percent identity of the match or match length were imposed. HLS Gene Repeat Analysis The HLS gene IMAGE clone sequences (see Table S4) were compared to the November 2002 build at UCSC using Dr. Jim Kent's BLAT program via the Human Genome Browser Gateway website (http://genome.ucsc.edu/cgi-bin/hgGateway). The BLAT hits were parsed such that only hits with a percent identity greater than or equal to 90% were reported. Furthermore, only hits with a match coverage (match length/query length) greater than or equal to 50% were reported. Repeat annotation was downloaded from UCSC (http://genome.ucsc.edu/goldenPath/14nov2002/database/). Using the position data obtained from the BLAT alignments along with a 50 kb buffer on both sides of the alignments, the relative repeat content was determined for each HLS gene region using a Perl script. As a comparison, the relative repeat content was determined for the entire genome. Annotated gaps within the regions and the human genome were subtracted from the percent content calculation so that these content values were not skewed by gaps. Only long interspersed nuclear element (LINE), long terminal repeat (LTR), short interspersed nuclear element (SINE), Simple, and Satellite classes of repeats were included in the analysis. LS Gene Cluster Repeat Analysis The 23 clusters of LS genes were compared to the human repeat database downloaded from UCSC (see HLS gene repeat analysis). Likewise, the Satellite repeat content for the LS genes within the 23 clusters was also determined in a similar fashion. GO Analysis of HLS and LS Genes Primary GenBank accession numbers associated with both the HLS and LS gene lists were parsed into separate lists and stored as tab delimited text files. GenBank accession numbers were used as unique identifiers, and gene lists were annotated and functionally characterized using DAVID (Database for Annotation Visualization and Integrated Discovery) (http://apps1.niaid.nih.gov/david/upload.asp) (Dennis et al. 2003). Analyses were performed at level one for DAVID and at a threshold cutoff of 1, which provides high coverage but relatively low specificity and considers all classifications. Analysis was carried out on both lists, first using those genes with GenBank accession numbers, and then only those genes with known gene symbols. The analysis based on gene symbols recapitulates the analysis based on GenBank accessions, but contains correspondingly fewer classified genes. In order to make meaningful comparisons between the LS genes, we identified and the entire genome, a nonredundant list of genome-wide UniGene numbers was adapted from EASE2.0 (Expression Analysis Systematic Explorer, http://apps1.niaid.nih.gov/david/) (Hosack et al. 2003), a program that facilitates the biological interpretation of gene lists. This tab-delimited text file, containing 33,655 unique UniGene numbers (updated 2 February 2004), was then uploaded to DAVID for GO analysis. The results for the molecular function analysis are graphically represented in Figure S4 and summarized in Table S5. GenBank accession numbers were used for the HLS and LS analysis due to nearly half of the genes lacking UniGene numbers, thus making GenBank accession numbers more inclusive of the entire HLS and LS dataset analysis. Alternatively, UniGene numbers were used for the the genome-wide analysis because they provide a nonredundant dataset which is a much closer estimate to the number of genes (33,655) in the human genome versus the human RefSeq accession numbers. When subtracting all computer-based models from human RefSeq, only 20,850 RefSeq accession numbers were available for analysis. Human versus Chimpanzee Comparison The HLS dataset is identical to that previously described. The random dataset chosen for this analysis was determined from UCSC's all_est annotation (http://genome.ucsc.edu/goldenPath/gbdDescriptions.html). From the all_est file, 200 random IMAGE clones were picked to ensure that at least one EST per IMAGE clone would map to the human genome. The EST sequences for both the HLS and random datasets were downloaded from GenBank and compared to the July 2003 human genome via a locally installed version of BLAT. BLAT output was parsed so that hits with a score greater than 200 and percent identity greater than 90% were examined for chimpanzee homology. The score and percent identity calculations mimic the calculations performed with the Web-based version of BLAT (http://genome.ucsc.edu/cgi-bin/hgBlat); the formula for these calculations was provided by Donna Karolchik. The BLAT hits, as defined as one or more blocks of alignment within score and percent identity cutoffs, were compared to the chimpanzee versus human reciprocal best chain alignment annotation (http://genome.ucsc.edu/goldenPath/hg16/versusPt0/). For each BLAT hit, each block of alignment was compared to the chimpanzee versus human best chain annotation and was scored as follows: “chimp positive” indicates the block is entirely homologous to chimp; “chimp partial” indicates the block is partially homologous to chimp but there are gaps in the homology; “chimp gap” indicates the block is within a gap of the chimp homology; “chimp negative” indicates the block has no homology to chimpanzee. The summary numbers are based on all of the blocks of alignments and how they are scored in reference to chimpanzee homology. The HLS dataset was compared to the “human > chimp” dataset by IMAGE clone identifiers. The “human > chimp” dataset is a redundant set that was not UniGene collapsed; thus, a redundant, non-UniGene collapsed HLS dataset was used for the comparison. RT-PCR Analysis RT-PCR analysis of interhominoid DNA copy number variation was carried out using an ABI Prism 7700 sequence detector (Perkin Elmer Corporation/Applied Biosystems [PE/ABI], Torrance, California, United States) (Livak et al. 1995; Heid et al. 1996). Exon-specific primers and probe for PLA2G4B, FLJ31659, BC040199, and CFTR genes/cDNAs were designed with the assistance of the Prism 7700 sequence detection software (Primer Express, PE/ABI). The following primer/probe sequences were used: PLA2G4B F 5′-GCAGGTCTGGGTGAGGGTT-3′, PLA2G4B R 5′-GCTGCACCTGATCCCCACT-3′, and the probe 5′-VIC-CAGGAAGTTGCCACACAGGTGAGCA-TAMRA-3′; FLJ31659 F 5′-GCTCAGACATCCAGGGACGA-3′, FLJ31659 R 5′-CGCTTCTCCCAGGATTGGT-3′, and the probe 5′-VIC-CACATTCGTCCAACAGCGGTCGC-TAMRA-3′; BC040199 F 5′-GAGGAAGGTTGGGTGTGGAG-3′, BC040199 R 5′-ACTGGGTGTCCTGCTGGCT-3′, and the probe 5′-VIC-TTGCTTGCTGTGGCCCCAAGCT-TAMRA-3′; CFTR F 5′-CGCGATTTATCTAGGCATAGGC-3′, CFTR R 5′-TGTGATGAAGGCCAAAAATGG-3′, and the probe 5′-6FAM-TGCCTTCTCTTTATTGTGAGGACACTGCTCC-TAMRA-3′. Amplification reactions were performed in MicroAmp optical tubes (PE/ABI) in a 50 μl mix containing 8% glycerol, 1× TaqMan buffer A (500 mM KCl, 100 mM Tris-HCl, 0.1 M EDTA, 600 nM passive reference dye ROX [pH 8.3 at room temperature]), 300 μM each of dATP, dGTP, and dCTP and 600 μM dUTP, 5.5 mM MgCl2, 900 nM forward primer, 300 nM reverse primer, 200 nM probe, 1.25 U AmpliTaq Gold DNA polymerase (PE/ABI), and the template genomic DNA. Thermal cycling conditions were as follows: activation of TaqGold at 95 °C for 10 min followed by 40 cycles of amplification at 95 °C for 15 s and 60 °C for 1 min. After amplification, data analysis was carried out using a ratio of test gene to reference gene to obtain a normalized copy number estimate of the test gene (Bieche et al. 1998). The starting copy number in the template DNA was determined by the threshold cycle (Ct), which represents the PCR cycle at which an increase in reporter fluorescence above a baseline signal can first be detected. The starting copy number of each test gene was quantified in the ape samples by determining the Ct of the test gene and using a standard curve for copy number. The standard curve for each gene was generated using the fluorescent data from five serial dilutions of human genomic DNA and calculating a single copy of each gene per haploid human genome, as annotated in the current genome build. Copy numbers of the test genes in ape samples were normalized to the copy number of the CFTR gene, which serves as a control representative of a single gene per haploid genome (Rochette et al. 2001). The ratio “N” of the test gene copy number to CFTR copy number in each sample normalized the results with respect to differing starting quantity and quality of the template DNA in each reaction (Bieche et al. 1998). Thus, “N” expresses the estimated copy number for each species using the derived standard curve and normalized to CFTR. RT-PCRs were carried out in triplicate for each gene in each species except human, in which five reactions were carried out for each gene to generate the standard curve. Supporting Information Figure S1 BAC FISH Analysis of Gene Predicted to Be Reduced Only in Orangutan FISH images of BAC probe RP11-93K3 containing sequences from IMAGE cDNA clone 1882505. (A) Normal human control, PHA-stimulated lymphocytes. Multiple (10–12) signals present: 9p12, 9q12, 4q arm, and two acrocentric p arm regions. The Chromosome 9 signals appear to flank the 9q heterochromatin and centromere regions, with the p arm signal a double signal. (B) Bonobo fibroblast culture. Multiple (10–12) signals. (C) Chimpanzee fibroblast culture. Multiple (10–12) signals. (D) Gorilla fibroblast culture. Multiple (12–15) signals. (E) Orangutan fibroblast culture. Two signals present on a pair of homologues (i.e., single copy in haploid genome). Also shown is a TreeView image (pseudocolor scale indicated) for IMAGE cDNA clone 1882505. (3.29 MB EPS). Click here for additional data file. Figure S2 TreeView Image of cDNAs Selected Using Relaxed HLS Criteria Figure shows a TreeView image of blocks of HLS genes selected using increasingly relaxed selection criteria. The top-most group represents HLS genes selected using the standard 0.5 cutoff value described in Materials and Methods, while successive groups (separated by gray bars) represent additional cDNAs that were selected as the cutoff was progressively reduced to 0.45, 0.4, 0.35, and 0.3. (1.9 MB EPS). Click here for additional data file. Figure S3 FISH Analysis with BAC Probe RP11-432G15 Containing the FLJ22004 Gene (A) Normal human control, PHA-stimulated lymphocytes. Signal at Chromosome 2q13 and 22qtel. (B) Bonobo fibroblast culture. Four signals. (C) Chimpanzee fibroblast culture. Four signals. (D) Gorilla fibroblast culture. More than 30 signals. Hybridization to most subtelomeric regions. (E) Orangutan fibroblast culture. No apparent red signal. Probe BAC RP11-1007701 in green included as internal hybridization control. Also shown are aCGH TreeView images (pseudocolor scale indicated) for three FLJ22004 cDNAs. (6.94 MB EPS). Click here for additional data file. Figure S4 GO Categories Pie graphs showing the distribution of GO molecular function categories within HLS, LS, and whole genome gene lists. The top 22 categories are named in the legend in descending order of representation for all three groups. Categories were ranked by normalizing each category value for HLS and LS lists to the genome-wide list and then ranking the sum of these values for each category. Less well-represented categories were omitted from the graphs in order to enhance legibility, and zero values are not listed. (1.1 MB EPS). Click here for additional data file. Figure S5 Interhominoid RT-PCR Analysis RT-PCR was used to provide an independent confirmation of interspecies cDNA aCGH data for three genes in which aCGH signals were different in the African great apes compared to human and orangutan. The chromosomal location, IMAGE clone ID, and GenBank accession numbers are provided for each cDNA. The species average log2 ratios for each cDNA clone and the copy number ratio of the test gene to the CFTR (control) gene, as determined by RT-PCR, are shown for the indicated species. Also shown are TreeView images of interhominoid aCGH results for the indicated cDNAs and a graphical depiction of the correlation between aCGH signal and copy number ratio to CFTR (RT-PCR). (A) PLA2G4B cDNA clone located on human Chromosome 15 using the UCSC November 2002 human genome assembly. The correlation between RT-PCR and aCGH-based copy number estimates is 0.94. (B) FLJ31659 cDNA clone located on human Chromosome 4 using the UCSC November 2002 human genome assembly. As in (A), the correlation between RT-PCR and aCGH data is 0.97. (C) BC040199 transcript located on human Chromosome 7 using the UCSC November 2002 human genome assembly. As in (A), the correlation between RT-PCR and aCGH data is 0.97. (1.29 MB EPS). Click here for additional data file. Figure S6 FISH Analysis with BAC Probe RP11–23P13 Containing the Human PLA2G4B and SPTBN5 Genes (A) Normal human control, PHA-stimulated lymphocytes. Two signals localized to Chromosome 15q15.1. (B) Chimpanzee fibroblast culture. Two signals on the chromosome syntenic to human Chromosome 15 (at arrows). Multiple additional signals in the subtelomeric regions. (C) Gorilla fibroblast culture. Two signals on the chromosome syntenic to Chromosome 15 (at arrows). Two additional signals on a large metacentric chromosome, which in interphase appear as amplified signals. (D) Orangutan fibroblast culture. Two signals on the chromosome syntenic to human Chromosome 15. (4.39 MB EPS). Click here for additional data file. Protocol S1 How to View aCGH Data Using TreeView (2 KB TXT). Click here for additional data file. Table S1 Genome-Wide Interhominoid cDNA aCGH Gene Dataset Values are provided for genes (cDNAs) queried by interhominoid aCGH. For each IMAGE clone queried, log2 aCGH values are listed for the human versus human samples (n = 5), human versus bonobo samples (n = 3), human versus chimpanzee (n = 4), human versus gorilla (n = 3), and human versus orangutan (n = 3). This table is tab-delimited and can be opened in Microsoft Excel to view the raw numbers or can be browsed using TreeView (see Protocol S1). Column B provides information regarding IMAGE clone number, chromosome, and nucleotide position (UCSC November 2002 freeze), Unigene ID, EST accession numbers, and known gene information. (12.84 MB TXT). Click here for additional data file. Table S2 Detailed Comparison of HLS Gene and WSSD Datasets For each IMAGE clone of the HLS genes, one or more EST sequences were used as a query for a BLAST search against the WSSD dataset. An expect value cutoff of e–20 was used and the best hit is reported in the table. Query refers to the HLS gene EST sequences; subject refers to the WSSD sequences. Score, expect value, and percent identity (ID) are reported for the best BLAST hit, while the start and stop positions and length for both query and subject are also reported. (434 KB DOC). Click here for additional data file. Table S3 Satellite Repeat Subclass Analysis for LS Gene Clusters For each of the 23 LS gene clusters, Satellite repeat subclass analysis was performed. The table lists the cluster's cytogenetic region, the chromosome and start and stop positions, and the adjusted length after accounting for gaps in the genomic sequence. The percent content for 24 subclasses of Satellite repeats is listed for each of the 23 gene clusters. Summary information includes the average content of the 24 subclasses of Satellite repeats for all of the clusters as well as the average for the entire human genome. The difference and fold change are calculated based on comparing the cluster averages to the entire human genome averages. (111 KB DOC). Click here for additional data file. Table S4 LS Gene Datasets Similar to Table S1, but only IMAGE clones with LS characteristics are listed, and each is ranked based on average fluorescence signal (highest to lowest) within each lineage. (269 KB TXT). Click here for additional data file. Table S5 GO Analysis Comparing HLS and LS Genes to the Whole Genome (52 KB DOC). Click here for additional data file. Table S6 Functional Assessment of Copies of HLS Genes Presented are pertinent data from GO analysis with DAVID, including numbers of classified and unclassified genes in each gene list, as well as the data returned for each of the 22 most represented molecular function categories. Listed are GO identification numbers (GOIDs) and names for each of the top 22 categories, as well as raw values and relative percent values for HLS, LS, and genome classifications. Relative percent columns are taken as the ratio of the number of classifications in each category to the number of genes classified in the list. The average percent is also provided as the average of these relative percent values across the three groups. This is intended as a metric to help gauge deviations in group relative percent values from the combined average value. (81 KB DOC). Click here for additional data file. Table S7 Comparison of Human HLS Genes to Chimpanzee Genomic Sequence The table has three sections: a summary showing the percentages of blocks in each respective chimpanzee homology scoring class; a table with the HLS versus chimpanzee data; and a table with the random versus chimpanzee data. The HLS versus chimpanzee and random versus chimpanzee tables have columns derived from both parsing the BLAT PSL data and from the chimpanzee homology comparison. The table lists the IMAGE clone and the EST accession number used as a query, the hit number, the score and percent identities, the start and stop positions in the query, the chromosome and chromosome start and stop positions, the number of blocks of alignment for the hit, the numbers of blocks that fall into each chimpanzee homology scoring class, and finally the respective chimpanzee scaffold(s) for each hit, if available. (3.58 MB DOC). Click here for additional data file. Accession Numbers The GenBank accession number (http://www.ncbi.nlm.nih.gov/Genbank/) for FGF7 is NM_002009 and for morpheus is AF132984. This paper is dedicated to the memory of Dr. William E. Hahn (1937–2003). Chimpanzee blood samples were contributed by the Yerkes National Primate Research Center (Atlanta, Georgia, United States). One gorilla DNA sample was donated by Dr. David Glenn Smith, Department of Anthropology, University of California, Davis. Bonobo, orangutan, and additional gorilla DNAs were obtained from the Coriell Institute (Camden, New Jersey, United States) and were derived from primary fibroblast cell lines. One orangutan DNA sample was provided by Drs. Morris Goodman and Derek Wildman, Center for Molecular Medicine and Genetics, Wayne State University School of Medicine. Cell lines for two bonobos and one gorilla were contributed by Dr. Edwin H. McConkey, Molecular, Cellular, and Developmental Biology Department, University of Colorado, Boulder. We thank Erin Dorning for help with manuscript preparation; William McNair for help with data analysis; Billie Carstens for FISH graphics; and Ken Krauter, Mark Spencer, Sandy Martin, and Zhaolei Zhang for helpful discussions. This work was supported by National Institute on Alcohol Abuse and Alcoholism R01AA11853 (to JMS), by National Cancer Institute K07CA88811 (to DHG) and by Butcher award 34-20121-33342 (to JMS and LM). Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. JP and JS conceived and designed the experiments. AF, YK, EM, and GH performed the experiments. AF, EM, KM, LM, MB, SB, AK-F, DG, LM, RB, JP, and JS analyzed the data. AF, YK, EM, RH, TH-B, and JP contributed reagents/materials/analysis tools. JS wrote the paper. Academic Editor: Chris Tyler-Smith, Sanger Institute Abbreviations aCGHarray-based comparative genomic hybridization BACbacterial artificial chromosome BLASTbasic local alignment search tool BLATBLAST-like alignment tool CGHcomparative genomic hybridization DAVIDDatabase for Annotation Visualization and Integrated Discovery ESTexpressed sequence tag FISHfluorescence in situ hybridization GABAγ-aminobutyric acid GGO Gorilla gorilla (gorilla) GOGene Ontology HLSrefers to genes that show interhominoid aCGH signals indicative of a human lineage-specific variation in copy number HSA Homo sapiens (human) IMAGEintegrated molecular analysis of genomes and their expression LINElong interspersed nuclear element LTRlong terminal repeat LOD scorelog base 10 of the likelihood ratio under the hypotheses of linkage and nonlinkage LSrefers to genes that show interhominoid aCGH signals indicative of a lineage-specific variation in copy number MYAmillion years ago NAIPneuronal apoptosis inhibitory protein PECNpublished estimate of gene copy number PPA Pan paniscus (bonobo) PPY Pongo pygmaeus (orangutan) PTR Pan troglodytes (chimpanzee) RT-PCRreal-time PCR SINEshort interspersed nuclear element SMAspinal muscular atrophy SMDStanford Microarray Database WSSD DatabaseWhole Genome Shotgun Segmental Duplication Database ==== Refs References Bailey JA Yavor AM Massa HF Trask BJ Eichler EE Segmental duplications: Organization and impact within the current human genome project assembly Genome Res 2001 11 1005 1017 11381028 Bailey JA Gu Z Clark RA Reinert K Samonte RV Recent segmental duplications in the human genome Science 2002a 297 1003 1007 12169732 Bailey JA Yavor AM Viggiano L Misceo D Horvath JE Human-specific duplication and mosaic transcripts: The recent paralogous structure of chromosome 22 Am J Hum Genet 2002b 70 83 100 11731936 Bieche I Olivi M Champeme MH Vidaud D Lidereau R Novel approach to quantitative polymerase chain reaction using real-time detection: Application to the detection of gene amplification in breast cancer Int J Cancer 1998 78 661 666 9808539 Chen FC Li WH Genomic divergences between humans and other hominoids and the effective population size of the common ancestor of humans and chimpanzees Am J Hum Genet 2001 68 444 456 11170892 Cheung J Estivill X Khaja R MacDonald JR Lau K Genome-wide detection of segmental duplications and potential assembly errors in the human genome sequence Genome Biol 2003 4 R25 12702206 Ciccodicola A D'Esposito M Esposito T Gianfrancesco F Migliaccio C Differentially regulated and evolved genes in the fully sequenced Xq/Yq pseudoautosomal region Hum Mol Genet 2000 9 395 401 10655549 Dennis G Sherman BT Hosack DA Yang J Gao W DAVID: Database for Annotation, Visualization, and Integrated Discovery Genome Biol 2003 4 R60 DiAntonio A Haghighi AP Portman SL Lee JD Amaranto AM Ubiquitination-dependent mechanisms regulate synaptic growth and function Nature 2001 412 449 452 11473321 Fan Y Linardopoulou E Friedman C Williams E Trask BJ Genomic structure and evolution of the ancestral chromosome fusion site in 2q13–2q14.1 and paralogous regions on other human chromosomes Genome Res 2002 12 1651 1662 12421751 Frazer KA Chen X Hinds DA Pant PV Patil N Genomic DNA insertions and deletions occur frequently between humans and nonhuman primates Genome Res 2003 13 341 346 12618364 Gao F Bailes E Robertson DL Chen Y Rodenburg CM Origin of HIV-1 in the chimpanzee Pan troglodytes troglodytes Nature 1999 397 436 441 9989410 Gilad Y Bustamante CD Lancet D Pääbo S Natural selection on the olfactory receptor gene family in humans and chimpanzees Am J Hum Genet 2003 73 489 501 12908129 Heid CA Stevens J Livak KJ Williams PM Real time quantitative PCR Genome Res 1996 6 986 994 8908518 Hirotsune S Yoshida N Chen A Garrett L Sugiyama F An expressed pseudogene regulates the messenger-RNA stability of its homologous coding gene Nature 2003 423 91 96 12721631 Horvath JE Schwartz S Eichler EE The mosaic structure of human pericentromeric DNA: A strategy for characterizing complex regions of the human genome Genome Res 2000 10 839 852 10854415 Hosack DA Dennis G Sherman BT Lane HC Lempicki RA Identifying biological themes within lists of genes with EASE Genome Biol 2003 4 R70 14519205 IJdo JW Baldini A Ward DC Reeders ST Wells RA Origin of human chromosome 2: An ancestral telomere–telomere fusion Proc Natl Acad Sci U S A 1991 88 9051 9055 1924367 Johnson ME Viggiano L Bailey JA Abdul-Rauf M Goodwin G Positive selection of a gene family during the emergence of humans and African apes Nature 2001 413 514 519 11586358 Kaessmann H Wiebe V Pääbo S Extensive nuclear DNA sequence diversity among chimpanzees Science 1999 286 1159 1162 10550054 Kallioniemi A Kallioniemi OP Sudar D Rutovitz D Gray JW Comparative genomic hybridization for molecular cytogenetic analysis of solid tumors Science 1992 258 818 821 1359641 Lamprecht R Farb CR LeDoux JE Fear memory formation involves p190 RhoGAP and ROCK proteins through a GRB2-mediated complex Neuron 2002 36 727 738 12441060 Lefebvre S Burglen L Reboullet S Clermont O Burlet P Identification and characterization of a spinal muscular atrophy-determining gene Cell 1995 80 155 165 7813012 Li WH Molecular evolution 1997 Sunderland, Massachusetts Sinauer Associates 432 Liston P Roy N Tamai K Lefebvre C Baird S Suppression of apoptosis in mammalian cells by NAIP and a related family of IAP genes Nature 1996 379 349 353 8552191 Livak KJ Flood SJ Marmaro J Giusti W Deetz K Oligonucleotides with fluorescent dyes at opposite ends provide a quenched probe system useful for detecting PCR product and nucleic acid hybridization PCR Methods Appl 1995 4 357 362 7580930 Locke DP Segraves R Carbone L Archidiacono N Albertson DG Large-scale variation among human and great ape genomes determined by array comparative genomic hybridization Genome Res 2003 13 347 357 12618365 Mefford HC Trask BJ The complex structure and dynamic evolution of human subtelomeres Nat Rev Genet 2002 3 91 102 11836503 Muchmore EA Diaz S Varki A A structural difference between the cell surfaces of humans and the great apes Am J Phys Anthropol 1998 107 187 198 9786333 Novembre FJ Saucier M Anderson DC Klumpp SA O'Neil SP Development of AIDS in a chimpanzee infected with human immunodeficiency virus type 1 J Virol 1997 71 4086 4091 9094687 Ohno S Evolution by gene duplication 1970 Berlin Springer 160 Pääbo S The mosaic that is our genome Nature 2003 421 409 412 12540910 Pinkel D Segraves R Sudar D Clark S Poole I High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays Nat Genet 1998 20 207 211 9771718 Pollack JR Perou CM Alizadeh AA Eisen MB Pergamenschikov A Genome-wide analysis of DNA copy-number changes using cDNA microarrays Nat Genet 1999 23 41 46 10471496 Pollack JR Sorlie T Perou CM Rees CA Jeffrey SS Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors Proc Natl Acad Sci U S A 2002 99 12963 12968 12297621 Ramakers GJ Rho proteins and the cellular mechanisms of mental retardation Am J Med Genet 2000 94 367 371 11050619 Rieder MJ Taylor SL Tobe VO Nickerson DA Automating the identification of DNA variations using quality-based fluorescence resequencing: Analysis of the human mitochondrial genome Nucleic Acids Res 1998 26 967 973 9461455 Rochette CF Gilbert N Simard LR SMN gene duplication and the emergence of the SMN2 gene occurred in distinct hominids: SMN2 is unique to Homo sapiens Hum Genet 2001 108 255 266 11354640 Samonte RV Eichler EE Segmental duplications and the evolution of the primate genome Nat Rev Genet 2002 3 65 72 11823792 Schena M Shalon D Davis RW Brown PO Quantitative monitoring of gene expression patterns with a complementary DNA microarray Science 1995 270 467 470 7569999 SLI Consortium A genomewide scan identifies two novel loci involved in specific language impairment Am J Hum Genet 2002 70 384 398 11791209 Snijders AM Nowak N Segraves R Blackwood S Brown N Assembly of microarrays for genome-wide measurement of DNA copy number Nat Genet 2001 29 263 264 11687795 Stankiewicz P Lupski JR Molecular-evolutionary mechanisms for genomic disorders Curr Opin Genet Dev 2002 12 312 319 12076675 Stanyon R Wienberg J Romagno D Bigoni F Jauch A Molecular and classical cytogenetic analyses demonstrate an apomorphic reciprocal chromosomal translocation in Gorilla gorilla Am J Phys Anthropol 1992 88 245 250 1605320 Wildman DE Uddin M Liu G Grossman LI Goodman M Implications of natural selection in shaping 99.4% nonsynonymous DNA identity between humans and chimpanzees: Enlarging genus Homo Proc Natl Acad Sci U S A 2003 100 7181 7188 12766228 Xu M Okada T Sakai H Miyamoto N Yanagisawa Y Functional human NAIP promoter transcription regulatory elements for the NAIP and PsiNAIP genes Biochim Biophys Acta 2002 1574 35 50 11955612 Yunis JJ Prakash O The origin of man: A chromosomal pictorial legacy Science 1982 215 1525 1530 7063861 Zimonjic DB Kelley MJ Rubin JS Aaronson SA Popescu NC Fluorescence in situ hybridization analysis of keratinocyte growth factor gene amplification and dispersion in evolution of great apes and humans Proc Natl Acad Sci U S A 1997 94 11461 11465 9326632

15252450

PMC449870

CC BY

2021-01-05 08:26:25

no

PLoS Biol. 2004 Jul 13; 2(7):e207

utf-8

PLoS Biol

10.1371/journal.pbio.0020207

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020208SynopsisEcologyEvolutionZoologyMammalsOn the Brink: How Biology and Humans Affect Extinction Risk Synopsis7 2004 13 7 2004 13 7 2004 2 7 e208Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Human Population Density and Extinction Risk in the World's Carnivores ==== Body Close to a quarter of the world's mammals are at high risk of extinction. Save for the periodic “great extinctions,” mammalian extinction has been a relatively rare event in geological terms, with one species disappearing from the fossil record every 1,000 years or so. Over the past 400 years, species have been disappearing 50 times faster than this “background” rate, with one vanishing every sixteen years. Human population growth and all its consequences—habitat destruction, propagation of invasive species, poaching—are largely to blame. Top predators often suffer heavily from encounters with humans, especially when those predators are perceived as economic threats. Thirty-four Mexican gray wolves have been reintroduced in Arizona since 1998, and five have been shot, reportedly by ranchers. The IUCN lists lions as vulnerable. Photo, with permission, by Nicky Jenner, Institute of Zoology, London Species in the most densely populated areas are expected to face the greatest risk, yet some survive while others perish, suggesting biological factors play a role in their fate. If, for example, the same external force drastically reduces populations of species with different biological profiles, then a species with a relatively short gestation period may stand a better chance of recovering than a long-gestating species. Effective conservation strategies depend on understanding which factors are likely to increase extinction risk, but it's unclear how important intrinsic biological traits are relative to external pressures from humans and whether biology's influence on survival depends on the intensity of the threat. Ecologists often use human population density as a proxy for anthropogenic threats such as habitat destruction and hunting. To tease out the relative importance of all these factors, Marcel Cardillo et al. analyzed the impact of various biological traits and human population density on extinction risk in the mammal order Carnivora, which includes the red panda, lion, and members of the photogenic weasel-like viverrid family. By identifying the most salient factors in predicting extinction, the authors have created a model to identify those species at greatest risk. The biology of a species combined with human population density, the researchers found, is a stronger predictor of risk than exposure to humans alone; those biological traits that increase risk vary depending on a species' exposure to human populations. Carnivores with low exposure to humans, for example, are likely to be at greater risk if their population density is low and they have small ranges, possibly because this makes them more vulnerable to loss of habitat. Species living near densely populated human areas must often contend with hunting and other direct threats on top of habitat loss and are more at risk if they also have long gestation periods—they can't repopulate fast enough to offset the additional pressures. Based on projected human population growth, this model predicts the addition of a number of species—mostly from Africa, where population growth rates largely exceed the global average—to the endangered list by the year 2030. Most of these species—including African viverrids such as the common genet, which not only lives in areas where human populations are rapidly expanding but is also biologically predisposed to decline—are currently considered a low conservation priority. While it's possible that the direct effects of human population density are past—that is, species most sensitive to human incursions are already gone—human population density likely modulates biology. That might explain why gestation length didn't predict risk for species living in sparsely populated areas—all else being equal, their numbers remained relatively stable. A species with a small population forebodes a high extinction risk regardless of human density, though species with long gestation periods, again, face greater danger in the company of humans. Altogether, these results suggest that as human population pressures increase, the importance of species-specific biology in predicting extinction risk also increases, with biology affecting which species are most vulnerable to external threats. With most conservation efforts focused on damage control, these findings make the case for interceding before a species reaches the brink of extinction. “There is no room for complacency about the security of species,” the authors warn, “simply because they are not currently considered threatened.”

0

PMC449891

CC BY

2021-01-05 08:21:11

no

PLoS Biol. 2004 Jul 13; 2(7):e208

utf-8

PLoS Biol

10.1371/journal.pbio.0020208

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020209SynopsisCell BiologyImmunologyInfectious DiseasesVirologyVirusesHomo (Human)Regulating the Regulators: Immune System Regulators Are Highly Susceptible to HIV Infection Synopsis7 2004 13 7 2004 13 7 2004 2 7 e209Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. HIV Infection of Naturally Occurring and Genetically Reprogrammed Human Regulatory T-cells ==== Body Nearly a dozen varieties of interdependent cells work in harmony to protect the body from infectious pathogens. Dendritic cells and B-cells carry remnants of pathogens to nearby helper T-cells (also known as CD4 cells because they express the CD4 protein on their surfaces), which coordinate a threat response by signaling killer T-cells to destroy the intruder. Yet another class of cells cleans up the debris such an encounter inevitably creates. Each of these cell types is further classified based on the combinations of surface proteins they express. Such proteins may simply identify the cell type or may help define its function. Inhibition of Treg function by HIV leads to T-cell hyperactivation An immune response can activate millions of cells; once these cells perform their respective jobs, it's crucial that they retire from active duty. That's where regulatory T-cells, or Treg cells, come in: an immune system that fails to retreat from an immune response can be just as dangerous as one that fails to organize. Research has recently focused on a subset of Treg cells that express CD4 and CD25 proteins (so-called CD4+CD25+ T-cells) and that suppress T-cell activation in mice and humans. Treg cells appear to be key immune system regulators, as their absence results in autoimmune and allergic diseases. Now a team led by Derya Unutmaz at Vanderbilt University report that human Treg cells are highly vulnerable to HIV infection. Treg cells isolated from healthy volunteers were not only highly susceptible to HIV infection but were also killed by the virus. Because Treg cells account for only about 1% of human T-cells and are difficult to grow in a test tube, Unutmaz and colleagues developed a way to manufacture sufficient quantities for study by introducing a transcription factor called FoxP3 into conventional “naïve” T-cells (cells not yet primed to recognize a specific target). FOXP3 is required for the development of Treg cells in mice. Though it's not clear what role the human form of the gene plays in human Treg cell development, FOXP3 mutations cause an autoimmune disease associated with hyperactive T-cells. Here the authors demonstrate that FoxP3 transforms T-cells into Treg cells. The FoxP3-engineered T-cells behaved just like naturally occurring Treg cells: when exposed to a population of resting naïve CD4 T-cells, the engineered cells suppressed their activation. Overexpression of FoxP3 also made activated T-cells more susceptible to infection. Since Treg cells are so susceptible to HIV, the researchers reasoned that these cells might be compromised in HIV-infected patients and that loss of Treg cells could lead to T-cell hyperactivation. Their logic was borne out by the finding that a portion of HIV patients with low CD4 counts and high levels of activated T-cells also had greatly depleted numbers of FoxP3-expressing CD4+CD25+ T-cells—a marker for Treg cells. The finding that HIV targets cells that normally suppress immune function is significant, given that HIV infection is characterized by chronic T-cell hyperactivation. Disruption of Treg cells, Unutmaz and colleagues conclude, could in turn disrupt the delicate balance of immune system function, setting the stage for hyperactivation—and a chronically hyperactive immune response could eventually exhaust the immune system. With a method to generate large numbers of Treg cells for study, Unutmaz and colleagues have paved the way for identifying mechanisms that mediate Treg cells' suppressive function and provided another resource for determining how HIV tips the scales toward disease progression.

0

PMC449894

CC BY

2021-01-05 08:21:12

no

PLoS Biol. 2004 Jul 13; 2(7):e209

utf-8

PLoS Biol

10.1371/journal.pbio.0020209

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020210Community PageGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyHomo (Human)Talking Science Community PagePollock Ludmila 7 2004 13 7 2004 13 7 2004 2 7 e210Copyright: © 2004 Ludmila Pollock.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.The Cold Spring Harbor Laboratory Oral History Collection presents interviews with over 80 leading scientists, drawn from the fields of molecular biology and genetics ==== Body “…where people do come with a sense that there's going to be openness. That there's going to be a great comfort with people standing up and challenging a particular perspective. That young people are welcome. That old codgers are welcome as well. This is sort of the idyllic way that science is supposed to happen. And Cold Spring Harbor [Laboratory] provides that opportunity.” —Francis Collins, Cold Spring Harbor Laboratory Oral History Collection Cold Spring Harbor Laboratory (CSHL) has played a significant role in the development of genetics, a role that began at the Laboratory in 1904, just a few years after the discovery of Mendel's work. In 1908, George Shull's development of hybrid corn (maize) shaped the future of modern agricultural genetics; 55 years ago, Barbara McClintock worked on maize cytogenetics and discovered “jumping genes”; and in 1977, Richard Roberts was a codiscoverer of “split genes.” Throughout the 1950s, the Phage Course at CSHL trained many of the leaders of the then new field of molecular genetics, and the Laboratory's courses continue to train young scientists. CSHL Archives is a unique repository of rare books, manuscripts, photos, and scientific reprints that document the institution's significant contributions to molecular genetics research over the past 100 years. In 2000, the Archives received James Watson's personal collection of original materials, including manuscripts, correspondence, photographs, lab notes, lectures, memorabilia and reprints, for processing and preservation. This is an invaluable documentary resource for the history of molecular biology. While organizing over 60 years of Watson's correspondence, we realized that many of his correspondents have passed through CSHL as researchers, participants in the laboratory's leading scientific meetings, or instructors of our courses. We decided to talk to these scientists and record their commentaries, stories, and personal memories. These oral history interviews will enhance our collection by adding the voices of participants in significant scientific events. The project began in 2000. In 2002, we received a grant from the Gladys Brook Foundation that enabled us to purchase state-of-the-art recording and editing equipment for carrying out this project, and created a web site exhibiting taped conversations. During the last three years, we have interviewed over 80 scientists from several generations working in different fields of molecular biology and genetics, and have developed a video collection at CSHL's Carnegie Library. (A customized DVD of this collection is available.) The unique environment of the CSHL facilitated our task. Throughout the year, researchers from all over the world attend our scientific meetings to exchange information and ideas about their research, so most of the interviews took place at the Laboratory during these meetings. The scientists were always very pleasant and receptive to our requests for interviews. Other interviews were conducted in Cambridge and Boston, Massachusetts; La Jolla, California; Washington, DC; and abroad, in Oxford, England, and Sydney and Melbourne, Australia. Interviewing scientists was very exciting. We often wanted to come back for an additional interview because one or two hours was not enough time to collect all the memories that they wanted to share with us. It is well known that Sydney Brenner is a superb storyteller. After spending a few hours listening to Sydney in La Jolla, California, I received a call confirming my meeting with Francis Crick, which was to take place in the next 30 minutes. Despite looking forward so much to this meeting, I was torn between my present interview, in which I was deeply involved, and my next, which I was gladly anticipating. The CSHL Oral History Collection web site (library.cshl.edu) provides an opportunity to “meet and get to know” the leading minds of molecular biology and genetics. We hope that our site makes the history of science come to life. These interviews can be helpful to all categories of users: students, scientists, historians, writers, journalists, and others. The site is searchable and allows visitors to cross-reference people and information, and links them to the world of narratives and anecdotes, as told in the voices of those interviewed. Selected interview clips are organized into five topics: CSHL, James D. Watson, prominent scientists, scientific experience, and genome research. Each topic is divided into subtopics; for example, “Genome Research” includes: involvement in genomics, mechanism of the Human Genome Project, challenges of the Human Genome Project, dangers of genomic research, the future of genomics, and others. “Woman in Science” (Figure 1) is one of the subtopics of “Scientific Experience.” Visitors also can choose a particular scientist and link to all the topics that he or she discussed. A brief biography of each participant has been included. Figure 1 Women in Science One of the pages from the “Scientific Experience” topic of the CSHL Oral History Collection. Our Web site adds a new dimension to the papers and books written by leading scientists—now you can listen to them discuss their work in their own voices! On this site you will meet a remarkable range of scientists, from friends and students of Barbara McClintock and Jim Watson, to the evolutionist Ernst Mayr, to the genome bioinformaticist James Kent. You'll hear Nancy Hopkins, Tom Maniatis, Matt Ridley, and Joan Steitz, to name just a few. We remain committed to our objective of providing an unprecedented and exciting approach for scientists, historians, scholars, and students to gain a firsthand account of the history of molecular biology and the stories behind the people who contributed to it. We have many more interviews planned, so in the future you can expect to meet even more of the fascinating figures who illuminate the glory days of the world of molecular biology. Oral History team: Clare Bunce, Kiryn Haslinger, Marisa Macari, Glen McAlpine, Mila Pollock, Jianna Tvedt, and Jan Witkowski. Editing help: Ellen Brenner, Margaret Henderson, and Jan Witkowski. Ludmila Pollock is the Director of Libraries and Archives at Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America. E-mail: [email protected] Abbreviation CSHLCold Spring Harbor Laboratory

15252453

PMC449895

CC BY

2021-01-05 08:26:25

no

PLoS Biol. 2004 Jul 13; 2(7):e210

utf-8

PLoS Biol

10.1371/journal.pbio.0020210

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020211Research ArticleAnimal BehaviorInsectsHoneybee Odometry: Performance in Varying Natural Terrain Honeybee OdometryTautz Juergen 1 Zhang Shaowu 2 Spaethe Johannes 1 ¤1Brockmann Axel 1 Si Aung 2 Srinivasan Mandyam [email protected] 2 1Beegroup Würzburg, Lehrstuhl für Verhaltensphysiologie und SoziobiologieWürzburg, Germany2Centre for Visual Sciences, Research School of Biological Sciences, Australian National UniversityCanberra, Australian Capital TerritoryAustralia7 2004 13 7 2004 13 7 2004 2 7 e2115 2 2004 6 5 2004 Copyright: © 2004 Tautz et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Dances as Windows into Insect Perception Honeybees' Distance Perception Changes with Terrain of Flight Path Recent studies have shown that honeybees flying through short, narrow tunnels with visually textured walls perform waggle dances that indicate a much greater flight distance than that actually flown. These studies suggest that the bee's “odometer” is driven by the optic flow (image motion) that is experienced during flight. One might therefore expect that, when bees fly to a food source through a varying outdoor landscape, their waggle dances would depend upon the nature of the terrain experienced en route. We trained honeybees to visit feeders positioned along two routes, each 580 m long. One route was exclusively over land. The other was initially over land, then over water and, finally, again over land. Flight over water resulted in a significantly flatter slope of the waggle-duration versus distance regression, compared to flight over land. The mean visual contrast of the scenes was significantly greater over land than over water. The results reveal that, in outdoor flight, the honeybee's odometer does not run at a constant rate; rather, the rate depends upon the properties of the terrain. The bee's perception of distance flown is therefore not absolute, but scene-dependent. These findings raise important and interesting questions about how these animals navigate reliably. After outdoor flight, honeybees represent distances they have travelled to find food differently, depending on the visual properties of the landscape ==== Body Introduction When a scout honeybee discovers an attractive patch of flowers, she returns to the hive and performs the famous “waggle dance” to advertise the location of the food source to her nestmates (von Frisch 1993). The dance consists of a series of alternating left-hand and right-hand loops, interspersed by a segment in which the bee waggles her abdomen from side to side. The duration of this “waggle phase” conveys to the potential recruits the distance of the food source from the hive: the longer the duration of the waggle, the greater the distance (von Frisch 1993). This information is used by the recruited bees to locate the food source. Clearly, then, the scout as well as the recruits are able to gauge how far they have flown in search of food. Early studies concluded that bees estimate distance flown by gauging the amount of energy they expend to reach the destination (von Frisch 1993; Neese 1988). More recent studies, however, are providing increasing evidence that this “energy hypothesis” is incorrect, at least for moderate distances of a few hundred meters (Esch et al. 1994, 2001; Esch and Burns 1995; Srinivasan et al. 1996, 1997, 2000). Over these distances, bees appear to gauge distance flown by measuring how much the image of the world appears to move in the eye en route to the food source (Esch and Burns 1995, 1996; Srinivasan et al. 1996, 1997, 2000; Esch et al. 2001). There are two kinds of experimental evidence that support this “optic flow” hypothesis. First, bees that fly a given distance close to the ground signal a much larger distance in their dances than do bees that fly the same distance at a considerable height above the ground and therefore experience a smaller rate of image motion (Esch and Burns 1996). Second, bees trained to fly to a feeder placed inside a short, narrow tunnel, the walls and floor of which are lined with a random visual texture, indicate a hugely exaggerated flight distance in their waggle dances (Srinivasan et al. 2000; Esch et al. 2001). Evidently, the proximity of the walls and floor of the tunnel greatly amplifies the magnitude of the optic flow that the bees experience, in comparison with the situation during outdoor flight in a natural environment. On the other hand, when the same tunnel was lined with axial stripes—so that a bee flying through it would experience very little optic flow because the stripes were parallel to the flight direction—the bees signaled a very small distance, even though they had flown the same physical distance as in the previous condition (Srinivasan et al. 2000). This experiment indicated that distance flown was being measured in terms of integrated optic flow, and not in terms of physical distance flown or energy consumed. If bees do indeed gauge distance traveled by measuring optic flow and integrating it over time, it is pertinent to enquire into the properties of their visually driven “odometer.” Given that the environment through which a bee flies can vary substantially in terms of its visual properties, such as color, contrast, texture, and the distribution of objects, it is important to know whether, and to what extent, the bee's perception of distance flown is affected by these environmental variables. In other words, how “robust” is the honeybee's odometer? In a recent study, this question was explored by training bees to fly into a short, narrow tunnel (as described above), and analyzing the waggle dances of the returning bees as the texture and the contrast of the patterns lining the walls and the floor were systematically varied (Si et al. 2003). The patterns used were black-and-white stripes and sinusoidal gratings of various spatial frequencies and contrasts. This study revealed that the honeybee's odometer is indeed rather robust to changes in the visual environment. For a flight of a given distance into the tunnel, the odometric signal is relatively unaffected by changes in the spatial frequency or contrast of the gratings, as long as the contrast of the grating is above 20%. (Contrast is defined here as 100(Imax−Imin)/(Imax+Imin), where Imax and Imin are the intensities of the bright and the dark bars of the grating, respectively.) At contrasts below 20%, the strength of the odometric signal starts to decrease. Another recent study revealed that the visual odometer of the honeybee is “color-blind” and driven exclusively by the green receptor (Chittka and Tautz 2003). Here we examine the robustness of the honeybee's odometer when it is performing in natural conditions, during flight through varying landscapes. Specifically, we compare the strength of the odometric signal during flight over land, where contrasts tend to be relatively high and textures are relatively rich, to that during flight over water, where contrast tends to be low and texture is sparse. The experiments described here involve training bees to fly over stretches of water or land, and comparing their waggle dances. These experiments also provide us with the opportunity to address two further questions that are somewhat controversial and not yet completely resolved. One question relates to whether bees can, and do, fly safely over large bodies of water, such as lakes. Heran and Lindauer (1963), for instance, suggested that bees experience difficulty in flying across lakes, often losing altitude and plunging into the water. The other question relates to whether bees, having discovered an attractive food source positioned in the middle of a large expanse of water, can successfully recruit their nestmates, through their dances, to visit the food source (Gould and Gould 1988). Results Experiment 1 In this experiment the feeder route was initially over land (segment 1), then over water (segment 2), and finally again over land (segment 3) (Figure 1). As the feeder was moved away from the hive in stepwise fashion, most of the marked bees followed. Virtually all of the marked bees continued to visit the feeder even when it was over water. Thus, the trained bees had no difficulty in flying over the water and finding the feeder in the boat. Figure 1 Aerial Photograph of Experimental Site In experiment 1 the bees were trained to fly due southwest from the hive initially over a stretch of land, then over water, and finally across the island. In experiment 2 the bees were trained to fly due northwest along a route that was entirely over land. The white dots depict successive locations of the feeding station. The red dots along the route of experiment 1 depict the shoreline stations, which represent the boundaries between segments 1 and 2, and between segments 2 and 3. The red dots along the route of experiment 2 represent the same distances from the hive as their counterparts in experiment 1. The situation with unmarked honeybees recruited to the feeder by the marked scouts was rather different. A few recruits were observed at the feeder as long as the feeder was on land. Once the feeder was on the water, however, very few recruits were observed. Recruits reappeared when the boat reached the island. In fact, they were observed searching at the shore of the island shortly before the boat reached that point. Recruits were present at all feeder positions on the island. For each feeder position along the route, the dances of the returning bees were filmed and analyzed as described in Materials and Methods. An example of a dance is given in Video S1. The variation of the mean waggle duration with feeder position is shown in Figure 2A. Waggle duration increased with feeder distance. Figure 2 Variation of Waggle Duration with Feeder Distance in Experiment 1 In this experiment, flight was over land (segment 1), water (segment 2), and again over land (segment 3) (see Figure 1). Symbols depict mean waggle durations. Bars represent standard error of the mean. R, correlation coefficient. (A) shows a global linear approximation of the data, using a single regression for the entire data set (solid line). Broken curves depict 95% CIs for regression slope. (B) shows a piecewise linear approximation of the data, using separate regressions for the data over land, water, and the island. Equations represent regression lines. Figure 2A also shows the best-fitting straight line (a linear regression) through the data. The slope of this regression line is 1.303 ± 0.150, where the limits denote the 95% confidence interval (CI). The root mean square (rms) error between the data and this linear approximation is 42.3 msec. Is this a good fit to the data? Visual inspection of the data in Figure 2 suggests that the rate at which the waggle duration increases with feeder distance (the slope of the curve) is higher in the first and third segments—where the bees fly over land—than in the second segment, where they fly over water. This impression is confirmed when linear regressions of the data are carried out separately for each of the three segments, as shown in Figure 2B. The results then reveal that the slope of the regression in segment 2 (water) is 0.546 ± 0.483, which is significantly lower than the slope in segment 1 (1.970 ± 0.348) and segment 3 (2.243 ± 0.500) (p < 0.02, pairwise comparison, Student t-test). On the other hand, there is no significant difference between the slopes for the land (segment 1) and the island (segment 3) segments (p > 0.2, Student t-test). Within each segment, the data fit a straight line quite well; the rms error between all of the data points and the piecewise linear approximation is 14.7 msec. This value is considerably lower than the rms error produced by the single straight line in Figure 2A (42.3 msec). Thus, the data are not well represented by a single linear approximation. They are better approximated by a piecewise linear relationship in which the slope over water is lower than that over land. Experiment 2 In experiment 2 the feeder route was of the same total length as in experiment 1, but was entirely over land (see Figure 1). In this experiment, unmarked recruits were observed at each feeder position. The variation of the mean waggle duration with feeder position for this experiment is shown in Figure 3A. Here, again, waggle duration increased with feeder distance. The best-fitting straight line through the entire data set is shown in Figure 3A; the slope of the resulting regression line is 1.431 ± 0.695. The rms error between this line and the data is 37.9 msec. In addition, to enable a comparison of the data from experiment 2 with those from experiment 1, the same data set was artificially divided into three segments, of lengths 190 m, 240 m, and 150 m, as it was for experiment 1 (see Figure 2B), and a linear regression was performed separately on each segment. The results of this piecewise linear approximation are shown in Figure 3B. In this case, the slopes of the regression lines were 1.712 ± 0.526 (segment 1), 1.925 ± 0.358 (segment 2), and 1.110 ± 1.500 (segment 3). There was no significant difference between any of these three slopes (p > 0.10, pairwise comparison, Student t-test). The rms error between the piecewise linear approximation and the data was 41.6 msec, a value similar to that obtained with the single, best-fitting straight line (37.9 msec, Figure 3A). Thus, in this case, a piecewise linear approximation does not improve the fit. This analysis indicates that the data of experiment 2, in which the bees flew exclusively over land, is well approximated by a single line of constant slope. The slope of this best-fitting line is 1.431 ± 0.695, which is not significantly different from the slopes calculated for either of the land segments in Figure 2A (p > 0.05, Student t-test). Figure 3 Variation of Waggle Duration with Feeder Distance in Experiment 2 For the purposes of comparative analysis, the data were artificially divided into segments corresponding to the land, water, and island segments of experiment 1 (see Figure 2). (A) shows a global linear approximation of the data, using a single regression for the entire data set. (B) shows a piecewise linear approximation of the data as they correspond to the segments of experiment 1. Other details are the same as in Figure 2. It is worth noting, however, that, in experiment 2, the three data points in the middle of segment 3 (Figure 3A and 3B) exhibit a much lower slope than the rest of the curve. A possible reason for this local variation will be discussed later. We may summarize the findings of experiments 1 and 2 by saying that the mean waggle duration increases more rapidly with distance flown when bees fly over land, than when they fly over water. The slope of the mean waggle duration versus distance curve is three to four times greater over land. In other words, land provides a stronger odometric signal than does water. Similar results were obtained when experiment 1 was repeated 3 weeks later using a different colony of bees. The new colony was placed in the same shed and allowed 10 d to acclimatize itself to the new location and familiarize itself with the surrounding terrain before the experiment commenced. The feeder was moved along exactly the same route as the original experiment 1. (Stakes had been placed on the ground to mark the locations of the feeding stations during the first experiment, to ensure that the same locations were used in the repetition. The feeder positions over the water were reproduced by using a marked rope, as described in Materials and Methods.) In this case, the slopes of the curve were 1.071 ± 0.127 (segment 1), 0.283 ± 0.693 (segment 2), and 1.990 ± 0.381 (segment 3). Here, again, the slope of the curve in segment 2 is substantially and significantly lower, as indicated by Student t-test results, than the slope in segment 1 (p < 0.01) and the slope in segment 3 (p < 0.001). Scene Analysis As described in Materials and Methods, a digital camera was used to acquire samples of images of the visual scenes that the bees would have encountered during the flights over land and water. Two such samples, one over land and the other over water, are shown in Figure 4. The mean contrasts of these and a few other scenes, as measured within windows of various sizes (Figure 4) are given in Tables 1 and 2. In each case, contrasts were measured for images that were obtained with and without a green filter placed in front of the camera lens. The green filter was chosen to mimic the spectral sensitivity of the honeybee's green photoreceptor channel, the channel involved in the sensing of image motion (Lehrer 1987; Chittka and Tautz 2003). Contrasts tend to be slightly higher when scenes—over land as well as over water—are viewed through the green filter. It is clear from Tables 1 and 2, however, that, in general, contrasts tend to be substantially greater over land than over water, regardless of whether the scenes are viewed directly or through a green filter. The contrast over water tends to be lowest when there is no wind, i.e., when the water is still and there are no ripples. However, we did not investigate this latter phenomenon systematically. Figure 4 Land and Water Terrain Examples of scenes over land (A) and water (B) that were photographed and analyzed for mean contrast within windows of various sizes, as illustrated by the red boxes in the center of the photographs. Results are given in Tables 1 and 2. Table 1 Mean Contrasts of Scenes over Land Table 2 Mean Contrasts of Scenes over Water We have assumed in this analysis that the image motion experienced by the bee is dominated by flow in the ventral visual field. This is not an unreasonable assumption, because the lateral structures (such as bushes and trees) were sparse and usually farther away than the ground, except in certain rare circumstances (see below). Discussion Our results demonstrate, first of all, that bees can be trained to fly reliably and without accident over stretches of water that span a few hundred meters. This has also been reported in earlier studies (Heran and Lindauer 1963; von Frisch 1993; Gould and Gould 1988), although some studies (e.g., Heran and Lindauer 1963) mention that bees experience difficulty in flying across lakes, often losing altitude and plunging into the water. As in earlier studies, we observed that individually marked bees trafficked regularly between the hive and the feeder, regardless of whether the feeder was on land or water. The short flight times (of a minute or two across the whole stretch of water, as established by radio communication) indicated that they had no difficulty in flying the direct route between origin and destination. We found, however, that the feeder attracted far fewer recruits when it was positioned over water. Although the scout bees that were trained to visit the feeder on land readily followed the feeder when it was moved over the water, recruits failed to appear once the water was reached. This was not because the trained scout bees ceased to dance once the feeder was over the water; they continued to dance with high vigor, and their waggles encoded a position over the water. Recruits reappeared when the feeder arrived at the island; in fact, they seemed to “anticipate” its arrival by patrolling the shore of the island even before the boat had landed there. Recruits were observed all along the route on the island, as well as along the entire stretch of the route used in experiment 2, which was exclusively over land. One possible explanation for the lack of recruitment when the feeder was over water could be the relatively low slope of the distance indication curve in this region, which would lead to a less precise indication of the position of the feeder, making it harder to pinpoint. This explanation seems unlikely, however, since the boat in which the feeder was carried was large and conspicuous, and was the only object on the water. Another possible reason for the lack of recruitment could have to do with the lack of assistance from experienced foragers. A recent study showed that experienced foragers can sometimes aid recruits in pinpointing a feeder by synchronizing their flights and performing buzzing flights around an unscented feeder (Tautz and Sandeman 2003). However, such assistance was not apparent in our study when the feeder was over the water. Why this assistance was not provided remains unexplained. A third possibility is that, when inexperienced recruits fly over water, they fly at a higher or lower altitude than the trained scouts, and thus experience a different magnitude of optic flow. Consequently, they may search for the feeder at the wrong location. This possibility remains to be explored. What our observations do suggest, however, is that the trained bees fly at about the same height over land as they do over water (see below). A fourth possibility relates to the controversial hypothesis, advanced by Gould and Gould (1988), that experienced bees have a “knowledge” of the surrounding landscape, including information about the existence and topography of bodies of water; and that they do not fly to locations signaled by other dancing bees that correspond to positions that are on the water, because such locations would be unlikely to bear food under natural circumstances. The primary contribution of the present study, however, is the demonstration that the honeybee's odometer does not run at a constant rate in outdoor flight. The results shown in Figures 2 and 3 reveal that the mean waggle duration in the dance increases at a slower rate when bees fly over water than when they fly over land. In other words, for routes of the same length over land and water, the bees' perception of distance flown (as indicated by their dances) is smaller for flights over water. Thus, the honeybee's odometer runs at a slower pace when flight is over water. There are a number of possible explanations for this finding. One reason could be that while flight over water is likely to stimulate only the ventral fields of view of the eyes with image motion, flight over land is likely to provide image motion signals in the lateral fields as well. In three earlier studies, honeybee odometry was investigated by training bees to fly through short, narrow tunnels lined with visual textures on the walls and/or or the floor. One of these studies (Srinivasan et al. 1997) suggested that the odometric signal is driven primarily by image motion in the lateral fields of view. However, the two others found that motion in the lateral as well as the ventral fields of view is important (Hrncir et al. 2003; Si et al. 2003). While the reasons for this discrepancy remain to be resolved, both studies suggest that lateral flow, if present, can contribute to the odometric signal. It must be noted, however, that in our experiments, the image motion experienced by the bee is likely to have been dominated by flow in the ventral visual field. This is because the lateral structures (such as bushes and trees) on both the water and land routes were sparse and were usually further away than the ground, except in rare circumstances. A second reason for the different sensitivities of the odometer to flight over land and water might be that bees increase their altitude when flying over water, thus reducing the extent of image motion that a given forward motion of the bee would elicit in the eye. Although we do not have precise information on flight heights in this study, visual observation of bees approaching the feeder over the water suggested that they flew at heights of between 1 m and 2 m above the water surface, similar to the heights at which they are reported to cruise over land (Heran and Lindauer 1963). In fact, Heran and Lindauer (1963) reported that bees tend to fly lower over water, and seem to experience difficulty in maintaining the same altitude as they do over land. While we did not observe this phenomenon, their evidence as well as ours seems to argue against the possibility that the reduced sensitivity of the odometer over water is caused by flight at a higher altitude. A third possible explanation has to do with the fact that the visual spatial texture of land could be considerably different from that of water. Investigation of this possibility would require a detailed analysis of the spatial frequency spectra of samples of land and water images, which we have not undertaken in the present study. However, a recent study examined the effects of varying visual texture on perceived distance flown in honeybees (Si et al. 2003). In that study, bees were trained to visit a feeder placed at the far end of a short, narrow tunnel, and their dances were analyzed as the texture and contrast of the patterns lining the floor and walls of the tunnel were systematically varied. The results revealed that perceived distance was almost invariant to changes in visual texture (i.e., changes in the spatial frequency content of the patterns). The same study, however, also found that the odometric signal dropped substantially when the contrast of the pattern was reduced to a level below 20% (Si et al. 2003). This finding is consistent with our present field data, which suggest that the odometric signal is strong when the bees fly over land (which possesses a mean contrast of about 20%) but weak when they fly over water (which possesses a mean contrast of about 9%). Thus, a fourth explanation—and the most likely one—is that water surfaces exhibit a substantially lower visual contrast than do land surfaces. Regardless of which of the above explanations is the valid one, our results indicate that the differences in the odometric signal between flight over land and over water are due to differences in the visual environment. Of course, the visual properties of land terrain can also vary considerably, depending upon the nature of the vegetation and on the existence of manmade structures. We suggest that this is the reason for some of the local fluctuations in slope that are evident in the data over land. In particular, we noted in the Results section that, in experiment 2, the three data points in the middle of segment 3 (Figures 3A and 3B) exhibit a much lower slope than the rest of the curve. This section of the land terrain was one in which the bees' flight took them along a paved bicycle path for a stretch of about 200 m. Two views of this section of the terrain, as would be experienced by a bee flying 1.7 m above the ground, are shown in Figure 5. These images were acquired without using any color filter (see Materials and Methods). It is evident that the surface of the bicycle path provides rather low contrast. (The mean contrast within the rectangle in Figure 5A was measured to be 14.30%.) The contrast of the surface is particularly low on a cloudy day (Figure 5B), when the surrounding vegetation does not cast any sharp shadows on the path. (The mean contrast within the rectangle in Figure 5B was measured to be 6.60%, which is even lower than that of most of the water surfaces that were measured.) The weather was indeed cloudy on the day the data for section 3 were obtained. This was confirmed by the weather entries in the experimental log book for that day, as well as by records from the Canberra Meteorological Station. Thus, the honeybee's odometer can run at different rates even on land, depending upon the nature of the local terrain. The three middle points in segment 3 show a progressively decreasing waggle duration, rather than a progressively increasing one. However, the decrease is not significant: A linear regression over these three data points reveals a slope of −0.369 ± 0.638, which is not significantly different from zero (p > 0.08). Thus, we interpret this as implying that the bees experienced very little optic flow during flight over this region. The final data point in section 3 shows an abrupt increase in waggle duration, compared to the previous three points. Interestingly, as we see from Figure 5, this is one of the rare segments of the bees' flight in which rows of trees appear close to the trajectory in the left and right lateral visual fields, potentially providing strong lateral flow. Figure 5 Terrain Along Which the Bees Were Trained to Fly in Segment 3 of Experiment 2 These two photographs show the terrain along which the bees were trained to fly in segment 3 of experiment 2. In (A) the sun was shining clearly, while in (B) it was behind a cloud. The rectangles depict the area within which mean contrast was measured (details in text). It is instructive to compare our results with the findings from an earlier study by von Frisch and Lindauer (von Frisch 1993). In 1962–1963, von Frisch and Lindauer compared the dance tempos of bees returning from a 340-m flight over water with those of bees returning from a flight of the same distance over land (von Frisch 1993). (The “dance tempo” is the number of dance circuits completed in 15 s [von Frisch 1993].) They found no significant difference in the dance tempos under the two conditions. Based on this observation, von Frisch and Lindauer concluded that land and water drive the odometer at the same rate, and suggested, therefore, that the honeybee's odometer is driven largely by nonvisual signals. However, they did not measure the duration of the waggle phase, which is now considered to be the true representation of distance traveled (Seeley et al. 2000). To investigate this issue more closely, we have measured four parameters of our dance data: mean circuit duration, mean waggle duration, mean return duration (the duration of the return phase of the circuit made during the dance), and mean dance tempo. The mean circuit duration is equal to the sum of the mean waggle duration and the mean return duration, and is proportional to the reciprocal of the dance tempo. The results of this analysis, for flights of the same distance over water and over land, are shown in Tables 3 and 4, respectively. These numbers were obtained by analyzing the dance data from experiments 1 and 2, respectively, for feeder distances ranging from 190 m to 390 m. Our results concur with those of von Frisch and Lindauer: As the feeder distance increased from 190 m to 390 m, the mean circuit duration exhibited a similar increase over land as it did over water (compare the first columns of Tables 3 and 4). Although the increase over land is somewhat larger than that over water, the difference is not statistically significant (p > 0.3, two-way ANOVA). The mean waggle duration increased much more rapidly on land than it does over water, just as Figures 2 and 3 indicate. However, the mean return duration increased much more rapidly on water than it did on land. As a consequence, the mean circuit duration showed a similar variation with distance over land as over water. This reconciles the present findings with those of von Frisch and Lindauer. The mean return duration is considered to be a measure of the “attractiveness” of the food source: the longer the duration, the lower the attractiveness (Seeley et al. 2000). Our data therefore suggest that (a) the attractiveness of a feeder diminishes as its distance from the hive is increased, and (b) the attractiveness decreases more rapidly with distance when flight is over water than when it is over land. The mean waggle duration, on the other hand, is a measure of the perceived distance flown; this quantity increases more rapidly on land than it does over water. Thus, in general, the odometer indeed runs at a faster rate over land than over water. Table 3 Analysis of Dances for Flights over Water (Experiment 1) Table 4 Analysis of Dances for Flights over Land (Experiment 2) Our present findings confirm the suggestions from earlier work (Esch and Burns 1995, 1996; Srinivasan et al. 1996, 1997, 2000; Esch et al. 2001; Si et al. 2003) that the honeybee dance does not convey information about distance traveled with absolute accuracy. Rather, the distance that is indicated (measured in terms of the mean waggle duration) depends upon the optical environment in which the bee flies. The odometer runs faster in terrain that presents a high contrast and rich texture (such as land with dense vegetation) than in terrain that carries low contrast and sparse texture (such as a water surface). Although the visual movement detection system that drives the odometer is impressively robust to variations in visual texture and contrast—as revealed by an earlier study in which bees were trained to fly through tunnels in which the textures lining the walls and floor were systematically varied (Si et al. 2003)—this robustness is not perfect. The study by Si et al. (2003) indicated that, for a flight of a given distance, the odometric reading is largely independent of visual contrast, as long as the contrast is above 20%. Below this value, however, the odometric reading begins to decline. This critical value of contrast is in approximate agreement with the findings of the present study, where we have investigated the properties of the odometric signal in a natural, rather than an artificial, environment. Given that the honeybee dance does not convey accurate information on distance flown, how do scout bees returning from a new food source usually manage to recruit other bees to visit it so quickly and so effectively? One explanation might be that the waggle dance conveys information on the direction as well as the distance of the food source. Thus, potential recruits that are persuaded by the scout's dance to seek out the source would fly in the direction signaled by the scout, and therefore experience approximately the same visual environment as the scout. Consequently, any terrain-induced variations in the odometric signal would be the same for the recruits as well as the scout, so that such variations would not compromise accurate pinpointing of the destination. The recruit would find the goal simply by flying in the specified direction until her odometric signal matched that indicated by the scout's dance. Thus, even though the scout's dance does not indicate distance in absolute terms, the recruits end up close to or at the correct location because their odometric signals evolve in the same way as that of the scout during the flight toward the food. Once the recruits are in the vicinity of the food source, the experienced foragers can assist them by providing visual and/or olfactory cues in the vicinity of the feeder to guide recruits to it (Tautz and Sandeman 2003). Materials and Methods Experimental site The experiments were conducted on the shore of Lake Burley Griffin in Canberra. A two-frame observation hive was set up 190 m from the shore of the lake, with the entrance facing the shore (see Figure 1). The ground sloped gently downwards toward the shore. The terrain was grassy and interspersed with shrubs and eucalyptus trees. Beyond the lake shore, 240 m into the water, was an island (Springbank Island) measuring 150 m across along the line of sight from the hive. The island contained dense tree vegetation around its circumference, with grass and a few trees in the middle. Beyond the island was a further 1000-m stretch of water extending to the opposite shore of the lake. Experiments Two experiments were carried out, both using honeybees (Apis mellifera) from the same hive. In experiment 1, bees were trained to fly a route due southwest toward the island. This route comprised an initial stretch over land, followed by a stretch over water, and then again over land (across the island), as shown in Figure 1. The total length of this route was 580 m. In experiment 2, bees were trained to fly a route of the same total distance due northwest that was entirely over land (see Figure 1). In experiment 1, about 20 bees were individually marked and trained to visit a feeder containing 1.0 M nonscented sucrose solution, initially placed 5 m from the hive. When the marked bees started to visit the feeder regularly, the feeder was moved step by step toward the shore. At each position, the dances of marked bees returning from the feeder were recorded at the hive. The distance from the hive to the lake shore was 190 m. The feeder was then placed in a rowboat and taken across the water, again stopping at several locations along the way to record dances back at the hive. In order to accustom the bees visually to the boat, we introduced them to the boat on land well before the water was reached. From about 50 m before the water's edge, the feeder was placed inside the boat. From this point on, the feeder was always in the boat, regardless of whether the location was on land or water. The stretch across the water was 240 m long. Upon reaching the shore of the island, the feeder (still placed in the boat) was moved stepwise across the island to the opposite shore, again recording the dances of bees returning from each location. The stretch across the island was 150 m long. The boat was an inflatable dinghy capable of carrying four adults in addition to the feeder. It was colored bright yellow to facilitate visual detection from a distance by the bees. A view of the boat on the lake is given in Figure S1. A buoyant rope, carrying brightly colored markers at 10 m intervals, was used to measure out distances on land as well as water. During periods of strong wind or water currents, a stable position over water was maintained with the aid of an anchor, supplemented when necessary by compensatory paddling. The weather was sunny and calm through most of the study, except for a few days (see below). The wind speed rarely exceeded 20 km/h. Most of the time, there were only a few ripples on the water surface. Temperature and weather conditions were recorded through the course of the experiment and were supplemented by records from the Canberra Meteorological Station. There were always at least two experimenters at the feeder (regardless of whether the feeder was on land or water), and two experimenters at the hive. The visit of each marked bee at the feeder, and its subsequent dance in the hive, were followed through radio communication between the feeder and the hive. At each feeder position, recording of a given bee's dances was commenced after it had made three visits to the feeder. This was done to allow adequate time for the dances to adjust to each new feeder position. Data were collected from between seven and 14 different individually marked bees at each feeder position. This procedure required a stay of about 30–60 min at each feeder position. In experiment 2, about 20 bees were trained by moving a feeder step by step from the hive, as in experiment 1, but along a route that was entirely over land, as described above. The total length of this route (580 m) was identical to that in experiment 1, thus enabling a direct comparison of the of the bees' dances along the two routes. Recording and analysis of bee dances The observation hive was housed in a specially constructed shed that afforded a weatherproof environment for observing and filming bee dances. Dances of marked bees were filmed at 25 frames/s, using a Sony (Tokyo, Japan) DCR-TRV310E video camera mounted on a tripod placed adjacent to one face of the observation hive, near the entrance. A mechanical gate at the entrance to the hive ensured that bees entered (and left) the hive only on the side facing the camera, thus facilitating the filming of dances. Bee dances were analyzed frame by frame to measure the mean duration of the waggle phase. The waggle duration was considered to be a measure of the bees' perception of the distance flown from the hive to the feeder: the longer the waggle duration, the greater the perceived distance (Esch et al. 2001). A total of between 68 and 217 dances were analyzed for each feeder position, from between seven and 14 individually marked bees. A total of over 6,000 dances were evaluated in the study. The dances were evaluated as follows. For each dance, the mean waggle duration was estimated by averaging the waggle durations over all loops. Then, the mean waggle duration for each bee was obtained by averaging the mean waggle durations over all of its dances at that feeder position. Finally, the mean waggle duration of all bees was calculated from the mean waggle durations for the individuals. The standard error of the mean was also calculated and displayed in the graphs (see Figures 2 and 3). Linear regressions of the data, and 95% CIs for the slopes of the fitted regression lines, were computed using the GraphPad Prism (GraphPad Software, San Diego, California, United States) statistical analysis package. Regression slopes of different data sets were also compared using the same package, which implemented the slope comparison test described in Sokal and Rohlf (1995). Scene photography and analysis Samples of the visual scenes that the bees would have experienced while flying over land and water were acquired by digital camera (Coolpix 950, Nikon, Tokyo, Japan), which produced color images of 1200 × 1600 pixel resolution. Sections of these images were analyzed to compare the mean visual contrast over land with that over water. The intensity of the image at each pixel was taken to be the average of the values of the three color subpixels at that location. These images were taken on a calm day with weather conditions similar to those on which the experiments were conducted. The surface of the lake was smooth, with relatively few ripples. The mean contrast in an image section was computed as the ratio of the standard deviation to the mean value of the intensities of all the pixels within that section, and was expressed as a percentage. It was recently demonstrated that the odometer is “color-blind” and is driven primarily by the green receptor channel (Chittka and Tautz 2003), as are other motion-sensitive pathways in the bee (Lehrer et al. 1988; Zhang et al. 1990; Zhang and Srinivasan 1993). Therefore, each scene was photographed twice: once without any color filter, and once through a color filter with a spectral transmission that approximated the spectral sensitivity of the honeybee's green receptor (B+W 30061 3X MRC [Schneider-Kreuznach, Bad Kreuznach, Germany]; with peak transmission at 530 nm and a bandwidth of 120 nm at half sensitivity). Supporting Information Figure S1 View of Boat at One of the Training Positions on the Lake (1.8 MB JPG). Click here for additional data file. Video S1 Marked Bee Dancing Upon Return from the Feeder When the Feeder Is Positioned 60 m into the Lake (250 m from the Hive) (27.8 MB AVI). Click here for additional data file. We thank Jason Wong and Judith Reinhard for their assistance with many aspects of the fieldwork. Harald Esch contributed to the analysis of the data presented in Table 3. The map in Figure 1 was kindly provided by Gordon Anderson of the Australian Capital Territory Land Information Centre. The reviewers' constructive comments helped improve the paper substantially. This research was supported partly by grants to MVS from the Australia–Germany Collaborative Research Scheme, the Human Frontiers in Science Program (RG 84/97), the United States Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-99-1-0506), the Alexander von Humboldt foundation, and the Deutsche Forschungsgemeinschaft (SFB 554 and GK 200). Conflicts of interest. The authors declare that no conflicts of interest exist. Author contributions.JT, MS, and SZ conceived and designed the experiments. JS, AB, and AS performed the experiments, with assistance from JT, MS, and SZ. SZ, JS, and AB analyzed the data. MS wrote the paper. Academic Editor: Tom Collett, University of Sussex ¤1 Current address: Department of Ecology and Evolutionary Biology, University of California, Irvine, California, United States of America Abbreviations CIconfidence interval rmsroot mean square SDstandard deviation ==== Refs References Chittka L Tautz J The spectral input to honeybee visual odometry J Exp Biol 2003 206 2393 2397 12796456 Esch H Burns JE Honeybees use optic flow to measure the distance of a food source Naturwissenschaften 1995 82 38 40 Esch H Burns JE Distance estimation by foraging honeybees J Exp Biol 1996 199 155 162 9317542 Esch H Goller F Burns JE Honeybee waggle dances: The “energy hypothesis” and thermoregulatory behavior of foragers J Comp Physiol B 1994 163 621 625 Esch HE Zhang SW Srinivasan MV Tautz J Honeybee dances communicate distances measured by optic flow Nature 2001 411 581 583 11385571 Gould JL Gould CG The honey bee 1988 New York W. H. 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Fischer 1 15 Seeley TD Mikheyev AS Pagano GJ Dancing bees tune both duration and rate of waggle run production in relation to nectar-source profitability J Comp Physiol A 2000 186 813 819 11085635 Si A Srinivasan MV Zhang SW Honeybee navigation: Properties of the visually driven “odometer.” J Exp Biol 2003 206 1265 1273 12624162 Sokal RR Rohlf FJ Biometry: The principles and practice of statistics in biological research, 3rd edition 1995 New York Freeman 887 Srinivasan MV Zhang SW Lehrer M Collett TS Honeybee navigation en route to the goal: Visual flight control and odometry J Exp Biol 1996 199 237 244 9317712 Srinivasan MV Zhang SW Bidwell NJ Visually mediated odometry in honeybees J Exp Biol 1997 200 2513 2522 9320443 Srinivasan MV Zhang SW Altwein M Tautz J Honeybee navigation: Nature and calibration of the “odometer.” Science 2000 287 281 283 Tautz J Sandeman DC Recruitment of honeybees to nonscented food sources J Comp Physiol 2003 189 293 300 12664091 von Frisch K The dance language and orientation of bees 1993 London Harvard University Press 566 Zhang SW Srinivasan MV Parallel information processing in the visual system of insects Jpn J Physiol 1993 43 S247 S258 8271505 Zhang SW Wang X Liu Z Srinivasan MV Visual tracking of moving targets by freely flying honeybees Vis Neurosci 1990 4 379 386 2271450

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020212Research ArticleAnimal BehaviorEcologyNeuroscienceSongbirdMigratory Sleeplessness in the White-Crowned Sparrow (Zonotrichia leucophrys gambelii) Migratory Sleeplessness in SparrowsRattenborg Niels C 1 Mandt Bruce H 1 Obermeyer William H 1 Winsauer Peter J 2 Huber Reto 1 Wikelski Martin 3 Benca Ruth M [email protected] 1 1Department of Psychiatry, University of WisconsinMadison, Wisconsin, United States of America2Department of Pharmacology and Experimental Therapeutics, Louisiana State UniversityNew Orleans, Louisiana, United States of America3Department of Ecology and Evolutionary Biology, Princeton UniversityPrinceton, New JerseyUnited States of America7 2004 13 7 2004 13 7 2004 2 7 e21219 3 2004 6 5 2004 Copyright: © 2004 Rattenborg et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. No Rest for the Weary: Migrating Songbirds Keep Their Wits without Sleep Twice a year, normally diurnal songbirds engage in long-distance nocturnal migrations between their wintering and breeding grounds. If and how songbirds sleep during these periods of increased activity has remained a mystery. We used a combination of electrophysiological recording and neurobehavioral testing to characterize seasonal changes in sleep and cognition in captive white-crowned sparrows (Zonotrichia leucophrys gambelii) across nonmigratory and migratory seasons. Compared to sparrows in a nonmigratory state, migratory sparrows spent approximately two-thirds less time sleeping. Despite reducing sleep during migration, accuracy and responding on a repeated-acquisition task remained at a high level in sparrows in a migratory state. This resistance to sleep loss during the prolonged migratory season is in direct contrast to the decline in accuracy and responding observed following as little as one night of experimenter-induced sleep restriction in the same birds during the nonmigratory season. Our results suggest that despite being adversely affected by sleep loss during the nonmigratory season, songbirds exhibit an unprecedented capacity to reduce sleep during migration for long periods of time without associated deficits in cognitive function. Understanding the mechanisms that mediate migratory sleeplessness may provide insights into the etiology of changes in sleep and behavior in seasonal mood disorders, as well as into the functions of sleep itself. Neurophysiological and behavioural studies suggest that sleep- loss during the migratory season does not adversely affect cognitive capacity in captive white-crowned sparrows ==== Body Introduction Seasonal behaviors occur in virtually all organisms, ranging from insects to mammals (Goldman et al. 2004). Just as circadian rhythms allow organisms to anticipate daily cycles of light and dark, circannual behaviors such as migration allow them to predict and respond to seasonal changes in environmental conditions. Like circadian rhythms, seasonal migratory behavior is both endogenously generated and shaped by external factors such as photoperiod length, weather, and food availability (Gwinner and Helm 2003). The most extraordinary examples of seasonal migration occur in birds, many species of which regularly migrate thousands of kilometers. Given the long distances traversed during migration, much research has focused on the timing of migratory flights, navigation during migration, and the energetic costs of migration (Berthold 1996; Gauthreaux 1996). One aspect of migration that is likely to impact all others, however, remains a complete mystery: Do birds sleep during migration and if so, how (Moore 1999; Schwilch et al. 2002; Jenni and Schaub 2003)? In many bird species, migration proceeds at a pace that does not seem to allow much time for sleep. The apparent conflict between migratory behavior and sleep may be particularly extreme for songbirds. In the nonmigratory seasons, songbirds sleep at night and are active during the day. In the migratory seasons, however, many songbirds undergo a profound behavioral shift and begin to fly at night while still remaining active during the day (Berthold 1996). In the only study to directly observe migratory behavior using telemetry, a Swainson's thrush (Catharus ustulatus) flew on six of seven nights and traveled 1,500 km, with flights that occurred under favorable weather conditions lasting up to7 h (Cochran 1987; see also, Cochran and Wikelski 2004; Cochran et al. 2004); however, daytime activity was not reported. Although some studies have observed brief periods of sleep behavior in the evening prior to the initiation of a nocturnal flight (Eyster 1954; Berthold and Querner 1988; Berthold 1996; Ramenofsky et al. 2003), the overall increase in activity during migration suggests a marked reduction in time available for sleep. Despite their apparent sleep loss, migrating songbirds are capable of engaging in adaptive waking behaviors, including prolonged flight, navigation, foraging, and evading predators in novel environments. The preservation of cognitive and physical performance during migration is surprising because sleep restriction in other animals causes profound deficits in neurobehavioral and physiological function. In humans, as little as one night of sleep deprivation adversely affects alertness, working memory, cognitive throughput (Van Dongen et al. 2003), divergent thinking (Wimmer et al. 1992; Harrison and Horne 1999), insight (Wagner et al. 2004), and memory consolidation (Karni et al. 1994; Stickgold et al. 2000, 2001; Maquet 2001; Fischer et al. 2002; Fenn et al. 2003), but see Siegel (2001). Even partial sleep restriction can have adverse effects on neurobehavioral function in humans; limiting sleep to 6 h per night (75% of the normal requirement) for ten nights decreases alertness to a level comparable to that following 24 h of total sleep deprivation (Van Dongen et al. 2003). The most prolonged sleep deprivation studies have been performed in rats, where near-total (>90%) sleep deprivation leads to physiological impairment culminating in death after as little as 2–3 wk (Rechtschaffen et al. 1983; Rechtschaffen and Bergmann 2002). Similarly, fruit flies (Drosophila melanogaster) deprived of sleep also die, suggesting that sleep serves a function vital to survival in all animals (Shaw et al. 2002). Given the dramatic effects of sleep deprivation in other animals, the preservation of adaptive waking function in spite of the apparent reduction in sleep during migration in songbirds seems paradoxical. Songbirds might have found a way to obtain sufficient amounts of sleep by either engaging in short but intense bouts of sleep or by sleeping in flight. Alternatively, songbirds might possess a capacity unprecedented in the animal kingdom to circumvent or withstand the effects of sleep loss during migration. We have performed long-term electrophysiological recordings of sleep and wakefulness during the nonmigratory and migratory seasons in a songbird, the white-crowned sparrow (Zonotrichia leucophrys gambelii), which migrates 4,300 km twice a year between Alaska and southern California (DeWolfe 1968; Chilton et al. 1995). We chose to study sleep in the white-crowned sparrow since migration has been investigated for over 50 years in this species, both in the wild and in captivity (Farner 1950; Ramenofsky et al. 2003). Furthermore, we measured cognitive function across an entire year to determine whether sleep loss has a differential effect on cognitive function during migratory and nonmigratory seasons. Results Demonstration of Migratory Restlessness in the Laboratory Setting The sleep patterns of birds in a migratory state were recorded in captivity, where migratory behavior manifests itself as migratory restlessness (Zugunruhe), i.e., nocturnal activity, including hopping and wing flapping (Berthold and Querner 1988; Berthold et al. 2000). Migratory restlessness is genetically controlled (Berthold and Querner 1981; Berthold 1990) and appears to reflect the natural migratory urge, since the number of nights during which birds exhibit episodes of migratory restlessness is positively correlated with the duration of migration in the wild (Gwinner 1986; Berthold and Querner 1988; Berthold 1996). Starting in June 2002, we established colonies of white-crowned sparrows at the University of Wisconsin-Madison. Seasonal changes in activity were continuously recorded with an infrared activity monitoring system. As shown in a representative activity plot spanning one calendar year (Figure 1) activity in the dark phase, as well as in the light phase, increased dramatically during the migratory seasons in the spring (March–June) and fall (August–December), when these birds would normally be migrating between California and Alaska (Chilton et al. 1995). As indicated in previous reports using similar techniques, the increase in activity was more pronounced in the spring than in the fall (Berthold 1996). The activity data demonstrate that the typical patterns of seasonal changes in migratory restlessness could be reproduced reliably in our laboratory setting. Figure 1 Activity Across the Year The purple line traces the (smoothed) percentage of 30-s epochs at night during which a bird (no. 38) broke an infrared beam by crossing the cage. The blue line shows the same information for the hours of light. Note the broad peak in activity between March and June and the broader and lower peak in activity between August and December, corresponding to the spring and fall migrations, respectively. The low levels of activity during July and December through March correspond to the summer and winter nonmigratory periods. The sharp high peaks (orange ovals) in early August of 2003 and early February of 2004 mark brief periods of experimenter-induced sleep restriction. To further characterize seasonal changes in behavior, we used infrared-sensitive cameras to record the sparrows' behavior. As reported previously (Berthold and Querner 1988; Berthold et al. 2000), the video recordings revealed that the infrared activity monitoring system underestimated the amount of time that birds were active. In addition to hopping back and forth across the infrared beam, the birds also spent a significant amount of time either hopping over or under the infrared beam. Occasionally, birds also flapped their wings with their heads raised while holding onto a perch, as if attempting to initiate flight (Video 1) a behavior that was restricted to the night and virtually never occurred in nonmigratory birds. The video recordings thus confirmed that the birds were displaying migratory behaviors consistent with previous reports in songbirds (Berthold and Querner 1988; Berthold et al. 2000), including white-crowned sparrows (Ramenofsky et al. 2003), and indicated that they were spending even more time active and behaviorally awake during migration than was indicated by the infrared activity monitoring system. Electrophysiological Correlates of Sleep and Wakefulness in Sparrows Although infrared activity and video monitoring suggested a reduction in the amount of sleep during migration, electrophysiological recordings are required to confirm behavioral state, as well as quantify potential changes in sleep stages and intensity. To characterize sleep patterns during the nonmigratory and migratory seasons, eight sparrows were instrumented for recording electroencephalographic activity (EEG) from both hemispheres, as well as electromyographic activity (EMG) (see Materials and Methods). To control for potential seasonal changes in sleep patterns related to the acute effects of photoperiod, all recordings were performed under a constant 12:12 light/dark (LD) cycle. Consequently, the seasonal changes in sleep patterns reported below can be attributed to endogenous changes in migratory state, rather than to the combined effects of migratory state and changes in photoperiod on behavior. All of the birds instrumented for EEG and EMG recording exhibited episodes of migratory restlessness starting between mid-July and mid-August, in synchrony with the other birds in the laboratory. Their behavior was indistinguishable from that displayed by the sparrows without electrophysiological implants. In three birds, bouts of migratory restlessness started prior to surgery or during postoperative recovery. The electrophysiological correlates of sleep and wakefulness in nonmigrating and migrating white-crowned sparrows were similar to each other, as well as to those previously described in other species of birds (Rattenborg and Amlaner 2002). Wakefulness Behavior during wakefulness included hopping and flying around the cage, feeding, drinking, feather preening, and actively scanning the room. Although movement artifacts obscured the EEG and EMG during gross movements (e.g., hopping and wing flapping), the recordings occurring immediately before and after such behaviors showed a low-amplitude, activated EEG in both hemispheres, and high EMG activity typical of alert wakefulness (Figure 2). Figure 2 Examples of Electrophysiological Correlates of Behavioral States This figure shows four 1-min samples each containing three EEG recordings (left hemisphere vs. right hemisphere [L v. R], left hemisphere vs. neutral reference [L], and right hemisphere vs. neutral reference [R]) and one EMG from one sparrow (no. 65) depicting the typical electrophysiological correlates of each behavioral state. (A) Transition from SWS (blue) to wakefulness (black). (B) Drowsiness (gray). (C) Transition from SWS (blue) to a bout of REM sleep (red), and then a brief awakening (black), followed by a return to SWS (blue). (D) Wakefulness during a period of migratory restlessness. High-amplitude artifacts associated with gross movements are shaded with a gray background. (A–C) were recorded on 11 August 2003, during the summer while the bird was in a nonmigratory state, and (D) was recorded on 11 October 2003, during the fall migratory period. Drowsiness Upon ceasing active wakeful behaviors, the sparrows entered drowsiness, a mixed state with behavioral and EEG characteristics of both wakefulness and slow-wave sleep (SWS) (Campbell and Tobler 1984). Sparrows in this state typically stood on the floor facing the front of the cages with their heads facing toward the center of the room (Video 2). Their heads were held close to their bodies, and the position of the eyelids fluctuated between open, partially closed, and completely closed states, while their heads moved from side to side. This behavior was intermittently interrupted by the birds opening their eyes completely and raising their heads, apparently attending to the activities of other birds in the room. The fact that the sparrows selected optimal vantage points in their cages from which to monitor the room, moved their heads from side to side in a scanning manner, and intermittently responded to stimuli in the environment indicates that several behavioral aspects of wakefulness were intact during this state. Although artifact from the head movements precluded reliable spectral analysis of the EEG during drowsiness, as in other studies of songbirds (Szymczak et al. 1993), visual assessments of the EEG showed activity intermediate between that of wakefulness and SWS (i.e., increased amplitude in the low-frequency range relative to wakefulness; Figure 2B). The EMG typically showed brief bursts of activity associated with head movements during drowsiness. The EEG amplitude and frequency approached that of SWS, but unlike unequivocal SWS, which is followed by rapid eye movement (REM) sleep in birds and mammals (Amlaner and Ball 1994; Zepelin 2000), REM sleep never occurred directly following drowsiness. Based on the mixture of behavioral and electrophysiological features of wakefulness and SWS, drowsiness was not included in either the calculation of time in wakefulness or sleep, but rather it constituted a separate behavioral category. Slow-wave sleep In contrast to the vigilant behaviors exhibited by sparrows in drowsiness, during SWS the birds were motionless, usually with closed eyes; the head was either pulled in toward the body and facing forward or resting on the bird's back (Video 3). The amplitude of low-frequency EEG reached its highest levels during SWS (see Figure 2A and 2C). As in other birds (Spooner 1964; Ookawa and Gotoh 1965; Ball et al. 1988; Rattenborg et al. 1999b), sparrows occasionally showed interhemispheric asymmetries in EEG low-frequency activity during SWS. However, such asymmetries were restricted to periods of immobility, and they never occurred during active behaviors. REM sleep An activated EEG pattern similar to that occurring during wakefulness characterized REM sleep (Figure 2C). Unlike most mammals, but similar to other birds, EMG activity recorded from the nuchal muscles rarely declined during REM sleep. Nevertheless, behavioral signs of reduced muscle tone made REM sleep readily distinguishable from brief awakenings; in contrast to wakefulness, during REM sleep the eyes were closed and the head either rolled to one side or fell forward, occasionally dropping to the bird's feet. In extreme cases, birds would sway and briefly lose their balance on the perch. Episodes of REM sleep typically lasted a maximum of 10 s, but often occurred in clusters separated by only a few seconds of SWS. Migratory restlessness During episodes of migratory restlessness, defined as active hopping and wing flapping at night interrupted by only brief pauses in motor activity, both eyes were completely open, and the EEG recorded from both hemispheres showed an activated pattern, indistinguishable from alert wakefulness (Figure 2D). There was no sign of either drowsiness or an interhemispheric asymmetry in SWS-related EEG, indicating that sparrows exhibiting migratory restlessness were completely awake. Comparisons of Sleep Patterns in Nonmigrating and Migrating Sparrows In nonmigrating sparrows, sleep and wakefulness patterns across the day were typical of those previously described in a songbird (Szymczak et al. 1993). The proportion of time spent in each state varied in a consistent manner across the LD cycle (Figure 3A). Wakefulness and drowsiness encompassed all of the time during the light phase and a small proportion of time during the dark phase. At night, SWS was the predominant sleep stage during all hours of the night. In a pattern similar to humans and other mammals (Borbély and Achermann 2000; Tobler 2000), the proportion of time spent in SWS declined across the night, whereas REM sleep increased. Figure 3 Changes in Sleep during Fall Migration Behavioral state was scored across 24-h (noon to noon) periods using a combination of video and electrophysiological recordings for birds in a nonmigratory (n = 5) and migratory (n = 8) state. The plots and table reflect the average for all birds in each group. All recordings were performed under a 12:12 LD photoperiod with lights turned off at 18:00 and on at 06:00. (A) Proportion of time in each behavioral state for nonmigrating (top) and migrating (bottom) birds. The proportion of every 10-min period spent in each sleep/wakefulness state was calculated for each bird and then averaged across all birds: wakefulness (black), drowsiness (gray), SWS (blue), and REM sleep (red). Note that overall sleep propensity in migrating birds is greatly diminished between approximately 22:30 and 06:00. Note also the increased propensity for REM sleep from 18:00 to 20:00 as compared to the same time period when not migrating. (B) Sleep and REM sleep latencies. Sleep latency was calculated as the length of time from lights out until the first occurrence of sleep (in all cases SWS) for birds in a nonmigratory and migratory state; average sleep latency did not differ significantly between nonmigrating and migrating birds. REM sleep latency was calculated as the length of time from sleep onset to the first occurrence of REM sleep. Note that REM sleep occurred earlier in sleep during migration for all five birds that were recorded in both a nonmigratory and migratory state (t = 3.3, paired, two-tailed, p < 0.05). Note also that the REM sleep latencies for the three birds recorded only in a migratory state were shorter than the shortest REM sleep latency in nonmigrating birds. (C) Sleep percentages. Average daily percentages of sleep and wakefulness states for birds in a nonmigrating (n = 5) and migrating (n = 8) state. Total sleep is the sum of SWS and REM sleep. For all states of vigilance, values for the migrating condition differed significantly from the nonmigrating condition (p < 0.01, after Bonferroni correction). The proportion of total sleep occupied by REM sleep was not significantly different between migratory states. The migratory state was marked by a dramatic change in both the amount and pattern of sleep across the day. The most striking difference was a large reduction in total amount of sleep in migrating birds. Figure 3A shows the average hypnogram for all birds recorded during nonmigratory (n = 5) and migratory (n = 8) conditions. Total time spent sleeping was reduced by an average of 63% in migrating birds compared to nonmigrating birds (Figure 3C). In the most extreme case, sleep time decreased from 9.05 h on a nonmigrating night to 1.39 h on a migrating night, representing a sleep reduction of about 85%. In all but one bird, most of the sleep obtained on migratory nights occurred during the first few hours of the night. One bird, however, obtained more sleep during the second half of the night, although it had a brief episode of sleep at the beginning of the night, followed by episodes of migratory restlessness. In addition to the restriction of sleep to the first few hours of the night in most migrating birds, there was also a shift in the timing of REM sleep. Although SWS latency was not affected by migratory status, the latency from SWS onset to REM sleep onset changed from 24.2 ± 6.9 min on nonmigratory nights to 10.3 ± 5.9 min on migratory nights (Figure 3B). Even in the bird that slept more during the second half of the night, REM sleep still occurred unusually early during the brief initial bout of sleep on its migratory night. In addition, REM sleep latencies for the three birds recorded only in a migratory state were all shorter than the shortest REM sleep latency for the five birds recorded in a nonmigratory state. Moreover, REM sleep as a proportion of total sleep time was elevated early in migratory nights (i.e., 18:00–20:00) when compared to the corresponding hours on nonmigrating nights (10.0% vs. 2.8%, t = 2.5, p < 0.05) (Figure 4). Although the migratory state significantly influenced the timing of REM sleep, the overall proportion of total sleep time spent in REM sleep was similar on nonmigratory (16.3%) and migratory (14.8%) nights (t = 0.56, p > 0.1) (see Figure 3C). Figure 4 REM Sleep across the Dark Phase REM sleep as a proportion of total sleep time is plotted for every 10-min period during the dark phase for birds in nonmigrating (n = 5) and migrating (n = 8) states. The individual dots represent the average for each 10-min period; the solid line is a spline fit to these data. The dashed line represents the absolute amount of REM sleep, as a percentage of recording time. Note that the fit for the migrating birds is truncated, not only because very few periods after midnight had any REM sleep, but also because no point after midnight was based on more than one bird. Finally, despite the marked reduction in nocturnal sleep during migration, sleep did not occur during the light phase in migrating birds; as in nonmigrating birds, SWS and REM sleep were restricted to the dark phase of the LD cycle. Nonetheless, time spent in drowsiness increased significantly during the light phase (38.4% vs. 27.7%, t = 2.68, p < 0.05) in migrating birds; drowsiness also increased during the dark phase, but this did not reach statistical significance (12.9% vs. 8.1%, t = 1.73, p > 0.1). On migratory nights, drowsiness usually occurred during the later half of night between bouts of migratory restlessness. Spectral Analysis of the SWS EEG in Nonmigrating and Migrating Sparrows In addition to determining changes in the amount and type of sleep, we also compared EEG activity during SWS on nonmigratory and migratory nights for evidence of changes in sleep intensity using fast Fourier transform (FFT) spectral analysis of the EEG. In mammals, SWS-related slow-wave activity (SWA) of 0.75- to 4.5-Hz appears to reflect sleep intensity, since arousal thresholds are positively correlated with the amount of SWA (Franken et al. 1991; Neckelmann and Ursin 1993). SWA in the 0.75- to 4.5-Hz band also increases as a function of prior time awake and shows a progressive decline across the sleep period in mammals, suggesting that SWA is an EEG marker of sleep-related homeostatic processes (Borbély 1982; Tobler 2000). We first examined the time course of SWA across the night on nonmigratory nights using the 0.75- to 4.5-Hz frequency band typically employed in mammals, but we were unable to detect a significant decline in spectral power. When we examined changes in spectral power across all frequencies, however, a significant and pronounced decline across the night was apparent in the 1.25- to 2.5-Hz band for both the left and right hemispheres (Figure 5). Assuming that EEG in this frequency range reflects homeostatic processes in sparrows, and given the marked reduction in sleep during migration, we predicted that sparrows might compensate for decrements in total amounts of sleep with increased spectral power in the 1.25- to 2.5-Hz range during SWS on migratory nights. In the five birds recorded on both nonmigratory and migratory nights, however, we did not detect a significant increase in 1.25- to 2.5-Hz spectral power during SWS on the migratory night when compared to the corresponding hours of the nonmigratory night; three birds showed an increase and two birds showed a decrease in 1.25- to 2.5-Hz spectral power. Figure 5 Time Course of EEG Power Density in the 1.5- to 2.5-Hz Band in SWS This figure shows the time course of EEG power density in the 1.5- to 2.5-Hz band in SWS during the dark phase for the left (blue) and right (red) hemisphere in nonmigrating sparrows. Curves represent mean 2-h values with standard error of the mean (n = 5). The EEG power density in the 1.5- to 2.5-Hz band of each 2-h interval is expressed as a percentage of the average EEG power in the 1.5- to 2.5-Hz band over all SWS epochs (dashed line = 100% of average 1.5- to 2.5-Hz power). The last 2-h interval is excluded since not all birds exhibited SWS during this time. A two-way, repeated measures ANOVA, with “hemisphere” and “2-h intervals” as factors, revealed a significant effect of the 2-h interval (F = 5.60, p < 0.05 with the Greenhouse–Geisser correction); neither an effect of hemisphere, nor an interaction between hemisphere and interval reached statistical significance (p > 0.1). Assessing Cognitive Function in Sparrows Given the reduction in time spent sleeping and the apparent lack of a compensatory increase in sleep intensity during migration, we were interested in determining whether sparrows in a migratory state showed associated changes in cognitive function. Because we were interested in detecting seasonal changes in cognition, we used a test that could be administered repeatedly to the sparrows over long periods of time and without the potential confound of “practice” effects, a repeated-acquisition task; it has been widely used in humans and animals to provide repeated measures of the acute and chronic effects of neurotoxic insult on the ability to acquire a new sequence of operant responses (Winsauer et al. 2002). When combined with a performance component that simply requires memory of a previously learned sequence of operant responses, these two tasks can be used to determine whether changes in responding and accuracy during acquisition are related to direct effects on learning or global effects on psychomotor performance. The repeated-acquisition procedure can also be used to assess the effects of sleep deprivation on both the quality (i.e., accuracy) and quantity (i.e., number of responses) of behavior (Cohn et al. 1992). We trained a group of eight sparrows not instrumented for electrophysiological recordings to respond under a multiple schedule of repeated-acquisition and performance in standard operant testing chambers (see Materials and Methods). In the acquisition component, birds learned a different three-response sequence of key pecks (e.g., left-right-center) during each session under a second-order fixed-ratio (FR) 3 schedule. In contrast, during the performance component, birds responded on the same three-response sequence each session under the same schedule of reinforcement. Despite being wild-caught, the sparrows adapted well to the testing apparatus and readily learned to respond in both tasks (Video 4). Effects of Sleep Restriction on Cognitive Function during the Nonmigratory Season To determine whether the task was sensitive to the effects of sleep restriction, as well as to provide a comparison for the effects of sleep loss occurring spontaneously during migration, sleep was restricted to the first 3 h of the dark phase on three consecutive nights during the nonmigratory (winter) season (see Materials and Methods). A 3-h sleep period at the start of the dark phase was chosen to mimic the general sleep pattern of sparrows during migration. Sleep restriction reduced accuracy (percentage correct responses) on both the acquisition (repeated measures analysis of variance [ANOVA], F = 12.65, p < 0.001) and performance (F = 3.12, p < 0.05) components of the task (Figure 6). The total number of responses also decreased following sleep restriction in both the acquisition (F = 33.91, p < 0.0001) and performance (F = 10.25, p < 0.0001) components of the task (see Figure 6). These effects of sleep restriction were evident following the first night and persisted following subsequent nights of sleep restriction. Figure 6 Operant Behavior and Sleep Restriction Average values for accuracy and the total number of responses on the acquisition and performance tasks are shown for the 3 d preceding sleep restriction, the 3 d of sleep restriction, and the 3 d following sleep restriction; boxes represent the 25th to 75th percentile of data with the median indicated by the line across the box. The “whiskers” extend from the quartiles to the most extreme value less than 1.5 times the interquartile range. Points outside the whiskers are plotted with small circles. Accuracy and total number of responses decreased significantly in both the acquisition and performance tasks following sleep restriction (n = 7). Comparison of Cognitive Function during Migratory and Nonmigratory Seasons The same group of eight sparrows was tested under the multiple schedule of repeated-acquisition and performance for one year, encompassing both spring and fall migrations. Figure 7 shows accuracy, the total number of responses in the repeated-acquisition component of the task, and nocturnal activity across the year for the entire group of birds. During periods of increased nocturnal activity corresponding to the spring (March–June) and fall (August–December) migratory periods, accuracy on the repeated-acquisition task remained stable. The number of responses was lowest during the winter, intermediate during the spring migration and summer, and actually reached the highest level during the fall migration, before returning to the low winter levels. Figure 7 Comparison of Operant Responding and Migratory Behavior The average value for all birds of nighttime (purple) and daytime (blue) activity (percentage of 30-s epochs containing at least one infrared beam break) and the accuracy (green) and total number of responses (red) on the acquisition task (n = 8). As in Figure 1, the sharp high peaks (orange ovals) in early August of 2003 and early February of 2004 mark brief periods of experimenter-induced sleep restriction. Because the sparrows did not all migrate at exactly the same time, we also examined the effect of migratory status on accuracy and responding more specifically by selecting for each bird two 3-wk periods with maximal nocturnal activity during the spring and fall migrations, and two 3-wk periods with minimal nocturnal activity during the summer and winter nonmigratory periods (see Materials and Methods). We chose a 3-wk window for analysis, as this was the longest period of relative nocturnal quiescence that could be found in all birds during the summer. Accuracy on the repeated-acquisition task was virtually unaffected by migratory status. The total number of responses during the repeated-acquisition task was lowest during the winter, intermediate during spring and summer, and highest during the fall (Figure 8). The preservation of accuracy and responding during migration is thus unlike the decline in accuracy and responding observed following three nights of sleep restriction during the nonmigratory season. Figure 8 Seasonal Aspects of Operant Behavior Accuracy and the total number of responses during the acquisition task are shown for the 3 wk of spring and fall during which each bird was most active (orange) at night and for the 3 wk during summer and winter during which each bird was the least active (purple) at night. Note that the data for winter are plotted twice to facilitate comparison. In contrast to sleep restriction imposed by the experimenters (see Figure 6), sparrows maintained high levels of accuracy on the acquisition and performance tasks during periods of sleep restriction associated with migration. Furthermore, the total number of responses reached the highest values during the fall migration, in contrast to the decline in responding following experimenter-imposed sleep restriction. Discussion Each spring and fall, songbirds switch from sleeping at night to migrating at night. Whether migrating songbirds sleep during flight, forgo sleep altogether, or compensate for night-time sleep loss by sleeping during the day has remained a mystery. Our EEG recordings of the white-crowned sparrow demonstrate a marked reduction in sleep, as well as distinct changes in sleep architecture during migration, including a shift in the timing of REM sleep to earlier in the night. Although an increase in drowsiness and a corresponding decrease in wakefulness were observed during the day, migrating sparrows did not compensate for sleep loss at night by sleeping more during the day or by increasing SWS intensity on migratory nights. Despite the apparent reduction in sleep occurring during migration, observations of songbirds in the wild suggest that they are fully capable of maintaining a high level of cognitive and physical function, including navigation during long-distance flights, foraging, and evading predators in novel environments. Our results from the repeated-acquisition and performance tasks also suggest that songbirds are not cognitively or physically impaired during episodes of migratory restlessness in the laboratory. Unlike sleep restriction during the nonmigratory season, which caused a decrease in accuracy and responding in both the acquisition and performance components of the task, accuracy and responding did not decrease during migratory periods (spring and fall), when compared to nonmigratory periods (summer and winter). In fact, responding was highest during the fall migration and lowest during the winter. We saw no evidence of sleep in active sparrows during periods of migratory restlessness in the laboratory setting, suggesting that songbirds do not sleep during migratory flights in the wild. Moreover, if birds have evolved the capacity to sleep while flying and depend upon it to avoid sleep deprivation, we would then expect them to be vulnerable to the effects of the sleep restriction resulting from migratory restlessness in the laboratory. The fact that birds exhibit migratory restlessness in captivity for periods of time similar to the duration of migration in the wild indicates an ability to withstand the effects of sleep restriction (Van Dongen et al. 2003). Furthermore, as discussed below, we did not see evidence of decrements in cognitive function similar to those observed following even a single night of sleep restriction during the nonmigratory season. Despite the evidence against sleep in flight, our results do not rule out the possibility that some sleep might occur during flight in the wild. In the laboratory, migratory behavior is characterized by hopping and attempts to initiate flight, a time when sleep is not likely to occur. In the wild, however, it is conceivable that once birds have initiated a nocturnal flight, SWS could occur either unihemispherically or bihemispherically. Precedent for the former is found in bottlenose dolphins (Tursiops truncates), northern fur seals (Callorhinus ursinus), and cape fur seals (Arctocephalus pussilus) that swim in a coordinated manner while exhibiting unihemispheric SWS, a state characterized by SWS-related EEG in one hemisphere and EEG indistinguishable from wakefulness in the other hemisphere (Mukhametov et al. 1977; Lyamin and Chetyrbok 1992; Rattenborg et al. 2000). Birds also show interhemispheric asymmetries in SWS-related EEG when sedentary, although the asymmetry is less pronounced than that in aquatic mammals (Rattenborg et al. 2001). SWS may even occur simultaneously in both hemispheres during flight because the motor control of flight is mediated by spinal reflexes and can persist in decerebrated birds (Cohen and Karten 1974; Steeves et al. 1987); REM sleep during flight seems unlikely, however, given the associated reduction in skeletal muscle tone (Heller et al. 1983; Dewasmes et al. 1985). The fact that nocturnal flights occur high in the generally unobstructed night sky where constant visual assessment of the environment may not be necessary also makes SWS in flight seem feasible. In such a scenario, navigational assessments and corrections could be made during brief awakenings. Ultimately, recordings from birds migrating in the wild are needed to determine whether any sleep occurs during migratory flights. The mechanisms that orchestrate the endogenous circannual rhythm of migratory behavior and associated changes in sleep remain largely unknown (Wingfield et al. 1990; Berthold 1996). Nevertheless, research into the neuroendocrine and circadian control of migratory behavior suggests possible interrelationships between migratory behavior and associated sleep patterns. Several studies suggest that migration is associated with increased hypothalamic–pituitary–adrenal (HPA) axis function (Meier and Fivizzani 1975; Ramenofsky et al. 1999; Wingfield 2003; Landys et al. 2004). In mammals, increased HPA function is associated with sleep disruption and REM sleep abnormalities. For example, plasma glucocorticoids are increased in rats (Meerlo et al. 2002) and humans (Spiegel et al. 1999) following sleep deprivation, and in patients with insomnia (Vgontzas et al. 2001). In depressed patients, increased plasma cortisol levels are correlated with reduced REM sleep latency (Poland et al. 1992). The reduction in sleep and shift in REM sleep timing observed during migration could therefore be related to activation of the HPA axis. Changes in sleep may also be linked to alterations in the circadian rhythm during migration. In particular, since the occurrence of REM sleep is closely tied to the circadian rhythm in humans (Czeisler et al. 1980; Dijk and Czeisler 1995), the shift toward more REM sleep early in the night during migration may reflect a phase advance in the circadian propensity for REM sleep. In garden warblers (Sylvia borin), however, rather than being phase advanced, the amplitude of the circadian rhythm is reduced during migration (Gwinner et al. 1993). Nonetheless, a link between the timing of REM sleep and changes in the circadian rhythm may exist, since depressed humans with short REM sleep latencies show circadian patterns similar to migrating songbirds; the amplitude of the circadian temperature rhythm is reduced while the phase remains unchanged compared to subjects with normal REM sleep latencies (Schulz and Lund 1983). Gwinner suggested that a dampened melatonin rhythm allows the phase relationship of coupled activity rhythms to shift, thereby resulting in activity during the day and night (Gwinner 1996), a mechanism that may also contribute to the changes in total sleep time and REM sleep timing observed during migration. Finally, the reduced latency to REM sleep during migratory nights might reflect a homeostatic response to prior REM sleep deprivation. In mammals, REM sleep deprivation or restriction leads to an increase in REM sleep during recovery sleep (Tobler 2000). As in mammals, pigeons (Columba livia) deprived of sleep also show an increase in REM sleep during recovery sleep (Tobler and Borbély 1988). Although the increase in REM sleep during the early portions of migratory nights is suggestive of a homeostatic response to prior REM sleep loss, the overall proportion of total sleep time spent in REM sleep was not consistently elevated on migratory nights. Consequently, the changes in REM sleep timing in migration are more suggestive of a shift in the circadian timing of REM sleep than a homeostatic response to REM sleep deprivation. Regardless of the mechanism, the changes in sleep architecture during the night in migratory sparrows are reminiscent of those seen in individuals with mood disorders. Like migrating sparrows, both depressed and manic patients show reduced latency to REM sleep, loss of SWS, and reduced amounts of total sleep, often with early morning awakening (Benca et al. 1992); sleep decrements are most profound during mania. Given the aspects of bipolar illness, such as increased energy, activity, and creativity, that may be adaptive under certain circumstances (Andreasen 1987; Jamison 1993; Wilson 1998; Brody 2001), and its many parallels with migratory behavior, including seasonality, it is possible that similar mechanisms may be involved in both migration and bipolar disorder. Despite the marked reduction in sleep during migration, we did not detect a significant increase in SWA during SWS on migratory nights, when compared to the corresponding hours of nonmigratory nights, in the five sparrows recorded during both nonmigratory and migratory states. This may indicate that migrating sparrows respond differently to sleep deprivation or that birds in general, unlike mammals, do not show increases in SWA during SWS following deprivation. The few studies that have examined sleep homeostasis in birds have produced conflicting results. Pigeons did not show a progressive decline in SWA (0.75- to 4.5-Hz) across the normal sleep period (SWS and REM sleep combined) when SWS was the predominant behavioral state, or an increase in SWA following 24 h of total sleep deprivation, suggesting a fundamental difference between sleep homeostasis in birds and mammals (Tobler and Borbély 1988). Unlike pigeons, however, blackbirds (Turdus merula) did show a decline in SWS-related SWA (0.5- to 4.0-Hz) across the major sleep period, indicating that aspects of mammalian SWS regulation may be present in some birds (Szymczak et al. 1996). In nonmigrating sparrows, we did not detect a decline in SWA during SWS across the night using the 0.75- to 4.5-Hz frequency range studied in pigeons and mammals, but a progressive decline in SWA was apparent in the 1.25- to 2.5-Hz frequency range, suggesting that this frequency band may reflect SWS homeostasis in sparrows. Nevertheless, even when this 1.25- to 2.5-Hz band was examined, migrating sparrows failed to show a consistent increase in spectral power. Assuming that the decline in 1.25- to 2.5-Hz power in nonmigrating sparrows reflects SWS homeostasis, the apparent absence of an increase in this band during migration suggests that, unlike nonmigrating sparrows, migrating sparrows may require less SWS. A reduced need for SWS during migration may also be reflected in the shorter REM sleep latencies on migratory nights, since REM sleep latency is positively correlated with SWS need in humans (Feinberg et al. 1992). Alternatively, the increase in drowsiness occurring during the light phase in migrating sparrows may have compensated for SWS loss during the previous night, thereby accounting for the absence of an increase in SWS-related SWA during the subsequent night. Deprivation of daytime drowsiness may clarify whether this state contributes to SWS homeostasis in nonmigrating and migrating sparrows. In addition, a link between the declining trend in 1.25- to 2.5-Hz spectral power and SWS homeostasis in nonmigrating sparrows will need to be established with additional studies of SWS deprivation in both nonmigrating and migrating sparrows. The results from the repeated-acquisition task suggest that songbirds appear resistant to the effects of sleep restriction during migration, although sleep restriction during the nonmigratory seasons appeared to impair accuracy and responding. It is possible that stress associated with the experimenter-induced sleep deprivation procedure may have contributed to the decrements observed; however, these birds were well acclimated to daily handling, and the methods used to deprive the birds of sleep (i.e., walking past the cage) were minimally intrusive. Regardless, birds in a migratory state were clearly able to maintain high levels of accuracy and responding during periods of spontaneous sleep loss occurring during migration. The only previous study to assess cognition in a migratory songbird compared the closely related nonmigratory Sardinian warbler (Sylvia melanocephala momus) to migratory garden warblers (S. borin); the migratory species performed better on a long-term memory task simulating habitat selection, despite being trained during a period of migratory restlessness, when sleep was presumably restricted (Mettke-Hofmann and Gwinner 2003). Although based only on a between-species comparison of one migratory and one nonmigratory species of songbird, these results and those from the white-crowned sparrow suggest that cognition is not impaired, and may even be enhanced, in migrating songbirds. Studies using other forms of neurobehavioral testing will be needed to determine whether migrating songbirds show a generalized resistance to the adverse effects of sleep restriction on specific cognitive functions known to be sensitive to sleep deprivation. In particular, the recent evidence suggesting that sleep is required for memory consolidation (Karni et al. 1994; Stickgold et al. 2000, 2001; Maquet 2001; Fischer et al. 2002; Fenn et al. 2003), a process not assessed with the repeated-acquisition task used herein, raises the question as to how birds consolidate memories during periods of migratory sleeplessness. The apparent resistance to the effects of sleep restriction in migrating songbirds is unprecedented and clearly needs to be confirmed with further neurobehavioral testing. Future studies aimed at understanding the mechanisms underlying migratory sleeplessness may provide insight into the etiology and treatment of certain sleep disorders, as well as psychiatric disorders such as bipolar disorder, where similar seasonal bouts of sleeplessness with high levels of cognitive function are diagnostic of hypomania. Furthermore, an understanding of the mechanisms involved in migratory sleeplessness may lead to the development of methods to temporarily mitigate the effects of sleep deprivation that otherwise compromise performance in humans engaged in sustained operations where the maintenance of high levels of cognitive and physical function is critical. Finally, revealing the mechanisms through which migratory songbirds resist the effects of sleep deprivation may yield important clues as to the function of sleep in general. Materials and Methods Birds Sparrows used for the operant testing were captured in Alaska (lat 64°49′ N, long 147°52′ E) during June 2002. Sparrows used for the EEG recordings were captured on their wintering grounds in the Sacramento valley in California (lat 39°00′ N, long 122°00′ E) during November 2002. All birds were collected using mist nets under the authority of a United States Fish and Wildlife Service permit. Birds were transported to the University of Wisconsin-Madison where they were individually housed in galvanized wire cages (L: 35 cm × W: 25 cm × H: 32 cm) in environmentally controlled rooms (L: 4.0 m × W: 2.7 m × H: 2.7 m; 22.0–24.5 °C, 40% relative humidity). Each bird was in visual and auditory contact with other birds in the room. To simulate seasonal changes in photoperiod, operant birds were exposed to photoperiods ranging from 9.5:14.5 LD to 16.5:7.5 LD. Sparrows used for the EEG recordings were maintained under a 12:12 LD schedule; lights went on at 06:00 with an illuminance level of 540–640 lux measured at the level of the cage floor. Illuminance during the dark phase was less than 0.5 lux. Birds were fed a mixed-seed and provided water ad libitum, and their diet was supplemented daily with lettuce, dried insects, live mealworms, and grit. Birds involved in operant testing were food restricted as described below. All experimental protocols were approved by the University of Wisconsin-Madison Animal Care and Use Committee. Activity monitoring As in previous studies (Wikelski et al. 1999), gross activity was measured using an infrared motion detector (no. 49–312, Radio Shack) connected to a system (VitalView version 4.0, Mini Mitter, Bend, Oregon, United States; http://www.minimitter.com) that logged the number of times that the bird crossed the infrared beam aimed across the center of the cage each 30-s interval. Although the infrared activity monitoring system may underestimate overall activity because it fails to quantify activity that does not result in a beam break, it nevertheless provides a rapid method for assessing gross seasonal changes in behavior. For the birds in which EEG was recorded, behavior was also continuously recorded using 16 infrared-sensitive cameras (two per bird) connected to a digital video storage system (Salient Systems, Austin, Texas, United States; http://www.salientsys.com). Infrared illuminators provided lighting for the cameras during the dark phase. Surgery In July 2003, eight adult white-crowned sparrows approximately 13–14 mo of age were randomly selected from our captive population and surgically instrumented for chronic EEG and EMG recordings. All surgical procedures were performed under isoflurane anesthesia (1.0%–3.5% isoflurane with 500 ml/min O2). The bird's head was stabilized in a Kopf Instruments (Tujunga, California, United States) stereotaxic device, using an adaptor developed for use with birds. A temperature-regulated heat pad set at 40 °C reduced heat loss during the procedure. After establishing a suitable anesthetic plane, the feathers overlying the cranium were clipped and the scalp was cleaned with 70% isopropyl alcohol. A longitudinal incision was made along the midline of the head to expose the cranium. After cleaning with 3% hydrogen peroxide and drying the cranium, four small holes were drilled through the cranium to the dura: two for the EEG electrodes, one for the reference electrode, and one for the ground electrode. To record the EEG from the left and right cerebral hemispheres, two holes were drilled 2 mm lateral of the midline, one over the left and one over the right Wulst, a brain region homologous to portions of the mammalian neocortex (Medina and Reiner 2000). A third hole for the reference electrode was positioned over the midline of the cerebellum. The fourth hole for the ground electrode was drilled over the right hemisphere. Stainless steel electrodes (no. AS 633, Cooner Wire, Chatsworth, California, United States) were inserted through the holes to the level of the dura and held in place using surgical glue. A final electrode was positioned over the nuchal muscles for recording EMG activity. Each electrode was connected to a lightweight, flexible, and electrically shielded recording cable (Dragonfly, Ridgeley, West Virginia, United States; http://www.dragonflyinc.com). The cable was attached to the bird's cranium using dental acrylic (Justi Products, Oxnard, California, United States). To form a strong adhesion, the acrylic was allowed to infiltrate the porous cavity between the inner and outer layers of the cranium through small holes drilled only through the dorsal layer of the cranium (Dave et al. 1999). Finally, the incision was closed around the acrylic with surgical adhesive (Tissuemend II, Veterinary Products Laboratories, Phoenix, Arizona, United States; http://www.vpl.com). Electrophysiological recording After surgery, each bird was placed in the recording cage (L: 35 cm × W: 25 cm × H: 32 cm) for at least 10 d of postoperative recovery and adaptation to the recording cable. The recording cable was attached to a low-torque, six-channel mercury commutator (Dragonfly) designed for use with small birds (Dave et al. 1999), and the weight of the recording cable was counterbalanced with a spring; these recording conditions allowed the sparrows to move unimpeded throughout the cage. The EEG and EMG signals were referenced to the cerebellar electrode, amplified, and band-pass filtered (0.3- to 30-Hz and 10- to 90-Hz, respectively), using Grass-Telefactor amplifiers (model 12 Neurodata and 7P511, Grass-Telefactor, West Warwick, Rhode Island, United States; http://www.grass-telefactor.com), digitized at 100 Hz, and visualized using Somnologica 3 software (Medcare, Reykjavik, Iceland; http://www.medcare.com). Sleep–wakefulness scoring Representative 24-h periods occurring prior to and during migration were selected for behavioral state scoring. The migratory nights selected for scoring were preceded by several nights with similar amounts of migratory restlessness. The behavioral state was scored visually, using both electrophysiological (i.e., EEG and EMG) and video recordings, and categorized as either wakefulness, drowsiness, SWS, or REM sleep. For accurate detection of REM sleep, the duration of scoring epochs was set at 4 s, since episodes of REM sleep may be as brief as only several seconds in birds (Rattenborg and Amlaner 2002). As in previous studies of sleep in birds (Rattenborg et al. 1999a, 1999b, 2001), the behavioral state was sampled across the 24-h period by scoring the first 4 s of each minute, resulting in a total of 1,440 samples per day. In addition, to determine the latency to SWS and REM sleep onsets precisely, every 4-s epoch was scored from lights out until the first unequivocal episodes of both SWS and REM sleep had occurred. Since SWS and REM sleep were restricted to the dark phase of the LD cycle in all birds during both migratory and nonmigratory seasons, sleep latency was calculated as the elapsed time from lights out to the first epoch of either SWS or REM sleep, and REM sleep latency was the elapsed time from the first epoch of SWS to the first epoch of REM sleep. Spectral analysis of the EEG EEG power spectra of the left and right hemisphere derivation were computed for all 4-s epochs, as described previously, by a FFT routine (Matlab function, Mathworks, Natick, Massachusetts, United States; using a Hanning window) within the frequency range of 0.25–25.0 Hz (Huber et al. 2000). Values were collapsed into 0.5-Hz bins. For the analysis of the time course of EEG power, artifact-free SWS epochs were selected. Cognitive testing To encourage responding by the sparrows, their food was restricted to maintain 90% of their free-feeding weights during nonmigratory periods. In practice, however, it was difficult to maintain birds at this weight, especially during periods of premigratory fattening, when even food-restricted birds gained weight. Sparrows were tested for 60-min sessions once per day on 5–7 d per week from 1 February 2003 through 15 February 2004. Testing was always performed between 09:30 and 16:00, and the testing order was counterbalanced across days for each bird. Activity levels in the home cages were measured using the infrared activity monitoring system. The amount of active time during the dark phase was used to select for analysis two 3-wk periods when the birds were migrating (spring and fall) and two 3-wk periods when they were not migrating (winter and summer). Multiple schedule of repeated-acquisition and performance Preliminary training for the repeated-acquisition task was described previously (Winsauer et al. 1995) and included shaping the approach to the food trough, shaping the response (key peck), and then reinforcing responses on each key when it was illuminated. To train repeated acquisition in all the sparrows, all three response keys were illuminated simultaneously with white light, but only one of the three response keys was chosen to be correct for a particular session, and each response emitted on that key resulted in the delivery of mixed-seed. Responding on either of the other two illuminated keys was considered an error and resulted in a 5-s time-out during which the key lights were extinguished and responding had no programmed consequence. For each daily session during this stage of training, the position for the correct response was varied pseudorandomly. After the sparrows acquired this task reliably, regardless of key position, a second response was added to the sequence or chain so that two correct responses were necessary to obtain seed. This type of sequential responding is procedurally defined as a “chain” because each response except the last produces a discriminative stimulus controlling the response that follows (Kelleher 1966). The key positions for the correct responses varied both within the two-response sequence and across sessions. The color of the key lights changed after each correct response. A third response was added to the sequence when stable responding was obtained under the two-response sequence. The average number of sessions required to train repeated acquisition of the first, second, and third member of the sequence was 38, 65, and 35, respectively. A second component was then added to the schedule so that sparrows responded under a multiple schedule of repeated acquisition and performance of response chains. During acquisition components, the three response keys were illuminated at the same time with one of three colors: green, red, or white. Responding on the correct key in the presence of one color (e.g., keys green, center correct; keys red, left correct; keys white, right correct) changed the color of the key lights as well as the position for the next correct response. When the subject completed the response sequence by emitting three correct responses (i.e., one correct response in the presence of each color), the key lights were extinguished, and the stimulus light in the mixed-seed trough was illuminated for 0.05 s. Subsequently, the response keys were illuminated with the first color (i.e., green), and the sequence was reset. Within a given session, the correct response that was associated with a particular color did not change, and the same sequence (in this case, center-left-right [C-L-R]) was repeated during all acquisition components of a given session. Responding on this sequence was maintained by food presentation under a second-order FR3 schedule such that every third completion of the sequence resulted in the presentation of 5 s of access to mixed-seed. When sparrows responded on an incorrect key (in the example, the left or right key when the green lights were illuminated), the error was followed by a 5-s time-out. An incorrect response did not reset the three-response sequence (i.e., the stimuli and the position of the correct response were the same before and after a time-out). To establish a steady state of repeated acquisition, the sequence was changed from session to session. An example of sequences for five consecutive sessions was C-L-R, L-R-C, C-R-L, R-L-C, and L-C-R. The sequences were carefully selected to be equivalent in several ways, and there were restrictions on their ordering across sessions. Briefly, each sequence was scheduled with equal frequency, and consecutive correct responses within a sequence were scheduled on different keys. Occasionally, a correct sequence position for a given color was the same for two consecutive sessions (in the list of sequences above, L-R-C and C-R-L). During performance components, the response keys and the houselights were illuminated, and the sequence remained the same (R-C-L) from session to session. In all other aspects (color of the stimuli for each response in the sequence, second-order FR3 schedule of food presentation, 5-s time-out, etc.), the performance components were identical to the acquisition components. Experimental sessions always began with an acquisition component, which then alternated with a performance component after 20 reinforcers or 20 min, whichever occurred first. The performance component alternated back to the acquisition component after 10 reinforcers or 20 min, whichever occurred first. Each session terminated after 60 min. Sleep deprivation To determine whether accuracy and response rate on the repeated-acquisition and performance task were affected by sleep restriction during the nonmigratory season, sleep was restricted to the first 3 h of the dark phase (18:00–21:00) for three consecutive nights starting on 10 February 2004. Birds were deprived of sleep starting at 21:00 until the following day at 18:00 by experimenters who entered the housing room at least once every 5 min or sooner if behavioral signs of sleep were observed via closed-circuit cameras. Walking quietly past the cages was always sufficiently stimulating to keep the birds awake; we never had to handle the birds to induce wakefulness. Statistics Comparisons were made using either Student's t-test, with Welch correction for sample size, or ANOVA. All tests were performed using “R” (http://www.r-project.org). Prior to analysis two procedures were performed on the data for the cognitive testing. One consequence of counterbalancing the order of testing the birds was that the length of time from withdrawal of food to the onset of testing (and presumably one component of food motivation) varied on a 3-d schedule. For this reason a 3-d running average of the cognitive testing variables was computed. There was also a linear trend across the year, particularly in acquisition percentage correct (r2 varied from 0.31 to as high as 0.67). This trend was removed before making seasonal comparisons. The spring and fall migratory periods and the summer and winter nonmigratory periods were determined as follows. For each bird, each date during the study was used to compute the average nighttime activity for the following 21 d. The periods in the spring (21 February–20 May) and fall (21 August–20 November) with the highest average activity for each bird were designated as peak migration times, and the periods in the summer and winter with the lowest activity for each bird were chosen as nonmigratory times. Video 1 Migratory Restlessness in a White-Crowned Sparrow Wing whirring while holding the perch (which occurred only at night) and perch hopping. Video from infrared camera. Bright object at the center of the screen is the source for the infrared motion detection beam. Video 2 Drowsiness in a White-Crowned Sparrow A brief example of active wakefulness followed by about 50 sec of drowsy behavior. Captured during the daytime. Video 3 SWS in a White-Crowned Sparrow Captured on surveillance camera. Bright object at the center of the screen is the source for the infrared motion detection beam. Video 4 Acquisition Component of the Operant Task A correct sequence of three presses must be repeated three times. Feedback is provided by the lighted keys. We thank Drs. Chiara Cirelli and Guilio Tononi for their valuable comments on the manuscript. We also thank Dr. Susan Sharbaugh at the University of Alaska in Fairbanks for her assistance in locating sparrows and the following people for their contribution to various aspects of the project: Brian Asti, Monique Duwell, Jennifer Fahy, Gretchen Fredericks, Bridgette Harder, Stephany Jones, Dolores Martinez-Gonzalez, Sarah Newman, Britta Nordberg, Roxanne Prichard, Michael Thalasinos, Brian Theyel, Rachel Uttech, Erika Vacha, and Annette Vee. This work was supported by the United States Department of Defense. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. NCR, WHO, PJW, and RMB conceived and designed the experiments. NCR and BHM performed the experiments. NCR, BHM, WHO, PJW, RH, and RMB analyzed the data. PJW, RH, and MW contributed reagents/materials/analysis tools. 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Los Angeles: University of California. 235 p 1964 Available from ProQuest (http://www.proquest.com ); AAT 6406387 Steeves JD Sholomenko GN Webster DM Stimulation of the pontomedullary reticular formation initiates locomotion in decerebrate birds Brain Res 1987 401 205 212 3815097 Stickgold R James L Hobson JA Visual discrimination learning requires sleep after training Nat Neurosci 2000 3 1237 1238 11100141 Stickgold R Hobson JA Fosse R Fosse M Sleep, learning, and dreams: Off-line memory reprocessing Science 2001 294 1052 1057 11691983 Szymczak JT Helb HW Kaiser W Electrophysiological and behavioral correlates of sleep in the blackbird (Turdus merula) Physiol Behav 1993 53 1201 1210 8346306 Szymczak JT Kaiser W Helb HW Beszczynska B A study of sleep in the European blackbird Physiol Behav 1996 60 1115 1120 8884941 Tobler I Phylogeny of sleep regulation. In: Kryger M, Roth T, Dement W, editors. 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In: Berthold P, Gwinner E, Sonnenschein E, editors. Avian migration 2003 Berlin Springer 113 125 Wingfield JC Schwabl H Mattocks PWJ Endocrine mechanisms of migration. In: Gwinner E, editor. Bird migration: Physiology and ecophysiology 1990 Berlin Springer-Verlag 232 256 Winsauer PJ Bixler MA Mele PC Differential effects of ionizing radiation on the acquisition and performance of response sequences in rats Neurotoxicology 1995 16 257 269 7566685 Winsauer PJ McCann UD Yuan J Delatte MS Stevenson MW Effects of fenfluramine, m-CPP and triazolam on repeated-acquisition in squirrel monkeys before and after neurotoxic MDMA administration Psychopharmacology (Berl) 2002 159 388 396 11823891 Zepelin H Mammalian sleep. In: Kryger MH, Roth T, Dement WC, editors. Principles and practice of sleep medicine, 3rd ed 2000 Philadelphia Saunders 82 92

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PLoS Biol. 2004 Jul 13; 2(7):e212

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020213FeatureBiotechnologyBotanyEcologyEvolutionGenetics/Genomics/Gene TherapyInfectious DiseasesMicrobiologyPlantsStopping the Rot FeatureNicholls Henry 7 2004 13 7 2004 13 7 2004 2 7 e213Copyright: © 2004 Henry Nicholls.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Phytophthora species blight potatoes and tomatoes, devastate soybean, rot cacao, and are the cause of sudden oak death. Understanding this versatile genus will be key to its control ==== Body In July 2000, the finger of blame for a mysterious mass killer of Californian oak trees came to rest on a previously undescribed plant pathogen. From the initial identification of Phytophthora ramorum, it took less than four years to produce a draft sequence of its genome, one of the fastest-ever discovery-to-sequence stories for a complex pathogen. This achievement was a United States initiative, facilitated by the injection of federal and state funding into Phytophthora research. But the US is not alone in the battle against this genus. Phytophthora species cause thousands of millions of dollars of damage to the world's commercial crops every year: they blight potatoes and tomatoes, devastate the lucrative soybean, and rot cacao, threatening the world's supply of chocolate (Figure 1). But for Sophien Kamoun, Associate Professor of Plant Pathology at Ohio State University (Wooster, Ohio, United States), these destructive organisms present an exciting opportunity. There are some 60 species of Phytophthora, but so little is known about the genus, he says, that there are things about its species we didn't even know we didn't know. ‘What I find really exciting,’ he says, ‘is discovering these unknown unknowns.’ Figure 1 Phytophthora Infestations (A) Potato and (B) cacao pod. (Reproduced courtesy of Sophien Kamoun, Ohio State University [A], and Christopher J. Saunders and the USDA Agricultural Research Service [B].) The most infamous of the Phytophthora pathogens is the potato late blight, P. infestans. It was this species that led to the Irish potato famine in the mid-1840s, which resulted in the death or displacement of millions. Today, P. infestans is estimated to cost potato and tomato farmers US$5,000,000,000 a year in lost revenue. The story for the soybean pathogen P. sojae is similar, causing loss of more than US$1,000,000,000 a year to soybean growers. In addition to the direct economic impact of these pathogens, introduced Phytophthora can cause severe damage to native flora. The most recent Phytophthora on the scene is P. ramorum, which has caused ‘sudden oak death’ (SOD) in tens of thousands of oak trees across the coastal counties of California, is now present in at least three other US states and is threatening to take on the native flora of the entire North American continent. It is also lurking in Europe, although apparently with less devastating consequences. Molecular Machinery With this kind of impact, it's no surprise that money has poured into research on Phytophthora. This year, the US federal government will channel US$7,400,000 toward research into SOD. A major focus of this funding is genomics. Sequencing the genomes of several Phytophthora will help clarify the phylogeny and evolution of these enigmatic organisms (Box 1) and improve methods of detection and identification. Ultimately, however, sequencing should reveal the molecular tricks that this genus uses to subvert the defences of its plant hosts, allowing scientists to come up with new ways to combat these troublesome organisms. The P. infestans sequencing initiative, coordinated by Kamoun, has recently completed a survey sequence of the genome that gives an initial understanding of how this organism is structured. Perhaps most striking is its size. ‘It's a huge genome,’ says Kamoun. At about 250 megabases (Mb), it's about twice the size of the Arabidopsis genome. His latest research, published in the Journal of Biological Chemistry (Tian et al. 2004), describes a P. infestans protease inhibitor—extracellular protease inhibitor 1 (EPI1)—that could be one of a unique class of suppressor proteins that Phytophthora deploy to infect and counteract host defences. The pathogen seems to upregulate the epi1 gene during colonisation of its host. EPI1 inhibits plant apoplastic proteases—extracellular enzymes that are part of the host's defensive armoury that have evolved to prevent foreign proteins entering cells. ‘Based on its biological activity and expression pattern, EPI1 may function as a disease effector molecule and may play an important role in P. infestans colonisation of host apoplast,’ Kamoun and his colleagues report (Tian et al. 2004). If further research confirms this function for EPI1, then it will become one of just a handful of pathogen molecules that have been shown to suppress host plant defenses. A search in sequence databases for matching motifs reveals just one similar sequence in the entire bacterial and fungal kingdoms. However, apicomplexans like Toxoplasma gondii that transit through the mammalian digestive tract also appear to secrete protease inhibitors allied to EPI1. This similarity suggests an analogy between plant apoplasts and mammalian digestive tracts. Both environments are rich in proteases, but nevertheless are colonised by a variety of microbial pathogens. In the case of an apoplast, the pathogen is P. infestans, whilst in the mammalian gut, it's T. gondii—and although they are phylogenetically distant, these pathogens seem to have independently recruited similar secreted proteins to inhibit the defensive proteases produced by their hosts. Interestingly, whilst T. gondii inhibitors inhibit gut enzymes trypsin and chymotrypsin, EPI1 does not, suggesting that coevolution between the inhibitors and their target proteases may shape the specificity of these pathogenic enzymes. Armed with this new insight into the molecular cunning of P. infestans, Kamoun hopes that it will be possible to come up with ways of slowing disease progression. Importantly, it looks likely that protease inhibitors such as EPI1 are present in other Phytophthora species. There are significant matches between the epi1 gene sequence and motifs from at least five other closely related species. So any methods of blocking the action of protease inhibitors in P. infestans might also work against other Phytophthora. The protease inhibitors are one of Kamoun's ‘unknown unknowns’. ‘It's an example of something that we had absolutely no idea was in the genome,’ he says. Even more advanced than the P. infestans genome project is an ongoing collaboration between the Virginia Bioinformatics Institute (Blacksburg, Virginia, United States) and the US Department of Energy's Joint Genome Project based in Walnut Creek, California. The focus here is the soybean pathogen P. sojae and the SOD pathogen P. ramorum, for which draft sequences are now complete (www.jgi.doe.gov/). The genomes are much smaller than that of the 250-Mb P. infestans—P. sojae is about 90 Mb and P. ramorum is just 55 Mb. This, in part, explains the comparative speed of these sequencing efforts. But another factor is undeniably the fear of the unknown P. ramorum, which in 2002 netted the Virginia–California initiative US$3,800,000 in federal funding to describe its genome. Ultimately, however, the sequence of one species will help to inform on the sequence of other related species. Brett Tyler, Research Professor at the Virginia Bioinformatics Institute, is focusing on the molecular tools used by P. sojae to infect its host. He agrees with Kamoun that understanding this machinery is the way to devise new control measures that could give plants the upper hand in the evolutionary arms race against their Phytophthora pests. At present, most strategies to limit the damage caused by species like P. infestans and P. sojae rely on developing resistant cultivars by selective breeding of varieties with major resistance genes—single genes that can block a pathogen. However, Phytophthora seem able to find ways to overcome these efforts. ‘P. infestans is absolutely notorious for its ability to genetically change in response to a major resistance gene,’ says Tyler. ‘Typically major resistance genes in potato barely last a single season.’ Things look better for the soybean. New cultivars containing major resistance genes show resilience to P. sojae for five to ten years. However, this resistance is starting to break down, and breeders are running out of major resistance genes with which to conjure new varieties. The alternative, says Tyler, is quantitative or multigenic resistance, which relies on getting plants to express several resistance genes at once, each of which makes a small contribution to the plant's overall resistance. It should be much harder for P. sojae to evolve a new attack against this kind of robust defence. The search is also on for new genes that could be used to encourage quantitative resistance to host species. One particularly promising approach is to pit pathogen against host to see which genes are switched on. The host should upregulate genes that defend it against infection and the pathogen should upregulate genes that it needs to attack. The discovery that plants produce proteases and that Phytophthora have responded by secreting protease inhibitors to disable them is important if long-lasting solutions are to be found. ‘We just need to find a way to introduce new protein-degrading enzymes into the plant that the pathogen doesn't know how to block,’ says Tyler. ‘With these genomic tools we can really accelerate the pace at which we can evaluate different possible protective measures.’ Epidemiology: Identifying the Culprit An additional benefit of the abundance of genetic information is that species identification is becoming increasingly sophisticated. At a glance, Phytophthora can be mistaken for a fungus, so DNA profiling of isolates is crucial if species and strains of species are to be identified correctly so that action appropriate to each infection can be taken. Since 2000, Matteo Garbelotto, Adjunct Professor of Mycology and Forest Pathology at the University of California at Berkeley (Berkeley, California, United States), has spent a significant part of his working life tracking the spread of P. ramorum, the Phytophthora that has killed off vast tracts of oak trees in native Californian forest (Box 2). DNA analysis has been crucial to confirm suspected cases of P. ramorum, and has now revealed that infections have reached at least three other US states. This spread is probably due to the movement of infected ornamentals like rhododendron (Rhododendron spp.) and viburnum (Viburnum spp.), which seem to act as carriers for the pathogen. ‘What we're seeing is a parallel to what has been happening throughout Europe, where the infection has basically moved using the commercial routes of the ornamental plant industry,’ Garbelotto says. P. ramorum in Europe P. ramorum does not appear to have the same devastating consequences in Europe as it does in the US—at least not yet. It's not entirely clear why, but it could have something to do with the structure of the bark of different host species, suggests Garbelotto. ‘We normally see more infection where we have more corrugation, and that's because water accumulates in the fissures … where the zoospores have a chance to infect the bark.’ However, he notes, European beech (Fagus sylvatica) appears to be extremely susceptible to P. ramorum. ‘If it reaches areas where there are a lot of beeches, it could potentially mirror what's happening in California,’ he warns. In Europe, symptoms characteristic of P. ramorum infection were first described on rhododendrons in The Netherlands in 1993. Once this was confirmed to be the same species as the pathogen responsible for Californian SOD, there was speculation that P. ramorum had either been introduced to the US from Europe or vice versa. However, in December 2002, it emerged that these two populations are of different mating types—A1 in Europe and A2 in the US. The latest research from Garbelotto and his colleagues, due to be published in Mycological Research (Ivors et al. 2004), supports this interpretation, demonstrating that although they belong to the same species, A1 and A2 are distinct lineages and have not exchanged genes for a long time. But last year, a batch of isolates from infected camellias (Camellia spp.) and rhododendrons in a nursery in Washington state showed that the A1 and A2 strains were living side by side. ‘That was a big surprise for us,’ recalls Garbelotto. ‘We had no knowledge at that point that both the European and the North American type could be present in the same nursery.’ The Threat of Recombination Hybridisation between different species of Phytophthora can produce a new species with different properties from those of either parent. One of the best cases comes from Europe, where a new Phytophthora emerged in 1993 that began to attack alder trees (Alnus spp.). Research carried out by scientists in the United Kingdom demonstrated that the alder Phytophthora was a product of a hybridisation event between P. cambivora and an unknown species similar to P. fragariae, neither of which attacks alder. Given this propensity for Phytophthora species to hybridise and new phenotypes to emerge, there is legitimate concern that sexual recombination between the A1 and A2 mating types could produce something more devastating than either form. Within the controlled confines of his laboratory, Garbelotto has been exploring whether the two types of P. ramorum can get it together. Initial findings are that oospores—the product of sexual recombination—are being produced, although most of them abort before they reach maturity. However, 30% progress further, and (microscopically, at least) look like they could be functional. ‘They'll germinate,’ he predicts. The threat that hybridisation could create a novel strain with a different host range is a concern that the UK is also taking seriously. In December 2003, an entirely new Phytophthora was isolated from two sites in England. Although the new species—currently referred to as Phytophthora taxon C sp. nov. (P. taxon C)— appears to cause relatively mild damage to its beech and rhododendron hosts, the UK's Department for Environment, Food and Rural Affairs (DEFRA) acknowledges that hybridisation of P. taxon C with P. ramorum could have serious consequences. ‘The potential for the pathogen to adapt further to its putative new environment intrinsically or via hybridisation is not known,’ note the authors of a DEFRA report on the mystery species (www.defra.gov.uk/planth/pra/forest.pdf). ‘Long-distance spread could easily occur through the movement of infected stock of rhododendron or beech and possibly other (as yet unknown) hosts,’ they warn. DEFRA is monitoring the situation closely (Figure 4). However, on the basis of a preliminary DNA analysis, P. taxon C and P. ramorum are only distantly related, making hybridisation unlikely, says Joan Webber, Head of Pathology at the UK government's Forestry Commission. The closest known relative of P. taxon C is P. boehmeriae, a pathogen that has been recorded on several species of tree in China and Australia, and on cotton in China and Greece, suggesting possible origins for the newly described species. But, says Webber, the sequence match between P. taxon C and P. boehmeriae is only 92%—not especially close. Much more evidence is needed to build a strong case for the origin of this new Phytophthora, she says. Figure 4 Infection of P. ramorum at a site in the UK DEFRA is monitoring closely for signs of hybridisation with P. taxon C sp. nov. Origins Indeed, it has taken more than 150 years to track down the geographical origin of the P. infestans strain that caused the Irish potato famine. Jean Beagle Ristaino of North Carolina State University (Raleigh, North Carolina, United States) is due to publish in Mycological Research an analysis of DNA extracted from diseased potato plants preserved from the nineteenth-century Irish epidemic (May and Ristaino 2004). It had long been suspected that the famine was caused by the Ib strain of P. infestans, which is presumed to have originated in Mexico. However, Ristaino's molecular evidence spotlights the Ia strain and traces its probable roots to the Andes. The infection could have found its way from South America to Europe and the US via exports of potato seed on steamships, she speculates. This kind of forensic treatment is more than just interesting. Tracing a Phytophthora species to its site of origin could reveal what keeps them at bay in the areas where they are native, and might suggest new ways to manage them when they are introduced to a different ecosystem, says Garbelotto: ‘There's a huge amount of information that can be learned from understanding where they're coming from.’ So where do pathogens like P. ramorum originate? The best lead, Garbelotto says, is the ease with which it infects rhododendron. These ornamentals are natives of Asia, but there are only a few places where the climate would suit P. ramorum. The most promising, he suggests, are the Southern Himalayas, the Tibetan plateau, or Yunnan province in China. But these are big places, and Garbelotto has plenty on his plate in his battle against the Californian SOD. ‘I am not very hopeful that we'll ever be able to find out where it comes from,’ he says. Figure 2 Reproductive Structures of the Phytophthora The asexual (A) sporangia, (B) zoospores, and (C) chlamydospores, and the sexual (D) oospores. (Reproduced courtesy of Matteo Garbelotto, UC Berkeley [A, D], and Edwin R. Florance, Lewis & Clark College [Portland, Oregon, United States] and the USDA Forest Service Pacific Southwest Research Station in Albany, California [B, C].) Figure 3 Coast Live Oaks Plagued by P. ramorum, Marin County, California (Reproduced courtesy of Matteo Garbelotto, UC Berkeley.) Box 1. A Closer Look at Phytophthora Phytophthora belong to the Kingdom Stramenophiles, so are most closely related to brown algae and diatoms. Their hyphal growth and variety of spores are morphologically and physiologically similar to fungi, for which they are occasionally mistaken, but their parasitic lifestyles have independent evolutionary origins, and therefore they have alternative mechanisms of pathogenicity. Within the class Oomycetes, which comprises all manner of heterotrophic blights, mildews, and molds, the Phytophthora—from the Greek for ‘plant destroyer’—is a pernicious genus, costing the world's farmers thousands of millions of dollars each year in control measures and lost yield. The majority of the 60 or so Phytophthora species that have been described are distinguished by their complex life histories, with both asexual and sexual phases and a bewildering array of associated reproductive structures. Sporangia are asexual spores that provide the pathogen with a short-lived mode of transmission. They are either broken off from the filamentous hyphae to become airborne as in the potato blight P. infestans (Figure 2A) or remain attached and divide into swimming zoospores following rains (Figure 2B). By contrast, chlamydospores are asexual structures that are adapted for longterm survival (Figure 2C). In the case of the Phytophthora causing ‘sudden oak death’ (P. ramorum) and forest dieback (P. cinnamomi), the chlamydospores play a crucial role, allowing the pathogen to survive from season to season. The formation of sexual structures is relatively rare, but oospores—the product of sexual recombination—occur in many species (Figure 2D). Some, such as P. sojae, are known as homothallic, with a single form that has both male and female reproductive structures, whilst others like P. ramorum are heterothallic and require two different mating types to meet for sexual recombination to occur. Box 2. Sudden Oak Death in California and Beyond In 1995, trees in oak forests in the coastal counties of California started to show a range of alarming and lethal symptoms. Since then, the disease has reached epidemic proportions, with tens of thousands of trees dying along approximately 300 km of the central Californian coastline (Figure 3). By 2000, the pathogen responsible had been identified as P. ramorum, and it is now clear that this pest has an extremely broad host range that extends to almost all woody plant species in the coastal forests of California. Many oak hosts suffer lethal branch or stem infections, whilst non-oak hosts only carry mild leaf or twig infections. The species hardest hit include tanoak (Lithocarpus densiflora) and the true oaks coast live oak (Quercus agrifolia), California black oak (Quercus kellogii) and Shreve's oak (Quercus parvula var. shrevei). These species develop large wounds or cankers in their woody tissue, which disrupt physiology and in extreme cases lead to death in a matter of months. In non-oak species, P. ramorum does not appear to cause the same damage, leaving hosts like the Californian bay laurel (Umbellularia californica) and rhododendrons (Rhododendron spp.) with the relatively mild symptoms of leaf blight and occasional branch dieback. However, these non-oak hosts probably act as reservoirs of disease and may even help it to spread. The sporangia and chlamydospores that are thought to be the main asexual propagules of the pathogen (Box 1) are readily produced on the foliage of such non-oak species, but do not seem to appear on the bark of most of the infected oak hosts. In March 2004, P. ramorum was found at a large wholesale horticultural nursery in Los Angeles County. The California Department of Food and Agriculture, the US Department of Agriculture's Animal and Plant Health Inspection Service, and state agriculture departments around the country are tracing all plants shipped from this nursery over the past year in an effort to identify and destroy any infected material, and hence prevent any further spread of the disease. However, the rot has already set in at nurseries in three states outside California: Oregon, Washington, and Florida. A National SOD Nursery Survey is underway to try to assess how far the disease might have spread. Henry Nicholls is a freelance writer based in London, United Kingdom. E-mail: [email protected]. Abbreviations DEFRAUnited Kingdom Department for Environment EPI1extracellular protease inhibitor 1 Mbmegabase(s) SODsudden oak death ==== Refs References and Further Reading Brasier CM Cooke DEL Duncan JM Origin of a new Phytophthora pathogen through interspecific hybridization Proc Natl Acad Sci U S A 1999 96 5878 5883 10318978 DEFRA Pest risk analysis. Phytophthora taxon C sp. nov 2004 3 12 Available: www.defra.gov.uk/planth/pra/forest.pdf via the Internet. Accessed 16 May 2004 Erwin DC Ribiero OK Phytophthora diseases worldwide 1996 Saint Paul (Minnesota) APS Press 592 Garbelotto M Davidson JM Ivors K Maloney P Huberli D Non-oak native plants are the main hosts for the sudden oak death pathogen in California Calif Agr 2003 57 18 23 Huitema E Bos JIB Tian M Win J Waugh ME Linking sequence to phenotype in Phytophthora –plant interactions Trends Microbiol 2004 12 193 200 15051070 Ivors KL Hayden KJ Bonats PJM Rizzo DM Garbelotto M AFLP and phylogenetic analyses of North American and European populations of Phytophthora ramorum Mycol Res 2004 In press May KJ Ristaino JB Identity of the mitochondrial DNA haplotype(s) of Phytophthora infestans in historical specimens from the Irish potato famine Mycol Res 2004 In press Rizzo DM Garbelotto M Sudden oak death: Endangering California and Oregon forest ecosystems Front Ecol Environ 2003 1 197 204 Tian M Huitema E da Cunha L Torto T Kamoun S A Kazal-like extracellular serine protease inhibitor from Phytophthora infestans targets the tomato pathogenesis-related protease P69B J Biol Chem 2004 In press 10.1074/jbc.M400941200 Tyler BM Genetics and genomics of the oomycete–host interaction Trends Genet 2001 17 611 614 11672843 Tyler BM Molecular basis of recognition between Phytophthora species and their hosts Annu Rev Phytopathol 2002 40 137 167 12147757 Werres S Zielke B First studies on the pairing of Phytophthora ramorum J Plant Dis Prot 2003 110 129 130

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PLoS Biol. 2004 Jul 13; 2(7):e213

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PLoS Biol

10.1371/journal.pbio.0020213

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020214EssayScience PolicyOtherHomo (Human)Doing Science in Uncertain Times EssayGelfand Mikhail 7 2004 13 7 2004 13 7 2004 2 7 e214Copyright: © 2004 Mikhail Gelfand.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Doing scientific research in post-Soviet Russia is challenging but there are solutions that could prevent the massive brain drain witnessed in recent decades ==== Body A scientist living in Russia is often asked two questions: “Why haven't you left?” and “Is it still possible to work there?” The best response to the first question is, “Why should I?”—which either terminates the conversation or leads to a stimulating discussion about the fate of the world. The second question, however, deserves a serious answer. In fact, this is the question that every one of us keeps asking ourselves. There is no simple answer. The biggest problems we face are brain drain, inadequate infrastructure, and lack of money (or perhaps, lack of money, lack of money, and lack of money). In the Soviet Union, fundamental science was supported to a great extent by military expenditure. Thus, it is not surprising that Soviet physics and mathematics were more successful than other fields, such as biology. In the 1990s, military spending on science declined sharply, although the exact numbers are hard to estimate. This year, the direct funding of science constitutes only 1.78% of Russia's national budget (an additional 0.46% is allocated to the space program), although the law stipulates that this figure should be at least 4%. Still, this funding amounts to 46.2 billion rubles (approximately US$1.6 billion), more than twice the amount spent in 2000. Although this figure looks negligible compared with spending on science in the United States and many European countries, it could still be sufficient to support existing actively working groups at a reasonable level. Funding There are several mechanisms for distributing funds for research. The major share comes via Russia's Department of Science and the Russian Academy of Sciences. The Academy, unlike its Western analogs, not only acts as a consulting body of experts, but also has the authority to distribute money (Figure 1 ). The funds come both as long-term support for scientific institutes and as National or Academy research programs. The former covers base salaries, which are small even by local standards (about US$200 per month for a laboratory chief), and basic infrastructure (water, electricity, etc.). This system of long-term support inherited all the old Soviet ills, such as the lack of correlation between scientific output and the level of funding. As a result, the available resources are spread thinly over hundreds of labs, most of which are just barely alive. The National or Academy research programs can provide funding at a higher level, sometimes even enough to do experimental research. However, the procedure of establishing such programs, though formally competitive, is often not transparent, and a major role is played by the so-called “administrative resource” (Allakhverdov and Pokrovsky 2003). Figure 1 The Golden Brain (the Praesidium of the Russian Academy of Sciences) (Photograph, with permission, by Nataliya Sadovskaya) Figure 2 Papers Published in Nature and Science in Which at Least One of the Coauthors Lists an Address inside the Soviet Union or Russia The actual number of such papers is shown by the solid line. Their “effective number,” into which each paper contributes with the coefficient equal to the fraction of addresses inside the Soviet Union or Russia from all the listed addresses, is shown by the broken line. In recent years, the contribution of ethnic Russians to high-quality research increased, but their work is mostly performed outside Russia. Figure 3 Papers Published in Nature and Science by Researchers with the 15 Most Common Russian Surnames (Ivanov[a], Kuznetsov[a], Smirnov[a], Etc) The number of all papers in which at least one coauthor has a name from this list is shown by the solid line, and the number of such papers that list at least one address inside the Soviet Union or Russia is shown by the broken line. Whereas before 1992 nearly 100% of ethnic Russians doing toplevel science resided inside the Soviet Union and Russia (hardly surprising!), by now, this number has dropped to below 25%. Money is also distributed through the Russian Foundation for Basic Research (RFBR). The decisionmaking mechanism used by RFBR is closer to Western standards, and involves anonymous refereeing followed by board discussions. Although its grants are rather small (at most, several thousand dollars per year for a maximum of three years), they provide important additional support for many small and mediumsized groups that may receive several such grants for different projects. In addition, RFBR supports the publication and translation of books, travel to international conferences, the organization of conferences in Russia, and similar activities. Unfortunately, several programs (in particular, support for young scientists) have recently been transferred from RFBR to a newly established government office, and have thus become less independent. Collaboration International collaboration and research grants are a major source of support for many active research groups (Table 1). Several agencies and foundations have programs for Eastern Europe, Russia, and/or former Soviet republics. Some of them, such as the Howard Hughes Medical Institute (Chevy Chase, Maryland, United States), fund individual groups; others—for example the International Science and Technology Center (Moscow, Russia)—stipulate that projects should be submitted jointly by academic and military researchers; and some agencies—in particular, European INTAS (Brussels, Belgium) and the American John E. Fogarty International Center (Bethesda, Maryland, United States)—support collaboration between Russian and Western laboratories. Table 1 International Agencies Supporting Russian Science aThe first three agencies are international organizations, followed by individual programs Another source of financial support is direct collaboration between Western and Russian laboratories. Even after a relatively short visit, the salary of a visiting researcher abroad can be stretched for several months back home in Russia; even more importantly, experimental biologists visiting foreign labs have access to modern instruments and chemicals, which allows them to do modern research. The hosts of such visits are often (although definitely not always) recent immigrants from Russia, and in such cases the collaborations may have roots in older days (Box 1). As well as supporting Russian science directly, international collaboration plays an important indirect role because it is less influenced by local politics. In fact, one of the main positive impacts of the New York–based International Science Foundation set up by George Soros in early 1990s was that it demonstrated the possibility of open competition with clearly defined rules—something unheard of in Soviet times—and thus served as a model for the RFBR, which was organized at approximately the same time. Unfortunately, international ties, especially with the United States, have been adversely affected by recent changes in visa procedures, which have become lengthy (leading to many missed conferences) and, even worse, completely unpredictable (e.g., Brumfiel 2004). The grapevine distributes stories of “bad words” that should be avoided when describing one's research area during an interview at the consulate. Examples of such words include the adjective “nuclear” (even within a innocuous terms like “nuclear magnetic resonance”) or, more recently, anything that involves “bacteria.” The demand for fundamental and even for applied biological research from Russian industry is almost nonexistent. The pharmaceutical industry is content to produce generics, while Russian biotech companies are still exploiting old strains developed in the Soviet Union. However, some laboratories are conducting outsourced research, and there are now research outposts of Western and Japanese companies in Russia organized as standard industrial labs. On one hand, this work is a dead end for Russian scientists, because the results of such research normally cannot be published. This is a serious problem, especially for young scientists who want to establish themselves. On the other hand, royalties from patents or commercialization of the products can be used to support further research. One group that has followed this path successfully is Sergey Lukyanov's lab at the Shemyakin Institute of Bioorganic Chemistry in Moscow, Russia. They have developed the subtractive hybridization technique for enrichment of clone libraries by rare transcripts or specific genomic fragments (Rebrikov et al. 2004), and are distributing it via a company called Evrogen (http://www.evrogen.com/about.shtml). Infrastructure and Bureaucracy Another major problem is the degradation of infrastructure. Only a few labs can afford modern equipment and instruments, and for many others, even standard chemicals are too expensive. This leads to a vicious circle: without equipment, a lab cannot conduct experiments at the level demanded by highimpact journals—and without such publications, it cannot compete for large grants. Smaller RFBR grants, while simpler to obtain, are insufficient to purchase large pieces of equipment, and funds from several grants or several years cannot be combined due to bureaucratic restrictions. Thus, the only hope for these labs, apart from international collaboration, is a personal connection with senior bureaucrats that might result in an (un)expected windfall. Having the funds to purchase modern equipment abroad is only the first hurdle; the many conflicting rules and restrictions, inefficiency, and corruption within the system can subsequently hold up the process. Some items, such as tissue samples or animals, are virtually impossible to import legally. The process of clearing the shipments through customs is a difficult, timeconsuming job. Grigory Kopelevich, the Howard Hughes Medical Institute's Russian representative, recalls a story of a grantee whose microscope was stopped at customs because the box contained two screwdrivers not specified in the order. Fortunately, to resolve the issue, it was sufficient to present one of the screwdrivers to a customs officer as a gift. Even basic access to journals is a problem, especially outside the main research centers. Indeed, out of a random sample of ten major universities where electronic library catalogs were available via the Internet, only six had subscribed to Nature, and only two to Science. More specialized journals are available only in Moscow and perhaps St. Petersburg. This is partially offset by the proliferating open-access journals from the Public Library of Science and BioMed Central, free electronic versions of older issues provided by some journals, free subscriptions for Russian academic institutes granted by some publishers or purchased by international foundations (e.g., the e-library.ru project organized by the RFBR and supported by the Open Society Institute [the Soros Foundation, based in New York] and the Department of Education) (Table 2), reprints at authors' Web pages, and last but not least, colleagues abroad who break copyright laws by e-mailing PDF files; there is even a popular bulletin board coordinating this activity. However, these are only partial solutions. Russia is not considered a developing country, and thus is excluded from many international efforts that provide free access to journals (such as HINARI). Moreover, many journals have page charges, but no Russian grants cover these, and the cost of publication may be prohibitively high for many groups. Table 2 Journals and Other Resources, Available to Russian Academic Institutes under the elibrary.ru Project (e-library.ru) Brain Drain These problems, along with low salaries, have naturally led to a huge brain drain. Entire generations have been decimated (Box 1); the dearth of researchers at the postdoc level, has caused a gap in the teaching and maintenance of scientific traditions. Many labs now consist of older chiefs and senior researchers, and graduate students who plan to leave immediately after getting the candidate degree (the equivalent of a Western doctorate). “Leaving” does not necessarily mean leaving the country; many capable young people go into business. While that might be good for the country in general, it is bad for science, at least in the short term. However, even emigration is not a completely negative thing; it creates a network of collaborators, and in many cases enhances ties with the international community. Despite all this, science in Russia is very much alive. Not-yet-Nobel-prizewinner Alexei Abrikosov's repeated exhorations to the scientific community “to help all the talented scientists leave Russia and to ignore the rest” were met by universal disgust (Hoffman 1993; Leskov 1993; Migdal 1993). There are several competitive Russian labs doing first-rate research and publishing in the top-tier journals. Old habits die hard; even in these days, very decent results are often published in Russianlanguage journals, the best of which have impact factors that are around 1. Each year, many intelligent and capable students enroll in universities, and competition for admission is steadily increasing from the lows of the mid-1990s. There are also well-attended international conferences in several Russian cities. Prospects What can be done by the international community to support what is left of Russian science? Of course, direct support in the form of competitive grants is important, especially if there are few restrictions on spending; even the most carefully considered procedure cannot foresee all possible situations. But even more useful is the creation of joint research centers, such as the one opened by the international Ludwig Institute for Cancer Research (LICR) based jointly in Zurich, New York, and London, and the Belozersky Institute of Physico-Chemical Biology of Moscow State University in Moscow, Russia. This research center began with limited support for several stronger groups, and is gradually moving toward integration of a research program in Moscow with other LICR projects, and real collaboration between Moscow groups and LICR labs elsewhere. One of the most essential elements of successful research is access to up-to-date information. Consequently, any initiative that provides open access to scientific literature and databases is extremely useful. Seminars, lecture courses (such as the Moscow University [Moscow, Russia] cycle on oncology and immunology sponsored by LICR; www.oncoimmunology.ru/index_e.htm), and the participation of Western scientists in scientific conferences in Russia are important not only because they provide a fresh understanding of emerging trends, but also because they create personal contacts between Russian and Western scientists that often lead to fruitful collaboration. By contrast, some other types of joint project may be less successful. Artificial programs aimed at creating various participant “networks” usually do not work as expected, and training programs in Western universities often attract potential emigrants rather that those willing to continue active research inside Russia. The contribution of the international community cannot be the sole decisive factor in the future growth of Russian science. Important as it is in this transition period, it is no substitute for a systemic change. The ills of Russian science are not unique; the same issues have been raised by scientists from other Eastern European countries (e.g., Wojcik 2004). Even the existing funds could go much further if scientific policies were more open, better structured, and more competitive. Large grants should be provided, on the basis of well-defined criteria, to only the strongest labs doing the best research. An often-heard opinion that no independent review is possible in a small, well-entrenched community is irrelevant, since international boards of experts can be formed—the example of the Soros foundation clearly demonstrates that this is feasible. However, smaller pilot grants are also needed to support young scientists and labs contemplating new projects. This would create competition at all levels and provide doctoral students and postdocs with an incentive to stay in Russia and enroll in a strong lab. But again, the procedure for awarding such grants should be well defined, transparent, and independent from administrative influences. Thus, the traditional model of top-down distribution of funds must be changed, and this may be difficult. The current system of decision making by Russian funding agencies is clearly inadequate. Moreover, the problems of Russian science mirror the problems of Russian society in general, and it would be naive to expect that they will be solved overnight, even given the political will. Still, if successful, this combination should provide both high-level research in established fields and sufficient flexibility to find new directions. Box 1. Top-Level Publications by Russian Scientists The vast majority of papers published in recent years in the best journals by scientists working in Russia have foreign coauthors (who are often Russian émigrés), indicating that international collaboration is the most reliable source of support for top-level research. (Text and figures in this box courtesy of Alexey Kondrashov.) Mikhail Gelfand works with the Department of Bioinformatics, Institute for Information Transmission Problems, Russian Academy of Science, in Moscow, Russia. E-mail: [email protected] Abbreviations LICRLudwig Institute for Cancer Research RFBRRussian Foundation for Basic Research ==== Refs References Allakhverdov A Pokrovsky V Academy plucks best biophysicists from a sea of mediocrity Science 2003 299 994 12586912 Brumfiel G Poor communication blamed for delay to US visas Nature 2004 427 766 Hoffman R Russian scientists are an endangered species [Response to Leskov article] New York Times 1993 7 25 Sect E:16 Leskov S America's Soviet scientists New York Times 1993 7 15 Sect A:25 Migdal AA Russian scientists are an endangered species [Response to Leskov article] New York Times 1993 7 25 Sect E:16 Rebrikov DV Desai SM Siebert PD Lukyanov SA Suppression subtractive hybridization Methods Mol Biol 2004 258 107 134 14970460 Wojcik C Eastern Europe: Progress stifled by old guard Nature 2004 427 196

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PLoS Biol. 2004 Jul 13; 2(7):e214

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PLoS Biol

10.1371/journal.pbio.0020214

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020215Journal ClubCell BiologyDevelopmentEukaryotesNanotubes Make Big Science Journal ClubDemontis Fabio 7 2004 13 7 2004 13 7 2004 2 7 e215Copyright: © 2004 Fabio Demontis.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Tiny protrusions on the surface of cells might be a more common mechanism for cell communication than previously expected ==== Body During development, cells need to communicate with each other to establish properly organised and functional tissues. Cells communicate with each other in various ways, such as by secreting and receiving diffusible molecules (morphogens, hormones, and neurotransmitters) or by establishing intercellular connections (gap junctions and cell protrusions) to allow a direct exchange of instructive factors. A recent paper has shown that communication via tiny cell protrusions might be a more common mechanism than previously expected (Rustom et al. 2004). Many different types of cell extensions have been described in a variety of developmental processes and organisms (Miller et al. 1995; Bryant 1999; Chou and Chien 2002; Rorth 2003), and for most of them a role in cell-to-cell communication has been hypothesized. For example, in the mouse, Salas-Vidal and Lomeli (2004) have described long processes (filopodia) that connect tissues in early embryos. Because these protrusions contain receptors for some well-known signalling molecules, it is thought that they might be responsible for receiving signals from neighbouring cells. Similarly, it has been proposed in the development of the Drosophila wing and eye imaginal discs (precursors of adult structures) that signals modulating the growth and patterning of one epithelial layer of cells are received through microtubule-based cell extensions arising from the apposing epithelium (Cho et al. 2000; Gibson and Schubiger 2000). Furthermore, in the wing imaginal disc, planar extensions called cytonemes arise from the periphery of the epithelium and grow towards a central area in the wing disc that produces the signalling molecule Decapentaplegic. This directionality of growth, and the observation of vesicles inside cytonemes, led Ramirez-Weber and Kornberg (1999) to propose that cells meant to receive a signal were searching actively for it, extending long cell protrusions towards the region from which signals were emanating. Although cell protrusions have been described in different developmental processes, tissues, and organisms, their potential role in cell signalling has been difficult to pin down. Most cell processes are very fragile, and their study is mainly limited to live tissues; in these conditions it is technically challenging to define how the signalling is mediated via protrusions. Possibly, it could occur through the release of free molecules, in a similar manner to synaptic neurotransmission, or shedding of vesicles as exosomes followed by endocytosis by the recipient cell. Alternatively, membranetethered ligands (such as Delta) on the protrusion could bind and activate receptors displayed on the surface of the receiving cell (De Joussineau et al. 2003). The paper by Rustom and colleagues has provided a new outlook on the role of cell protrusions, by reporting a novel mechanism employed to transmit signals between cells connected by a protrusion. Surprisingly, they did not observe any of the mechanisms described above. Rather, transfer of molecules and organelles occurred directly from the cytoplasm of one cell to the other, passing through a protrusion that established membrane continuity between the connected cells. Using rat PC12 cells, Rustom and colleagues observed ultrafine protrusions (with a diameter of only 50–200 nm and a length spanning several cell diameters) connecting sparse cells in culture (Figure 1). Similar to other cell protrusions, these structures, termed tunnelling nanotubes (TNTs), displayed a pronounced sensitivity to both mechanical stress and chemical fixation and even to prolonged light excitation, resulting in the rupture of many of them. TNTs are actin-based and devoid of microtubules: interestingly most other types of cell protrusions also contain actin (Condeelis 1993; Rorth 2003). The researchers also confirmed the existence of TNTs in a human cell line (human embryonic kidney cells) and rat primary cells (normal rat kidney cells), suggesting that TNTs are not a peculiarity of PC12 cells. Figure 1 A TNT Connecting Two Neighbouring Cells Immunofluorescence analysis of wheat germ agglutinin–stained PC12 cells that shows a TNT, a novel type of cell protrusion that establishes membrane continuity between two neighbouring cells. Transfer of molecules and organelles can occur directly from the cytoplasm of one cell to that of the other. (Image courtesy of Hans-Hermann Gerdes.) Aiming to investigate how TNTs were linking cells, the authors performed scanning and transmission electron microscopy of TNTs. They observed a seamless transition between TNTs and the cells they were connected to, suggesting that indeed there was continuity between the membranes of the two connected cells. Rustom and colleagues then went on to test whether TNTs could be used to transmit signals between cells. The experimental approach used was to mark two populations of cells in a distinct way, either by introducing genes that encoded proteins tagged with green fluorescent proteins or by using dyes. The two different cell populations were then mixed, cocultured, and analysed for transfer of marked proteins or dye-stained organelles from one cell to another, between cells that were differently marked and connected by a TNT. Strikingly, soluble cytoplasmic molecules could not pass freely along the TNTs (with actin tagged with green fluorescent protein being the only exception), whereas membrane-bound proteins were transferred along TNTs and detected in the receiving cells, further supporting the likelihood of membrane continuity between connected cells. Rustom and colleagues also observed transport of vesicles, which seemed to be unidirectional. Finally, in transfer experiments performed at close to 0 °C, where endo-, exo-, and phagocytosis would be blocked, vesicle exchange still occurred, suggesting that these events are not required for vesicle transfer and further supporting the idea that membrane continuity exists between connected cells. By contrast, interfering with actin polymerization, using the drug latrunculin-B, led to protrusion removal and arrest in organelle transfer, indicating that actin is required both for protrusion biogenesis and organelle transport. Taken together, the experiments performed by Rustom and colleagues strongly suggest a role for cell protrusions in cell-to-cell communication. They also provide evidence, in culture, for a novel mechanism used by cell protrusions to transport molecules and organelles. It will be interesting to test whether TNTs also exist in living tissues and, if so, what molecules they transport. TNTs could be distinct from the protrusions known so far and could be responsible for establishing another type of connection between cells. They could connect all cells in a tissue, directly or indirectly, establishing a global interaction network potentially important in exchanging basic survival information as well as positional cues (Milan et al. 2001). Another interesting question is how connections such as TNTs are established. Rustom et al. have shown that, initially, many filopodial extensions arise from one cell and are directed toward a neighbour. As soon as one of them reaches the target, it is stabilised, while the others degenerate. It is possible that membrane fusion occurs between the tip of the protrusion and the planar plasma membrane of the target cell. However, membrane fusion can be more easily achieved if the tips of two cell protrusions fuse with each other, thus suggesting the participation, in the process of membrane fusion, of microvilli or other tiny protrusions belonging to the target cell. Fusion between two protrusions is reported to rely on the cylindrical shape and narrow diameter of cell protrusions and also on the localised concentration of adhesion/fusion molecules at the tips of the cell protrusions, such as microvilli, that display particular tip-specific membrane microdomains (Monck and Fernandez 1996; Wilson and Snell 1998; Roper et al. 2000). The work performed by Rustom and colleagues suggests that cell protrusions are a general mechanism for cell-to-cell communication and that information exchange is occurring through the direct membrane continuity of connected cells, independently of exo- and endocytosis. It is important to determine whether events similar to these seen in cell culture are occurring in tissues and what functions cell protrusions perform during tissue morphogenesis. In my work as a graduate student, I am trying to address this question. We need to identify the types of cell protrusions that are present in tissues and the molecular complexes localizing on them as well as their functions. To then prove that cell protrusions are important in cell-to-cell communication in tissues, we would need to remove the protrusions and see how this affects tissue architecture and function. However, the necessary tools are still missing, given the lack of knowledge of the specific molecules important for the biogenesis of these protrusions. Thus far, the function of cell protrusions has been hypothesized mainly on the basis of their location in tissues and on crude attempts to remove them, for example by altering the actin cytoskeleton or even by removing the entire epithelium they belong to. The paper by Rustom et al. has shed some new light on these still mysterious cellular arms and has further boosted my interest in this emerging field of cell and developmental biology. I would like to thank Christian Dahmann, Heather Thompson, Hans-Hermann Gerdes and Naomi Foster for critically reading the manuscript. Hans-Hermann Gerdes kindly provided the image for Figure 1. Fabio Demontis is a graduate student of the International Max Planck Research School working with Christian Dahmann at the Max Planck Institute for Cell Biology and Genetics in Dresden, Germany. E-mail: [email protected] Abbreviation TNTtunnelling nanotube ==== Refs References Bryant PJ Filopodia: Fickle fingers of cell fate? Curr Biol 1999 9 R655 R657 10508575 Cho KO Chern J Izaddoost S Choi KW Novel signaling from the peripodial membrane is essential for eye disc patterning in Drosophila Cell 2000 103 331 342 11057905 Chou YH Chien CT Scabrous controls ommatidial rotation in the Drosophila compound eye Dev Cell 2002 3 839 850 12479809 Condeelis J Life at the leading edge: The formation of cell protrusions Annu Rev Cell Biol 1993 9 411 444 8280467 De Joussineau C Soule J Martin M Anguille C Montcourrier P Delta-promoted filopodia mediate long-range lateral inhibition in Drosophila Nature 2003 426 555 559 14654840 Gibson MC Schubiger G Peripodial cells regulate proliferation and patterning of Drosophila imaginal discs Cell 2000 103 343 350 11057906 Milan M Weihe U Perez L Cohen SM The LRR proteins Capricious and Tartan mediate cell interactions during DV boundary formation in the Drosophila wing Cell 2001 106 785 794 11572783 Miller J Fraser SE McClay D Dynamics of thin filopodia during sea urchin gastrulation Development 1995 121 2501 2511 7671814 Monck JR Fernandez JM The fusion pore and mechanisms of biological membrane fusion Curr Opin Cell Biol 1996 8 524 533 8791451 Ramirez-Weber FA Kornberg TB Cytonemes: Cellular processes that project to the principal signaling center in Drosophila imaginal discs Cell 1999 97 599 607 10367889 Roper K Corbeil D Huttner WB Retention of prominin in microvilli reveals distinct cholesterol-based lipid micro-domains in the apical plasma membrane Nat Cell Biol 2000 2 582 592 10980698 Rorth P Communication by touch: Role of cellular extensions in complex animals Cell 2003 112 595 598 12628180 Rustom A Saffrich R Markovic I Walther P Gerdes HH Nanotubular highways for intercellular organelle transport Science 2004 303 1007 1010 14963329 Salas-Vidal E Lomeli Imaging filopodia dynamics in the mouse blastocyst Dev Biol 2004 265 75 89 14697354 Wilson NF Snell WJ Microvilli and cell–cell fusion during fertilization Trends Cell Biol 1998 8 93 96 9695816

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PLoS Biol. 2004 Jul 13; 2(7):e215

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PLoS Biol

10.1371/journal.pbio.0020215

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020216PrimerAnimal BehaviorInsectsDances as Windows into Insect Perception PrimerChittka Lars 7 2004 13 7 2004 13 7 2004 2 7 e216Copyright: © 2004 Lars Chittka.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Honeybee Odometry: Performance in Varying Natural Terrain Honeybees signal the location of food sources to their hive- mates using a "dancing" flight pattern. Translating these patterns, scientists learn what bees perceive ==== Body Experimental psychologists working with humans have a fundamental advantage over scientists studying the behaviour of other animals. This is because human subjects can give a verbal account of their experience. For example, they can report: ‘These two lights of different colour look equally bright’ or ‘This object looks further away than that one’. Such direct reports facilitate studying how information from the sensory periphery, that is, the sense organs that actually interface with the environment, is processed in the brain. The perceptual world of animals is often very different from that of humans. Many animals have sensory facilities that we humans lack; for example, insects can see ultraviolet and polarised light. But how they actually perceive the world, based on information from their sensory periphery, is often beyond our grasp. Because animals cannot describe their sensations, our access to them is often based on indirect psychophysical tests, where animal performance depends fundamentally on motivation and training method (Chittka et al. 2003). However, some animals do in fact describe the world around them, but not necessarily in ways that we might intuitively understand. Perhaps the best example of this are the honeybees (genus Apis), which have a symbolic ‘language’ that nestmates use to communicate with each other about profitable food sources. By eavesdropping on this communication, scientists have recently obtained a unique perspective into the perceptual world of insects. How does the dance language work? A triumphant scout bee returns from the field, and advertises the location of a newly discovered food source to nestmates. To do this, the forager performs a repetitive sequence of movements, the so-called waggle dance, which is one of the most intriguing examples of complex animal behaviour. The successful forager wiggles her abdomen provocatively from side to side, moving forward in a straight line. Then she runs in a half circle to the left, back to her starting point, performs another straight wiggle run along the path of her first, and then circles to the right (Figure 1). This pattern is repeated multiple times, and is eagerly attended by unemployed bees in the hive. Shortly after such dances commence, dozens of newly recruited foragers arrive at the food source being advertised. Figure 1 Figure-Eight-Shaped Waggle Dance of the Honeybee (Apis mellifera) A waggle run oriented 45° to the right of ‘up’ on the vertical comb (A) indicates a food source 45° to the right of the direction of the sun outside the hive (B). The abdomen of the dancer appears blurred because of the rapid motion from side to side. (Figure design: J. Tautz and M. Kleinhenz, Beegroup Würzburg.) In the 1940s, Nobel laureate Karl von Frisch deciphered the code hidden in this seemingly senseless choreography performed on vertical honeycombs in the darkness of the hive (reviewed in von Frisch 1967). He found that the angle of the waggle run from the vertical is equal to the angle between the sun's azimuth and the indicated food source outside the hive. For example, if a food source is found in the direction of the sun, the dancer will waggle ‘straight up’ the vertical comb. If food is found 45° to the right of the sun's direction, the waggle run will be oriented 45° to the right of vertical on the comb (Figure 1). The distance to the target, a flower patch with abundant nectar or pollen, is encoded in the duration of the waggle run: the longer the bee waggles, the larger the distance of the food from the hive. No other species (besides humans) uses a similarly symbolic representation to communicate information from the real world. But how do bees measure the flight distance that they communicate so precisely? It was previously thought they do this by measuring the energy used as they fly (Heran 1956). However, doubts emerged when it was found that distance estimation by bees could be manipulated by altering the number of landmarks between the hive and a food source, suggesting bees were counting landmarks encountered en route (Chittka and Geiger 1995). In an elegant experiment, Esch and Burns (1995) tapped into the bees' dance language to access their subjective assessment of flight distance. They let bees forage from a food source 70 m from the hive and recorded the dance distance code of the returning foragers. Subsequently, the feeder was attached to a weather balloon, and slowly lifted to an altitude of 90 m—so that the distance between the hive and the food now increased from 70 m to 114 m. Correspondingly, foragers should have indicated a longer distance, by stretching their waggle run duration. But, in fact, the perceived distance (as indicated in the dance) decreased by more than 50%! This clearly shows that bee perception of distance cannot solely be based on energy expenditure, since a longer flight that cost more energy was danced as a shorter ‘distance’ in the waggle run. So what actually drives the bee odometer? Because the landscape bees pass in flight moves more slowly when bees fly at higher altitudes, Esch and Burns (1995) conjectured that foragers process the speed with which visual contours move across the eye (optic flow), and integrate this with travel time. To confirm this hypothesis, Srinivasan et al. (2000) further exaggerated the experienced image flow, by training bees to fly through narrow chequered tunnels. These bees grossly overestimated actual travel distance, bragging to their nestmates that they had flown 195 m when in fact they had flown 6 m. Attendees of these dances promptly believed the high-class swindle, and searched for food at remote locations that the dancers had never even visited (Esch et al. 2001). The quality of information available about the velocity of the passing landscape will depend, of course, on the sensitivity of the eyes. The eyes of bees contain three types of colour receptors, with maximum sensitivity in the ultraviolet, blue, and green domains of the spectrum (Autrum and von Zwehl 1964). Their excellent colour vision is optimal for flower identification (Chittka 1996), but do they also use it to measure the image velocity of the passing landscape? Surprisingly, the answer is no—bee odometry is in fact totally colour blind. Chittka and Tautz (2003) found that bees use exclusively the signal from their green receptors for measuring image velocity (Figure 2), confirming earlier reports that motion vision in bees is mediated only by this receptor type (Giurfa and Lehrer 2001; Spaethe et al. 2001). Thus, the level of intensity contrast present in the scene strongly influences the bees' subjective experience of flight distance (Chittka and Tautz 2003; Si et al. 2003). Figure 2 Bees Use Different Visual Cues When Viewing Flowers and Landscape Image Motion Although bees see flowers in colour, they do not analyse the colours of the landscape image that moves across the eye as they fly. Their perception of landscape motion is colour-blind; motion vision is driven solely by a single spectral receptor type, the bees' green receptor. This is reflected in the distance code of the dance: the more green contrast is present in the scene, the further bees ‘think’ they have flown. (Figure design: F. Bock, Beegroup Würzburg.) With so many external variables influencing distance estimation, it seems unlikely that the honeybee odometer would be very robust in natural conditions. Now, as reported in this issue of PLoS Biology, Tautz et al. (2004) have quantified the bees' subjective experience of distance travelled when they fly over natural terrain with varying levels of contrast. Specifically, they compared the dances of bees flying over water (scenery with low visual contrast) with those of bees flying over land (scenery with relatively high contrast). They trained bees to forage at a feeder on a boat, which was paddled increasing distances from the hive, until it reached an island. All the while, observers at the hive deciphered the dances of the bees returning from the feeder. Interestingly, bees flying 200 m over water hardly appeared to register an increase in travel distance, whereas the same increase in distance flown over land resulted in a substantial increase in perceived flight distance. This is consistent with the hypothesis that the bees' odometer is largely based on visual, external cues and demonstrates that this system is sensitive to visual contrast. But there must be something else beside visual cues. Navigation over water, in the near absence of visible ground features, is extremely difficult without a reliable internal instrument measuring travel speed. This is the case even for us humans with sophisticated measuring devices: malfunctioning air speed indicators have been responsible for several airplane crashes into water, for example Birgenair Flight 301 and AeroPeru Flight 603 in 1996. Heran and Lindauer (1963) likewise observed that honeybees flying over lakes sometimes lost altitude and plunged into the water. However, the new study by Tautz et al. (2004) also shows that most bees will reliably fly over prolonged stretches of water without accident. Furthermore, even though bees experience only a small increase in subjective travel distance when flying over water, it is not zero. This indicates that bees do perhaps resort to an internal measure of flight distance when other cues fail. For example, bumblebees walking to a food source in absolute darkness, that is, in the complete absence of visual cues, are able to correctly gauge travel distance (Chittka et al. 1999), indicating that an internal odometer, possibly based on energy consumption, also exists. It appears that animal navigation, just like aviation, relies on multiple backup systems that support each other and can compensate if one system fails in a certain context. Spying on honeybee dances can not only tell us about the cues they use for navigation, but also allows insights into the cognitive architecture that governs other aspects of bee behaviour, such as the assessment of flower quality. We've learned that bees prefer high over low nectar concentrations because this is reflected in their dances. When bees find better nectar, they dance more enthusiastically, that is, the number of dance circuits per minute increases (Seeley et al. 2000; Waddington 2001). However, Waddington (2001) found that the relationship between actual and perceived nectar quality is nonlinear. In fact, it is a positive but decelerating relationship, so that an increase in sucrose concentration from 10% to 20% results in twice the difference in dance rate that an increase from 50% to 60% does. Interestingly, the perceived change in quality is stronger when there is a decrease than when there is an increase in nectar quality of the same magnitude. Such asymmetric perception of gains and losses is well known in humans, where it has been linked to risk-aversive behaviour (Tversky and Kahnemann 1981). Unfortunately, animal subjects often do not yield this type of information very readily. Only in their own language do they reveal many of their perceptual peculiarities. Using the bee language as a window into insect visual perception has been a wonderful tool and is a promising avenue for further research into the question of how miniature brains encode the world around them. Lars Chittka is at the School of Biological Sciences, Queen Mary College, University of London, London, United Kingdom. E-mail: [email protected] ==== Refs References Autrum HJ von Zwehl V Die spektrale Empfindlichkeit einzelner Sehzellen des Bienenauges Z Vergl Physiol 1964 48 357 384 Chittka L Optimal sets of colour receptors and opponent processes for coding of natural objects in insect vision J Theor Biol 1996 181 179 196 Chittka L Geiger K Can honeybees count landmarks? Anim Behav 1995 49 159 164 Chittka L Tautz J The spectral input to honeybee visual odometry J Exp Biol 2003 206 2393 2397 12796456 Chittka L Williams NM Rasmussen H Thomson JD Navigation without vision: Bumblebee orientation in complete darkness Proc R Soc Lond B Biol Sci 1999 266 45 50 Chittka L Dyer AG Bock F Dornhaus A Bees trade off foraging speed for accuracy Nature 2003 424 388 388 12879057 Esch HE Burns JE Honeybees use optic flow to measure the distance of a food source Naturwissenschaften 1995 82 38 40 Esch HE Zhang S Srinivasan MV Tautz J Honeybee dances communicate distances measured by optic flow Nature 2001 411 581 583 11385571 Giurfa M Lehrer M Chittka L Thomson JD Honeybee vision and floral displays: From detection to close-up recognition Cognitive ecology of pollination 2001 Cambridge Cambridge University Press 61 82 Heran H Ein Beitrag zur Frage nach der Wahrnehmungsgrundlage der Entfernungsweisung der Bienen Z Vergl Physiol 1956 42 103 163 Heran H Lindauer M Windkompensation und Seitenwindkorrektur der Bienen beim Flug ueber Wasser Z Vergl Physiol 1963 47 39 55 Seeley TD Mikheyev AS Pagano GJ Dancing bees tune both duration and rate of waggle-run production in relation to nectar-source profitability J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2000 186 813 819 Si A Srinivasan MV Zhang SW Honeybee navigation: Properties of the visually driven ‘odometer’ J Exp Biol 2003 206 1265 1273 12624162 Spaethe J Tautz J Chittka L Visual constraints in foraging bumblebees: Flower size and color affect search time and flight behavior Proc Nat Acad Sci U S A 2001 98 3898 3903 Srinivasan MV Zhang S Altwein M Tautz J Honeybee navigation: Nature and calibration of the ‘odometer’ Science 2000 287 851 853 10657298 Tautz J Zhang S Spaethe J Brockmann A Si A Honeybee odometry: Performance in varying natural terrain PLoS Biol 2004 2 e211 10.1371/journal.pbio.0020211 15252454 Tversky A Kahnemann D The framing of decisions and psychology of choice Science 1981 211 453 458 7455683 von Frisch K The dance language and orientation of bees 1967 Cambridge (Massachusetts) Harvard University Press 566 Waddington KD Chittka L Thomson JD Subjective evaluation and choice behavior by nectar- and pollencollecting bees Cognitive ecology of pollination 2001 Cambridge Cambridge University Press 41 60

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PLoS Biol. 2004 Jul 13; 2(7):e216

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PLoS Biol

10.1371/journal.pbio.0020216

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020221SynopsisAnimal BehaviorNeuroscienceAnimalsBirdsSongbirdNo Rest for the Weary: Migrating Songbirds Keep Their Wits without Sleep Synopsis7 2004 13 7 2004 13 7 2004 2 7 e221Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Migratory Sleeplessness in the White-Crowned Sparrow (Zonotrichia leucophrys gambelii) ==== Body Every spring and fall, billions of songbirds fly thousands of miles between their summer breeding grounds in North America and their wintering grounds in the more hospitable climes of southern California, Mexico, and Central and South America. While some birds fly during the day, most, including the white-crowned sparrow, fly under cover of night. Many aspects of this remarkable voyage remain obscure, especially if, and how, nocturnal migrators get any sleep at night. White-crowned sparrow (Zonotrichia leucophrys gambelii) A tracking study of the Swainson's thrush found that the roughly seven-inch birds flew up to seven hours straight on six of seven nights, racking up over 930 miles. While the study didn't track their daytime behavior, the birds' migratory pace—as well as the increased activity required to sustain migrations—suggests little time for sleep. Yet field observations indicate that presumably sleep-deprived fliers appear no worse for wear, foraging, navigating, and avoiding predators with aplomb. Researchers are left trying to reconcile this observation with the vast body of evidence linking sleep deprivation to impaired neurobehavioral and physiological function. How do songbirds cope with so little sleep? Do they take power naps? Have they taken “sleep walking” to new heights? Or have they managed to selectively short-circuit the adverse effects of sleep deprivation during migratory stints? To investigate these questions, Ruth Benca and colleagues studied cognitive and sleep behaviors in captive white-crowned sparrows over the course of a year. The sparrows fly nearly 2,700 miles twice a year between their Alaska and southern California homes. In laboratory cages, the birds' migratory instincts manifest as increased restlessness at night during the migratory season, with lots of hopping around and wing flapping. Niels Rattenborg et al. characterized the birds' activity levels with motion-detection measurements and video recordings, and placed sensors on their brains to monitor their seasonal sleep patterns. The brain recordings showed a marked seasonal difference in both the amount and type of sleep during a 24-hour period: migrating birds spent roughly two-thirds less time sleeping than nonmigratory birds and fell into REM sleep (the dream stage of sleep, marked by rapid eye movements) much sooner. Birds displaying active migratory behavior appeared completely awake during such activity. Cognitive tests—birds performed a task that involved pecking a key in exchange for seed—revealed that birds in the nonmigrating state suffered cognitive deficits when sleep-deprived but displayed an “unprecedented” ability to maintain cognitive function in the face of ongoing sleep loss in the migratory state. These results suggest that wild songbirds drastically reduce sleep time during migration, though Benca and colleagues concede it's impossible to know for sure without recording the birds in action. And it is unclear what molecular mechanisms jumpstart the migratory mindset. Such an ability to temporarily circumvent the need for sleep, however, could prove useful for humans in situations that demand continuous performance. Some studies link migration with increased neuroendocrine activity, which is in turn associated with sleep disruption, accelerated timing of REM cycles, and mood disorders in humans. “Like migrating sparrows,” the authors note, “both depressed and manic patients show reduced latency to REM sleep, loss of slow-wave sleep, and reduced amounts of total sleep.” Given the parallels between migratory behaviors and bipolar illness, it's possible that similar mechanisms may be involved in both. Whatever the mechanism, the unprecedented imperviousness of migrating songbirds to sleep deprivation, the authors conclude, clearly warrants further testing. But it also raises interesting questions about the role of sleep, which recent studies suggest is required to incorporate novel perceptions into the brain's memory banks. If this is true, how do songbirds consolidate memories of migratory events with so little sleep? Understanding the mechanisms that power the sleepless flight of songbirds promises to unravel one of the longstanding mysteries of their improbable journey. It may also shed light on the origins of sleep-related seasonal disorders and the much-debated role of sleep itself.

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2021-01-05 08:21:12

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PLoS Biol. 2004 Jul 13; 2(7):e221

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PLoS Biol

10.1371/journal.pbio.0020221

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020223PrimerBiotechnologyIn VitroTranslating DNA into Synthetic Molecules Liu David R 7 2004 13 7 2004 13 7 2004 2 7 e223Copyright: © 2004 David R. Liu.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. DNA Display II. Genetic Manipulation of Combinatorial Chemistry Libraries for Small-Molecule Evolution DNA Display III. Solid-Phase Organic Synthesis on Unprotected DNA DNA Display I. Sequence-Encoded Routing of DNA Populations Harnessing DNA-Based Technology for Drug Discovery The unique ability of nucleic acids to replicate has recently been combined with the power of combinatorial chemistry, providing a new approach to the science of drug design ==== Body At some time almost 4 billion years ago, nature likely was faced with a chemical dilemma. Nucleic acids had emerged as replicable information carriers and primitive catalysts (Joyce 2002), yet their functional potential was constrained by their structural homogeneity and lack of reactive groups. These properties rendered nucleic acids well suited for storing information, but flawed for mediating the diverse chemistries required to sustain and improve increasingly complex biological systems. It is tempting to speculate that translation emerged as the solution to this dilemma. Translation, defined here as the conversion of an informationcarrying molecule into a corresponding encoded structure, enabled the expanded functional potential of proteins to be explored using powerful evolutionary methods that depend on the unique ability of nucleic acids to replicate. A small but growing number of researchers have begun to tackle a modern version of this dilemma. While proteins and nucleic acids can be manipulated using powerful molecular biology techniques that enable their directed evolution, the size, fragility, and relatively limited functional group diversity of biological macromolecules make them poorly suited for solving many problems in the chemical sciences. Ideally, researchers would like to apply evolution-based approaches to the discovery of functional synthetic, rather than biological, molecules. A solution analogous to nature's translation of mRNA into protein could, in principle, address this contemporary problem (Orgel 1995; Gartner and Liu 2001). If a laboratory system were developed that could translate amplifiable information carriers such as DNA into arbitrary synthetic molecules, the evolution of synthetic molecules using iterated cycles of translation, selection, amplification, and diversification would be possible. The translation of DNA into synthetic molecules is conceptually distinct from the use of DNA simply as a tag during the solidphase synthesis of a molecule that is part of a combinatorial library (Brenner and Lerner 1992). The latter process uses DNA to record the history of a series of chemical reactions by cosynthesizing a portion of a DNA oligonucleotide during each step of a molecule's solidphase synthesis. As a result, the identity of compounds that pass screening can be inferred by PCR amplification and sequencing of the DNA associated with a given bead (Needels et al. 1993). The resulting DNA, however, cannot redirect the synthesis of active compounds. In contrast, the translation of DNA into synthetic molecules uses the sequence of nucleotides in a strand of DNA to direct the synthesis of a nascent molecule. As a result, a complete cycle of translation, selection, and amplification can be applied to the discovery of synthetic molecules in a manner that is analogous to the processes that take place during biological evolution. DNA-templated organic synthesis (DTS) has emerged as one way to translate DNA sequences into a variety of complex synthetic small molecules (Gartner and Liu 2001; Gartner et al. 2002; Li and Liu 2004). In this approach, starting materials covalently linked to DNA templates approximately 20–50 nucleotides in length are combined in very dilute solutions with reagents that are covalently linked to complementary DNA oligonucleotides. Upon Watson-Crick base pairing, the proximity of the synthetic reactive groups elevates their effective molarity by several orders of magnitude, inducing a chemical reaction. Because reactions do not take place between reactants linked to mismatched (noncomplementary) DNA, DTS generates synthetic products in a manner that is programmed by the sequence of bases in the template strand. In a series of three papers in this issue of PLoS Biology, Harbury and co-workers describe an elegant new approach to translating DNA into synthetic peptides called “DNA display.” Their approach uses DNA hybridization to separate mixtures of DNA sequences into spatially distinct locations. The first paper (Halpin and Harbury 2004a) reports the development of resin-linked oligonucleotides that efficiently and sequence-specifically capture DNA containing complementary subsequences. This immobilization process is efficient enough to be iterated, so that DNA sequences specifying multiple amino acids can be routed to the appropriate miniature resin-filled columns during each step. In the second paper (Halpin and Harbury 2004b), Harbury and coworkers detail solid-phase peptide synthesis performed on unprotected DNA 340mers bound to DEAE Sepharose. Optimization of amino acid side-chain-protecting groups and peptide coupling conditions enabled a variety of amino acids to undergo efficient peptide coupling to bound oligonucleotides containing an amine group. The third paper (Halpin et al. 2004) integrates the routing and peptide synthesis described above into the translation of a library of 106 DNA 340mers into a corresponding library of up to 106 synthetic pentapeptides. To achieve chemical translation, the DNA library was subjected to iterated cycles of routing and solidphase peptide synthesis. After each routing step, the appropriate amino acid was coupled to each DNA-linked subpopulation. DNA routing was therefore used to achieve the splitting step of “split-and-pool” combinatorial peptide synthesis. The completed library of peptide–DNA conjugates was then subjected to in vitro selection based on the ability to bind an antibody with known affinity for the [Leu]enkephalin pentapeptide Tyr-Gly-Gly-Phe-Leu. After two rounds of routing, synthesis, and selection, followed by DNA sequencing, the remaining oligonucleotides predominantly encoded the Tyr-Gly-Gly-Phe-Leu sequence or close variants thereof. This result demonstrates that the DNA display method is capable of facilitating the discovery of functional molecules by enabling in vitro selection methods to be applied to molecules generated by split-and-pool combinatorial synthesis. The fundamental distinctions between DTS and DNA display approaches to chemical translation imply that these two strategies will be applicable to different types of synthetic structures. Because the DNA display approach separates the DNA hybridization step from the chemical synthesis step, it does not require the coupling of synthetic reagents to oligonucleotides (beyond the starting material), and can use reaction conditions such as high temperatures or high pH that may not be compatible with DNA hybridization. These features suggest that DNA display may be able to access structures that cannot be created by DTS. Likewise, because DTS approaches use effective molarity rather than intermolecular reactivity to direct organic synthesis, they enable modes of controlling reactivity (such as using otherwise incompatible reactions in a single solution [Calderone et al. 2002]) and classes of chemical reactions (such as heterocoupling of substrates that preferentially homocouple) that cannot be accessed using split-and-pool synthesis. In principle, these two approaches are complementary, and it is tantalizing to envision the use of both DNA display and DTS to direct different steps during a single chemical translation. In order for either approach to fully realize its potential of truly evolving libraries of diverse synthetic molecules, rather than simply enriching libraries that already contain at the outset the “most fit” molecule, researchers must develop sophisticated library syntheses that generate remarkable complexity (vast numbers of different compounds) in a relatively modest number of DNA-compatible synthetic steps. True evolution takes place when the theoretical complexity of a population exceeds the number of different molecules that can be created in a single library translation step, and when diversification is required to access compounds in later generations that are more fit than any member of the starting pool. To my knowledge, no synthetic library to date contains this degree of complexity (indeed, the total size of the Chemical Abstracts Service database of known chemical substances is presently less than 108 compounds). However, because so few copies of a DNA-linked synthetic molecule are required for in vitro selection (Doyon et al. 2003)—compared with the relatively large quantities of material that are required for conventional screening approaches—these chemical translation methods offer the first hope of achieving such synthetic complexity without requiring an impractical amount of material or storage space. For comparison, a conventional-format synthetic library containing 100 µg of each of 108 different structures would represent 10 kg of material, not including the mass of beads or plates associated with the library, while a chemically translated library containing 10,000 copies of 108 different species represents less than 1 µg of total material. While significant remaining challenges face efforts to develop and apply chemical translation, the promise of marrying evolution and organic synthesis is an irresistible combination for some researchers. The work of Harbury and co-workers described in this issue represents the latest approach to the very ancient problem of translating replicable information into functional structures. David R. Liu is Associate Professor in the Department of Chemistry and Chemical Biology at Harvard University, Cambridge, Massachusetts, United States. E-mail: [email protected] Abbreviation DTSDNA-templated organic synthesis ==== Refs References Brenner S Lerner RA Encoded combinatorial chemistry Proc Natl Acad Sci U S A 1992 89 5181 5183 1350680 Calderone CT Puckett JW Gartner ZJ Liu DR Directing otherwise incompatible reactions in a single solution by using DNAtemplated organic synthesis Angew Chem Int Ed 2002 41 4104 4108 Doyon JB Snyder TM Liu DR Highly sensitive in vitro selections for DNA-linked synthetic small molecules with protein binding affinity and specificity J Am Chem Soc 2003 125 12372 12373 14531656 Gartner ZJ Liu DR The generality of DNAtemplated synthesis as a basis for evolving nonnatural small molecules J Am Chem Soc 2001 123 6961 6963 11448217 Gartner ZJ Kanan MW Liu DR Expanding the reaction scope of DNA-templated synthesis Angew Chem Int Ed 2002 41 1796 1800 Halpin DR Harbury PB DNA display I. Sequence-encoded routing of DNA populations PLoS Biol 2004a 2 e173 10.1371/journal.pbio.0020173 15221027 Halpin DR Harbury PB DNA display II. Genetic manipulation of combinatorial chemistry libraries for small-molecule evolution PLoS Biol 2004b 2 e174 10.1371/journal.pbio.0020174 15221028 Halpin DR Lee JA Wreen SJ Harbury PB DNA display III: Solidphase organic synthesis on unprotected DNA PLoS Biol 2004 2 e175 10.1371/journal.pbio.0020175 15221029 Joyce GF The antiquity of RNA-based evolution Nature 2002 418 214 221 12110897 Li X Liu DR DNA-templated organic synthesis: Nature's strategy for controlling chemical reactivity applied to synthetic molecules Angew Chem Int Ed 2004 In press Needels MN Jones DG Tate EH Heinkel GL Kochersperger LM Generation and screening of an oligonucleotide-encoded synthetic peptide library Proc Natl Acad Sci U S A 1993 90 10700 10704 7504279 Orgel LE Unnatural selection in chemical systems Acc Chem Res 1995 28 109 118 11542502

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PLoS Biol. 2004 Jul 13; 2(7):e223

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PLoS Biol

10.1371/journal.pbio.0020223

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020226SynopsisCell BiologyImmunologyMammalsTraining the Immune Response: B-cells' Master Regulator Synopsis7 2004 13 7 2004 13 7 2004 2 7 e226Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Activation-Induced Cytidine Deaminase Initiates Immunoglobulin Gene Conversion and Hypermutation by a Common Intermediate ==== Body Viruses, bacteria, and other pathogens betray their presence in the body through exterior proteins, distinct to each strain. To prepare for the multitude of potential infectious agents, developing B-cells shuffle their genes to produce as many as a billion different antibodies, one to match almost any foreign protein. Upon infection, a limited subset of these antibodies will recognize a particular pathogen and mobilize a larger, targeted immune response. B-cells producing the “recognizing” antibody refine and test genetic modifications, adjusting the antibody's fit to the foreign entity. B-cells compete for the best match, or highest affinity; the winners survive to produce more cells and more antibodies against the invader. Occurrence of Ig gene conversion and hypermutation on an evolutionary tree B-cells require an enzyme called activation-induced cytidine deaminase (AID) to develop the most effective antibody. AID generates mutations in the highly variable target-recognition region of an antibody. Removing the AID gene prevents antibody refinement in mature human and mouse B-cells—which use a process called somatic hypermutation to alter single nucleotides in the antibody gene—as well as chicken cells that use a different process called gene conversion to produce variation. Unlike the single nucleotide changes caused by hypermutation, gene conversion modifies an antibody by swapping part of its antigen-binding region for a replacement gene segment. Preference for hypermutation versus gene conversion varies across species, and can even vary within a species. B-cells in chickens use gene conversion through adolescence, when the cells move from a hindgut organ called the bursa into the spleen, where hypermutation takes over. It is unclear precisely how AID induces either somatic hypermutation or gene conversion, and how it chooses one over the other. Several recent studies suggest that AID's effectiveness may depend on damage to a single DNA base—specifically, changing a cytidine to uracil, which AID can do in either DNA or RNA. To test whether AID causes hypermutation and gene conversion through a common pathway, Jean-Marie Buerstedde and colleagues at the National Research Center for Environment and Health in Munich, Germany, deleted the donor genes that supply replacement segments for gene conversion in chicken bursa cells. The cells not only stopped performing gene conversion; they revved up single nucleotide mutations in a pattern that looked suspiciously like somatic hypermutation. The mutations targeted hotspots for gene conversion, suggesting that hypermutation and gene conversion share common starting points along antibody genes. This paper adds evidence that AID functions by swapping a single DNA base to induce multiple modes of gene shuffling and refinement in B-cells.

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PLoS Biol. 2004 Jul 13; 2(7):e226

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PLoS Biol

10.1371/journal.pbio.0020226

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020227SynopsisAnimal BehaviorInsectsHoneybees' Distance Perception Changes with Terrain of Flight Path Synopsis7 2004 13 7 2004 13 7 2004 2 7 e227Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Honeybee Odometry: Performance in Varying Natural Terrain ==== Body When a trip for food can require a three-mile flight, it pays to get the directions right, especially if you're a bee. Bees more typically forage within a 600- to 800-yard radius, expending a significant amount of energy—a fact they seem keenly aware of: for reasons that remain unclear, bees tend to ignore directions that send them to a target on water. It's been known since Aristotle's time that returning foragers dance a little jig for their hivemates, presumably regaling them with tales of nectar-laden flora. Some 2,300 years later, zoologist Karl von Frisch correlated dance choreography to the direction and distance of a food source, eventually winning the Nobel prize for his work. Since then, researchers have been working out the details of bee communication, such as how fellow foragers interpret the “waggle dance” and how dancers perceive and convey navigational details of a trip. A key aspect of this information exchange is how bees estimate distance. A honeybee's “odometer” generally runs faster when it flies over land than water Recent studies suggest that bee odometers are driven by the “optic flow” experienced during flight—or, simply put, bees appear to log distance by measuring the rate that images of passing terrain move in their eye during flight. This theory comes from observations that when bees fly a given distance, they indicate a much longer distance—by performing a longer waggle—for low-flying trips than for those at higher altitudes, presumably because flying at higher altitudes limits the bees' ability to perceive changing images. Likewise, bees flying through short, narrow tunnels filled with visual elements waggle a disproportionately long distance. These observations also raise questions about which visual aspects of the environment—contrast, texture, distribution of objects—are most important to a bee's perception of image flow. To investigate the factors driving the bee trip odometer, Mandyam Srinivasan and colleagues trained bees to feed at locations along two different routes in a natural environment, then compared their waggle dances. One route was entirely over land; the other started on land, shifted to water, and ended back on land. Both routes were the same distance, about 630 yards. Bees trained to feed at a boat in the middle of a lake had no trouble getting there, which was no guarantee based on reports that bees have trouble flying across lakes, often plunging into the drink. They had less success recruiting their colleagues to share in the bounty, even though their waggles clearly placed the feeder on the lake. (This finding supports an earlier, controversial theory that suggests experienced bees know water rarely harbors bee food.) The length of the bees' waggle dance increased faster with distance when they flew over land than when they flew over water. This disparity indicates that land provides a stronger “odometric signal” than water. “The honeybee's odometer,” the authors explain, “runs at a slower pace when flight is over water.” Overland flights tend to offer high contrasts and rich textures, while flights over water tend to offer low contrasts and sparse textures. Most likely, it is the high contrast of land surfaces that triggers a stronger odometric signal. But land surfaces also show variation in contrast, which was reflected in the bees' dance. One section of the land-only route was a paved bicycle path, a low contrast surface that the bees waggled as a relatively shorter distance. Whether or not the contrast theory holds, Srinivasan and colleagues conclude, differences in the visual environment trigger differences in odometric signal. The odometer racks up yards depending on the nature of the terrain, whether it be land or water, during flight. The great Belgian playwright and avid bee-keeper Maurice Maeterlinck wondered at the language of bees in his 1901 book, The Life of the Bee, deciding it must correspond “to senses and properties of matter wholly unknown to ourselves.” As Srinivasan and colleagues show here, the bee's view of the world indeed corresponds to a unique way of interpreting the landscape—and of sharing news of their travels with their hivemates.

0

PMC449905

CC BY

2021-01-05 08:26:25

no

PLoS Biol. 2004 Jul 13; 2(7):e227

utf-8

PLoS Biol

10.1371/journal.pbio.0020227

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020228EditorialScience PolicyHomo (Human)Whose Copy? Whose Rights? EditorialGass Andy Doyle Helen Kennison Rebecca 7 2004 13 7 2004 13 7 2004 2 7 e228Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.Primary scientific and medical literature is written for academic communities and the public good -- so should be governed by copyright licenses that permit the full range of uses that could benefit stakeholders in research. ==== Body This is the third installment in a series of editorials on the implications of open-access publishing for established publishing practices and stakeholders in scientific and medical research. The questions, tensions, and social concerns surrounding copyright and the Internet are very different for scientific and medical literature than for other kinds of easily reproducible digital works. Peer-reviewed publications are often the sole tangible products of the tremendously time-consuming and expensive process of conducting primary research in biology and medicine. Who should own the primary research articles that are the culmination of years of work by scientists and staggering financial investments by governments, universities, and tax-exempt foundations? What uses should the documents' owners permit? In practice, academic authors typically assign full copyrights to their articles to the publishers of the journals in which the works appear. Scientists do not benefit financially from the transaction; indeed, they often subsidize the cost of their articles' publication in the form of page charges, color charges, and other fees levied by publishers. The prestige associated with publishing in a selective journal is sufficiently valuable that scientists are generally willing to abdicate the legal rights to their own work without remuneration. In recent years, however, several technological and legal innovations have led a growing number of scientists to begin to question the sagacity of this arrangement. The advent of electronic publishing and the Internet itself have made technically possible a slew of novel uses of primary research papers. Simultaneously, the traditional “all rights reserved” copyright license has been supplemented by a variety of alternative licenses—of equal legal validity and available at no charge to anyone who wants them—that allow copyright holders to prevent some uses of a work without permission, but to authorize others. Different licenses created by the nonprofit organization Creative Commons (www.creativecommons.org), for example, allow copyright holders to mark their work with freedoms—to permit a work's reproduction for any noncommercial purpose (the Noncommercial License) or for any purpose at all provided that the original authorship is properly attributed (the Attribution License). The upshot of these developments is that copyright holders can now permit a spectrum of uses of a paper by prospective researchers, anthologizers, archivists, teachers, patients, policy makers, journalists, and other interested parties. Precisely which uses are permitted and which are not is far from a trivial matter. The particular copyright license under which an article is published largely determines how the document can be stored, searched, and built upon by other scientists. Authors' Rights and Users' Rights One implication of the variety of copyright licenses now widely available is that the right to use an article in one way or another is largely independent of its accessibility online. A paper that is touted as “freely available” or “free access” is very different from one that is “open access.” (See http://www.plos.org/openaccess for the formal definition of an open-access article, drafted at the 11 April 2003 Bethesda Meeting on Open Access Publishing.) When a document is “freely available,” someone who comes across it may be permitted to do nothing more than read it online on a publisher's Web site; the right to use the article in any other way is typically granted only at the publisher's discretion. When a document is open access, however, a wide range of additional uses are perpetually and irrevocably allowed— from the reproduction and distribution of the paper by a professor for his or her students to the archiving of the paper in a searchable online repository available to anyone in the world with an Internet connection, and more. The Creative Commons Attribution License (CCAL), which governs this editorial and all other content in PLoS Biology, permits a number of uses of articles that are typically restricted and for which there is an immediate demand. All articles published by PLoS can be included in coursepacks—a use-right that most authors would want to allow without exception, but that most traditional publishers grant only for a substantial fee (which they rarely share with authors). The CCAL also ensures that institutions are permitted to archive not only articles written by their own faculty, but all other works published under the same legal terms as well, thereby facilitating their permanent accessibility and preservation. For example, the LOCKSS (“Lots of Copies Keep Stuff Safe”) program (http://lockss.stanford.edu/projectdescbrief.htm), an ongoing project to support libraries' efforts to “create, preserve, and archive local electronic collections,” is viable only insofar as institutions are permitted to store information themselves, rather than access it exclusively via publishers' Web sites. Many collaborative projects between libraries and publishers have been complicated by legal constraints, including the stipulation that archives remain “dark,” or inaccessible to users, until any commercial incentive for restricting access to articles has been exhausted—clearly a suboptimal arrangement for researchers, and one that is unnecessary for collections of works governed by the CCAL. One of the truly revolutionary implications of open-access articles, however, is that we simply do not know the full range of their potential applications. They are available for any use that any entrepreneur can envision, so long as the authors of the papers are properly credited. The only certainty, then, is that the utility of open-access research articles will be limited solely by the imagination of those that are inspired by the possibilities—rather than by legal constraints. Authors' Protections Authors retain the copyright to all articles in PLoS Biology and license their works under nonexclusive terms that reserve only some—rather than all—rights. There are several common objections, generally leveled by publishers, against this practice. For example, it is sometimes argued that the traditional copyright arrangement in scientific publishing protects against uses of articles that authors would object to—while the CCAL permits such uses and renders authors helpless to prevent them. To the extent that the uses in question are for academic or archival purposes, such as those discussed above, it is certainly true that the CCAL permits practices that “all rights reserved” licenses do not. Indeed, the expanded range of legitimate academic uses of articles is among the primary selling points of the CCAL in the context of scientific publishing. To the extent that the ostensibly objectionable uses are commercial, the problem is easily remedied with the Creative Commons Noncommercial License, which prohibits commercial reuse of a work without the copyright holder's consent. PLoS has chosen, for reasons both philosophical and pragmatic, to permit the commercial use of works we publish. As a matter of principle, all of our policies reflect the view that scientific publishers are service providers and should not themselves restrict the potential applications of the largely publicly funded work in their journals. More concretely, if a commercial enterprise is interested in repackaging the articles that PLoS has published, we are loath to prevent an author's work from wider distribution. Any risk that a company will use an article for a purpose its author would be uncomfortable with is, in our view, substantially outweighed by the benefits of allowing—not on a case-by-case basis, but across the board—the reproduction of the article for inclusion in online encyclopedias, or for distribution in countries in which Internet access is unreliable, or, indeed, for creative uses we hope to inspire by making primary research articles legally available to commercial interests. Another recurring objection to the copyright arrangement that PLoS employs is that authors are inappropriate copyright holders because they are ill-equipped to protect their own works against plagiarism, misattribution, and other misuse. Most scientists, however, have enough familiarity with cases of plagiarism in their own field to know that their strongest protection against mis- or nonattribution is derived not from the threat of prosecution for copyright infringement, but from community standards of conduct. Furthermore, among the benefits of open-access articles is the fact that their full texts, rather than just their abstracts, are searchable—which, as any teacher knows, makes plagiarism much easier to detect. Beyond plagiarism and misattribution, it is not clear what uses of primary research articles authors would actually want to prevent (other than, perhaps, the commercial uses that their work is already susceptible to, in many cases, when publishers hold copyrights). Scientists do not receive royalties for their published work. The more widely their articles are read and cited, the more their professional reputations are bolstered. Certainly, research articles have a wide range of uses that publishers typically object to— and indeed often file suit over—such as their compilation in coursepacks by copy shops. Those applications, however, tend not to constitute “misuse” in many authors' eyes. Authors' Voice There is no question that the licensing arrangement PLoS employs is relatively novel—and therefore untested over the long term—in biomedical publishing. However, it hardly takes a radical understanding of the interests of authors and users of primary research articles to conclude that the open-access terms of copyright promise substantial benefits for both groups. What, then, can scientists do to encourage other publishers to follow suit and strike similar legal arrangements with authors as a matter of course? One answer is to “vote with your submissions;” that is, authors should submit their work preferentially to journals with copyright and licensing practices that genuinely serve their interests. Another equally important action for scientists is to raise the issue with their professional societies. Scholarly associations exist, among other reasons, to serve the needs of their members—and society members should actively urge their society journals to employ the CCAL or a similar license for their research articles. Scientific and medical literature is different from fiction or movies or music. The United States government invests more than $28 billion per year in the National Institutes of Health alone to fund research in biology and medicine. The scientists who conduct that research and the research paid for by other public-minded institutions in the United States and abroad have an affirmative moral obligation to share the knowledge they create—not just with students and faculty at elite Western universities, but with everyone who could use it and build upon it. When authors publish their work in journals with restrictive copyright practices, it becomes illegal (often for even the authors themselves) to store primary research articles in many archives or include them in coursepacks or use them for other responsible purposes. Those obstacles to sharing knowledge can be avoided without legislative intervention, however, if scientists and publishers alike embrace a legal paradigm for disseminating new discoveries that maximizes their utility. Andy Gass is the outreach coordinator, Helen Doyle is the director of development and strategic alliances, and Rebecca Kennison is the director of journal production at the Public Library of Science (PLoS).

15252465

PMC449906

CC BY

2021-01-05 08:21:12

no

PLoS Biol. 2004 Jul 13; 2(7):e228

utf-8

PLoS Biol

10.1371/journal.pbio.0020228

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020236SynopsisEvolutionGenetics/Genomics/Gene TherapyHomo (Human)PrimatesGreat Ape Genomes Offer Insight into Human Evolution Synopsis7 2004 13 7 2004 13 7 2004 2 7 e236Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Lineage-Specific Gene Duplication and Loss in Human and Great Ape Evolution ==== Body Some primatologists have argued that to understand human nature we must understand the behavior of apes. In the social interactions and organization of modern primates, the theory goes, we can see the evolutionary roots of our own social relationships. In the genomic era, the age-old question, What makes us human? has become, Why are we not apes? As scientists become more adept at extracting biological meaning from an ever expanding repository of sequenced genomes, it is likely that our next of kin will again hold promising clues to our own identity. Lineage-specific gene gains and losses in humans and great apes Many comparative genomics studies have looked to our more distant evolutionary relatives, such as the mouse and even yeast, to help interpret the human genome. Because the genomes of mice, yeast, and humans have diverged significantly since their last common ancestor—about 75 million years ago for mouse and human, and about 1 billion years ago for yeast and human—there are enough differences between the functional and nonfunctional regions to home in on biologically significant sequences, based on their similarity. Sequences that are similar, or conserved, in such divergent species are assumed to encode important biological functions. These comparative studies have successfully identified and characterized many human genes. And a similar approach comparing primate genomes can help scientists understand the genetic basis of the physical and biochemical traits that distinguish primate species. In this approach, however, rather than looking for genes that are shared across many species, scientists look for those that are unique to a species. One of the primary agents of genome evolution is gene duplication. Duplicated genes provide the raw material for the generation of novel genes and biological functions, which in turn allow the evolution of organismal complexity and new species. (For more on duplicated genes, see the primer by Hurles in this issue.) James Sikela and colleagues set out to compare gene duplications between humans and four of our closest primate relatives to find the genetic roots of our evolutionary split from the other great apes. Collecting the DNA of humans, chimpanzees, bonobos, gorillas, and orangutans from blood and experimental cell lines, the researchers used microarray analysis to identify variations in the number of copies of individual genes among the different species. They analyzed nearly 30,000 human genes and compared their copy numbers in the genomes of humans and the four great apes. Overall, Sikela and colleagues found more than 1,000 genes with lineage-specific changes in copy number, representing 3.4% of the genes tested. All the great ape species showed more increases than decreases in gene copy numbers, but relative to the evolutionary age of each lineage, humans showed the highest number of genes with increased copy numbers, at 134. Many of these duplicated human genes are implicated in brain structure and function. The gene changes identified in the study, the authors conclude, likely represent most of the major lineage-specific gene expansions (or losses) that have taken place since orangutans split from the other great apes, some 15 million years ago. (Humans diverged from their closest cousins, the chimp and bonobo, roughly 5 million to 7 million years ago.) And because some of these gene changes were unique to each of the species examined, they will likely account for some of the physiological and morphological characteristics that are unique to each species. One cluster of genes that amplified only in humans was mapped to a genomic area that appears prone to instability in human, chimp, bonobo, and gorilla. This region, which corresponds to an ancestral region in the orangutan genome, has undergone modifications in each of the other descendent primate species, suggesting an evolutionary role. In humans, gene mutations in this region are also associated with the inherited disorder spinal muscular atrophy. This fact, along with the observation that there are human-specific gene duplications in this region, suggests a link between genome instability, disease processes, and evolutionary adaptation. In their genome-wide hunt for gene duplications and losses in humans and great apes, Sikela and colleagues have highlighted genomic regions likely to have influenced primate evolution. With the impending release of the chimp genome and more primate sequences to follow, scientists can take advantage of both sequence-based and microarray-based genome information to wrest additional insights from our primate cousins and flesh out the details of the human story.

0

PMC449907

CC BY

2021-01-05 08:26:25

no

PLoS Biol. 2004 Jul 13; 2(7):e236

utf-8

PLoS Biol

10.1371/journal.pbio.0020236

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020238SynopsisCell BiologyDevelopmentMolecular Biology/Structural BiologyMus (Mouse)MammalsRemembering Which X Chromosome to Use Synopsis7 2004 13 7 2004 13 7 2004 2 7 e238Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Chromosomal Memory Triggered by Xist Regulates Histone Methylation in X Inactivation ==== Body In mammals, males usually have one X and one Y chromosome and females have two X chromosomes. This crucial difference sets the sexes apart, but also creates a problem—female cells have the potential to turn out twice as much X-based gene product as necessary. Males with multiple–X chromosome syndromes face a similar problem. As if to avoid an overdose of X-related proteins, cells in the early embryo inactivate all but one X chromosome. The choice of which X (or Xs) to inactivate is apparently random, but once made, it persists across cell divisions and the specializations that determine a cell's ultimate fate, or type. Chromosome-wide histone H3 lysine 27 trimethylation caused by Xist expression A gene called Xist—which resides on the X chromosome—has a central role in X chromosome inactivation. It creates a special RNA molecule that spreads from its point of production down the length of the X chromosome, repressing its genes and inactivating the chromosome. After about one cell cycle, this gene silencing no longer requires Xist RNA. Daughter cells somehow remember which X to keep mute. In this month's PLoS Biology, geneticist Anton Wutz and colleagues at the Research Institute of Molecular Pathology in Vienna show that Xist expression during a critical period very early in embryonic development creates a chromosomal memory, independent of X silencing, that might help maintain X inactivation across cell generations. The molecular underpinnings of X inactivation seem to center on histones, the protein spools around which DNA coils its length. DNA and histones form complexes called chromatin, which undergoes many structural modifications that have important effects on gene expression. For example, tightly packed chromatin inhibits gene expression in its closely curled segments. Not surprisingly, the inactivated X chromosome is coiled into this dense form, called heterochromatin. In the standing model of X inactivation, the Xist gene mediates alterations to histones (such as the addition of chemical compounds called methyl groups) along the X chromosome, which result in heterochromatin formation. As this structure is passed on to daughter cells, X silencing is perpetuated. To explore the molecular changes that mediate X chromosome inactivation, Wutz and colleagues inserted a special Xist gene into the X chromosome of male mouse embryonic stem cells, so they could turn Xist expression “on” and “off” at will. The stem cells represent the earliest, unspecialized cells of a mouse embryo. Since the cells can be induced to differentiate in culture, they provide the opportunity to study the relationship between differentiation and X chromosome inactivation (which would not normally happen at all in these “male” cells). Using this system, the authors have shown previously that Xist must act during a critical window very early during stem cell differentiation—within the first 24 hours. Wutz and colleagues now show that after that window the X chromosome inactivation can still be reversed, but after an additional 24 hours, it cannot. There appears to be a “memory” of Xist action, which leads to the permanent shutting down of the chromosome. The importance of this observation is that it establishes a new step in the process of X chromosome inactivation—between the action of Xist and the establishment of irreversible silencing. By looking at the kinetics of histone modification, gene silencing, and Xist action, Wutz and colleagues further show that although certain histones are methylated at specific locations during this period in response to Xist, these modifications do not themselves constitute the chromosomal memory. The nature of the memory remains mysterious. Further experiments, perhaps looking at different histone modifications, will be required. Clarification of the events that lead to X inactivation will also improve our knowledge of how changes in the organization and structure of chromosomes can influence the activity of genes.

0

PMC449908

CC BY

2021-01-05 08:21:12

no

PLoS Biol. 2004 Jul 13; 2(7):e238

utf-8

PLoS Biol

10.1371/journal.pbio.0020238

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020238SynopsisCell BiologyDevelopmentMolecular Biology/Structural BiologyMus (Mouse)MammalsRemembering Which X Chromosome to Use Synopsis7 2004 13 7 2004 13 7 2004 2 7 e238Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. A Chromosomal Memory Triggered by Xist Regulates Histone Methylation in X Inactivation ==== Body In mammals, males usually have one X and one Y chromosome and females have two X chromosomes. This crucial difference sets the sexes apart, but also creates a problem—female cells have the potential to turn out twice as much X-based gene product as necessary. Males with multiple–X chromosome syndromes face a similar problem. As if to avoid an overdose of X-related proteins, cells in the early embryo inactivate all but one X chromosome. The choice of which X (or Xs) to inactivate is apparently random, but once made, it persists across cell divisions and the specializations that determine a cell's ultimate fate, or type. Chromosome-wide histone H3 lysine 27 trimethylation caused by Xist expression A gene called Xist—which resides on the X chromosome—has a central role in X chromosome inactivation. It creates a special RNA molecule that spreads from its point of production down the length of the X chromosome, repressing its genes and inactivating the chromosome. After about one cell cycle, this gene silencing no longer requires Xist RNA. Daughter cells somehow remember which X to keep mute. In this month's PLoS Biology, geneticist Anton Wutz and colleagues at the Research Institute of Molecular Pathology in Vienna show that Xist expression during a critical period very early in embryonic development creates a chromosomal memory, independent of X silencing, that might help maintain X inactivation across cell generations. The molecular underpinnings of X inactivation seem to center on histones, the protein spools around which DNA coils its length. DNA and histones form complexes called chromatin, which undergoes many structural modifications that have important effects on gene expression. For example, tightly packed chromatin inhibits gene expression in its closely curled segments. Not surprisingly, the inactivated X chromosome is coiled into this dense form, called heterochromatin. In the standing model of X inactivation, the Xist gene mediates alterations to histones (such as the addition of chemical compounds called methyl groups) along the X chromosome, which result in heterochromatin formation. As this structure is passed on to daughter cells, X silencing is perpetuated. To explore the molecular changes that mediate X chromosome inactivation, Wutz and colleagues inserted a special Xist gene into the X chromosome of male mouse embryonic stem cells, so they could turn Xist expression “on” and “off” at will. The stem cells represent the earliest, unspecialized cells of a mouse embryo. Since the cells can be induced to differentiate in culture, they provide the opportunity to study the relationship between differentiation and X chromosome inactivation (which would not normally happen at all in these “male” cells). Using this system, the authors have shown previously that Xist must act during a critical window very early during stem cell differentiation—within the first 24 hours. Wutz and colleagues now show that after that window the X chromosome inactivation can still be reversed, but after an additional 24 hours, it cannot. There appears to be a “memory” of Xist action, which leads to the permanent shutting down of the chromosome. The importance of this observation is that it establishes a new step in the process of X chromosome inactivation—between the action of Xist and the establishment of irreversible silencing. By looking at the kinetics of histone modification, gene silencing, and Xist action, Wutz and colleagues further show that although certain histones are methylated at specific locations during this period in response to Xist, these modifications do not themselves constitute the chromosomal memory. The nature of the memory remains mysterious. Further experiments, perhaps looking at different histone modifications, will be required. Clarification of the events that lead to X inactivation will also improve our knowledge of how changes in the organization and structure of chromosomes can influence the activity of genes.

0

PMC450293

CC BY

2021-01-05 08:21:12

no

PLoS Biol. 2004 Jul 13; 2(7):e256

latin-1

PLoS Biol

10.1371/journal.pbio.0020256

oa_comm

==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020239Research ArticleCell BiologyPhysiologyDrosophilaCellular and Genetic Analysis of Wound Healing in Drosophila Larvae Wound Healing in DrosophilaGalko Michael J [email protected] 1 Krasnow Mark A [email protected] 1 1Howard Hughes Medical Institute and Department of BiochemistryStanford University School of MedicineStanford, CaliforniaUnited States of America8 2004 20 7 2004 20 7 2004 2 8 e23920 2 2004 26 5 2004 Copyright: © 2004 Galko and Krasnow.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. The Molecular Biology of Wound Healing To establish a genetic system to study postembryonic wound healing, we characterized epidermal wound healing in Drosophila larvae. Following puncture wounding, larvae begin to bleed but within an hour a plug forms in the wound gap. Over the next couple of hours the outer part of the plug melanizes to form a scab, and epidermal cells surrounding the plug orient toward it and then fuse to form a syncytium. Subsequently, more-peripheral cells orient toward and fuse with the central syncytium. During this time, the Jun N-terminal kinase (JNK) pathway is activated in a gradient emanating out from the wound, and the epidermal cells spread along or through the wound plug to reestablish a continuous epithelium and its basal lamina and apical cuticle lining. Inactivation of the JNK pathway inhibits epidermal spreading and reepithelialization but does not affect scab formation or other wound healing responses. Conversely, mutations that block scab formation, and a scabless wounding procedure, provide evidence that the scab stabilizes the wound site but is not required to initiate other wound responses. However, in the absence of a scab, the JNK pathway is hyperinduced, reepithelialization initiates but is not always completed, and a chronic wound ensues. The results demonstrate that the cellular responses of wound healing are under separate genetic control, and that the responses are coordinated by multiple signals emanating from the wound site, including a negative feedback signal between scab formation and the JNK pathway. Cell biological and molecular parallels to vertebrate wound healing lead us to speculate that wound healing is an ancient response that has diversified during evolution. A powerful new system for studying wound healing in the fruitfly is helping to unearth the genetic and cellular requirements of the healing process ==== Body Introduction The capacity to heal wounds is essential for organisms to endure and thrive despite an occasionally hostile environment. Organisms throughout the animal kingdom can heal wounds, but mammalian wound healing has been studied most intensively because of its medical relevance. Wound healing must occur to restore health after trauma or surgery, or in conditions such as cancer or peptic ulcers in which internal processes cause tissue damage. Mammalian epithelial tissues display a characteristic set of responses to tissue damage, including the rapid formation of a blood clot at the site of injury, followed by spreading of the damaged epithelium across the wound gap to restore tissue integrity (Martin 1997; Singer and Clark 1999). However, there are significant differences in the wound healing response depending on the specific tissue affected, its developmental stage, and the nature of the damage. For example, damaged fetal epidermis heals without leaving a scar (Colwell et al. 2003), and a few adult tissues, including human liver, can regenerate large portions of the damaged tissue (Diehl 2002). Some wounds, such as the common foot ulcers of diabetics, heal slowly or not at all (Greenhalgh 2003), whereas others display an exaggerated response that results in disfiguring keloid scars (Alster and Tanzi 2003). One important goal of wound healing research is to find ways to speed or alter the healing process. Another is to understand the fundamental cellular and molecular mechanisms by which cells sense tissue damage and signal to neighboring healthy cells to contain and repair it. Cellular studies of mammalian wound healing have shown that it is a complex process that takes weeks to complete and involves not just the damaged epithelial cells and their neighbors, but also fibroblasts and blood vessels in the underlying stroma, and inflammatory cells that are recruited to the wound site (Martin 1997; Singer and Clark 1999). Only the first step in mammalian wound healing, the proteolytic cascade that culminates in fibrin deposition and clot formation, is well understood at the molecular level (Furie and Furie 1992). As the clot forms, platelets bound to it and to the damaged tissue release additional procoagulant proteins as well as growth factors and chemokines that can attract neutrophils and monocytes that mediate an early inflammatory response. Keratinocytes at the wound margin become activated, break down their cell junctions, and assume a lamellipodial crawling morphology as they spread across the wound site to restore epithelial integrity (Odland and Ross 1968; Clark et al. 1982). The early inflammatory cells release additional signals that can attract and activate fibroblasts, macrophages, and blood vessel endothelial cells. These cells infiltrate the wound site and form a specialized stroma called granulation tissue, which facilitates reepithelialization, helps contract the wound, and is later remodeled to form the scar. Although many different cell types are present at the wound site, and dozens of signaling molecules, receptors, matrix proteins, and proteases are known to be expressed during the healing process (Martin 1997; Singer and Clark 1999), their roles in the process have been difficult to establish. This difficulty is due to the cellular and molecular complexity of wound healing and the challenges in manipulating wound gene expression and function in vivo. Hence, models of gene function in wound healing derive primarily from results of gene expression studies at wound sites, application of exogenous gene products to wounds, and studies in simple cell culture models such as keratinocyte monolayers. Analyses of wound healing defects in mouse knockouts of candidate genes have also begun to provide insight into the genes' roles in the process (Werner et al. 1994; Romer et al. 1996). However, some of the genetic results challenge fundamental aspects of the prevailing models (Ashcroft et al. 1999; Drew et al. 2001; Martin et al. 2003). The establishment of simpler, more tractable genetic systems to study wound healing could allow systematic genetic dissection of the process in vivo and complement studies in vertebrates and clinical settings. Over a half century ago, Wigglesworth demonstrated that the large hemipteran insect Rhodnius prolixus has a robust wound healing response (Wigglesworth 1937). He characterized the response by light microscopy and described the proliferation and spreading of epidermal cells and the accumulation of blood cells (hemocytes) at the wound site. Since this pioneering work, only a few follow-up studies have appeared (Lai-Fook 1966, 1968). There has been little work on other insects aside from a number of studies of wound healing during imaginal disc and leg regeneration (Reinhardt et al. 1977; Truby 1985; Bryant and Fraser 1988) and the recent discoveries that Drosophila embryos undergo a scarless wound healing process involving actin cable formation and filopodial extension (Kiehart et al. 2000; Wood et al. 2002) and that wounded adult cells activate the Jun N-terminal kinase (JNK) signaling pathway (Ramet et al. 2002; see below). Some attention has also focused on melanization, the formation of a heteropolymer of orthoquinones generated by phenoloxidase-catalyzed oxidation of mono- and diphenols (Wright 1987) that accompanies certain infections, tumors, and wound healing (De Gregorio et al. 2002; Ligoxygakis et al. 2002). We set out to investigate wound healing in Drosophila melanogaster because of the powerful genetic and genomic approaches available in this organism. These approaches have elucidated the molecular pathways that control many developmental and physiological processes. For example, genetic studies revealed a prominent role for a JNK signaling pathway in Drosophila dorsal closure, a developmentally programmed spreading of the embryonic epidermis (Noselli and Agnes 1999). This process resembles epithelial spreading during vertebrate wound healing, and indeed this similarity and the expression patterns of JNK pathway transcription factors near wounds (Verrier et al. 1986; Martin and Nobes 1992) prompted two recent genetic studies of JNK pathway activity in adult wound healing (Ramet et al. 2002; Li et al. 2003). In this paper, we describe the cellular events and genetic requirements of epidermal wound healing in Drosophila larvae. A simple puncture wound assay was developed, and we use it to show that a plug rapidly forms at the wound site and subsequently melanizes to form a scab. We describe how epidermal cells surrounding the plug orient toward it and fuse to form a syncytium, and how the cells spread along and through the plug to reestablish epithelial continuity. We then use JNK pathway reporters and genetic analysis to demonstrate the induction and function of the JNK pathway in the process, and we use mutants that block scab formation, and a scabless wounding procedure, to elucidate the function of the scab. The results demonstrate that the cellular responses of wound healing are under separate genetic control, and that they are coordinated by multiple signals emanating from the wound site, including a negative feedback signal between scab formation and the JNK pathway. This establishes a tractable genetic system to study postembryonic wound healing, and the cellular and molecular parallels with vertebrate wound healing suggest that some of the fundamental steps in the process are evolutionarily conserved. Results A Larval Epidermal Wound Healing Assay A puncture wounding procedure was developed in which early third instar (L3) Drosophila larvae were lightly anesthetized and then stabbed with a 0.1-mm–diameter steel needle, about the size of six epidermal cells (Figure 1; see also Figure 3A). To ensure reproducibility, larvae were always stabbed at the dorsal midline halfway between the hair stripes of abdominal segments A3 or A4. Wounding did not cause a developmental arrest, because the wounded larvae continued to grow and pupariated 48 h after wounding, similar to mock-wounded controls (Figure 1B–1G), and 90% or more of the wounded larvae survived the procedure (see below). We then analyzed the major morphological, cellular, and molecular events of healing (Figure 1N) by visualizing wounds at different stages of healing in live and heat-killed whole-mount larvae, in histochemically or immunostained larval fillets, and in sections through wounds that we examined by transmission electron microscopy (TEM) (schematized in Figure 1A). Figure 1 Scab Formation and Resolution during Puncture Wound Healing (A) Puncture wound assay. L3 larvae are punctured at the dorsal midline with a 100-μm diameter pin; they are then cultured and the healing wounds analyzed as shown. (B–E) Photomicrographs of heat-killed L3 larvae before wounding (B) and at the indicated times after wounding (C–E). Note larval growth during wound healing. Anterior is up. (F) L2 larva wounded as above and analyzed in L3, 60 h after wounding. Wounding in L2 allows visualization of late stages of wound healing without the complication of pupariation, which begins about 48 h after wounding in the standard L3 assay. (G) A mock-wounded L2 larva visualized 60 h after wounding. Note that it and the wounded larva (F) grew to a similar extent. (H–M) Close-up images of (B–G) showing unwounded cuticle (H and M) or wound sites (I–L, boxed regions in C–F) to show detail of scab. Micrographs are of living larvae taken shortly before the corresponding images of the whole heat-killed larvae above. Bar, 500 μm (for B–G), 50 μm (for H–M). (N) Timing of wound responses. Solid lines, time response was most often observed; dashed extensions to left, time response was occasionally observed; dashed extensions to right, time response was diminishing; BL, basal lamina. Figure 3 Epidermal Cell Orientation and Fusion around Puncture Wounds (A–E) w; UAS-GFP.nls/+; A58-Gal4/+ larvae that express GFP (green) in epidermal cell nuclei were mock-wounded (A) or puncture wounded (B–E), cultured for the indicated time, filleted open, fixed, and immunostained for Fasciclin III (red) to label the basolateral surface of the cells. (A) Pre-wounding. Dashed circle, size of the 100-μm pin used for wounding. (B) 2 h postwounding. Some cells at the wound margin have elongated and oriented toward the wound (arrowheads). (C) 8 h postwounding. Cells at the wound margin have begun fusing to form a syncytium. Note the syncytium with four nuclei that contains a partially degraded, radially-oriented membrane domain (arrow) and scattered puncta of Fasciclin III staining in the cytoplasm (arrowhead) that may be membrane breakdown intermediates. (D) 48 h postwounding. The central syncytium contains ten or more nuclei, some of which are located in extensions (arrowheads) that may represent recent fusions of peripheral cells with the central syncytium. Other peripheral cells have oriented toward the syncytium but not fused with it. (E) 60 h postwounding. A large syncytium with more than 30 nuclei. (F) 8 h postwounding. Larva was treated as above but immunostained for Coracle (red), a septate junction component. The central syncytium contains nine nuclei. Bar, 50 μm. Bleeding and Scab Formation at the Wound Site Unwounded larvae have a semitransparent white cuticular surface with rare or no blemishes (see Figure 1B, 1G, 1H, and 1M). Beneath the cuticle is the epidermis (Figure 2), an epithelial monolayer that secretes the cuticle at its apical (external) surface and is lined by a basal lamina along its basal surface (Figure 2A, 2C–2F). Immediately after puncture wounding, a variable amount of blood (hemolymph) escapes from the wound site (data not shown). Within 10–15 min, the wound site begins to darken (see Figure 1C and 1I) and a plug forms in the gap (Figure 2B and 2G). The plug is composed of debris, presumably the remnants of necrotic cells damaged by wounding that are disorganized and highly vesiculated and not bound by a cell membrane or basal lamina (Figure 2H and 2K). The plug may also contain blood coagulation products (see Discussion). Figure 2 Ultrastructural Analysis of Puncture Wound Healing (A) Schematic of unwounded epidermis showing the cell monolayer, its apical cuticle lining, and basal lamina. White ovals indicate nuclei. (B) Schematic of recently wounded epidermis showing a plug of cell debris in the wound gap. Cells and ruptured cuticle at the wound margin are shown. (C–S) TEM sections of unwounded (C–F) and wounded (G–S) larvae at the times indicated after wounding. Transverse sections through each wound site are shown (C, G, J, N, and Q) along with close-ups of the boxed regions at right. c, cuticle; d, debris; e, epidermis; ec, epicuticle; m, muscle; n, epidermal nucleus; p, plug; pc, procuticle; s, scab; t, trachea (C) Pre-wounding. The epidermis and cuticle are intact. (D) Apical surface of epidermal cell showing villi (arrowhead) that secrete cuticle. (E) Basal surface. Arrowhead, basal lamina. (F) Epidermal cuticle. The epicuticle (top three layers) overlies the striated procuticle layer. (G) 1 h postwounding. The epidermis and cuticle are discontinuous but the gap is filled with a plug (outlined by dashed line) of cell debris. The epidermis has partially separated from the cuticle beyond the wound margin (asterisks). (H) The plug contains highly vesiculated cell debris. (I) The epidermis separating (asterisk) from overlying cuticle appears vesiculated and is presumably necrotic. (J) 2 h postwounding. The outer portion of the plug has melanized to form an electron-dense scab (outlined by white dashed line). The epidermis and cuticle are still discontinuous. (K) Debris, including a necrotic trachea, in the plug. The plug is not bounded by a membrane or basal lamina. (L) Portion of scab showing melanized debris and trachea. (M) Close-up of a lamellipodium (bracket) extending into a plug at the outer edge of another 2-h wound. Note basal lamina (arrowhead) along the lamellipodium. (N) 8 h postwounding. The epidermis has migrated across the gap to reestablish continuity, and has secreted new cuticle beneath the scab. (O) A region of epidermal cell cytoplasm near wound plug debris contains vesiculated material (outlined by dashed line) that is probably phagocytosed debris. (P) The newly established epidermis under the wound has a continuous basal lamina (arrowhead) and apical villi (arrow) secreting cuticle. (Q) 24 h postwounding. The new cuticle underlying the scab is thicker and the scab is more electron dense. Four nuclei in close apposition are in a syncytium because there are no membranes separating them. (R) Portion of scab and old cuticle. Note that cuticle in contact with the scab is melanized. (S) Cytoplasmic extension (arrowhead) engulfing debris at the basal surface of the epidermis of another 24-h wound. Bar, 10 μm (C, G, J, N, and Q), 0.33 μm (D and E), 0.83 μm (F), 1 μm (H, M, and P), 2 μm (I and O), 1.67 μm (K and L), 4 μm (R), 1.25 μm (S). Over the next 24 h, the outer part of the plug is converted into a scab. This part of the plug becomes electron dense (Figure 2J and 2L) as the scab enlarges and darkens (see Figure 1D, 1E, 1J, and 1K), presumably due to a melanization reaction. Melanization affects all of the external structures at the wound site including the debris, the edges of the damaged cuticle (Figure 2N, 2Q, and 2R), and even entrapped tissues such as tracheae (Figure 2L). By 2 or 3 d after wounding, debris is cleared, the scab resolves, and the exterior of the animal resumes a nearly normal appearance (see Figure 1F and 1L). Epidermal cells that grow back across the wound gap (see below) appear to participate in debris clearance, because they extend processes that engulf the debris (Figure 2S) and contain within their cytoplasm vesiculated material resembling debris (Figure 2O). Other components of the plug and scab may be degraded extracellularly or passively shed from the wound site. Epidermal Cells Orient toward the Wound and Fuse to Form a Syncytium The response of epidermal cells to wounding was examined in transgenic larvae in which epidermal cell nuclei were labeled with green fluorescent protein (GFP) and cell membranes were immunostained for the basolateral membrane marker Fasciclin III or the septate junction protein Coracle (Figure 3). Epidermal cells at the wound site underwent two dramatic morphological changes in the several hours following wounding. First, beginning about a half hour after wounding, cells at the wound margin began to elongate and orient toward the wound, often tapering toward the wound site (Figure 3B). Second, these cells fused with each other to form a syncytium. Normally, epidermal cells are mononuclear (Figure 3A). However, as early as 1 h after wounding, the radially oriented plasma membrane domains (parallel to the long cell axis) began to break down as the circumferential domains joined, creating multinucleate cells around the wound. This can be seen in the Fasciclin III and Coracle stains, which showed incomplete (Figure 3C and 3) or absent (Figure 3C–3E) radial domains of plasma membrane staining; the loss of these membrane domains was sometimes accompanied by scattered puncta of staining in the cytoplasm, which may be membrane breakdown intermediates (see Figure 3C). TEM analysis confirmed the absence of plasma membrane between epidermal nuclei beneath and adjacent to the wound site (see Figure 2Q). Syncytia were nearly always present by 4 h after wounding. As healing progressed, the polarization and fusion of epidermal cells spread outward from the wound. As cells bordering the wound fused, the more-peripheral cells just beyond the syncytium began to polarize toward the wound (see Figure 3D). Some of these cells apparently proceed to fuse with the central syncytium, because the average number of nuclei per syncytium increased over the 2 d following wounding, creating a large syncytium with as many as 30 nuclei surrounding the wound (see Figure 3E). Epidermal cell or nuclear division do not contribute to growth of the syncytium, because neither was detected around the wound site or elsewhere in the epidermis by immunostaining for phosphorylated histone H3, a marker of condensed mitotic chromosomes, 4–24 h after wounding (data not shown). Highly asymmetrical syncytia like the one shown in Figure 3D probably represent cases in which a subset of polarized peripheral cells had fused with the central syncytium. Peripheral cells may fuse to each other before fusing to the central syncytium, because satellite syncytia separate from the central syncytium were occasionally observed. Epidermal Cells Spread along and through the Plug to Reestablish Epithelial Integrity A key step in wound healing is the closure of the epidermal gap and reestablishment of epithelial integrity. By 2 h after wounding, the epidermis was still discontinuous but the breach was filled by the plug and developing scab (see Figure 2J). Ultrastructural studies showed that during the next 6 h, as the epidermal cells oriented toward the puncture site and fused to form a syncytium, they also spread along and through the plug, led by lamellipodial extensions (see Figure 2M), until epithelial continuity was reestablished (see Figure 2N). No multicellular actin cable indicative of the “purse string” closure mechanism of embryonic wound healing (Wood et al. 2002) was observed in the spreading cells (see Materials and Methods). A thin basal lamina was present along the length of the lamellipodia (see Figure 2M), suggesting that basal lamina is synthesized by the cells before or during their migration. Following reepithelialization, new cuticle was secreted (see Figure 2N–2P). By 24 h after wounding, a thick new cuticle layer was present that was continuous with the old cuticle at the wound margin (see Figure 2Q). Most of the wound plug debris ended up outside the new cuticle layer and eventually melanized to form scab (see Figure 2Q and 2R), although occasionally some was left beneath the epidermis and was later phagocytosed or degraded (see Figure 2S). The JNK Pathway Is Activated in a Gradient and Promotes Reepithelialization To elucidate the genetic control and interdependence of the cellular events of wound healing, we investigated the activity and function of the JNK signaling pathway in the process (Figure 4). The epidermal spreading in some ways resembles the epidermal spreading of dorsal closure, which depends on the JNK pathway. During dorsal closure, the mitogen-activated protein kinase kinase kinase kinase Misshapen (Su et al. 1998) is activated, triggering a phosphorylation cascade that ultimately activates the JNK Basket (Riesgo-Escovar et al. 1996; Sluss et al. 1996). Basket phosphorylates the Drosophila Jun and Drosophila Fos transcription factors (Riesgo-Escovar and Hafen 1997), thus inducing expression of puckered (puc), which encodes a phosphatase that negatively regulates Basket, and other targets (Martin-Blanco et al. 1998). To test for JNK pathway activation in the larval puncture wound assay, we assayed expression of lacZ transcriptional reporters of puc and misshapen (msn), two genes induced by JNK pathway activation in other contexts (Martin-Blanco et al. 1998; Ramet et al. 2002). Figure 4 Induction and Function of the JNK Pathway around Puncture Wounds (A–C) Larvae carrying the JNK pathway reporter puc-lacZ, which expresses a nuclear β-galactosidase, were mock-wounded (A) or puncture wounded (B and C), and then cultured for the indicated times before staining with X-gal to visualize reporter activity (blue). There is little reporter activity in unwounded epidermis (A), but 4 h after wounding the reporter is expressed in a gradient emanating from the wound, with highest expression in the row of epidermal nuclei at the wound margin and decreasing levels in surrounding nuclei out to five cell diameters away. At 24 h (C), reporter expression has declined. (D–F) Larvae carrying the JNK pathway reporter msn-lacZ, treated as above. Wounding-induced reporter expression is seen out to seven cell diameters. (G–I) Larvae carrying msn-lacZ and A58-Gal4 and UAS-bskDN transgenes (to inactivate the JNK pathway in larval epidermis), treated as above. Reporter induction is inhibited, but the scab forms normally. (J and K) Larvae carrying msn-lacZ and either UAS-bskDN alone as control (J) or A58-Gal4 and UAS-bskDN transgenes (K), wounded as above and analyzed 24 h later by immunostaining for Fasciclin III and β-galactosidase. Reporter induction is inhibited in (K), but epidermal cells have oriented toward the wound, and although nuclear β-galactosidase staining is faint, careful inspection shows that the cells closest to the wound have fused to form a syncytium. Syncytium formation was confirmed using the A58-Gal4>UAS-GFP.nls marker. (L and M) Larvae carrying msn-lacZ and either UAS-bskDN alone as control (L) or A58-Gal4 and UAS-bskDN transgenes (M), wounded and analyzed 24 h later by TEM. Note that the epidermis in M has failed to spread across the wound gap and is still discontinuous (asterisks). No cuticle has been synthesized in the wound gap, but the cuticle flanking the wound appears thickened. Bar in (I), 100 μm (for [A–I]). Bar in (K), 50 μm (for [J and K]). Bar in (M), 5 μm (for [L and M]). In unwounded larval epidermis, there was little or no detectable expression of either the msn or the puc reporter (Figure 4A and 4D). However, within 1 h after wounding, expression of both reporters was readily detected in epidermal cells surrounding the wound, and by 4 h both exhibited robust expression (Figure 4B and 4E; unpublished data). The msn and puc reporters were induced in large, roughly symmetrical zones extending three to seven cell diameters out from the puncture site. Within each zone, the reporters were expressed in a gradient, with cells closest to the puncture site exhibiting the highest level of expression, suggesting that the reporters are induced by a signal emanating from the wound site. The zone of expression of the msn reporter was typically broader than that of puc, perhaps because it is more sensitive to the inducing signal. Expression of both reporters peaked between 4 and 8 h after wounding and declined thereafter, with expression restricting to cells closest to the wound (Figure 4C and 4F). To determine the function of JNK pathway induction, we analyzed wound healing in larvae in which the JNK pathway was inactivated. Because null mutations in JNK pathway genes block dorsal closure and are embryonic lethal, we selectively inhibited the pathway in larval epidermis by expressing a dominant-negative form of Basket JNK (upstream activation sequence-basketdominant negative [UAS-bskDN]) under the control of the A58-Gal4 driver, an epidermal-specific driver that turns on early in larval development. UAS-bskDN was used because it is the most potent JNK pathway inhibitor available (see Materials and Methods): it gave a severe dorsal closure phenotype and lethality when expressed in the embryonic epidermis using e22c-Gal4 or69B-Gal4 drivers. By contrast, larvae expressing UAS-bskDN under control of the A58-Gal4 driver were viable and active and did not display any morphological abnormalities, suggesting that the JNK pathway does not play a critical role in the larval epidermis under normal environmental conditions. However, following wounding, induction of the msn reporter was almost completely abolished (Figure 4G–4I), and the wound healing process was dramatically affected. We analyzed the effect of JNK pathway inhibition on wound healing using the assays used for wild-type larvae. There were no detectable defects in the early steps in wound healing, including scab formation, epidermal cell orientation toward the wound, and epidermal cell fusion to form a syncytium (see Figure 4G–4K). However, ultrastructural analysis showed that reepithelialization was blocked or defective, with no cytoplasmic processes or only extremely fine or distorted processes and no new cuticle synthesis beneath the scab 16 h after wounding (Figure 4L and 4M; data not shown). To further test the requirement of the JNK pathway in reepithelialization, we analyzed larvae in which a portion of the epidermis was abraded by a nonpenetrating pinch wounding procedure (described further below) that leaves a much larger gap in the epidermis than does a fine puncture wound and hence provides a more rigorous test of wound reepithelialization (Figure 5). In control larvae in which the JNK pathway was not inhibited, the epidermis spread to close the gap, and full reepithelialization was evident within 24 h after wounding (Figure 5A and 5B). By contrast, in larvae in which the JNK pathway was inhibited, the epidermis did not spread, and a large gap remained (Figure 5C). We conclude that induction of the JNK pathway promotes spreading and reepithelialization of the larval epidermis but appears to be dispensable for other steps in wound healing, including scab formation, cell orientation, and cell fusion. Figure 5 Cellular Responses and Genetic Requirements of Pinch Wound Healing (A–D) Larvae carrying the msn-lacZ reporter and the indicated transgenes or mutations were pinched with a forceps to abrade a region of dorsal epidermis but leave the overlying cuticle intact. Wounded larvae were cultured for the indicated times and immunostained for Fasciclin III (red) and β-galactosidase (green). (A) 6 h after pinch wounding. Note the large epidermal gap (asterisk) at the wound site. Some cells at the wound margin have elongated and oriented toward the wound (arrowheads). Others have fused to form syncytia (arrow). (B) 24 h after pinch wounding. The epidermis has spread to close the gap. Note disorganization of epidermis and syncytia (arrows) at site of healed wound. (C) An A58-Gal4 and UAS-bskDN larva 24 h after pinch wounding. Epidermal spreading is inhibited and a large wound gap remains (asterisk). However, cells at the wound margin still orient toward the wound (arrowheads) and fuse to form syncytia (arrows). (D) A hemizygous lzr15 mutant larva 24 h after pinch wounding. lzr15 blocks crystal cell development and scab formation at puncture wounds (Figure 6), but no defects are observed in pinch wound healing. (E and F) Larvae carrying msn-lacZ reporter were mock-wounded (E) or pinch wounded (F), cultured for 4 h, and stained with X-gal (blue). Wounding induces reporter expression in a gradient extending out four cell diameters. The gap (asterisk) lacks a scab. Bar, 100 μM. Crystal Cells Promote Scab Formation To investigate the role of the scab in puncture wound healing, we sought ways to block scab formation genetically (Figure 6). Crystal cells are a special type of blood cell that contain distinctive, crystal-like intracellular inclusions and have long been hypothesized to play a role in melanization responses such as those in scab formation (Rizki and Rizki 1959, 1984). The gene lozenge (lz) encodes a transcription factor required for development of the crystal cell lineage (Lebestky et al. 2000), and crystal cells are severely reduced or absent in lzr15 homozygous or hemizygous larvae (Figure 6A and 6B). The lzr15 mutant larvae failed to form a scab detectable by light microscopy (Figure 6C and 6D), and TEM analysis showed a diffuse plug at the wound site instead of the consolidated, electron-dense plug and scab that are normally present 24 h after wounding (Figure 6E and 6F). This defect in scab formation is likely due to the effect of lzr15 on crystal cells, and not some other effect of the mutation, because scab formation was also inhibited in larvae homozygous for Black cells (Bc) (data not shown), a mutation that alters crystal cell morphology and eliminates serum phenoloxidase activity (Rizki et al. 1980). We conclude that crystal cells are required to consolidate and melanize the plug to form a scab during wound healing. Figure 6 Effect of lz on Scab Formation and the Other Events of Puncture Wound Healing (A and B) Posterior of lz+ (w1118) (A) and lzr15 mutant (B) L3 larvae. Larvae were heated so crystal cells appear as tiny black dots. No crystal cells are apparent in the lzr15 mutant. Bar, 200 μm. (C and D) Micrographs of control lz+ (w1118) (C) and lzr15 mutant (D) L3 larvae 4 h after puncture wounding. No scab is seen at the lzr15 wound site (encircled). Bar, 50 μm. (E and F) TEM sections through 24-h–old puncture wounds of a control lzr15/+ heterozygote (E) and a hemizygous lzr15 mutant larva (F), both carrying the msn-lacZ transgene. A consolidated, electron-dense scab has formed in the control larva (E), but only a diffuse plug with peripheral electron density is present at the lzr15 hemizygous wound (F). The electron density of the lzr15 plug might be due to residual melanization activity in the lzr15 mutant. Although reepithelialization is complete in the lzr15 mutant wound, the epidermis contains large vacuoles and abundant apical processes, and it is separated by a gap (asterisks) from the old cuticle and has not secreted new cuticle. Other 24-h lzr15 mutant wounds analyzed had necrotic or discontinuous epidermis at the wound site (not shown). Bar, 10 μm. (G and H) Fluorescence micrographs of 20-h puncture wounds in control (G) and lzr15 mutant (H) larvae carrying the msn-lacZ reporter that were treated as above and immunostained for Fasciclin III (red) and β-galactosidase (green). Epidermal cells at both control and lzr15 mutant wounds have fused to form syncytia (arrows), and cells in the control are oriented toward the wound site (arrowheads). The orientation response of epidermal cells in the lzr15 mutant is difficult to assess because cell borders out to six cell diameters away from the wound appear slack and wavy. Bar, 50 μm. (I–L) X-gal stains of 6-h–old puncture wounds of control lz+ (I and K) or lzr15 hemizygous mutant larvae (J and L) carrying either msn-lacZ (I and J) or puc-lacZ (K and L). Note the absence of scabs and the increase in reporter activity (blue) in lzr15. The basal level of reporter expression in unwounded epidermis was not increased in lzr15 (not shown). Bar, 50 μm. Untreated lzr15 larvae were viable and active, but few survived the normal puncture wound procedure (Figure 7). By 4 h after wounding, only 55% of lzr15 larvae were alive, and by 24 h only 15% survived, most of which were sluggish and flaccid. By contrast, 85% or more of the lz+ control larvae survived the wounding procedure. Thus, scab formation is critical for healing puncture wounds. Figure 7 Effect of lz on Survival Following Puncture Wounding Control lz+ (w1118) and lzr15 mutant larvae were puncture wounded or mock-wounded and cultured for 4 or 24 h. The percentage of treated larvae alive and motile at each time is shown. Values are the average (± standard error of the mean) of three to six independent experiments with ten or more treated larvae per time point. The Scab Stabilizes the Wound Site and Prevents Superinduction of the JNK Pathway We next investigated the cellular events of wound healing in lzr15 larvae, using the methods described above for wild-type and JNK pathway mutants, except that sharper pins were used for wounding to increase survival and allow analysis of the later stages of wound healing. Most of the cellular responses to wounding appeared to initiate in lzr15 mutants, although they did not progress normally. Epidermal cell fusion occurred, but the syncytium often occupied a greater area than in control larvae (see Figure 6G and 6H). The surrounding epidermal cells also appeared to organize around the wound, but their cell borders were slack and wavy, even several cell diameters out from the wound, making it difficult to assess whether they had oriented toward the wound (see Figure 6H). A similar though less severe “wavy border” phenotype was observed in Bc mutant larvae. TEM analysis revealed that the epidermal cells up to 200 μm or more beyond the wound margin separated from the overlying cuticle around the wound (see Figure 6F). However, the detached cells extended numerous fine cellular processes in an apparent attempt to close the wound. Sometimes the edges of the punctured epidermis met to restore epithelial integrity, but in most cases they did not (see Figure 6F; data not shown). The lzr15 mutation also caused superinduction of the JNK pathway reporters. Although the basal expression level of the msn and puc reporters in unwounded epidermis was unchanged, both were expressed at higher levels and in an expanded zone around the wound site at 3, 6, and 24 h after wounding (see Figure 6I–6L; data not shown). A similar effect was observed in Bc mutants. Thus, scab formation limits induction of the JNK pathway around puncture wounds. To further investigate the role of the scab in wound healing, a scabless wound healing procedure was developed. The larval cuticle was gently pinched with dissecting forceps, leaving the cuticle intact but abrading a patch of epidermal cells from its inner surface (see Figure 5A). Although these pinch wounds did not bleed or form scabs, the epidermal cells at the wound site underwent many of the same responses seen at puncture wounds. Many cells at the wound margin oriented toward the wound, and some fused with neighboring cells to form syncytia (see Figure 5A and 5B). Also, the msn reporter was induced in a gradient in the cells surrounding the wound (see Figure 5A and 5F), and the cells spread to close the wound gap within 24 h (see Figure 5B). Thus, each of the major epidermal cell responses to wounding can occur normally in the absence of a scab, provided the cuticle remains intact. Indeed, the primary function of the scab may be to restore integrity to the cuticle and wound site, because lzr15mutant larvae did not display any defects in the healing of pinch wounds: epidermal cells around the wound polarized and fused like in lz+ controls, the JNK pathway reporters were induced at their normal levels and in their normal domain around the wound site, and the epidermal cells spread across the wound and healed with normal kinetics (see Figure 5D). Thus, the critical function of the scab appears to be to provide stability to the damaged cuticle and wound site, and the defects observed in the epidermal cell responses following puncture wounding of lzr15 mutants most likely arise secondarily to the persistent instability of the wound site. Discussion We established an epidermal wound healing assay in Drosophila larvae and elucidated the cellular events and genetic requirements of the healing process. Following puncture wounding, the damaged epidermal cells and their neighbors execute a series of responses that limit blood loss and restore integrity to the epidermis and overlying cuticle (see Figure 1N). Shortly after wounding, a plug forms in the wound gap. Over the next several hours, the outer portion of the plug melanizes to form a scab, and epidermal cells at the wound margin begin to elongate and orient toward the wound. They then fuse with each other to form a syncytium surrounding the wound. Subsequently, more-peripheral cells orient toward and fuse with the central syncytium. No proliferation of epidermal cells or actin cable formation was detected at the wound site. Instead, the epidermal cells surrounding the wound migrate along or through the plug to restore continuity of the epithelium and its basal lamina and cuticle lining. Each of these responses—scab formation, epidermal cell orientation and fusion, and epidermal spreading and reepithelialization—occurs at characteristic times and positions during wound healing. However, our results suggest that these responses are under separate genetic control and are not contingently coupled (Figure 8). Scab formation is dependent on crystal cells and is inhibited by the lzr15 and Bc mutations. Epidermal spreading and reepithelialization require bsk and JNK pathway activity, which is rapidly induced in epidermal cells surrounding the wound site. Epidermal cell orientation and fusion can proceed even in the absence of scab formation or JNK pathway activity. Although the different responses have distinct genetic requirements and can initiate independently of each other, we identified one important interaction between them. In lzr15 and Bc mutants, reepithelialization initiated but was not always completed, and the JNK pathway was hyperinduced, implying that the scab normally facilitates reepithelialization and restrains JNK activation. Figure 8 Model of the Cellular Events and Genetic Requirements of Larval Wound Healing Puncture wounding disrupts the epidermis and overlying cuticle and triggers the three parallel series of events shown, each with distinct genetic requirements. Plug and scab formation stabilize the wound site, which promotes epidermal cell spreading and suppresses JNK activation, perhaps by a negative feedback mechanism (dashed line). The lz and Bc genes promote scab formation, presumably by promoting crystal cell development and the production and secretion of serum melanization factors by these cells. The spreading epidermal cells synthesize cuticle and basal lamina, and they clear wound site debris by phagocytosis. Pinch wounding disrupts the epidermis but not the overlying cuticle and triggers only the events shown in black. However, cuticle and basal lamina synthesis and phagocytosis have not been examined in pinch wounds and are only inferred to occur from the puncture wound studies. Wounding may induce additional signals (not indicated) that attract blood cells (plasmatocytes) and tracheal branches. Below, we discuss the mechanisms and functions of each of these wound healing responses and the signals that trigger them, and suggest a mechanistic basis for the observed interaction between scab formation, reepithelialization, and JNK activation. We also compare wound healing in Drosophila with the related processes in mammals and speculate on their evolutionary relationship. Formation and Function of the Scab The wound plug that forms shortly after puncture wounding contains cell debris, and it may also contain blood coagulation products like those identified in other arthropods (Nakamura et al. 1976; Barwig 1985; Geng and Dunn 1988) and recently in Drosophila (Scherfer et al. 2004). Over the next few hours the plug rapidly darkens and becomes electron dense, presumably the result of a melanization reaction. Although the nature and extent of melanin cross-linking to tissues has not been studied, it seems likely that the polymer links to wound plug components and cuticle to strengthen and stabilize the wound site. Our results identify two important requirements for maturation of the plug and scab formation. One is crystal cells. The mutations lzr15 and Bc, which block crystal cell development or function, inhibited scab formation at puncture wound sites. The effect was particularly striking in lzr15 mutants: no scab was detected by light microscopy, and ultrastructural studies revealed only disorganized, amorphous debris where the scab normally forms. Because crystal cells are not commonly found at puncture wound sites (G. Fish, M. J. Galko, and M. A. Krasnow, unpublished data), these results support a model in which crystal cells promote scab formation by supplying serum factors such as prophenoloxidase that are necessary to form or consolidate the scab. The other critical requirement for scab formation is a breach spanning both the epidermis and cuticle. In both puncture and pinch wounds, the epidermal layer is disrupted, but only puncture wounds formed scabs. The most obvious difference between the two types of wounds is that the cuticle layer remains intact after pinch wounding. This leads us to propose that scab formation is initiated by a signal generated or liberated by cuticle rupture, or by contact between serum and ruptured cuticle or air. One consequence of this would be local activation of prophenoloxidase by serine proteases that are present as inactive zymogens in insect cuticle (Ashida and Brey 1995; Jiang et al. 1998). The scab appears to serve at least three functions in wound healing. One is to prevent exsanguination. Drosophila has an open circulatory system, so any rupture of both epidermis and cuticle will lead to blood loss. lzr15 mutants did not form scabs and survived poorly after puncture wounding; the few surviving larvae appeared flaccid, suggesting continued blood loss from the wound. Although the wound plug likely provides a temporary stop to bleeding, scab formation appears necessary to form a stable hemostatic barrier. Second, the scab likely serves an immune function, which may also enhance survival upon puncture wounding. The orthoquinone precursors of melanin are cytotoxic to microorganisms (Nappi and Ottaviani 2000) and may guard against infection even before the scab matures to form a physical barrier to microbe entry. The third function of the scab is to provide structural stability to the wound, which is critical for the next phase of wound healing, reepithelialization. This is inferred from the failure of reepithelialization following puncture wounding of lz mutants that are unable to form a normal scab. lz loss of function does not cause any intrinsic defect in reepithelialization, because reepithelialization of pinch wounds proceeded normally in the mutant. Also, the JNK pathway was activated in the wounded epidermis of lz mutant puncture wounds, and the cells at the wound margin appeared to initiate reepithelialization by extending processes into the wound gap. However, the epidermis did not always complete closure and seal the gap. These results suggest that when both epidermis and cuticle are disrupted, the scab is necessary to stabilize the wound gap to allow the epidermis to spread across and close it. In the absence of a scab, the JNK pathway is hyperinduced, epidermal cells at the wound margins separate from the overlying cuticle and extend abundant cytoplasmic processes, and a chronic wound ensues. Epidermal Cell Orientation and Fusion Two intriguing cellular responses during wound healing are the orientation of epidermal cells toward the wound site and their subsequent fusion to form a syncytium. During orientation, epidermal cells at the wound margin lengthen along the axis radial to the puncture site and contract along the axis circumferential to it, with the part of the cell closest to the wound contracting most, imparting a characteristic taper. These cells then fuse by joining their circumferentially-oriented plasma membrane domains and eliminating their radially-oriented membrane domains that contact neighboring cells. This implies that epidermal cells are able to sense their location with respect to the wound and organize their cytoskeleton and plasma membrane domains with respect to it. As wound healing proceeds, cell orientation and fusion typically spread to include more-peripheral cells, resulting in large syncytia with up to 30 nuclei at puncture wounds and smaller, scattered syncytia at pinch wounds. The occurrence of these responses in cells beyond the wound margin suggests that they are not a direct result of damage but rather are induced and oriented by a signal produced by wounding that can spread several cell diameters away from the wound. The function of epidermal cell orientation and fusion may be to fit more cells around the wound and help seal off the wound site by eliminating intercellular spaces. This may be similar to the fusion of mammalian macrophages into multinucleate giant cells as they surround and engulf large foreign bodies (Chambers 1977). Indeed, like macrophages, the fusing epidermal cells appear to be phagocytically active, engulfing debris at the wound site. Although the close temporal and spatial relationship between epidermal cell orientation and fusion suggests that these responses are likely to be coupled, mutants that specifically block each process will be required to determine if they are contingently coupled or just coordinated by a common upstream signal. Epidermal Spreading and Reepithelialization The most important cellular response for the long-term health of the animal is the restoration of epithelial integrity. However, spreading of the epithelium does not usually manifest until several hours after wounding. This allows time to induce the JNK pathway and activate the cell migration machinery in the epidermal cells, and to assemble a mature wound plug through or along which the cells move. Spreading appears to be an active process of epidermal cell migration, as no evidence of a purse-string closure mechanism or cell division was detected during spreading; instead, the earliest morphological manifestation of spreading was lamellipodial extensions, a hallmark of active cell migration, that course along and through the wound plug. Spreading likely requires a shift in the adhesion properties of epidermal cells from their normal tight association with the overlying cuticle to an affinity for the plug, and an ability to burrow through the plug. Spreading also requires a signal at the wound site that induces the JNK pathway in surrounding cells and activates the cell migration machinery. This must be a local signal emanating from the wound site that can influence cells up to seven cell diameters away. The activating signal might guide the migrations across the wound gap, or the cells might spread randomly along the matrix until their movement is arrested by contact inhibition. The main function of reepithelialization is to restore the normal barrier function of the epidermis. Indeed, the spreading epidermal cells ultimately secrete a thick layer of cuticle at their apical surface that displaces the scab, and they also supply the new basal lamina. The spreading cells also appear to play an important role in clearing wound site debris, as they were occasionally seen engulfing debris and often contained material resembling debris in phagosomes. Epidermal cells may share this scavenging role with plasmatocytes, circulating phagocytes recruited to wound sites after wounding (G. Fish, M. J. Galko, and M. A. Krasnow, unpublished data). Once reepithelialization is completed, debris is cleared, and the scab is sloughed or degraded, it is difficult to discern the old wound site by light microscopy. However, healing is not scar-free; the syncytium formed during healing persists and marks the wound site at least until metamorphosis begins. Occasionally, such syncytia are also seen in untreated larvae; these may be scars of naturally occurring wounds suffered earlier in larval life. The Wound as a Signaling Center The results suggest that there are multiple signals induced by wounding that control and coordinate the different events of larval wound healing: a signal that initiates formation of the wound plug and scab, one that orients surrounding epidermal cells and induces them to fuse, one that activates the JNK pathway and epidermal cell migration, and one dependent on scab formation that suppresses the JNK pathway. There may also be signals that attract plasmatocytes to combat infection and tracheal branches to increase wound oxygenation (M. J. Galko, unpublished data). These signals have distinct properties. One obvious difference is their range of activation around the wound. The signal that triggers plug and scab formation does so only at the epidermal and cuticular breach, whereas the JNK pathway activator influences cells up to seven cell diameters away. Some signals influence only the damaged cells and their neighbors, whereas others like the putative plasmatocyte and tracheal attractants must reach circulating cells and other tissues. Some of the signals are likely to be diffusible molecules released by damaged cells. These could be intracellular components such as uric acid, histones, or heat shock proteins, all of which have been shown to be released by necrotic mammalian cells and are implicated as intercellular signals (Ohashi et al. 2000; Li et al. 2001; Scaffidi et al. 2002; Shi et al. 2003). They could also be more conventional signaling molecules like the fibroblast growth factors secreted upon vertebrate wounding (Werner et al. 1992). Not all signals need be freely diffusible. Surface-bound signals could be sequentially propagated from one cell to the next, and some signals might be mechanical rather than chemical. Wounding appears to alter the tensile properties of the epidermis around the wound site (see Figure 6H), which could serve as a mechanical stimulus for some responses. This is an attractive idea for the control of JNK pathway activation, because changes in mechanical stress have been shown to activate JNK signaling in other cell types (Ingram et al. 2000; Kippenberger et al. 2000; Martineau and Gardiner 2001). Once reepithelialization is complete, tension could be restored, and signaling would diminish. Indeed, such a feedback circuit provides a plausible mechanistic basis for the inhibitory effect of scab formation on the JNK pathway (see Figure 8). In the absence of a scab, reepithelialization fails and tension is not restored, leaving the JNK pathway unconstrained. It is not obvious how many distinct signals are generated by wounding, because individual signals might regulate multiple responses. A high priority now is to molecularly identify the signals and the mechanisms by which they control and coordinate the wound healing responses. Comparison with Other Wound Healing Processes The healing of larval puncture wounds bears little resemblance to wound healing in the developing embryo, which occurs rapidly via actin cable assembly and filopodial extension by cells at the wound margin, and proceeds without scab formation (Kiehart et al. 2000; Wood et al. 2002). Despite the substantial structural differences between Drosophila and mammalian epidermis, embryonic wound healing appears similar to that in mammalian embryos, where it is also a rapid process involving actin cable formation but no apparent hemostatic or inflammatory response (Martin and Lewis 1992). Likewise, larval wound healing displays many similarities to postembryonic wound healing in mammals. Both processes commence with formation of a plug or clot that fills the wound gap. Both use the plug as a provisional substratum through which surrounding epidermal cells migrate. In both processes, the surrounding epidermal cells orient toward the wound site, become activated for migration, and spread through the plug in a similar manner—by extending lamellipodia and then their cell bodies into the plug until epidermal continuity is reestablished. The cells then redifferentiate to restore epidermal morphology. In addition, inflammatory cells are recruited to the wound in both processes, and the plug is remodeled to form a scab that is degraded or sloughed when repair and redifferentiation are complete. Despite these general similarities, there are many specific differences between each parallel step in Drosophila and mammals. For example, the composition of the Drosophila plug and the mammalian clot probably differ, because clotting mechanisms in arthropods involve proteolytic cascades similar to those in mammals but different coagulogens (Nakamura et al. 1976; Barwig 1985; Geng and Dunn 1988; Scherfer et al. 2004). Also, Drosophila epidermal cells near the wound do not proliferate during reepithelialization as do their mammalian counterparts (Martin 1997). The cells surrounding a Drosophila wound fuse to form a syncytium, whereas mammalian epidermal cells remain distinct but dynamically rearrange their junctions with neighboring cells as they spread. Spreading Drosophila cells carry a basal lamina with them, whereas migrating mammalian epidermal cells detach from the basal lamina (Odland and Ross 1968; Clark et al. 1982). The most important difference may be the extent of cell recruitment to the wound site and subsequent remodeling of the plug, which are substantial in mammals but limited in Drosophila. Evolution of the Wound Healing Response The similarities between Drosophila and mammalian wound healing responses prompt the question of whether these are homologous processes or the result of convergent evolution. Because there would likely have been strong selective pressure early in evolution for a wound healing response, we favor the idea that wound healing is an ancient process that evolved before the divergence of flies and mammals and subsequently diversified. Indeed, the parallels in the embryonic and postembryonic processes suggest that distinct embryonic and postembryonic wound healing mechanisms were already in place at the time of divergence. If this evolutionary hypothesis is correct, then there should still be common molecular manifestations in Drosophila and mammals of the ancestral processes. Actin cable formation in embryonic wound healing may be one such manifestation, and the induction of JNK signaling pathways and their involvement in reepithelialization of postembryonic wounds may be another (Ramet et al. 2002; Li et al. 2003). Others may become apparent once the wound healing processes have been genetically dissected. The wound healing process described here, with its simple tissue architecture, streamlined response, and accessible genetics, provides a tractable system for identifying additional genes and fundamental mechanisms of wound healing. Materials and Methods Fly strains and genetics The mutant lz r15 is a molecular null allele (Daga et al. 1996). Bc1 is a dominant mutation that was used in the homozygous condition (Rizki et al. 1980). The msn-lacZ allele was l(3)06946 (Spradling et al. 1999) and the puc-lacZ allele was l(3)A251.1 (Martin-Blanco et al. 1998); both are P[lacZ, rosy+] enhancer trap insertions in the respective loci that express a nuclear β-galactosidase; heterozygotes were used to monitor reporter activity. For analysis of msn-lacZ reporter activity in the lzr15 mutant background, lzr15, FRT18E/Y; msn-lacZ/+ hemizygous male larvae were compared to lzr15, FRT18E/white [w]1118; msn-lacZ/+ heterozygous female siblings; similar comparisons were made for the puc-lacZ reporter. w 1118 was used as a control strain because most of the other strains employed carried a background w– mutation. The Gal4/UAS system (Brand and Perrimon 1993) was used for protein misexpression. The A58-Gal4 driver expresses the yeast Gal4 transcription factor throughout the larval epidermis beginning in L1 (A. Ghabrial, M. J. Galko, and M. A. Krasnow, unpublished data); e22c-Gal4 (Lawrence et al. 1995) and 69B-Gal4 (Brand and Perrimon 1993) express Gal4 throughout the embryonic epidermis. UAS-GFP-actin (Verkhusha et al. 1999) was driven by A58-Gal4 to visualize actin dynamics within the larval epidermis. UAS-GFP.nls (Shiga et al. 1996) expresses a nuclear-localized GFP. UAS-bskDN (Adachi-Yamada et al. 1999), UAS-puc (Martin-Blanco 1998), UAS-Jra.bZip (Kockel et al. 1997), and UAS-kayak.bZip (Zeitlinger et al. 1997) express different JNK pathway inhibitors. When crossed to the e22c-Gal4 or 69B-Gal4 drivers, only UAS-bskDN and UAS-puc gave a strong dorsal closure defect like JNK pathway mutants. To express BasketDN in larval epidermis, female w1118, UAS-bskDN, UAS-bskDN/w1118; msn-lacZ, A58-Gal4 /+ larvae (and sibling males lacking the w1118 chromosome) were used. To express Puckered, w1118; UAS-puc/ msn-lacZ, A58-Gal4 larvae were used. Larvae of the same genotypes but lacking A58-Gal4 served as controls. Wounding assays Animals were reared on standard cornmeal-dextrose fly media at 25 °C. L3 larvae were rinsed with water, lightly anesthetized with ether, and then visualized under a stereomicroscope and impaled with a 0.1-mm steel needle (Fine Science Tools, Foster City, California, United States) at the dorsal midline between the hair stripes of abdominal segment A3 or A4. Typically, the needle pierced through the larva but only the entry wound was analyzed. After wounding, larvae were rinsed and returned to fly media in 1-dram vials and cultured at 25 °C. For experiments depicted in Figure 7, care was taken to select both larvae and wounding pins of uniform size because larval survival following wounding is significantly influenced by these variables. For pinch wounds, L3 larvae prepared as above were pinched with #5 dissecting forceps (Fine Science Tools) at midbody on the dorsal side for approximately 10 s and then cultured as above. Mock-wounded control larvae were prepared and cultured as above, except that needle impalement and pinching were omitted. Incisional wounds were not analyzed because incision caused early L3 larvae to burst and die. TEM Larvae were dissected at 4 °C in EM fixative (3% glutaraldehyde, 2% paraformaldehyde, and 2.5% dimethylsulfoxide in 0.2 M sodium phosphate buffer [pH 7.2]) and pinned ventral side up on a Sylgard (Dow Corning, Midland, Michigan, United States) surface. A ventral incision along the length of the animal was made with dissecting scissors, and the four corners of the epidermis were stretched with forceps and pinned to the surface. Internal tissues were removed, and the epidermis was fixed an additional 15 min at room temperature and then trimmed to a flat piece of epidermis surrounding the wound. Tissue samples were incubated for 1 h at 4 °C in 1% osmium tetroxide, stained overnight at 4 °C in 0.5% uranyl acetate, dehydrated through a graded series of ethanol concentrations and propylene oxide, and embedded in EMbed 812 (Electron Microscope Sciences, Hatfield, Pennsylvania, United States) with N, N-dimethylbenzylamine, which was polymerized overnight at 55 °C. Transverse sections (75–90 nm) were cut through the wound site with a Leica Ultracut ultramicrotome (Leica, Wetzlar, Germany) and collected on formvar/carbon-coated 75 mesh copper grids and stained for 20 s in supersaturated uranyl acetate:acetone (1:1) followed by 0.2% lead citrate for 3–4 min. Specimens were observed with an 80-kV beam on a JEOL TEM-1230 microscope (JEOL, Peabody, Massachusetts, United States), and images were captured on a Gatan Multiscan 791 digital camera (Gatan, Pleasanton, California, United States). For TEM analysis of lzr15 mutants, there was no scab to mark the wound site, so lzr15, FRT18E/Y; msn-lacZ/+ larvae were used and stained with X-gal (5-bromo-4-chloro-3-indolyl--D-galactopyranoside) (see below) to locate the wound. Wounded larvae were dissected in phosphate-buffered saline (PBS), fixed in 2% glutaraldehyde for 15 min at room temperature, and stained with X-gal as described below, except the staining solution lacked Triton X-100 and contained 20 mM K4[FeII(CN)6], 20 mM K3[FeIII(CN)6], 2 mM MgCl2, and 0.2% X-gal in PBS. The propylene oxide dehydration steps during TEM sample preparation were omitted to preserve the X-gal reaction product. Histochemistry and immunohistochemistry For β-galactosidase histochemistry, larvae carrying lacZ transgenes were dissected open in PBS, fixed for 15 min at room temperature with cold 2% glutaraldehyde, rinsed with PBS, and then stained at room temperature for 6 h (puc-lacZ) or 2 h (msn-lacZ) in 150 mM NaCl, 10 mM Na2HPO4, 3 mM K4[FeII(CN)6], 3 mM K3[FeIII(CN)6], 1 mM MgCl2, 0.1% Triton X-100, and 0.2% X-gal. For immunostaining, primary antibodies were anti-Coracle monoclonal antibodies 9C and C61516B (Fehon et al. 1994) (1:500 dilution), anti-Fasciclin III monoclonal antibody 7G10 (Patel et al. 1987) (1:50 dilution), and rabbit anti-β-galactosidase serum (Roche, Basel, Switzerland) (1:150 dilution) preadsorbed against Drosophila embryos. Secondary antibodies (Jackson Immunoresearch, West Grove, Pennsylvania, United States) were goat anti-mouse IgG-Cy3 (1:1000 dilution) and goat anti-rabbit IgG-FITC (1:300 dilution). Samples were blocked in PHT buffer (Ca++/Mg++-free PBS containing 1% heat-inactivated normal goat serum and 0.3% Triton X-100) for 1 h or more and then incubated overnight at 4 °C with primary antibody diluted in PHT. Samples were washed with fresh PHT at least six times for 1 h at room temperature, incubated overnight at 4 °C with secondary antibody, and washed as before. Samples were mounted in 70% (v/v) glycerol or Vectashield (Vector Laboratories, Burlingame, California, United States) mounting medium and observed with a Bio-Rad confocal microscope (Bio-Rad, Hercules, California, United States). Cy3 and FITC channels were sequentially excited and captured for each specimen; a Z-series of optical sections was collected and merged to avoid loss of out-of-plane information due to tissue wrinkling. Supporting Information Accession numbers The GenBank (http://www.ncbi.nlm.nih.gov/) accession numbers for the genes discussed in this paper are bsk (NM_164901), Jra (NM_165739), kay (NM_170427), lz (NM_078544), msn (NM_079940), and puc (NM_079549). We are grateful to Greg Fish for expert technical assistance and to John Perrino of the Beckman Center Cell Sciences Imaging Facility for assistance with TEM analysis. We thank Amin Ghabrial, Joseph Lipsick, Dirk Bohmann, Marek Mlodzik, Richard Fehon, the Bloomington Drosophila Stock Center, and the Developmental Studies Hybridoma Bank for fly stocks and antisera. We thank Marc Dionne, Ranjiv Khush, David Schneider, and members of the Krasnow lab for advice and for comments on the manuscript. MJG was supported by an American Heart Association postdoctoral fellowship and a Beckman Scholar Award. MAK is an investigator of the Howard Hughes Medical Institute. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. MJG and MAK conceived the experiments. MJG designed and performed the experiments. MJG and MAK analyzed the data, contributed reagents/materials/analysis tools, and wrote the paper. Academic Editor: Alfonso Martinez Arias, Cambridge University Citation: Galko MJ, Krasnow MA (2004) Cellular and genetic analysis of wound healing in Drosophila larvae. PLoS Biol 2(8): e239. Abbreviations Bc Black cells bsk basket DNdominant negative GFPgreen fluorescent protein JNKJun N-terminal kinase L[number]larval instar [number] lz lozenge msn misshapen PBSphosphate-buffered saline puc puckered TEMtransmission electron microscopy UASupstream activation sequence w white ==== Refs References Adachi-Yamada T Nakamura M Irie K Tomoyasu Y Sano Y p38 mitogen-activated protein kinase can be involved in transforming growth factor beta superfamily signal transduction in Drosophila wing morphogenesis Mol Cell Biol 1999 19 2322 2329 10022918 Alster TS Tanzi EL Hypertrophic scars and keloids: Etiology and management Am J Clin Dermatol 2003 4 235 243 12680802 Ashcroft GS Yang X Glick AB Weinstein M Letterio JL Mice lacking Smad3 show accelerated wound healing and an impaired local inflammatory response Nat Cell Biol 1999 1 260 266 10559937 Ashida M Brey PT Role of the integument in insect defense: Pro-phenol oxidase cascade in the cuticular matrix Proc Natl Acad Sci U S A 1995 92 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Insect ultrastructure 1984 New York Plenum 579 604 Rizki TM Rizki RM Grell EH A mutant affecting the crystal cells in Drosophila melanogaster Rouxs Arch Dev Biol 1980 188 91 99 Romer J Bugge TH Pyke C Lund LR Flick MJ Impaired wound healing in mice with a disrupted plasminogen gene Nat Med 1996 2 287 292 8612226 Scaffidi P Misteli T Bianchi ME Release of chromatin protein HMGB1 by necrotic cells triggers inflammation Nature 2002 418 191 195 12110890 Scherfer C Karlsson C Loseva O Bidla G Goto A Isolation and characterization of hemolymph clotting factors in Drosophila melanogaster by a pullout method Curr Biol 2004 14 625 629 15062105 Shi Y Evans JE Rock KL Molecular identification of a danger signal that alerts the immune system to dying cells Nature 2003 425 516 521 14520412 Shiga Y Tanaka-Matakatsu M Hayashi S A nuclear GFP/beta-galactosidase fusion protein as a marker for morphogenesis in living Drosophila Dev Growth Differ 1996 38 99 106 Singer AJ Clark RA Cutaneous wound healing N Engl J Med 1999 341 738 746 10471461 Sluss HK Han Z Barrett T Davis RJ Ip YT A JNK signal transduction pathway that mediates morphogenesis and an immune response in Drosophila Genes Dev 1996 10 2745 2758 8946915 Spradling AC Stern D Beaton A Rhem EJ Laverty T The Berkeley Drosophila Genome Project gene disruption project: Single P-element insertions mutating 25% of vital Drosophila genes Genetics 1999 153 135 177 10471706 Su YC Treisman JE Skolnik EY The Drosophila Ste20-related kinase misshapen is required for embryonic dorsal closure and acts through a JNK MAPK module on an evolutionarily conserved signaling pathway Genes Dev 1998 12 2371 2380 9694801 Truby PR Separation of wound healing from regeneration in the cockroach leg J Embryol Exp Morphol 1985 85 177 190 3989448 Verkhusha VV Tsukita S Oda H Actin dynamics in lammelipodia of migrating border cells in the Drosophila ovary revealed by a GFP-actin fusion protein FEBS Lett 1999 445 395 401 10094496 Verrier B Muller D Bravo R Muller R Wounding a fibroblast monolayer results in the rapid induction of the c-fos proto-oncogene EMBO J 1986 5 913 917 3522222 Werner S Peters KG Longaker MT Fuller-Pace F Banda MJ Large induction of keratinocyte growth factor expression in the dermis during wound healing Proc Natl Acad Sci U S A 1992 89 6896 6900 1379725 Werner S Smola H Liao X Longaker MT Krieg T The function of KGF in morphogenesis of epithelium and reepithelialization of wounds Science 1994 266 819 822 7973639 Wigglesworth VB Wound healing in an insect (Rhodnius prolixus Hemiptera) J Exp Biol 1937 14 364 381 Wood W Jacinto A Grose R Woolner S Gale J Wound healing recapitulates morphogenesis in Drosophila embryos Nat Cell Biol 2002 4 907 912 12402048 Wright TR The genetics of biogenic amine metabolism, sclerotization, and melanization in Drosophila melanogaster Adv Genet 1987 24 127 222 3124532 Zeitlinger J Kockel L Peverali FA Jackson DB Mlodzik M Defective dorsal closure and loss of epidermal decapentaplegic expression in Drosophila fos mutants EMBO J 1997 16 7393 7401 9405368

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020244Research ArticleCell BiologyDevelopmentGenetics/Genomics/Gene TherapyDanio (Zebrafish)An Integrin-Dependent Role of Pouch Endoderm in Hyoid Cartilage Development Integrin and Pouch Pattern Hyoid CartilageCrump Justin Gage [email protected] 1 Swartz Mary E 1 Kimmel Charles B 1 1Institute of Neuroscience, University of OregonEugene, OregonUnited States of America9 2004 20 7 2004 20 7 2004 2 9 e24423 2 2004 1 6 2004 Copyright: © 2004 Crump et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Patterning the Face Pharyngeal endoderm is essential for and can reprogram development of the head skeleton. Here we investigate the roles of specific endodermal structures in regulating craniofacial development. We have isolated an integrinα5 mutant in zebrafish that has region-specific losses of facial cartilages derived from hyoid neural crest cells. In addition, the cranial muscles that normally attach to the affected cartilage region and their associated nerve are secondarily reduced in integrinα5− animals. Earlier in development, integrinα5 mutants also have specific defects in the formation of the first pouch, an outpocketing of the pharyngeal endoderm. By fate mapping, we show that the cartilage regions that are lost in integrinα5 mutants develop from neural crest cells directly adjacent to the first pouch in wild-type animals. Furthermore, we demonstrate that Integrinα5 functions in the endoderm to control pouch formation and cartilage development. Time-lapse recordings suggest that the first pouch promotes region-specific cartilage development by regulating the local compaction and survival of skeletogenic neural crest cells. Thus, our results reveal a hierarchy of tissue interactions, at the top of which is the first endodermal pouch, which locally coordinates the development of multiple tissues in a specific region of the vertebrate face. Lastly, we discuss the implications of a mosaic assembly of the facial skeleton for the evolution of ray-finned fish. A developmental mutant of the zebrafish is helping to uncover the processes by which the facial skeleton is patterned in vertebrates ==== Body Introduction The skeletal elements that form and support the vertebrate jaw and gills are derived from a specialized population of ectomesenchyme cells, the cranial neural crest (Platt 1893; Le Douarin 1982; Schilling and Kimmel 1994; but see Weston et al. 2004). In the larval zebrafish, crest cells of the first, or mandibular, arch give rise to Meckel's and palatoquadrate cartilages that constitute the lower and upper jaws, respectively. Several cartilages are derived from the second, or hyoid, arch, including the ceratohyal (CH) and hyosymplectic (HS) cartilages that support the jaw. In particular, the HS cartilage serves to connect the upper jaw to the skull by means of a hyomandibula (HM) plate and a symplectic (SY) anterior rod-like extension. In addition, the HM plate supports the overlying opercular apparatus that helps to ventilate the gills (Hughes and Shelton 1958). The tetrapod stapes is homologous to HM. During vertebrate development, cranial neural crest cells delaminate from near the dorsal neural primordium and migrate to ventrolateral positions where they populate a series of pharyngeal arches (reviewed in Le Douarin 1982). Once the pharyngeal arches are established, skeletal elements develop from cylinders of neural crest whose mesodermal cores undergo stereotypic divisions to form the cranial muscles (Edgeworth 1935; Schilling and Kimmel 1997; Kimmel et al. 2001). The segmentally organized pharyngeal arches are separated from one another by reiterative outpocketings of the pharyngeal endoderm called pouches. Recent work in chicken has demonstrated an important role for endoderm in patterning cartilages of all the pharyngeal arches (Couly et al. 2002; Ruhin et al. 2003). Using grafting and ablation experiments, these researchers divided the pharyngeal endoderm into anterior-posterior (A-P) and mediolateral domains that are required for and have the ability to induce segment-specific pharyngeal cartilages. In zebrafish, experiments have demonstrated a genetic requirement for endoderm in pharyngeal cartilage development. casanova mutant embryos make no endoderm and pharyngeal cartilages fail to form, and transplantation experiments show that wild-type endoderm is sufficient to rescue cartilage development (Alexander et al. 1999; David et al. 2002). In addition, a role for pharyngeal pouches in segmentation and survival of cranial neural crest has been shown. In zebrafish tbx1 (vgo) mutant embryos, most pouches fail to develop and posterior pharyngeal cartilages are reduced and fused together (Piotrowski and Nusslein-Volhard 2000; Piotrowski et al. 2003). However, though endoderm is clearly critical for pharyngeal cartilage development, it is not well understood how interactions between neural crest–derived cells and endoderm produce segment-specific patterns of cartilage. In this work, we isolate and characterize a zebrafish integrinα5 loss-of-function mutant. Integrins are a family of heterodimeric receptors, composed of α and β subunits, that bind to ligands in the extracellular matrix such as fibronectin and laminin. Integrins have structural roles in adhesion that promote tissue integrity and cell migration, and signaling functions important for cell differentiation and survival (reviewed in Bokel and Brown 2002). Various studies in mouse and chick have shown a role for integrins and their ligands in neural crest migration. Integrins α4, α1, and αV are expressed early in neural crest development, and function-blocking antibodies against these integrins perturb crest migration in vitro (Delannet et al. 1994; Desban and Duband 1997; Kil et al. 1998; Testaz and Duband 2001). The in vivo roles of specific integrins in neural crest development are less clear (Yang et al. 1995; Gardner et al. 1996; Bader et al. 1998). Mice lacking Integrinα5, which in complex with primarily β1 forms the major fibronectin receptor, die early during embryogenesis because of mesodermal defects (Yang et al. 1993, 1999). Analysis of integrinα5−/− mouse embryos showed that Integrinα5 is required for the survival of a subset of hyoid crest (Goh et al. 1997). However, it was not known where Integrinα5 functions to control hyoid crest development. Here we report that, in zebrafish, Integrinα5 functions in the pharyngeal endoderm to control hyoid crest development. In integrinα5− embryos, the first pharyngeal pouch fails to develop, and the lack of a first pouch correlates with reductions in specific regions of the HS cartilage. integrinα5 mutants also have defects in a subset of dorsal first and second arch muscles and facial motor nerve VII, suggesting that Integrinα5 is required for region-specific development of multiple pharyngeal tissues. However, both expression and penetrance data suggest that the muscle and nerve defects are likely secondary to the cartilage and pouch defects. integrinα5 is expressed in pharyngeal endoderm during pouch formation and is required in endoderm for both first pouch and hyoid cartilage development. In order to understand the remarkable specificity of the integrinα5− cartilage phenotype, we fate mapped regions of the HS cartilage in the hyoid arch. We found that the regions of the HS cartilage that are lost in integrinα5 mutants develop from anterior crest–derived cells immediately adjacent to the first pouch. Analysis of integrinα5 mutants suggests that the first endodermal pouch specifies a portion of the hyoid cartilage pattern by locally stabilizing hyoid crest. Lastly, we present a model in which new local interactions of endodermal structures with hyoid crest underlie the elaboration of the jaw support apparatus during the evolution of ray-finned fish. Results Isolation of a Mutation in Zebrafish integrinα5 In a genetic screen for zebrafish with altered pharyngeal cartilages, we isolated a single recessive mutant allele, b926, with specific defects in the hyoid cartilage pattern. Using polymorphism mapping, we placed b926 on Linkage Group (LG) 23 between the markers Z5141 and Z20492, a region containing a zebrafish integrinα5 homolog (Figure 1A). We performed reverse transcription polymerase chain reaction (RT-PCR) to obtain a full-length cDNA encoding a protein product with 54% identity to mouse Integrinα5. Sequencing of integrinα5 in b926 revealed a T to A nucleotide substitution that segregated with the mutant phenotype. The b926 mutation converts a conserved tyrosine residue to an asparagine in the third beta-propeller repeat of the extracellular domain, a region known to be important for ligand binding (Figure 1B and 1C) (Springer 1997; Mould et al. 2000). In addition, a morpholino designed against the exon13-intron splice site of integrinα5 (integrinα5-MO; Figure 1D) phenocopies both the cartilage and first pouch defects of b926. We confirmed by RT-PCR that itga5-MO effectively inhibits the splicing of integrinα5 (Figure 1E and 1F). We conclude that b926 is a loss-of-function mutation in zebrafish integrinα5. Figure 1 Identification of a Zebrafish integrinα5 Mutant (A) Based on the hyoid cartilage phenotype the b926 allele was mapped to LG23. Using polymorphism mapping, we placed b926 between the markers Z5141 (2/362 recombinants/meioses) and Z20494 (3/362 recombinants/meioses). Databases of the partial zebrafish genomic sequence revealed that a homolog of integrinα5 mapped to this region. (B) Zebrafish Integrinα5 protein is predicted to have seven extracellular beta-propeller repeats (stippled), a transmembrane domain, and a short intracellular cytoplasmic tail. Integrinα5 forms heterodimeric complexes with Integrin β chains, primarily β1, and binds extracellular matrix ligands containing the RGD motif. Sequencing of integrinα5 in b926 revealed a T to A nucleotide substitution at position 952 of the cDNA that converts a tyrosine to an asparagine at amino acid 218, a residue within the third beta-propeller repeat. (C) Alignment of the third beta-propeller repeat of the Integrinα5 proteins of zebrafish, human, mouse, Xenopus, and Fugu. Y218 is absolutely conserved among all five species and is mutated to N in b926. (D) The genomic locus of integrinα5 consists of 30 exons (not drawn to scale). The integrinα5 morpholino, itga5-MO, was designed against the exon13-intron splice site (arrow). (E) The primers GC145 and GC147 were used to amplify a 497-bp fragment from wild-type cDNA. PCR amplification was then performed using cDNA from 24-hpf embryos that had been injected with 10 ng of itga5-MO at the one-cell stage, the same concentration of morpholino used for our phenotypic analysis. The resultant product was 587 bp long, and sequencing confirmed that the increased length was due to the failure to splice out the intron between exons 13 and 14. The left lane of the agarose gel contains standard size markers in basepairs. (F) A schematic of the wild-type Integrinα5 protein shows seven extracellular repeats and the transmembrane (TM) domain. Inhibition of splicing by itga5-MO would be predicted to result in a nonfunctional protein that is truncated in the seventh repeat of the extracellular domain. Region-Specific Pharyngeal Defects in integrinα5 Mutants integrinα5 mutant zebrafish showed partially penetrant and variably expressive losses of specific hyoid cartilage regions at 4 d (Figure 2; Table 1). The most frequent phenotype seen in integrinα5− animals was a specific loss of the anterior half of the HM plate (anterior HM [aHM]) (Figure 2B). In other integrinα5 mutants, we saw variable reductions of the SY element in addition to aHM (Figure 2C). In what we interpret as the most severe integrinα5 (b926) phenotype, HS was reduced to a rod that variably fused with CH; rarely, the first arch joint was fused as well (Figure 2D). However, even in the most severe class of integrinα5 mutants, the posterior half of HM (posterior HM [pHM]), the CH, and the hyoid opercle bone remained unaffected. In addition, integrinα5 mutants had variably reduced numbers of ceratobranchial (CB) cartilages and rare fusions of adjacent CBs (Figure 2G; Table 1). Animals injected with itga5-MO displayed a similar range and spectrum of cartilage phenotypes (Figure 2E; Table 1). Figure 2 Region-Specific Pharyngeal Defects in integrinα5 Mutants (A–E) Flat mount dissections of hyoid and mandibular cartilages from fixed, 4-d-old wild-type (A), integrinα5− (B–D), and itga5-MO (E) animals. Meckel's (M) and palatoquadrate (PQ) cartilages are derived from the mandibular arch (1), and CH, SY, and HM cartilages and the opercle (Op) bone are derived from the hyoid arch (2). A phenotypic series (B–D) shows that the anterior half of HM (arrows) is absent and SY is progressively reduced in integrinα5− animals. Rarely, mandibular and hyoid joints are also missing in integrinα5− animals (asterisks in D). (E) Animals treated with itga5-MO display similar reductions of HM (arrow) and SY. (F and G) Flat-mount dissections of the pharyngeal cartilages of 4-d-old wild-type (F) and integrinα5− (G) animals. In addition to the mandibular and hyoid cartilages, the five CB cartilages (CB1–CB5) that are derived from the third through seventh arches are shown. Note the teeth on CB5 (dots in F). In integrinα5− embryos we see rare fusions of CB cartilages (arrow in G). (H–J) Confocal micrographs of the pharyngeal arches of wild-type fli1-GFP (H) and integrinα5−; fli1-GFP (I and J) embryos stained with anti-GFP and Zn8 antibodies at 38 hpf. Neural crest cells of the pharyngeal arches are labeled with fli1-GFP (green, numbered in [H]), and the pharyngeal pouches are labeled by the Zn8 antibody (red, numbered p1–p5 in [H]). In integrinα5−; fli1-GFP embryos, the first pouch is absent or very reduced at 38 hpf (arrows in I and J). Less frequently, we also see reductions in more posterior pouches in integrinα5−; fli1-GFP embryos (arrowhead in J shows a single endodermal mass where p3–p5 would be in wild-type embryos). The Zn8 antibody also recognizes cranial sensory ganglia (dots). (K and L) In situ hybridizations of wild-type (K) and integrinα5− (L) embryos stained with the pharyngeal pouch marker pea3 at 36 hpf (arrowhead denotes first pouch). The first pouch of integrinα5− embryos is very reduced, but still expresses pea3. Sensory ganglia also stain with pea3 (dots). (M and N) Cranial muscles of 4-d-old wild-type fli1-GFP (M) and integrinα5−; fli1-GFP (N) embryos stained with MF20 antibody. Mandibular muscles (intermandibularis posterior [imp], adductor mandibulae [am], levator arcus palatine [lap], and do) and hyoid muscles (interhyal , hyohyal [hh], ah, ao, and lo) are labeled in wild-type. integrinα5− embryos have a selective reduction of do and ah muscles (arrow in [N]). Confocal projections of integrinα5− animals did not include ocular muscles (asterisks in M). (O and P) Cranial motor nerves of wild-type islet1-GFP (O) and integrinα5−; islet1-GFP (P) live embryos at 54 hpf. islet1-GFP-expressing cranial motor neurons innervate muscles of the pharyngeal arches with the following strict segmental correspondence: trigeminal (V)—mandibular; facial (VII)—hyoid; glossopharyngeal (IX)—third; and vagus (X)—fourth through seventh. In integrinα5−; islet1-GFP embryos, facial nerve VII (arrowhead in P) is reduced and/or fails to branch. (Q and R) Summary of integrinα5 regional pharyngeal defects extrapolated to a 4-d-old embryo and color-coded for cartilage (blue), muscle (red), and nerve (green). Shown in black are the eye (filled circle within larger circle), ear (two dots within oval), and opercle bone (mushroom). In wild-type animals, facial nerve VII innervates and passes by do and ah muscles that are in close association with the aHM cartilage region (enlarged in Q′). In integrinα5 mutants, we see specific reductions of the first pouch (not shown), the aHM cartilage region, do and ah muscles, and facial nerve VII (enlarged in R′). Scale bars: 50 μm. Table 1 Pharyngeal Defects in Animals Reduced for integrinα5 Percentage of sides with each phenotype is listed for integrinα5−(b926); fli1-GFP animals and fli1-GFP animals injected with 10 ng of itga5-MO. In 100 wild-type animals, none of these phenotypes was seen. For cartilage defects, percentage of mutant clutch showing any hyoid cartilage defect is listed. Hyoid cartilage defects are divided into categories based on the morphology of the HS element: HS rod-shaped with SY extension (“aHM lost”), HS rod-shaped without SY extension (“aHM and SY lost”), and HS rod-shaped and fused to CH (“losses and fusion to CH”). Average number of CB cartilages per side is listed; wild-type animals invariably have five CBs per side. Fusions of CBs in mutant clutches were rare and not quantified. Pharyngeal endoderm defects were scored on a fluorescence dissecting microscope as percentage of mutant clutch displaying any pouch defect at 34 hpf. Pouch defects were divided into two classes based on whether more posterior pouches were lost in addition to the first pouch. Cranial muscle defects were scored as percentage of mutant clutch with reductions of do and ah muscles. In addition, we found five animals from integrinα5; islet1-GFP mutant clutches (n = 200) with defects in the facial (VII) motor nerve. Animals with potential nerve defects were identified in the dissecting microscope and subsequently confirmed on the confocal microscope. As such, we cannot give an absolute percentage of animals with nerve defects. All animals with muscle and nerve defects also had hyoid cartilage defects. In addition to the phenotypes listed, some integrinα5 mutants developed heart edema and had smaller eyes by 4 d. Animals injected with higher doses of itga5-MO (15 ng) also developed severe heart edema, had shorter bodies, and often stained poorly for cartilage In addition to cartilage defects, we found partially penetrant reductions of the first pharyngeal pouch, an endodermal outpocketing, in integrinα5 mutants (Figure 2I and 2J). Loss of the first pouch was apparent as early as 24 hours post fertilization (hpf), and similar first pouch reductions were seen in animals injected with itga5-MO (data not shown; Table 1). Although the majority of integrinα5− embryos showed defects restricted to the first pouch, a few integrinα5− embryos had graded reductions of more posterior pouches as well (Figure 2J; Table 1). In order to examine whether the reduced first pouches in integrinα5− embryos retained pouch identity, we examined expression of the pharyngeal pouch marker pea3, a downstream effector of Fgf signaling (Figure 2K) (Roehl and Nusslein-Volhard 2001). We found that the reduced first pouches of integrinα5 mutants still expressed pea3 at 36 hpf (Figure 2L). As both the pouch and cartilage phenotypes were incompletely penetrant in integrinα5 mutants, often with one side of an embryo showing defects and the other side not, we examined how tightly coupled the phenotypes were in individual sides. In order to visualize early pharyngeal arch structure in live animals, we made use of a fli1–green fluorescent protein (GFP) transgenic line (Lawson and Weinstein 2002). fli1-GFP expression initiates in neural crest cells of the pharyngeal arches shortly after crest migration (ca. 18 hpf) and persists as pharyngeal cartilages and bones develop. Pouches are evident as nonexpressing regions between the GFP-labeled crest-derived cells of the arches. We sorted live integrinα5− animals for first pouch defects at 36 hpf and then grew these animals to 4 d to analyze pharyngeal cartilages. Strikingly, we found a strong correlation between reductions of the first pouch and later hyoid cartilage defects in integrinα5 mutants. integrinα5− sides that lacked the first pouch early had hyoid cartilage defects 93% of the time (n = 116), whereas only 7% of integrinα5− sides with a normal first pouch early developed hyoid cartilage defects (n = 108). The correlation was highly significant (p < 0.0001). We next asked whether Integrinα5 was required for the development of cranial muscles. By 4 d of development, the mesoderm of the first pharyngeal arch has undergone stereotypic subdivisions to form, ventrally, intermandibularis anterior and intermandibularis posterior; medially, adductor mandibulae; and, dorsally, levator arcus palatini and dilatator operculi (do) cranial muscles. Second arch mesoderm gives rise to interhyal and hyohyal muscles, ventrally, and adductor hyomandibulae (ah), adductor operculi (ao), and levator operculi (lo) muscles, dorsally (Edgeworth 1935; Schilling et al. 1997; Figure 2M). In a few integrinα5 mutants, the dorsal first and second arch muscles, do and ah, were selectively reduced, whereas levator arcus palatini, ao, and lo were present but appeared closer together (Figure 2N; Table 1). The muscles that were disrupted in integrinα5 mutants correspond to those that associate most closely with the aHM cartilage (schematized in Figure 2Q and 2R). Lastly, we found that integrinα5 mutants had specific defects in the nerve that innervates second arch muscles. Facial motor neurons send a ventral-directed nerve (VII) that innervates dorsal second arch muscles ah and ao, passes through the foramen of HM, and subsequently branches to innervate ventral muscles interhyal and hyohyal (Higashijima et al. 2000; Figure 2O). In a small fraction of integrinα5 mutants, facial nerve VII failed to branch and/or was hypotrophic (Figure 2P). Both nerve and muscle defects were only seen in integrinα5 mutants that also displayed cartilage defects. Moreover, the lower penetrance of nerve and muscle defects suggests that they are secondary to endodermal and/or crest defects. In conclusion, we have found a requirement for Integrinα5 in the development of multiple pharyngeal tissues in the vicinity of the first pouch and aHM cartilage region. integrinα5 Is Expressed Dynamically in Pharyngeal Endoderm and Cranial Neural Crest In order to understand where Integrinα5 might be acting to control pharyngeal arch development, we examined the expression of integrinα5 mRNA by in situ hybridization. The integrinα5 expression domains were complex and dynamic, and we do not give an exhaustive description of domains other than the pharynx here. We observed integrinα5 expression as early as the 32-cell stage, indicating a maternal integrinα5 contribution (Figure 3A). Maternal expression of integrinα5 also has been reported in frog (Whittaker and DeSimone 1993). By gastrulation stage (60% epiboly), the mesendoderm broadly expressed integrinα5 (Figure 3B). At the 1-somite (s) stage, integrinα5 expression was in ectoderm adjacent to the anterior neural plate, a domain consistent with cranial neural crest and placode precursors (Figure 3C). In addition, we saw strong expression in the first somite and posterior mesoderm and weaker expression in scattered, large cells lateral and anterior to the notochord that we interpret as early endoderm (Warga and Nusslein-Volhard 1999). By 5-s stages, cranial neural crest and the otic placode expressed integrinα5 (Figure 3D), and pharyngeal endoderm expression was seen ventrally along the surface of the yolk (Figure 3E). From 12-s to 18-s (18 hpf), pharyngeal endoderm continued to express integrinα5, and the ectodermal expression domain became more restricted to hyoid crest–derived tissue and the otic placode (Figure 3F and 3G). Endoderm and ectoderm expression domains of integrinα5 were apparent most clearly in 18 hpf cross-sections (Figure 3H–3J). In particular, we observed strong integrinα5 expression throughout pharyngeal endoderm, including the first pouch (Figure 3H). At later times, we saw dynamic integrinα5 expression in both crest derivatives and pharyngeal endoderm. The six pharyngeal pouches form in an anterior to posterior wave of development. By 26 hpf, the fourth pouch, which was the most posterior pouch forming at this time (J.G.C. and C.B.K., unpublished data), expressed integrinα5, whereas integrinα5 expression was no longer seen in the first pouch (Figure 3K). At 38 hpf, the sixth pouch, the last to form, strongly expressed integrinα5, yet the fourth pouch was nonexpressing (Figure 3L). In addition, dynamic integrinα5 expression was seen in patchy zones of pharyngeal crest. Finally, integrinα5 expression was not affected by the b926 mutation (examined at 12-s; data not shown). In conclusion, integrinα5 expression in the pharyngeal endoderm is in a pattern that both spatially and temporally corresponds to regions of pouch formation, and expression of integrinα5 in the crest begins during premigratory stages and later becomes refined to patches of crest derivatives within the pharyngeal arches. Figure 3 integrinα5 Expression in Pharyngeal Endoderm and Cranial Neural Crest (A) At the 32-cell stage, strong maternal integrinα5 expression is seen. (B) At 60% epiboly, integrinα5 expresses broadly throughout the mesendoderm. (C) Dorsal view of a 1-s-stage embryo. integrinα5 transcript is concentrated in the ectoderm at the edge of the neural plate (black arrow), in scattered presumptive endodermal cells, and in the first somite (white arrow). (D and E) Dorsal (D) and lateral (E) views of a 5-s-stage embryo show ectodermal (arrows) and pharyngeal endodermal (arrowhead) expression domains of integrinα5. Ectodermal integrinα5 expression includes migratory hyoid crest, otic placode, and forebrain. (F) At the 12-s stage, integrinα5 continues to be expressed in the pharyngeal endoderm (black arrowhead), postmigratory hyoid crest (arrow), ear (red arrowhead), and forebrain. (G–J) At 18 hpf, a dorsal view of an embryo stained for integrinα5 transcript (G) shows approximate axial levels at which cross-sections were prepared. (H) A cross-section at the level of the first pouch shows strong integrinα5 expression in the pharyngeal endoderm (arrowhead). (I) A cross-section at the level of the hyoid arch shows expression of integrinα5 in neural crest (arrow) and pharyngeal endoderm (arrowhead). (J) A cross-section at the level of the ear shows integrinα5 expression in the otic epithelium (red arrowhead) and pharyngeal endoderm (black arrowhead). (K and L) At 26-hpf (K) and 38-hpf (L) stages, integrinα5 transcript is enriched in the region of the most recent forming pharyngeal pouch (arrowheads) and in patches of crest (arrows). Scale bars: (A–C), (F), and (G): 100 μm; (D), (E), and (H–L): 50 μm. Integrinα5 Is Required in Endoderm but Not Crest for First Pouch and Hyoid Cartilage Development As integrinα5 expression was observed in both pharyngeal endoderm and neural crest, we used transplantation experiments to determine in which tissues Integrinα5 was required for first pouch and hyoid cartilage development (Figure 4). Since it is difficult to transplant large amounts of endoderm from normal zebrafish embryos, we used forced expression of the activated Taram-A receptor (TAR*) to generate donor embryos consisting almost entirely of endoderm (see David et al. [2002] for details). This method allows the specific and unilateral introduction of large amounts of endoderm into mutant hosts. Transplants were performed at 40% epiboly (late blastula; ca. 4 hpf), and the first pouch was scored at 38 hpf in live animals (Figure 4A). In wild-type to wild-type control transplants, 8% of recipient sides developed first pouch defects, suggesting a low level of toxicity of the TAR* construct. We then transplanted wild-type TAR* endoderm into integrinα5−; fli1-GFP hosts. Control mutant sides that did not receive donor endoderm had first pouch defects in 83% of cases. In contrast, first pouch defects were seen in only 17% of mutant sides that received wild-type donor endoderm (Figure 4C and 4D; summarized in Figure 4I). Thus, wild-type endoderm was able to rescue pouch formation in integrinα5 mutants. Furthermore, this rescue was dependent on donor endoderm contributing to the first pouch. Figure 4 integrinα5 Requirement in Endoderm but Not Crest (A and B) Schematics of endoderm (A) and crest (B) transplant experiments. (C–E) In endoderm transplants, confocal projections at 38 hpf (C) and 4 d (D) of a single integrinα5−; fli1-GFP host animal show that wild-type TAR* donor tissue contributed efficiently to pharyngeal endoderm (red) but not crest (green). (E) Flat-mount dissection of mandibular and hyoid cartilages from the individual in (C) and (D). Wild-type TAR* endoderm rescues first pouch development (arrow in [C]) and partially rescues hyoid cartilage development (arrowhead in [D] and arrow in [E]) in integrinα5− embryos. (F–H) In crest transplants, confocal projections of a single integrinα5−; fli1-GFP host animal show extensive colocalization (yellow) of donor tissue (red) with crest (green) at 38 hpf (F) and 4 d (G). Donor tissue does not contribute to endoderm or mesoderm. (H) Flat-mount dissection of mandibular and hyoid cartilages from the individual in (F) and (G). Neither the first pouch defects (arrow in [F]) nor hyoid cartilage defects (arrowhead in [G], arrow in [H]) of integrinα5− animals were rescued by wild-type crest. (I and J) Wild-type and integrinα5− sides that received wild-type endoderm (nwt = 39; nitga5 = 12) or crest (nwt = 30; nitga5 = 12) transplants are plotted against the contralateral integrinα5− control sides that did not receive transplants. (I) First pouch defects are quantified as percent of sides missing the first pouch. For endoderm transplants, integrinα5− recipient sides were rescued to wild-type levels. For crest transplants, integrinα5− recipient sides were not rescued compared to control sides. (J) Hyoid cartilage defects are quantified according to a mutant cartilage index: 0, wild-type; 1, partial aHM reduction; 2, full aHM loss; 3, aHM and SY losses; and 4, aHM and SY losses and fusion to CH. For endoderm transplants, integrinα5− recipient sides were rescued to wild-type index. For crest transplants, integrinα5− recipient sides were not rescued compared to control sides. Lowercase letters (a, b) in plots designate statistically significant groupings using Tukey-Kramer HSD test. Scale bars: 50 μm. We next asked whether wild-type endoderm also was able to rescue hyoid cartilage defects in integrinα5− embryos. In order to quantify the severity of hyoid defects in integrinα5 mutants, we devised a mutant cartilage index that ranged from zero for wild-type to four for the most severe hyoid defects (see legend for Figure 4). In wild-type to wild-type control transplants, the mutant cartilage index was 0.46, consistent with TAR* causing a low level of defects on its own. The control nonrecipient sides of integrinα5−; fli1-GFP animals had an index of 2.5. In contrast, the index of mutant sides that received the TAR* endoderm transplant was rescued to 0.92 (Figure 4D and 4E; summarized in Figure 4J). Hence, wild-type endoderm can nonautonomously rescue hyoid cartilage development in integrinα5 mutants. We also tested whether Integrinα5 was required only in crest for first pouch and hyoid cartilage development. We modified the hindbrain transplantation technique described in Maves et al. (2002) to transplant neural crest precursor cells at shield stages (Figure 4B). In wild-type to wild-type controls, transplants resulted in donor cells constituting a large proportion of the crest cells within the pharyngeal arches and resultant cartilages. In contrast to wild-type endoderm rescue, introduction of substantial amounts of wild-type crest failed to rescue first pouch formation in integrinα5−; fli1-GFP animals (Figure 4F and 4G; summarized in Figure 4I). Furthermore, transplanted wild-type crest did not improve the mutant cartilage index of integrinα5−; fli1-GFP animals (2.50 for recipient sides and 2.29 for nonrecipient sides) (Figure 4G and 4H; summarized in Figure 4J). Thus, wild-type crest was not able to rescue first pouch and hyoid cartilage development in integrinα5 mutants. Fate Map of Hyoid Cartilages Understanding the developmental basis for the specificity of the integrinα5− cartilage phenotype requires a fate map of pharyngeal cartilages in wild-type animals. Here we focus on the origins of the SY, aHM, and pHM regions of HS, and a more complete mandibular and hyoid fate map will be published elsewhere. We used in vivo microelectroporation (Lyons et al. 2003) to label crest cells at 24 hpf and later monitor their cartilage fate (see Materials and Methods; Figure 5A). Representative examples of 24-hpf in vivo microelectroporations show cells that contributed to SY, aHM, or pHM regions at 4 d (Figure 5B–5J). We plotted the origins of cells that contribute to each region along normalized A-P and dorsal-ventral (D-V) axes (Figure 5K). A comparison of mean distances along the A-P axis showed that cells that contributed to aHM and SY clustered on average 6–7 μm, or 1–2 cell diameters, from the first pouch (the anterior border of the arch). In contrast, cells contributing to pHM were on average 16 μm, or three cell diameters, away from the first pouch. A comparison of mean distances along the D-V axis showed that cells contributing to SY were more ventral than cells contributing to aHM and pHM. No statistically significant differences along the mediolateral axis were seen between cells contributing to different HS regions (data not shown; see legend of Figure 5). We conclude that HS cartilage regions most sensitive to loss of Integrinα5 are those developing just beside the first pouch. Figure 5 Fate Map of Hyoid Cartilages (A) In in vivo microelectroporation, a glass needle coupled to a positive electrode and filled with Alexa568 amine dextrans (red) is positioned in the hyoid arch (2) of wild-type fli1-GFP embryos immobilized adjacent to a negative electrode. A short pulse of current delivers dye into single or pairs of cells. A-P and D-V axes, the mandibular arch (1), and first pouch (p1) are designated in (A) and (B). (B–D) Confocal sections of fli1-GFP-labeled hyoid arches (2) (green) show the positions of Alexa568-labeled cells (red) shortly after microelectroporation (24 hpf). (E-J) At 4 d, confocal micrographs (E–G) (schematized in [H–J]) show the resultant fate of labeled crest cells (red) in the hyoid cartilage regions (green). Examples shown include labeled hyoid cells that contributed exclusively to SY (B, E, and H), aHM (C, F, and I), and pHM (D, G, and J) cartilage regions. (K) The relative distances (normalized to one) of hyoid crest cells at 24 hpf that contributed to SY (red), aHM (blue), and pHM (green) regions are plotted along A-P and D-V axes. The first pouch and partial outline of the mandibular arch (1) are drawn for reference. One cell gave rise to an aHM/pHM (blue/green) mixed lineage, and another cell gave rise to pHM and unidentified cells (green/light blue). SY and aHM progenitors map to more anterior domains (i.e., closer to the first pouch) than do pHM progenitors (relative distances from anterior: SY, 0.12 ± 0.11; aHM, 0.17 ± 0.06; pHM 0.43 ± 0.05; statistically different using Tukey-Kramer HSD test). SY progenitors map to a more ventral domain than do aHM and pHM progenitors (relative distances from dorsal: SY, 0.68 ± 0.11; aHM, 0.33 ± 0.06; pHM 0.37 ± 0.05; statistically different using Tukey-Kramer HSD test). No significant differences along the mediolateral axis were seen between regions (relative distances from lateral: SY, 0.47 ± 0.12; aHM, 0.53 ± 0.07; pHM 0.43 ± 0.06). (L) For the fate analysis, the 4-d HS cartilage was subdivided into SY (red), aHM (blue), and pHM (green) regions. The outline of the CH cartilage is also shown. Scale bars: 50 μm. Increased Cell Death and Disorganized goosecoid Expression in the Hyoid Arches of integrinα5− Embryos We next investigated whether the losses of aHM and SY regions in integrinα5− embryos correlated with increased cell death in the hyoid arch (Figure 6). At 25 hpf, TUNEL staining revealed a greater than 2-fold increase in apoptosis over wild-type in the hyoid arches of integrinα5−; fli1-GFP embryos (Figure 6A, 6B, and 6E). A moderate tendency toward increased apoptosis was also seen in the hyoid arches of integrinα5−; fli1-GFP embryos from 29 to 35 hpf (Figure 6D and 6E). Apoptotic nuclei appeared to cluster in the dorsal anterior portion of the hyoid arch (Figure 6B and 6D), and colocalization with fli1-GFP, a marker of neural crest, in confocal sections showed that some of these nuclei corresponded to dying crest cells (Figure 6B′). Interestingly, we observed an increase in hyoid apoptosis only in integrinα5−; fli1-GFP sides in which the first pouch failed to develop (Figure 6E). In addition, at 14 hpf and 18 hpf, stages before which the first pouch has normally fully formed, no increase in cell death was seen in the cranial neural crest of integrinα5− animals (data not shown). These results are consistent with the first pouch being required for the survival of hyoid crest that contributes to aHM and SY. Figure 6 Increased Apoptosis and Disorganized gsc Expression in the Hyoid Arches of integrinα5 Mutants (A–D) TUNEL staining of wild-type fli1-GFP (A and C) and integrinα5−; fli1-GFP (B and D) animals shows apoptotic nuclei (red) relative to the GFP-expressing crest of the pharyngeal arches (green) at 25 hpf (A and B) and 29 hpf (C and D). In wild-type confocal projections arches are numbered. (A′) and (B′) are representative confocal sections taken from the projections in A and B. In integrinα5− animals lacking the first pouch, increased apoptosis (arrows in [B] and [D]) is seen in the dorsal anterior hyoid arch adjacent to where the first pouch would be in wild-type animals. In mutant sections (B′), TUNEL-positive cells (arrow) colocalize with the fli1-GFP crest marker. (E) The number of apoptotic nuclei per hyoid arch is plotted versus time for wild-type sides (blue) and integrinα5− sides without (p1−; red) or with (p1+; green) a normal first pouch. At 25 hpf, integrinα5− hyoid arches had more apoptotic nuclei than wild-type hyoid arches only when the first pouch was defective (p < 0.0001). At later time points, integrinα5− hyoid arches missing the first pouch had a tendency to have more apoptotic nuclei than wild-type or integrinα5− arches with normal first pouches (only itga5− with a normal first pouch versus itga5− without at 35 hpf is statistically significant, p < 0.05). Total sides examined: 25 hpf: nwt = 40, nitga5 = 38; 29 hpf: nwt = 30, nitga5 = 26; 30 hpf: nwt = 30, nitga5 = 20; and 35 hpf: nwt = 30, nitga5 = 14. (F and G) gsc expression at 36 hpf labels dorsal and ventral domains of hyoid crest. Mandibular (1) and hyoid (2) arches are numbered, and the first pouch is denoted by arrowhead. In wild-type animals, dorsal and ventral hyoid gsc domains are well separated. In this integrinα5− animal, dorsal and ventral hyoid gsc domains are fused, and disorganized gsc-expressing cells envelop the reduced first pouch (arrowhead). Scale bars: 50 μm. We also examined whether hyoid crest was correctly specified in integrinα5 mutant embryos. Hyoid crest expresses Hox class 2 genes, whereas mandibular crest is Hox nonexpressing (Hunt et al. 1991). No defects were seen in the expression of hoxa2 in the hyoid arches of integrinα5 mutants at 36 hpf (data not shown). In 36-hpf wild-type animals, goosecoid (gsc) expression marks dorsal and ventral domains within the hyoid arch (Figure 6F). In integrinα5− embryos, gsc domains were present, although in 12% of mutants they were variably disorganized. In the example shown in Figure 6G, the dorsal hyoid gsc domain was disorganized and fused to the ventral hyoid gsc domain. However, as the gsc defects were of significantly lower penetrance than the cartilage defects, we conclude that the majority of specific cartilage defects seen in integrinα5− embryos are not due to altered gsc expression. A Subset of Hyoid Crest Shows Aberrant Behavior and Does Not Contribute to Cartilage in integrinα5 Mutants The first pharyngeal cartilages begin to chondrify around 48 hpf (Schilling and Kimmel 1997). In order to understand neural crest cell behavior during cartilage formation in wild-type animals, we used the fli1-GFP line to make extended time-lapse recordings of hyoid arch development that began at 38 hpf and ended at 86 hpf, an endpoint when cartilage elements are readily identifiable (Figure 7). In one focal plane, the SY region was observed to form from tightly packed cells adjacent to the ventral tip of the first pouch at 38 hpf (Video S1; Figure 7A–7F). In another focal plane, aHM was observed to form from a tightly packed mass of fli1-GFP-labeled cells located directly adjacent to the first pouch in the dorsal, anterior portion of the hyoid arch at 38 hpf (Video S2; Figure 7G–7L). We found that crest cells that contributed to aHM remained fairly static during the period of observation, though local rearrangements that contribute to the flattening of the HM plate were not analyzed in detail here (enlarged in Figure 7G′–7L′). The pHM region formed from cells located posterior to the aHM mass at 38 hpf. In general, our time-lapse recordings of wild-type hyoid development supported and extended the conclusions generated from the 24-hpf fate map. In the hyoid arch, crest cells that will contribute to the aHM and SY regions are tightly packed masses directly adjacent to the first pouch prior to chondrogenesis. Figure 7 Anterior Hyoid Crest Cells Display Aberrant Behavior in integrinα5 Mutants Confocal time-lapse recordings show hyoid cartilage development in wild-type fli1-GFP (Videos S1 and S2) and integrinα5−; fli1-GFP (Video S3) animals from 38 hpf to 86 hpf (nwt = 3; nitga5 = 4). Videos S1 and S2 are different depths of the same time-lapse recording. Representative imaging stills of Video S1 (A–F), Video S2 (G–L), and Video S3 (M–R) were taken at 38 hpf (A, G, and M), 44 hpf (B, H, and N), 50 hpf (C, I , and O), 56 hpf (D, J, and P), 62 hpf (E, K, and Q), and 86 hpf (F, L, and R). At the beginning of the recordings (A, G, and M), the mandibular (1) and hyoid (2) arches are numbered and an arrow denotes the first pouch (p1). At the end of the recordings (F, L, and R), the cartilage regions are clearly visible as large cells with thick matrix (pseudocolored blue). The outline of the HS cartilage, a composite of SY and HM regions, is shown in (F) and (L). As a reference, the opercle bone and ao/lo hyoid muscle mass are pseudocolored purple and red, respectively, and the eye and ear are labeled. In Video S1 (A–F), red arrowheads denote a cluster of cells adjacent to the first pouch that undergo cellular rearrangements and form the long, anterior SY extension in wild-type animals. (G′–R′) show magnifications of HM-forming regions taken from (G–R) and correspond to areas within white boxes given in (G) and (L) for (G–L) and in (M) and (R) for (M–R). In wild-type development, hyoid crest cells adjacent to the first pouch remain a tightly packed mass as aHM chondrifies (e.g., cells denoted by red arrowheads in G′–L′). In integrinα5 mutants, the first pouch is missing (white arrow in [M]), and anterior hyoid crest cells are disorganized at 38 hpf (e.g., arrowhead in [M′]). Over time, anterior hyoid crest cells migrate out of the region and do not contribute to cartilage (e.g., arrowheads in [N′–Q′]). In contrast, the pHM region and the opercle bone develop normally from more posterior hyoid crest in integrinα5− animals (R). Scale bar: 50 μm. In order to understand the cellular basis for the losses of the aHM and SY regions in integrinα5 mutants, we made time-lapse recordings of hyoid crest development in integrinα5−; fli1-GFP embryos from 38 hpf to 86 hpf (Video S3; Figure 7M–7R). Whereas in wild-type animals anterior hyoid crest cells were tightly packed masses adjacent to the first pouch at 38 hpf, in integrinα5−; fli1-GFP embryos, crest cells in the dorsal, anterior portion of the hyoid arch were more loosely packed (Figure 7M′). Strikingly, over the next day the crest-derived cells migrated out of the dorsal, anterior region of the mutant hyoid arch (enlarged in Figure 7N′–7Q′). By 86 hpf, the pHM cartilage region had formed, yet no fli1-GFP-positive cells were seen anterior to pHM (Figure 7R). Thus, we found a strong correlation between the lack of compaction and stabilization of dorsal, anterior hyoid crest cells and the loss of the aHM cartilage region in integrinα5−; fli1-GFP embryos. Discussion Isolation of a Zebrafish integrinα5 Mutant In this work, we isolated and characterized a zebrafish mutant allele (b926) that has variably penetrant and expressive reductions of the first pouch and hyoid aHM and SY cartilage regions. By positional mapping, allele segregation, and morpholino phenocopy, we identified the genetic basis of the lesion as a missense mutation in the ligand-binding domain of Integrinα5. As similar pharyngeal phenotypes were observed in mutant and morpholino-treated animals, we conclude that b926 is a loss-of-function allele of integrinα5. However, we do not know if Integrinα5 activity is completely eliminated in b926. In addition, we observed strong maternal expression of integrinα5 that could mitigate the zygotic loss of integrinα5 in b926. Thus, the variable penetrance and expressivity of the integrinα5 phenotype could be due to partial activity of mutant Integrinα5 or the presence of maternally supplied Integrinα5. Additionally, other integrins may act redundantly with Integrinα5 in pharyngeal development. A survey of nearly finished genome sequence (http://www.ensembl.org/Danio_rerio/) has uncovered at least 15 additional Integrin α chains, for which no expression or phenotypic data are known in zebrafish. Lastly, integrinα5 is expressed strongly in many tissues, such as the otic placode, for which no overt phenotypes were observed in b926. Future studies, in particular those using animals in which both maternal and zygotic integrinα5 have been eliminated, may reveal new functions of Integrinα5 in zebrafish development. The First Pouch Is Required for the Development of a Subset of Hyoid Cartilage Our results point to an important role for the first pouch in the development of specific hyoid cartilage regions. We have used the incomplete penetrance of the pouch and cartilage phenotypes of integrinα5− animals to show that early first pouch defects are strongly predictive of later hyoid cartilage defects. Furthermore, transplantation experiments show that wild-type endoderm, but not crest, rescues first pouch and hyoid cartilage development in integrinα5 mutants. We infer that Integrinα5 functions in the pharyngeal endoderm for the formation of the first pouch, and that the first pouch, in turn, interacts with postmigratory neural crest to promote cartilage development in a region of the hyoid arch. A role for the first endodermal pouch in promoting regional hyoid cartilage development is consistent with work in chicken showing that domains of pharyngeal endoderm specify region-specific cartilage shapes (Couly et al. 2002; Ruhin et al. 2003). Our data extend these findings, arguing that the formation of the first pouch is a critical step in allowing pharyngeal endoderm to interact with hyoid crest and promote the development of specific cartilage regions, aHM and SY. It will be interesting to see the extent to which the ability of different pharyngeal endoderm domains to induce cartilage elements of specific shapes depends on their ability to form discrete morphological structures such as the first pouch. How might the first pouch control development of specific cartilage regions within the hyoid arch? The first pouch forms at a time when hyoid crest cells are migrating to ventrolateral positions to form the hyoid arch (Veitch et al. 1999; J.G.C. and C.B.K., unpublished data). Upon reaching the developing arch, crest cells become less motile and form tightly packed masses adjacent to the first pouch. Our wild-type fate map shows that the hyoid cartilage regions that are lost in integrinα5 mutants, aHM and SY, develop from crest cells immediately adjacent to the first pouch (Figure 8A). Our time-lapse recordings of wild-type cartilage development show that crest cells that will form aHM remain a tightly packed mass as the aHM region chondrifies (Figure 8B and 8C). In contrast, in integrinα5 mutants, dorsal anterior hyoid crest cells are aberrantly motile and do not contribute to cartilage, whereas more posterior dorsal hyoid crest cells contribute normally to pHM (Figure 8D–8F). In addition, we observe increased apoptosis in the dorsal, anterior domain of integrinα5− hyoid arches from 25 to 35 hpf. Importantly, increased death of postmigratory hyoid crest cells was seen only when the first pouch was reduced. It will be interesting to examine whether the increased apoptosis observed in the dorsal anterior hyoid arches of integrinα5−/− mice (Goh et al. 1997) is a secondary consequence of a missing first pouch as well. In contrast to integrinα5−/− mice, in which an increase in cell death was seen earlier during crest migration, we found no evidence for increased death of migratory crest in integrinα5− zebrafish. However, our analysis cannot rule out that increased crest death during migratory stages may contribute to the infrequent, most severe cartilage phenotypes seen in integrinα5− embryos. Indeed, given the strong expression of integrinα5 in migratory hyoid crest, future studies that further reduce Integrinα5 activity, for example by removing its maternal component, may uncover crest-autonomous functions of zebrafish Integrinα5 in the survival and/or migration of hyoid crest cells. Figure 8 Model for Development and Evolution of Hyoid Cartilage (A–F) Models of hyoid development in wild-type (A–C) and integrinα5− (D–F) animals show the structure of hyoid arches at 24 hpf (A and D) and 38 hpf (B and E) and mandibular and hyoid cartilages at 4 d (C and F). (A) At 24 hpf of wild-type development, crest that will form aHM (dark green), pHM (medium green), and SY (light green) cartilage regions occupy distinct domains within the hyoid arch. Signals (red arrows) from the first pouch (orange) stabilize adjacent aHM- and SY-producing crest. (B) At 38 hpf of wild-type development, aHM- and SY-producing crest tightly pack along the first pouch. Cranial mesoderm (red) and some mandibular crest (blue) are also shown. (C) At 4 d of wild-type development, the HS cartilage is a composite of aHM, pHM, and SY regions. Also shown are the hyoid CH (yellow) and mandibular Meckel's (light blue) and palatoquadrate (dark blue) cartilages. (D) In integrinα5− animals, the first pouch is missing or very reduced at 24 hpf. (E) By 38 hpf, as a consequence of the lack of a first pouch, aHM and SY progenitors are disorganized and undergo gradual apoptosis. In contrast, the development of pHM progenitor cells does not require the first pouch. (F) At 4 d, aHM and SY cartilage regions are selectively reduced in integrinα5− animals. (G–I) The HS element has undergone extensive change during vertebrate evolution. In the illustrations (adapted from De Beer [1937]), the neurocranium is grey or outlined in black and mandibular and hyoid cartilages are color-coded as described above. Based on relations to morphological landmarks and data presented here on the tripartite mosaic development of HS, an evolutionary scheme is proposed. (G) In the dogfish shark Scyliorhinus canicula, a single rod-shaped element corresponds to pro-aHM/SY regions. (H) In the basal actinopterygian Polypterus senegalus, separate aHM and SY regions are present. (I) As shown for salmon, during actinopterygian evolution a new region, pHM, develops posterior to and fuses with aHM to create a wide HM plate that articulates with the neurocranium and supports an enlarged, overlying opercular apparatus (not shown). Our data show that the first pouch is required for the stabilization and survival of crest cells that will become aHM and SY. Interactions between the first pouch and adjacent hyoid crest could involve direct adhesion and/or diffusible signaling molecules. aHM and SY cartilage regions develop from crest-derived cells immediately adjacent to the first pouch at 24 hpf, whereas the pHM region develops from crest three cell diameters away. The remarkable specificity of the integrinα5 cartilage phenotype suggests that pouch-derived signals act very locally, perhaps through cell–cell contract, to promote development of aHM and SY regions. In support of this, explant studies in the newt show that physical contact between pharyngeal endoderm and neural crest cells is necessary to promote the compaction and differentiation of crest into cartilage (Epperlein and Lehmann 1975). On the other hand, a signaling role for pharyngeal endoderm in crest survival also has been shown. In zebrafish, Fgf3 produced from the pharyngeal pouches acts as a secreted survival factor for neural crest (David et al. 2002; Nissen et al. 2003). In addition to promoting stabilization and survival, might endoderm also control local gene expression in hyoid crest? In a small fraction of integrinα5− embryos, gsc expression domains in the hyoid arches were present but disorganized. However, it is possible that the aberrant gsc expression reflects a disorganization of the hyoid arch and not ectopic gene expression. Although disorganized gsc expression may correlate with more severe integrinα5 phenotypes such as hyoid cartilage fusions (see Figure 2D), the significantly lower penetrance of gsc phenotypes compared to cartilage phenotypes implies that disorganized gsc expression is not the major cause of the specific cartilage defects. In addition, hoxA2 expression was unaffected in the hyoid arches of integrinα5− embryos. Thus, although additional markers of hyoid crest need to be examined in integrinα5− embryos, we have found no strong evidence for the first pouch controlling gene expression in neighboring crest. Instead, our data argue that the first pouch locally controls cartilage development by promoting the compaction and survival of immediately adjacent crest-derived cells. Integrin-Mediated Outgrowth of the First Pharyngeal Pouch We have shown that zebrafish integrinα5 is expressed in pharyngeal endoderm during pouch formation and is required in the endoderm for development of the first pouch. The specificity for the first pouch of the integrinα5 phenotype could be due to either redundancy with other integrins that function preferentially in posterior pouches or greater sensitivity of the first pouch to loss of integrin function. Although the most common phenotype in integrinα5 mutants is loss of just the first pouch, we do occasionally see reductions of more posterior pouches as well, suggesting that Integrinα5 functions in the formation of most or all pouches. Moreover, as the more posterior pouches are required to segment the posterior crest mass into the five branchial arches from which the CB cartilages develop (Piotrowski and Nusslein-Volhard 2000), the variable loss of these pouches likely explains the reductions and rare fusions of CB cartilages seen in some integrinα5− animals. How might Integrinα5 control pouch formation? The elaboration of a relatively uniform tissue into an organ of more complex curvature and ramifications, termed branching morphogenesis, is a common developmental program in both vertebrates and invertebrates. The formation of an iterative series of outpocketings, or pouches, from the pharyngeal endoderm can be thought of as analogous to branching morphogenesis. Integrins have well-documented roles in cell migration that could promote the outgrowth of branches (reviewed in Bokel and Brown 2002). From our unpublished observations in zebrafish, we know that pouches form by the directed lateral migration of pharyngeal endodermal cells (unpublished data). In this work, we find that integrinα5 is expressed transiently in pouch-forming regions of pharyngeal endoderm and is required in endoderm for pouch formation. One possibility is that Integrinα5 adhesion promotes the lateral migration of endodermal cells to form pouches. Alternatively, Integrinα5 may be required for the specification or survival of pharyngeal endoderm that forms pouches. Future time-lapse imaging studies, in which pharyngeal endoderm morphogenesis is analyzed directly in integrinα5− embryos, will help to clarify the role of Integrinα5 in pouch formation. A Hierarchy of Tissue Interactions Control Regional Development in the Pharyngeal Arches The exquisite functionality of the vertebrate jaw and pharynx requires the precise developmental coordination of their component parts. Arch-specific patterns of muscle connect with pharyngeal skeletal elements and are innervated by motor neurons of appropriate axial levels to orchestrate behaviors such as feeding and gill pumping. In integrinα5 mutants, we see specific defects not only in the endodermal pouches and crest-derived cartilages, but also in cranial muscles and their associated motor nerves (schematized in Figure 2R). Both a dorsal mandibular (do) and a dorsal hyoid (ah) muscle are reduced in integrinα5 mutants, and facial nerve VII, which innervates ah and other hyoid muscles, is reduced and/or fails to make a characteristic branch into two main fascicles. However, it is likely that muscle and nerve defects in integrinα5 mutants are secondary to pouch and cartilage defects. Whereas integrinα5 is expressed in endoderm and crest during pharyngeal morphogenesis, we were unable to detect integrinα5 expression in cranial mesoderm or hindbrain neurons during axon outgrowth. In addition, muscle and nerve defects in integrinα5 mutants were of significantly lower penetrance than the first pouch and hyoid cartilage defects. The low penetrance of the muscle and nerve defects might be explained by the variably expressive loss, in integrinα5 mutants, of the pouch- and/or crest-derived signals on which muscle and nerve development depend. Unfortunately, due to the low penetrance of both muscle and nerve defects in integrinα5 mutants, we were unable to directly test the tissue autonomy of these defects. Our analysis of the integrinα5 mutant does not distinguish between roles for endoderm and crest in patterning cranial muscle and nerves. The mesodermal cores that give rise to do and ah, the muscles affected in integrinα5− animals, lie close to and on opposite sides of the first pouch during pharyngeal arch development. The first endodermal pouch could have an early organizing role for cranial mesoderm. However, increasing evidence suggests that crest has a major role in patterning cranial mesoderm. Analysis of the chinless mutation in zebrafish has shown that chinless functions nonautonomously within the crest to promote muscle development (Schilling et al. 1996). In classic experiments in chicken, grafting of mandibular crest into more posterior arches can reprogram both skeletal and muscular fates (Noden 1983), though recent work suggests this effect is mediated by an isthmus-organizing activity included in the grafts (Trainor et al. 2002). In the larval zebrafish, do and ah are found in close association with the aHM region that is lost in integrinα5 mutants. It is possible that loss of dorsal anterior hyoid crest in integrinα5 mutants results in reductions not only of the aHM cartilage region but also of crest-derived signals that support development of do and ah muscles. As has been proposed by others (Noden 1983; Kontges and Lumsden 1996), the development of cranial muscles may depend less on their arch origin and more on the crest-derived structures, such as the aHM cartilage region and associated connective tissue, onto which they attach. Likewise, the reduction of facial nerve VII in integrinα5 mutants could be due to either reductions in hyoid muscles and their associated survival signals or reductions in nerve outgrowth–promoting cues normally produced by the crest and/or endoderm. In conclusion, we see evidence for a local hierarchy of interactions that control the development of a specific region of the head encompassing dorsal hyoid and mandibular elements. At the top of the hierarchy is the endoderm-derived first pouch that promotes the development of a subset of hyoid crest into cartilage; in turn, this subset of hyoid crest may control development of neighboring muscles and, directly or indirectly, their associated nerve. Mosaic Assembly of Hyoid Cartilage: Implications for Evolution The shape of the dorsal hyoid cartilage element has undergone extensive modification during actinopterygian evolution. In sharks (Figure 8G) and basal ray-finned fishes such as the bichir, Polypterus senegalus (Figure 8H), HM is a rod and SY is absent or not well elongated (De Beer 1937). In teleosts, highly derived ray-finned fish, the dorsal hyoid cartilage consists of a wider HM plate and a long SY extension (Figure 8I). The elaboration of HM and SY regions before teleosts emerged may have served to more efficiently support the jaw and increase gill pumping. The origin of the HM plate has long been a subject of debate. Allis (1915) proposed that the teleost HM plate consists of two regions that become fused together, whereas Edgeworth (1926), based on his staging series of the bowfin Amia, a relative of teleosts, concluded that the HM plate develops from a single anterior region that undergoes posterior growth to form a plate. There is ample precedence for differential growth as a mechanism of morphological change. For example, beautiful interspecies mosaic experiments have shown that the difference in beak length between ducks and quails is due to an autonomous growth potential of mandibular crest (Schneider and Helms 2003). However, our data support the composite two-region HM theory of Allis. We see a clear genetic dissociation between the development of aHM and pHM. Whereas aHM is absent in the majority of integrinα5 mutants, pHM and the connecting opercle bone are still present even in the most severe class of integrinα5 mutants. In addition, our fate mapping data show that, although aHM and pHM form a seamless HM plate in the larva, their progenitor cells occupy distinct, albeit contiguous, domains within the hyoid arch at 24 hpf, a result inconsistent with the posterior growth hypothesis of Edgeworth. Thus, aHM and pHM regions develop from spatially distinct domains of crest that depend on different sources of inductive signals. Our data show that the first endodermal pouch is required for the development of aHM, yet in mutants that lack all pharyngeal endoderm, such as casanova, both aHM and pHM are lost (David et al. 2002). Thus, other structures of the pharyngeal endoderm besides the first pouch may be required for the development of pHM. One attractive possibility is that, whereas the first pouch induces aHM in the anterior part of the hyoid arch, the second pouch induces pHM in the posterior part of the arch. Allis (1915) concluded, based on relationships to morphological landmarks, that the rod-shaped HM cartilage in Polypterus represents the anterior portion of the teleost HM plate (i.e., aHM). Although more embryological studies of Polypterus need to be done, the evolution of the HM plate in ray-finned fishes such as teleosts appears to have involved a new induction event that led to a new region, pHM, being added to an older region, aHM. We propose that the de novo addition of regions to the skeletal pattern represents another mechanism, in addition to differential growth, of generating skeletal diversity during evolution. Materials and Methods Zebrafish strains and mutant screen. Zebrafish (Danio rerio) raised at 28.5 °C were staged as previously described (Kimmel et al. 1995; Westerfield 1995). The wild-type line used was AB. fli1-GFP albino transgenic fish are the same as TG(fli1:EGFP)y1; albb4 (Lawson and Weinstein 2002), and islet1-GFP fish are as described (Higashijima et al. 2000). For the cartilage screen, ENU-mutagenized F2 parthenogenic diploid fish were generated by early pressure treatment (Streisinger et al. 1981; Solnica-Krezel et al. 1994) and fixed and stained with Alcian at 4 d. The b926 allele was outcrossed to the AB strain and subsequently crossed onto the fli1-GFP and islet1-GFP backgrounds. integrinα5 identification and morpholino. b926 was mapped with respect to polymorphic microsatellites based on the hyoid cartilage phenotype. Initial mapping was performed on an AB background, and fine mapping was performed on an islet1-GFP background selected for a high degree of LG23 polymorphisms with respect to AB. Full-length integrinα5 cDNA was obtained by 5′- and 3′-RACE. Standard molecular biological techniques were used. For genotyping, primers were designed to turn b926 into a codominant polymorphism digestible with XmnI (GC156, TGACTGTGACCTTCAGCTCAATGTAAACGC; GC158, TGGATCTGGCCACCCACTGAGGTCGAAAAG). A morpholino (Genetools, Philomath, Oregon, United States) was designed against the exon13-intron splice site of integrinα5 (itga5-MO) with the following sequence: ATGCTTTCTCACCTGGGTAGCCATT. Embryos were pressure-injected with 5 nl of 2 mg/ml itga5-MO as previously described (Maves et al. 2002). Phenotypic analysis. Alcian Green staining was performed as described (Miller et al. 2003). For flat mount dissections, Alcian-stained animals were digested for 1 h in 8% trypsin at 37 °C and transferred to 100% glycerol. Cartilages were dissected free from surrounding tissues with fine stainless-steel insect pins and photographed using a Zeiss (Oberkochen, Germany) Axiophot 2 microscope. Image background was cleaned up with Adobe Photoshop. For immunocytochemistry, embryos were prepared as described (Maves et al. 2002). Antibodies were used at the following dilutions: rabbit anti-GFP, 1:1,000 (Molecular Probes, Eugene, Oregon, United States); Zn-8, 1:400 (Trevarrow et al. 1990); MF-20, 1:10 (Developmental Studies Hybridoma Bank, University of Iowa, Iowa City, Iowa, United States); goat anti-rabbit Alexa Fluor 488 and anti-mouse Alexa Fluor 568 (Alexa568), both 1:300 (Molecular Probes). TUNEL staining was performed on embryos that were fixed overnight in 4% PFA, MeOH-permeabilized for 20 min, rehydrated, and treated with ProteinaseK (Sigma, St. Louis, Missouri, United States) for 2–20 min at room temperature. TdT/Fluorescein-dUTP reaction (Roche, Basel, Switzerland) was performed for 1 h on ice, followed by 1 h at room temperature. After labeling with Fluorescein-dUTP, immunocytochemistry was performed using rabbit anti-Fluorescein Fab fragments (1:20,000, Molecular Probes) and goat anti-rabbit Alexa Fluor 568 antibodies (1:200, Molecular Probes). GFP fluorescence survived the procedure. Probe syntheses and whole-mount in situ hybridizations were performed as previously described (Westerfield 1995). Embryos were mounted in glycerol and photographed using a Zeiss Axiophot 2 microscope. integrinα5 RNA probes were made from plasmids pINT4150 and pINT4853, constructed by inserting RT-PCR fragments corresponding to nucleotides 254–2,207 and 1,679–2,960 of the integrinα5 cDNA, respectively, into the TA vector (Invitrogen, Carlsbad, California, United States). Plasmids were linearized with BamHI, and T7 RNA polymerase was used for probe synthesis. Both probes gave identical expression patterns, and pINT4853 was used for photographs. pea3 (Brown et al. 1998) and gsc (Schulte-Merker et al. 1994) probes were prepared as described, and mutant embryos were PCR genotyped. Single-cell microelectroporation. The microelectroporation technique was similar to that described by Lyons et al. (2003). fli1-GFP embryos, 24 hpf, were dechorionated, anesthetized with tricane solution, and bathed in a solution of 5 mg/ml pronase (Sigma) for 1 min to allow passage of the microelectrode through the skin. Agar mounting of embryos on slides was performed as described in Westerfield (1995). Under 50× Nomarski optics a micropipette filled with Alexa Fluor 568 dextran amines (Molecular Probes) was positioned next to the cell of interest, and a ground electrode was placed in the bath next to the embryo. Pulses of current between 1 and 4 uA were used to mobilize the dye. Shortly after electroporation, the location of labeled cells relative to the fli1-GFP-expressing hyoid arch was assessed using confocal microscopy. Only embryos with one or two adjacently labeled cells were used in the analysis. Three-dimensional projections were constructed to determine the position of labeled cells in the arch relative to landmarks. All cell distances were made from the mid point of the cell to the landmark. Distance of the labeled cell from the edge of the first pouch was used to determine A-P position. Similarly, distances of labeled cells from the dorsal and lateral edges of the arch were used to determine D-V and mediolateral positions. In electroporations where two adjacent cells were labeled, the positions of each cell were measured and averaged. To control for variation in arch dimensions among individuals, measurements along the three axes were normalized to total axes lengths. At 4 d, embryos were imaged again to determine the fate of labeled cells in hyoid cartilage. The aHM region was defined as the anterior portion of HM that is characteristically lost in integrinα5 mutants. pHM comprises the rest of the HM region. The SY region begins at the point of attachment to pHM. All graphing and statistical analysis were done using JMP (2002, SAS Institute, Cary, North Carolina, United States). Time-lapse analysis and confocal imaging. Embryos were manually dechorionated, anesthetized with tricane solution, transferred to 0.2% agarose in embryo media with 10 mM HEPES and tricane, and then mounted onto a drop of 3% methylcellulose on a rectangular coverslip with three superglued #1 square coverslips on each side. A ring of vacuum grease was added around the embryo to make an airtight seal upon addition of the top coverslip. A heated stage kept the embryos at 28.5 °C. Approximately 80-μm Z-stacks at 2-μm intervals were captured every 10 min using a Zeiss LSM 5 Pascal confocal fluorescence microscope. Movies of individual Z-sections were made by manually following cells and concatenating sections; further processing was done with Adobe Premiere. For single time point confocal sections, embryos were mounted without vacuum grease. Endoderm and crest transplants. Transplant techniques were as described (Maves et al. 2002). For endoderm transplants, donor embryos were injected at the one-cell stage with a mixture of 2% Alexa Fluor 568 dextran and 3% lysine-fixable biotin dextran (10,000 MW, Molecular Probes) along with TAR* RNA prepared according to David et al. (2002). At 40% epiboly (ca. 4 hpf), donor TAR* tissue was moved to the margins of fli1-GFP host embryos. For the crest transplants, donor fli1-GFP embryos were injected at the one-cell stage with the Alexa Fluor 568 mixture (“Alexa568”). At shield stage (ca. 6 hpf), donor tissue was taken from the animal cap and moved to a position approximately two germ ring widths from the margin and 90° from dorsal in a fli1-GFP host embryo. Confocal images of host embryos were captured at 38 hpf and 4 d, and embryos were subsequently fixed and Alcian-stained to visualize cartilages. For endoderm transplants, only embryos in which donor tissue contributed to the second and at least one other pouch were included in the analysis. For crest transplants, only embryos in which at least half the hyoid arch was composed of donor tissue were included in the analysis. Supporting Information Video S1 Wild-Type Development of SY Cartilage Confocal time-lapse recording shows hyoid cartilage development in a wild-type fli1-GFP animal from 38 hpf to 86 hpf. At the beginning of the video, the mandibular (1) and hyoid (2) arches are numbered and an arrow denotes the normal position of the first pouch. At the end of the video (see Figure 7 for representative still images), the SY cartilage is pseudocolored blue. A red arrowhead denotes a cluster of cells adjacent to the first pouch that undergo cellular rearrangements and form the long anterior SY extension in wild-type animals. (6.0 MB MOV). Click here for additional data file. Video S2 Wild-Type Development of HM Cartilage Confocal time-lapse recording shows hyoid cartilage development in a wild-type fli1-GFP animal from 38 hpf to 86 hpf. This video is a different depth of the same time-lapse recording as Video S1. At the beginning of the video, the mandibular (1) and hyoid (2) arches are numbered and an arrow denotes the normal position of the first pouch. At the end of the video (see Figure 7 for representative still images), the HM cartilage is pseudocolored blue, and the ao/lo muscle quadrant and opercle bone are pseudocolored red and purple, respectively, for reference. A red arrowhead points to hyoid crest–derived cells immediately adjacent to the first pouch that give rise to the aHM cartilage region in wild-type animals. (7.1 mB MOV). Click here for additional data file. Video S3 Development of HM Cartilage in integrinα5 Mutants Confocal time-lapse recording shows hyoid cartilage development in an integrinα5−; fli1-GFP animal from 38 hpf to 86 hpf. At the beginning of the video, the mandibular (1) and hyoid (2) arches are numbered and an arrow denotes the normal position of the first pouch. At the end of the video (see Figure 7 for representative still images), the HM cartilage is pseudocolored blue, and the ao/lo muscle quadrant and opercle bone are pseudocolored red and purple, respectively, for reference. A red arrowhead points to hyoid crest cells that display increased motility in integrinα5− animals and do not contribute to cartilage as in wild-type. (6.0 KB MOV). Click here for additional data file. Accession Numbers The Genbank (http://www.ncbi.nlm.nih.gov) accession number for the zebrafish integrinα5 cDNA is AY550244. Accession numbers for the related Integrinα5 proteins described in Figure 1 are Homo sapiens (Genbank P08648), Mus musculus (Genbank P11688), Xenopus laevis (Genbank Q06274), and Fugu rubripes (manually assembled from Fugublast M002304). We thank Jennifer Wofford nee Lawson for help in mapping and stocks; John Dowd, Bill Trevarrow, and the UO Fish Facility for abundant help with raising fish; Brant Weinstein for providing transgenic GFP lines before publication; Jonathan Clarke for help setting up the microelectroporation technique; Lisa Maves for technical guidance; Le Trinh for the TAR* plasmid; Craig T. Miller, Macie. B. Walker, Le Trinh, Didier Stainier, and members of the Kimmel Lab for helpful discussions; and Johann Eberhart and Rebecca Cheeks for comments on the manuscript. JGC is an O'Donnell Fellow of the Life Sciences Research Foundation. Research is funded by National Institutes of Health grants DE13834 and HD22486. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. JGC conceived and designed the experiments. JGC and MES performed the experiments and analyzed the data. CBK contributed reagents/materials/analysis tools. JGC wrote the paper. Academic Editor: Robb Krumlauf, Stowers Institute for Medical Research Citation: Crump JG, Swartz ME, Kimmel CB (2004) An integrin-dependent role of pouch endoderm in hyoid cartilage development. PLoS Biol 2(8): e244. Abbreviations A-Panterior-posterior ahadductor hyomandibulae aHManterior hyomandibula aoadductor operculi CBceratobranchial CHceratohyal D-Vdorsal-ventral dodilatator operculi GFPgreen fluorescent protein gsc goosecoid HMhyomandibula hpfhours post fertilization HShyosymplectic itga5 integrinα5 LGLinkage Group lolevator operculi pHMposterior hyomandibula RT-PCRreverse transcription polymerase chain reaction ssomites SYsymplectic TAR*activated Taram-A receptor ==== Refs References Alexander J Rothenberg M Henry GL Stainier DY casanova plays an early and essential role in endoderm formation in zebrafish Dev Biol 1999 215 343 357 10545242 Allis EP The homologies of the hyomandibula of the gnathostome fishes J Morphol 1915 26 563 624 Bader BL Rayburn H Crowley D Hynes RO Extensive vasculogenesis, angiogenesis, and organogenesis precede lethality in mice lacking all alpha v integrins Cell 1998 95 507 519 9827803 Bokel C Brown NH Integrins in development: Moving on, responding to, and sticking to the extracellular matrix Dev Cell 2002 3 311 321 12361595 Brown LA Amores A Schilling TF Jowett T Baert JL Molecular characterization of the zebrafish PEA3 ETS-domain transcription factor Oncogene 1998 17 93 104 9671318 Couly G Creuzet S Bennaceur S Vincent C Le Douarin NM Interactions between Hox-negative cephalic neural crest cells and the foregut endoderm in patterning the facial skeleton in the vertebrate head Development 2002 129 1061 1073 11861488 David NB Saint-Etienne L Tsang M Schilling TF Rosa FM Requirement for endoderm and FGF3 in ventral head skeleton formation Development 2002 129 4457 4468 12223404 De Beer GR The development of the vertebrate skull 1937 Oxford Clarendon Press 544 Delannet M Martin F Bossy B Cheresh DA Reichardt LF Specific roles of the alpha V beta 1, alpha V beta 3 and alpha V beta 5 integrins in avian neural crest cell adhesion and migration on vitronectin Development 1994 120 2687 2702 7525179 Desban N Duband JL Avian neural crest cell migration on laminin: Interaction of the alpha1beta1 integrin with distinct laminin-1 domains mediates different adhesive responses J Cell Sci 1997 110 2729 2744 9427390 Edgeworth FH On the hyomandibula of Selachii, Teleostomi and Ceratodus J Anat 1926 60 173 193 17104094 Edgeworth FH The cranial muscles of vertebrates 1935 Cambridge Cambridge University Press 493 Epperlein HH Lehmann R The ectomesenchymal-endodermal interaction-system (EEIS) of Triturus alpestris in tissue culture. 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Specificity of ligand binding is determined by amino acid sequences in the second and third NH2-terminal repeats of the alpha subunit J Biol Chem 2000 275 20324 20336 10764748 Nissen RM Yan J Amsterdam A Hopkins N Burgess SM Zebrafish foxi one modulates cellular responses to Fgf signaling required for the integrity of ear and jaw patterning Development 2003 130 2543 2554 12702667 Noden DM The role of the neural crest in patterning of avian cranial skeletal, connective, and muscle tissues Dev Biol 1983 96 144 165 6825950 Piotrowski T Nusslein-Volhard C The endoderm plays an important role in patterning the segmented pharyngeal region in zebrafish (Danio rerio) Dev Biol 2000 225 339 356 10985854 Piotrowski T Ahn DG Schilling TF Nair S Ruvinsky I The zebrafish van gogh mutation disrupts tbx1, which is involved in the DiGeorge deletion syndrome in humans Development 2003 130 5043 5052 12952905 Platt JB Ectodermic origin of the cartilages of the head Anat Anz 1893 8 506 509 Roehl H Nusslein-Volhard C Zebrafish pea3 and erm are general targets of FGF8 signaling Curr Biol 2001 11 503 507 11413000 Ruhin B Creuzet S Vincent C Benouaiche L Le Douarin NM Patterning of the hyoid cartilage depends upon signals arising from the ventral foregut endoderm Dev Dyn 2003 228 239 246 14517995 Schilling TF Kimmel CB Segment and cell type lineage restrictions during pharyngeal arch development in the zebrafish embryo Development 1994 120 483 494 8162849 Schilling TF Kimmel CB Musculoskeletal patterning in the pharyngeal segments of the zebrafish embryo Development 1997 124 2945 2960 9247337 Schilling TF Walker C Kimmel CB The chinless mutation and neural crest cell interactions in zebrafish jaw development Development 1996 122 1417 1426 8625830 Schneider RA Helms JA The cellular and molecular origins of beak morphology Science 2003 299 565 568 12543976 Schulte-Merker S Hammerschmidt M Beuchle D Cho KW De Robertis EM Expression of zebrafish goosecoid and no tail gene products in wild-type and mutant no tail embryos Development 1994 120 843 852 7600961 Solnica-Krezel L Schier AF Driever W Efficient recovery of ENU-induced mutations from the zebrafish germline Genetics 1994 136 1401 1420 8013916 Springer TA Folding of the N-terminal, ligand-binding region of integrin alpha-subunits into a beta-propeller domain Proc Natl Acad Sci U S A 1997 94 65 72 8990162 Streisinger G Walker C Dower N Knauber D Singer F Production of clones of homozygous diploid zebra fish (Brachydanio rerio) Nature 1981 291 293 296 7248006 Testaz S Duband JL Central role of the alpha4beta1 integrin in the coordination of avian truncal neural crest cell adhesion, migration, and survival Dev Dyn 2001 222 127 140 11668592 Trainor PA Ariza-McNaughton L Krumlauf R Role of the isthmus and FGFs in resolving the paradox of neural crest plasticity and prepatterning Science 2002 295 1288 1291 11847340 Trevarrow B Marks DL Kimmel CB Organization of hindbrain segments in the zebrafish embryo Neuron 1990 4 669 679 2344406 Veitch E Begbie J Schilling TF Smith MM Graham A Pharyngeal arch patterning in the absence of neural crest Curr Biol 1999 9 1481 1484 10607595 Warga RM Nusslein-Volhard C Origin and development of the zebrafish endoderm Development 1999 126 827 838 9895329 Westerfield M The zebrafish book 1995 Eugene (Oregon) University of Oregon Press Weston JA Yoshida H Robinson V Nishikawa S Fraser ST Neural crest and the origin of ectomesenchyme: Neural fold heterogeneity suggests an alternative hypothesis Dev Dyn 2004 229 118 130 14699583 Whittaker CA DeSimone DW Integrin alpha subunit mRNAs are differentially expressed in early Xenopus embryos Development 1993 117 1239 1249 8404528 Yang JT Rayburn H Hynes RO Embryonic mesodermal defects in alpha 5 integrin-deficient mice Development 1993 119 1093 1105 7508365 Yang JT Rayburn H Hynes RO Cell adhesion events mediated by alpha 4 integrins are essential in placental and cardiac development Development 1995 121 549 560 7539359 Yang JT Bader BL Kreidberg JA Ullman-Cullere M Trevithick JE Overlapping and independent functions of fibronectin receptor integrins in early mesodermal development Dev Biol 1999 215 264 277 10545236

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020275Research ArticleEvolutionGenetics/Genomics/Gene TherapyImmunologyInfectious DiseasesMicrobiologyVirologyZoologyVirusesAncient Adaptive Evolution of the Primate Antiviral DNA-Editing Enzyme APOBEC3G Positive Selection of Primate APOBEC3GSawyer Sara L 1 Emerman Michael 1 2 Malik Harmit S 1 1Division of Basic Sciences, Fred Hutchinson Cancer Research CenterSeattle, Washington, United States of America2Human Biology, Fred Hutchinson Cancer Research CenterSeattle, WashingtonUnited States of America9 2004 20 7 2004 20 7 2004 2 9 e27530 4 2004 21 6 2004 Copyright: © 2004 Sawyer et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Evolution of a Primate Defense Against Intragenomic Infiltrators Adaptation and Immunity Host genomes have adopted several strategies to curb the proliferation of transposable elements and viruses. A recently discovered novel primate defense against retroviral infection involves a single-stranded DNA-editing enzyme, APOBEC3G, that causes hypermutation of HIV. The HIV-encoded virion infectivity factor (Vif) protein targets APOBEC3G for destruction, setting up a genetic conflict between the APOBEC3G and Vif genes. This kind of conflict leads to rapid fixation of mutations that alter amino acids at the protein–protein interface, referred to as positive selection. We show that the APOBEC3G gene has been subject to strong positive selection throughout the history of primate evolution. Unexpectedly, this selection appears more ancient than, and is likely only partially caused by, modern lentiviruses. Furthermore, five additional APOBEC genes in the human genome appear to be engaged in similar genetic conflicts, displaying some of the highest signals for positive selection in the human genome. Despite being only recently discovered, editing of RNA and DNA may thus represent an ancient form of host defense in primate genomes. APOBEC3G, a gene that edits retroviral DNA like HIV, is under positive selection that predates the origin of HIV, implying that RNA/DNA editing represents an ancient form of intragenomic host defense ==== Body Introduction Mobile genetic elements have been in conflict with host genomes for over a billion years. Our own genomes reveal the remarkable effects of retrotransposition, as about 45% of our genomic DNA results directly from this process (Lander et al. 2001). This perennial state of conflict has led eukaryotes to adopt several strategies to curb the proliferation of transposable elements and viruses. These include transcriptional silencing through DNA and histone methylation (Tamaru and Selker 2001; Selker et al. 2003) or RNA interference (Ketting et al. 1999; Tabara et al. 1999; Aufsatz et al. 2002), and even directed mutagenesis of mobile elements (Selker et al. 2003). Despite facing this gauntlet of defense strategies, transposable elements have thrived in eukaryotic genomes (with Neurospora crassa being a notable exception [Selker et al. 2003]) by evolving suitable countermeasures. Our current understanding of the intracellular interplay between host defenses and the assault of transposable elements suffers from a paucity of cases where both counterstrategies have been clearly identified. This is in contrast to extracellular cases, where interactions between viral proteins and either host immune surveillance or host receptors have been well established. Understanding the nature and evolutionary time-frame of intracellular conflict is key to understanding the current state of eukaryotic genomes. Recent studies of host inhibition of HIV have uncovered mutations introduced by DNA editing as a novel means by which host genomes battle viruses intracellularly. Furthermore, the means by which viruses combat this defense strategy are also identified, thus providing an unprecedented opportunity to study the evolution of intracellular genetic conflict. Different human cell lines vary in their susceptibility to HIV infection. The gene responsible for this differential susceptibility was identified as apolipoprotein B–editing catalytic polypeptide 3G (APOBEC3G) (Sheehy et al. 2002), whose product targets HIV and simian immunodeficiency virus (SIV) for editing as their genomes undergo reverse transcription in the cytoplasm of host cells. APOBEC3G is a cytidine deaminase that edits cytosines to uracils in the minus strand DNA copied from the viral RNA genome, resulting in promiscuous guanine-to-adenine (G-to-A) hypermutation of the plus (protein-coding) strand of the viral DNA (Harris et al. 2003; Mangeat et al. 2003; Zhang et al. 2003). APOBEC3G is expressed in testes, ovary, spleen, peripheral blood leukocytes, and T-lymphocytes (Jarmuz et al. 2002; Sheehy et al. 2002) and is packaged in nascent virions and delivered into new host cells along with the viral genome (Harris et al. 2003). How this editing reduces the evolutionary fitness of the virus is not well established. The mutations introduced by the editing process may either directly reduce viral fitness, or target the uracil-containing viral DNA for destruction (Gu and Sundquist 2003). Before the discovery of APOBEC3G, RNA editing was thought to function solely in the diversification of gene-encoded information. The discovery of viral targeting by APOBEC3G represents a new phase in our understanding of nucleic acid editing in primates. APOBEC3G belongs to a family of nine primate genes that catalyze the deamination of cytosine to uracil in DNA and/or RNA (Figure 1). Two other members of this family are known to have important in vivo editing functions. APOBEC1 encodes a protein that site-specifically edits the mRNA of apolipoprotein B (APOB), leading to a truncated form of the APOB lipid-transport protein (Chan et al. 1997), which is important for determining levels of low-density lipoprotein production. Another member of this family, activation-induced deaminase (AID), is important for all steps following V(D)J recombination in B lymphocytes (Fugmann and Schatz 2002), from generating antibody diversity to class-switching events. Significantly, APOBEC1 and AID act within the nucleus, whereas APOBEC3G is exclusively cytoplasmic, which prevents it from mutating “self” DNA molecules. Whereas rodents have a single APOBEC3 gene, humans have at least six (Jarmuz et al. 2002), including APOBEC3G. The functions of the other members of this expanded APOBEC3 cluster are unknown, although APOBEC3C has been shown to be catalytically active, exhibiting DNA mutator activity in a bacterial system that is like APOBEC3G (Harris et al. 2002). More recently, APOBEC3F has also been associated with anti-HIV biological activity (Wiegand et al. 2004; Zheng et al. 2004). Figure 1 The Primate APOBEC Family (A) The human genome contains nine known members of the APOBEC family. AID and APOBEC1 are located approximately 900 kb apart on human Chromosome 12. The primate-specific APOBEC3 cluster of six genes resides on human Chromosome 22, and likely arose through a series of gene duplication events (Jarmuz et al. 2002; Wedekind et al. 2003). The single APOBEC3-like gene found in mouse resides on Chromosome 15 (not shown), which is syntenic to human Chromosome 22 (Sheehy et al. 2002). There is EST evidence for both APOBEC3D and APOBEC3DE (see Materials and Methods), and we treat these as three separate transcripts in our analysis because currently there is no evidence for the relevant protein products. (B) All members of the APOBEC family contain an active site that encodes a zinc-dependent cytidine deaminase domain with the HXE, PCXXC signature (Mian et al. 1998), a linker peptide, and a pseudoactive domain (Navaratnam et al. 1998; Jarmuz et al. 2002). The active and pseudoactive domains are related by structure only, and likely originated from a gene duplication event followed by degeneration of the catalytic activity of the pseudoactive domain. Several members of the human APOBEC3 gene cluster (APOBEC3B, 3DE, 3F, and3G) have undergone an additional duplication/recombination event and now contain two each of the active and pseudoactive sites (Jarmuz et al. 2002; Wedekind et al. 2003), as does the single APOBEC3-like gene found in mouse. DOI:10.1371/journal.pbio.0020275.g001 Most lentiviruses encode an accessory gene, virion infectivity factor (Vif), whose product counteracts the antiviral activity of APOBEC3G. Vif interacts with APOBEC3G and targets it for ubiquitination and proteasome-dependent degradation, thus preventing its incorporation into nascent virions (Madani and Kabat 1998; Simon et al. 1998; Marin et al. 2003; Sheehy et al. 2003; Stopak et al. 2003; Yu et al. 2003). This interaction can be species-specific, as the Vif protein of one lentivirus will counteract APOBEC3G from its host species, but not always the APOBEC3G from a different primate species (Mariani et al. 2003). Thus, APOBEC3G and Vif are predicted to be under selection to decrease and enhance, respectively, their interaction with one another, each driving rapid change in the other. Genetic conflicts like this one are predicted to result in the rapid fixation of mutations that alter amino acids, specifically those that affect this protein–protein interaction. This scenario is referred to as positive selection and is commonly seen in host–pathogen interactions. In this report, we directly test this prediction by studying the paleontology of selective pressures that have acted on APOBEC3G in the primate lineage, to ask whether APOBEC3G has been subject to positive selection, and to date the origins of this genetic conflict. We find that APOBEC3G has been under remarkably strong positive selection, and has undergone several episodes of adaptive evolution throughout the history of primates. Unexpectedly, we find that the positive selection acting on APOBEC3G predates modern lentiviruses, indicating that a more ancient, and perhaps ongoing, conflict has shaped its evolution. We also report evidence for strong positive selection acting on a majority of the APOBEC genes, suggesting that this family of genes may have expanded in primate genomes for genome defense via RNA/DNA editing. Results/Discussion APOBEC3G Has Been Evolving under Positive Selection in Primates To determine what selective pressures have shaped APOBEC3G evolution, we sequenced the APOBEC3G gene from a panel of primate genomes representing 33 million years of evolution. We sequenced the complete APOBEC3G coding sequence (approximately 1,155 bp) from ten primate species, including four hominids (other than human), four Old World monkeys (OWMs), and two New World monkeys (NWMs) (Figure 2). A phylogeny constructed using either complete APOBEC3G sequences or individual exons (unpublished data) is congruent to the widely accepted primate phylogeny (Purvis 1995), indicating that all sequences isolated by our PCR strategy are truly orthologous. Figure 2 APOBEC3G Has Been Under Positive Selection for at Least 33 Million Years The ω values and actual numbers of non-synonymous and synonymous changes (R:S, included in parentheses) in APOBEC3G are indicated on the accepted primate phylogeny (Purvis 1995) that includes five hominids, five OWMs, and two NWMs. OWMs diverged from hominids about 23 million years ago, whereas NWMs diverged around 33 million years ago (Nei and Glazko 2002). ω values were calculated using the PAML package of programs using the free-ratio model that allows ω to vary along each branch. In some instances, zero synonymous substitutions lead to an apparent ω of infinity. HIV/SIV-infected species are indicated by asterisks. DOI:10.1371/journal.pbio.0020275.g002 The hallmark of positive selection is an excess of non-synonymous substitutions (which alter the amino acid being encoded) relative to synonymous substitutions (which retain the encoded amino acid). Because non-synonymous changes are more likely to be deleterious, they are typically culled out by selection (Hurst 2002) (referred to as purifying or negative selection). Therefore, in protein-coding open reading frames, the number of observed changes per synonymous site (Ks) usually exceeds the number of observed changes per non-synonymous site (Ka). In the case of the APOBEC3G, however, we found that a majority of branches of the phylogeny (including internal branches) show evidence of positive selection (defined as Ka/Ks [ω] greater than one; see Figure 2). This implies that the APOBEC3G has been subject to positive selection throughout the history of primate evolution. In support of this conclusion, all pairwise comparisons of the entire APOBEC3G gene between the various primates have ω greater than one (unpublished data). Maximum likelihood analysis using the PAML (phylogenetic analysis by maximum likelihood) suite of programs also finds strong evidence that the full-length APOBEC3G gene has been subject to positive selection (p < 10–13). Numbers in parenthesis in Figure 2 indicate the actual number of non-synonymous and synonymous changes (R:S) that have occurred along each branch. The average Ks in APOBEC3G is not unusually low; it is about 0.09 between hominids and OWMs and 0.26 between hominids and NWMs, compared to 0.08 and 0.15 respectively for comparisons of various intronic and noncoding regions of primate genomes (Li 1997). Thus, we can rule out the possibility that selection has led to deflated Ks values in APOBEC3G that lead to artificially high ω ratios. Indeed, these high ω ratios can be explained only by a significantly higher rate of non-synonymous substitutions. Of the primates analyzed, lentiviral infections have been observed only in the African monkeys, chimpanzees, and humans (Peeters and Courgnaud 2002). HIV/SIV-infected species are indicated with asterisks in Figure 2. Estimating the age of lentiviruses is difficult because of their rapid rate of evolution and frequent cross-species transfer, but it has been suggested that primate lentiviruses are no older than 1 million years (Sharp et al. 1999). The presence of modern lentiviruses appears to bear no correlation to either the presence or the strength of positive selection. For instance, the lineage leading to hominids has a ω of 3.3, the highest overall. The positive selection acting on APOBEC3G thus appears to predate modern lentiviruses, and interactions with lentiviral Vif proteins are not likely to be a major cause of this unusually strong signal of positive selection. In support of this conclusion, HIV has not been in the human population long enough to account for the positive selection of APOBEC3G specific to the human lineage (a 7:0 R:S ratio) arguing that, although the positive selection of Vif may be explained in large part by that of APOBEC3G, the reverse is certainly not the case. Positive Selection in APOBEC3G Is Not Localized to One Domain We wanted to identify the specific domains in APOBEC3G that were subject to positive selection, because this might suggest the driving evolutionary force. For instance, the positive selection in the major histocompatibility complex proteins is confined to only small segments of the protein that constitute the antigen-recognition site (Hughes and Nei 1988; Yang and Swanson 2002), because only these sites participate in protein–protein interactions subject to genetic conflict. All members of the APOBEC family contain a similar domain organization (see Figure 1B) that consists of an active site that encodes a zinc-dependent cytidine deaminase domain with the HXE, PCXXC (H, histidine; X, any amino acid; E, glutamic acid; P, proline; C, cysteine) signature (Mian et al. 1998), a linker peptide, and a pseudoactive domain (Navaratnam et al. 1998; Jarmuz et al. 2002). The active and pseudoactive domains are believed to have originated from a gene duplication event followed by degeneration of the catalytic activity of the pseudoactive domain. APOBEC3G and some other APOBEC genes have also undergone a second gene duplication/fusion event (Jarmuz et al. 2002; Wedekind et al. 2003). Representative examples of pairwise (sliding window) comparisons of Ka/Ks ratios between two hominids, two OWMs, and two NWMs suggest that the same domain of APOBEC3G has not been subject to positive selection throughout primate evolution (Figure 3A–3C). In both the hominid and NWM comparisons, the second half of the gene shows evidence of positive selection (Figure 3A and 3C), but in an OWM comparison, it is the first half that is under positive selection (Figure 3B). When the APOBEC3G gene is divided into structural domains, we find that all domains, including the active site domains, have undergone multiple distinct episodes of positive selection (Figure S1). This highly unusual pattern suggests that the genetic conflicts that have shaped APOBEC3G evolution have involved episodic protein–protein interactions with different parts of the entire APOBEC3G protein. Figure 3 Episodic Positive Selection on Different Regions of the APOBEC3G Gene (A–C) Sliding window (300-bp window; 50-bp slide) analysis of Ka and Ks was performed on three representative pairs of primate APOBEC3G sequences, between two hominids (human–orangutan) (A), between two OWMs (crested macaque–baboon) (B), and between two NWMs (tamarin–woolly monkey) (C). Ka/Ks, Ka, and Ks are plotted against the length of the gene (with a schematic of protein domains along the x-axis) to illustrate that different domains of APOBEC3G have undergone positive selection, depending on which lineage is examined. The value for ω, indicated by Ka/Ks, is not shown for part of the crested macaque–baboon comparison (B), because Ks is zero in this region (see plot below). (D) A schematic of the domains of human APOBEC3G illustrates the N-terminal domain (aa 1–29), the two active sites (aa 30–120 and 215–311), and the pseudoactive sites (aa 162–214 and 348–384). Also illustrated is the Vif-interaction domain of APOBEC3G (aa 54–124) (Conticello et al. 2003) as well as the single amino acid residue responsible for species-specific sensitivity to Vif (aspartic acid 128; cross shape in linker 1) (Bogerd et al. 2004; Schrofelbauer et al. 2004). PAML (Yang 1997) was used to identify individual residues (codons) that have significant posterior probabilities of ω greater than 1.0 (see Materials and Methods). Those codons with posterior probabilities greater than 0.95 and greater than 0.99 are indicated by open and closed inverted triangles, respectively (listed in Figures S2 and S3). This represents only a subset of the residues that are likely to be under positive selection, highlighting those residues that have repeatedly undergone non-synonymous substitutions. For instance, residue 128 is not highlighted, as it has a posterior probability of only 0.55 because it has undergone only one fixed non-synonymous change (along the OWM lineage). Domains have been defined by protein sequence alignment to APOBEC1 (Jarmuz et al. 2002). The first pseudoactive domain is likely to include in its C-terminus a second duplication of the N-terminal domain, although this boundary cannot be resolved because of sequence divergence. DOI:10.1371/journal.pbio.0020275.g003 We also employed a maximum-likelihood approach (see Materials and Methods), using the PAML suite of programs (Yang 1997) to identify the specific residues that have been repeatedly subject to positive selection in primates. These analyses (in the best fit model) identify 30% of the codons as having evolved under stringent purifying selection (ω of approximately zero). These include the catalytically important residues that are invariant throughout all APOBECs. The same analysis also identifies approximately 30% of the codons as having evolved under positive selection with an average ω of nearly 3.5 (residues that are evolving without selective constraint would be expected to have an average ω of one). Even among adaptively evolving proteins, this is an unusually high proportion of sites, once again implicating a large number of residues in APOBEC3G as having participated in some kind of genetic conflict. Of these, several residues are identified as being under positive selection with high confidence (posterior probability greater than 0.95, inverted triangles in Figure 3D). In simulations using datasets with comparable levels of sequence divergence and strength of positive selection to our APOBEC3G dataset (tree length = 1.59), PAML analyses were found to be highly accurate in identifying residues subject to positive selection (Anisimova et al. 2002). The schematic in Figure 3D highlights the region where Vif is believed to interact with human APOBEC3G (Conticello et al. 2003). It also highlights the single amino acid residue (cross symbol in linker 1) that is responsible for the species-specific interactions seen between Vif and APOBEC3G in African green monkeys (SIV) and humans (HIV) (Bogerd et al. 2004; Schrofelbauer et al. 2004). There is a noticeable lack of correlation between the sites on APOBEC3G that are important for Vif interaction and those sites that are identified by PAML with high confidence, supporting our earlier conclusion that Vif interactions have played only a small role in dictating the positive selection of APOBEC3G. Other APOBEC Genes May Participate in Host Defense The discovery that APOBEC3G is involved in host defense was predicated on the tissue-specific inhibition of HIV. Other studies have investigated a possible inhibitory role of other APOBEC genes but found that only APOBEC3G and APOBEC3F exert an antiviral defense against HIV (Mariani et al. 2003; Wiegand et al. 2004; Zheng et al. 2004). An unbiased look at selective pressures among other APOBEC genes could reveal clues to their function. We calculated whole-gene Ka/Ks ratios for other members of the human APOBEC family, using orthologs from the chimpanzee genome project (Table 1, second column). This analysis reveals strong evidence of purifying selection acting on AID and APOBEC3A but positive selection acting on APOBEC3B and APOBEC3DE (as well as APOBEC3D and APOBEC3E alone) in addition to APOBEC3G. There is no expression evidence for APOBEC3E, and it is unclear whether it occurs as a stand-alone gene, but its ω ratio of 5.6 is among the highest seen for any human–chimp comparison and argues strongly that it is a functional gene and an active participant in some form of genetic conflict. Whole-gene analyses are notoriously poor at identifying specific domains of positive selection, especially when the rest of the gene is subject to purifying selection. We therefore performed a sliding window Ka/Ks test (Endo et al. 1996), which also reveals positive selection acting on APOBEC3F (amino acids [aa] 117–250). Table 1 Positive Selection throughout the APOBEC3 Gene Cluster * p < 0.05; ** p < 0.01; *** p < 0.001 a189 out of 200 amino acids analyzed in human–chimp comparison b376 out of 383 amino acids analyzed in human–chimp comparison c196 out of 202 amino acids analyzed in human–chimp comparison d119 out of 186 amino acids analyzed in human–chimp comparison e314 out of 387 amino acids analyzed in human–chimp comparison N.D., not determined ω ratios were calculated for human–chimp orthologs, and tested against the neutral expectation that ω = 1 (p-values obtained from simulations performed in K-estimator). Values of ω significantly less and greater than one imply purifying and positive/diversifying selection, respectively. We were unable to obtain enough APOBEC2 sequence from the chimpanzee genome project to do this analysis, so APOBEC2 was sequenced from orangutan. When sliding window analysis was performed, APOBEC1 (human–orangutan; see Figure 4), APOBEC3C (human–gorilla), and APOBEC3F (human–chimp) show regions of both significant positive and purifying selection. Windows of positive selection in these genes are indicated as amino acid ranges (e.g., aa 1–100 for APOBEC1) along with the associated ω values and statistical significance DOI:10.1371/journal.pbio.0020275.t001 The limited divergence between human and chimp genomes leads to some comparisons not being informative enough to detect selection (APOBEC1 and APOBEC3C), and there was insufficient chimpanzee sequence available in one case (APOBEC2). To gain further information about these genes, we sequenced them from either orangutan or gorilla (Table 1, third column). These comparisons reveal that strong purifying selection has acted on APOBEC2, but positive selection can be detected in both APOBEC1 (aa 1–100; also see Figure 4) and APOBEC3C (aa 34–133). Although we might have expected APOBEC1 to be evolving only under purifying selection based on its important editing of APOB mRNA, our analysis suggests that APOBEC1 has also participated in some kind of genetic conflict involving its first active site, and suggests that the rapid evolution of APOBEC1 seen previously in mouse–rat comparisons may also be due to positive selection (Nakamuta et al. 1995). Figure 4 shows representative sliding window analyses of genes undergoing gene-wide purifying (APOBEC2) and positive (APOBEC3E) selection. These findings greatly extend the current understanding of the APOBEC family, and implicate a majority of APOBEC genes as participants in host defense. They also raise the possibility of other editing systems being involved in genome defense; for instance, hepatitis delta virus is known to be edited by adenosine deaminase (Polson et al. 1996). Figure 4 Selective Pressures on APOBEC1, APOBEC2, and APOBEC3E Sliding window analysis (250-bp window; 50-bp slide) was performed on three APOBEC genes. Although APOBEC1 demonstrates purifying selection when the whole gene is analyzed (Table 1), the sliding window analysis of a human–orangutan comparison reveals a window (aa 1–100) in the first active site (dark gray bar), which shows evidence of positive selection (p < 0.01). Sliding window analysis of APOBEC2, which is also evolving under purifying selection (Table 1), does not show any windows where ω is greater than one. APOBEC3E, which gives the strongest signal for positive selection (Table 1), has ω greater than one for almost all windows. (Note that ω is not plotted where Ks = 0). DOI:10.1371/journal.pbio.0020275.g004 Human APOBEC3G Polymorphisms and AIDS The antiviral activity of APOBEC3G and the excess of non-synonymous changes specific to human APOBEC3G (see Figure 2) implicate non-synonymous polymorphisms as being functionally very important. Because binding by Vif inhibits APOBEC3G's antiviral ability, we might predict that APOBEC3G should be subject to overdominant selection (heterozygous individuals being at a selective advantage), especially in populations with a high incidence of HIV infection, since different alleles of APOBEC3G may have different susceptibility to various viral strains. The action of APOBEC3G on viral evolution could also be complex because, although it is ineffective as an antiviral mechanism in the presence of Vif, its action could also result in an increased likelihood of adaptive changes and viral diversity in the host due to the introduced G-to-A hypermutations. Polymorphisms in APOBEC3G may thus have direct impact on the progression time from initial HIV infection to AIDS, and should be investigated as such. What Drives the Long-Term Evolution of APOBEC3G? The evidence for positive selection of APOBEC3G does not identify the biological step that exerts this selective pressure. Formally, this step could be the yet-undefined mechanism by which APOBEC3G is packaged into virions, the interaction of APOBEC3G with Vif-like destruction proteins encoded by other viruses, and/or its interaction with the proteasome machinery. APOBEC3G may indeed interact with other viruses, because G-to-A hypermutation—a hallmark of the single-stranded DNA–editing activity of APOBEC3G-like enzymes—has been observed in some nonlentivirus viruses (Vartanian et al. 2003), and because APOBEC3G has recently been shown to inhibit the replication of the hepatitis B virus upon deliberate coexpression (Turelli et al. 2004). However, this inhibition of hepatitis B is not correlated with G-to-A hypermutation, suggesting that APOBEC3G may also inhibit viral replication independent of its catalytic activity. The ancient, constant pressure of positive selection on APOBEC3G in primates raises the possibility that at least some of its evolution may be explained by a struggle not in the lymphocytes, but in the germline, where APOBEC3G is also abundantly expressed (Jarmuz et al. 2002), and where genome-restricted mobile genetic elements need to transpose to ensure survival. Of the three main classes of eukaryotic mobile elements, only two are active in humans and, most likely, other primate genomes. The first and major class includes the LINE1 (long interspersed element–1) non-LTR (long terminal repeat) retroposons that are not a likely target for APOBEC3G, because they carry out their reverse transcription in the nucleus (APOBEC3G is restricted to the cytoplasm). A second class, the LTR-bearing human endogenous retroviruses (HERVs), is identical in many aspects of its life cycle to retroviruses. While the selective disadvantage to an individual organism conferred by endogenous retroviruses may pale in comparison to that of pathogenic viruses, over time the steady retrotransposition of endogenous retroviruses is likely to be more detrimental to a species than scattered, episodic interactions with viruses. Thus, the constant efforts of HERVs to jockey for evolutionary dominance may provide a more likely explanation for the positive selection of APOBEC3G and other APOBEC genes in primate genomes. Materials and Methods Genomic DNA sequencing of primate samples. Genomic DNA was obtained from Coriell (Camden, New Jersey, United States). Species and Coriell repository numbers are: Pan troglodytes (chimpanzee) (NAO3448A), Pan paniscus (bonobo) (NGO5253), Gorilla gorilla (gorilla) (NG05251B), Pongo pygmaeus (orangutan) (NAO4272), Macaca nigra (Celebes crested macaque) (NG07101), Macaca fascicularis (crab-eating macaque) (NA03446), Erythrocebus patas (patas monkey) (NG06254), Lagothrix lagotricha (common woolly monkey) (NG05356), and Saguinus labiatus (red-chested mustached tamarin) (NG05308). Papio anubis (baboon) DNA was a personal gift from Dr. Trent Colbert. The APOBEC3G, APOBEC1, APOBEC2, and APOBEC3C genes were amplified exon-by-exon from genomic DNA with PCR Supermix High Fidelity (Invitrogen, Carlsbad, California, United States), and PCR products were sequenced directly. PCR and sequencing primers are shown in Table S1. The human APOBEC3G sequence was obtained from the Ensembl database of the human genome project (ENSG00000100289). The Chlorocebus aethiops (African green monkey) APOBEC3G sequence (GenBank AY331714.1) is missing the last 21 bp of the coding sequence because it was sequenced from mRNA (Mariani et al. 2003) in a previous study. Exon–intron boundaries are conserved, except in APOBEC3G from NWMs (woolly monkey and tamarin) where the “AG” directly 5′ of the eighth coding exon is missing. Sequences have been deposited in GenBank under the following accession numbers: APOBEC3G (AY622514–AY622593), APOBEC3C (AY622594–AY622597), APOBEC2 (AY622598–AY622599), APOBEC1 (AY622600–AY622604). Sequences of other APOBEC family members. Human sequences were obtained from the Ensembl or GenBank databases: APOBEC1 (ENSG00000111701), APOBEC2 (ENSG00000124701), AID (ENSG00000111732), APOBEC3A (ENSG00000128383), APOBEC3B (NM_004900.3), APOBEC3C (ENSG00000179750), APOBEC3DE (ENSG00000179007), and APOBEC3F (ENSG00000128394). Transcripts for both APOBEC3D (NM_152426) and APOBEC3DE (BC017022.1) exist in the database. Chimp sequences were obtained from orthology to human genes assigned on the University of California at Santa Cruz Genome Bioinformatics Website (http://www.genome.ucsc.edu). All orthologous chimp exons were checked for AG and GT flanking the 5′ and 3′ boundaries, respectively, an indication that human splice sites are conserved. The mouse APOBEC3 protein sequence can be found in GenBank (NP_084531.1). Sequence analysis. DNA sequences were aligned using Clustal_X (Thompson et al. 1997), with hand alignment of small indels based on amino acid sequence. Changes along each lineage (see Figure 2) were assigned using parsimony and counted by hand. Changes at 18 positions could not be unambiguously assigned as non-synonymous or synonymous and were excluded from the R:S ratios. Ka and Ks for pairwise comparisons (Figures 3A–3C and 4; Table 1), as well as their confidence values, were calculated using the K-estimator software package (Comeron 1999). For confidence values, simulations were carried out under the condition where Ka equals Ks and compared to actual Ka from that region, and multiple parameters for transition:transversion ratios were simulated. Maximum likelihood analysis was performed with the PAML software package (Yang 1997). Global ω ratios for the tree (see Figure 2) were calculated by a free-ratio model, which allows ω to vary along different branches. To detect selection, the multiple alignments were fitted to either the F3×4 or F61 models of codon frequencies. We then compared the log-likelihood ratios of the data using different NSsites models: model 1 (two-state, neutral, ω > 1 disallowed) to model 2 (similar to model 1 but ω >1 allowed), and model 7 (fit to a beta distribution, ω > 1 disallowed) to model 8 (similar to model 7 but ω >1 allowed). In both cases, permitting sites to evolve under positive selection gave a much better fit to the data (p < 10−13) with a significant fraction of the sites (more than 30%) predicted to evolve at average ω ratios greater than 3.5 (see Figure S2 for details). These analyses also identified certain amino acid residues with high posterior probabilities (greater than 0.95) of having evolved under positive selection (Figures 3D and S2). Supporting Information Figure S1 Episodic Evolution of APOBEC3G Protein Domains The evolutionary history of APOBEC3G is represented. R:S ratios are indicated along each branch of the primate cladogram. The N-terminal domain (A) has undergone adaptive evolution in at least three distinct periods. Despite being only 29 codons long, this domain has accumulated ten non-synonymous changes and only two synonymous changes in the African green monkey since it and the patas monkey last shared a common ancestor. Similarly, the orangutan has retained eight non-synonymous changes and no synonymous changes since it split from the rest of the hominids. Finally, a ratio of 6:0 R:S changes is seen in the split between the NWMs and the common ancestor of OWMs and hominids. Surprisingly, even the two active site structures of APOBEC3G (B and E) show evidence for adaptive evolution (despite all the putative catalytic residues being conserved), including along the branch leading to the common ancestor of all hominids. The first pseudoactive domain (D) acquired ten non-synonymous and no synonymous changes since the hominids split from the OWMs. (343 KB PDF). Click here for additional data file. Figure S2 PAML Analysis of APOBEC3G Maximum likelihood analysis was performed on APOBEC3G sequences using the PAML software package. To detect selection, the multiple alignments were fitted to either the F3×4 (A) or F61 (B) models of codon frequencies. We compared the log-likelihood ratios of the data using comparisons of different NSsites models: model 1 (two-state, neutral, ω > 1 disallowed) versus model 2 (similar to model 1 but ω > 1 allowed) and model 7 (fit to a beta distribution, ω > 1 disallowed) versus model 8 (similar to model 7 but ω > 1 allowed). In both cases, permitting sites to evolve under positive selection gave a much better fit to the data (p <10−13) (C) with a significant fraction of the sites (more than 30%) predicted to evolve at average ω ratios greater than 3.5. These analyses also identified certain amino acid residues with high posterior probabilities (greater than 0.95) of having evolved under positive selection (A and B). (60KB PDF). Click here for additional data file. Figure S3 Alignment of APOBEC3G Protein Sequences The individual domains of the APOBEC3G protein are demarcated. Catalytically important residues are highlighted in bold, and those residues identified by PAML analysis as being under positive selection are indicated with gray shading. Blue shading highlights the single amino acid residue that can switch specificity of Vif interaction with APOBEC3G. AGM, African green monkey. (46 KB PDF). Click here for additional data file. Table S1 Complete List of Primers Used in This Study (39 KB PDF). Click here for additional data file. Accession numbers The GenBank (http://www.ncbi.nlm.nih.gov/) and Ensembl (http://www.ensembl.org/) accession numbers for the genes and gene products discussed in this paper are as follows. GenBank: APOBEC1 (AY622600–AY622604), APOBEC2 (AY622598–AY622599), mouse APOBEC3 (NP_084531.1), human APOBEC3B (NM_004900.3), APOBEC3C (AY622594–AY622597), APOBEC3D (NM_152426), APOBEC3DE (BC017022.1), APOBEC3G (AY622514–AY622593), and African green monkey APOBEC3G (AY331714.1). Ensembl (all human sequences): APOBEC1 (ENSG00000111701), APOBEC2 (ENSG00000124701), AID (ENSG00000111732), APOBEC3A (ENSG00000128383), APOBEC3C (ENSG00000179750), APOBEC3DE (ENSG00000179007), APOBEC3F (ENSG00000128394), and APOBEC3G (ENSG00000100289). Coriell (http://www.coriell.undmj.edu/) repository numbers for primate genomic DNAs are Pan troglodytes (chimpanzee) (NAO3448A), Pan paniscus (bonobo) (NGO5253), Gorilla gorilla (gorilla) (NG05251B), Pongo pygmaeus (orangutan) (NAO4272), Macaca nigra (Celebes crested macaque) (NG07101), Macaca fascicularis (long-tailed macaque) (NA03446), Erythrocebus patas (patas monkey) (NG06254), Lagothrix lagotricha (common woolly monkey) (NG05356), and Saguinus labiatus (red-chested mustached tamarin) (NG05308). We would like to thank Steve Henikoff, Danielle Vermaak, Maxine Linial, and Janet Young for their valuable comments on the manuscript, and Jorja Henikoff for help with the PAML implementation. SLS and HSM are supported by startup funds from the Fred Hutchinson Cancer Research Center, and ME is supported by National Institutes of Health grant R37 AI30927. Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. SLS, ME, and HSM conceived and designed the experiments. SLS performed the experiments. SLS and HSM analyzed the data. SLS, ME, and HSM wrote the paper. Academic Editor: Paul Harvey, University of Oxford Citation: Sawyer SL, Emerman M, Malik HS (2004) Ancient adaptive evolution of the primate antiviral DNA-editing enzyme APOBEC3G. PLoS Biol 2(9): e275. Abbreviations aaamino acids AIDactivation-induced deaminase APOBapolipoprotein B APOBECapolipoprotein B–editing catalytic polypeptide G-to-Aguanine-to-adenine Kanumber of substitutions per non-synonymous site Ksnumber of substitutions per synonymous site LTRlong terminal repeat NWMNew World monkey OWMOld World monkey PAMLphylogenetic analysis by maximum likelihood R:Sratio of non-synonymous to synonymous changes SIVsimian immunodeficiency virus Vifvirion infectivity factor ωKa/Ks ratio ==== Refs References Anisimova M Bielawski JP Yang Z Accuracy and power of Bayes prediction of amino acid sites under positive selection Mol Biol Evol 2002 19 950 958 12032251 Aufsatz W Mette MF van der Winden J Matzke AJ Matzke M RNA-directed DNA methylation in Arabidopsis Proc Natl Acad Sci U S A 2002 99 Suppl 4 16499 16506 12169664 Bogerd HP Doehle BP Wiegand HL Cullen BR A single amino acid difference in the host APOBEC3G protein controls the primate species specificity of HIV type 1 virion infectivity factor Proc Natl Acad Sci U S A 2004 101 3770 3774 14999100 Chan L Chang BHJ Nakamuta M Li WH Smith LC Apobec-1 and apolipoprotein B mRNA editing Biochim Biophys Acta 1997 1345 11 26 9084497 Comeron JM K-estimator: Calculation of the number of nucleotide substitutions per site and the confidence intervals Bioinformatics 1999 15 763 764 10498777 Conticello SG Harris RS Neuberger MS The Vif protein of HIV triggers degradation of the human antiretroviral DNA deaminase APOBEC3G Curr Biol 2003 13 2009 2013 14614829 Endo T Ikeo K Gojobori T Large-scale search for genes on which positive selection may operate Mol Biol Evol 1996 13 685 690 8676743 Fugmann SD Schatz DG One AID to unite them all Science 2002 295 1244 1245 11847327 Gu Y Sundquist WI Good to CU Nature 2003 424 21 22 12840737 Harris RS Petersen-Mahrt SK Neuberger MS RNA editing enzyme APOBEC1 and some of its homologs can act as DNA mutators Mol Cell 2002 10 1247 1253 12453430 Harris RS Bishop KN Sheehy AM Craig HM Petersen-Mahrt SK DNA determination mediates innate immunity to retroviral infection Cell 2003 113 803 809 12809610 Hughes AL Nei M Pattern of nucleotide substitution at major histocompatibility complex class I loci reveals overdominant selection Nature 1988 335 167 170 3412472 Hurst LD The Ka/Ks ratio: Diagnosing the form of sequence evolution Trends Genet 2002 18 486 487 12175810 Jarmuz A Chester A Bayliss J Gisbourne J Dunham I An anthropoid-specific locus of orphan C to U RNA-editing enzymes on chromosome 22 Genomics 2002 79 285 296 11863358 Ketting RF Haverkamp TH van Luenen HG Plasterk RH Mut-7 of C. elegans required for transposon silencing and RNA interference, is a homolog of Werner syndrome helicase and RNaseD Cell 1999 99 133 141 10535732 Lander ES Linton LM Birren B Nusbaum C Zody MC Initial sequencing and analysis of the human genome Nature 2001 409 860 921 11237011 Lau P Zhu H Baldini A Charnsangavej C Chan L Dimeric structure of a human apolipoprotein B mRNA editing protein and cloning and chromosomal localization of its gene Proc Natl Acad Sci U S A 1994 91 8522 8526 8078915 Li WH Molecular evolution 1997 Sunderland (Massachusetts) Sinauer Associates 487 Liao W Hong SH Chan BHJ Rudolph FB Clark SC APOBEC-2, a cardiac- and skeletal muscle-specific member of the cytidine deaminase supergene family Biochem Biophys Res Commun 1999 260 398 404 10403781 Madani N Kabat D An endogenous inhibitor of human immunodeficiency virus in human lymphocytes is overcome by the viral Vif protein J Virol 1998 72 10251 10255 9811770 Mangeat B Turelli P Caron G Friedli M Perrin L Broad antiretroviral defence by human APOBEC3G through lethal editing of nascent reverse transcripts Nature 2003 424 99 103 12808466 Mariani R Chen D Schrofelbauer B Navarro F Konig R Species-specific exclusion of APOBEC3G from HIV-1 virions by Vif Cell 2003 114 21 31 12859895 Marin M Rose KM Kozak SL Kabat D HIV-1 Vif protein binds the editing enzyme APOBEC3G and induces its degradation Nat Med 2003 9 1398 1403 14528301 Mian IS Moser MJ Holley WR Chatterjee A Statistical modelling and phylogenetic analysis of a deaminase domain J Comput Biol 1998 5 57 72 9541871 Muramatsu M Sankaranand VS Anant S Sugai M Kinoshita K Specific expression of activation-induced cytidine deaminase (AID), a novel member of the RNA-editing deaminase family in germinal center B cells J Biol Chem 1999 274 18470 18476 10373455 Nakamuta M Oka K Krushkal J Kobayashi K Yamamoto M Alternative mRNA splicing and differential promoter utilization determine tissue-specific expression of the apolipoprotein B mRNA-editing protein (Apobec1) gene in mice J Biol Chem 1995 270 13042 13056 7768898 Navaratnam N Fujino T Bayliss J Jarmuz A How A Escherichia coli cytidine deaminase provides a molecular model for ApoB RNA editing and a mechanism for RNA substrate recognition J Mol Biol 1998 275 695 714 9466941 Nei M Glazko GV The Wilhelmine E. Key 2001 invitational lecture. Estimation of divergence times for a few mammalian and several primate species J Hered 2002 93 157 164 12195029 Peeters M Courgnaud V Overview of primate lentiviruses and their evolution in non-human primates in Africa. HIV Sequence Database. Available: http://www.hiv.lanl.gov/content/hiv-db/REVIEWS/PEETERS2002/Peeters2002.html via the Internet 2002 Accessed 1 July 2004 Polson AG Bass BL Casey JL RNA editing of hepatitis delta virus antigenome by dsRNA-adenosine deaminase Nature 1996 380 454 456 8602246 Purvis A A composite estimate of primate phylogeny Philos Trans R Soc Lond B Biol Sci 1995 348 405 421 7480112 Schrofelbauer B Chen D Landau NR A single amino acid of APOBEC3G controls its species-specific interaction with virion infectivity factor (Vif) Proc Natl Acad Sci U S A 2004 101 3927 3932 14978281 Selker EU Tountas NA Cross SH Margolin BS Murphy JG The methylated component of the Neurospora crassa genome Nature 2003 422 893 897 12712205 Sharp PM Bailes E Robertson DL Gao F Hahn BH Origins and evolution of AIDS viruses Biol Bull 1999 196 338 342 10390833 Sheehy AM Gaddis NC Choi JD Malim MH Isolation of a human gene that inhibits HIV-1 infection and is supressed by the viral Vif protein Nature 2002 418 646 650 12167863 Sheehy AM Gaddis NC Malim MH The antiretroviral enzyme APOBEC3G is degraded by the proteasome in response to HIV-1 Vif Nat Med 2003 9 1404 1407 14528300 Simon JH Gaddis NC Fouchier RA Malim MH Evidence for a newly discovered cellular anti-HIV-1 phenotype Nat Med 1998 4 1397 1400 9846577 Stopak K de Noronha C Yonemoto W Greene WC HIV-1 Vif blocks the antiviral activity of APOBEC3G by impairing both its translation and intracellular stability Mol Cell 2003 12 591 601 14527406 Tabara H Sarkissian M Kelly WG Fleenor J Grishok A The rde-1 gene, RNA interference, and transposon silencing in C. elegans Cell 1999 99 123 132 10535731 Tamaru H Selker EU A histone H3 methyltransferase controls DNA methylation in Neurospora crassa Nature 2001 414 277 283 11713521 Thompson JD Gibson TJ Plewniak F Jeanmougin F Higgins DG The CLUSTAL_X windows interface: Flexible strategies for multiple sequence alignment aided by quality analysis tools Nucleic Acids Res 1997 25 4876 4882 9396791 Turelli P Mangeat B Jost S Vianin S Trono D Inhibition of hepatitis B virus replication by APOBEC3G Science 2004 303 1829 15031497 Vartanian JP Sommer P Wain-Hobson S Death and the retrovirus Trends Mol Med 2003 9 409 413 14557052 Wedekind JE Dance GSC Sowden MP Smith HC Messenger RNA editing in mammals: New members of the APOBEC family seeking roles in the family business Trends Genet 2003 19 207 216 12683974 Wiegand HL Doehle BP Bogerd HP Cullen BR A second human antiretroviral factor, Apobec3F, is suppressed by the HIV-1 and HIV-2 Vif proteins EMBO J 2004 23 2451 2458 15152192 Yang Z PAML: A program package for phylogenetic analysis by maximum likelihood Comput Appl Biosci 1997 13 555 556 9367129 Yang Z Swanson WJ Codon-substitution models to detect adaptive evolution that account for heterogeneous selective pressures among site classes Mol Biol Evol 2002 19 49 57 11752189 Yu XH Yu YK Liu BD Luo K Kong W Induction of APOBEC3G ubiquitination and degradation by an HIV-1 Vif-Cul5-SCF complex Science 2003 302 1056 1060 14564014 Zhang H Yang B Pomerantz RJ Zhang C Arunachalam SC The cytidine deaminase CEM15 induces hypermutation in newly synthesized HIV-1 DNA Nature 2003 424 94 98 12808465 Zheng YH Irwin D Kurosu T Tokunaga K Sata T Human Apobec3F is another host factor that blocks human immunodeficiency virus type 1 replication J Virol 2004 78 6073 6076 15141007

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PMC479043

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PLoS Biol. 2004 Sep 20; 2(9):e275

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PLoS Biol

10.1371/journal.pbio.0020275

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020278SynopsisGenetics/Genomics/Gene TherapyPhysiologyDrosophilaThe Molecular Biology of Wound Healing Synopsis8 2004 20 7 2004 20 7 2004 2 8 e278Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Cellular and Genetic Analysis of Wound Healing in Drosophila Larvae ==== Body Anyone who's endured their share of childhood scrapes has probably heard some version of the motherly admonishment, “Don't pick that scab, you'll just make it worse!” It turns out, Mom was on to something. Tissue damage in humans triggers a well-characterized response marked by rapid blood clotting and a recruitment of epidermal cells to the injury. When you remove a scab, you're also removing some of the newly regenerated tissues growing underneath, thereby interfering with the healing process. Many different cell types and proteins have been linked to the repair process, but the complexity of the mammalian wound response has challenged efforts to determine their individual roles. Only the first step—the signaling cascade that promotes blood clotting—is understood at the molecular level. Since dissecting the wound response pathway at the molecular level requires an organism that lends itself to relatively easy genetic manipulations, Michael Galko and Mark Krasnow of Stanford University turned to the quintessential genetics organism, Drosophila melanogaster, to create a novel system for studying wound healing. After stabbing fruitfly larvae with a needle to create a nonfatal puncture wound, Galko and Krasnow then analyzed significant morphological, cellular, and molecular events at various stages of healing. Immediately after wounding, the larvae begin to bleed, and about 15 minutes after wounding, the wound darkens and a “plug” of cellular debris forms in the wound. The plug staunches the bleeding and provides a protective layer. Within two hours the plug's outer layer forms a dark, tough scab that presumably serves as an effective barrier to further blood loss. Two or three days later, the healing process is complete. Galko and Krasnow observed the activity of epidermal cells during healing by labeling their nuclei with a fluorescent protein and staining their membranes. Soon after wounding, cells along the wound margin elongated and aligned themselves toward the wound, then fused together to form a large, multinucleate (multiple nuclei) epidermal cell surrounding the scab. Over the next six hours, these cells also spread along and through the plug, re-establishing a continuous epidermis. Since this epidermal spreading resembles that seen during a developmental stage of the fruitfly, where the process depends on the JNK signaling pathway, the authors investigated JNK signaling to shed light on the genetics and cellular events of healing. Sure enough, they found that the JNK pathway was activated during the peak hours of wound healing. Inhibiting the pathway in fly mutants—the classic approach in fly genetics—had dramatic effects on the wound-healing process. The early stages of healing—including plug and scab formation—weren't affected, but epidermal spreading to regenerate the intact epidermis was either blocked or defective. In contrast, larvae with defects in a gene required for the generation of crystal cells—a type of blood cell implicated in processes linked to scab formation—could not properly form scabs. In these scabless wounds, the JNK pathway was hyperactive, epidermal cells at the wound's margin started to spread to close the wound but often failed, and the wound did not heal. Score one for Mom. But apparently Mom wasn't totally right: As Galko and Krasnow discovered, a scab isn't always necessary. When the authors pinched larvae without puncturing the overlying cuticle, the wounds did not form scabs. However, they still saw many of the same processes at work around this “pinch” wound that they saw around the puncture wound, and the pinch wounds healed efficiently. This indicates, the authors argue, that the scab functions primarily to provide stability to the wound site and help restore tissue integrity when both the epidermis and overlying cuticle are damaged during wounding. While the stages of wound healing outlined here occur at a particular time and place, these results suggest that each stage is controlled by distinct genetic programs and signaling pathways triggered by the wound. Since many aspects of the fly wound response resemble those in mammals, it's likely that the molecular components are also shared. That makes identifying the molecular underpinnings of wound healing a high research priority. And thanks to the powerful system presented here, this task should be all the easier.

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PLoS Biol. 2004 Aug 20; 2(8):e278

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PLoS Biol

10.1371/journal.pbio.0020278

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020292SynopsisEvolutionGenetics/Genomics/Gene TherapyImmunologyInfectious DiseasesMicrobiologyVirologyZoologyVirusesEvolution of a Primate Defense against Intragenomic Infiltrators Synopsis9 2004 20 7 2004 20 7 2004 2 9 e292Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Ancient Adaptive Evolution of the Primate Antiviral DNA-Editing Enzyme APOBEC3G ==== Body Anyone who uses a word processor is likely thankful for the spell checker program. But that autocorrect function can introduce errors, “correcting” the spelling of words to fit its stored repertoire, which is decidedly limited. Take that one step further and imagine a rogue program that destroys the coherence and meaning of your prose by swapping out one letter for another throughout the document. That's the situation retroviruses like the human immunodeficiency virus (HIV) face during the course of their infectious cycle, when a protein encoded by the host genome slips into the virus, mutates the virus's genetic material, and alters the viral genome. The gene, APOBEC3G, belongs to a family of primate genes that produce enzymes (in this case, APOBEC3G) that “edit” DNA and RNA, by slipping into viral particles and inducing mutations that replace one base (cytosine) with another (uracil) as the virus undergoes reverse transcription in the host cell's cytoplasm. The edited virus fails to replicate. HIV, in turn, generates a protein called Vif that binds to the APOBEC3G enzyme and targets it for degradation, thereby eliminating its antiviral activity. Since the protein-binding regions that govern these interactions have a direct effect on the fitness of both virus and host, one would expect to see the proteins angling for advantage, with Vif maximizing its ability to recognize APOBEC3G and APOBEC3G doing its best to evade Vif. Such battles are thought to result in frequent mutations that alter the amino acids involved in the interaction; the perpetuation of such advantageous mutations is called positive selection. In this issue of PLoS Biology, Sara Sawyer, Michael Emerman, and Harmit Malik investigate the genetic roots of this battle for evolutionary advantage and find something surprising. As predicted, the APOBEC3G gene is under strong positive selection. But that selection appears to predate the existence of HIV-type viruses. To characterize the selective pressures on APOBEC3G evolution, Sawyer et al. analyzed the gene from twelve primates—New World monkeys, Old World monkeys, and great apes, including humans—spanning 33 million years of evolution. Most of the primate lineages showed evidence of positive selection, indicating that the gene has been under pressure to adapt throughout the history of primate evolution. But viruses like HIV have been found in only five of the primates studied—three African monkeys, chimpanzees, and humans—and appear to be at most one million years old. And HIV infection in human populations is too recent to account for the positive selection of APOBEC3G in humans—so what has been fueling APOBEC3G's rapid evolution? Genetic conflict between the host antiviral editing enzyme APOBEC3G, and the viral Vif protein leads to rapid fixation of amino acid replacements in both proteins APOBEC3G and Vif interact in T-cells, but the fact that selective pressure on APOBEC3G has been constant over the course of primate evolution suggests that another force is also acting on the gene. Sawyer et al. propose that this force is most likely occurring in germline cells (sperm and egg precursors), which also produce high levels of APOBEC3G and can pass mobile genetic elements on to the next generation. Despite being non-infectious, these elements increase their own copy number in the host genome, moving from one part of the genome to another. The human genome is littered with such “retrotransposons,” and it is these mobile genetic elements, the authors conclude, that likely antagonize APOBEC3G. One class of retrotransposons, called human endogenous retroviruses, acts in many ways like foreign retroviruses. A retrovirus emanating from one's own genome poses less of an immediate threat than a retrovirus like HIV. But the constant efforts of the endogenous retrovirus to “jockey for evolutionary dominance,” the authors conclude, could eventually take a toll and would be expected to provoke efforts to contain it. And it may be that this ancient intragenomic conflict endowed APOBEC3G with the means to do battle with foreign retroviruses like HIV. Sawyer et al. also found evidence that five other APOBEC human genes appear to be engaged in similar conflicts. Combined with the finding that rodents have only one APOBEC3G gene and that five out of the six human APOBEC3 genes have been under positive selection, these results suggest that this gene family expanded in mammalian evolution as a means of defending the germline from the promiscuous intrusions of mobile genetic elements.

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PLoS Biol. 2004 Sep 20; 2(9):e292

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PLoS Biol

10.1371/journal.pbio.0020292

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020295SynopsisCell BiologyDevelopmentGenetics/Genomics/Gene TherapyDanio (Zebrafish)Patterning the Face Synopsis9 2004 20 7 2004 20 7 2004 2 9 e295Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. An Integrin-Dependent Role of Pouch Endoderm in Hyoid Cartilage Development ==== Body Vertebrates come in a dazzling array of shapes and sizes, from blue whales to pygmy bats, their overt morphology determined largely by the skeleton. The head skeleton in particular has undergone remarkable diversification, as is beautifully illustrated in Darwin's examination of beak morphology in Galapagos finches. It is now appreciated that a large part of the facial skeleton is derived from a newly identified, vertebrate-specific population of cells, called the cranial neural crest, that has its origins at the border of the dorsal neural plate (the future brain). Vertebrates develop from three germ layers—the endoderm, mesoderm, and ectoderm—which each give rise to distinct elements in the emerging body plan, and interactions between these layers are a common feature of embryogenesis. For example, early in development, cranial neural crest cells migrate to positions along the bottom (ventral side) of the future head, where they form a series of developmental intermediate structures called pharyngeal arches. The arches facilitate interactions between crest cells (derived from ectoderm) and neighboring tissues (such as endoderm and surface ectoderm), which induce specific bone and cartilage patterns in the face. Recent chick studies showed that head endoderm, which contributes to the lining of the pharynx and gills, can pattern the facial skeleton. But the question remained, by what mechanism does endodermal signaling induce specific patterns of cartilage and bone? In this issue of PLoS Biology, Justin Crump, Mary Swartz, and Charles Kimmel study the patterning of a jaw-support cartilage called the hyosymplectic in the larval zebrafish and find a “hierarchy of tissue interactions” at work. In zebrafish mutated for a gene called integrinα5, the authors report, a specific region of the hyosymplectic cartilage fails to develop. The loss of this cartilage region correlates with the loss of the first endodermal pouch. Pouches are outpocketings of the head endoderm that fuse with the skin to form the gill slits later in development. By labeling individual crest cells with fluorescent dye and making time-lapse recordings of these cells in transgenic fish, Crump et al. show that the hyosymplectic cartilage regions lost in the integrinα5 mutant are normally derived from crest cells directly adjacent to the first pouch. Pharyngeal development in a zebrafish embryo Integrins are transmembrane receptors that promote cell adhesion and signaling. Although integrins function in crest cell migration, Crump et al. show that the Integrinα5 receptor is required in endoderm for hyosymplectic cartilage development and appears to promote development of the first pouch. The first pouch in turn acts as a template, by promoting both the survival and local clustering of crest cells, to pattern a specific region of the hyosymplectic cartilage. But the pouch may have more far-reaching effects. Since integrinα5 mutants also have region-specific defects in cranial muscles and nerves, the first pouch may serve to organize an entire functional unit in a region of the head. As the hyosymplectic element has undergone considerable change during evolution—from a jaw-support element in fish to a tiny, sound-conducting bone called the stapes in mammals—Crump et al. speculate that such a local, interconnected strategy of development would facilitate evolution of the vertebrate head. Changes in endodermal signaling would allow a particular skeletal element to vary in shape or size, in coordination with the muscles and nerves that move the skeletal element and independent of other regions of the head. It will be interesting to determine, the authors note, whether this hierarchical organization applies to other skeletal elements in the head. But for now, these results will inform efforts to understand the specificity of interrelated defects seen in human craniofacial syndromes such as DiGeorge Syndrome, whose underlying causes lie in the development of the endoderm.

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PLoS Biol. 2004 Sep 20; 2(9):e295

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PLoS Biol

10.1371/journal.pbio.0020295

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020259Research ArticleCell BiologySaccharomycesGenome-Wide Mapping of the Cohesin Complex in the Yeast Saccharomyces cerevisiae Genome-Wide Mapping of Yeast CohesinGlynn Earl F 1 Megee Paul C 2 Yu Hong-Guo 3 Mistrot Cathy 3 Unal Elcin 3 Koshland Douglas E 3 DeRisi Joseph L 4 Gerton Jennifer L [email protected] 1 1Stowers Institute for Medical Research, Kansas CityMissouri, United States of America2Department of Biochemistry and Molecular Genetics, University of ColoradoDenver, Colorado, United States of America3Howard Hughes Medical Institute, Department of EmbryologyCarnegie Institution of Washington, Baltimore, Maryland, United States of America4Department of Biochemistry and Biophysics, University of CaliforniaSan Francisco, CaliforniaUnited States of America9 2004 27 7 2004 27 7 2004 2 9 e25912 12 2003 14 5 2004 Copyright: © 2004 Glynn et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. The Kinetochore Is an Enhancer of Pericentric Cohesin Binding Genome-Wide Survey of Cohesin: A Molecular Guardian of Genomic Fidelity In eukaryotic cells, cohesin holds sister chromatids together until they separate into daughter cells during mitosis. We have used chromatin immunoprecipitation coupled with microarray analysis (ChIP chip) to produce a genome-wide description of cohesin binding to meiotic and mitotic chromosomes of Saccharomyces cerevisiae. A computer program, PeakFinder, enables flexible, automated identification and annotation of cohesin binding peaks in ChIP chip data. Cohesin sites are highly conserved in meiosis and mitosis, suggesting that chromosomes share a common underlying structure during different developmental programs. These sites occur with a semiperiodic spacing of 11 kb that correlates with AT content. The number of sites correlates with chromosome size; however, binding to neighboring sites does not appear to be cooperative. We observed a very strong correlation between cohesin sites and regions between convergent transcription units. The apparent incompatibility between transcription and cohesin binding exists in both meiosis and mitosis. Further experiments reveal that transcript elongation into a cohesin-binding site removes cohesin. A negative correlation between cohesin sites and meiotic recombination sites suggests meiotic exchange is sensitive to the chromosome structure provided by cohesin. The genome-wide view of mitotic and meiotic cohesin binding provides an important framework for the exploration of cohesins and cohesion in other genomes. Chromatin immunoprecipitation reveals that cohesin sites -- those responsible for holding sister chromatids together until they separate into daughter cells -- are conserved in meiosis and mitosis ==== Body Introduction Sister chromatid cohesion ensures that daughter cells inherit complete copies of their genome. Cohesion in eukaryotic cells is mediated by a multisubunit protein complex called cohesin. Cohesin consists of four proteins: Smc1, Smc3, Scc1/Mcd1, which is called kleisin and is the target of the protease separase, and Scc3. These proteins have recently been proposed to form a ring structure that encircles sister chromatids (Gruber et al. 2003). Alternately, the ring may act as a snap (Milutinovich and Koshland 2003). Cohesion is established during replication and maintained until metaphase in mitosis (Uhlmann and Nasmyth 1998). All members of the cohesin complex are essential in Saccharomyces cerevisiae, since mutation results in the precocious dissociation of sister chromatids. Cohesion serves at least three roles in the cell with respect to accurate genome transmission. Firstly, cohesion proximal to the centromere facilitates biorientation of chromosomes with respect to the spindle (Tanaka et al. 2000). Secondly, it prevents splitting of chromosomes once bipolar attachments are made (Tanaka et al. 2000). Thirdly, cohesin bound along chromosome arms may be essential for proper chromosome condensation in yeast (Guacci et al. 1997). In meiosis, cohesin at most arm sites disappears prior to the first nuclear division. The meiotic cohesin complex contains Rec8 instead of Scc1/Mcd1 (Klein et al. 1999). Cohesion is maintained distal to crossovers between homologs, which links them and facilitates their biorientation on the meiotic I spindle. Cohesin is also maintained at pericentric regions, which is essential for biorientation of chromosomes at the spindle for the second nuclear division (Buonomo et al. 2000). We are interested in understanding the cis determinants of cohesin binding. Cohesin-associated regions have been identified in yeast using chromatin immunoprecipitation. In these studies cohesin association with chromatin was followed at low resolution along the entire length of Chromosome III (3-kb intervals) or high resolution (300-bp intervals) at limited regions on Chromosome III, V, and XII (Blat and Kleckner 1999; Megee et al. 1999; Tanaka et al. 1999; Laloraya et al. 2000). These studies showed associations of cohesin with specific regions of chromosomes; one of the regions of intense association is centromeres. In addition to the enrichment of cohesin in the pericentric region of Chromosome III, Blat and Kleckner (1999) found a spacing of cohesin-associated regions of 13 kb along the arms of Chromosome III. A similar spacing was observed in a limited region of Chromosome XII (Laloraya et al. 2000). These studies also noted a correlation of cohesins with elevated AT content. The average size of the mapped sites was 0.8–1 kb (Laloraya et al. 2000). Based on three sites mapped to high resolution, cohesin was proposed to associate with the boundaries of transcriptionally silent regions (Laloraya et al. 2000). Despite these insights into cis determinants of cohesin binding, many aspects of the cohesin–DNA interaction remain obscure. The high resolution studies sampled a small portion of the genome, and the low-resolution analysis of Chromosome III does not address questions about the position of cohesin relative to smaller-scale genome features, such as individual transcription units. Furthermore, Chromosome III is the sex chromosome of budding yeast, and, similar to other organisms, it has unusual properties including large domains of repressed recombination, silent mating type loci, and different patterns of replication (Reynolds et al. 1989; Wu et al. 1997). Some discrepancies between high- and low-resolution studies have emerged, including, for example, whether cohesin is found at telomeres (Blat and Kleckner 1999; Laloraya et al. 2000). One approach to better understand cis determinants of cohesin binding is to analyze them across the whole genome. To obtain a genome-wide picture of cohesin binding to S. cerevisiae chromosomes at 1–2-kb resolution, we used a combination of chromatin immunoprecipitation (ChIP) and microarray methods, often referred to as ChIP chip technology. To aid identification of peaks of cohesin binding we developed a program, PeakFinder, for extraction of peaks from raw ChIP data. We further used this approach to map all the cohesin-binding sites on an “ectopic chromosome,” a yeast artifical chromosome containing a 334-kb insert from human Chromosome VII. Information from a large number of sites greatly facilitates the assessment of cohesin distribution and of the significance of correlations with many local properties of the genome, such as base composition and coding content. Furthermore, it allows us to evaluate the impact of several factors, such as strain background, transcription, and developmental programs like meiosis, on cohesin binding, and to test the predictions by engineering individual cohesin sites. Results Determining Sites at Which Cohesin Interacts with the Yeast Genome We used the genome-wide approach of ChIP chip to identify and evaluate cis determinants of cohesin sites (detailed protocol for ChIP available at http://www.uchsc.edu/sm/bbgn/images/ChIP%20protocol.htm; see also Protocol S1). We began the study using Mcd1-18Myc as the protein target in the W303a strain background. Cells were arrested in metaphase by a temperature-sensitive mutation in CDC16, a subunit of the anaphase-promoting complex required to degrade Pds1p, a negative regulator of anaphase (Yamamoto et al. 1996; Cohen-Fix and Koshland 1997). We were interested in determining potential correlations between cohesin binding and genome features such as base composition, transcriptional state, and known cis determinants of chromosome transmission. Earlier studies have used simple ratio “thresholds” to define binding sites in ChIP chip data (Iyer et al. 2001; Lieb et al. 2001; Wyrick et al. 2001). A single genome-wide threshold would be of limited value in our experiments because (1) peaks representing the intensity of cohesin binding are much higher at pericentric regions than towards the end of chromosomes, therefore, a threshold constant would have the effect of skewing all the binding sites towards the centromere-proximal regions; (2) binding sites in ChIP chip data are frequently defined by several array elements, complicating the identification of cis determinants; and (3) much of the analysis has to be done manually. A better approach would be to use the local parameters of cohesin binding to identify the peaks. To aid such a task, we have written a Windows program, PeakFinder, which discerns and filters the peaks from a variable local background and maps the tip of the peak to a single array element. The program is freely available (http://research.stowers-institute.org/jeg/2004/cohesin/peakfinder). We validated our methods by comparing the results to previously collected data. Laloraya et al. (2000) discovered nine sites in the arms of Chromosomes III and XII using ChIP followed by semiquantitative PCR. All of those sites could be identified as peaks in the microarray data using PeakFinder (for Chromosome III see Figure 1). Blat and Kleckner (1999) mapped 23 cohesin sites to 3-kb resolution on Chromosome III. Although the number of identified peaks is increased in our data (33 versus 23), the increase can largely be accounted for by higher-resolution mapping. Qualitatively the results are comparable, including that (1) the peak height at CEN3 is the highest for the chromosome, (2) the height of the peaks declines towards the ends of the chromosome, and (3) peaks correlate with AT content (Figure 2). Therefore, the results of previous studies are reproduced by our methods. Figure 1 Interactions between Cohesin and CHRIII in S. cerevisiae The centromere is indicated with a black circle; the smoothed data are indicated with a green line. 50-kb intervals are indicated by vertical grey lines. (A) Data generated from a cdc16-arrest ChIP for Mcd1-18Myc in W303a. The midpoint of each feature is used to represent the log2 of the median red:green ratio (left y-axis) with a black line, high firing replication origins are indicated with black triangles, and previously mapped CARC2, CARC1, CARC3, CARC4, CARC5, and CARC6 (Laloraya et al., 2000) correspond to peaks 9, 10, 29, 30, 31, and 32, respectively. Peaks are located and numbered by PeakFinder (with the exception of telomeres) using the parameters described in the Materials and Methods. (B) Smoothed data from cdc16-arrest ChIP for Mcd1/Scc1-6HA in A364a. (C) Smoothed data from cdc16-arrest ChIP for Smc3-6Myc in A364a. Figure 2 Visual Representation of the Interactions between Mcd1-18Myc and the S. cerevisiae Genome in W303a For each of the 16 chromosomes the centromere is indicated with a black circle, the smoothed data (based on the log2 of the ratio) is indicated with a green line (left y-axis), and the percent GC is indicated by a red line (right y-axis). Vertical grey bars mark 50-kb intervals. Peaks are located and numbered by PeakFinder (with the exception of telomeres) using the parameters described in the Materials and Methods. For Chromosome XII, peaks 41 and 42 correspond to the previously described peaks CARL1 and CARL2 (Laloraya et al. 2000). Three additional controls were performed to validate our methods. First, immunoprecipitation from a strain without any epitope tags on the cohesin complex was performed and did not yield any signal (Megee et al. 1999). Second, for each ChIP performed with the anti-Myc antibody, a second ChIP was performed with the same chromatin solution in which the anti-Myc antibody was omitted. The immunoprecipitated DNA was subjected to semiquantitative PCR for centromere sequence (SGD coordinates 114318–114561) on Chromosome III. When the amplification was in the linear range, the difference in signal between the two templates was 11-fold ± SD 3.2, demonstrating that the enrichment for this particular sequence is specific to the interaction between the Myc epitope and the anti-Myc antibody. The ChIP samples were subjected to 20–25 cycles of random PCR amplification (http://microarrays.org/protocols.html; see also Protocol S2) prior to labeling and hybridization to microarrays. This amplification procedure was performed side by side on ChIP samples obtained with and without primary antibody. After 25 cycles of PCR, equal amounts of the samples were loaded on an agarose gel. The sample generated in the absence of primary antibody did not contain any detectable DNA, while the sample obtained with primary antibody generated a robust smear of DNA (unpublished data). While additional cycles of PCR did produce detectable DNA for the sample generated in the absence of a primary antibody, the lack of DNA under the amplification conditions used for the microarray experiment demonstrates that nonspecific immunoprecipitation of DNA was not a confounding factor for our microarray analysis. Third, the same chromatin solution was subjected to immunoprecipitation with an anti-Mif2 antibody. Mif2 is a centromere-binding protein. Centromeres in S. cerevisiae are approximately 125 bp. The peak of Mif2 binding spanned approximately 500 bp, as assessed by PCR amplification (see Weber et al. 2004, Figure 4). This demonstrates that the shearing of fragments in the chromatin solution was sufficient to give resolution on the order of 500 bp in a case where this level of resolution is expected. Figure 4 Peaks and AT Content (A) The AT content for each array element was calculated and put into bins in 1% intervals (grey bars, left y-axis). The AT content for each array element that is a cohesin peak was also put into bins (red bars, right y-axis). (B) The AT content for each intergenic array element was put into bins in 1% intervals (grey bars, left y-axis). The AT content for each intergenic array element that is a cohesin peak was also put into bins (red bars, right y-axis). (C) The AT content for each convergent intergenic array element was put into bins (grey bars, left y-axis) and the AT content for each convergent intergenic array element that is a cohesin peak was also put into bins (red bars, right y-axis). To demonstrate the internal consistency and reproducibility of our data, we compared peaks of cohesin binding for Mcd1-18Myc in W303a (see Figure 1A), Scc1/Mcd1-6HA in A364a (Figure 1B), and Smc3-6Myc in A364a (Figure 1C). There is good agreement between the location of cohesin peaks in different strain backgrounds (correlation coefficient = 0.76 for Mcd1/Scc1 ChIP in strains A364a and W303a). This is the relevant comparison for the data in Figure 1 here and the data in Figure 1 of Weber et al. (2004), which shows the genome-wide results of ChIP for Scc1/Mcd1-6HA in the A364a background. The agreement is even stronger when different members of the cohesin complex are used as ChIP targets in the same genetic background (correlation coefficient = 0.96 for Mcd1/Scc1 and Smc3 ChIP in strain A364a). All data from individual arrays and datasets are available at http://research.stowers-institute.org/jeg/2004/cohesin/data/index.html and as supporting information (Datasets S1–S58). Genomic Distribution of Cohesin The levels of cohesin on all the chromosomes are similar and follow a clear pattern: large regions of intense binding in the pericentric domain and less intense, smaller regions distributed in a semiperiodic manner throughout the arms. We evaluated whether cohesin was associated with cis determinants of chromosome transmission including centromeres, telomeres, and origins of replication. Cohesin shows a large (30–50 kb), dense region of binding in pericentric domains (Figure 2). Although it has been proposed that telomeres do not associate with cohesin (Blat and Kleckner 1999), we found that nine of the 32 telomeres in fact were associated with cohesin. However, the height of the peaks associated with telomeres and subtelomeric regions is lower than at internal regions, which may reflect lower affinity or occupancy of cohesin at these regions (Figure 3A). On Chromosome III, cohesin peaks appear to be associated with replication origins that have been functionally mapped (Poloumienko et al. 2001) (see Figure 1A, only the origins with the strongest signal are indicated). Cohesin enrichment at the centromeres clearly supports previous studies implicating a requirement for the coupled function of cohesion and the centromere in chromosome segregation (Hill and Bloom 1987; Megee et al. 1999), while the significance of cohesin association with telomeres and origins is unclear. Figure 3 Features of Peaks (A) Using all cohesin-binding peaks within 40 kb of a telomere ordered based on distance from the telomere, we calculated a five-point moving average for distance in kilobases from the telomere (x-axis) and plotted this as a function of the five-point moving average of the log2 value for the associated peaks (y-axis). (B) Chromosome length (x-axis) is plotted as a function of the number of cohesin peaks (y-axis). A line was fitted using the least squares method and R2 = 0.96. (C) The distance between peaks was put into 1-kb bins; the average distance between peaks is 10.9 kb and the median is 9.3 kb. While some cohesin is associated with the known cis determinants of chromosome transmission, the vast majority of sites are not. The number of cohesin-binding peaks per chromosome shows an excellent correlation to chromosome length (R2 = 0.96; Figure 3B). The mean distance between sites was 10.9 kb, with a standard deviation of 6.7 kb (Figure 3C). There are 50 regions of the genome with large gaps between neighboring peaks (24 kb or greater, i.e., more than 2 s.d. from the mean); these appear to be randomly scattered throughout the arms of the larger chromosomes, and are never located on any of the four smallest chromosomes. The spacing of peaks is conserved for the most part in pericentric regions, with an additional “baseline” level of binding. Cohesin distribution therefore appears to be nonrandom with a tendency for even distribution over the genome. Cohesin Tends to Bind AT-Rich Sequences Cohesin peaks were strongly associated with AT-rich regions (Figure 4). We found that 810 of the 1,095 array elements defined as cohesin-binding sites have AT content above the yeast median of 62.6% (p < 0.0001) (Figure 4A). Cohesin peaks are significantly associated with intergenic regions, with 765 out of 1,095, or 70%, of all peaks located in such regions (p < 0.0001) even though intergenic regions make up only 27% of the genome length. Intergenic regions in S. cerevisiae are more AT-rich than open reading frames (ORFs). Therefore, we tested whether the AT bias could be explained by the bias towards binding intergenic sequences by comparing the AT content of all intergenic regions with the AT content of intergenic regions associated with cohesin (598 out of 765 are above the median, p < 0.0001; Figure 4B). The peaks observed at ORFs are also higher in AT content than ORFs on average (p = 0.0005). Thus, AT content appears to be a major determinant for cohesin association. We observed local oscillations of AT content in a 5-kb sliding window, which corresponded to cohesin-binding peaks in chromosome arms (see Figure 2), thus extending to the whole genome the observation for Chromosome III (Blat and Kleckner 1999). Furthermore, all 16 pericentric regions have local peaks of AT content (Figure 2). Interestingly, the sequence elements associated with cohesin in the pericentric domain contain nearly equal numbers of ORF and intergenic sequences, as might be expected if binding is mainly directed by the centromere and base composition and disregards other genomic features such as transcription units (Weber et al. 2004). Distribution of Cohesin on a YAC The semiregular spacing of cohesin and the correlation with local oscillations of base composition suggested that AT content and/or a measuring mechanism might control cohesin distribution on the chromosome. In order to test these possibilities, we used a nonessential ectopic yeast artificial chromosome (YAC). We used ChIP followed by quantitative PCR to map cohesin-binding sites in a YAC containing 334 kb of human DNA from Chromosome VII. The pericentric region on the right end of the YAC shows a broad (approximately 35 kb), intense association with cohesin. This is similar to the cohesin association with endogenous pericentric regions. However, the spacing of cohesin does not have the same periodic nature as on endogenous chromosomes. For example, the leftmost 83 kb of the YAC contains only one peak of cohesin binding, resulting in two large gaps for cohesin of 38 and 45 kb (Figure 5A). These two gaps are larger than any of the gaps found on the smaller endogenous yeast chromosomes, which have a comparable size to the YAC. The human DNA fragment does not contain oscillations of AT content similar to those observed in the yeast genome, nor does the pattern of cohesin association appear to reflect base composition in this context. Thus, the difference in the distribution of cohesin-binding sites in the arms of the YAC supports the idea that sequence contributes to cohesin distribution in yeast and that evolution has selected for an even distribution on endogenous chromosomes. Figure 5 Cohesin Sites Mapped Using ChIP Followed by Semiquantitative PCR with Primers at 1-kb Intervals in a YAC Containing Human DNA (A) Cohesin binding for the entire YAC is shown. (B) Cohesin binding in the region spanning 135–180 kb is shown for the wild-type YAC (black diamonds) and for the YAC containing a replacement of the sequences at 156–162 kb with the gene encoding geneticin resistance (grey squares). To further test the contribution of sequence to cohesin binding, we took advantage of the fact that none of the human sequences were essential for yeast survival. When we replaced the region from 156 to 162 kb, which contains a cohesin-binding site, with the gene encoding for geneticin resistance, this region was no longer associated with cohesin (Figure 5B). Neighboring regions were unaffected, and de novo cohesin binding was not observed. This suggested that some property of the sequence, rather than its precise location or context, was responsible for cohesin binding. Transcription and Cohesin Inspection of the intergenic regions associated with cohesin revealed a strong preference towards the intergenic regions in which transcription is converging, and additionally, an extreme bias against association with intergenic regions in which transcription is diverging. Among the cohesin-associated intergenic regions that could be assigned to a category, 86% were in intergenic regions with converging transcription, 12% were in intergenic regions with surrounding unidirectional transcription, and only 2% were in intergenic regions that are between two divergently transcribed genes. In contrast, the genome as a whole has these regions in approximately a 1:2:1 ratio, respectively, making this result highly statistically significant (p < 0.0001). In fact, 39% of the convergent intergenic regions in the genome have peaks of cohesin binding, and nearly half of all cohesin-binding sites are in convergent intergenic regions. These percentages approach the predictive power of consensus sequences for identifying binding sites of their cognate transcription factor (Chu et al. 1998; Lieb et al. 2001). Convergent intergenic regions have high AT content compared to the genome at large; the bias of the intergenic sequences associated with cohesin can be partly explained by the AT bias of convergent intergenic regions (see Figure 4C). Of the sites, 865 of 1,095 can be explained by one or more of the following three factors: (1) location within 25 kb of a centromere, (2) above average AT content, or (3) location in an intergenic region with converging transcription. This leaves 230 sites unexplained. Of these, 43 are intergenic. Most of these are simply difficult to assign to a transcriptional category. Interestingly, of the 230 “unexplained” sequences, 187 are in ORFs, which is more than half of all the ORFs associated with cohesin. These ORFs do not appear to have any unifying theme with regard to function, dubiousness, transcription level, or essentiality. The 187 peaks have similar height to other peaks. Thus, unlike the cis determinants in the intergenic regions, the factors within the ORFs that enable cohesin binding are not well understood. Attempts to identify a genome-wide linear consensus binding site for cohesin using BioProspector (Liu et al. 2002), MobyDick (Bussemaker et al. 2000), AlignACE (Hughes et al. 2000), and MEME (Bailey and Elkan 1994) did not return any sequence model with predictive value. However, when we took the group of “unexplained” ORF sequences and looked for a common motif using BioProspector, we identified two repetitive sequences that were strongly enriched relative to the genome: (CAR)5 (p = 10−65) and (GAN)10 (p = 10−76). The p values reflect the significance of these sequences as calculated based on a Monte Carlo simulation for the yeast genome. These sequences were rare in intergenic regions in yeast. These sequences were not present in the YAC. Further experiments will be required to determine whether these repeats are targeted by cohesin. Interestingly, human cohesin has been localized in Alu repeats (Hakimi et al. 2002), suggesting that repetitive DNA may be prone to a particular structure or chromatin configuration preferred by cohesin, or may accumulate in regions that are bound by cohesin. Alu repeats do not bear any obvious similarity to the sequences we identified. Binding of cohesin may help modulate transcription of these repeated sequences. The Mechanism of the Negative Association between Transcription and Cohesin Binding The link between intergenic tail-to-tail regions and cohesin suggests a general incompatibility between transcription and cohesin association. The observations that transcription can disrupt centromeric cohesin and results in chromosome missegregation and cell death (Tanaka et al. 1999), and that cohesin binds at the boundaries of silent chromatin in several loci (Laloraya et al. 2000) are in agreement with this. We analyzed whether changing the transcriptional program could change the association of cohesin with a locus. We grew cultures with either 2% glucose or 2% galactose as the carbon source and arrested them in metaphase using nocodazole. The main difference between metaphase arrest in the presence of nocodazole and in cdc16-ts is that cohesin binding at pericentric regions is increased in the former case (unpublished data). We carried out ChIP chip analysis in parallel with monitoring gene expression in the same cells using ORF arrays. Of the regions where gene expression changed 5-fold or more, only two regions were associated with cohesin. One peak of cohesin binding in glucose was associated with the promoter of the GAL2 gene (Figure 6A), which was induced 42-fold in galactose. This had a dramatic effect on the local profile of cohesin binding (Figure 6B). The promoter region of GAL2 became a trough of cohesin binding, and the single peak observed in glucose was split into two peaks, in effect adding a new peak to the region. The second region was an uncharacterized ORF, YDL218W, a membrane protein distantly related to secretory factor NCE102/YPR149W and to metazoan synaptogyrin family (unpublished data). Expression of YDL218W was induced 11-fold in the presence of glucose compared to galactose. This ORF is associated with cohesin in the presence of galactose, but this association is reduced when glucose is present (unpublished data). The peaks surrounding both regions were unaffected. These results demonstrate that high levels of transcription are incompatible with cohesin binding. It also supports the results observed with the human DNA that neighboring cohesin sites behave independently. Figure 6 Transcription Affects the Cohesin Peak at the Promoter of GAL2 SGD coordinates 260–320 kb (x-axis) and a gene map are depicted for Chromosome XII. The strain 1827-22D (isogenic to the strain in Figure 1 except CDC16) was grown with either 2% glucose (A) or 2% galactose (B) as the carbon source. Cultures were arrested with nocodazole, and ChIP chip was performed. The smoothed data (as the log2 of the ratio) is depicted in green, the peaks found by PeakFinder are indicated with black dots, and the region corresponding to the GAL2 promoter is indicated with a grey bar. Transcription of GAL2 is up-regulated 42-fold in (B). There are a number of mechanisms that could account for the incompatibility between transcription and cohesin binding. Transcription of a region during G1 may prevent new association of cohesin, or transcription may displace cohesin in G2. We explored the mechanistic link between transcription and cohesin association at the previously characterized cohesin sites cohesin-associated region on Chromosome III (C) (CARC1) and cohesin-associated region on Chromosome XII (L) (CARL2) (Laloraya et al. 2000) by inserting 0.8 kb of CARC1 of 1.4 kb of CARL2 into a plasmid construct next to a galactose-inducible promoter (pGAL1-10). Strains containing one of these two plasmids were grown with 2% raffinose as the carbon source, arrested in G1 with alpha factor, and then released from G1 in the presence of nocodazole, producing a metaphase arrest. Galactose was added to half the culture. One hour after the addition of galactose, cultures were fixed with formaldehyde and processed for ChIP. Semiquantitative PCR was used to monitor the distribution of cohesin. With the appropriate use of primers, cohesin association with CARC1 and CARL2 on the plasmid and the endogenous locus could be distinguished. Galactose-induced transcription had no effect on association of cohesin with the endogenous loci (unpublished data) but disrupted cohesin associated with the 5′ end of both CARC1 and CARL2 on the plasmid (Figure 7A). This result demonstrates that transcription during G2 can displace cohesin. Figure 7 Effect of Transcript Elongation on Cohesin Associated with CARC1 and CARL2 Located on a Plasmid Next to a Galactose-Inducible Promoter (A) The fold reduction in cohesin binding in the presence (+) or absence (−) of galactose-induced transcription is depicted as a function of the 5′ or 3′ end of the locus. (B) The fold reduction in cohesin binding at CARL2 during galactose-induced transcription in the presence (+) or absence (−) of thiolutin, an inhibitor of transcript elongation. The displacement of cohesin may be due to a competition between RNA pol II/chromatin–remodeling factors and cohesin for DNA, or transcript elongation may remove cohesin. We tested if the incompatibility was dependent on transcript elongation. A culture was arrested with nocodazole, and galactose-responsive transcription was induced by the addition of galactose, as described above. Thiolutin was added to half of this culture immediately prior to the addition of galactose. Thiolutin inhibits transcript elongation but presumably does not inhibit the binding of RNA pol II (Parker et al. 1991). The effect as monitored at CARL2 was dependent upon elongation since the addition of thiolutin prevented the displacement of cohesin (Figure 7B). This result demonstrates that the binding of RNA pol II per se does not affect cohesin binding, but transcript elongation can displace cohesin within the G2 portion of a single cell cycle. Meiotic Cohesin The transcriptional program of a cell changes under different conditions, such as the developmental program of sporulation. We used ChIP chip to analyze the location of the meiosis-specific cohesin complex (Klein et al. 1999; Watanabe and Nurse 1999). The protein composition of the meiosis-specific complex has been described, but no information on the cis determinants of this complex has been reported. We expressed Rec8-3HA in SK1, a rapidly and synchronously sporulating strain, and analyzed the location of the cohesin–DNA complex in ChIP experiments (Figure 8). The pattern of cohesin association in meiotic and mitotic cells appears to be similar (correlation coefficient = 0.77 across SK1 genome comparing Rec8 to Mcd1; see Figure 8 for coordinates 295–345 kb and 440–460 kb on Chromosome XII; additional data regarding the timecourse of sporulation is provided in Figure 9). In both cases, pericentric regions contain broad, intense regions of cohesin association and there are nonrandomly spaced cohesin sites in the arms. Figure 8 Meiotic Cohesin DSB data are shown in red, Rec8 data in black, and Mcd1 data in grey. (A) Ratios for meiotic cohesin are compared to mitotic cohesin in SK1 for kilobasepairs 440–461 on Chromosome XII. For the mitotic culture, cells were arrested with nocodazole. For meiotic cells, timepoints were collected every 2 h from hour 4 to hour 12 after transfer to SPM. The median ratio value was used to represent the data. Meiosis is slower in an SK1 strain with an HA-epitope-tagged Rec8 than in a wild-type strain (see Figure 9). The gene structure for this locus is shown below the graph, with genes encoded by the Watson strand labeled on top and genes encoded by the Crick strand labeled on the bottom. (B) Ratios for meiotic cohesin are compared to mitotic cohesin and DSBs for kilobasepairs 295–345 on Chromosome XII. Figure 9 Meiotic Timecourse for an SK1 Strain Containing Rec8-3HA Cells were collected at the indicated timepoints throughout meiosis using the same experimental regime used to collect the binding sites of Rec8-3HA presented in Figure 8. The epitope tag appears to slow meiosis by 3–4 h as compared to an untagged strain. Three assays were developed to monitor culture synchrony during meiosis. (A) FACS profile of the REC8-3HA strain. Aliquots of cells were fixed with 70% EtOH, followed by FACS analysis. (B) Nuclear division of the REC8-3HA strain. Aliquots of cells were fixed with 1% formaldehyde for 1 h at room temperature. Nuclear DNA was stained by DAPI and visualized under a fluorescence microscope. At least 200 cells were scored at each timepoint. (C) Rec8-3HA protein level. Protein extracts were prepared and subjected to SDS-PAGE and Western blot. The Rec8-3HA protein level was detected by an anti-HA antibody (12CA5). The same blot was stripped and reprobed with anti-β-tubulin antibody to detect the level of β-tubulin, which served as a loading control. PCD1 has been shown to be a cohesin-binding site (Laloraya et al. 2000). Binding to this site is diminished for meiotic cohesin (see Figure 8A). The transcription of this gene is induced early in meiosis, which may explain why cohesin binding to this site is diminished (Chu et al. 1998). Other genes show a similar pattern, namely that they are binding sites for cohesin in mitotic cells, but their transcription is induced early in meiosis, and they do not appear to be binding sites for the Rec8-containing cohesin complex (e.g., YPR006C, YDL238C, and YER179W). This suggests that binding of the meiotic complex, like the mitotic complex, is not compatible with transcription. We compared the location of meiotic cohesin to the location of the double-strand breaks (DSBs) that initiate meiotic recombination (Gerton et al. 2000). We found a negative correlation (correlation coefficient = −0.26); DSBs tend to occur in regions where meiotic cohesin is absent, and meiotic cohesin tends to be located in regions that contain low levels of DSBs (see Figure 8B). Cohesin has been shown to be required for the formation of the axial elements that become the lateral elements of the proteinaceous structure known as the synaptonemal complex (SC) (Klein et al. 1999). The SC organizes meiotic chromosomes and aids interhomolog recombination. In fact, meiotic cohesin has been shown to be required for meiotic recombination (Klein et al. 1999). This result suggests that recombination proteins can recognize chromosome structure/organization provided by cohesin. The most notable difference between meiotic and mitotic cohesin is at the ribosomal DNA (rDNA) locus. (Nocodazole arrest does not affect cohesin binding in the rDNA in A364a or W303a, unpublished data) The rDNA is encoded in an approximately 1–2-Mb region on the right arm of Chromosome XII consisting of 100–200 tandem copies of a 9.1-kb repeat. There is a peak of cohesin binding that localizes to the left border of the rDNA repeat (Laloraya et al. 2000) that is absent for meiotic cohesin (see Figure 8A). Although information regarding the transcription of rDNA in meiosis is unavailable, genes involved in the processing of the 35S transcript, such as ROK1, RRS1, and EBP2 (Wade et al. 2001), are down-regulated 5- to 15-fold by 0.5 h in meiosis, and ribosomal protein genes are also down-regulated an average of 5-fold (Chu et al. 1998), suggesting that transcription of this region is significantly reduced in meiosis. The rDNA is located in the nucleolus in meiotic cells and is associated with proteins that repress interhomolog recombination (Petes and Botstein 1977; San-Segundo and Roeder 1999, 2000). This region may have a chromatin structure in meiosis that suppresses transcription, recombination (so as to avoid chaotic exchange between repeated elements), and cohesin binding. Discussion We have used ChIP chip to map, to 1–2-kb resolution, the genome-wide pattern of cohesin association under several different growth conditions (metaphase arrest by cdc16-ts or nocodazole, galactose versus glucose as a carbon source, and induction of meiosis) and in three different yeast-strain backgrounds (W303a, SK1, and A364a). Using PeakFinder, a program that assesses cohesin binding by comparison of signal to variable local background, we find that the majority of cohesin-binding sites are remarkably constant under these different circumstances. Distribution of cohesins throughout the genome appears to depend on a combination of base composition, sequence, and transcriptional activity. We find evidence for three types of cohesin sites in the genome: (1) the centromere and pericentric domain, (2) intergenic regions in chromosome arms, and (3) ORFs in chromosome arms. The association of cohesin with these three types of sites is subject to different genomic parameters. Cohesin at centromeres and pericentric regions is spread over a broad domain with an elevated “baseline” level and is not affected by the natural transcriptional and coding status. Much of the cohesin in chromosome arms is located in transcriptionally converging intergenic regions. ORFs in chromosome arms where cohesin is found are enriched for repetitive sequences. This suggests that there may be three mechanisms to load cohesin, consistent with what has been proposed for cohesin in meiotic chromosomes for S. pombe (Kitajima et al. 2003). A unifying feature of all three types of sites is high AT content. Pericentric regions contain the most intense and broadest levels of cohesin in the genome (for a more complete analysis of pericentric cohesin see Weber et al. [2004]). This finding is consistent with a model in which a centromere contains determinants of two opposing processes: (1) pulling the chromosomes apart, via the assembled kinetochore attached to a microtubule, and (2) keeping chromosomes together, via pericentric cohesion. The intensity and breadth of cohesin binding at pericentric regions is similar for all chromosomes, implying microtubules pull all chromosomes with comparable force, regardless of their length. On the other hand, the number of binding sites per chromosome is proportional to chromosome length. This result implies that arm cohesion is not a direct measure of the force exerted by spindle microtubules, and may serve a different function, for instance, to achieve similar levels of condensation. The model in budding yeast that cohesin can participate in genome maintenance in two ways, namely condensation via arm cohesin and biorientation via pericentric cohesin, is intriguing in light of the recent finding that cohesin complexes with different subunits are found on arms and pericentric regions on meiotic chromosomes in S. pombe and apparently serve different functions (Kitajima et al. 2003). Cohesin cannot stay bound to DNA in the face of active transcript elongation based on three independent cohesin sites (promoter of GAL2, CARC1, and CARL2). If cohesin and transcript elongation were incompatible, then we would also expect to find sites biased towards intergenic regions, which we do. However, we find a strong bias towards intergenic regions with converging transcription, and a bias against intergenic regions with surrounding unidirectional transcription or diverging transcription, suggesting that intergenic regions with converging transcription may have especially low transcription. These regions may have evolved particularly strong transcriptional stops since they are quite short on average and the cell may need to avoid transcription from one side extending to the other to prevent the synthesis of antisense RNA. The protection of sequence elements important for the replication and segregation of eukaryotic chromosomes from transcription may be a general necessity for their proper function in vivo. For instance, transcription through an autonomous replicating sequence (Snyder et al. 1988) or a centromere (Hill and Bloom 1987) disrupts their function. The observed antagonistic relationship between transcription and cohesin binding in chromosome arms can be explained in two ways. Firstly, transcript elongation may be directly responsible for displacing cohesin. In this type of model, cohesin loading/binding is random, and transcription (and possibly other DNA metabolic processes) “pushes” cohesin into place or strips cohesin from inappropriate locations in each cell cycle. Secondly, transcript elongation may be indirectly responsible for localizing cohesins, for example by accumulation of “nonpermissive” chromatin in transcribed regions and “permissive” chromatin in nontranscribed regions. This type of genome-wide demarcation of transcription units has been shown to occur in S. cerevisiae (Nagy et al. 2003) and may depend on nucleosomes (Lee 2004) and histone variants. The chromatin remodeling complex RSC (Remodels the Structure of Chromatin) has recently been shown to be important for establishment of cohesin in chromosome arms (Baetz et al. 2004; Huang et al. 2004). The preferential location of cohesin in heterochromatin in S. pombe also supports the idea of chromatin modification/structure as the basis for cohesin localization (Bernard et al. 2001; Nonaka et al. 2002). The possibility also exists that cohesin itself may influence transcriptional status and act as a transcriptional boundary (Hagstrom and Meyer 2003; Rollins et al. 1999). Despite the subunit difference between the meiotic and mitotic cohesin complex, we find that the association of cohesin with DNA in meiotic cells is similar to that in mitotic cells. In addition, we find that the constitutive peaks of meiotic cohesin binding are negatively correlated with DSB sites. This negative correlation is consistent with the model proposed by Blat et al. (2002) for the relationship between recombination and cohesin. This model suggests that cohesins are at meiotic chromosome cores and that recombination occurs in chromatin loops emanating from these cores where the part of the loop undergoing recombination is transiently localized to the axis. Thus, the recombination machinery can sense chromosome organization provided by cohesin. The differences in binding of meiotic and mitotic cohesin suggest that the location of the meiotic complex is also dependent on gene transcription. Hence, meiotic recombination in a given organism may be somewhat dependent on the spacing of cohesin as established in premeiotic S phase, which is in turn responsive to transcription. The genome-wide distribution of DSBs is positively correlated with regions of high GC content, divergent promoters, and transcription factor binding (Gerton et al. 2000). Thus transcription, recombination, and cohesion all display intimate connections to chromosome and chromatin structure. Genome-wide studies of protein–DNA complexes afford a better understanding of the role of these complexes in the biology of an organism and its genome. In the process of analyzing the first genome-wide map of cohesin in any organism, we developed PeakFinder, a program able to sensitively identify binding sites of protein–DNA complexes in their local genomic environment, and potentially useful for analysis of any other genome-wide measurements. While budding yeast appears to have largely opted for placement of cohesin in AT-rich, transcriptionally inactive regions, other organisms with much longer and more complicated transcriptional units, different base composition properties, or different levels of condensation may employ different strategies for the placement of cohesin, which may in turn affect the stability of those genomes. The genome-wide analysis of cohesin in S. cerevisiae will serve as a useful framework upon which to explore attributes of cohesin localization in higher eukaryotes. Materials and Methods ChIP methods. ChIPs were performed as previously described (Meluh and Koshland 1997; Laloraya et al. 2000). Semiquantitative PCR analysis was performed as previously described (Laloraya et al. 2000). ChIP using the same experimental regime in a strain lacking the Myc epitope was performed and did not yield any appreciable signal (Megee et al. 1999). Cell culture. For the meiotic timecourse, cultures were grown in YPA, then transferred to SPM. Timepoints were removed for ChIP at 4, 6, 8, 10, and 12 h after transfer to SPM. Nocodazole-mediated arrest was accomplished by adding nocodazole to a final concentration of 15 μg/ml to the media. All cultures were grown at 30 °C. Shifting cultures to 37 °C in prewarmed media induced metaphase arrest in cdc16-ts cells. DNA amplification, labeling, and hybridization. Preparation of Cy5- and Cy3-labeled DNA, hybridization, and analysis were performed as previously described (Bohlander et al. 1992; Gerton et al. 2000). The polyL-lysine-coated spotted glass microarrays used in this study contained each ORF and each intergenic region in the yeast genome as individual spots (Iyer et al. 2001). For each experimental condition, a minimum of two independent immunoprecipitations was performed. DNA from the immunoprecipitation was labeled with Cy5 and competitively hybridized with total genomic DNA labeled with Cy3. Hybridizations with fluor reversal were also performed for DNA from at least one of the immunoprecipitations for each condition. At least three arrays were analyzed per experimental condition, and the median values were used to represent the dataset. Hybridizations were performed at 63 °C overnight under standard conditions, and slides were washed successively with 0.6X SSC/0.03% SDS and then 0.06X SSC prior to scanning (see also http://microarrays.org). The meiotic experiments were done in the SK1 strain background and although two independent timecourses were performed, the results from a single representative timecourse were used for analysis. The resolution of these genome-wide maps is limited by (1) the shear size of the DNA fragments (range of 200–1000 bp) and (2) the size of the elements on our microarrays (mean of 0.9 kb). We do not expect these fragment-size distributions to introduce a significant bias in our mapping effort since the previously estimated size of a cohesin-binding region at an arm site is 0.8–1.0 kb (Laloraya et al. 2000). Computational methods. The arrays were scanned using an Axon Instruments (Union City, California, United States) 4000B scanner and quantitated using GenePix 4.0. Results were stored in the AMAD database. Data were normalized and filtered by requiring intensity to be 200 or more, and spots to have a correlation coefficient of 0.5 or more. For analysis purposes, any feature with less than two measurements was excluded (with the exception of the meiotic timecourse). Data were analyzed using PeakFinder, a program developed specifically for finding peaks in ChIP data, but generally applicable for plotting any measurement against genomic coordinates, smoothing the curves, and annotating peaks on the basis of local properties of the curve. Extensive documentation for PeakFinder is available at http://research.stowers-institute.org/jeg/2004/cohesin/peakfinder/. Briefly, PeakFinder takes the fluorescence ratios and samples them at the indicated interval of basepairs. The log2 of the data are then smoothed. The first derivative of the smoothed line is used to identify peaks, and the absolute value of the corresponding peak is then extracted from the raw data (this is necessary because the nature of the smoothing algorithm dampens the peak height). PeakFinder allows filtering of peaks based on the parameters of the peak. For example, cohesin peaks analyzed in the cdc16-ts dataset were identified using the following set of parameters: (1) sampling log2-transformed ratios at 100-bp intervals, (2) smoothing over eight rounds using a nine-point Gaussian-weighted moving average, and (3) filtering of peaks with a left and right rise of less than 0.1 and a height less than 0.4 (log2 space). These conditions identified all peaks mapped to high resolution on Chromosomes III and XII (Laloraya et al. 2000). A current limitation of PeakFinder is that it is unable to identify one-sided peaks; therefore telomeres were manually inspected for cohesin binding. PeakFinder is written in Delphi, runs on a Windows platform, and is distributed under the GNU General Public License. YAC. PCR primers were designed to amplify 150–300-bp sequences, at 1-kb intervals along the entire length of the 1572 YAC (Green et al. 1995). Nucleotides 1–3,683 contain the vector sequences from pYAC4 including the telomere and URA3. Nucleotides 326,702—332,707 contain vector sequences from pYAC4 including the centromere, TRP1, and ARS1. The YAC was introduced into the strain 1377 A1 4B, two independent cultures were grown to exponential phase, and nocodazole was added. After 3 h of growth at 23 °C, more than 90% of cells were arrested in metaphase. Cultures were processed for ChIP as described previously. Thiolutin. 0.8 kb from CARC1 and 1.4 kb from CARL2 were cloned into pUNI and then recombined with pYCE to form pCM34 and pCM38. This places the pGAL1-10 promoter immediately adjacent to cohesin-associated regions. pCM34 and pCM38 were introduced into 1377 A1 4B by transformation. Strains with pCM34 and pCM38 were initially grown in complete medium lacking uracil with raffinose as a carbon source. This medium selects for retention of the plasmids and prevents transcription from the Gal-inducible promoter. These cultures were diluted approximately 100-fold in YEP raffinose and grown to 7 × 106/ml. Cultures were arrested in G1 with alpha factor, released from G1 in the presence of nocodazole, and grown for 3 h to generate an M phase arrest. Cultures were split in two, and one half received galactose to a final concentration of 4%. One hour after addition of galactose, cultures were fixed and processed for ChIP. Experiments with thiolutin were performed as described above except thiolutin was added to a final concentration of 3 μg/ml just prior to galactose addition. Primers were generated that amplified 5′ and 3′ regions of CARC1 and CARL2 in the endogenous locus and on pCM34 and pCM38. Reduction in cohesin binding was expressed as the ratio of (1) the amount of cohesin bound to the 5′ or 3′ ends of the CAR on the plasmids to (2) the amount of cohesin bound to the 5′ or 3′ regions of the CAR in the genome. Supporting Information Datasets S10–S58 correspond to the individual GenePix results (GPR) files for each array performed. For each dataset we have listed the Cy3 channel sample and the Cy5 channel sample. Dataset S1 W303 Strain Arrested by cdc16-ts with ChIP Performed for Mcd1-18Myc File cdc16_Mcd1-18Myc_W303. (483 KB TXT). Click here for additional data file. Dataset S2 A364a Strain Arrested by cdc16-ts with ChIP Performed for Mcd1-6HA File cdc16_Mcd1-6HA_A364a. (957 KB TXT). Click here for additional data file. Dataset S3 A364a Strain Arrested by cdc16-ts with ChIP Performed for Smc3-6Myc File cdc16_Smc3-6Myc_A364a. (701 KB TXT). Click here for additional data file. Dataset S4 W303 Strain Grown in Galactose and Arrested by Nocodazole with ChIP Performed for Mcd1-18Myc File Mcd1_18Myc_W303_NZgalCHIP. (406 KB TXT). Click here for additional data file. Dataset S5 W303 Strain with Mcd1-18Myc Grown in Galactose and Arrested by Nocodazole with RNA Harvested for Gene Expression File Mcd1-18Myc_W303_NZgal_exp. (196 KB TXT). Click here for additional data file. Dataset S6 W303 Strain with Mcd1-18Myc Grown in Glucose and Arrested by Nocodazole with RNA Harvested for Gene Expression File Mcd1-18Myc_W303_NZglu_exp. (221 KB TXT). Click here for additional data file. Dataset S7 W303 Strain Grown in Glucose and Arrested with Nocodazole with ChIP Performed for Mcd1-18Myc File Mcd1-18Myc_W303_NZgluChIP. (694 KB TXT). Click here for additional data file. Dataset S8 SK1 Strain Arrested with Nocodazole with ChIP Performed for Mcd1-3HA File Mcd1-3HA_SK1_NZ. (339 KB TXT). Click here for additional data file. Dataset S9 SK1 Strain in Meiosis with ChIP Performed for Rec8-3HA File Rec8-3HA_SK1. (710 KB TXT). Click here for additional data file. Dataset S10 SIMRUP2_147 Cy3 = ChIP cdc16-ts Mcd1-6HA in A364a; Cy5 = genomic DNA. (4.6 MB XLS). Click here for additional data file. Dataset S11 SIMRUP2_170 Cy3 = ChIP cdc16-ts Mcd1-6HA in A364a; Cy5 = genomic DNA. (4.6 MB XLS). Click here for additional data file. Dataset S12 SIMRUP2_171 Cy3 = ChIP cdc16-ts Mcd1-6HA in A364a; Cy5 = genomic DNA. (4.6 MB XLS). Click here for additional data file. Dataset S13 SIMRUP2_178 Cy3 = genomic DNA; Cy5 = ChIP cdc16-ts Mcd1-6HA in A364a. (4.6 MB XLS). Click here for additional data file. Dataset S14 SIMRUP2_180 Cy3 = genomic DNA; Cy5 = ChIP cdc16-ts Mcd1-6HA in A364a. (4.6 MB XLS). Click here for additional data file. Dataset S15 SIMRUP2_187 Cy3 = ChIP cdc16-ts Smc3-6Myc in A364a; Cy5 = genomic DNA. (4.6 MB XLS). Click here for additional data file. Dataset S16 SIMRUP2_190 Cy3 = ChIP cdc16-ts Smc3-6Myc Mcd1-6HA ChIP for HA in A364a; Cy5 = genomic DNA. (4.6 MB XLS). Click here for additional data file. Dataset S17 SIMRUP2_191 Cy3 = ChIP cdc16-ts Smc3-6Myc Mcd1-6HA ChIP for Myc in A364a; Cy5 = genomic DNA. (4.6 MB XLS). Click here for additional data file. Dataset S18 SIMRUP2_226 Cy3 = genomic DNA; Cy5 = ChIP cdc16-ts Smc3-6Myc in A364a. (4.6 MB XLS). Click here for additional data file. Dataset S19 SIMRUP2_244 Cy3 = genomic DNA; Cy5 = ChIP cdc16-ts Smc3-6Myc Mcd1-6HA ChIP for HA in A364a. (4.6 MB XLS). Click here for additional data file. Dataset S20 SIMRUP2_254 Cy3 = genomic DNA; Cy5 = ChIP cdc16-ts Smc3-6Myc Mcd1-6HA ChIP for Myc in A364a. (4.6 MB XLS). Click here for additional data file. Dataset S21 UP2_13 Cy3 = genomic DNA; Cy5 = ChIP cdc16-ts Mcd1-18Myc in W303. (2.5 MB XLS). Click here for additional data file. Dataset S22 UP2_19 Cy3 = genomic DNA; Cy5 = ChIP cdc16-ts Mcd1-18Myc in W303. (2.5 MB XLS). Click here for additional data file. Dataset S23 UP3_186 Cy3 = ChIP 6h Rec8-3HA in SK1; Cy5 = genomic DNA. (2.6 MB XLS). Click here for additional data file. Dataset S24 UP3_187 Cy3 = ChIP 8h Rec8-3HA in SK1; Cy5 = genomic DNA. (2.5 MB XLS). Click here for additional data file. Dataset S25 UP3_188 Cy3 = ChIP 10h Rec8-3HA in SK1; Cy5 = genomic DNA. (2.6 MB XLS). Click here for additional data file. Dataset S26 UP3_190 Cy3 = ChIP 6h Rec8-3HA in SK1; Cy5 = genomic DNA. (2.6 MB XLS). Click here for additional data file. Dataset S27 UP3_191 Cy3 = ChIP 8h Rec8-3HA in SK1; Cy5 = genomic DNA. (2.6 MB XLS). Click here for additional data file. Dataset S28 UP3_29 Cy3 = genomic DNA; Cy5 = ChIP 12h Rec8-3HA in SK1. (2.6 MB XLS). Click here for additional data file. Dataset S29 UP3_30 Cy3 = genomic DNA; Cy5 = ChIP 4h Rec8-3HA in SK1. (2.6 MB XLS). Click here for additional data file. Dataset S30 UP3_48 Cy3 = genomic DNA; Cy5 = ChIP cdc16-ts Mcd1-18Myc in W303. (2.6 MB XLS). Click here for additional data file. Dataset S31 UP3_51 Cy3 = genomic DNA; Cy5 = ChIP cdc16-ts Mcd1-18Myc in W303. (2.6 MB XLS). Click here for additional data file. Dataset S32 UP3_84 Cy3 = ChIP cdc16-ts Mcd1-18Myc in W303; Cy5 = genomic DNA. (2.6 MB XLS). Click here for additional data file. Dataset S33 UP3_85 Cy3 = ChIP cdc16-ts Mcd1-18Myc in W303; Cy5 = genomic DNA. (2.6 MB XLS). Click here for additional data file. Dataset S34 UP3_86 Cy3 = genomic DNA; Cy5 = ChIP 4h Rec8-3HA in SK1. (2.6 MB XLS). Click here for additional data file. Dataset S35 UP3_87 Cy3 = genomic DNA; Cy5 = ChIP 10h Rec8-3HA in SK1. (2.6 MB XLS). Click here for additional data file. Dataset S36 UP3_89 Cy3 = genomic DNA; Cy5 = ChIP 12h Rec8-3HA in SK1. (5.6 MB XLS). Click here for additional data file. Dataset S37 UP4_224 Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest Mcd1-3HA in SK1. (2.6 MB XLS). Click here for additional data file. Dataset S38 UP4_225 Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest Mcd1-3HA in SK1. (2.6 MB XLS). Click here for additional data file. Dataset S39 UP5_164 Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303. (2.7 MB XLS). Click here for additional data file. Dataset S40 UP5_80 Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303. (2.7 MB XLS). Click here for additional data file. Dataset S41 UP6_124 Cy3 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303; Cy5 = genomic DNA. (2.6 MB XLS). Click here for additional data file. Dataset S42 UP6_210 Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303. (4.4 MB XLS). Click here for additional data file. Dataset S43 UP6_213 Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303. (4.4 MB XLS). Click here for additional data file. Dataset S44 UP6_214 Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest galactose Mcd1-18Myc in W303. (4.4 MB XLS). Click here for additional data file. Dataset S45 UP6_217 Cy3 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303; Cy5 = genomic DNA. (4.4 MB XLS). Click here for additional data file. Dataset S46 UP6_218 Cy3 = ChIP nocodazole arrest galactose Mcd1-18Myc in W303; Cy5 = genomic DNA. (4.4 MB XLS). Click here for additional data file. Dataset S47 UP6_221 Cy3 = ChIP nocodazole arrest galactose Mcd1-18Myc in W303; Cy5 = genomic DNA. (4.4 MB XLS). Click here for additional data file. Dataset S48 UP6_223 Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303. (4.4 MB XLS). Click here for additional data file. Dataset S49 UP6_225 Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest galactose Mcd1-18Myc in W303. (4.4 MB XLS). Click here for additional data file. Dataset S50 YA1S4P2_106 Cy3 = polyA+ reference RNA; Cy5 = polyA+ RNA nocodazole arrest galactose Mcd1-18Myc in W303. (2.2 MB XLS). Click here for additional data file. Dataset S51 YA1S4P2_108 Cy3 = polyA+ RNA nocodazole arrest galactose Mcd1-18Myc in W303; Cy5 = polyA+ reference RNA. (2.2 MB XLS). Click here for additional data file. Dataset S52 YA1S4P2_109 Cy3 = polyA+ reference RNA; Cy5 = polyA+ RNA nocodazole arrest glucose Mcd1-18Myc in W303. (2.2 MB XLS). Click here for additional data file. Dataset S53 YA1S4P2_110 Cy3 = polyA+ reference RNA; Cy5 = polyA+ RNA nocodazole arrest galactose Mcd1-18Myc in W303. (2.2 MB XLS). Click here for additional data file. Dataset S54 YA1S4P2_111 Cy3 = polyA+ RNA nocodazole arrest glucose Mcd1-18Myc in W303; Cy5 = polyA+ reference RNA. (2.2 MB XLS). Click here for additional data file. Dataset S55 YA1S4P2_112 Cy3 = polyA+ reference RNA; Cy5 = polyA+ RNA nocodazole arrest glucose Mcd1-18Myc in W303. (2.2 MB XLS). Click here for additional data file. Dataset S56 YA1S4P2_114 Cy3 = polyA+ RNA nocodazole arrest galactose Mcd1-18Myc in W303; Cy5 = polyA+ reference RNA. (2.2 MB XLS). Click here for additional data file. Dataset S57 YA1S4P2_115 Cy3 = polyA+ RNA nocodazole arrest glucose Mcd1-18Myc in W303; Cy5 = polyA+ reference RNA. (2.2 MB XLS). Click here for additional data file. Dataset S58 YA1S4P2_125 Cy3 = polyA+ reference RNA; Cy5 = polyA+ RNA nocodazole arrest glucose Mcd1-18Myc in W303. (2.2 MB XLS). Click here for additional data file. Protocol S1 ChIP for Yeast (61 KB DOC). Click here for additional data file. Protocol S2 Round A/B/C Random Amplification of DNA (37 KB DOC). Click here for additional data file. Accession Numbers The Saccharomyces Genome Database (http://www.yeastgenome.org/) accession numbers for the genes and gene products discussed in this paper are CDC16 (SGDID S0001505), EBP2 (SGDID S0001655), GAL2 (SGDID S0004071), Mif2 (SGDID S0001572), NCE102/YPR149W (SGDID S0006353), PCD1 (SGDID S0004141), Pds1p (SGDID S0002520), Rec8 (SGDID S0006211), ROK1 (SGDID S0003139), RRS1 (SGDID S0005820), Scc1/Mcd1 (SGDID S0002161), Scc3 (SGDID S0001288), Smc1 (SGDID S0001886), Smc3 (SGDID S0003610), YDL218W (SGDID S0002377), YDL238C (SGDID S0002397), YER179W (SGDID S0000981), and YPR006C (SGDID S0006210). We thank A. Mushegian, J. Workman, and N. Kleckner for valuable comments on the manuscript. This research was supported in part by a Herb Boyer postdoctoral fellowship (JLG), March of Dimes Basil O'Connor grant 5-FY02-251 (JLG), National Institutes of Health grant RO1 GM66213 (PCM), and the Howard Hughes Medical Institute (DEK). Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. PCM, JLD, JLG, and DEK conceived and designed the experiments. PCM, JLG, DEK, CM, EU, and HGY performed the experiments. JLD, EFG, JLG, PCM, and DEK analyzed the data. JLD contributed reagents/materials/analysis tools. PCM, JLD, JLG, and DEK wrote the paper. EFG wrote PeakFinder. Academic Editor: Bruce Stillman, Cold Spring Harbor Laboratory Citation: Glynn F, Megee PC, Yu HG, Mistrot C, Unal E, et al. (2004) Genome-wide mapping of the cohesion complex in the yeast Saccharomyces cerevisiae. PLoS Biol 2(9): e259. Abbreviations CARCcohesin-associated region on Chromosome III (C) CARLcohesin-associated region on Chromosome XII (L) ChIPchromatin immunoprecipitation ChIP chipchromatin immunoprecipitation coupled with microarray analysis DSBdouble-strand break ORFopen reading frame rDNAribosomal DNA SCsynaptonemal complex YACyeast artificial chromosome ==== Refs References Baetz KK Krogan NJ The ctf13–30/CTF13 genomic haploinsufficiency modifier screen identifies the yeast chromatin remodeling complex RSC, which is required for the establishment of sister chromatid cohesion Mol Cell Biol 2004 24 1232 1244 14729968 Bailey TL Elkan C Fitting a mixture model by expectation maximization to discover motifs in biopolymers Proc Int Conf Intell Syst Mol Biol 1994 2 28 36 7584402 Bernard P Maure JF Partridge JF Genier S Javerzat JP Requirement of heterochromatin for cohesion at centromeres Science 2001 294 2539 2542 11598266 Blat Y Kleckner N Cohesins bind to preferential sites along yeast chromosome III, 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10.1371/journal.pbio.0020259

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020260Research ArticleCell BiologySaccharomycesThe Kinetochore Is an Enhancer of Pericentric Cohesin Binding Kinetochores Enhance Cohesin BindingWeber Stewart A 1 Gerton Jennifer L 2 Polancic Joan E 1 DeRisi Joseph L 3 Koshland Douglas 4 Megee Paul C [email protected] 1 1Department of Biochemistry and Molecular Genetics, University of Colorado Health Sciences Center at FitzsimonsAurora, Colorado, United States of America2Stowers Institute, Kansas CityMissouri, United States of America3Department of Biochemistry and Biophysics, University of CaliforniaSan Francisco, California, United States of America4Howard Hughes Medical Institute, Department of EmbryologyCarnegie Institution of Washington, Baltimore, MarylandUnited States of America9 2004 27 7 2004 27 7 2004 2 9 e26012 12 2003 14 5 2004 Copyright: © 2004 Weber et al.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. Genome-Wide Mapping of the Cohesin Complex in the Yeast Saccharomyces cerevisiae Genome-Wide Survey of Cohesin: A Molecular Guardian of Genomic Fidelity The recruitment of cohesins to pericentric chromatin in some organisms appears to require heterochromatin associated with repetitive DNA. However, neocentromeres and budding yeast centromeres lack flanking repetitive DNA, indicating that cohesin recruitment occurs through an alternative pathway. Here, we demonstrate that all budding yeast chromosomes assemble cohesin domains that extend over 20–50 kb of unique pericentric sequences flanking the conserved 120-bp centromeric DNA. The assembly of these cohesin domains requires the presence of a functional kinetochore in every cell cycle. A similar enhancement of cohesin binding was also observed in regions flanking an ectopic centromere. At both endogenous and ectopic locations, the centromeric enhancer amplified the inherent levels of cohesin binding that are unique to each region. Thus, kinetochores are enhancers of cohesin association that act over tens of kilobases to assemble pericentric cohesin domains. These domains are larger than the pericentric regions stretched by microtubule attachments, and thus are likely to counter microtubule-dependent forces. Kinetochores mediate two essential segregation functions: chromosome movement through microtubule attachment and biorientation of sister chromatids through the recruitment of high levels of cohesin to pericentric regions. We suggest that the coordination of chromosome movement and biorientation makes the kinetochore an autonomous segregation unit. Kinetochores mediate chromosome movement through microtubule attachment and biorientation of sister chromatids through the recruitment of high levels of cohesin to pericentric regions ==== Body Introduction The proper segregation of replicated chromosomes, or sister chromatids, to daughter cells during mitosis requires that sister chromatids establish stable attachments to microtubules emanating from opposite spindle poles, known as chromosome biorientation, and that sister chromatids move to opposite poles of the cell during anaphase. Chromosome biorientation is made possible by the cohesion of replicated sister chromatids, which occurs along the entire length of the chromosome and is especially robust in large centromere-flanking or “pericentric” domains (Sumner 1991). The centromere is the site of assembly of the kinetochore, a protein complex that mediates the attachment and movement of chromosomes along the mitotic spindle. The centromere-flanking domains of cohesion are thought to play an important role in biorientation by constraining the kinetochores on paired sister chromatids in opposite directions. This orientation of sister kinetochores facilitates the capture of microtubules originating from different spindle poles, thereby ensuring that sister chromatids are segregated in opposition later in mitosis. A second function of pericentric cohesion and possibly cohesion along chromosome arms is to resist the poleward forces that are imposed by these bipolar spindle microtubule attachments. The resistance that is provided by cohesion prevents the premature dissociation of sister chromatids and contributes to a tension-based mechanism that stabilizes kinetochore–microtubule interactions (Nicklas and Ward 1994). Given the importance of cohesion in pericentric regions, it is critical to understand how these large functional domains are assembled. An important clue came with the discovery of a group of proteins that mediate cohesion and are conserved in organisms from the yeasts to vertebrates (Guacci et al. 1997; Michaelis et al. 1997; Furuya et al. 1998; Losada et al. 1998; Skibbens et al. 1999; Toth et al. 1999; Hartman et al. 2000; Tomonaga et al. 2000; Wang et al. 2000; Hanna et al. 2001; Losada and Hirano 2001). This group includes Pds5p and the members of a multisubunit “cohesin” complex, Mcd1/Scc1p, Irr1p/Scc3p, Smc1p, and Smc3p. Cohesins were shown to bind to specific regions of chromosomes (Blat and Kleckner 1999; Megee et al. 1999; Tanaka et al. 1999; Hartman et al. 2000; Laloraya et al. 2000; Panizza et al. 2000). At centromere-distal locations, cohesin-binding sites span only approximately 0.8 kb (Laloraya et al. 2000). In striking contrast, cohesin binding is highly enriched within an approximately 50-kb pericentric region of budding yeast Chromosome (CHR) III flanking the centromere–kinetochore complex, which occupies only a 0.25-kb nuclease-resistant region (Bloom and Carbon 1982; Saunders et al. 1988; Blat and Kleckner 1999). It remains to be determined whether pericentric cohesin enrichment is unique to budding yeast CHRIII or is, in fact, a property of all pericentric regions. However, cytological observations support the notion that cohesin may be enriched in the centromere-proximal regions of all higher eukaryotic chromosomes, given that the chromosomes of mitotically arrested cells remain tightly associated in these regions (Gonzalez et al. 1991). These observations suggest that these large domains of pericentric cohesion result from an enrichment of cohesin binding. How are these large cohesin domains assembled in kinetochore-flanking regions? Recent experiments in Schizosaccharomyces pombe have provided evidence for the role of repetitive heterochromatic DNA in pericentric cohesion. In this organism, the recruitment of high levels of cohesion factors to centromeric regions is dependent on Swi6 (SPAC664.01c), the fission yeast homolog of the heterochromatin protein HP1 (Bernard et al. 2001; Nonaka et al. 2002). However, this is unlikely to be the only mechanism for generating large pericentric cohesin domains because the centromere on budding yeast CHRIII and neocentromeres in human cells are devoid of surrounding repetitive sequences characteristic of heterochromatin. One clue for a potential mechanism came from our observation that CEN3 placed ectopically on a minichromosome could direct the binding of cohesin to approximately 2 kb of centromere-flanking DNA even if that DNA did not normally associate with cohesin, establishing that centromeres could modulate cohesin binding on neighboring sequences (Megee et al. 1999). The presence of these cohesin-enriched domains flanking CEN3 on both the endogenous chromosome and a minichromosome is intriguing, and their existence has raised many interesting questions. Here, we endeavored to determine whether large pericentric cohesin domains exist on all budding yeast chromosomes, and if so, whether these domains are equivalent in size and have a similar distribution of cohesin. Furthermore, we examined the possible roles of the centromere and centromere-flanking sequences in the assembly of the pericentric cohesin domains. Our results show that the budding yeast kinetochore behaves as an enhancer in the assembly of approximately 20-kb to 50-kb pericentric cohesin domains. Our observations suggest that the kinetochore mediates two essential segregation functions, coordinating not only the attachment of chromosomes to the mitotic spindle, but also the recruitment of sufficient levels of cohesin within pericentric regions to promote biorientation of sister kinetochores and to resist microtubule-dependent poleward forces. Thus, the kinetochore functions as a modular segregation unit. The integration of these functions by the kinetochore is therefore likely to play an important role in the maintenance of genomic integrity. Results Cohesin Is Enriched throughout Large Pericentric Domains Relative to Arm Sites Previous studies have demonstrated that cohesin binding is highly enriched in the centromere-proximal region of CHRIII in comparison to arm sites and also within the centromere-flanking region of a CEN3-containing minichromosome in budding yeast arrested in mitosis using nocodazole, an inhibitor of microtubule assembly (Blat and Kleckner 1999; Megee et al. 1999; Laloraya et al. 2000). To examine cohesin binding throughout centromere-proximal and -distal regions of the budding yeast genome, we have performed chromatin immunoprecipitation (ChIP) using epitope-tagged alleles of cohesin subunits (Mcd1-6HAp or Smc3-6Mycp) as markers for the cohesin complex. The immunoprecipitated (ChIP) and input DNA samples not subject to immunoprecipitation were then analyzed using two approaches (Materials and Methods). In some cases, PCR reactions that respond linearly to the amount of input DNA were performed for both ChIP DNA and diluted input DNA to determine the percentage of total chromatin bound by cohesin subunits in the ChIPs. Alternatively, input and ChIP DNA samples were labeled with aminoallyl dUTP conjugated to different fluorescent tags, and then hybridized competitively to DNA microarrays containing all budding yeast open reading frames (ORFs) and intergenic regions to analyze cohesin binding genome-wide. In both types of experiments, we examined the distribution (i.e., the locations of peaks and valleys) and the magnitude (peak height) of cohesin subunit binding. To determine whether the pericentric regions of all budding yeast chromosomes are similarly enriched for cohesin binding, input DNA and DNA crosslinked to Mcd1p in mitotically arrested cells were used to probe microarrays of the budding yeast genome. The profile of Mcd1p binding within each ORF and intergenic region was then superimposed on a map of the sixteen budding yeast chromosomes to visualize the distribution of Mcd1p association (Figure 1A). This analysis demonstrated dramatic differences in the distribution of Mcd1p in centromere-proximal and -distal regions of all sixteen budding yeast chromosomes, as had been observed previously in a study of CHRIII (Blat and Kleckner 1999). While centromere-distal regions contained short, discrete foci (approximately 0.8 kb) of Mcd1p binding that were distributed at intervals of approximately 10 kb, centromere-proximal regions of all chromosomes contained large (approximately 20–50 kb) domains that were highly enriched for Mcd1p binding. Microarray analyses were also performed using ChIP DNA isolated from strains containing both Myc-tagged Smc3p (Smc3-6Mycp) and HA-tagged Mcd1p (Mcd1-6HAp), or Smc3-6Myc alone to confirm that another subunit of the cohesin complex showed a similar enrichment in pericentric chromatin. As was the case with Mcd1p, the magnitude of Smc3p association was higher in pericentric regions than at arm locations (Figure 1B). Furthermore, the pattern of Smc3p binding observed within pericentric regions in independent immunoprecipitations of chromatin isolated from the singly or doubly tagged strains was strikingly similar to that observed for Mcd1p (Figure 2). Thus, the high level of concurrence in cohesin subunit binding patterns suggests that the association of the entire cohesin complex is enriched in pericentric chromatin compared to chromosome arm locations. In addition, we compared the datasets obtained by microarray analyses in this study to those obtained using Myc-tagged Mcd1p (Mcd1-18Mycp) in another commonly used budding yeast strain background as described in the accompanying report by Glynn et al. (2004). These studies showed good agreement for the presence of enriched Mcd1p binding in pericentric regions and a similar distribution of peaks and valleys of binding at both pericentric and arm locations (correlation coefficient = 0.76; Glynn et al. 2004). Figure 1 Microarray Analyses of Mcd1p and Smc3p Binding DNA isolated from cohesin subunit ChIPs and control input DNA was labeled with aminoallyl dUTP, conjugated to Cy5 (red) or Cy3 (green) fluorescent tags, and then hybridized competitively to microarrays. Although the samples were labeled by different fluorescent tags depending on the experiment, for the purposes of analysis, the ratios are converted such that the ChIP signal is represented by red and control DNA by green. The red-to-green (R:G) ratio for each ORF and intergenic region was calculated for cells arrested in mitosis using the cdc16 mutation and assigned a color, and the median value obtained for each element was then plotted on a map of the sixteen chromosomes to determine the chromosomal distribution of Mcd1–6HAp. Regions with a R:G ratio less than 1.8 are shown in gray, and those with ratios of 1.8 or higher are shown in red. The red shading is an indicator of the intensity of cohesin subunit binding, such that regions with larger R:G ratios have lighter shades of red. Hybridization data are unavailable for regions shaded in blue (see Materials and Methods), and genomic regions not present on the arrays are indicated in white. Centromere position is indicated by an asterisk. (A) Chromatin isolated from strains containing Mcd1-6HAp (1377A1-4B, 1829-15B, and PMY270) was immunoprecipitated using anti-HA antibodies. (B) Chromatin isolated from strains containing Smc3-6Mycp (PMY270 and 1839-3D) was immunoprecipitated with anti-Myc antibodies. Figure 2 Comparison of Mcd1p and Smc3p Binding Distributions in Centromere-Flanking Regions The log2 of the median of the R:G ratios for Smc3-6Mycp (triangles) and Mcd1-6HAp (squares) binding within approximately 60-kb pericentric regions of CHRV, CHRVI, and CHRIX in cdc16-arrested cells is plotted as a function of the indicated SGD coordinates. The relative position of the centromere within each pericentric region is indicated by the oval. To further investigate the nature of this pericentric cohesin enrichment, Mcd1p association profiles were examined within the approximately 50-kb pericentric regions of endogenous CHRI, CHRIII and CHRXIV using PCR analyses of ChIP DNA. For comparison, Mcd1p association was also examined within an approximately 37-kb centromere-distal region on the right arm of CHRIII (Saccharomyces Genome Database [SGD] coordinates 242–279 kb) (Figure 3). We observed that the magnitude of Mcd1p binding throughout these pericentric regions is on average 3- to 5-fold higher than the levels of association observed at the CHRIII centromere-distal location. Within each of the pericentric domains examined, the distribution of Mcd1p binding was not uniform, but instead consisted of peaks and troughs of association (Figure 3A–3C). These peaks of Mcd1p binding were much broader than those observed at the CHRIII centromere-distal location (Figure 3D) (Blat and Kleckner 1999; Laloraya et al. 2000). The peaks of Mcd1p association within pericentric chromatin were separated by troughs having values approximately 0.5% of those of the input chromatin. Although significantly reduced in comparison to the peaks, Mcd1p association in these troughs reflects significant binding, given that other chromosomal regions such as ARS1 and ADE3 are absent from Mcd1p ChIPs (≤0.04% of input chromatin; unpublished data not shown). Furthermore, this binding within troughs is unlikely to reflect poor shearing of the pericentric chromatin by sonication, as ChIPs performed using antibodies specific for the kinetochore protein Mif2p showed an enrichment within centromeric DNA that decreased 5-fold in flanking regions only approximately 245 bp away from CEN3 (Figure 4). Lastly, it is interesting to note that in many of the pericentric regions, cohesin subunit association in the interval that contains the centromeric DNA was reduced in comparison to the regions immediately flanking the centromere, possibly because the large complex of kinetochore proteins precludes the association of cohesin with the relatively small 120-bp centromeric DNA (see Figures 2 and 3A–3C). Thus, microarray and PCR analyses of DNA crosslinked to cohesin subunits demonstrate that cohesin binding is highly enriched within large pericentric domains on all budding yeast chromosomes. Figure 3 Mcd1p Binding Profiles in Centromere-Proximal and -Distal Regions Cells containing Mcd1-6HAp were first staged in G1 using αF, and then released from G1 into medium containing nocodazole to arrest the cells in mitosis. For the centromere excision experiments (B–D), the cultures were divided in half after G1 arrest, and one half of each culture was treated with galactose for 2 h to induce centromere excision (see Materials and Methods). Both the induced (acentric) and uninduced (centric) control cultures were then released from the G1 arrest into fresh medium and rearrested in mitosis. Once arrested in mitosis, cells were fixed in formaldehyde and then processed for ChIP using antiserum against epitope-tagged Mcd1p (Mcd1-6HAp) as an indicator of the cohesin complex. DNA isolated from the ChIPs and diluted input DNA not subject to immunoprecipitation were then subjected to PCR analysis using oligonucleotide primer pairs that amplify approximately 300-bp fragments within the indicated regions. Quantitation of DNA in the Mcd1p ChIPs, expressed as a percentage of the input DNA, is plotted as a function of the locations of the midpoints of those DNA fragments based on the SGD coordinates. Centromere position is indicated by an oval (not drawn to scale). (A) The Mcd1p association profile for the CHRI pericentric region in strain 1377A1-4B is shown. Mcd1p binding adjacent to CEN1 is difficult to assess fully because of the presence of a moderately repetitive Ty element in the region from approximately 160 to 166 kb, indicated with the dashed line. Similarly, the Mcd1p binding profiles in the pericentric regions of CHRIII (B) and CHRXIV (C) are shown in the presence (black squares) and absence (gray circles) of CEN3 and CEN14 using strains PMY185 and PMY206, respectively. (D) The Mcd1p binding profiles for a centromere-distal region of CHRIII are shown for comparison in the presence (black squares) and absence (gray circles) of CEN3. Figure 4 Shearing of Centromere-Proximal Chromatin by Sonication As a control for the shearing of chromatin, a precipitation of chromatin was performed in each experiment using a polyclonal antiserum specific for the kinetochore protein Mif2p, which has been shown to interact with centromeric DNA (Meluh and Koshland 1997). DNA crosslinked to Mif2 was then subjected to PCR analysis throughout a 2-kb region spanning CEN3, using a series of primer pairs that amplify 240 ± 21–bp fragments. CEN3 DNA spans SGD coordinates 114382 to 114498, indicated with the ovals. Data were plotted on a scale similar to cohesin subunit ChIP data for comparison, and the inset shows in detail the magnitude of binding within a 2-kb centromere-flanking region. While all pericentric regions were enriched for Mcd1p and Smc3p binding, we found that the pattern of cohesin subunit association was different within each pericentric region (Figure 3A–3C). For example, the CHRIII pericentric region had its highest levels of Mcd1p association within an approximately 2-kb region spanning the centromere, and this region was flanked symmetrically by peaks of Mcd1p association of lesser magnitude, located approximately 15 kb from the centromere. In contrast, the CHRI centromere was located in a local trough of Mcd1p association and was flanked by peaks of Mcd1p binding that were approximately 5 kb away. Furthermore, the pericentric region of CHRXIV contained a series of closely spaced peaks of Mcd1p association that were roughly equivalent in magnitude. Thus, although all pericentric regions were indeed enriched for cohesin association, the uniqueness of the distribution of cohesin within each pericentric region suggests that cohesin binding is influenced by local sequence characteristics. Enhancement of Pericentric Cohesin Binding Is Mediated by the Kinetochore Although pericentric cohesin recruitment in S. pombe likely occurs through an interaction with the heterochromatin constituent HP1 bound to repetitive DNA sequences, the absence of repetitive DNA flanking budding yeast centromeres indicates that some other mechanism is used for the recruitment of cohesin throughout large pericentric domains. One possibility is that the high density of cohesin within pericentric chromatin is dependent on the centromere–kinetochore complex. To test this possibility, we examined Mcd1p association in the centromere-flanking regions of chromosomes in the presence and absence of the centromere. For these experiments, the endogenous centromeres on CHRIII and CHRXIV were replaced by CEN3 and CEN14 sequences, respectively, flanked by site-specific recombination target sites for the R recombinase from Zygosaccharomyces rouxii (Materials and Methods). The 120-bp centromeric DNA and approximately 200 bp of flanking sequences, corresponding roughly to the nuclease-resistant region spanning budding yeast centromeres (Bloom and Carbon 1982), were then excised from the chromosome in G1 cells by activating the expression of a galactose-inducible R recombinase, resulting in the generation of an acentric chromosome. The absence of Mcd1 protein in G1-staged cells and the interdependency of cohesin subunit association with chromosomes suggest that centromere excision occurs prior to the loading of the cohesin complex onto chromosomes in our experimental regimen (Guacci et al. 1997; Toth et al. 1999). After centromere excision (≥95% efficiency; Figure 5), the cells were released from the G1 arrest, rearrested in mitosis using nocodazole, and then processed for ChIP to assess Mcd1p binding. Figure 5 Centromere Excision (A) CEN1 on CHRI was replaced with a CEN3-URA3 cassette flanked by head-to-tail-oriented site-specific recombination target sites (red arrows) for the R recombinase from Zygosaccharomyces rouxii, as described in Materials and Methods. This strain (1824-23B) contained the R recombinase under the control of a galactose-inducible promoter. Genomic DNA samples, taken prior to the addition of galactose to the culture medium (0) and at 0.5-h intervals for 4.5 h after the galactose addition, were digested to completion with PvuII (black arrows) and analyzed by Southern blot analysis using a 1.25-kb probe corresponding to CHRI SGD coordinates 151823 to 153080. (B) The percentage of centromere excision was determined for the timecourse shown in (A). Briefly, a phosphorimage of the Southern blot and ImageQuant software were used to determine the pixel intensities of the unexcised and excised bands (top and bottom bands, respectively). The percent excision was then calculated as the pixel intensity present in the excised band divided by the total pixel intensities of both bands at each timepoint. (C) A Southern blot analysis of centromere excision from CHRIII. The endogenous CEN3 on CHRIII was replaced by R-recombinase target-site-flanked CEN3 in strain 1829-15B, as described in Materials and Methods. The efficiency of centromere excision from CHRIII was determined by Southern blot analysis in two independent experiments using genomic DNA samples digested with SnaBI and a probe corresponding to CHRIII SGD coordinates 113799-114336. Lanes 1 and 3 represent uninduced controls, and lanes 2 and 4 represent the extent of centromere excision after 2 h of recombinase induction. The percent excision was determined as in (B). In nocodazole-arrested cells, the magnitude of Mcd1p binding within the approximately 50-kb region flanking the site of the excised CEN3 was reduced significantly compared to control cells that retained the centromere (see Figure 3B). This reduction in Mcd1p binding occurred symmetrically throughout the entire 50-kb pericentric region flanking CEN3. In contrast, the magnitude of Mcd1p binding within a centromere-distal location on the arm of CHRIII was similar in both the centric and acentric chromosomes, indicating that cohesin association on chromosome arms is unaffected by centromere excision (see Figure 3D). Furthermore, the magnitude of Mcd1p association within the pericentric region of an endogenous chromosome (CHRI) that did not undergo centromere excision was also unaltered (unpublished data). In agreement with the results for CHRIII, we also observed a symmetrical reduction in Mcd1p binding throughout an approximately 50-kb pericentric region on an acentric CHRXIV when compared to control cells that retained CEN14 (see Figure 3C). While the magnitude of Mcd1p association was dramatically reduced in the former pericentric regions following centromere excision, the relative positions of the peaks and troughs of Mcd1p binding were unaltered. These results suggest that the centromere is required for the enrichment of Mcd1p binding in pericentric regions, where it amplifies an intrinsic local pattern of Mcd1p association. This amplification appears to occur bidirectionally, even though the DNA and protein components of the centromere–kinetochore complex are inherently asymmetric (Espelin et al. 1997). In addition, the loss of pericentric cohesin binding upon centromere excision occurred despite the presence of centromeres on the remaining chromosomes. Thus, the enhancement of cohesin binding in pericentric chromatin requires a centromere in cis. These observations suggest that the budding yeast centromere and its associated factors together behave as a bidirectional enhancer to increase cohesin binding throughout large pericentric regions in every cell cycle. While the removal of the centromere by site-specific recombination greatly reduced the levels of cohesin bound within pericentric DNA, we noted that the levels of binding within the valleys of the pericentric regions remained higher than the valleys observed in centromere-distal regions (see Figure 3B and 3D). This observation suggested that pericentric sequences might contribute to the enhancement of cohesin association independent of the centromere. However, when the CHRIII centromere was moved to an ectopic location on the right arm of the chromosome, the residual levels of Mcd1p binding within the troughs throughout the former pericentric region were further reduced (Figure 6). In fact, this region now more closely resembled typical arm cohesin-association sites, where peaks of binding occur at approximately 10-kb intervals and are separated by regions with minimal or undetectable levels of cohesin association. Thus, this result suggests that cohesin enrichment within pericentric regions is mediated exclusively by the centromere–kinetochore complex and that a centromere-independent pathway does not contribute to the enhanced levels of cohesin binding in pericentric chromatin. The residual levels of cohesin that remain bound in the troughs immediately following centromere excision may reflect a difference in the timing of centromere loss. In the centromere excision experiment, Mcd1p association was examined during mitosis of the same cell cycle in which the centromere was removed, whereas in the ectopic centromere strain, Mcd1p association was examined many generations after centromere removal. Thus, the persistence of cohesin binding during the first cell cycle following centromere excision suggests the existence of an epigenetic component in the recruitment of cohesin to pericentric chromatin. Indeed, possible epigenetic contributions to kinetochore function have been suggested previously in budding yeast (Mythreye and Bloom 2003). Figure 6 Mcd1p Binding within the Endogenous CHRIII Pericentric Region after Centromere Excision or Centromere Movement The Mcd1p binding profiles in the endogenous CHRIII pericentric region are shown in cells in which the centromere is absent, either because of centromere excision (gray circles, PMY185) or because of the movement of the centromere to an ectopic location on the right arm of CHRIII (black triangles, PMY318). PMY185 and PMY318 are highly related strains; PMY185 was one of the parental strains used to generate PMY318. In the centromere excision strain, Mcd1p binding was examined in the same cell cycle in which the centromere was lost, whereas in the ectopic centromere strain, Mcd1p association was determined many generations after centromere relocation (see text for further discussion). Mcd1p binding data from the centromere excision experiment are the same as those shown in Figure 2B, but are replotted here for clarity. CEN3 normally occupies the interval between SGD coordinates 114382-114498. While the 120-bp centromeric DNA was the only conserved DNA sequence present within the excised regions of the two chromosomes, it was possible that some unidentified motif within the excised DNA was instead responsible for the enhancement of pericentric cohesin binding. To rule out this possibility, we tested whether enhancer activity was mediated specifically by the centromere and its associated factors by examining pericentric cohesin binding in cells lacking functional kinetochores. Kinetochore assembly was disrupted using a conditional mutant in the NDC10 gene. NDC10 encodes an essential subunit of CBF3, a complex of kinetochore proteins that binds to the conserved centromere DNA element CDEIII and nucleates kinetochore assembly (Goh and Kilmartin 1993; Jiang et al. 1993). At the restrictive temperature of 37 °C, ndc10-42 mutants assemble a defective kinetochore, and consequently, arrest in G2/M due to the activation of the spindle assembly checkpoint (Doheny et al. 1993). Cultures of the ndc10-42 mutant and an isogenic wild-type control strain were staged in G1 and then released from the G1 arrest at the restrictive temperature in medium containing nocodazole. After reaching a mitotic arrest, both the mutant and wild-type cultures were processed for ChIP to assess Mcd1p association in pericentric regions. We observed that Mcd1p binding was reduced 5-fold on average throughout an approximately 50-kb CHRIII pericentric region in the ndc10-42 cells at the restrictive temperature when compared to the isogenic wild-type control (Figure 7A). In fact, Mcd1p association was reduced throughout the same region that was affected by centromere excision. Similarly, Mcd1p binding throughout an approximately 40-kb pericentric region of CHRI was also reduced approximately 5-fold in the ndc10-42 mutant when placed at the restrictive temperature (Figure 7B). These results differ from those of a previous study which reported no change in cohesin association at an established endogenous centromere in ndc10-1 cells (Tanaka et al. 1999). This difference is likely explained by the fact that the previous study examined Mcd1p binding within only one approximately 300-bp centromere-spanning region, whereas we examined Mcd1p association at multiple locations throughout 40-kb pericentric regions. To determine the extent to which kinetochore inactivation affected cohesin association at more centromere-distal locations, we examined global Mcd1p association in the ndc10-42 mutant at the restrictive temperature using the hybridization of ChIP DNA to microarrays. Consistent with the PCR quantitation of CHRI and CHRIII, we found that the magnitude of Mcd1p binding in the pericentric regions of all chromosomes was indeed reduced in the ndc10-42 mutant at the restrictive temperature when compared to the isogenic wild-type strain (Figure 7C; for brevity, only CHRV, CHRVI, and CHRX are shown). However, Mcd1p binding at centromere-distal locations was equivalent in wild type and ndc10-42 mutant cells, indicating that cohesin association in these regions is independent of the centromere–kinetochore complex (Figure 7C). These observations demonstrate that the centromere– kinetochore complex can increase the magnitude of cohesin association bidirectionally from the centromere over regions as large as 25 kb, thereby generating approximately 50-kb pericentric domains that are highly enriched for cohesin binding. Figure 7 A Functional Centromere–Kinetochore Complex Is Essential for Enhanced Pericentric Cohesin Association Cultures of isogenic wild-type (1846-15A) and ndc10-42 mutant (1846-15C) cells were arrested in αF at 23 °C and then released into fresh medium containing nocodazole at 37 °C. After the cells arrested in mitosis (approximately 3 h), the cultures were crosslinked with formaldehyde and processed for ChIP using a monoclonal antiserum against epitope-tagged Mcd1p (Mcd1-6HAp) as an indicator of the cohesin complex. The cohesin association profiles in the pericentric regions of CHRIII (A) and CHRI (B) are shown for NDC10 (black squares) and ndc10-42 (gray circles) cultures. The positions of the centromeres are indicated by ovals (not drawn to scale). The dashed line in (B) indicates a region containing a Ty element. (C) To identify chromosomal regions depleted for cohesin binding in the absence of a functional kinetochore, the Mcd1p-ChIP-to-input fluorescence ratio obtained for each ORF and intergenic region in genomic microarray analyses of CHRV, CHRVI, and CHRIX in ndc10-42 cells was divided by the ratio obtained for NDC10 cells and plotted on a map of the chromosomes. Regions that demonstrated 2.5-fold or greater reduction in Mcd1p binding in the ndc10-42 mutant are shaded dark green, while lighter green hues represent further fold reductions in Mcd1p binding. Regions where the magnitude of Mcd1p binding was similar in NDC10 and ndc10-42 cells are shown in gray. Gaps in the chromosomal maps are genomic regions not represented on the microarrays, while regions shaded blue were present on the arrays but gave no data during hybridizations for reasons described in Materials and Methods. The location of the centromere on each chromosome is indicated by an asterisk. Centromeric Enhancer Activity Is Context- and Orientation-Independent Our observations suggested that kinetochores mediate the enhancement of cohesin binding within pericentric regions, but that the distribution of cohesin-binding peaks and valleys is an intrinsic property of the flanking DNA. If correct, we reasoned that the distribution of cohesin within pericentric DNA would be unaltered by the replacement of centromeric DNA with centromeric sequences from a different chromosome. To test this hypothesis directly, we removed CEN1 and approximately 440 bp of flanking sequences from CHRI and replaced it with approximately 320 bp of pericentric DNA from CHRIII that contained CEN3. The patterns of Mcd1p association within the pericentric regions of cells containing the altered or endogenous CHRI were then determined by ChIP in nocodazole-arrested cells. We observed that both the distribution and the magnitude of Mcd1p binding within the pericentric region of the modified CHRI were similar to those observed on the wild-type CHRI (Figure 8, compare with Figure 3A). Furthermore, the excision of CEN3 sequences from CHRI using the same experimental regimen described above also resulted in a bidirectional reduction in Mcd1p association in the regions flanking the centromere at both high- and low-affinity regions, as observed previously for CHRIII and CHRXIV (Figure 8). The finding that the magnitude of Mcd1p binding was equivalent within the altered and endogenous CHRI pericentric regions suggested that centromeric enhancers have similar abilities to mediate cohesin association within other pericentric regions. Furthermore, the replacement of CEN1 with CEN3 sequences was done in such a way that CEN3 was present in the opposite orientation with respect to the endogenous centromere, and the context of CEN3 within the CHRI pericentric region was further modified by the introduction of the URA3 gene immediately adjacent to the centromere. Thus, these results demonstrate that the centromeric enhancer can function in an altered chromosomal context and that the enhancement of cohesin binding in pericentric DNA is independent of both the primary sequence of the centromere and its orientation with respect to the pericentric sequences. Figure 8 Centromeric Enhancer Activity Is Context and Orientation-Independent Cells containing an endogenous CHRI (1377A1-4B) and those in which CEN1 was replaced with CEN3 marked with URA3 (1824-23B), as described in Materials and Methods, were staged in G1 using αF and then released into fresh medium containing nocodazole. In the case of strain 1824-23B, the G1-arrested culture was split in half, and one half was treated with galactose to induce excision of CEN3 from CHRI prior to release into medium containing nocodazole. After reaching a mitotic arrest, the cultures were crosslinked with formaldehyde and processed for ChIP using a monoclonal antiserum against epitope-tagged Mcd1p (Mcd1-6HAp). The cohesin association profiles for the modified CHRI with and without CEN3 are shown (squares and circles, respectively). The position of the centromere is indicated by the oval (not drawn to scale). The dashed line indicates the region containing a Ty element. See Figure 2A for the Mcd1p association profile of the endogenous CHRI. Centromeric Enhancer Is Active at an Ectopic Location The importance of pericentric cohesion in the promotion of sister kinetochore biorientation may have maintained an evolutionary selection for pericentric sequences that favors higher levels of cohesin association. Consequently, these sequences may be particularly susceptible to centromeric enhancer activity. To test whether naive sequences that have never resided near a centromere can also respond to the presence of the centromere–kinetochore complex with increased cohesin association, we determined whether the movement of centromeric DNA to an ectopic location resulted in increased Mcd1p association in the flanking chromatin. In this experiment, Mcd1p association was examined within an approximately 37-kb region on the right arm of CHRIII spanning SGD coordinates 242–279 kb after the insertion of CEN6 DNA at SGD coordinate 260 kb. Endogenous CEN3 sequences were removed and the new centromere was inserted concurrently to prevent the production of a dicentric CHRIII (Materials and Methods). Cultures of cells containing the ectopic centromere or an isogenic control strain with the centromere at its endogenous location were staged in G1, and then released into fresh media containing nocodazole to arrest the cells in mitosis. Once the cells were arrested, they were processed for ChIP. Quantitation of the levels of Mcd1p-associated sequences in the wild-type cells revealed a major peak of Mcd1p binding at SGD coordinate approximately 277 kb and three minor peaks at 248 kb, 261 kb, and 272 kb (Figure 9A). In cells containing the ectopically placed centromere, the peaks of Mcd1p binding occurred at the same locations as those observed in the wild-type cells, but the magnitude of binding was significantly higher, most notably within the peaks at 248 kb and 273 kb. Moreover, the levels of Mcd1p binding in the troughs proximal to the ectopic centromere were also dramatically elevated. Indeed, when the fold increases in cohesin binding flanking the ectopic centromere were plotted, the analysis revealed that similar levels of enhancement are reached throughout the approximately 37-kb region examined (median fold increase of 6.0 ± 2.1), with the majority of the increase occurring in the trough regions (Figure 9B). Thus, the insertion of the centromeric enhancer resulted in the generation of a cohesin domain (≥37 kb) similar in size to that mediated by an endogenous centromere–kinetochore complex. In addition, cohesin binding at the ectopic location was not random, but instead, appeared to amplify the intrinsic pattern of association. Taken together, these observations indicate that the ability of the centromere to enhance cohesin binding in flanking DNA is not limited to endogenous pericentric sequences. Figure 9 The Centromeric Enhancer Is Active at an Ectopic Location The endogenous centromere on CHRIII was removed, and CEN6 was inserted at an ectopic location (SGD coordinate approximately 260 kb), producing yeast strain PMY318, as described in Materials and Methods. Cells containing the ectopic centromere and isogenic wild-type cells (1829-15B) were staged in G1 using αF, and then released into fresh medium containing nocodazole to arrest cells in mitosis. Cells were then fixed in formaldehyde and processed for ChIP using epitope-tagged Mcd1-6HAp as a marker for the cohesin complex. (A) The Mcd1p binding profiles at the ectopic location on endogenous CHRIII (black squares) and in the presence of the ectopic centromere (gray circles) are shown. The location of the ectopic centromere is indicated by the black oval. (B) The levels of Mcd1p binding in the region flanking the ectopically placed centromere were divided by those observed in the isogenic wild-type control strain to determine the fold increases in Mcd1p binding in the presence of the centromere. Data are plotted as a function of the SGD coordinates for this region. Discussion In this report we show that the approximately 120-bp point centromere of budding yeast increases the magnitude of cohesin association within large approximately 20–50-kb pericentric regions. The budding yeast centromere–kinetochore complex generates a specialized chromatin structure, consisting of an approximately 250-bp nuclease-resistant region flanked by several positioned nucleosomes that together span approximately 3 kb (Bloom and Carbon 1982). Thus, the centromere-flanking cohesin domains are approximately 80–200 times larger than the nuclease-resistant region of the kinetochore. The ability of kinetochores to mediate increased cohesin association over large domains was not limited to endogenous pericentric sequences, but also occurred when the centromere was moved to an ectopic location. Although rare, other examples exist in which cis DNA elements have been shown to mediate the generation of large chromosomal domains, namely telomeric silencing and X chromosome inactivation (Renauld et al. 1993; Hecht et al. 1996; Lee et al. 1999). Finally, the kinetochore enhanced cohesin association in pericentric regions only in cis and in an orientation-independent manner. Thus, the kinetochore functions as an enhancer of pericentric cohesin binding in addition to mediating the attachment to the mitotic spindle. The kinetochore-dependent generation of these extended pericentric cohesin domains may be the consequence of kinetochore-mediated de novo loading of cohesin, or, alternatively, the domains may reflect a role for the kinetochore in maintaining or protecting cohesin association in centromere-flanking regions. Evidence in support of both models exists. Cohesin association in the centromere-flanking region of a minichromosome was reduced upon centromere excision in M phase–arrested cells (Megee et al. 1999). In addition, high levels of cohesin association were observed in the CHRIII pericentric region of cells arrested in S phase or mitosis, while cohesin binding along the arms was lower in mitotically arrested cells compared to cells arrested in S phase (Blat and Kleckner 1999). These observations are consistent with a role for the kinetochore in maintaining cohesin association in pericentric regions. However, the elevated levels of pericentric cohesin binding were not identical in the S phase– and M phase–arrested cells, but were in fact higher in the mitotically arrested cells (Blat and Kleckner 1999). Furthermore, we have found that the magnitude of cohesin binding within pericentric regions of mitotically arrested cells increases in response to environmental cues (P. Megee, unpublished data). These observations are consistent with the de novo loading of cohesins by the centromere–kinetochore complex. Thus, the kinetochore may mediate increased cohesin binding by multiple pathways. Our finding that kinetochores mediate cohesin binding throughout large approximately 50-kb pericentric domains may potentially reconcile seemingly paradoxical observations concerning the enrichment of cohesin in pericentric chromatin and the transient loss of cohesion between sister chromatids in these regions. Despite the presence of these large pericentric cohesin domains, sister chromatids undergo transient separations within pericentric regions shortly after the formation of bipolar spindle microtubule attachments (Goshima and Yanagida 2000; He et al. 2000; Tanaka et al. 2000). However, the extent of sister chromatid separation observed in these studies is consistent with the deformation of only approximately 20 kb of pericentric DNA (He et al. 2000, 2001). Thus, our results indicate that pericentric cohesin domains extend well beyond the approximately 20 kb of chromatin undergoing significant microtubule-dependent stretching. These observations suggest that pericentric cohesion may play dual roles in the maintenance of genomic integrity. First, cohesin bound immediately adjacent to the kinetochore may sterically constrain the kinetochores on paired sister chromatids to face opposite poles, thereby facilitating the establishment of chromosome biorientation. Once biorientation is achieved, the poleward forces imposed by the microtubule attachments disrupt cohesin binding within approximately 20-kb pericentric regions, giving rise to transient sister chromatid separation. Because the kinetochore-mediated pericentric cohesin domain extends beyond the region of stretched chromatin, this domain may provide the resistance that is required for the production of tension between sister kinetochores (Skibbens et al. 1995). This tension is thought to promote the stability of kinetochore–microtubule attachments (Nicklas and Ward 1994). Our results also demonstrate that the distribution of cohesin within each pericentric domain differs between chromosomes and that the observed pattern is unrelated to the specific centromeric DNA sequence present on the chromosome. Instead, the distribution of cohesin in either endogenous or ectopic centromere-proximal locations is specified by an intrinsic property of the chromatin. Furthermore, although the ability of the centromeric enhancer to mediate cohesin binding extended over large domains, the loss of the kinetochore did not affect cohesin binding in centromere-distal regions (this study). This observation is consistent with two alternative explanations. First, the centromeric enhancer may form a gradient of activity that dissipates with distance from the kinetochore. This model is unlikely, however, since the fold increases in cohesin association adjacent to the ectopic centromere were similar throughout the flanking approximately 37-kb region. Alternatively, the length of pericentric chromatin that can be influenced by the centromeric enhancer may be constrained by cis factors, such as boundary elements or sequences nonpermissive for cohesin binding. Such a model is supported by the relatively small pericentric cohesin domain present on minichromosomes, where local sequence context was suggested to affect cohesin binding adjacent to the centromere (Megee et al. 1999). Interestingly, during meiosis cohesins are removed from chromosome arms but remain bound in the large pericentric regions (Klein et al. 1999; Watanabe and Nurse 1999). It is possible that the boundaries that dictate the pericentric region and constrain the centromeric enhancer are the same that modulate cohesin binding during meiosis. Moreover, since the distribution of cohesin association in pericentric regions appears to be unique to each chromosome, this variability in cohesin distribution may provide a basis for the range of nondisjunction frequencies associated with different chromosomes within an organism (Campbell et al. 1981). In this report we have provided insights into the control of cohesin binding within pericentric chromatin in budding yeast. We have demonstrated that the centromere–kinetochore complex behaves as an enhancer for cohesin association in pericentric chromatin and appears to be largely responsible for the increased levels of pericentric cohesin association at both endogenous and ectopic locations. Presumably, the enhancer functions by activating a trans factor that either recruits cohesin to pericentric chromatin or maintains high levels of pericentric cohesin binding. One candidate for such a trans factor could be a histone-modifying enzyme. The centromeric enhancer would augment the recruitment of the histone-modifying enzyme and enhance cohesin association. Indeed, results from S. pombe indicate that a histone methyltransferase is targeted to centromere-proximal heterochromatin, and this modification is important for cohesin recruitment (Bernard et al. 2001; Nonaka et al. 2002). Since budding yeast lacks centromere-proximal heterochromatin, the targeting method for pericentric cohesin recruitment is likely to be different. However, since pericentric cohesin domains are highly conserved, we suspect that the enhancer activity of the kinetochore may well be ubiquitous in all eukaryotes. Our results suggest that the coordination of microtubule attachment and pericentric cohesin recruitment by the budding yeast kinetochore generates an autonomous segregation unit that ensures sister kinetochore biorientation and, consequently, the maintenance of genomic integrity. The integration of these activities by the kinetochore was likely key for the first identification of budding yeast centromeric DNA, since microtubule attachment in the absence of pericentric cohesion is unlikely to have increased the mitotic stability of minichromosomes, the assay used for centromere DNA identification (Clarke and Carbon 1980). Similarly, the coordination of cohesion and microtubule attachment by the kinetochore may also explain how neocentromeres in humans acquire full chromosome segregation capabilities in the absence of flanking repetitive DNA or cytologically detectable levels of pericentric heterochromatin (Aagaard et al. 2000; Saffery et al. 2000; Amor and Choo 2002). Our observations suggest that kinetochore-mediated cohesin recruitment may compensate for the lack of heterochromatin-dependent cohesin recruitment, thereby promoting biorientation of sister kinetochores. Moreover, the ability of a neocentromere to generate new domains of cohesin binding is likely to have been instrumental for chromosome evolution by allowing some degree of flexibility for chromosomal rearrangements. Materials and Methods Yeast cell culture The genotypes of the yeast strains used in this study are listed in Table 1. Cultures used for ChIP were synchronized in G1 using α-factor (αF) mating pheromone at final concentrations of 3 μM and 15 nM for BAR1 wild-type and mutant strains, respectively. Cells were then released from the G1 arrest by two washes in the appropriate growth medium containing 0.1 mg/ml Pronase (Sigma, St. Louis, Missouri, United States) to proteolyze the αF. Cells were then allowed to grow in medium containing 0.1 mg/ml Pronase and then arrested in mitosis using either 15 μg/ml nocodazole (Sigma) resuspended in 1% DMSO, or a temperature-sensitive cdc16 mutation, as indicated. Mitotic arrest, as determined by a large-budded cell morphology, was generally reached after 2.5–3 h of growth. The R-site-specific recombinase from Zygosaccharomyces rouxii used for centromere excision events was induced in G1 cells by the addition of galactose (2% final concentration) to rich medium containing 2% raffinose. Table 1 Saccharomyces cerevisiae Strains aIndicated strains contain two tandem copies of the Zygosaccharomyces rouxii recombinase under the control of the galactose promoter integrated at LEU2. bR signifies an R-recombinase target site cStrain background Centromere excision and strain construction Centromeres were excised from endogenous chromosomes by site-specific recombination events using a galactose-inducible R recombinase from Zygosaccharomyces rouxii (Matsuzaki et al. 1990). A 320-bp BamHI CEN3 fragment was inserted between head-to-tail-oriented recombination target sites, as described previously (Megee and Koshland 1999). URA3 was subcloned adjacent to the CEN3 centromere cassette outside the recombination target sites, and this construct replaced the endogenous centromere on CHRIII by a one-step replacement (Rothstein 1983), selecting for uracil prototrophs. To restore the chromosomal context of the centromere on CHRIII, the one-step replacement was then repeated with a centromere cassette lacking URA3, and transformants were grown on 5-fluoroorotic acid to screen for the loss of URA3. The constructs were then confirmed by Southern analysis of genomic DNA. Similarly, a 334-bp PCR fragment (SGD coordinates 628717-629051) containing CEN14 and approximately 200 bp of flanking DNA was inserted between head-to-tail-oriented recombination target sites. A URA3-marked version of this construct was used to replace the endogenous centromere on CHRXIV by one-step gene replacement, and the chromosomal context of CEN14 was then restored using the same strategy outlined above for CEN3. The construction of the CEN3 replacement of CEN1 DNA was performed similarly, except that URA3 was placed adjacent to the centromere between the recombination target sites. Replacement of CEN1 sequences with the CEN3-URA3 cassette resulted in the deletion of 558 bp containing CEN1 (CHRI, SDG coordinates 151263-151820), and was confirmed by Southern analysis of genomic DNA (unpublished data). To obtain PMY318, a strain having the centromere at an ectopic location on CHRIII, strain 1829-15B was transformed with DNA encoding a CEN6-URA3 cassette flanked by regions homologous to the TRX3 locus. The TRX3 ORF and a small amount of flanking sequences, encompassing SGD coordinates 259521-260061, were deleted by the integration of the CEN6-URA3 cassette. Prior to transformation, 1829–15B cells were grown in rich medium containing raffinose, and then plated on medium containing galactose to induce excision of endogenous CEN3 and approximately 200 bp of the flanking region by site-specific recombination, as described above. ChIP ChIP was performed as described (Megee et al. 1999). A detailed protocol is available at http://www.uchsc.edu/sm/bbgn/megee.html (see also Protocol S1 in the accompanying paper by Glynn et al. [2004]). Immunoprecipitations were done using 12CA5 anti-HA antibody (Roche, Basel, Switzerland), A-14 anti-Myc antibody (Santa Cruz Biotechnology, Santa Cruz, California, United States), or rabbit polyclonal anti-Mif2p antibody (Meluh and Koshland 1997), as indicated. Immunoprecipitations of crosslinked chromatin prepared from strains lacking epitope-tagged versions of cohesin subunits did not precipitate centromeric DNA, as demonstrated previously (Megee et al. 1999). In addition, mock immunoprecipitations were performed to exclude the possibility that centromere-flanking chromatin was nonspecifically enriched in the immunoprecipitates obtained from epitope-tagged strains. We observed that centromeric sequences were enriched over those observed in the mock immunoprecipitations at least 500-fold and 11-fold using HA-tagged or Myc-tagged cohesin subunits, respectively. This difference likely reflects the observation that for any cohesin-associated region, a larger percentage of total chromatin is precipitated with the HA epitope tag than with the Myc tag (Mcd1-6HAp compared to Mcd1-18Mycp) (P. Megee, unpublished data). Lastly, analysis of the input DNA showed that our samples were routinely sheared to an average size of 500 bp, with a range of 200–1,000 bp. In all experiments, duplicate immunoprecipitations were performed for cohesin subunits and subjected to a preliminary PCR analysis using centromere-specific or -proximal oligonucleotide primer pairs. The duplicates had values that were routinely within 10% of one another, and, thus, one sample was chosen randomly for further analysis. The linearity of PCR under our experimental conditions was tested empirically. Briefly, input DNA was diluted 90-fold relative to immunoprecipitated DNA before PCR analysis. Increasing amounts of input DNA were then used to program PCR reactions, and the resulting products were quantitated to determine whether the amount of product responded linearly to the levels of input DNA (unpublished data; Megee et al. 1999). This relationship was determined empirically in experiments initiated with approximately 1.65 × 109 cells, and all subsequent experiments were performed with the same cell number to maintain linearity of PCR. PCR fragments were separated on 2.5% NuSieve (Cambrex, East Rutherford, New Jersey, United States) gels containing 0.15 μg/ml ethidium bromide. Digital images of ethidium bromide–stained gels were quantitated using ImageQuant software (Molecular Dynamics, Sunnyvale, California, United States). The details of oligonucleotide primer pairs used for PCR analyses are available upon request. In general, PCR primer pairs amplified approximately 300-bp sequences and were separated from neighboring pairs by an average of approximately 1.5 kb. In the CHRIII pericentric region, the mean size of the 37 PCR products used in the ChIP analysis was 304 ± 68 bp. For CHRXIV, the mean size of the 41 PCR products used in the analysis was 309 ± 36 bp. In the CHRI pericentric region, the mean size of the 22 PCR products used in the analysis was 306 ± 31 bp. ChIP experiments were performed at least twice, and representative data from one experiment are presented. Microarray analyses of ChIP DNA Preparation of Cy5- and Cy3-labeled DNA, hybridization, and analysis were performed as previously described (Gerton et al. 2000). Briefly, the recovered ChIP DNA was randomly PCR amplified in the presence of amino-allyl dUTP (Bohlander et al. 1992; Gerton et al. 2000; see also Protocol S2 in the accompanying paper by Glynn et al. [2004]), which was then coupled to a fluorescent dye (e.g., Cy5) and competitively hybridized to a polyL-lysine-coated spotted glass DNA microarray in the presence of total genomic DNA similarly labeled with a second fluorescent dye (e.g., Cy3). Hybridizations were performed at 63 °C overnight under standard conditions and slides were washed successively with 0.6X SSC/0.03% SDS and then 0.06X SSC prior to scanning (see also http://microarrays.org). The fluorescence in each spot on the microarray was detected using an Axon 4000B laser scanner/detector (Axon Instruments, Union City, California, United States), and the ratio of the two signals was determined using GenePix 4.0 software (Axon Instruments). This ratio indicates the enrichment for a given sequence in the ChIP. The AMAD database was used to normalize and store microarray data. The microarrays used in this study contained all the ORFs and intergenic regions in the yeast genome as individual spots (Iyer et al. 2001). For each experimental condition, a minimum of two hybridizations from two independent immunoprecipitations was performed. Data were normalized and then filtered in the following ways: the spot intensity was required to be 200 or greater; flagged spots were not used; and spots were required to have a correlation coefficient of 0.5 or greater. For analysis purposes, any feature with less than two measurements was excluded, and excluded regions are colored blue in the maps of cohesin binding. The raw data and datasets for the microarrays presented herein are provided as Datasets S1–S22, and can also be viewed at http://research.stowers-institute.org/jeg/2004/cohesin/index.html. Supporting Information Datasets S5–S22 correspond to the individual GenePix results (GPR) files for each array performed. For each dataset, the Cy3 and Cy5 channel samples and the method of mitotic arrest are listed. Dataset S1 Dataset for Smc3-6Myc ChIPs Performed in cdc16-Arrested Cells File cdc16_Smc3-6Myc_A364a. (701 KB TXT). Click here for additional data file. Dataset S2 Dataset for Mcd1-6HA ChIPs Performed in cdc16-Arrested Cells File cdc16_Mcd1-6HA_A364a. (957 KB TXT). Click here for additional data file. Dataset S3 Dataset for Mcd1-6HA ChIPs Performed in Nocodazole-Arrested NDC10 Cells File Mcd1-6HA_S288c_NZ. (449 KB TXT). Click here for additional data file. Dataset S4 Dataset for Mcd1-6HA ChIPs Performed in Nocodazole-Arrested ndc10-42 Cells File Mcd1-6HA_ncd10-42_S288c_NZ. (418 KB TXT). Click here for additional data file. Dataset S5 SIMRUP2_147 Cy3 = cdc16-ts Mcd1-6HA ChIP in A364a; Cy5 = genomic DNA. (4.6 MB XLS). Click here for additional data file. Dataset S6 SIMRUP2_170 Cy3 = cdc16-ts Mcd1-6HA ChIP in A364a; Cy5 = genomic DNA. (4.6 MB XLS). Click here for additional data file. Dataset S7 SIMRUP2_171 Cy3 = cdc16-ts Mcd1-6HA ChIP in A364a; Cy5 = genomic DNA. (4.6 MB XLS). Click here for additional data file. Dataset S8 SIMRUP2_178 Cy3 = genomic DNA; Cy5 = cdc16-ts Mcd1-6HA ChIP in A364a. (4.6 MB XLS). Click here for additional data file. Dataset S9 SIMRUP2_180 Cy3 = genomic DNA; Cy5 = cdc16-ts Mcd1-6HA ChIP in A364a. (4.6 MB XLS). Click here for additional data file. Dataset S10 SIMRUP2_183 Cy3 = Mcd1-6HA ChIP in nocodazole-arrested S288C background; Cy5 = genomic DNA. (4.6 MB XLS). Click here for additional data file. Dataset S11 SIMRUP2_184 Cy3 = ndc10-42 Mcd1-6HA ChIP in nocodazole-arrested S288C background; Cy5 = genomic DNA. (4.6 MB XLS). Click here for additional data file. Dataset S12 SIMRUP2_185 Cy3 = Mcd1-6HA ChIP in nocodazole-arrested S288C background; Cy5 = genomic DNA. (4.6 MB XLS). Click here for additional data file. Dataset S13 SIMRUP2_187 Cy3 = cdc16-ts Smc3-6Myc ChIP in A364a; Cy5 = genomic DNA. (4.6 MB XLS). Click here for additional data file. Dataset S14 SIMRUP2_190 Cy3 = ChIP of Mcd1-6HA in cdc16-ts Smc3-6Myc Mcd1-6HA A364a strain; Cy5 = genomic DNA. (4.6 MB XLS). Click here for additional data file. Dataset S15 SIMRUP2_191 Cy3 = ChIP of SMC3-6Myc in cdc16-ts SMC3-6Myc MCD1-6HA A364a strain; Cy5 = genomic DNA. (4.6 MB XLS). Click here for additional data file. Dataset S16 SIMRUP2_226 Cy3 = genomic DNA; Cy5 = cdc16-ts Smc3-6Myc ChIP in A364a. (4.6 MB XLS). Click here for additional data file. Dataset S17 SIMRUP2_228 Cy3 = genomic DNA; Cy5 = Mcd1-6HA ChIP in nocodazole-arrested S288C background. (4.6 MB XLS). Click here for additional data file. Dataset S18 SIMRUP2_229 Cy3 = genomic DNA; Cy5 = ndc10-42 Mcd1-6HA ChIP in nocodazole-arrested S288C background. (4.6 MB XLS). Click here for additional data file. Dataset S19 SIMRUP2_230 Cy3 = genomic DNA; Cy5 = Mcd1-6HA ChIP in nocodazole-arrested S288C background. (4.6 MB XLS). Click here for additional data file. Dataset S20 SIMRUP2_231 Cy3 = genomic DNA; Cy5 = ndc10-42 Mcd1-6HA ChIP in nocodazole-arrested S288C background. (4.6 MB XLS). Click here for additional data file. Dataset S21 SIMRUP2_254 Cy3 = genomic DNA; Cy5 = ChIP of SMC3-6Myc in cdc16-ts Smc3-6Myc Mcd1-6HA A364a strain. (4.6 MB XLS). Click here for additional data file. Dataset S22 UP6_205 Cy3 = genomic DNA; Cy5 = Mcd1-6HA ChIP in nocodazole-arrested S288C background. (4.4 MB XLS). Click here for additional data file. Accession Numbers The Saccharomyces Genome Database (http://www.yeastgenome.org/) accession numbers for the genes and gene products discussed in this paper are Irr1p/Scc3p (SGDID S0001288), Mcd1/Scc1p (SGDID S0002161), Mif2p (SGDID S0001572), Pds5p (SGDID S0004681), Smc1p (SGDID S0001886), and Smc3p (SGDID S0003610). The S. pombe Genome Project (http://www.sanger.ac.uk/Projects/S_pombe/) accession number for Swi6 is SPAC664.01c. We thank Drs. Vincent Guacci and Pamela Meluh for generously providing strains and Drs. David Bentley, Orna Cohen-Fix, Vincent Guacci, and Jessica Tyler and members of our laboratories for valuable comments on the manuscript. This work was supported in part by the National Institutes of Health (grant RO1 GM66213 to PCM), the Howard Hughes Medical Institute (DK), and the March of Dimes (grant 5-FY02–251 to JLG). Conflicts of interest. The authors have declared that no conflicts of interest exist. Author contributions. PCM, JLG, and DEK conceived and designed the experiments. PCM, JLG, SAW, and JEP performed the experiments. JLD contributed reagents, materials, and analysis tools. PCM, JLG, and DEK wrote the manuscript. Academic Editor: Bruce Stillman, Cold Spring Harbor Laboratory Citation: Weber SA, Gerton JL, Polancic JE, DeRisi JL, Koshland D, et al. (2004) The kinetochore is an enhancer of pericentric cohesion binding. PLoS Biol 2(9): e260. Abbreviations αFα-factor mating pheromone ChIPchromatin immunoprecipitation CHRchromosome ORFopen reading frame R:Gred-to-green SGD Saccharomyces Genome Database ==== Refs References Aagaard L Schmid M Warburton P Jenuwein T Mitotic phosphorylation of SUV39H1, a novel component of active centromeres, coincides with transient accumulation at mammalian centromeres J Cell Sci 2000 113 817 829 10671371 Amor DJ Choo KH Neocentromeres: Role in human disease, evolution, and centromere study Am J Hum Genet 2002 71 695 714 12196915 Bernard P Maure JF Partridge JF Genier S Javerzat JP Requirement of heterochromatin for cohesion at centromeres Science 2001 294 2539 2542 11598266 Blat Y Kleckner N Cohesins bind to preferential sites along yeast chromosome III, with differential regulation along arms versus the centric region Cell 1999 98 249 259 10428036 Bloom KS Carbon J Yeast centromere DNA is in a unique and highly ordered structure in chromosomes and small circular minichromosomes Cell 1982 29 305 317 6288253 Bohlander SK Espinosa R Le Beau MM Rowley JD Diaz MO A method for the rapid sequence-independent amplification of microdissected chromosomal material Genomics 1992 13 1322 1324 1505965 Campbell D Doctor JS Feuersanger JH Doolittle MM Differential mitotic stability of yeast disomes derived from triploid meiosis Genetics 1981 98 239 255 7035289 Clarke L Carbon J Isolation of a yeast centromere and construction of functional small circular chromosomes Nature 1980 287 504 509 6999364 Doheny KF Sorger PK Hyman AA Tugendreich S Spencer F Identification of essential components of the S. cerevisiae kinetochore Cell 1993 73 761 774 8500169 Espelin CW Kaplan KB Sorger PK Probing the architecture of a simple kinetochore using DNA-protein crosslinking J Cell Biol 1997 139 1383 1396 9396745 Furuya K Takahashi K Yanagida M Faithful anaphase is ensured by Mis4, a sister chromatid cohesion molecule required in S phase and not destroyed in G1 phase Genes Dev 1998 12 3408 3418 9808627 Gerton JL DeRisi J Shroff R Lichten M Brown PO Inaugural article: Global mapping of meiotic recombination hotspots and coldspots in the yeast Saccharomyces cerevisiae Proc Natl Acad Sci U S A 2000 97 11383 11390 11027339 Glynn F Megee PC Yu HG Mistrot C Unal E Genome-wide mapping of the cohesion complex in the yeast Saccharomyces cerevisiae PLoS Biol 2004 2 (9) e259 10.1371/journal.pbio.0020259 15309048 Goh PY Kilmartin JV NDC10: A gene involved in chromosome segregation in Saccharomyces cerevisiae J Cell Biol 1993 121 503 512 8486732 Gonzalez C Casal Jimenez J Ripoll P Sunkel CE The spindle is required for the process of sister chromatid separation in Drosophila neuroblasts Exp Cell Res 1991 192 10 15 1898588 Goshima G Yanagida M Establishing biorientation occurs with precocious separation of the sister kinetochores, but not the arms, in the early spindle of budding yeast Cell 2000 100 619 633 10761928 Guacci V Koshland D Strunnikov A A direct link between sister chromatid cohesion and chromosome condensation revealed through the analysis of MCD1 in S. cerevisiae Cell 1997 91 47 57 9335334 Hanna JS Kroll ES Lundblad V Spencer FA Saccharomyces cerevisiae CTF18 and CTF4 are required for sister chromatid cohesion Mol Cell Biol 2001 21 3144 3158 11287619 Hartman T Stead K Koshland D Guacci V Pds5p is an essential chromosomal protein required for both sister chromatid cohesion and condensation in Saccharomyces cerevisiae J Cell Biol 2000 151 613 626 11062262 He X Asthana S Sorger PK Transient sister chromatid separation and elastic deformation of chromosomes during mitosis in budding yeast Cell 2000 101 763 775 10892747 He X Rines DR Espelin CW Sorger PK Molecular analysis of kinetochore-microtubule attachment in budding yeast Cell 2001 106 195 206 11511347 Hecht A Strahl-Bolsinger S Grunstein M Spreading of transcriptional repressor SIR3 from telomeric heterochromatin Nature 1996 383 92 96 8779721 Iyer VR Horak CE Scafe CS Botstein D Snyder M Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF Nature 2001 409 533 538 11206552 Jiang W Lechner J Carbon J Isolation and characterization of a gene (CBF2) specifying a protein component of the budding yeast kinetochore J Cell Biol 1993 121 513 519 8486733 Klein F Mahr P Galova M Buonomo SB Michaelis C A central role for cohesins in sister chromatid cohesion, formation of axial elements, and recombination during yeast meiosis Cell 1999 98 91 103 10412984 Laloraya S Guacci V Koshland D Chromosomal addresses of the cohesin component Mcd1p J Cell Biol 2000 151 1047 1056 11086006 Lee JT Lu N Han Y Genetic analysis of the mouse X inactivation center defines an 80-kb multifunction domain Proc Natl Acad Sci U S A 1999 96 3836 3841 10097124 Losada A Hirano T Intermolecular DNA interactions stimulated by the cohesin complex in vitro. Implications for sister chromatid cohesion Curr Biol 2001 11 268 272 11250156 Losada A Hirano M Hirano T Identification of Xenopus SMC protein complexes required for sister chromatid cohesion Genes Dev 1998 12 1986 1997 9649503 Matsuzaki H Nakajima R Nishiyama J Araki H Oshima Y Chromosome engineering in Saccharomyces cerevisiae by using a site-specific recombination system of a yeast plasmid J Bacteriol 1990 172 610 618 2404945 Megee PC Koshland D A functional assay for centromere-associated sister chromatid cohesion Science 1999 285 254 257 10398602 Megee PC Mistrot C Guacci V Koshland D The centromeric sister chromatid cohesion site directs Mcd1p binding to adjacent sequences Mol Cell 1999 4 445 450 10518226 Meluh PB Koshland D Budding yeast centromere composition and assembly as revealed by in vivo cross-linking Genes Dev 1997 11 3401 3412 9407032 Michaelis C Ciosk R Nasmyth K Cohesins: Chromosomal proteins that prevent premature separation of sister chromatids Cell 1997 91 35 45 9335333 Mythreye K Bloom KS Differential kinetochore protein requirements for establishment versus propagation of centromere activity in Saccharomyces cerevisiae J Cell Biol 2003 160 833 843 12642611 Nicklas RB Ward SC Elements of error correction in mitosis: Microtubule capture, release, and tension J Cell Biol 1994 126 1241 1253 8063861 Nonaka N Kitajima T Yokobayashi S Xiao G Yamamoto M Recruitment of cohesin to heterochromatic regions by Swi6/HP1 in fission yeast Nat Cell Biol 2002 4 89 93 11780129 Panizza S Tanaka T Hochwagen A Eisenhaber F Nasmyth K Pds5 cooperates with cohesin in maintaining sister chromatid cohesion Curr Biol 2000 10 1557 1564 11137006 Renauld H Aparicio OM Zierath PD Billington BL Chhablani SK Silent domains are assembled continuously from the telomere and are defined by promoter distance and strength, and by SIR3 dosage Genes Dev 1993 7 1133 1145 8319906 Rothstein RJ One-step gene disruption in yeast Methods Enzymol 1983 101 202 211 6310324 Saffery R Irvine DV Griffiths B Kalitsis P Wordeman L Human centromeres and neocentromeres show identical distribution patterns of >20 functionally important kinetochore-associated proteins Hum Mol Genet 2000 9 175 185 10607828 Saunders M Fitzgerald-Hayes M Bloom K Chromatin structure of altered yeast centromeres Proc Natl Acad Sci U S A 1988 85 175 179 2829168 Skibbens RV Rieder CL Salmon ED Kinetochore motility after severing between sister centromeres using laser microsurgery: Evidence that kinetochore directional instability and position is regulated by tension J Cell Sci 1995 108 2537 2548 7593295 Skibbens RV Corson LB Koshland D Hieter P Ctf7p is essential for sister chromatid cohesion and links mitotic chromosome structure to the DNA replication machinery Genes Dev 1999 13 307 319 9990855 Sumner AT Scanning electron microscopy of mammalian chromosomes from prophase to telophase Chromosoma 1991 100 410 418 1893796 Tanaka T Cosma MP Wirth K Nasmyth K Identification of cohesin association sites at centromeres and along chromosome arms Cell 1999 98 847 858 10499801 Tanaka T Fuchs J Loidl J Nasmyth K Cohesin ensures bipolar attachment of microtubules to sister centromeres and resists their precocious separation Nat Cell Biol 2000 2 492 499 10934469 Tomonaga T Nagao K Kawasaki Y Furuya K Murakami A Characterization of fission yeast cohesin: Essential anaphase proteolysis of Rad21 phosphorylated in the S phase Genes Dev 2000 14 2757 2570 11069892 Toth A Ciosk R Uhlmann F Galova M Schleiffer A Yeast cohesin complex requires a conserved protein, Eco1p(Ctf7), to establish cohesion between sister chromatids during DNA replication Genes Dev 1999 13 320 333 9990856 Wang Z Castano IB De Las Penas A Adams C Christman MF Pol kappa: A DNA polymerase required for sister chromatid cohesion Science 2000 289 774 779 10926539 Watanabe Y Nurse P Cohesin Rec8 is required for reductional chromosome segregation at meiosis Nature 1999 400 461 464 10440376

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PLoS Biol. 2004 Sep 27; 2(9):e260

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==== Front PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0020291SynopsisCell BiologySaccharomycesGenome-Wide Survey of Cohesin: A Molecular Guardian of Genomic Fidelity Synopsis9 2004 27 7 2004 27 7 2004 2 9 e291Copyright: © 2004 Public Library of Science.2004This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. The Kinetochore Is an Enhancer of Pericentric Cohesin Binding Genome-Wide Mapping of the Cohesin Complex in the Yeast Saccharomyces cerevisiae ==== Body At a fundamental level, the continuity of life depends on cell division. Humans generate many millions of cells per second just to stay alive, with most cell types dividing and multiplying repeatedly during a lifetime. Details of cell division vary from cell to cell and organism to organism, but certain features are universal, including what is arguably a cell's most crucial task: the faithful duplication and segregation of its genetic material. During mitosis, a cell copies its nuclear DNA, then splits into two identical daughter cells, a process that involves moving the replicated chromosomes (called sister chromatids) toward opposite ends of the cell. After chromosomes replicate, a protein complex called cohesin binds the sister chromatids together. Cohesion helps the cell distinguish between the copies, which in turn aids proper distribution. Improper sister chromatid segregation can yield an abnormal number of chromosomes (called aneuploidy) in the daughter cells, a condition associated with cancer. During meiosis—the cell division that produces egg and sperm cells—aneuploidy causes a number of congenital disorders, including Down's syndrome. PeakFinder automates identification of peaks in ChIP data To end up in their appropriate positions, sister chromatids must establish attachments to tentacle-like protein polymers called spindle microtubules, which emanate from spindle poles at opposite ends of a cell. Cohesion between the chromatids makes these bipolar attachments possible and keeps sister chromatids from separating after they attach to the spindle. Cohesion occurs along the length of a chromosome and is particularly strong around centromeres, the pinched region of a chromosome. Centromeres, in turn, assemble another protein complex called the kinetochore, which mediates the attachment of chromosomes to spindle microtubules; together, they guide chromosomes to their respective destinations. Cohesin's binding locations were discovered by removing chromatin—the mass of DNA and proteins that forms chromosomes—from cells, and purifying the regions associated with cohesin. These studies looked at cohesin's binding distribution either genome-wide or at select regions of a few chromosomes. Here, two research groups use a similar approach to provide a broader picture in their analysis of cohesin binding in the budding yeast Saccharomyces cerevisiae, a favorite system for cell biologists. In the first paper, Jennifer Gerton and colleagues generated a map for the entire yeast genome of locations where cohesin binds to chromosomes during meiosis and mitosis. In the second paper, Paul Megee and colleagues found that centromeres attract large concentrations of cohesin to their flanks and that the assembly of these cohesin domains is mediated by centromere–kinetochore complexes. Gerton's group reports that large regions surrounding centromeres have “intense” cohesin binding. These binding sites correlate with DNA base composition—DNA is composed of four chemical bases, or nucleotides, that are referred to as A, C, G, and T—showing a strong association with AT-rich regions. In meiotic chromosomes, cohesin binding sites are interspersed between the DNA double-strand breaks that initiate the exchange of genetic information characteristic of meiosis, perhaps keeping the chromatids attached without interfering with genetic recombination. Most striking, the authors note, is the observation that cohesin binding changes according to the cell's gene transcription program. Cohesin prefers DNA that lies between active transcription zones and is unceremoniously displaced from regions where RNA transcripts are being made (a process called elongation). This suggests that elongation through a region and cohesion binding may be incompatible. These observations support previous work indicating that DNA sequences required for the replication and segregation of chromosomes must be protected from transcription to function properly. Whatever the explanation, this finding begs the question of how more complicated genomes can accommodate these two seemingly contradictory processes. Megee's group investigated whether all yeast chromosomes have these large centromere-flanking cohesin regions and whether the centromeres and DNA sequences that surround them somehow facilitate the assembly of cohesin complexes. By removing centromeres and generating cells incapable of assembling kinetochores, the researchers show that the assembly of these cohesin regions is mediated solely by the centromere–kinetochore complex. What's more, inserting centromeric DNA sequences in abnormal chromosomal locations produced new cohesin-assembling regions around these “neo” centromeres. The kinetochores' influence appears to stretch over tens of thousands of DNA bases and serves chromatid segregation in two crucial ways: by recruiting high levels of cohesin to centromeres' sides, which attaches chromatids to their bipolar spindles, and by attaching chromatids to microtubules, which provides their passage to the cell's opposite sides. The maintenance of genomic integrity, the authors conclude, likely relies on the coordination of these essential functions.

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PLoS Biol. 2004 Sep 27; 2(9):e291

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PLoS Biol

10.1371/journal.pbio.0020291

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==== Front BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-5-481526523710.1186/1471-2164-5-48Research ArticleCharacterization of the GATC regulatory network in E. coli Riva Alessandra [email protected] Marie-Odile [email protected] Tony [email protected] Nicolas [email protected]énaut Corinne [email protected]énaut Alain [email protected] Laboratoire Génome et Informatique, UMR 8116, CNRS – Université d'Evry Val d'Essonne, Tour Evry 2, 523 Place des Terrasses, 91034 Evry cedex, France2 METabolic EXplorer S.A., Biop ô le Clermont-Limagne, 63 360 Saint-Beauzire, France3 Previous address: Hong Kong University- Pasteur Research Centre Ltd, Dexter HC Man Building, 8 Sassoon Road, Pokfulam, Hong Kong2004 20 7 2004 5 48 48 30 3 2004 20 7 2004 Copyright © 2004 Riva et al; licensee BioMed Central Ltd.2004Riva et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background The tetranucleotide GATC is methylated in Escherichia. coli by the DNA methyltransferase (Dam) and is known to be implicated in numerous cellular processes. Mutants lacking Dam are characterized by a pleiotropic phenotype. The existence of a GATC regulated network, thought to be involved in cold and oxygen shift, had been proposed and its existence has recently been confirmed. The aim of this article is to describe the components of the GATC regulated network of E. coli in detail and propose a role of this network in the light of an evolutionary advantage for the organism. Results We have classified the genes of the GATC network according to the EcoCyc functional classes. Comparisons with all of E. coli's genes and the genes involved in the SOS and stress response show that the GATC network forms a group apart. The functional classes that characterize the network are the Energy metabolism (in particular respiration), Fatty acid/ Phospholipid metabolism and Nucleotide metabolism. Conclusions The network is thought to come into play when the cell undergoes coldshock and is likely to enter stationary phase. The respiration is almost completely under GATC control and according to our hypothesis it will be blocked at the moment of coldshock; this might give the cell a selective advantage as it increases its chances for survival when entering stationary phase under coldshock. We predict the accumulation of formate and possibly succinate, which might increase the cell's resistance, in this case to antimicrobial agents, when entering stationary phase. ==== Body Background The tetranucleotide GATC is methylated in Escherichia. coli by the DNA methyltransferase (Dam); this enzyme methylates the adenine residue within 5'-GATC-3' sequences in double stranded DNA. GATC motifs and their methylation by Dam play an important role in E. coli; they are involved in mismatch repair (see [1] for a review on the subject of mismatch repair and [2] for a review concerning E. coli only) and the control of chromosome replication (see [3] for a concise overview on the subject). The methylation state of GATC is also involved in the expression of the pap operon; this operon codes for the Pap pili, which are of great importance in the pathogenicity of uropathogenic E. coli [4]. Mutants that lack Dam are characterized by a pleiotropic phenotype; they show for example an increased sensitivity to DNA-damaging agents, have a higher mutability and increased hyper-recombination [5]. Recent transcriptome analyses on Dam mutants show that nearly 10% of E. coli's genes are affected [6]. When we sort these genes according to their EcoCyc functional classes and compare their distribution with all of E. coli's genes, one can observe that the two distributions are different (p-value = 1.7 × 10-7) and that the class Energy metabolism is particularly overrepresented in the genes sensitive to the dam genotype (see Figure 1). Figure 1 Comparison of the distribution of the "Oshima genes" with all of E. coli's genes. The genes of E. coli have been classified according to the 15 classes used by EcoCyc. In this figure we compare the distribution of the genes sensitive to the dam+/ dam- background according to a transcriptome analysis carried out by [6] ("Oshima genes") with all of E. coli's genes. The distributions are different (p-value = 1.7 × 10-7): the "Oshima genes" contain a much smaller proportion of "Hypothetical" genes, whilst the group "Energy metabolism" is particularly overrepresented. In 1996, Hénaut et al. [7] suggested the existence of a GATC regulated network thought to be involved in cold and oxygen shift. Its existence has been recently confirmed by Riva et al. [8], who have worked with a virtual chromosome and used Salmonella -a close relative of E. coli- as a control organism. The network consists of a number of genes which contain clusters of GATC within their coding sequences. In fast growing cells (for example in the intestine of their warm blooded host) Dam is a limiting factor and the DNA will be undermethylated [9,10]; this undermethylated DNA possesses an increased melting temperature and thus an increased stability [11]. According to the hypothesis of [7] and [8], the increased stability of the DNA comes into play when the bacteria undergo coldshock (and an oxygen shift), caused by the passage from the intestine to the external environment. At that moment, the transcription of genes containing a GATC cluster will be blocked at the level of the cluster because of the high stability of the hemi-methylated DNA. The aim of this article is to describe the GATC regulated network of E. coli in detail. In order to obtain more information about the characteristics of the network, we compare it with the genes known to be involved in the stress and SOS response. By examining the functions of the genes belonging to the GATC regulated network, we try to address the question about the possible evolutionary advantage of developing a regulatory network controlled by GATC clusters present within the coding regions. Results Description of the network and its conservation Firstly we assigned each gene in E. coli to one of fifteen functional classes according to EcoCyc [12]. This allowed us to examine whether the "GATC genes" (those genes in E. coli containing a GATC cluster, listed in Table 1 [see additional file 1]) follow the distribution of all of E. coli's genes or not. The results are displayed in Table 2 and Figure 2 and show that the distribution (in percentage) of the "GATC genes" in the fifteen classes is significantly different from all of E. coli's genes (p-value = 6 × 10-15). As the GATC network is thought to come into play when the cell suffers from stress, namely coldshock (coupled to an oxygen shift), we compared the "GATC genes" with genes induced under various stress conditions according to EcoCyc ("EcoCyc genes") as the two groups might overlap. The results of this second comparison are displayed in Table 2 and Figure 3 and show that the distribution (in percentage) of the genes in the fifteen classes is clearly different in the two groups (p-value = 5 × 10-9). A third comparison was made, between the "GATC genes" and the group of genes, whose expression is strongly affected by mitomycin C ("Mitomycin C genes") according to a recent transcriptome analysis [13]. Mitomycin C is an antitumor drug and antibiotic which has a strong ability to cause interstrand DNA cross-links [14]. Addition of mitomycin C to a culture of E. coli provokes a general stress in the bacteria, affecting almost 30 % of E. coli's genes; 7% of E. coli's genes show particularly strong changes in expression levels, including the genes in the SOS response and genes belonging to other stress response pathways [13]. We have compared this latter group of genes with the "GATC genes". Again the comparison shows the two groups of genes to be distinct (p-value = 2 × 10-6, see Table 2, Figure 4). Table 2 Distribution of the different groups of genes according to the EcoCyc functional classification Functional class "GATC genes" all of E. coli's genes "EcoCyc genes" "Mitomycin C genes" Amino acid metabolism 2 134 0 5 Biosynthesis of cofactors, prosthetic groups, carriers 2 127 2 1 Cell envelope 2 194 5 10 Cellular process 1 102 18 14 Central intermediary metabolism 4 149 9 15 Energy metabolism 16 363 6 29 Fatty acid/Phospholipid metabolism 8 64 1 4 Hypothetical 11 1847 12 62 Nucleotide metabolism 11 120 2 14 Other categories 3 236 30 15 Regulatory functions 1 104 12 5 Replication 4 89 14 19 Transcription 1 47 5 7 Translation 0 150 2 61 Transport/binding protein 10 369 28 42 Total 76 4095 146 303 The table shows the distributions of four groups of genes discussed in this paper, (classified according to the EcoCyc functional classification): the "GATC genes" (genes containing a GATC cluster), all of E. coli's genes, the "EcoCyc genes" (genes induced under various stress conditions according to EcoCyc) and the "Mitomycin C genes" (genes induced by the stress caused by the antibiotic mitomycin C). This table allows the reader to perform the statistical analyses carried out in this paper. The comparison of the distribution of the "GATC genes" with each of the other three groups shows that in all three comparisons three functional classes are overrepresented in the "GATC genes": Energy metabolism, Fatty acid/ Phospholipid metabolism and Nucleotide metabolism. These three classes characterize the GATC regulated network. For an easier interpretation these data are also displayed in the form of histograms in Figures 2, 3 and 4. Figure 2 Comparison of the distribution of the "GATC genes" with all of E. coli's genes. The genes of E. coli have been classified according to the 15 classes used by EcoCyc. In this figure we compare the distribution of the genes containing a GATC cluster ("GATC genes") with all of E. coli's genes. (See Table 2 for the corresponding numerical data.) The distributions are different (p-value = 6 × 10-15): the "GATC genes" contain a much smaller proportion of "Hypothetical" genes, whilst the groups "Fatty acid/ Phospholipid metabolism", "Nucleotide metabolism" and "Energy metabolism" are overrepresented. Figure 3 Comparison of the distribution of the "GATC genes" with the "EcoCyc genes". The genes of E. coli have been classified according to the 15 classes used by EcoCyc. In this figure we compare the distribution of the genes containing a GATC cluster ("GATC genes") with the genes induced under various stress conditions according to EcoCyc ("EcoCyc genes"). (See Table 2 for the corresponding numerical data.) The distributions are different (p-value = 5 × 10-9): the "GATC genes" contain a much smaller proportion of genes belonging to "Other categories", genes involved in "Cellular processes" and "Regulatory functions", whilst the groups "Fatty acid/ Phospholipid metabolism", "Nucleotide metabolism" and "Energy metabolism" are overrepresented. Figure 4 Comparison of the distribution of the "GATC genes" with the "Mitomycin C genes". The genes of E. coli have been classified according to the 15 classes used by EcoCyc. In this figure we compare the distribution of the genes containing a GATC cluster ("GATC genes") with the genes whose expression is sensitive to the stress caused by the antibiotic mitomycin C (genes involved in the SOS response and other stress response pathways) according to the recent transcriptome analysis carried out by [13] ("Mitomycin C genes"). (See Table 2 for the corresponding numerical data.) The distributions are different (p-value = 2 × 10-6): the "GATC genes" contain a much smaller proportion genes involved in "Translation", whilst the groups "Fatty acid/ Phospholipid metabolism", "Nucleotide metabolism" and "Energy metabolism" are overrepresented. The three comparisons show us that the "GATC genes" form a group apart. A characteristic of the GATC network is the overrepresentation of three classes, namely Fatty acid/ Phospholipid metabolism, Nucleotide metabolism and Energy metabolism, indicating that the genes belonging to these three classes are the ones which characterize the network and therefore deserve a closer examination. Two distinct arguments corroborate the fact that the three classes (Fatty acid/ Phospholipid metabolism, Nucleotide metabolism and Energy metabolism) are at the heart of the GATC Network: The first argument comes from an analysis of some transcriptome data. Oshima et al. [6] carried out a transcriptome study on E. coli in a dam+/dam- background. We have classified the genes affected by this background according to the 15 functional classes in EcoCyc and compared the distribution of these genes with the "GATC genes". At first sight the distribution (in percentage) of the genes in the fifteen classes is different in the two groups (p-value = 1 × 10-4). If, however, we do not take into consideration the class "Hypothetical", the two distributions do no longer differ from each other, with a p-value of 0.03 (Table 3 [see additional file 2]. If, on the other hand, we ignore the class "Hypothetical" in the three comparisons mentioned above, i.e. the comparisons of the "GATC genes" with all of E. coli's genes, the stress-induced genes according to EcoCyc and the mitomycin C sensitive genes, the distributions continue to be significantly different. The second argument stems from the comparison of E. coli's "GATC genes" with the "GATC genes" present in Salmonella. E. coli contains 76 "GATC genes", Salmonella 57 (and 3 pseudogenes) and they follow the same distribution in the 15 EcoCyc functional classes (p-value = 0.75). Twenty-three genes are in common to the two bacteria (i.e. they contain a GATC cluster in both organisms); even if we do not take these genes into account, the rest of the "GATC genes" in the two organisms still follow the same distribution (p-value = 0.33). We expected the network to be evolutionarily conserved, but not to be identical in the two organisms. A particularly interesting case is given when the same pathway is affected in both organisms, but through different genes, as this represents a particularly strong argument for the evolutionary conservation of the GATC regulated network, beyond the mere conservation of the sequences themselves. The following serve as examples: The first regards the propionate catabolism, where three genes contain a cluster. prpE contains a GATC cluster in both E. coli and Salmonella. prpB, which belongs to the same catabolic pathway, is affected in E. coli only. According to our hypothesis, transcription from these two genes would be halted during coldshock. prpR codes for the positive regulator of the propionate catabolism operon, to which prpE and prpB belong to. It contains a cluster in Salmonella. According to our hypothesis, transcription of prpR would be halted under coldshock, thus inhibiting transcriptional activation of the propionate catabolism operon, effectively blocking propionate catabolism. It is interesting to note that the GATC regulated transcription block acts on two different levels: the genes involved in catabolism, as well as their operon's regulator. Formate metabolism is affected by GATC clusters in both organisms; according to our hypothesis, the insertion of selenocysteine (required by all three formate dehydrogenases) is blocked in E. coli via selB, in Salmonella through cysN (see the corresponding entry in Table 1 [see additional file 1] for details). Furthermore, we find that in E. coli hyfR contains a cluster, whose product is required for the induction of the formate hydrogenlyase 2 [15]; by blocking hyfR, the formation of the complex would be hindered and thus formate metabolism further inhibited. In both organisms, a nitrate/ nitrite response regulator is part of the GATC network: narL in E. coli and narP in Salmonella. The two enzymes fulfil equivalent roles in respiration, both being involved in the (co-) regulation of a number of genes encoding oxidoreductases and dehydrogenases. The regulated oxidoreductases include a periplasmic nitrite reductase (nrfABCD) and two nitrate reductases (narGHJI and napFDAGHBC); the formate dehydrogenase-N (fdnGHI) is regulated by NarL [16-19]. Discussion Evolutionary advantage of GATC network regulation According to our hypothesis, the GATC network comes into play, when E. coli passes from a warm, nutrient rich environment, where it grows rapidly, to a cold, nutrient poor environment, where growth might be expected to be considerably slower, if not completely arrested. This process occurs naturally, when the cells pass from the intestine of their warm blooded host to the external environment. When growing rapidly, the DNA is undermethylated [9,10] and possesses an increased melting temperature and thus an increased stability [11]. We hypothesize that when the bacterium undergoes coldshock, the transcription of genes containing a GATC cluster will be blocked at the level of the cluster because of the high stability of the hemi-methylated DNA. We will now draw the logical consequences of this hypothesis: if a gene contains a GATC cluster, its transcription will be blocked when the bacterium undergoes coldshock and the product the gene codes for will no longer be formed, affecting the biological process it is involved in. We will apply this principle systematically to the genes belonging to the three functional classes characterizing the GATC network (Fatty acid/ Phospholipid metabolism, Nucleotide metabolism and Energy metabolism), in order to try answering the following question: is there one and the same selection pressure that can explain why these three particular classes are affected by the GATC network? Nucleotide metabolism Two main groups of genes can be distinguished: those involved in the synthesis of Nucleotides ("Nucleotide Synthesis", see column "Subclass", Table 1 [see additional file 1]) and those involved in DNA repair ("DNA repair", see column 'Subclass", Table 1 [see additional file 1]). The shut down of macromolecule synthesis such as DNA, RNA and ATP when undergoing coldshock might be seen as the "effort" of the cell to eliminate wasteful energy expenditure, as growth conditions in the new environment will probably be less comfortable than in the host's intestine. The halt of the DNA repair machinery, on the other hand, might have a different reason: when the cell passes from the intestine to the outside environment, it is halted in the middle of a rapid growth phase, which means that the DNA double helix will be open in certain regions and "loose ends" of newly formed DNA will be present (like, for example, Okazaki fragments). The DNA repair machinery might interpret these fragments as damaged pieces of DNA and proceed to its elimination [20]. An immediate halt of the DNA repair machinery when undergoing coldshock would prevent such "erroneous" repair. Energy metabolism Again, we will look at two classes of genes, which are particularly interesting: those involved in respiration and those involved in the metabolism of succinate ("Respiration" and "Succinate", column "Subclass", Table 1 [see additional file 1]). Respiration The respiratory system of E. coli has a modular character (see [21] for a comprehensive introduction to the subject). There are three types of respiratory components (see Figure 5): Figure 5 The respiratory components of E. coli and Salmonella . This figure gives an overview of the respiration of E. coli and Salmonella and how it is influenced by the GATC network. As can be seen, the large majority of enzymes involved in respiration are affected, directly or indirectly, by the GATC network. Listed on the left hand side are the gene complexes that make up the dehydrogenases, together with the reactions they catalyse. The resulting reducing equivalents are passed through the common quinone pool and are used by the oxidoreductases, listed on the right hand side, together with the reactions they catalyse. Also given are the means by which the various dehydrogenases and oxidoreductases are affected through the GATC cluster network. "Heme": the gene complex contains a heme, whose synthesis is blocked by a GATC cluster in gltX. "NarL/ NarP" the gene complex is under control by NarL/ NarP whose respective genes are blocked by a GATC cluster. "Selenocysteine": the gene complex contains selenocysteine whose insertion into the protein is blocked (GATC clusters in selB/ cysN). "GATC cluster": the gene complex itself contains a GATC cluster. For further details please refer to the results section."Sal.": Salmonella. 1) Substrate specific dehydrogenases which oxidize their substrates and feed electrons to the mobile quinone pool. 2) Quinones, which deliver reducing equivalents to the terminal oxidoreductases. 3) Terminal oxidoreductases which reduce the terminal electron acceptors. The heme synthesis is blocked in E. coli (and Salmonella) at the level of the aminolevulinic acid (ALA) production. ALA is synthesized from glutamate through the action of GltX, whose corresponding gene, gltX, contains a GATC cluster (in both organism). Hemes play a fundamental role in the energy conserving electron transport chains and are also present as cofactors in a number of enzymes [22]. Thus, if heme synthesis is blocked, the repercussions are felt by the heme containing enzymes, notably by the dehydrogenases poxB, sdhABCD, fdnGHI, fdoGHI, fdhF and lldD and the oxidoreductases torCAD, torYz, nirB, nrfAB, cyoABCD, cydAB and cyxAB. Respiration is further affected through narL, containing a GATC cluster in E. coli (in Salmonella it is narP which contains a GATC cluster) which codes for a nitrate / nitrite response regulator and is involved in the (co-) regulation of a number of genes encoding oxidoreductases and dehydrogenases. The oxidoreductases regulated include a periplasmic nitrite reductase (nrfABCD) and two nitrate reductases (narGHJI and napFDAGHBC); the formate dehydrogenase-N (fdnGHI) is regulated by NarL [15-18]. As discussed in the section above, the formate dehydrogenases require selenocysteine, whose incorporation is halted. We can thus expect formate to accumulate in the cell when it undergoes coldshock, an aspect discussed further below. Two more dehydrogenases – glpA and lldD – and oxidoreductases – dmsA and nirB- are blocked in E. coli. If we now look at the respiration as a whole, at all the respiratory enzymes affected by the clusters, directly or indirectly, the following picture emerges (see Figure 5): the great majority of enzymes are blocked. Interestingly, Nyström [23] points out that during aerobic starving conditions or stasis the aerobic respiration in E. coli is blocked (by ArcA). Applying our hypothesis we come to the conclusion that genes involved in aerobic respiration are blocked and moreover, that almost the entire respiration, aerobic as well as anaerobic, is brought to a standstill. A possible explanation for this can be found in the work of Dukan & Nyström [24]. They point to the fact that when encountering stress (oxidative, nutritive, osmotic or thermal) the cell is likely to enter the stationary phase. Dukan & Nyström studied E. coli cultures when entering stationary phase under different conditions; they found that cell viability depends on the conditions the cell was prior to entering the stationary phase. Cells entering the stationary phase aerobically showed a very high mortality. Cells entering the stationary phase anaerobically, though, showed a much higher viability. Seen from this perspective it could be that what we predict to happen through the GATC regulation is the "attempt" of the cell to stay in an "anaerobic mode": the bacterium does not, for example, start producing enzyme complexes needed for aerobic respiration. Blocking respiration (when entering stationary phase) might be a preventive measure because all respiration, in the presence of oxygen, is a potential danger as it can lead to the production of free radicals and peroxide. Metabolism of succinate In the section above, we predict that formate probably accumulates in the cell when undergoing coldshock. A second prediction is that succinate might also be accumulating. If we look at the TCA cycle in anaerobiosis we note that the reductive branch (leading from oxaloacetate to succinyl CoA) is not affected by GATC clusters except for the last step, leading from succinate to succinyl CoA. Thus, we expect succinate to accumulate during coldshock. (In Salmonella, a recently discovered pathway leading from succinate to propionate is also blocked through ygfH (see the relevant entry in Table 1 [see additional file 1]), also suggesting an accumulation of succinate in the cell.) As mentioned above, Dukan & Nyström point out that a cell undergoing stress (including coldshock) is likely to enter stationary phase; this coupled with recent work carried out on Salmonella and E. coli [25] might give an explanation: the authors suggest that when the cells are in stationary phase, an accumulation of formate and succinate has a protective effect against antimicrobial agents. The mechanisms and exact nature of this protection are not yet understood. Fatty acid / Phospholipid metabolism We can make two observations regarding this group. The first is, that the synthesis of two enzymes which are essential for fatty acid synthesis are under GATC cluster control: the biosynthesis of ACP, the central coenzyme of fatty acid biosynthesis [26] is halted through acpS and ilvD (see the relevant entry in Table 1 [see additional file 1]); furthermore, the synthesis of biotin, an essential cofactor in fatty acid synthesis, is hindered through mioC, which contains a GATC cluster (recent work suggesting that MioC is an essential cofactor for the biotin synthase [27]). The second observation regards the metabolism of propionate. As mentioned above, propionate catabolism is halted in both E. coli and Salmonella. Propionate, a short chain fatty acid (SCFA) is present in relative abundance in the warm blooded hosts' intestine [28,29], and less so in the external environment. It might be, that the GATC network acts to quickly stop a catabolic process for which it will have less use once in the external environment. The (control) mechanism So far we have stressed the importance of the physical property of hemi- or unmethylated GATC motifs, especially when present in clusters: the increased stability of the DNA, which comes into play when the ambient temperature of the bacterium is suddenly lowered. We hypothesize that this increased stability hinders transcription of the gene containing a GATC cluster when the cell undergoes coldshock and is likely to enter stationary phase; we have looked at the effect this might have on the cell. A question we might ask is that of the control mechanism: apart from the control exerted by the physical property of hemi- or unmethylated GATC motifs, is there something else that recognizes these motifs? The protein SeqA, for example, recognizes hemimethylated GATC sequences placed on the same face of the DNA double helix and it does so in a cooperative, histone-like manner, forming a homotetramer [30]. It might indeed be that the GATC cluster network also requires the intervention of such a protein in order to fully exert its functions. This question, however, reaches beyond the abilities of the research in silico and would require an intervention from the wet lab. Conclusions E. coli and Salmonella possess a GATC cluster regulated network. The clusters are found within the coding sequences and their distribution is not at random. Three functional classes characterize the network: Nucleotide metabolism, Energy metabolism and Fatty acid / Phospholipid metabolism. We hypothesize that the network comes into play when the cell passes from the warm, nutrient rich environment of its warm blooded host's intestine to the external environment, i.e., when the cell undergoes coldshock and is likely to enter stationary phase. According to our theory, the transcription of genes containing a GATC cluster will be blocked at the level of the cluster when the bacterium undergoes coldshock and the product the gene codes for will no longer be formed, affecting the biological process it is involved in. We have applied this principle to the three functional classes that characterize the GATC network and come to the conclusion that respiration is almost completely under GATC control and will be blocked at the moment of coldshock; this might give the cell a selective advantage as it increases its chances for survival when entering stationary phase under coldshock. We also predict the accumulation of formate and possibly succinate, which might increase the cell's resistance, in this case to antimicrobial agents, when entering stationary phase. Methods Procedure The procedure has been described elsewhere [8], here a general outline: We have taken a classic genomic approach, analyzing the statistical distribution of GATC along the chromosome, using a realistic model of the chromosome as theoretical reference. We thus identify local enrichments, or clusters, in the real chromosome. A GATC cluster is identified in the following manner: GATC pairs separated by less than 8 bp and triplets in regions spanning less than 62 bp are kept in a preliminary screening. In a second step we retain only those regions where there are at least four GATC motifs and where the average distance between pairs is shorter than 18 bp. We confirm the presence of GATC clusters within the genes. In order to verify that the particular distribution observed in E. coli is not a statistical artefact, but has a physiological role, we have carried out the same analysis on Salmonella, making the hypothesis that the genes containing a GATC clusters should be largely the same in the two bacteria. This has been indeed observed, showing that the genes containing a GATC cluster are part of a regulation network We thus obtain a list of genes for E. coli (76) and for Salmonella (57 and three pseudo genes) containing GATC clusters, displayed in Table 1 [see additional file 1]. With the help of the data mining tools listed below, we try to attribute a function to each of these genes and group them into classes according to EcoCyc's system [12]. For certain genes, which have more than one function, we have chosen the class based on the particular context of the current study (see Table 1 [see additional file 1] for details). In order to compare the genes belonging to E. coli's GATC network (the "GATC genes", Table 4 [see additional file 3], column F) with those involved in the stress and SOS response, we retrieved the genes listed under "adaptation" and "SOS response" at EcoCyc (the "EcoCyc genes", Table 4 [see additional file 3], column D). We also retrieved he genes whose expression is sensitive to mitomycin C, which in addition to the SOS response also includes genes belonging to other stress response pathways, according to a recent transcriptome analysis carried out by [12] (the "Mitomycin C genes", Table 4 [see additional file 3], column E). Again, the "EcoCyc genes" and "Mitomycin C genes" were classified according to EcoCyc's system. A summary of the results is displayed in Table 2. Data mining tools In order to gain information about the "GATC genes" identified, we have used the following databases: • EcoCyc [12], • KEGG [34], • METAVISTA® [35](proprietary data base of the Metabolic Explorer society), • PubMed [36], • Swiss-Prot [37] using the SRS search tool to interrogate the SWALL (SPTR) database (accessible as SWall on the SRS server). Authors' contributions AR performed the molecular genetic studies, participated in the statistical analysis and drafted the manuscript, MOD participated in the design of the study and the statistical analysis, TC, NG and CH were responsible for the data mining, AH conceived the study, participated in its design and coordination. All authors read and approved the final manuscript. Supplementary Material Additional File 1 The "GATC genes" in E. coli and Salmonella The table lists all genes that contain a GATC cluster in E. coli or in Salmonella. ORF(#ID): the ORF number and #ID of the genes according to Oshima et al. [6]. Empty cells indicate genes not analyzed in their work. The column "EcoCyc genes" denotes the genes induced under various stress conditions according to EcoCyc, "Mitomycin C genes" denotes the genes induced by the stress caused by the antibiotic mitomycin C (genes involved in the SOS response and other stress response pathways) according to the recent transcriptome analysis carried out by [13], the two columns "GATC genes" denote genes containing a GATC cluster (in E. coli or in Salmonella). The column "Oshima genes" denotes genes in E coli sensitive to the dam+/ dam- background. "EcoCyc functional class (modified)" gives the genes, classified according to the EcoCyc functional classes, with the modifications made by us (changes are justified in the last column). "Subclass" refers to the various groups of genes discussed in the results/ discussion section of this paper. "1": the gene is affected, "0": the gene is not affected, "N.A.": no information was available. Click here for file Additional File 2 Distribution of the different groups of genes according to the EcoCyc functional classification, without the class "Hypothetical" The table shows the distributions of the five groups of genes discussed in this paper, (classified according to the EcoCyc functional classification) after removal of the class "Hypothetical". The five groups are: the "GATC genes" (genes containing a GATC cluster), all of E. coli's genes, the "EcoCyc genes" (genes induced under various stress conditions according to EcoCyc), the "Mitomycin C genes" (genes induced by the stress caused by the antibiotic mitomycin C) and the "Oshima genes" (genes sensitive to the dam+/ dam- background). The distributions of the "GATC genes" and the "Oshima genes" do not differ from each other (p-value = 0.03). The comparisons of the "GATC genes" with all of E. coli's, genes, the "EcoCyc genes" and the "Mitomycin C genes", however, show that the distributions continue to be significantly different, even after the removal of the class "Hypothetical". Click here for file Additional File 3 The file contains the raw data used for this article and allows the reader to re-trace all the calculations made Click here for file Acknowledgements The authors wish to thank J.-L. Risler for critical reading of the manuscript. This work was supported in part by the French Ministry of the Economy, Finance and Industry (contract ASG n°01 4 90 6093). ==== Refs Marti TM Kunz C Fleck O DNA mismatch repair and mutation avoidance pathways J Cell Physiol 2002 191 28 41 11920679 10.1002/jcp.10077 Bhagwat AS Lieb M Cooperation and competition in mismatch repair: very short-patch repair and methyl-directed mismatch repair in Escherichia coli Mol Microbiol 2002 44 1421 8 12067333 10.1046/j.1365-2958.2002.02989.x Donachie WD Co-ordinate regulation of the Escherichia coli cell cycle or The cloud of unknowing Mol Microbiol 2001 40 779 785 11401685 10.1046/j.1365-2958.2001.02439.x Hale WB van der Woude MW Braaten BA Low DA Regulation of uropathogenic Escherichia coli adhesin expression by DNA methylation Mol Genet Metab 1998 65 191 6 9851883 10.1006/mgme.1998.2744 Marinus MG Recombination is essential for viability of an Escherichia coli dam (DNA adenine methyltransferase) Mutant J Bacteriol 2000 182 463 468 10629194 10.1128/JB.182.2.463-468.2000 Oshima T Wade C Kawagoe Y Ara T Maeda M Masuda Y Hiraga S Mori H Genome-wide analysis of deoxyadenosine methyltransferase-mediated control of gene expression in Escherichia coli Mol Microbiol 2002 45 673 695 12139615 10.1046/j.1365-2958.2002.03037.x Hénaut A Rouxel T Gleizes A Moszer I Danchin A Uneven distribution of GATC motifs in the Escherichia coli chromosome, its plasmids and its phages J Mol Biol 1996 257 574 585 8648625 10.1006/jmbi.1996.0186 Riva A Delorme MO Chevalier T Guilhot N Hénaut C Hénaut A The difficult interpretation of transcriptome data: the case of the GATC regulatory network CBAC 2004 28 109 118 15130539 10.1016/j.compbiolchem.2003.12.004 Boye E Marinus MG Lobner-Olesen A Quantitation of dam methyltransferase in Escherichia coli J Bacteriol 1992 174 1682 1685 1537808 Plumbridge J Söll D The effect of dam methylation on the expression of glnS in E. coli Biochimie 1987 69 539 541 2960382 10.1016/0300-9084(87)90091-5 Fazakerley GV Teoule R Guy A Fritzsche H Guschlbauer W NMR studies on oligodeoxyribonucleotides containing the dam methylation site GATC. Comparison between d(GGATCC) and d(GGm6ATCC) Biochemistry 1985 24 4540 8 4063337 EcoCyc Encyclopedia of Escherichia coli K12 Genes and Metabolism Khil PP Camerini-Otero RD Over 1000 genes are involved in the DNA damage response of Escherichia coli Mol Microbiol 2002 44 89 105 11967071 10.1046/j.1365-2958.2002.02878.x Tomasz M Mitomycin C: small, fast and deadly (but very selective) Chem Biol 1995 2 575 579 9383461 10.1016/1074-5521(95)90120-5 Andrews SC Berks BC McClay J Ambler A Quail MA Golby P Guest JR A 12-cistron Escherichia coli operon (hyf) encoding a putative proton-translocating formate hydrogenlyase system Microbiology 1997 143 3633 3647 9387241 Browning DF Beatty CM Wolfe AJ Cole JA Busby SJ Independent regulation of the divergent Escherichia coli nrfA and acsP1 promoters by a nucleoprotein assembly at a shared regulatory region Mol Microbiol 2002 43 687 701 11929525 10.1046/j.1365-2958.2002.02776.x Darwin A Stewart V Expression of the narX, narL, narP, and narQ genes of Escherichia coli K-12: regulation of the regulators J Bacteriol 1995 177 3865 3869 7601854 Darwin AJ Ziegelhoffer EC Kiley PJ Stewart V Fnr, NarP, and NarL regulation of Escherichia coli K-12 napF (periplasmic nitrate reductase) operon transcription in vitro J Bacteriol 1998 180 4192 4198 9696769 Wang H Gunsalus RP The nrfA and nirB nitrite reductase operons in Escherichia coli are expressed differently in response to nitrate than to nitrite J Bacteriol 2000 182 5183 5822 Rupp WD Neidhardt FC, Curtiss III R, Ingraham JL, Lin ECC, Low KB, Magasanik B, Reznikoff WS, Riley M, Schaechter M, Umbarger HE DNA repair mechanisms In Escherichia coli and Salmonella: cellular and molecular biology 1996 2 Washington, DC: ASM Press 2277 2294 Gennis RB Stewart V Neidhardt FC, Curtiss III R, Ingraham JL, Lin ECC, Low KB, Magasanik B, Reznikoff WS, Riley M, Schaechter M, Umbarger HE Respiration In Escherichia coli and Salmonella: cellular and molecular biology 1996 2 Washington, DC: ASM Press 217 261 Schobert M Jahn D Regulation of heme biosynthesis in non-phototrophic bacteria J Mol Microbiol Biotechnol 2002 4 387 94 Nyström T To be or not to be: the ultimate decision of the growth-arrested bacterial cell FEMS Microbiol Rev 1997 21 283 290 10.1016/S0168-6445(97)00060-0 Dukan S Nyström T Oxidative stress defense and deterioration of growth-arrested Escherichia coli cells J Biol Chem 1999 274 26027 26032 10473549 10.1074/jbc.274.37.26027 Barker HC Kinsella N Jaspe Z Friedrich T O'Connor CD Formate protects stationary-phase Escherichia coli and Salmonella cells from killing by a cationic antimicrobial peptide Mol Microbiol 2000 35 1518 1529 10760151 10.1046/j.1365-2958.2000.01820.x Flugel RS Hwangbo Y Lambalot RH Cronan JE JrWalsh CT Holo-(acyl carrier protein) synthase and phosphopantetheinyl transfer in Escherichia coli J Biol Chem 2000 275 959 68 10625633 10.1074/jbc.275.2.959 Birch OM Hewitson KS Fuhrmann M Burgdorf K Baldwin JE Roach PL Shaw NM MioC is an FMN-binding protein that is essential for Escherichia coli biotin synthase activity in vitro J Biol Chem 2000 275 32277 32280 10913144 10.1074/jbc.M004497200 Macfarlane S Macfarlane GT Regulation of short-chain fatty acid production Proc Nutr Soc 2003 62 67 72 12740060 10.1079/PNS2002207 Pouteau E Nguyen P Ballevre O Krempf M Production rates and metabolism of short-chain fatty acids in the colon and whole body using stable isotopes Proc Nutr Soc 2003 62 87 93 12740063 10.1079/PNS2003208 Lee H Kang S Bae SH Choi BS Hwang DS SeqA protein aggregation is necessary for SeqA function J Biol Chem 2001 276 34600 34606 11457824 10.1074/jbc.M101339200 Haller T Buckel T Retey J Gerlt JA Discovering new enzymes and metabolic pathways: conversion of succinate to propionate by Escherichia coli Biochemistry 2000 39 4622 9 10769117 10.1021/bi992888d Onogi T Yamazoe M Ichinose C Niki H Hiraga S Null mutation of the dam or seqA gene suppresses temperature-sensitive lethality but not hypersensitivity to novobiocin of muk null mutants J Bacteriol 2000 182 5898 5901 11004192 10.1128/JB.182.20.5898-5901.2000 Yamazoe M Onogi T Sunako Y Niki H Yamanaka K Ichimura T Hiraga S Complex formation of MukB, MukE and MukF proteins involved in chromosome partitioning in Escherichia coli EMBO J 1999 18 5873 5884 10545099 10.1093/emboj/18.21.5873 KEGG Kyoto Encyclopedia of Genes and Genomes METabolic EXplorer Entrez PubMed UniProt-Swiss-Prot

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BMC Genomics. 2004 Jul 20; 5:48

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==== Front BMC NephrolBMC Nephrology1471-2369BioMed Central London 1471-2369-5-81526523410.1186/1471-2369-5-8Research ArticleEffects of chemokines on proliferation and apoptosis of human mesangial cells Wörnle Markus [email protected] Holger [email protected] Monika [email protected] Bernhard [email protected] Medical Policlinic, Ludwig-Maximilians-University, Munich, Germany2004 20 7 2004 5 8 8 11 1 2004 20 7 2004 Copyright © 2004 Wörnle et al; licensee BioMed Central Ltd.2004Wörnle et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Proliferation and apoptosis of mesangial cells (MC) are important mechanisms during nephrogenesis, for the maintenance of glomerular homeostasis as well as in renal disease and glomerular regeneration. Expression of chemokines and chemokine receptors by intrinsic renal cells, e.g. SLC/CCL21 on podocytes and CCR7 on MC is suggested to play a pivotal role during these processes. Therefore the effect of selected chemokines on MC proliferation and apoptosis was studied. Methods Proliferation assays, cell death assays including cell cycle analysis, hoechst stain and measurement of caspase-3 activity were performed. Results A dose-dependent, mesangioproliferative effect of the chemokine SLC/CCL21, which is constitutively expressed on human podocytes was seen via activation of the chemokine receptor CCR7, which is constitutively expressed on MC. In addition, in cultured MC SLC/CCL21 had a protective effect on cell survival in Fas-mediated apoptosis. The CXCR3 ligands IP-10/CXCL10 and Mig/CXCL9 revealed a proproliferative effect but did not influence apoptosis of MC. Both the CCR1 ligand RANTES/CCL5 and the amino-terminally modified RANTES analogue Met-RANTES which blocks CCR1 signalling had no effect on proliferation and apoptosis. Conclusions The different effects of chemokines and their respective receptors on proliferation and apoptosis of MC suggest highly regulated, novel biological functions of chemokine/chemokine receptor pairs in processes involved in renal inflammation, regeneration and glomerular homeostasis. Mesangial cellproliferationapoptosischemokinechemokine receptor ==== Body Background Originally chemokines (chemotactic cytokines) were described as key mediators for the selective migration of leukocytes into sites of tissue injury [1]. Later on chemokines and chemokine receptors have also been described as important mediators in noninflammatory processes, including normal cellular trafficking, hematopoesis, angiogenesis, organ development, tissue remodelling, and tumor metastasis [2-4]. To date more than 40 different human chemokines are characterized. The chemokine superfamily is separated into the C, CC, CXC, and CX3C subfamilies (Where X represents any intervening amino acid residue between the first two cysteines in the amino acid sequence) [5,6]. Chemokines mediate their biological activity by ligation and interaction with seven-transmembrane-spanning G protein-coupled receptors (i.e. C, CC, CXC, and CX3C receptors) [7]. In the kidney expression of chemokines and chemokine receptors are important for the initiation and regulation of inflammatory glomerular diseases [8]. Expression of the chemokines monocyte chemoattractant protein-1 (MCP-1/CCL2), regulated upon activation, normal T cell expressed and secreted (RANTES/CCL5), interleukin-8 (IL-8), and interferon-γ (IFN-γ)-inducible protein of 10 kD (IP-10/CXCL10) by human mesangial cells (MC) was shown by our group [9] and others [1,10]. Inducible expression of the chemokine receptor CCR1 by human MC was previously described [9]. The expression of the chemokine receptor CXCR3 on human MC was published by Romagnani and colleagues [11]. A high level of expression of this receptor by MC was seen by immunohistochemistry in kidney biopsies from patients with IgA nephropathy, membranoproliferative glomerulonephritis or rapidly progressive glomerulonephritis. Recently our group showed that SLC/CCL21 is constitutively expressed by glomerular podocytes and CCR7 constitutively expressed by MC [12]. In the kidney the well-regulated relationship among resident cell proliferation and apoptosis is important for the development of the sophisticated glomerular architecture during ontogenesis as well as maintaining normal function of adult human glomeruli. Dysfunction of the balance between glomerular cell proliferation and apoptosis after leukocyte infiltration has been discussed for many inflammatory kidney diseases [13]. The finding of expression of chemokine receptors and their respective ligands by intrinsic renal cells not only under inflammatory conditions led to the hypothesis of an involvement of these receptors in glomerular homeostasis. Therefore the influence of chemokines on mesangial cell growth was investigated. We describe the different effects of the chemokines SLC/CCL21, IP-10/CXCL10, Mig/CXCL9, RANTES/CCL5 and the amino-terminally modified RANTES/CCL5 analogue Met-RANTES on mesangial cell proliferation and apoptosis, suggesting novel functions of chemokine/chemokine receptor pairs on local immunomodulation, glomerular regeneration and homeostasis. Methods Cell culture conditions for human mesangial cells Immortalized human mesangial cells (MC) were grown as described previously [9]. This MC line was characterized for antigenic markers typically expressed by MC in vivo and in vitro and showed no dedifferentiation within approximately 100 passages during a 36 months cultivation period. For all experiments cells in passages 51 to 65 were used. Different preparations of primary human MC served as controls and were cultured as previously described [14]. Proliferation assay To assess proliferation we performed a MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide, Sigma, Germany) assay [15]. Aliquots of 20 × 103 cells in 100 μl medium were cultured in 96-well microtitre plates for 24 hours under standard conditions to yield firmly attached and stably growing cells. Subsequently the medium was changed to 100 μl of medium containing test substances and the cells were incubated from 24 to 72 hours. After discarding the supernatants 50 μl of a 1 mg/ml solution of MTT were added. The cells were incubated for 3 hours at 37°C and then formazan crystals were dissolved by addition of 50 μl isopropanol. Absorbance was measured at 550 nm against 630 nm reference using a DYNATECH (Germany) MR7000 ELISA reader. For each experiment at least 6 wells were analyzed per experimental condition and time point. Cell death assays Apoptosis of MC was induced by Fas/CD 95 ligation as previously described [16]. Fas/CD 95 surface expression was induced by starvation of the cells in serum-free medium and pre-stimulation with IFN-γ (70 ng/ml) for 48 h. For analysis of chemokine effects, cells were pretreated with SLC/CCL21 250 ng/ml, IP-10/CXCL10 100 ng/ml, Mig/CXCL9 100 ng/ml, RANTES/CCL5 100 ng/ml, and Met-RANTES 100 ng/ml prior to adding the activating anti-human Fas antibody (400 ng/ml, incubation over 18 hours) (Biomol, Germany). To induce expression of the chemokine receptor CCR1 in a different experiment MC were preincubated with a combination of IFN-γ (20 ng/ml), TNF-α (25 ng/ml) and IL-1β (10 ng/ml) for 24 h before starvation of the cells in serum-free medium and stimulation with IFN-γ (70 ng/ml), TNF-α (25 ng/ml) and IL-1β (10 ng/ml) for 48 h. Apoptosis was studied with several assays: Flow cytometric cell cycle analysis was performed using propidium iodide staining as described previously [17]. For visualization of chromatin fragmentation, MC were seeded on chamber slides (NUNC, Germany). After treatment with test substances cells were fixed with ethanol and stained with the nuclear dye Hoechst 33258 (Hoechst, Germany) 5 μmol/ml. The percentage of apoptotic cells was determined by immunofluorescence microscopy, counting nuclei with condensed and fragmented chromatin. Three different experiments were performed, at least 300 cells were analyzed per condition. Counting was performed in a blinded manner by two investigators. For measurement of caspase-3 activity [18] a commercial assay (R&D systems, Germany) was used according to the manufacturer's specifications. After induction of apoptosis as described above, MC were lysed and caspase-3 specific proteolytic activity was quantitated spectrophotometrically. Three experiments were done analyzing duplicates for any experimental condition. Statistical analysis Values are provided as mean ± SEM. Statistical analysis was performed by unpaired t test. Significant differences are indicated for p values < 0.05 (*) or < 0.01 (**), respectively. Results Effects of SLC/CCL21, IP-10/CXCL10, Mig/CXCL9, RANTES/CCL5 and Met-RANTES on proliferation of human mesangial cells SLC/CCL21 induces proliferation of human MC To demonstrate that activation of CCR7 has an influence on the proliferative activity of MC MTT assays were performed as described in Methods. The CCR7 ligand SLC/CCL21 led to a concentration dependent increase of proliferation of human MC in a range from 10 to 250 ng/ml after 24 hours of stimulation (Figure 1A). In a time course experiment from 24 to 72 hours the maximum increase of proliferation was seen after incubation with SLC/CCL21 for 48 hours (Figure 1B). Figure 1 Dose- and time- dependent effect of SLC/CCL21 on proliferation of human MC. (A) Incubation of human MC with various concentrations of SLC/CCL21 (10 ng/ml, 50 ng/ml, 100 ng/ml, 250 ng/ml) induces proliferation of MC in a dose-dependent manner. (B) Time-course stimulation of MC with SLC/CCL21 for various time intervals (24 h, 48 h, 72 h). Cell proliferation was analyzed with the MTT assay as described in Methods. Cells growing under standard conditions served as control. Changes in proliferative activity are given as relative values to the respective controls. Each bar represents a mean ± SEM of 7 parallel incubations for each condition. Statistically significant differences to the control are depicted with * = p < 0.05 and ** = p < 0.01, resp. Comparable results were obtained in three series of independent experiments. IP-10/CXCL10 and Mig/CXCL9 induce proliferation of human MC CXCR3 activation had an influence on the proliferative activity of MC. As shown in Figure 2, stimulation with the CXCR3 ligands IP-10/CXCL10 and Mig/CXCL9 had an increasing effect on the proliferation of human MC in concentrations 10 to 250 ng/ml (Figure 2). Incubation period was 24 hours. Figure 2 Dose-dependent effect of IP-10/CXCL10 and Mig/CXCL9 on proliferation of human MCIncubation of human MC for 24 hours with various concentrations of IP-10/CXCL10 and Mig/CXCL9 (10 ng/ml, 100 ng/ml, 250 ng/ml) induces proliferation of MC in a dose-dependent manner. Cell proliferation was analyzed with the MTT assay as described in Methods. Cells growing under standard conditions served as control. Changes in proliferative activity are given as relative values to the respective controls. Each bar represents a mean ± SEM of 5 parallel incubations for each condition. Statistically significant differences to the control are depicted with * = p < 0.05 and ** = p < 0.01, resp. Comparable results were obtained in three series of independent experiments. RANTES/CCL5 and Met-RANTES have no effect on proliferation of human MC Incubation of human MC with various concentrations of the CCR1 ligands RANTES/CCL5 (10 ng/ml, 50 ng/ml, 100 ng/ml, 250 ng/ml) and Met-RANTES (10 ng/ml, 50 ng/ml, 100 ng/ml, 250 ng/ml) had no effect on the proliferation of MC at an incubation period of 24 hours under standard conditions (Figure 3). To induce chemokine receptor CCR1 expression cells were pretreated with a combination of IFN-γ (20 ng/ml), TNF-α (25 ng/ml) and IL-1β (10 ng/ml) for 24 hours prior to adding test substances. Stimulation with RANTES/CCL5 or Met-RANTES without pretreatment with cytokines has no effect on proliferation (data not shown). Figure 3 Effect of RANTES/CCL5 and Met-RANTES on proliferation of human MCIncubation of human MC for 24 hours with various concentrations of RANTES/CCL5 (10 ng/ml, 100 ng/ml, 250 ng/ml, 1 μg/ml, 10 μg/ml) and Met-RANTES (10 ng/ml, 100 ng/ml, 250 ng/ml, 1 μg/ml, 10 μg/ml) has no effect on proliferation of MC under standard conditions. To induce expression of chemokine receptor CCR1 MC were prestimulated with IFN-γ (20 ng/ml), TNF-α (25 ng/ml) and IL-1β (10 ng/ml) for 24 hours prior to adding test substances. Cell proliferation was analyzed with the MTT assay as described in Methods. Cells growing under standard conditions served as control. Changes in proliferative activity are given as relative values to the respective controls. Each bar represents a mean ± SEM of 7 parallel incubations for each condition. Comparable results were obtained in three series of independent experiments. Effects of SLC/CCL21, IP-10/CXCL10, Mig/CXCL9, RANTES/CCL5 and Met-RANTES on apoptosis of human mesangial cells SLC/CCL21 has an anti-apoptotic effect on Fas/CD95-induced apoptosis The effect of CCR7 activation for MC survival in Fas/CD95-induced cell death was studied with three different methods. Flow cytometric cell cycle analysis using propidium iodide staining showed 8.3 ± 1.1% apoptotic cells under normal conditions. After serum-starvation and incubation with IFN-γ for 48 hours human MC express Fas/CD95 on surface (data not shown). Under these conditions the population with a sub-G1 DNA content was 20.4 ± 3.6% (Figure 4A). Serum-starvation and stimulation with IFN-γ showed no difference in the number of apoptotic MC compared to serum-starvation without stimulation with IFN-γ (data not shown). Subsequent stimulation with an activating anti-Fas antibody increased the amount of MC with a sub-G1 DNA content to 50.1 ± 3.6% consistent with a marked increases in apoptosis (Figure 4B). When MC were prestimulated with SLC/CCL21 prior to induction of cell death the percentage of apoptotic cells was reduced markedly to 31.2 ± 4.4% (Figure 4C). The results are from four independent experimental series. The data are shown in Table 1. Staining with Hoechst visualizes cells with fragmented chromatin. Figure 5 shows a fluorescent microscopic analysis of MC. Figure 5A represents cells stimulated with IFN-γ and Figure 5B cells stimulated with IFN-γ and Fas ligation. Figure 5C shows a significantly reduced number of apoptotic cells when MC were prestimulated with SLC/CCL21 prior to induction of cell death. Apoptotic nuclei were analyzed microscopically in three different sets of experiments counting at least 300 cells per condition. After serum starvation and IFN-γ stimulation 9.4 ± 2.5% of the cells were found to be apoptotic. Subsequent Fas ligation induced apoptosis in 37.3 ± 3.2% of human MC. Prestimulation with SLC/CCL21 reduced Fas-induced cell death effectively to 15.0 ± 2.6% (Figure 6A). Induction of cell death of MC by Fas ligation increased Caspase-3 activity 2.8-fold compared with control conditions. Coincubation with SLC/CCL21 reduced caspase-3 activity significantly (Figure 6B) Figure 4 Effect of SLC/CCL21 on Fas-induced cell death of human MC in cell cycle analysis by flow cytometry. Percentage of apoptotic cells was analyzed in cell cycle analysis by flow cytometry after staining with propidium iodide. Histograms represent cell counts (y-axis) versus DNA content (x-axis) with the percentage of apoptotic cells containing sub-G1 DNA indicated. Figure 4A shows the cell cycle analysis of MC after serum starvation and stimulation with IFN-γ for 48 hours. Figure 4B: Apoptosis was induced subsequently by Fas ligation. Figure 4C: Effect of preincubation with SLC/CCL21 250 ng/ml on Fas-induced apoptosis of MC. The profiles shown are representative for four independent experiments. Table 1 Effect of SLC/CCL21, IP-10/CXCL10, Mig/CXCL9, RANTES/CCL5 and Met-RANTES on Fas-induced cell death of human mesangial cells. IFN IFN/aFas IFN/aFas/ SLC IFN IFN/aFas IFN/aFas/IP-10 IFN/aFas/Mig IFN IFN/aFas IFN/aFas/RANTES IFN/aFas/Met-RANTES 15.8 47.2 24.7 11.1 36.7 21.1 25.8 18.3 40.9 31.6 35.5 22.4 53.1 33.7 16.3 33.1 25.3 28.2 19.3 34.1 40.7 41.8 23.9 53.4 34.1 20.2 29.9 28.7 28.9 24.8 46.2 39.6 35.5 19.6 46.8 32.4 13.5 25.9 31.1 36.3 16.2 37.9 34.2 42.6 Mean 20.4 50.1 31.2 15.3 31.4 26.6 29.8 19.7 39.8 36.5 38.1 SEM 3.6 3.6 4.4 3.9 4.6 4.3 4.5 3.7 5.1 4.3 4.9 Data of four separate FACS analyses, respectively. Values are means ± SEM. Figure 5 Effect of SLC/CCL21 on Fas-induced cell death of human MC in Hoechst stain on a fluorescent microscopic analysis of MC (A) MC stimulated with IFN-γ. (B) MC stimulated wit IFN-γ and Fas ligation. (C) Significantly reduced number of apoptotic cells when MC were prestimulated with SLC/CCL21 prior to induction of cell death. Figure 6 Effect of SLC/CCL21 on Fas-induced cell death of human MC in cell count and caspase-3 assay. (A) The percentage of apoptotic MC was determined after visualisation of fragmented chromatin with Hoechst dye. Apoptotic nuclei were analyzed microscopically in three different sets of experiments counting at least 300 cells per condition. (B) Caspase-3 activity was quantitated spectrophotometrically in MC lysates. Data are from three independent sets of experiments, each performed in duplicate. Statistically significant differences are depicted: * = p < 0.05 and ** = p < 0.01, resp. IP-10/CXCL10 and Mig/CXCL9 have no effect on Fas/CD95-induced cell death of human MC To investigate the effect of the CXCR3 ligands IP-10/CXCL10 and Mig/CXCL9 on Fas-induced cell death MC were grown without serum and stimulated with IFN-γ. Induction of cell death by an activating anti-Fas-antibody led to significantly increased number of apoptotic cells. Coincubation with IP-10/CXCL10 or Mig/CXCL9 had no effect on Fas-induced apoptosis of MC (Figure 7). The data of four separate experiments are shown in Table 1. Figure 7 Effect of IP-10/CXCL10 and Mig/CXCL9 on Fas-induced cell death of human MC in cell cycle analysis by flow cytometry. Percentage of apoptotic cells was analyzed in cell cycle analysis by flow cytometry after staining with propidium iodide. Histograms represent cell counts (y-axis) versus DNA content (x-axis) with the percentage of apoptotic cells containing sub-G1 DNA indicated. Cell cycle analysis of MC was performed after serum starvation and stimulation with IFN-γ for 48 hours. (A) Cell cycle analysis of MC after serum starvation and stimulation with IFN-γ for 48 hours. (B) Apoptosis was induced subsequently by Fas ligation. (C, D) Preincubation with IP-10/CXCL10 and Mig/CXCL9 has no effect on Fas-induced apoptosis of MC. The profiles shown are representative for four independent experiments. RANTES/CCL5 and Met-RANTES have no effect on Fas/CD95-induced cell death of human MC To induce expression of chemokine receptor CCR1 cells were pretreated with a combination of IFN-γ (20 ng/ml), TNF-α (25 ng/ml) and IL-1β (10 ng/ml) for 24 hours, followed by serum-starvation and incubation with IFN-γ for 48 hours (Figure 8A). Cell death was induced again by stimulation with an activating anti-Fas antibody (Figure 8B). Stimulation with RANTES/CCL5 (250 ng/ml) (Figure 8C) or Met-RANTES (250 ng/ml) (Figure 8D) prior to induction of cell death was ineffective in maintaining cell survival. The results are from four independent experimental series (See Table 1). The percentage of apoptotic cells varies between the experiments due to biological variance. Figure 8 Effect of RANTES/CCL5 and Met-RANTES on Fas-induced cell death of human MC in cell cycle analysis by flow cytometry. Percentage of apoptotic cells was analyzed in cell cycle analysis by flow cytometry after staining with propidium iodide. Histograms represent cell counts (y-axis) versus DNA content (x-axis) with the percentage of apoptotic cells containing sub-G1 DNA indicated. (A) Cell cycle analysis of MC was performed after serum starvation and stimulation with IFN-γ for 48 hours. (B) Apoptosis was induced subsequently by Fas ligation. Prior to serum starvation the expression of chemokine receptor CCR1 was induced by pretreatment with a combination of IFN-γ (20 ng/ml), TNF-α (25 ng/ml) and IL-1β (10 ng/ml) for 24 hours. (C,D) RANTES/CCL5 250 ng/ml and Met-RANTES 250 ng/ml have no effect on Fas-induced cell death of MC. Discussion Proliferation and apoptosis play a pivotal role in a variety of biological processes, such as morphogenesis during the embryonic stage, cell selection during lymphoid development, tissue repair after injury, regression of inflammation, elimination of cells at risk of developing into a tumor and lymphocyte-mediated killing [19]. A balance of proliferation and apoptosis is essential for the tissue homeostasis [20,21]. Apoptosis as mechanism of controlled cell death is a well-controlled process and progresses through a series of morphological and biochemical phases, including chromatin condensation and activation of proteolytic enzymes [22,23]. A number of mediators involved in the induction of apoptosis have been identified during recent years. Probably the most thoroughly characterized death receptor is the cell membrane receptor Fas (CD 95), a member of the TNF receptor family. Cross-linking of Fas, either via specific antibodies or via its specific ligand, activates a cascade reaction of caspases, which are responsible for induction of membrane alterations, breakdown of cellular constituents and DNA, and finally cell death [19]. In the kidney beside the damage induced by infiltrating inflammatory cells the relationship among resident cell proliferation and apoptosis in glomeruli determines the outcome in various glomerulonephritides [13]. Several groups reported that apoptosis plays an important role in the repair process in experimental and human glomerulonephritis [24,25]. Apoptosis has an additional role in the sclerosing process in the glomeruli [26]. Cell number abnormalities are frequent in renal diseases, and range from the hypercellularity of postinfectious glomerulonephritis to the cell depletion of chronic renal atrophy. Death ligands and receptors, such as TNF and Fas-ligand, pro-apoptotic and anti-apoptotic Bcl-2 family members and caspases have all been shown to participate in apoptosis regulation in the course of renal injury [27]. Some reports suggest that altered apoptotic signaling and regulatory mechanisms contribute to further progressive renal impairment, tubular atrophy, interstitial fibrosis, and glomerulosclerosis in a model of focal and segmental glomerulosclerosis in rats [28]. In the glomerulus a balance between endothelial, mesangial and visceral epithelial cells and their extracellular matrix is essential for structural and functional integrity. During glomerular injury function and morphology of these cells are altered. Intrinsic cell proliferation in the glomerulus is regulated by a large number of mediators and growth factors like IL-10 [29], insulin-like growth factors [30] and platelet derived growth factor [31]. Some of these factors also influence apoptosis in the glomerulus [32]. The basis for the experiments performed in this work was the hypothesis that chemokines and chemokine receptors expressed by intrinsic renal cells may be involved both in the maintenance of glomerular homeostasis in normal adult human kidney and regulation of glomerular cell numbers during disease states. We previously showed constitutive expression of CCR7 protein and its ligand secondary lymphoid tissue chemokine (SLC/CCL21) in human renal tissue. In immunohistochemistry we found a clear staining pattern for SLC/CCL21 on podocytes and CCR7 on MC during nephrogenesis and in adult kidney. Also constitutive mRNA expression has been shown for CCR7 in cultured MC and for SLC/CCL21 in isolated human glomeruli. Furthermore it was demonstrated that mesangial CCR7 is functionally active since for example SCL/CCL21 induced a dose-dependent migration of MC [12]. We therefore investigated the influence of chemokines on MC growth and found an significant increase in proliferation of MC after stimulation with SLC/CCL21 in a dose-dependent manner. To study the role of SLC/CCL21 in MC apoptosis cell death was induced by activating mesangial Fas/CD95 receptors. SLC/CCL21 was found to prevent MC apoptosis as shown by cell cycle analysis and Hoechst stain. Since caspase-3 assays revealed impaired activity it can be assumed that this molecule is involved in chemokine-influenced intracellular apoptosis pathways in MC. The finding of an anti-apoptotic function of SLC/CCL21 is novel. At present SLC/CCL21 is known to be constitutively produced by high endothelial venules and stromal cells within T cell zones of lymph nodes [33]. Its corresponding receptor CCR7 is expressed on naive T cell subpopulations and up-regulated by maturing dendritic cells [34]. Therefore this chemokine/chemokine receptor pair was described as an prototypic model for the homing of immune cells to lymphoid tissue [35,36]. Anti-apoptotic effects of chemokines seem not to be restricted to SLC/CCL21. The CX3CR1-binding chemokine fractalkine which is constitutively expressed on neuronal cells has been demonstrated as survival factor for brain microglia in Fas-induced cell death [37]. A role of the chemokine receptor CXCR3 in inflammatory glomerular disease has been proposed before by the group of Romagnani et al. since a mesangial expression of CXCR3 in biopsies from patients with mesangioproliferative glomerulonephritis could be demonstrated [11]. We therefore investigated the effects of the CXCR3 ligands IP-10/CXCL10 and Mig/CXCL9 on MC and also found a concentration dependent increase of proliferation of MC after stimulation with these chemokines. Interestingly, in contrast to the effect observed with SLC/CCL21 both IP-10/CXCL10 and Mig/CXCL9 had no effect on Fas induced apoptosis of MC. The third chemokine receptor of interest was CCR1 since our group has demonstrated functionally active expression of CCR1 on human MC, inducible after stimulation with a combination of the proinflammatory cytokines TNF-α, IL-1β and IFN-γ [9]. Futhermore upregulation of CCR1 expression is also known in an animal model for immune complex glomerulonephritis [38]. In contrast to the effects observed with CCR7 and CXCR3 ligands, stimulation of MC with the CCR1 ligand RANTES/CCL5 had no effect on cell proliferation and apoptosis. In this context an article of Topham et al. is of special interest. This group demonstrated that CCR1 may have anti-inflammatory functions since mice negative for CCR1 showed enhanced Th1 immune responses and worsened histology in a model of nephrotoxic serum nephritis [39]. Our group showed in a model of horse apoferritin (HAF)-induced glomerulonephritis that CC chemokine ligand 5/RANTES chemokine antagonists aggravate glomerulonephrtis despite reduction of glomerular leukocyte infiltration. These findings were associated with an enhancing effect of the CCL5/RANTES analogs on the macrophage activation state in vitro and in vivo. The humoral response and the Th1/Th2 balance in HAF-glomerulonephritis and mesangial cell proliferation in vitro were not affected by the CCL5/RANTES analogs [40]. Therefore also the effects of the CCR1 blocker Met-RANTES were studied but showed no influence on MC proliferation and apoptosis. Conclusions In summary it is tempting to speculate that the different effects of SLC/CCL21 and IP-10/CXCL10 and Mig/CXCL9 on proliferation and apoptosis of MC represent specialized functions of chemokine receptos on non-immune cells. CCR7 could be a chemokine receptor important for the development of the glomerular architecture during ontogenesis and for maintaining glomerular homeostasis in adult human kidney. CXCR3 may have its main functions important for mesangial expansion in mesangioproliferative disease. In contrast, the chemokine receptor CCR1 and its ligands RANTES/CCL5 and Met-RANTES seems not to have a special impact for the regulation of proliferation and apoptosis on MC. CCR1 may be important for local immunomodulation especially in glomerular inflammation. The chemokines and their receptors we have analyzed seem to be part of a complex system of factors which regulate proliferation and apoptosis in kidney and therefore play a pivotal role in regulation of glomerulogenesis, during glomerular injury and in homeostatic balance in the glomerulum. Studying these locally synthesized chemokines and their interaction with corresponding receptors on non-immune cells deserves further investigation and will reveal novel chemokine/chemokine receptor functions far beyond their orignal functions in guiding inflammatory cells to sites of tissue injury. Competing interests None declared. Authors' contributions All authors were involved in experimental procedures and manuscript preparation. Abbreviations mesangial cell (MC); chemokines monocyte chemoattractant protein-1 (MCP-1); regulated upon activation, normal T cell expressed and secreted (RANTES/CCL5); interferon-γ (IFN-γ)-inducible protein of 10 kD (IP-10/CXCL10); monokine induced by IFN-γ (Mig/CXCL9); (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements This work was supported in part by grants from the Ludwig-Maximilians-University Munich (FöFoLe 231) to Markus Wörnle and the Deutsche Forschungsgemeinschaft (BA 2137/1-1) to Bernhard Banas ==== Refs Luster AD Chemokines: Chemotactic cytokines that mediate inflammation N Engl J Med 1998 338 436 445 9459648 10.1056/NEJM199802123380706 Muller A Homey B Soto N Ge N Catron D Buchanan ME McClanahan T Murphy E Yuan W Wagner SN Barrera JL Mohar A Verastegui E Zlotnik A Involvement of chemokine receptors in breast cancer metastasis Nature 2001 410 50 56 11242036 10.1038/35065016 Tachibana K Hirota S Iizasa H Yoshida H Kawabata K Kataoka Y Kitamura K Matsushima N Yoshida N Nishikawa S Kishimoto T Nagasawa T The chemokine receptor CXCR4 is essential for vascularization of the gastrointestinal tract Nature 1998 393 591 594 9634237 10.1038/31261 Zou YR Kottmann AH Kuroda M Taniuchi I Littmann DR Function of the chemokine receptor CXCR4 in haematopoiesis and in cerebellar development Nature 1998 393 595 599 9634238 10.1038/31269 Mackay CR Chemokines What chemokine is that? 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==== Front BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-281526523110.1186/1471-2458-4-28Research ArticleSmoking cigarettes of low nicotine yield does not reduce nicotine intake as expected: a study of nicotine dependency in Japanese males Nakazawa Atsuko [email protected] Masako [email protected] Kotaro [email protected] Division of Health Check-up, Kyoto First Red Cross Hospital 15 Honmachi, Higashiyama-ku, Kyoto, 605-0981, Japan2 Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine Graduate School of Medical Science, Kawaramachi Hirokoji, Kamigyo-ku, Kyoto, 602-8566, Japan2004 20 7 2004 4 28 28 27 2 2004 20 7 2004 Copyright © 2004 Nakazawa et al; licensee BioMed Central Ltd.2004Nakazawa et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Many Japanese believe that low-yield cigarettes are less hazardous than regular cigarettes, and many smokers consume low-yield cigarettes to reduce their risks from smoking. We evaluate the association between actual nicotine intake and brand nicotine yield, and the influence of nicotine dependence on this association. Methods The study subjects included 458 Japanese male smokers, aged 51.2 ± 9.9 years, who participated in health check-ups in a hospital in 1998 and 2000. Each subject filled out a self-administered smoking questionnaire and the score of each on the Fagerström Test for Nicotine Dependence was calculated. Urinary cotinine concentration was measured at the time of participation. Results The geometric mean of urinary cotinine concentration was 535 ng/mgCr for those who smoked brands with the lowest nicotine (0.1 mg on the package), compared with 1010 ng/mgCr for those who smoked brands with the highest (0.9–2.4 mg, weighted mean of 1.1 mg). Thus, despite the 11-fold ratio of nicotine yield on the packages, the ratio of urinary cotinine level was less than twofold. Both nicotine yield on the package and nicotine dependence significantly increased urinary cotinine concentration, and the negative interaction between them almost attained statistical significance. Cotinine concentration in heavily dependent smokers was consistently high regardless of the nicotine yield of brands. Conclusions The nicotine yield of cigarettes measured by machine-smoking does not reliably predict the exposure of smokers. Smokers consuming low-yield nicotine cigarettes did not reduce actual intake of nicotine to the level that might be expected, especially for those heavily dependent on nicotine. Current labeling practices are misleading for the two-third of smokers who are moderately or highly dependent on nicotine. ==== Body Background 'Low-yield nicotine' cigarettes, which have brand names that include 'light,' 'mild,' or similar words, and which have nicotine yields on their packages of 0.8 mg or less, are widely consumed inside and outside Japan, and their market share is increasing. The Tobacco Institute of Japan reported that, in 2001, of the 20 top brands that share 84% of the total cigarettes consumed in Japan, 6.9% had a nicotine yield reported on their package of 0.1 mg, 73.4% had a yield of 0.2–0.8 mg, and 19.7% had a yield of 0.9 mg or higher [1]. The average nicotine yield of these 20 top brands was 0.8 mg, weighted by number of cigarettes consumed [1]. Many smokers would like to avoid the health risks associated with smoking, but not want to quit. These individuals would like to use less hazardous cigarettes or cigarettes that cause less irritation to their throats [2]. In response, the tobacco industry has developed low-yield nicotine brands [3-5]. There have been many studies examining whether low-yield cigarettes are less hazardous than regular brands. For example, in the 1980s and 90s, the actual intake of nicotine [6-9], as well as tar and carbon monoxide [7,9], from smoking low-yield brand cigarettes was similar to that from high-yield brands. More recent studies, which have included ultra-low yield cigarettes (0.1 mg nicotine yield on the package), have shown similar results [10,11]. In addition mortality from lung cancer in the United States has not decreased over the past 30 years, although low-yield brand cigarette increased in market share during that time [12]. Thus consumers of low-yield cigarettes are at a higher health risk than they expected. It has been assumed that high nicotine levels in the blood of smokers of low-yield cigarettes are caused by compensatory behavior due to nicotine dependence. Most of these comparisons have been determined in Western countries. Fewer comparisons have been reported in Japan, and nicotine dependence was partially taken into account during analysis [11]. In Japan, the rate of smoking is still high, being >50% among males[13]. These smokers are likely to consume low-yield cigarettes and to decrease the number of cigarettes consumed in order to reduce the health risks of smoking. For example, a study of smokers in a medical school showed that about 70% of males and 100% of females consumed low-yield nicotine brands [14]. In 1999, some physicians recommended that smokers change to low-yield nicotine cigarettes as the first step toward quitting [15]. In 2000, however, a TV program on scientific issues in Japan reported that low-yield nicotine cigarettes did not reduce the health hazards of smoking [16]. In 2002, the Ministry of Health, Labour and Welfare of Japan published in major Japanese newspapers the findings of a study showing that the nicotine and tar yield of 7 popular cigarette brands in Japan measured by simulating the manner of actual human smoking was larger than that obtained by machines using the Federal Trade Commission (FTC) method [17,18]. Two days later, Japan Tobacco Inc. advertised in these newspapers that the FTC method was authorized throughout the world and that the nicotine yield on the cigarette package was valid for consumers [19]. Thus, issues regarding nicotine yield are less known in Japan than in Western countries. Moreover tobacco companies seem to target young people, especially young women, by intensive advertisement of low-yield cigarette brands [20]. It is important, therefore, to emphasize to smokers the health hazards of low-yield nicotine cigarettes, but evidence in Japanese smokers is still scarce [11]. In the present study, we examined the relationship between nicotine yield and nicotine metabolites excreted in the urine, and the influence of nicotine dependence on this relationship among the Japanese male smokers. Methods The subjects of this study were male smokers who participated in a health check-up at the Kyoto First Red Cross Hospital from July to December in 1998, or from January to February in 2000. The latter subjects were supplementary to the main group, but the two groups exhibited similar demographics. Smokers were recruited using a routine health check-up questionnaire and were defined in this study as those who smoked at least one cigarette per day. Of the 1,579 male participants in the health check-up during the study period, 513 were identified as smokers, and 479 agreed to participate in this study. Each participant filled out a self-administered questionnaire, which was checked during an interview with a physician. This questionnaire included questions determining score on the Fagerström Test of Nicotine Dependence (FTND) [21]. These included questions on the number of cigarettes smoked per day, time from awaking to the first cigarette, difficulty in refraining from smoking in places where smoking is forbidden, the number of cigarettes smoked during the morning compared with the number smoked during the rest of the day, cigarettes that could not be give up, and smoking for most of the day while ill in bed. Also, included were questions about the brand(s) of cigarettes smoked, inhalation pattern (deep inhalation, some deep inhalations, or no inhalation), attempt to quit smoking, and stage of behavioral change in the quitting process [22]. Smokers who would continue to smoke during their lifetimes were defined as being on precontemplation-1, and smokers who would continue to smoke for at least one year further but who would quit smoking some day were defined as being on precontemplation-2. About three quarter of plasma nicotine is converted to cotinine, which is excreted in the urine. The half-life of nicotine is 30 minutes [23] and that of cotinine is about 20 hours [24]. Measurement of cotinine in the plasma, urine is widely used to assess the level of nicotine intake [25]. Therefore, each subject's urinary cotinine concentration was measured. Actual nicotine intake was evaluated from urinary cotinine concentration adjusted for urinary creatinine concentration. Although collection of urine over 24 hours may represent nicotine intake more accurately than a spot urine test, for practical reasons we measured cotinine concentration in the first urine in the morning, as this can reflect smoking from the previous day. Each participant was asked to fast from 21:00 the night before until urine was collected around 9:00 the following morning. The urine samples were frozen at -80°C with in the same day and transported to SRL Laboratory, Hachioji, Tokyo, at which cotinine was measured by gas chromatography [26,27]. For machinery nicotine yield by the FTC method, we used the value indicated on the cigarettes packages. Statistical analysis was performed using data from 458 male smokers who completed the FTND question and whose urinary cotinine levels were measured. We compared the characteristics of subjects among three groups categorized by machine-measured nicotine yield (0.1 mg, 0.2–0.8 mg, 0.9+ mg). For each group, we calculated mean machine-measured nicotine yield weighted by the number of subjects. Log-transformed data were used for the urinary cotinine concentrations because it was distributed log-normally. Means were compared using Student's t-test or analysis of variance, and proportions were determined using the chi-square test. The effects on urinary cotinine concentration of machine-measured nicotine yield, number of cigarettes consumed per day, and nicotine dependence were analyzed using a regression model, in which urinary cotinine concentration was the dependent variable, and two of the other parameters were independent variables. Main effect and interaction were evaluated by regression coefficients and partial correlation coefficients. The effect of different cigarette brands was also examined. All statistical procedures were performed by SPSS [28]. A P value <0.05 was considered statistically significant. Results Of the 458 subjects, 87 (19.0%) smoked cigarette brands yielding 0.1 mg nicotine, 223 (48.7%) smoked cigarettes of 0.2–0.8 mg, and 148 (32.3%) smoked cigarettes of 0.9+ mg (Table 1). The highest machine-measured nicotine yield for cigarettes consumed by the subjects was 2.4 mg. The weighted mean of brands yielding 0.2–0.8 mg was 0.5 mg, whereas the weighted mean of brands yielding 0.9–2.4 mg nicotine was 1.1 mg. The subjects ranged in age from 23 to 83 years, and the number of cigarettes consumed per day was 1 to 60. Smokers of brands yielding nicotine of 0.1 mg were slightly older than those smoking brands yielding 0.2–0.8 mg and of 0.9–2.4 mg nicotine (p = 0.08). These two groups did not differ with respect to the numbers of cigarettes consumed per day and the FTND score (p = 0.93 and p = 0.20, respectively). Table 1 Characteristics of the subjects by machine-measured nicotine yield of cigarette Characteristics Machine-measured nicotine yield (mg/cigarette) Total 0.1 0.2–0.8 0.9–2.4 p Number of subjects 87 223 148 458 Mean and SD of mahchine-measured nicotine yield (mg/cigarette) 0.1 0.5 ± 0.2 1.1 ± 0.4 0.6 ± 0.4 Mean and SD of age 53.6 ± 10.4 50.6 ± 9.34 50.8 ± 10.4 51.2 ± 9.9 0.07 Mean and SD of number of cigarettes per day 23.4 ± 12.2 24.5 ± 10.7 24.4 ± 9.5 24.4 ± 10.6 0.93 Mean and SD of FTND 5.1 ± 2.5 5.4 ± 2.3 5.6 ± 2.0 5.4 ± 2.2 0.21 Number of smokers at each category of FTND   FTND score 0–3 19 (21.8%) 47 (21.1%) 21 (14.2%) 87 (19.0%)   FTND score 4–6 40 (46.0%) 100 (44.8%) 78 (52.7%) 218 (47.6%) 0.41   FTND score 7–10 28 (32.2%) 76 (34.1%) 49 (33.1%) 153 (33.4%) Number of smokers having attempted to quit 50 (61.0%) 132 (59.5%) 84 (56.8%) 269 (58.9%) 0.73 Number of smokers at each stage of behavioral change in quitting process   Precontemplation-1 16 (18.6%) 47 (21.3%) 58 (39.7%) 121 (26.7%) 0.001   Precontemplation-2 48 (55.8%) 131 (59.3%) 62 (42.5%) 241 (53.2%)   Contemplation 20 (23.3%) 33 (14.9%) 23 (15.6%) 76 (16.8%)   Preparation 2 (2.3%) 10 (4.5%) 3 (2.1%) 15 (3.3%) Mean and SD of urinary cotinine concentration (mean-SD, mean+SD*) 535 (1782,160) 770 (1981,299) 1010 (2071,492) 784 (484, 1264) <0.001 FTND:Fagerström Test for Nicotine Dependence P for difference is examined by analysis of variance or chi-square test * Back-transformation of log-transformed data Of all subjects, 153 (33.4%) were heavily dependent on nicotine (FTND score>= 7), whereas 87 (19.0%) had low dependence (FTND score< = 3). About 60% of all subjects had attempted to quit, with the proportion similar in the high- and low-nicotine dependent groups (p = 0.73). The stage of behavioral change was different (p = 0.001), however, with smokers of cigarettes yielding 0.1 mg of nicotine being at more advanced stages. The geometric mean of urinary cotinine concentration in all subjects was 784 ng/mg creatinine (Cr), with a distribution of 484 ng/mg Cr (mean-SD) to 1264 ng/mg Cr (mean+SD), as determined by back-transformation of log-transformed data (range; 10–4770 ng/mgCr). The levels differed significantly between the machine-measured nicotine yield groups (p < 0.001). Urinary cotinine levels did not differ among smokers of individual brands of yielding 0.1 mg of nicotine (p = 0.51 by analysis of variance to adjust for number of cigarettes consumed per day). After integration of similar brands, geometric cotinine concentration means were 686 ng/mgCr for those who smoked American brands and 460 ng/mgCr for those who smoked Japanese brands (p = 0.19). Urinary cotinine concentrations also did not differ among smokers of individual cigarettes brands yielding 0.2–0.8 mg nicotine (p = 0.71, adjusted for number of cigarettes and nicotine yield). The geometric means were 823 ng/mgCr for smokers of Japanese brands and 724 ng/mgCr for smokers of American brands. Mentholated cigarettes were consumed by only 5 subjects and were therefore not examined. When we assayed the relationship between urinary cotinine concentrations and number of cigarettes consumed per day by machine-measured nicotine yield of cigarettes, we found that cotinine concentrations were related to number of cigarettes consumed per day (Figure 1). The correlations were different between machine-measured nicotine yield groups, in that there was a stronger correlation for the low nicotine-yield group. There was some negative interaction between the number of cigarettes smoked and machine-measured nicotine yield (Table 2 upper). When the data restricted with <30 cigarettes consumed per day in which the relationship was assumed to be linear (n = 394), the regression coefficient for machine nicotine yield was 0.834 (p = 0.006), 0.074 (p < 0.001) for number of cigarettes consumed per day, and -0.017 (p = 0.20) for interaction term. Thus, among smokers who consumed a small number of cigarettes, cotinine level of those who smoked high nicotine cigarettes was considerably higher than the level of those who smoked low nicotine cigarettes. In contrast, cotinine level differed little among smokers who consumed 40–60 cigarettes per day, regardless of machine-measured nicotine yield of cigarettes. Figure 1 Number of cigarettes per day and urinary cotinine concentration by machine yield of nicotine Table 2 Regression coefficients and partial correlation coefficients for urinary cotinine concentration in a multiple regression model Variable Regression coefficients Partial correlation coefficients B p r p Figure 1 Intercept 5.270 <0.001 Nicotine yield by machine 0.762 0.001 0.23 <0.001 Number of cigarettes consumed 0.045 <0.001 0.43 <0.001 Interaction -0.012 0.16 Figure 2 Intercept 4.994 <0.001 Nicotine yield by machine 0.793 0.001 0.18 <0.001 FTND score 0.268 <0.001 0.52 <0.001 Interaction -0.079 0.057 FTND: Fagerström Test for Nicotine Dependence When we assayed the relationship between urinary cotinine concentrations and machine-measured nicotine yield of cigarettes by FTND score (Figure 2), we found little difference between machine-measured nicotine yield groups among heavily nicotine dependent smokers, although there was a correlation between urinary cotinine concentration and nicotine yield among smokers with low dependence. According to the regression model, there was an almost significant negative interaction between FTND score and machine-measured nicotine yield (Table 2, lower). Figure 2 Geometric means of urinary cotinine concentration by nicotine yield and nicotine dependence The ratio of mean nicotine yield was 0.45 for cigarettes yielding 0.2–0.8 mg nicotine, and 0.09 for cigarettes yielding 0.1 mg nicotine, compared with the brands yielding 0.9–2.4 mg (Table 3). In contrast, the ratio of mean cotinine concentration was 0.76 for those who smoked cigarettes yielding 0.2–0.8 mg nicotine, and 0.53 for those who smoked cigarettes yielding 0.1 mg nicotine, compared with those who smoked cigarettes yielding 0.9–2.4 mg nicotine. Among heavily dependent smokers the ratios of urinary cotinine concentration were much nearer to 1 (0.92 and 0.85, respectively) than among smokers with low dependence (0.58 and 0.32, respectively). Table 3 Ratios of mean urinary cotinine concentration for nicotine yield by nicotine dependence Machine measured nicotine yield Ratio of mean urinary cotinine concentration FTND score Category (mean) (mg) Ratio of mean nicotine yield Total 0–3 4–6 7–10 0.9–2.4 (1.1) 1 1 (560) 1 (993) 1 (1333) 1 (1010) 0.2–0.8 (0.5) 0.45 0.58 (327) 0.81 (808) 0.92 (1226) 0.76 (770) 0.1 (0.1) 0.09 0.32 (179) 0.53 (529) 0.85 (1138) 0.53 (535) Numbers in parentheses are geometric means of urinary cotinine concentration (ng/mgCr) FTND:Fagerström Test for Nicotine Dependence Self-reported inhalation patterns did not influence the average urinary cotinine concentration (p = 0.54) when the variable of inhalation pattern was added to the above model with nicotine yield and FTND score. Discussion In Japan, low nicotine-yield cigarettes seem to be recognized as less hazardous, and smokers likely think that the hazards of smoking are directly proportional to nicotine or tar yield shown on the cigarette packages. This is supported by the results of the present study, which indicate that smokers of low-yield nicotine cigarettes were more advanced behaviorally in wishing to quit. This is additionally supported by circumstantial evidence [1-5,14-18] and by our experience in a check-up clinic, despite the paucity of formal studies of this issue in Japan. We have shown here that smokers of low nicotine cigarettes did not reduce their actual intake of nicotine to the degree that would be expected from the nicotine yield on the packages. Although smokers of cigarettes yielding 0.1 mg nicotine would be expected to ingest one-eleventh of the nicotine ingested by smokers of cigarettes yielding 0.9–2.4 mg nicotine (average of 1.1 mg), the average urinary cotinine concentration of the former group was more than half that of the latter (535 ng/mg Cr vs. 1010 ng/mg Cr). Moreover, smokers of cigarettes yielding 0.2–0.8 mg nicotine (average of 0.5 mg) had about a 25% decrease in urinary cotinine concentration (770 ng/mg Cr) compared with smokers of cigarettes yielding 0.9–2.4 mg nicotine, despite the two-fold reduction expected from the nicotine yield on the packages. These differences were even smaller in smokers who consumed large numbers of cigarettes per day as well as in smokers with heavy nicotine dependence. The number of cigarettes consumed per day is regarded as a component of nicotine dependence and is included in FTND. There were negative interactions between machine-measured nicotine yield and number of cigarettes consumed per day, and between machine-measured nicotine yield and nicotine dependence. In particular, smokers with heavy nicotine dependence tended to have a high urinary cotinine concentration (about 1200 ng/mgCr) despite differences in machine-measured nicotine yield of cigarettes, which may explain this negative interaction. In contrast, the actual nicotine intake of smokers who consumed small numbers of cigarettes and smokers with a low level of dependency was more strongly correlated with the machine-measured nicotine yield of the cigarettes they consumed. That is, those who smoked light cigarettes absorbed a smaller amount of nicotine, but, again, the amount absorbed was not equal to the difference in nicotine yields on the packages. These associations are evident in the ratios of means shown in Table 3. Significantly high values of the intercept in the regression models in Table 2 also provide an explanation for the insufficient decrease in urinary cotinine compared with the decrease in nicotine yield on the packages. We determined the full FTND score in each of our subjects, although some components of FTND, including the number of cigarettes consumed and the time from awakening until the first cigarette, were measured in the previous study of smoking in Japan [11]. It has not been previously reported that smokers with a strong dependency on nicotine showed constantly high levels of urinary cotinine regardless of nicotine yield of the cigarette brands they consumed. Moreover, we recruited a larger number of smokers of cigarettes yielding 0.1 mg nicotine than the previous report [11]. We were thus clearly able to show associations among nicotine dependency, the machine yield of nicotine, and urinary cotinine concentration. Smokers heavily dependent on nicotine obtained no advantage by smoking low-yield cigarettes. Moreover, they may actually increase their risk due to compensatory behavior, for example, by inhaling more carbon monoxide or other harmful substances contained in cigarette smoke. Our results suggest that tobacco industry advertising may have led these smokers, especially those heavily dependent on nicotine, to underestimate the health risks posed by low-yield cigarettes. This is similar to the results of other studies, which suggested that 'Light' or 'Ultra Light' cigarettes could deliver as much tar and nicotine as 'Regular' cigarettes [6-11]. The compensation mechanisms that may keep blood nicotine at a high level include more puffs per cigarette, greater volume per puff, and greater depth of inhalation, all of which may be conscious or unconscious on the part of smokers. In addition, the filters of low-yield cigarettes are sometimes treated with ammonium to increase absorption of nicotine, thus eliminating the need for deep inhalation [5]. These filters may also be processed to reduce throat irritation, for example in mentholated cigarettes, so that smokers do not realize that are inhaling deeply or frequently [5,29]. This mechanism may increase the inhalation volume, and consequently increase the absorption of carbon monoxide or other harmful substances. In our results, the urinary cotinine concentration did not differ according to self-reported inhalation pattern, suggesting that smokers regulate nicotine intake without being aware of their inhalation patterns. In this study, mentholated cigarettes were not consumed by a sufficient number of subjects for examination. Cigarette brand, however, showed no apparent difference in urinary cotinine level. Another important mechanism by which smokers of low-yield cigarettes increase their nicotine intake is by blocking the ventilation holes on the filter with their fingers or lips while holding the cigarette and smoking [29]. These holes are made to inspire fresh air and dilute the smoke. During measurement of nicotine yield by the FTC method, however, these holes are not blocked [17]. The tobacco companies have admitted that they have known of the relationship between the nicotine yield reported on the packages and the actual quantities of tobacco smoke components inhaled [30]. Low-yield brands appear to mislead smokers who want to avoid health risks without quitting smoking. Smokers who had not previously considered quitting smoking, however, were found to begin to consider quitting after learning that low-yield cigarettes are processed to make them less irritating and that they did not reduce health risks [31,32]. Most smokers of low-yield brand should be informed of these findings. The subjects of this study were participants in a so-called 'human dry dock,' a detailed health check-up system for middle-aged and elderly people, which started in Japan in the 1950's and currently enroll about 10 million [33]. Most of them are socioeconomically well off, because each subject pays seven or eight thousand yen ($70–80) on average (up to about forty thousand yen) out-of-pocket for this check-up, and most subjects get annual check-ups. Most participants in this system are health-conscious and have some knowledge about health hazards of smoking. It is likely, therefore, that the subjects of our study are similar with respect to those characteristics, although their occupation, education level, and socioeconomic state were not surveyed. Moreover, in the health check-up associated with this study, many smokers were advised to quit smoking [34], which may explain the relatively low smoking rate of male candidates for this study (32%), compared with an average of 50% or higher in the general community. Female participants were not examined in this study, mostly because smoking rate of females in our health check-up clinic is less than 10% and some participants refused to answer questions about smoking. However, the advertisement of 'light' cigarettes seems to target young people, especially young women [20], and the smoking rate among young women is increasing in Japan [13]. Thus further studies focused on young people, particularly young females, would provide important information. In conclusion, we have shown here that the difference in intake of nicotine into a smoker's body was smaller than the difference in machine-measured nicotine yield among cigarette brands. Smokers consuming cigarettes with a low nicotine yield did not reduce actual intake of nicotine to the level that they expected. This was especially true for smokers with heavy nicotine dependence. This result should be emphasized in public health messages to smokers as well as to young people likely to start smoking. Abbreviations FTND: Fagerström Test of Nicotine Dependence, FTC: Federal Trade Commission Competing interests None declared. Authors' contributions AN participated in the design of the study, collected the data, performed the statistical analysis, drafted the manuscript, and was the principal investigator. MNS conceived of the study, participated in its design, and collected the data. KO supervised the data analysis and the manuscript. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgments The authors wish to thank Professor Yoshiyuki Watanabe, Department of Epidemiology for Community Health and Medicine, Kyoto Prefectural University of Medicine Graduate School of Medical Science for his scientific advice. This study was supported in part by a Grant for Health Promotion Research from Kyoto city. A part of this study was presented in the 12th World Conference on Tobacco or Health, Helsinki, 4–8 August 2003. ==== Refs Japan Tobacco Inc JT News Release 2002 Shiffman S Pillitteri JL Burton SL Rohay JM Gitchell JG Smokers' beliefs about "Light" and "Ultra Light" cigarettes Tob Control 2001 10 i17 23 11740040 Kozlowski LT Goldberg ME Yost BA White EL Sweeney CT Pillitteri JL Smokers' misperceptions of light and ultra-light cigarettes may keep them smoking Am J Prev Med 1998 15 9 16 9651633 10.1016/S0749-3797(98)00004-X Warner KE Slade J Low tar, high toll Am J Public Health 1992 82 17 8 1536326 Wilkenfeld J Henningfield J Slade J Burns D Pinney J It's time for a change: cigarette smokers deserve meaningful information about their cigarettes J Natl Cancer Inst 2000 92 90 2 10639500 10.1093/jnci/92.2.90 Benowitz NL Hall SM Herning RI Jacob P 3rdJones RT Osman AL Smokers of low-yield cigarettes do not consume less nicotine N Engl J Med 1983 309 139 42 6866013 Gori GB Lynch CJ Analytical cigarette yields as predictors of smoke bioavailability Regul Toxicol Pharmacol 1985 5 314 26 4059592 Woodward M Tunstall-Pedoe H Do smokers of lower tar cigarettes consume lower amounts of smoke components? Results from the Scottish Heart Health Study Br J Addict 1992 87 921 8 1326360 Maron DJ Fortmann SP Nicotine yield and measures of cigarette smoke exposure in a large population: are lower-yield cigarettes safer? Am J Public Health 1987 77 546 9 3565645 Jarvis MJ Boreham R Primatesta P Feyerabend C Bryant A Nicotine yield from machine-smoked cigarettes and nicotine intakes in smokers: evidence from a representative population survey J Natl Cancer Inst 2001 93 134 8 11208883 10.1093/jnci/93.2.134 Ueda K Kawachi I Nakamura M Nogami H Shirokawa N Masui S Okayama A Oshima A Cigarette nicotine yields and nicotine intake among Japanese male workers Tob Control 2002 11 55 60 11891369 10.1136/tc.11.1.55 Thun MJ Burns DM Health impact of "reduced yield" cigarettes: a critical assessment of the epidemiological evidence Tob Control 2001 10 i4 11 11740038 Ministry of Health, Labour and Welfare Report on the Survey for Smoking and Related Health Problems, 1998 Tokyo 2000 [in Japanese]. Kawane HMT Smoking Status of Students at a Medical School, 1998–1999 Kawasaki Medical Journal 2000 26 129 134 Tomoeda T Kubota K Kurosaki R Horikawa J Imai K Moriguchi J Ezaki T Furuki K Ikeda M Yamada C Akashi K Inui S Influence of suggestion to switch into 'Ultra-Low cigarettes' [abstract] Sangyo Eiseigaku Zasshi 1999 41 420 [in Japanese] NHK Science Eye [TV program] Tokyo 4 November 2000. Shopland DR The FTC Cigarette Test Method for Determining Tar, Nicotine and Carbon Monoxide Yields of U. S. Cigarettes 1996 DIANE Publishing Co Asahi Shinbun (Newspaper) Amount of nicotine and tar by simulation of actual human smoking is larger than the value of nicotine yield on cigarette packages Tokyo 12 May 2002 [in Japanese]. Asahi Shinbun Advertisement by Japan Tobacco Inc Tokyo 14 May 2002 [in Japanese]. Shigeta MN Nakazawa A Ueda M Ozasa K Tobacco sales campaigns invade Japan by powerful tools of prizes In Abstracts of the 12th World Conference on Tobacco or Health: 4–8 August 2003; Helsinki 2003 573 Heatherton TF Kozlowski LT Frecker RC Fagerström KO The Fagerström Test for Nicotine Dependence: a revision of the Fagerström Tolerance Questionnaire Br J Addict 1991 86 1119 27 1932883 Velicer WF Rossi JS Diclemente CC Prochaska JO A criterion measurement model for health behavior change Addict Behav 1996 21 555 84 8876758 10.1016/0306-4603(95)00083-6 Jarvis MJ Why people smoke BMJ 2004 328 277 9 14751901 10.1136/bmj.328.7434.277 Scherer G Jarczyk L Heller WD Biber A Neurath GBFA Pharmacokinetics of nicotine, cotinine, and 3'-hydroxycotinine in cigarette smokers Klin Wochenschr 1988 66 5 11 3184779 Haufroid V Lison D Urinary cotinine as a tobacco-smoke exposure index: a minireview Int Arch Occup Environ Health 1998 71 162 8 9591157 10.1007/s004200050266 Feyerabend C Russell MA Improved gas chromatographic method and micro-extraction technique for the measurement of nicotine in biological fluids J Pharm Pharmacol 1979 31 73 6 33255 Hengen N Hengen M Gas-liquid chromatographic determination of nicotine and cotinine in plasma Clin Chem 1978 24 50 3 618667 SPSS Inc SPSS Base, Ver 100j Chicago 1999 Kozlowski LT O'Connor RJ Cigarette filter ventilation is a defective design because of misleading taste, bigger puffs, and blocked vents Tob Control 2002 11 I40 50 11893814 Hurt RD Robertson CR Prying open the door to the tobacco industry's secrets about nicotine: the Minnesota Tobacco Trial JAMA 1998 280 1173 81 9777818 10.1001/jama.280.13.1173 Kozlowski LT Pillitteri JL Beliefs about "Light" and "Ultra Light" cigarettes and efforts to change those beliefs: an overview of early efforts and published research Tob Control 2001 10 i12 6 11740039 Shopland DR Historical perspective: the low tar lie Tob Control 2001 10 i1 3 11740037 Japan Hospital Association Report of the Committee for Preventive Medicine Tokyo 2000 [in Japanese]. Ozasa K Shigeta M Nakazawa A Nishimura S Watanabe Y Higashi A The Role of the Human Dry Dock in Smoking Cessation in Japan Asian Pacific J Cancer Prev 2000 1 207 209

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==== Front BMC Womens HealthBMC Women's Health1472-6874BioMed Central London 1472-6874-4-51526523010.1186/1472-6874-4-5Research ArticleRofecoxib for dysmenorrhoea: meta-analysis using individual patient data Edwards Jayne E [email protected] R Andrew [email protected] Henry J [email protected] Pain Research Unit & Nuffield Department of Anaesthetics University of Oxford The Churchill Headington Oxford OX3 7LJ UK2004 20 7 2004 4 5 5 23 3 2004 20 7 2004 Copyright © 2004 Edwards et al; licensee BioMed Central Ltd.2004Edwards et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Individual patient meta-analysis to determine the analgesic efficacy and adverse effects of single-dose rofecoxib in primary dysmenorrhoea. Methods Individual patient information was available from three randomised, double blind, placebo and active controlled trials of rofecoxib. Data were combined through meta-analysis. Number-needed-to-treat (NNT) for at least 50% pain relief and the proportion of patients who had taken rescue medication over 12 hours were calculated. Information was collected on adverse effects. Results For single-dose rofecoxib 50 mg compared with placebo, the NNTs (with 95% CI) for at least 50% pain relief were 3.2 (2.4 to 4.5) at six, 3.1 (2.4 to 9.0) at eight, and 3.7 (2.8 to 5.6) at 12 hours. For naproxen sodium 550 mg they were 3.1 (2.4 to 4.4) at six, 3.0 (2.3 to 4.2) at eight, and 3.8 (2.7 to 6.1) at 12 hours. The proportion of patients who needed rescue medication within 12 hours was 27% with rofecoxib 50 mg, 29% with naproxen sodium 550 mg, and 50% with placebo. In the single-dose trial, the proportion of patients reporting any adverse effect was 8% (4/49) with rofecoxib 50 mg, 12% (6/49) with ibuprofen 400 mg, and 6% (3/49) with placebo. In the other two multiple dose trials, the proportion of patients reporting any adverse effect was 23% (42/179) with rofecoxib 50 mg, 24% (45/181) with naproxen sodium 550 mg, and 18% (33/178) with placebo. Conclusions Single dose rofecoxib 50 mg provided similar pain relief to naproxen sodium 550 mg over 12 hours. The duration of analgesia with rofecoxib 50 mg was similar to that of naproxen sodium 550 mg. Adverse effects were uncommon suggesting safety in short-term use of rofecoxib and naproxen sodium. Future research should include restriction on daily life and absence from work or school as outcomes. ==== Body Background Dysmenorrhoea is associated with painful cramping of the lower abdominal or back muscles, with or without other symptoms such as nausea, vomiting, and diarrhoea. Onset of dysmenorrhoea is common during adolescence, and up to 50% of women of reproductive age may be affected [1], and 10% incapacitated for up to three days each menstrual cycle. The pain caused by dysmenorrhoea can be debilitating, resulting in women being unable to perform daily activities, and being absent from work or school. In consequence, dysmenorrhoea is associated with emotional, social, and economic burdens. Despite the impact of dysmenorrhoea on daily living, few women seek medical advice [2], or know which treatments work [3]. Raised concentrations of uterine prostaglandins are thought to cause the pain and cramping associated with dysmenorrhoea [4-6]. Nonsteroidal anti-inflammatory drugs (NSAIDs) inhibit prostaglandin synthesis and are commonly used to treat the condition. The newer Cox-2 selective inhibitors (coxibs) also inhibit prostaglandin synthesis, providing an alternative to conventional NSAIDs. Relatively low rates of gastrointestinal adverse effects allow the use of higher doses of coxibs in acute pain and dysmenorrhoea. These high doses may have the additional advantage of longer duration analgesia with extended dosing intervals. Systematic reviews have shown NSAIDs to be effective in the treatment of primary dysmenorrhoea [7,8] While the latest, Cochrane, review [8] reported on 4,066 women in trials of NSAIDs in dysmenorrhoea, the trials themselves were small, with an average of about 50 women per trial. These 63 randomised double-blind trials investigated 21 different NSAIDS, at different doses, in studies of varying design, varying outcomes, and varying duration. At least moderate pain relief over a cycle was reported in 14 comparisons between NSAID (any NSAID, any dose) and placebo in 599 women, with an NNT of 2.1 (1.9 to 2.5). The most studied NSAID was naproxen, with 287 women in seven trials, with an NNT of 2.5 (2.0 to 3.3) for this outcome. An earlier review [7] included more trials, some not double-blind, but came to substantially the same conclusion about analgesic efficacy in primary dysmenorrhoea. NSAIDs also improved activities of daily living, though reported in only 216 women [8]. Adverse event information in these trials was not informative given the small number of women and trials, and that adverse events were rare in younger women taking NSAIDs for limited time. Cox-2 selective inhibitors (coxibs) provide an alternative to conventional NSAIDs, with a potential advantage of once daily dosing [9-11]. This individual patient data meta-analysis of rofecoxib in dysmenorrhoea aimed to determine the efficacy and duration of analgesic activity of single dose rofecoxib, and to evaluate adverse effects. Methods QUOROM guidelines [12] for quality of reporting of meta-analyses were followed though no flow chart was used since trial data all came from a single source. Merck Research Laboratories, Rahway, New Jersey provided individual patient data from three Phase III trials of rofecoxib in dysmenorrhoea (studies 38, 55 and 56), with the guarantee that all relevant studies completed by July 2002 had been made available. One of the trials has been published in full [9]. Searching PubMed to January 2004 identified one other randomised but open trial of rofecoxib [13] not sponsored by Merck. For inclusion, trials had to be randomised and double blind, compare rofecoxib with placebo, and provide single dose efficacy information. Outcome data were available for pain relief, pain intensity, time to remedication (use of rescue analgesic), and adverse effects. Each trial report was independently read and scored for quality using a three item, 0–5 point scale [14]. For inclusion a trial had to score a minimum of two points, one each for randomisation and double blinding, out of a maximum of 5. A sixteen-point scale was also used to assess trial validity [15]. Our intention was to use two pain outcomes (pain relief over the first dose, and pain relief over the whole cycle) with the outcome being that closest to at least half pain relief. Over the first dose, this might be a measure of total pain relief (TOTPAR), while over a whole cycle it may be a patient global evaluation of good or excellent, rather than mild, fair, no improvement, or worse. Analyses for comparator treatments were based on information available only from the trials of rofecoxib in this report. Outcome data were pooled in an intention to treat (number of patients randomised) analysis. Neither heterogeneity tests nor funnel plots were used [16,17]. Instead clinical homogeneity of trials was examined graphically [18]. Relative benefit (or risk) was calculated using a fixed effects model [19] with no statistically significant difference between treatments assumed when the 95% confidence intervals included 1. Number-needed-to-treat (or harm) was calculated using the method of Cook and Sackett [20] using pooled observations. NNT is the reciprocal of the absolute risk reduction or increase; for instance, if 75 out of 100 patients benefit with treatment and only 25 out of 100 benefit with placebo, the absolute risk increase is 0.75–0.25 = 0.5, and the NNT is 1/0.5 = 2. The z test [21] was used to determine statistical differences between NNTs for different doses, treatments or outcomes. Mean adverse event rates were calculated, weighting by treatment group size. Use of rescue medication was analysed as the proportion of patients remedicating at different time points within 12 hours. Results The mean age of women in the trials was 31 years, and at baseline pain was moderate in 66% of women and severe in 34%. All trials were randomised, double blind, and compared oral doses of rofecoxib with an active control and placebo in women with moderate to severe pain due to dysmenorrhoea. One trial was a single dose crossover (study 38), and two were multiple dose crossovers (studies 55 and 56), where the crossover was between single doses in different menstrual cycles. Single dose efficacy data were available for the first 12 hours of treatment in all trials, but not summary estimates over a cycle. Study designs and quality and validity scores are shown in Table 1. All trials scored the maximum five points for quality and at least 13/16 points for trial validity; some of the criteria for validity were not appropriate because of the individual patient presentation of results. Table 1 Trial details Trial ID Study drug and dose, number of women Design Observations after 8 hrs Quality score Validity score 38 49 women Rofecoxib 25 mg Rofecoxib 50 mg Ibuprofen 400 mg Placebo Single oral dose, parallel 3 menstrual cycles 12, 24 5/5 ≥13/16 55 60 women Rofecoxib 50 mg then 25 mg as required Naproxen sodium 550 mg every 12 hrs Placebo Oral. Multiple dose study with single dose efficacy data, multiple dose adverse events Cross-over, 1 of 6 drug sequences 3 menstrual cycles 12 hour obervations after a single dose in a three-day study 5/5 ≥13/16 56 122 women Rofecoxib 50 mg as required Rofecoxib 50 mg then 25 mg as required Naproxen sodium 550 mg every 12 hrs Placebo Oral. Multiple dose study with single dose efficacy data, multiple dose adverse events Cross-over, 1 of 4 drug sequences 4 menstrual cycles 12 hour obervations after a single dose in a three-day study 5/5 ≥13/16 The single dose trial (study 38) was conducted over three cycles. Each of 49 women received three of four treatments (rofecoxib 25 mg, rofecoxib 50 mg, ibuprofen 400 mg, or placebo). For the two multiple dose studies, one (study 55) compared a single dose of rofecoxib 50 mg followed by 25 mg daily as required, naproxen sodium 550 mg every 12 hours, or placebo in 60 women over three menstrual cycles. The other (study 56) compared rofecoxib 50 mg as required, rofecoxib 50 mg followed by 25 mg daily as required, naproxen sodium 550 mg every 12 hours, or placebo in 122 women over four menstrual cycles. Both trials reported multiple dose adverse effects. In the multiple dose trials, all women received each treatment regimen for one cycle. Pain intensity and pain relief were measured using the standard 4-point categorical pain intensity scale (0 none, 1 mild, 2 moderate, 3 severe) and a 5-point point pain relief scale (0 none, 1 a little, 2 some, 3 a lot, 4 complete). Pain measurements were collected using patient diaries. Patients were assessed at baseline, then at least hourly for eight hours, and again at 12 hours for single dose efficacy data. The exact time at which a patient requested remedication (or rescue analgesic), if required, was recorded. Adverse effects were recorded as the number of patients with any adverse effect(s), or particular adverse effects. Efficacy Full efficacy results over six, eight and 12 hours are shown in Table 2. All active treatments were significantly more effective than placebo at all time points. Table 2 Number needed to treat for at least 50% pain relief Improved with % improved Number of trials Drug and dose (mg) Active Placebo Active Placebo Relative risk (95% CI) NNT (95% CI) Six hour ourcomes  1 Rofecoxib 25 66/115 45/118 57 38 1.5 (1.1 to 2.0) 5.0 (3.7 to 7.8)  3 Rofecoxib 50 140/226 70/225 62 31 2.0 (1.6 to 2.5) 3.2 (2.4 to 4.5)  1 Ibuprofen 400 31/49 10/47 63 21 3.0 (1.7 to 5.4) 2.4 (1.7 to 4.2)  2 Naproxen sodium 550 120/181 60/178 66 34 2.0 (1.6 to 2.5) 3.1 (2.4 to 4.4) Eight hour ourcomes  1 Rofecoxib 25 70/115 44/118 61 37 1.6 (1.2 to 2.2) 4.2 (2.8 to 9.0)  3 Rofecoxib 50 147/226 73/225 65 32 2.0 (1.6 to 2.5) 3.1 (2.4 to 9.0)  1 Ibuprofen 400 30/47 11/47 61 21 2.6 (1.5 to 4.6) 2.6 (1.8 to 5.1)  2 Naproxen sodium 550 121/181 62/178 68 35 2.0 (1.6 to 2.4) 3.0 (2.3 to 4.3) Twelve hour ourcomes  1 Rofecoxib 25 64/115 45/118 56 38 1.5 (1.1 to 1.9) 5.7 (3.3 to 20)  3 Rofecoxib 50 135/226 74/225 60 33 1.8 (1.5 to 2.3) 3.7 (2.8 to 5.6)  1 Ibuprofen 400 27/49 12/47 55 26 2.2 (1.3 to 3.7) 3.4 (2.1 to 9.2)  2 Naproxen sodium 550 111/181 62/178 61 35 1.8 (1.4 to 2.2) 3.8 (2.7 to 6.1) Rofecoxib 25 mg was tested in a single trial, rofecoxib 50 mg in three, ibuprofen 400 mg in one and naproxen sodium 550 mg in two. For all the active analgesics the proportion of patients with at least 50% pain relief was about 60% at all time points, and with placebo it was about 30% at all time points (Table 2). Numbers needed to treat tended to be much the same for rofecoxib 50 mg, ibuprofen 400 mg and naproxen sodium 550 mg, though somewhat higher (worse) for rofecoxib 25 mg (Table 2). There was no significant difference between NNTs for single doses of study treatments at six, eight or 12 hours. For instance, no significant difference at the 12 hour comparison was seen between the NNTs of rofecoxib 25 mg and rofecoxib 50 mg (z score 1.19, p = 0.23), ibuprofen 400 mg (z score 0.28, p = 0.78), or naproxen sodium 550 mg(z score 1.09, p = 0.28), or between NNTs of rofecoxib 50 mg and ibuprofen 400 mg (z score 0.26, p = 0.79), or naproxen sodium 550 mg (z score 0.096, p = 0.60). Remedication The proportion of patients who remedicated at different time points over 12 hours is shown in Figure 1. At 12 hours remedication occurred with 29% on rofecoxib 25 mg, 28% on rofecoxib 50 mg, 29% on naproxen sodium 550 mg, 41% on ibuprofen 400 mg, and 50% with placebo. Figure 1 Remedication time for all drugs Adverse effects Few adverse effects of a particular type were reported, and none were serious in any trial. The most commonly reported adverse effects were nausea and somnolence, but these occurred infrequently. A single dose in one trial (study 38) gave the proportion of patients reporting any adverse effect(s) as 10% (5/49 patients) with rofecoxib 25 mg, 8% (4/49) with rofecoxib 50 mg, 12% (6/49) with ibuprofen 400 mg, and 6% (3/49) with placebo. With multiple doses over a cycle, the proportion of patients reporting any adverse effect(s) was 23% (42/179 patients) with rofecoxib 50 mg, 24% (45/181) with naproxen sodium 550 mg, and 18% (33/178) with placebo. Discussion Pain with dysmenorrhoea usually lasts for about three days, though with considerable individual variation. Trials of analgesics can have various forms. The simplest might be to give the same analgesic for the whole of the painful cycle, and ask a global question concerning efficacy at the end. Women might then be crossed over to a different treatment at the next cycle. A variation would be to use the same basic structure, but make more detailed evaluations of pain or pain relief over a limited time during the first day, though a global question could always be added. A more complicated design would use a cross-over within a single cycle. The three trials described here used a cross-over between cycles, with detailed pain measurements over 12–24 hours in the first painful day. Twelve-hour and 24-hour outcomes have also been reported in two other recent studies of coxibs in dysmenorrhoea [10,11]. With previous NSAID studies [8] the outcome most often used in placebo-controlled trials was at least moderate pain relief or an equivalent outcome over a whole cycle. A recent open-label study had a crossover design with drugs given each successive day [13]. All three trials were of the highest reporting quality, and had high validity scores, indicating that known sources of bias are unlikely to occur [22,8]. We know that to be sure of a result (as an NNT) we need information from about 400 patients when the NNT is about 2, but much more when the NNT is higher (worse) [23]. Here we have information from about 450 women with rofecoxib 50 mg and 360 with naproxen sodium 550 mg, but only about 200 women contributed for rofecoxib 25 mg and fewer than 100 for ibuprofen 400 mg (Table 2). For rofecoxib 25 mg and ibuprofen 400 mg, therefore, uncertainty over the size of the effect continues. Individual patient information from three trials of high quality showed that for the outcome of at least half pain relief over 12 hours, rofecoxib, naproxen sodium and ibuprofen were similarly effective in the treatment of pain associated with dysmenorrhoea. This confirms what was known from previous meta-analysis [8], in which most information was for naproxen at various doses with an NNT of 2.5 (2.0 to 3.3) for the outcome of at least moderate pain relief over 3–5 days compared with placebo. In this analysis the NNT for a single dose of 550 mg naproxen sodium was between 3.0 and 3.8 over six to 12 hours. The Cochrane review had information on 287 women in seven placebo-controlled trials, while two of the three trials here had information on 359 women taking naproxen sodium. Rofecoxib 50 mg was statistically indistinguishable from naproxen sodium 550 mg (Table 2) at all times, though rofecoxib 25 mg tended to have numerically higher (worse) NNTs at all times. Remedication over 12 hours was statistically indistinguishable between rofecoxib doses and naproxen. Here the number of women studied was even larger, with 451 women involved in the trials comparing rofecoxib 50 mg and placebo. The difference between rofecoxib 50 mg and naproxen sodium 550 mg would be in dosing schedules, with once versus twice a day dosing. Pain relief and duration of analgesia are not the only issues of importance in dysmenorrhoea. The impact of dysmenorrhoea on activities of daily living, disability or function, and absence from work or school are additional factors to be considered. These outcomes were not addressed by the trials for rofecoxib, which were conducted for regulatory purposes. This limits the utility of the information, but trials of other coxibs (also conducted for regulatory purposes) have also concentrated on pain relief and duration of analgesia [10,11]. Future trials should examine a range of short-term analgesia and longer outcomes like interference with daily living or absence from work or school. The analysis by Zhang and colleagues [7] did examine these additional outcomes, and found daily life to be less restricted with naproxen or ibuprofen than with placebo, and fewer absences from work or school to occur with naproxen than with placebo. These outcomes are infrequently reported [8], but are likely to be associated with pain, so decreased pain should improve these other outcomes as well. Verification of this assumption with data from high quality clinical trials would be welcome, though. Future individual patient analysis of trials in dysmenorrhoea would have the potential to examine issues around the efficacy of analgesics in women with heavy menstrual loss, or who use combined oral contraceptive pills. In this analysis information was not available for these analyses, and in any event any sub-groups would probably have been too small for any definitive answer. In the trials for rofecoxib, information on adverse effects was collected using diaries. Few adverse effects were reported to have occurred and none were serious. The most common adverse effects were nausea and somnolence. These and headache have been frequently reported with other coxibs [10,11] and NSAIDs [7,24]. The problem when interpreting information on adverse effects, though, is that any symptom can be recorded as an adverse event however tenuous its association to the study drug. We cannot be certain whether these symptoms were due to the condition or to the drug. Conclusions Based on information from three trials, a single dose of rofecoxib 50 mg is as effective as a single dose of naproxen sodium 500 mg in controlling the pain associated with dysmenorrhoea, and causes relatively few adverse effects. Competing interests RAM has been a consultant for Merck, Sharpe and Dohme Ltd, UK. RAM, JE and HJM have received lecture fees from pharmaceutical companies. The authors have received research support from charities and government sources at various times, but no such support was received for this work. Neither author has any direct stock holding in any pharmaceutical company. The terms of the financial support from MSD included freedom for authors to reach their own conclusions, and an absolute right to publish the results of their research, irrespective of any conclusions reached. MSD did have the right to view the final manuscript before publication, and did so. Authors' contributions JE conducted the analyses, which were checked by RAM. All authors contributed equally to the design, writing and reviewing of the paper. Table 3 Proportion of women who used rescue analgesic Percent who remedicated by (hrs) Drug & dose (mg) 6 8 12 Rofecoxib 25 21 25 28 Rofecoxib 50 22 24 27 Ibuprofen 400 22 35 41 Naproxen sodium 550 18 24 29 Placebo 37 44 50 Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements Merck Research Laboratories, Rahway, New Jersey provided individual patient data for use in this review. Financial support was provided by an unconditional educational grant from Merck Sharp and Dohme Ltd, UK. Additional support was provided by the Oxford Pain Relief Trust and Oxford Pain Research funds. ==== Refs Dawood MY Nonsteroidal anti-inflammatory drugs and changing attitudes toward dysmenorrhoea American Journal of Medicine 1988 84 23 29 3287908 10.1016/0002-9343(88)90473-1 Hewison A van den Akker OB Dysmenorrhoea, menstrual attitude and GP consultation British Journal of Nursing 1996 5 480 4 8788460 Hillen TI Grbavac SL Johnston PJ Straton JA Keogh JM Primary dysmenorrhoea in young Western Australian women: prevalence, impact and knowledge of treatment Journal of Adolescent Health 1999 25 40 5 10418884 10.1016/S1054-139X(98)00147-5 Dawood MY Nonsteroidal antiinflammatory drugs and reproduction American Journal of Obstetrics and Gynecology 1993 169 1255 65 8238194 Bieglmayer C Hofer G Kainz C Reinthaller A Kopp B Janisch H Concentrations of various arachidonic acid metabolites in menstrual fluid are associated with menstrual pain and are influenced by hormonal contraceptives Gynecological Endocrinolology 1995 9 307 12 Nigam S Benedetto C Zonca M Leo-Rossberg I Lubbert H Hammerstein J Increased concentrations of eicosanoids and platelet-activating factor in menstrual blood from women with primary dysmenorrhea Eicosanoids 1991 4 137 41 1772686 Zhang WY Li Wan Po A Efficacy of minor analgesics in primary dysmenorrhoea: a systematic review British Journal of Obstetrics and Gynaecology 1998 105 780 9 9692420 Marjoribanks J Proctor M Farquhar C Nonsteroidal anti-inflammatory drugs for primary dysmenorrhoea Cochrane Database of Systematic Reviews 2003 4 CD001751 Morrison BW Daniels SE Kotey P Cantu N Seidenberg B Rofecoxib, a specific cyclooxygenase-2 inhibitor, in primary dysmenorrhea: a randomized controlled trial Obstetrics & Gynecology 1999 94 504 8 10511349 10.1016/S0029-7844(99)00360-9 Daniels SE Talwalker S Torri S Snabes MC Recker DP Verburg KM Valdecoxib, a cyclooxygenase-2-specific inhibitor, is effective in treating primary dysmenorrhea Obstetrics & Gynecology 2002 100 350 8 12151162 10.1016/S0029-7844(02)02085-9 Malmstrom K Kotey P Cichanowitz N Daniels S Desjardins PJ Analgesic efficacy of etoricoxib in primary dysmenorrhea: results of a randomized, controlled trial Gynecology Obstetrics Investigations 2003 56 65 9 10.1159/000072735 Moher D Cook DJ Eastwood S Olkin I Rennie D Stroup DF Improving the quality of meta-analyses of randomised controlled trials: the QUOROM statement Lancet 1999 354 1896 1900 10584742 10.1016/S0140-6736(99)04149-5 Sahin I Saracoglu F Kurban Y Turkkani B Dysmenorrhea treatment with a single daily dose of rofecoxib International Journal of Gynaecology and Obstetrics 2003 83 285 91 14643039 10.1016/S0020-7292(03)00260-1 Jadad AR More RA Carroll D Jenkinson C Reynolds DJM Gavaghan DJ McQuay HJ Assessing the quality of reports of randomised clinical trials: is blinding necessary? Controlled Clinical Trials 1996 17 1 12 8721797 10.1016/0197-2456(95)00134-4 Smith LA Oldman AD McQuay HJ Moore RA Teasing apart quality and validity in systematic reviews: an example from acupuncture trials in chronic neck and back pain Pain 2000 86 119 132 10779669 10.1016/S0304-3959(00)00234-7 Gavaghan DJ Moore RA McQuay HJ An evaluation of homogeneity tests in meta-analyses in pain using simulations of individual patient data Pain 2000 85 415 424 10781914 10.1016/S0304-3959(99)00302-4 Tang J-L Liu JLY Misleading funnel plot for detection of bias in meta-analysis Journal of Clinical Epidemiology 2000 53 477 484 10812319 10.1016/S0895-4356(99)00204-8 L'Abbé KA Detsky AS O'Rourke K Meta-analysis in clinical research Annals of International Medicine 1987 107 224 233 Morris JA Gardner MJ Gardner MJ, Altman DG Calculating confidence intervals for relative risk, odds ratios and standardised ratios and rates Statistics with confidence – confidence intervals and statistical guidelines 1995 London, British Medical Journal Books 50 63 Cook RJ Sackett DL The number needed to treat: a clinically useful measure of treatment effect British Medical Journal 1995 310 452 454 7873954 Tramèr MR Reynolds DJM Moore RA McQuay HJ Impact of covert duplicate publication on meta-analysis: a case study British Medical Journal 1997 315 635 9 9310564 Moore RA Gavaghan D Tramer MR Collins SL McQuay HJ Size is everything-large amounts of information are needed to overcome random effects in estimating direction and magnitude of treatment effects Pain 1998 78 209 216 9870574 10.1016/S0304-3959(98)00140-7 Khan KS Daya S Jadad AR The importance of quality of primary studies in producing unbiased systematic reviews Arch Intern Med 1996 156 661 666 8629879 10.1001/archinte.156.6.661 Milsom I Minic M Dawood MY Akin MD Spann J Niland NF Squire RA Comparison of the efficacy and safety of nonprescription doses of naproxen and naproxen sodium with ibuprofen, acetaminophen, and placebo in the treatment of primary dysmenorrhea: a pooled analysis of five studies Clinical Therapeutics 2002 24 1384 400 12380631 10.1016/S0149-2918(02)80043-1

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==== Front BMC Med Inform Decis MakBMC Medical Informatics and Decision Making1472-6947BioMed Central London 1472-6947-4-101526524010.1186/1472-6947-4-10Research ArticleAlgorithms for optimizing drug therapy Wanger Peter [email protected] Lene [email protected] Department of Clinical Neuroscience, Karolinska Institutet St Erik's Eye Hospital S-112 82 Stockholm Sweden2004 20 7 2004 4 10 10 31 3 2004 20 7 2004 Copyright © 2004 Wanger and Martin; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Drug therapy has become increasingly efficient, with more drugs available for treatment of an ever-growing number of conditions. Yet, drug use is reported to be sub optimal in several aspects, such as dosage, patient's adherence and outcome of therapy. The aim of the current study was to investigate the possibility to optimize drug therapy using computer programs, available on the Internet. Methods One hundred and ten officially endorsed text documents, published between 1996 and 2004, containing guidelines for drug therapy in 246 disorders, were analyzed with regard to information about patient-, disease- and drug-related factors and relationships between these factors. This information was used to construct algorithms for identifying optimum treatment in each of the studied disorders. These algorithms were categorized in order to define as few models as possible that still could accommodate the identified factors and the relationships between them. The resulting program prototypes were implemented in HTML (user interface) and JavaScript (program logic). Results Three types of algorithms were sufficient for the intended purpose. The simplest type is a list of factors, each of which implies that the particular patient should or should not receive treatment. This is adequate in situations where only one treatment exists. The second type, a more elaborate model, is required when treatment can by provided using drugs from different pharmacological classes and the selection of drug class is dependent on patient characteristics. An easily implemented set of if-then statements was able to manage the identified information in such instances. The third type was needed in the few situations where the selection and dosage of drugs were depending on the degree to which one or more patient-specific factors were present. In these cases the implementation of an established decision model based on fuzzy sets was required. Computer programs based on one of these three models could be constructed regarding all but one of the studied disorders. The single exception was depression, where reliable relationships between patient characteristics, drug classes and outcome of therapy remain to be defined. Conclusion Algorithms for optimizing drug therapy can, with presumably rare exceptions, be developed for any disorder, using standard Internet programming methods. ==== Body Background During the last decades the possibility of computer support for optimum management of various disorders has attracted interest [1-3]. Regarding drug therapy the approach has usually been limited to one particular disease [4-7]. Diabetes has been extensively studied and thoroughly analyzed also from a systems perspective [8]. Several attempts have been made to develop a general model for medical guidelines, which could be used also for the development of computer programs [9-12]. Computerized physician order entry has been shown to reduce the frequency of serious medication errors. Decision support tools such as alerting functions for patient medication allergy are a key part of these applications. However, Abookire and co-workers [13] analyzed trend data obtained over a five-year period that showed decreasing compliance to allergy alert functions within computerized order entry. They concluded that optimal performance requires iterative refinement and that, as systems become increasingly complex, mechanisms to monitor their performance become increasingly critical [13]. In another study large differences were seen for all main types of medication errors: dose errors, frequency errors, route errors, substitution errors, and allergies [14]. In a university hospital setting, the reductions in transcription errors and medication turn-around times supported the view that computerized physician order entry and an electronic medication administration record can provide a good return on investment [15]. In the current study guidelines for 246 diseases were analyzed regarding the content of facts, and relationships between these facts, influencing the selection and dosage of therapeutic drugs. The aim of study was to identify one or more algorithms (descriptions of how to solve a problem in a definite number of steps), which could be used to support optimum drug therapy in any disease. Methods The studied material consisted of 110 officially endorsed text documents, published between 1996 and 2004. Forty-four of these documents were published by the Swedish Medical Products Agency [16], 13 by the Swedish Agency for Evaluation of Medical Technologies [17] and 53 (in three booklets) by the Swedish Strategic Programme for The Rational Use of Antimicrobial Agents and Surveillance of Resistance (STRAMA) [18], established in order to prevent bacterial resistance to common antibiotics. The documents contained guidelines for drug therapy in altogether 246 disorders. Information regarding contraindications, dosage etc was available from an existing pharmaceutical website [19] and was retrieved by linking to context-relevant pages. These documents were analyzed as follows: All statements in the particular document regarding patient-, disease- and drug-related factors were recorded. In a second step the statements in the texts describing the relationship between these factors were identified. After that the obtained information was rewritten in a format similar to a generic computer language, using if-then statements and logical operators as needed. These rewritten documents were grouped according to similarity in structure, based on the number of if-then statements, the need for branching or calculations in the code and the occurrence of 'left-overs', i.e. information in the text that could not be explicitly expressed using if-then statements and logical operators. In theory this procedure could result in an intractable combinatorial explosion. This was observed only in one situation, regarding side effects of anti-hypertensive medication. Results Figure 1 shows the number of facts, relevant for the selection of appropriate drug class in the 246 studied diseases. Three algorithms were sufficient for the intended purpose. The simplest model is a list, containing the data, which imply that the patient should or should not receive a particular treatment. This model is adequate in situations where only one pharmacological treatment modality exists, such as influenza, where neuramidase inhibitors are the only drug class approved in Sweden, see figure 2. A 16 page summary of 35 pages of background documentation contained the following data items influencing drug therapy: four risk groups, time from start of symptom, two available neuramidases, one of which was approved also for use in children. Pharmaceutical properties regarding all approved drugs is available online (accessed from the program using 'deep linking'). In the actual program implementation information was added about current epidemiological state regarding influenza by linking to a government authority website, diagnostic criteria and some management aspects (in pop-up windows) (Fig. 2). In situations, where treatment can by provided using drugs from different pharmacological classes, a more elaborate model is required. An example is heart failure. A 25-page summary of 69 pages of background documentation described six physiological states and 11 available drug classes. This knowledge was modelled using an ordered set of it-then statements, see figure 3. The function containing these sets of ordered rules is activated every time the user enters some information on the screen. For an example of user interface, see figure 4. In the actual program implementation information was added about treatment in emergency situations and in different clinical stages of the disease (in pop-up windows) (Fig. 4). The algorithms mainly consist of sets of if-then statements, grouped in a single function, which is activated each time the user enters some information by checking a check box or radio button on the screen [20]. This information is equivalent to a fact list, and the if-then rules mimic a knowledge base. Thus in these aspects the algorithms are similar to conventional expert systems [21], but a separate inference engine was not needed. However, the if-then rules had to be ordered according to the output such that positive information, i.e. recommendations to use a particular drug, always was overwritten when factors were present that implied that this particular drug was contra-indicated or less suitable. Such sets of if-then statements were able to manage the identified information in all, but one instance. The exception was depression, where explicit knowledge regarding the relationships between patient characteristics and optimum choice of drug is lacking. In some diseases the selection and dosage of drugs depend on the degree to which one or more particular factors are present. In such cases a specific program implementation of an established decision model based on fuzzy set theory [22] could be used. An example is the adjustment of medication according the patient's description of side effects in the treatment of high blood pressure [6,23], see figure 5. The data in this program were obtained from a study of 1013 treated and 125 untreated hypertensive patients [24]. Thirteen types of complaints were recorded and correlated to the ten available drug classes. The resulting matrix was analysed by four physicians, one professor of endocrinology, with special interest in hypertension, two clinical pharmacologists and one family physician, and modified according to scientific evidence and clinical experience. Clicking on radio buttons enters the type and degree of complaint and the program ranks the available drug classes according to the degree to which they can be assumed not to cause the patient's complaint(s). The algorithm is an implementation of a decision theory, based on fuzzy sets [22]. A verbal description of the implementation would be quite lengthy, but the workings of the algorithm and the code can be seen on the author's website [23]. The pharmaceutical information about all therapeutics drugs, approved in Sweden, is available on the Internet at a site managed by the association of pharmaceutical industries [19]. By linking to this site, the programs provide access to the relevant and continously updated drug information regarding all drugs, registered for use in the current disease. In addition, each program is linked to the appropriate guideline document, so that the user can verify the program's treatment advice. Discussion The main findings in the current study are that simple algorithms can support drug therapy and that such algorithms can be easily implemented using standard Internet programming techniques. Problems in the implementation of information system designed to support actual care are discussed in a study by Bates et. al. [25]. The authors concluded that 'ten commandments' have to be taken into account. The most important factors for success were speed, anticipation of needs and good fit into the user's workflow. The programs developed in the current study easily meet these criteria, provided that the users have access to the Internet. However, it should be noted that the programs require no communication of patient-related information over the Internet, other than transmission of the prescription to the pharmacy, if such a system is implemented. One disorder, depression, was identified for which relevant computer program support could not be constructed. Initially, the program model with the above-mentioned decision algorithm, based on fuzzy sets, was thought to be applicable, since the number of relevant facts is well within manageable range; four major classes of drugs, slightly more than ten diagnostic variables, some ten receptors and seven neurotransmitters. However, the information in the official consensus document [26], did not provide sufficient information, due to the lack of explicit relationships between the patient's characteristics, as described e.g. by diagnostic rating scales, and the pharmacological properties of the various types of antidepressants. Outcome studies regarding depression also give little information about which drug should be recommended for a particular patient. The exception is a recent report by Joyce et al [27], who found a markedly superior response to nortriptyline compared to fluoxetine in men above 40 years of age, and the reverse effect in women, aged 18 – 24 years. Problems in pharmacotherapy related to the simultaneous occurrence of multiple disorders in the same patient are well studied and presented in the pharmacological information as contraindications or recommendations of "caution in patients with disease ...". This information is used as output from the programs in the current study. The information in the analyzed text documents was not always sufficiently detailed and logically consistent to allow the construction of computer code. Additional information could usually be gathered from other sources, but such procedures may induce errors and should be avoided. Methods are currently available to support logical structure in text documents, e.g. the Guideline Elements Model [8]. This model relies on XML tagging, a standard for content description in documents intended for electronic processing. A formal structure would increase the usefulness of guidelines [24], since it would be possible to present them in the format of fast, easily available and "easy-to-use" programs, as in the recently published PresGuid system [11], which relies on XML tagging of text based guidelines for automatic conversion to computer programs. Experience will tell whether this approach will be more efficient than the simpler strategy of manual extraction of relevant data from the guideline document, combined with JavaScript coding, used in the current study. The introduction of a new drug for treatment of a particular disease, or the results from new scientific studies may have considerable influence on guidelines. Yet only a few documents were changed during the study period and the lifespan of pharmacotherapeutic guidelines can be assumed to be 2–5 years. However, new information may appear unexpectedly, and a reasonably complete system for pharmacotherapy requires frequent updates. The approach taken in the current study, with the programs stored on a website as scripted HTML pages, facilitates the maintenance and update of the system and ensures that all users always have access to the latest version. An additional way to increase relevance over time is to link from the programs to recently published studies, e.g. by providing automatic searches in the PubMed database [28]. Conclusion In the current study based on guidelines regarding two hundred and forty-six diseases, three basic program models were found to be sufficient for computerized decision-support in pharmacotherapy. The programs could be developed using HTML and JavaScript, i.e. simple and widespread programming techniques. No specific development tools are needed although standard programming tools such as Macromedia Dreamweaver® are helpful. The use of Internet implies that only one version of the programs is in use, that upgrading is very convenient, and that no interference occurs with existing systems. No confidential patient data has to be communicated and the programs are always available from any Internet connected computer. In summary, algorithms for optimizing drug therapy can, with presumably rare exceptions, be developed for any disorder using standard Internet programming methods. Competing interests None declared. Authors' contributions PW conceived and designed the study, collected data, supervised data analysis, did most of the JavaScript and some of the HTML programming and contributed to the writing of the paper. LM did most of the HTML and some of the JavaScript programming and contributed to the data analysis and the writing of the paper. Both authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Figures and Tables Figure 1 Number of facts influencing the selection of therapeutic drugs in the studied disorders (n = 246). Figure 2 Example of user interface, one drug class. Underlined headings, e.g. Neuramidase inhibitors, are links to pop-up windows Figure 3 Example of a set of rules (verbose pseudocode) Figure 4 Example of user interface. Underlined headings are links to pop-up windows, check boxes refer to facts influencing selection of drugs. Figure 5 Output from a program for adjusting medication for high blood pressure according to the patient's side effects, in this case ankle edema ==== Refs Montani S Bellazzi R Quaglini S d'Annunzio G Meta-analysis of the effect of the use of computer-based systems on the metabolic control of patients with diabetes mellitus Diabetes Technol Ther 2001 3 347 56 11762513 10.1089/15209150152607123 Blackshear JL Schwartz GL Step care therapy for hypertension in diabetic patients Mayo Clin Proc 2001 76 266 74 Schmidt R Gierl L Case-based reasoning for antibiotics therapy advice: an investigation of retrieval algorithms and prototypes Artif Intell Med 2001 23 171 86 11583924 10.1016/S0933-3657(01)00083-5 Wanger P Martin L Computer-assisted management of patients with open-angle glaucoma or ocular hypertension Acta Ophthalmol 1997 75 700 704 Martin L Wanger P Prescription of anti-glaucoma drugs, patient characteristics and treatment results 3rd International Glaucoma Symposium, Prag, Mars 2001 (accessed 2004-05-28) Wanger P Martin L Evaluation of a computerized decision-support system for managing patients with high blood pressure [abstract] J Hypertension 2001 19 s226 Seroussi B Bouaud J Dreau H Falcoff H Riou C Joubert M Simon C Simon G Venot A ASTI: a guideline-based drug-ordering system for primary care Medinfo 2001 10 528 32 11604796 Carson ER Decision support systems in diabetes: a systems perspective Comput Methods Programs Biomed 1998 56 77 91 9700425 10.1016/S0169-2607(98)00017-0 PROFORMA (accessed 2004-06-13) Prodigy (accessed 2004-06-13) Dufour J-C Fieschi D Fieschi M Coupling computer-interpretable guidelines with a drug-database through a web-based system – The PRESGUID project BMC Medical Informatics and Decision Making 2004 4 2 15053828 10.1186/1472-6947-4-2 Schiffman RN Karras BT Agrawal A Chen R Marenco L Nath S GEM: A proposal for a more comprehensive guideline document model using XML J Am Med Inform Assoc 2000 7 488 498 10984468 Abookire SA Teich JM Sandige H Paterno MD Martin MT Kuperman GJ Bates DW Improving allergy alerting in a computerized physician order entry system Proc AMIA Symp 2000 2 6 Bates DW Teich JM Lee J Seger D Kuperman GJ Ma'Luf N Boyle D Leape L The impact of computerized physician order entry on medication error prevention J Am Med Inform Assoc 1999 6 313 21 10428004 Mekhjian HS Immediate benefits realized following implementation of physician order entry at an academic medical center J Am Med Inform Assoc 2002 9 529 539 12223505 10.1197/jamia.M1038 Swedish Medical Products Agency (accessed 2004-05-28) Swedish Agency for Evaluation of Medical Technologies (accessed 2004-05-28) Strategy Group for Rational Use of Antibiotics (accessed 2004-05-28) Swedish Association of Pharmaceutical Manufacturers (accessed 2004-05-28) Heart Failure Treatment Guide (accessed 2004-05-28) Giarratano J Riley G Expert Systems, Principles and Programming 1998 Third PWS Publishing Company Boston Yager RR A New Methodology for Ordinal Multiple Aspect Decisions Based on Fuzzy Sets Decision Sciences 1981 12 589 600 Demo of ordinal multiple aspects decision algorithm (accessed 2004-05-28) Kjellgren KI Ahlner J Dahllöf B Gill H Hedner T Säljö R Perceived symptoms among hypertensive patients in routine clinical practise – a population-based study J Intern Med 1998 244 325 32 9797496 10.1046/j.1365-2796.1998.00377.x Bates DW Kupferman GJ Wang S Gandhi T Kittler A Volk L Spurr C Khorasani R Tanasijevic M Middleton B Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality J Am Med Inform Assoc 2000 10 6 Swedish Agency for Evaluation of Medical Technologies (accessed 2004-05-28) Joyce PR Mulder RT Luty SE McKenzie JM Rae AM A differential response to nortriptyline and fluoxetine in melancholic depression: the importance of age and gender Acta Psychiatr Scand 2003 108 20 23 12807373 10.1034/j.1600-0447.2003.00120.x PubMed (accessed 2004-05-28)

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BMC Med Inform Decis Mak. 2004 Jul 20; 4:10

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BMC Med Inform Decis Mak

10.1186/1472-6947-4-10

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==== Front Cardiovasc UltrasoundCardiovascular Ultrasound1476-7120BioMed Central London 1476-7120-2-71526523910.1186/1476-7120-2-7ResearchColor Doppler imaging of cervicocephalic fibromuscular dysplasia Arning Christian [email protected] Ulrich [email protected] Department of Neurology, Allgemeines Krankenhaus Hamburg-Wandsbek, Alphonsstr. 14, D-22043 Hamburg, Germany2 Department of Neuroradiology, Universitätskrankenhaus Hamburg-Eppendorf, Martinistr. 52, D-20246 Hamburg, Germany2004 20 7 2004 2 7 7 8 7 2004 20 7 2004 Copyright © 2004 Arning and Grzyska; licensee BioMed Central Ltd.2004Arning and Grzyska; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Fibromuscular dysplasia (FMD) is a possible cause of stroke, especially in middle-aged women. However, only few reports are available on ultrasonographic detection and monitoring. Methods Among the 15,000 patients who underwent color Doppler imaging (CDI) of the cervicocephalic arteries during the study period, all cases fulfilling ultrasound criteria of FMD were included into the case series. Criteria of FMD were: 1. Segmental string-of-beads pattern, 2. Localization in the distal extracranial part of internal carotid artery (ICA) or vertebral artery (VA), and 3. (optional): Direct and/or indirect criteria of stenosis. Results CDI detected FMD in 39 vessels (37 ICA and 2 VA segments) of 21 patients. 16 patients had bilateral manifestation on ICA, one of those also on VA, bilaterally. CDI disclosed 4 symptomatic high-grade ICA stenoses, 3 of them underwent endovascular treatment. 5 patients with moderate symptomatic ICA stenoses got medical treatment. In 6 patients FMD was the most likely cause of headache and in one patient FMD was diagnosed as a cause of vertigo. Conclusions CDI may be used for detection of cervicocephalic FMD. Due to the unfavourable localisation of FMD for CDI, the sensitivity of CDI is lower in comparison to angiography. However, high-grade FMD stenoses that require invasive treatment can be recognized on the basis of indirect hemodynamic criteria. ==== Body Background Fibromuscular dysplasia (FMD) is a non-atheromatous, non-inflammatory arteriopathy of unknown etiology with segmental manifestation on medium-sized arteries in various regions of the body [1]. Manifestation on the renal arteries with the possible consequence of renovascular hypertension is remarkably frequent [2]. The cervico-cephalic arteries, especially the internal carotid artery (ICA) are attacked with an incidence of about 0.6 – 1%, often bilaterally [3]; manifestation also occurs on the vertebral artery (VA) [4]. The disease can occur at any age but is usually diagnosed in middle-aged, predominately female individuals [4]. Angiography reveals in most cases the typical string-of-beads pattern (fig. 1) with alternating regions of lumen narrowing and vessel dilatation over a length of 3 – 5 cm [3]; the proximal section of the ICA is generally not affected, except in a rare FMD subtype characterised by proximal involvement with a web-like membrane [5]. Figure 1 The string-of-beads sign with alternating regions of lumen narrowing and vessel dilatation on angiogram of the ICA (arrows) in a 52-year-old woman sufferning from recurrent transient ischemic attacks. Clinical manifestations of FMD on the ICA are transitoric ischemic attacks or cerebral infarctions [6] as well as unspecific symptoms such as headache and vertigo. In cases of cerebrovascular events, endovascular or surgical treatment is recommended [7-9], therefore detection of FMD is of considerable importance. Patients and methods Among the 15,000 patients who attended the neurosonography department of our clinic during the study period, 21 cases were identified fulfilling ultrasound criteria of FMD (Table 1). The presenting symptoms of the patients are listed in table 2. Table 1 Color Doppler ultrasound criteria of FMD 1. Morphological criteria:  Segmental string-of-beads pattern with alternating regions of lumen narrowing and vessel dilatation 2. Localization:  Distal extracranial part of ICA (VA). 3. Hemodynamics (optional):  Direct and/or indirect criteria of stenosis (in distal extracranial part of ICA / VA). Table 2 Patients and symptoms No. Age Male/female Symptoms 1 52 f Transient ischemic attack 2 55 f Bruit 3 55 f Headache 4 75 f Bruit 5 61 f Vertigo 6 53 f Pulsatile tinnitus 7 63 f Vertigo 8 65 f Amaurosis fugax 9 47 f Amaurosis fugax, vertigo 10 41 f Minor stroke 11 54 f Minor stroke 12 52 f Headache, vertigo 13 57 f Minor stroke 14 46 f Vertigo, bruit 15 73 f Bruit, headache, vertigo 16 55 f Headache 17 51 f Headache 18 62 f Headache 19 42 M Transient ischemic attack 20 62 f Transient ischemic attack 21 40 f Headache The color Doppler examinations were performed as described by Arning [10] and included the common carotid, external carotid, and internal carotid arteries as well as the vertebral arteries. CDI was performed with 5 MHz and 7 MHz linear array transducers using one of the following systems: Acuson Sequoia (Siemens AG, Erlangen, Germany), Toshiba Powervision 6000 or Toshiba Aplio (Toshiba Medical Systems Europe, Zoetermeer, Netherlands), or ATL HDI 5000 (Philips Medical Systems, Andover, MA). Results Using the criteria of table 1, FMD was diagnosed in 21 patients (1 male, 20 female). In total, CDI detected FMD in 39 vessels (37 ICA and 2 VA segments). 16 patients had bilateral manifestation on ICA, one of those also on VA, bilaterally. 5 patients had unilateral manifestation on ICA. The degree of stenosis was low in 2 patients (Fig. 2) and moderate in the majority of cases (Fig. 3,4,5). 5 patients with moderate symptomatic ICA stenoses got medical treatment. 4 symptomatic high-grade ICA stenoses (Fig. 6,7,8) were detected, 3 of them underwent endovascular treatment (Fig. 9). In 6 patients FMD was the most likely cause of headache and in one patient FMD was diagnosed as the cause of vertigo, involving vertebral artery (fig. 10). Figure 2 The string-of-beads sign in the color Doppler image in a 51-year-old patient with low-grade stenosing FMD of the ICA. The patient suffered from migraine-like headache. Figure 3 FMD of the ICA in a 53-year-old woman suffering from headache. Power Doppler image of the left ICA shows the string-of-beads pattern. Figure 4 The same case as in fig. 3: Color Doppler and spectral Doppler examination of the left ICA revealing stenoses of about 70%. Figure 5 The same case as in fig. 3: Power Doppler image of the right ICA. Figure 6 High-grade stenosis of the ICA caused by FMD in a 52-year-old woman sufferning from recurrent transient ischemic attacks. CDI shows the string-of-beads pattern distally to a longer section of normal vessel. Figure 7 The same case as in fig. 6 (enlarged), showing the string-of-beads pattern distally to a longer section of normal vessel. Figure 8 The same case as in fig. 6: Spectral Doppler examinations reveal a high-grade stenosis. Figure 9 The same case as in fig. 6: Findings after endovascular treatment (stenting). Figure 10 The string-of-beads sign on the VA (C2-C1) in a in a 55-year-old woman with bilateral manifestation of FMD on ICA and VA. The patient suffered from vertigo. Discussion FMD is an uncommon angiopathy with an incidence on the ICA of about 0.6 – 1% [3]. However, the frequency of FMD detection by ultrasound imaging is considerably lower: 0,14% in our case series. Only few reports are available on the detection and monitoring of cervicocephal FMD with ultrasonography [11-14]. Ultrasound criteria of FMD correspond to those of angiography (Fig. 1). CDI reveals the segmental string-of-beads pattern with alternating regions of lumen narrowing and vessel dilatation (Fig. 2,3), distally to a completely normal segment of the vessel (Fig. 6). Dependent on the degree of stenosis, direct (Fig. 8) or indirect hemodynamic criteria may be recognized [14]. In comparison to angiography, the sensitivity of CDI is low: The vascular lesion can only be visualized sonographically when it is located not too far cranially on the ICA [15]. However, high-grade FMD stenoses will be detected on the basis of indirect hemodynamic criteria. To overlook asymptomatic cases of low grade or medium grade stenosing lesions will not have a negative consequence since they do not require any treatment [16]. Conclusions CDI allows diagnosis of FMD in numerous cases. Due to the unfavourable localisation of FMD for CDI, the sensitivity of CDI is low in comparison to angiography. However, high-grade FMD stenoses that require invasive treatment can be recognized on the basis of indirect hemodynamic criteria. Competing interests None declared. List of abbreviations CDI Color Doppler Imaging FMD Fibromuscular Dysplasia ICA Internal Carotid Artery VA Vertebral Artery ==== Refs Russo CP Smoker WRK Nonatheromatous carotid artery disease Neuroimaging Clinics of North America 1996 6 811 830 8824133 Slovut DP Olin JW Fibromuscular dysplasia N Engl J Med 2004 350 1862 1871 15115832 10.1056/NEJMra032393 Sandok BA Fibromuscular dysplasia of the internal carotid artery Neurol Clin 1983 1 17 26 6680159 Mas JL Bousser MG Hasboun D Laplane D Extracranial vertebral artery dissections: a review of 13 cases Stroke 1987 18 1037 1047 3318002 Morgenlander JC Goldstein LV Recurrent transient ischemic attacks and stroke in association with an internal carotid artery web Stroke 1991 22 94 98 1987677 Sandmann J Hojer D Bewermeyer H Bamborschke S Neufang KF Fibromuscular dysplasia as a cause of cerebral infarct Nervenarzt 1992 63 335 340 1635615 Curry TK Messina LM Fibromuscular dysplasia: when is intervention warranted? Semin Vasc Surg 2003 16 190 199 12975758 10.1016/S0895-7967(03)00024-3 Chiche L Bahnini A Koskas F Kieffer E Occlusive fibromuscular disease of arteries supplying the brain: results of surgical treatment Ann Vasc Surg 1997 11 496 504 9302062 10.1007/s100169900081 Van Damme H Sakalihasan N Limet R Fibromuscular dysplasia of the internal carotid artery. Personal experience with 13 cases and literature review Acta Chir Belg 1999 99 163 168 10499386 Arning C Farbkodierte Duplexsonographie der hirnversorgenden Arterien 2002 3 Stuttgart, New York: Thieme Edell SL Huang P Sonographic demonstration of fibromuscular hyperplasia of the cervical internal carotid artery Stroke 1981 12 518 520 7314175 Kliewer MA Carroll BA Ultrasound case of the day. Internal carotid artery web (atypical fibromuscular dysplasia) Radiographics 1991 11 504 505 1852941 Krzanowski M Fibromuscular dysplasia of the internal carotid artery as a cause of transient cerebral ischemia episodes Pol Arch Med Wewn 1997 98 546 550 9640084 Arning C Nonatherosclerotic disease of the cervical arteries: Role of ultrasonography for diagnosis VASA 2001 30 160 167 11582945 Wells RP Smith RR Fibromuscular dysplasia of the internal carotid artery: a long term follow-up Neurosurgery 1982 10 39 43 7057976 Wesen CA Elliott BM Fibromuscular dysplasia of the carotid arteries Am J Surg 1986 151 448 451 3515980 10.1016/0002-9610(86)90100-5

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Cardiovasc Ultrasound. 2004 Jul 20; 2:7

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Cardiovasc Ultrasound

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==== Front Reprod Biol EndocrinolReproductive biology and endocrinology : RB&E1477-7827BioMed Central London 1477-7827-2-581526523810.1186/1477-7827-2-58ResearchEndometrial glands as a source of nutrients, growth factors and cytokines during the first trimester of human pregnancy: A morphological and immunohistochemical study Hempstock Joanne [email protected] Tereza [email protected] Eric [email protected] Graham J [email protected] Department of Anatomy, University of Cambridge, Cambridge, UK2 Academic Department of Obstetrics and Gynaecology, Royal Free and University College, London, UK2004 20 7 2004 2 58 58 14 6 2004 20 7 2004 Copyright © 2004 Hempstock et al; licensee BioMed Central Ltd.2004Hempstock et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background The maternal circulation to the human placenta is not fully established until 10–12 weeks of pregnancy. During the first trimester the intervillous space is filled by a clear fluid, in part derived from secretions from the endometrial glands via openings in the basal plate. The aim was to determine the activity of the glands throughout the first trimester, and to identify components of the secretions. Methods Samples of human decidua basalis from 5–14 weeks gestational age were examined by transmission electron microscopy and immunohistochemically. An archival collection of placenta-in-situ samples was also reviewed. Results The thickness of the endometrium beneath the implantation site reduced from approximately 5 mm at 6 weeks to 1 mm at 14 weeks of gestation. The glandular epithelium also transformed from tall columnar cells, packed with secretory organelles, to a low cuboidal layer over this period. The lumens of the glands were always filled with precipitated secretions, and communications with the intervillous space could be traced until at least 10 weeks. The glandular epithelium reacted strongly for leukaemia inhibitory factor, vascular endothelial growth factor, epidermal growth factor, transforming growth factor beta, alpha tocopherol transfer protein, MUC-1 and glycodelin, and weakly for lactoferrin. As gestation advanced uterine natural killer cells became closely approximated to the basal surface of the epithelium. These cells were also immunopositive for epidermal growth factor. Conclusions Morphologically the endometrial glands are best developed and most active during early human pregnancy. The glands gradually regress over the first trimester, but still communicate with the intervillous space until at least 10 weeks. Hence, they could provide an important source of nutrients, growth factors and cytokines for the feto-placental unit. The endometrium may therefore play a greater role in regulating placental growth and differentiation post-implantation than previously appreciated. ==== Body Background The realisation that the maternal circulation to the human placenta is extremely limited prior to 10–12 weeks of pregnancy prompted us to investigate other potential sources of fetal nutrition during the first trimester [1-4]. During the evolution of ovoviviparity and viviparity secretions from the uterus became an increasingly important supplement to the yolk contained within the embryo's yolk sac [5]. In chondrichthyan fishes they represent an important source of nutrients, even in those species that do not possess a placenta [6]. Amongst eutherian mammals the uptake of secretions derived from the endometrial glands by the trophoblast continues to provide an important pathway for nutritional exchange in the earliest stages of pregnancy, before the placenta is established. These secretions contain a complex array of carbohydrates, proteins and lipids, and have been referred to variously as uterine milk or histiotroph [5]. They are particularly significant in ruminants and equids where there is a relatively long interval between the arrival of the conceptus within the uterine cavity and the establishment of placentation. In some species, such as the pig and mouse, they represent a parallel pathway for the exchange of large proteins throughout most of pregnancy [7,8]. More recently, it has been appreciated that the secretions may perform wider functions beyond the simple provision of nutrients. Some components, such as glycodelin, have potent immunosuppresive properties [9], while others, such as leukaemia inhibitory factor (LIF) and MUC-1, play key roles in regulating implantation [10,11]. Histiotroph may therefore modulate materno-fetal interactions and regulate diverse aspects of placental development. Its importance during the preimplantation period has been powerfully demonstrated in the sheep, where suppression of endometrial gland development leads to failure of the conceptus to survive and develop [12]. Equally, in the horse increased expression of epidermal growth factor (EGF) in the endometrial glands correlates closely both temporally and spatially with cell proliferation in the overlying fetal membranes [13]. In the human histiotrophic nutrition has always been considered to be of little importance for two principal reasons. Firstly, the invasive form of implantation displayed by the human blastocyst removes it from the uterine lumen, and hence it was believed the uterine secretions, by day 7–10 post-fertilisation. Secondly, the contemporaneous appearance of maternal erythrocytes within the lacunar spaces of the syncytiotrophoblastic mantle has been widely interpreted as evidence of early onset of the maternal circulation, and hence haemotrophic exchange [14,15]. However, there is now a substantial body of evidence from a variety of techniques indicating that an effective maternal circulation is not established until the start of the second trimester [1,3,16,17]. Indeed, the human placenta cannot be considered haemochorial prior to this time for the intervillous space is filled with a clear fluid only [16]. Initially it was considered this fluid was derived as a plasma filtrate percolating through the trophoblastic plugs occluding the tips of the spiral arteries. However, we recently demonstrated that the uterine glands deliver secretions into the intervillous space until at least 10 weeks of gestation, suggesting that they may at least contribute to formation of the fluid [18]. This raises the possibility that they may play greater roles during early pregnancy than previously anticipated, equivalent to those in other species. The aim of this study was therefore to examine the secretory activity of the endometrial glands within the decidua basalis both morphologically and immunohistochemically over the first trimester in order to assess their potential contribution to fetal nutrition and placental development. Methods Samples of decidua basalis were obtained with informed written consent from patients undergoing surgical termination of normal pregnancies at University College Hospital, London. The study had been approved by the University College London Hospitals Committee on the Ethics of Human Research. Samples were obtained under ultrasound guidance, and were available from 30 cases ranging in gestational age from 5 weeks to 14 weeks (median age 9 weeks). Gestational age was estimated from the crown rump length of the fetus. Immediately after removal the tissues were fixed by immersion for 2 hours in either 4% paraformaldehyde in 0.1 M PIPES buffer for immunohistochemistry or 2% glutaraldehyde for electron microscopy, or frozen in OCT medium. Colourimetric immunohistochemistry Paraformaldehyde-fixed tissues were embedded in paraffin wax and sectioned at 5 μm. After blocking of endogenous peroxidases by incubation with 1% H2O2 for 30 min, the sections were incubated with non-immune serum for 20 min. The primary antibodies (Table 1) were applied for 3 hrs at room temperature, and binding was detected using Vectastain Elite ABC kits (Vector Laboratories) and SigmaFast DAB (Sigma), according to the manufacturers' instructions. Sections were then lightly counterstained with haematoxylin. When necessary antigen retrieval was performed prior to blocking using 0.01 M sodium citrate buffer at pH6.0 in a pressure cooker for 3 min. Negative controls were performed by omission of the primary antibody. Table 1 Primary antibodies used for immunohistochemistry. Antigen Species Type Dilution Retrieval Supplier Alpha tocopherol transfer protein Rabbit Polyclonal 1:300 Yes Dr D Kaempf-Rotzoll Cathepsin D Rabbit Polyclonal 1:200 No (frozen) Biogenesis CD56 Mouse Monoclonal 1:100 Yes Zymed CD68 Mouse Monoclonal 1:100 Yes Dako Cytokeratin 7 Mouse Monoclonal 1:100 No Dako Epidermal growth factor Rabbit Polyclonal 1:50 No Autogen Bioclear Glycodelin Mouse Monoclonal 1:10 No (frozen) Prof. M Seppälä Human placental lactogen Rabbit Polyclonal 1:200 Yes Dako Lactoferrin Rabbit Polyclonal 1:200 No Dako Leukaemia inhibitory factor Goat Polyclonal 1:200 No Santa Cruz R5Mucin-1 Mouse Monoclonal 1:100 Yes Abcam Transforming growth factor β3 Rabbit Polyclonal 1:300 Yes Santa Cruz Vascular endothelial growth factor Goat Polyclonal 1:400 No Santa Cruz Fluorescent dual-labelling immunohistochemistry Paraformaldehyde-fixed samples of decidua basalis were embedded in paraffin wax, and sectioned at 5 μm. After rehydration sections were subjected to antigen retrieval by proteinase K (20 μg/ml for 30 min), permeabilised in TBS containing Triton X-100 (0.1%) and Tween 20 (0.1%) (TBS-TT) for 30–60 min and blocked in 5 % goat serum for 30 min at room temperature. A mixture of a rabbit polyclonal and a mouse monoclonal antibody diluted in TBS-TT was applied, and sections were incubated overnight at 4°C. Negative control sections were left at the blocking stage and were not covered with primary antibodies. After three 10-minute washes in TBS-TT, sections were incubated for 1 hr at room temperature with a mixture of fluorescent secondary antibodies, containing goat anti-rabbit Alexa 488 and goat anti-mouse Alexa 568 (both used 1/200; Molecular Probes) in TBS-TT. Sections were washed in TBS-TT as before and then twice in distilled water for 5 min and subsequently mounted in Vectashield mounting medium containing DAPI (Vector, UK). Frozen sections were used for dual labelling with anti-cathepsin D (rabbit) and glycodelin (mouse). Samples were frozen in cryoembedding medium. Sections (10–12 μm) were cut on a Reichert cryomicrotome, air-dried, fixed briefly in cold methanol/acetone (at -20°C) and permeabilised in TBS-TT for 30–60 min. All subsequent immunolabelling steps were carried out as with the paraffin embedded sections. Images were captured using a Leica confocal microscope (LeicaTCS-NT, Leica Instruments GmbH, Germany). Electron microscopy Glutaraldehyde fixed tissues were secondary fixed in 1% osmium tetroxide for 1 hour, and embedded in Araldite epoxy resin. Semi-thin sections (1 μm) were stained with methylene blue, whereas ultra-thin sections (50 nm) were counterstained with uranyl acetate followed by lead citrate and viewed using a Philips CM100 microscope (Eindhoven, The Netherlands). Archival histological material The Boyd Collection housed within the Department of Anatomy, University of Cambridge, contains a number of placenta-in-situ specimens. Only those with no recorded history of pathology were reviewed, and twelve specimens met this criterion. The gestational age (from the last menstrual period) was estimated from the recorded crown-rump length [19], and ranged from 43 to 130 days. For each specimen the thickness of the endometrium was measured from the junction of the endometrium with the cytotrophoblastic shell perpendicularly to the border of the endometrium with the myometrium at a minimum of 50 randomly selected points spread over at least 5 slides using the VIDS system (Synoptics, Cambridge). Statistical analyses All analyses were performed using Statview (SAS Institute Inc., Cary, USA). Results Endometrial histology In the earliest specimen available, H710 estimated to be of 43 days menstrual age, the conceptus was embedded within the superficial layer of a highly secretory endometrium (Figure 1A). The uterine glands displayed the sawtooth appearance characteristic of the late secretory phase of the menstrual cycle, and were filled with copius secretions. These were heterogenous in nature, comprising a carbohydrate-rich flocculent material in which were interspersed numerous smooth round droplets resembling lipid (Figure 1B). The cytotrophoblastic shell was well developed, and formed a smooth interface with the endometrium. As gestational age advanced the thickness of the decidua basalis reduced dramatically from over 5 mm at 6 weeks to approximately 1 mm at 14 weeks (Figure 2). Although there was considerable variation between samples a statistically significant negative correlation existed between the two parameters (r = -0.644, P = 0.0216). As gestation advanced there was also increasing variability in the thickness of the endometrium across the placental bed, reflecting the formation of placental septa. The profiles of the glands became smoother and more regular, but they still contained precipitated secretions (Figure 3). Communications with the intervillous space could be traced until at least 10 weeks gestational age. Figure 1 A) In the earliest specimen available, H710, the conceptus (C) can be seen embedded in the superficial endometrium overlying well-developed endometrial glands (G). M, myometrium. (Haematoxylin and eosin) Scale bar = 1.0 cm. B) The secretions within the lumens of the glands are heterogenous, being a mixture of carbohydrate-rich flocculent material (blue) and what appear to be lipid droplets (red). (Alcian blue and Neutral red) Scale bar = 100 μm. Figure 2 Scattergram showing the relationship between endometrial thickness and gestational age. Figure 3 Placenta-in-situ specimen (H1094) of 13.5 weeks gestational age showing the reduction in thickness of the endometrium (E) at this stage of pregnancy. The glands (G) have a more regular outline, but still contain precipitated secretions within their lumens. M, myometrium; IVS, intervillous space. (Haematoxylin and eosin) Scale bar = 1.0 mm. Glandular epithelium In the early specimens the epithelial cells displayed a tall columnar morphology, often with large apical projections extending into the glandular lumen (Figure 4A). This was confirmed at the ultrastructural level, at which it could be seen that the apical membrane bounding these projections displayed only scanty short microvilli. Tight junctions were present at the base of the projections, linking the cells. Within the cytoplasm there were numerous mitochondria and large quantities of rough endoplasmic reticulum (Figure 4B). Numerous droplets resembling lipid were observed in the basal portions of the cells, and this was confirmed by staining with Oil RedO (data not shown). The cells were attached to a well-developed basal lamina, beneath which were occasional stromal cell processes and collagen fibres. Figure 4 A) Photomicrograph of a 1 μm resin section of 6 week decidua illustrating the columnar epithelium of the glands, their large apical projections and the heterogeneous nature of the secretions. (Methylene blue) Scale bar = 10 μm. B) At the ultrastructural level it can be seen that the cells possess large quantities of mitochondria and endoplasmic reticulum, and lipid droplets are abundant in the basal region. The cells are attached to a well-formed basal lamina (arrowed). Scale bar = 2 μm. By 10–11 weeks the cells were more cuboidal in nature with fewer apical projections (Figure 5A), although there was considerable variation between the glandular profiles even within the same sample. The apical cell membrane was frequently covered with long microvilli, and both Golgi apparatus and short strands of rough endoplasmic reticulum were present within the cytoplasm (Figure 5B). It was notable that other cell types were now present closely approximated to the deep surface of the basal lamina (Figures 5A and 6). One population possessed an irregularly shaped nucleus with dense peripheral heterochromatin, and osmiophilic membrane-bound granules were frequently present in the cytoplasm. Morphologically these resembled uterine natural killer (NK) cells, and this was confirmed using fluorescent immunohistochemistry and antibodies against CD56 (Figure 7F). The other cell type was larger, less osmiophilic and the cytoplasm resembled that of the stromal decidual cells. In order to attempt to identify these cells further immunostaining was performed for human placental lactogen and cytokeratin as markers for extravillous trophoblast, and CD68 as a marker for macrophages. Many invading trophoblasts and macrophages were present in the stroma between the glands, but only the latter were seen in particularly close proximity to the basal lamina (Figures 7G and 7H). The secretions within the glandular lumens reacted positively for placental lactogen, indicating communication with the intervillous space, as did the macrophages, suggesting phagocytic uptake of the hormone (Figure 7G). Figure 5 A) Photomicrograph of a 1 μm resin section of 10 week decidua. By now the epithelium is cuboidal in nature, although secretions are still present within the lumens. There appears to be an almost complete layer of additional cells (arrowed) beneath the basal lamina. Scale bar = 10 μm. B) At the ultrastructural level the cells appear more quiescent at this stage of gestation, although Golgi bodies and a few strands of rough endoplasmic reticulum remain. Scale bar = 1 μm. Figure 6 Low power transmission electron micrograph of 10 week decidua demonstrating the heterogenous population of cells accumulated immediately beneath the epithelial basal lamina (arrowheads) at this stage of gestation. The smaller cells (arrowed) with large numbers of granules resemble uterine NK cells, whereas the larger more electron lucent cells (asterisks) resemble decidual cells. Scale bar = 5 μm. Figure 7 Confocal immunofluorescent images of decidua at 8 weeks (C, E, G, H) and 12 weeks (A, B, D, F) gestational age. In A) and B) the glandular epithelium has been immunolabelled for tocopherol transfer protein (green) and NK cells with CD56 (red). NK cells can be seen within the stroma between the glands, but also closely approximated (arrowed) to the basal lamina of the glandular epithelium. In C-F sections were immunolabelled for epidermal growth factor (EGF) (green) and CD56 (red). The epithelium reacts strongly at 8 weeks for EGF (C), but less so at 12 weeks (D). The NK cells lying beneath the glandular epithelium also react strongly for EGF (co-localisation yellow) (E and F). In G) and H) the sections were immunolabelled for human placental lactogen (red), and in G) for CD68 (green) and in H) for cytokeratin (green). Cells positive for both placental lactogen and CD68 (yellow) were considered to be macrophages, and were observed throughout the stroma but also closely approximated to the glandular epithelium (arrowed in G). Cells reacting only for placental lactogen, or for both placental lactogen and cytokeratin, were considered to be invading extravillous trophoblast cells (arrowheads in G and H), and were not found to be closely associated with the epithelium (E). Blue, DAPI; L, gland lumen. Scale bars C, D. = 60 μm and E - H = 30 μm. By 14 weeks the glandular epithelial cells were markedly flattened and only a few short microvilli were present on the apical surface (Figure 8A). Few organelles were present in the cytoplasm, but instead there were numerous membrane vesicles containing a highly osmiophilic flocculent material resembling lipofuschin (Figure 8B). Decidual cells made extensive contact with the basal lamina beneath the epithelium, often extending long processes in order to do so (Figure 8A). Figure 8 Transmission electron micrographs of 15 week decidua illustrating A) the flattened nature of the glandular cells at this stage of gestation, and B) the accumulation of a flocculent osmiophilic material in their cytoplasm. L, gland lumen. Scale bars = 5 μm and 1 μm. Mucin and Cytokine production The glandular epithelium reacted strongly for leukaemia inhibitory factor (LIF), VEGF and MUC-1 at all gestational ages from 5 to 14 weeks (Figure 9). In the early specimens the staining was particularly strong in the apical protrusions of the epithelial cells, whereas in the older cases it was more generalised throughout the cells. The secretions within the lumens also reacted positively for LIF and MUC-1. Figure 9 Photomicrographs of immunolabelled decidua at 6 weeks (A, D, G, J, M, P) and 12 weeks (B, E, H, K, N, Q) gestational age. The glandular epithelium reacted positively for LIF (A, B), VEGF (D, E), MUC-1 (G, H), alpha tocopherol transfer protein (J, K), TGFβ3 (M, N), and weakly for lactoferrin (P, Q). Negative controls; C, F, I, L, O and R. Scale bar = 200 μm. A similar pattern was observed for alpha tocopherol transfer protein (TTP) and transforming growth factor beta (TGFβ3), although many of the decidual cells also reacted positively as gestational age increased (Figure 9). In addition, some of the interstitial cells and those just beneath the epithelium reacted intensely for TGFβ3. These were presumed to be macrophages and uterine NK cells. The pattern was different for epidermal growth factor (EGF), for although the glandular epithelium initially displayed strong reactivity, the intensity reduced considerably by 9–10 weeks. By contrast, in the older specimens the cells immediately beneath the epithelium reacted strongly. As these cells also reacted positively for CD56 it was assumed they were NK cells (Figures 7C,7D,7E,7F). Immunoreactivity for lactoferrin was weak even in the earliest specimens, although occasional cells reacted strongly. In the older specimens only faint staining could be identified (Figure 9). In all cases the negative controls showed no staining. Fate of the secretions In order to determine whether the glandular secretions taken up by the trophoblast enter the digestive pathway frozen sections of first trimester villi were dual-labelled for glycodelin, a glandular product, and cathepsin D, a marker of the lysosomal pathway. In the syncytiotrophoblast numerous vesicles immunoreactive exclusively for glycodelin were observed within the superficial layer of the syncytioplasm abutting the intervillous space, whereas lysosomes positive only for cathepsin D were observed in the basal region. In the midzone of the syncytioplasm the two labels were co-localised, indicating lysosomal fusion with the glycodelin-containing vesicles (Figure 10). Figure 10 Confocal photomicrograph of a frozen section of an 8 week villus A) immunolabelled for glycodelin (green) and cathepsin D (red) and B) under phase contrast. Vesicles labelled solely for glycodelin predominate in the apical region of the syncytiotrophoblast (S), and those for cathepsin D in the basal region. In the mid-zone the two labels co-localise (yellow) indicating that maternal proteins enter the trophoblast digestion pathway. IVS, intervillous space. Discussion It is clear from this study of placenta-in-situ specimens that the uterine glands are still well-developed and highly active at 6 weeks of pregnancy, and that although there is considerable individual variation they gradually regress, both in terms of their length and the height of their epithelium, as the first trimester advances. Some of this variation may reflect differences in the thickness of the endometrium across the placental bed, for it was generally thinnest in the centre and thicker towards the periphery. Sampling at different sites may therefore yield different measurements. Nonetheless, by the start of the second trimester the endometrium beneath the placenta is very thin, the glandular epithelium is cuboidal and secretory organelles are no longer predominant. Indeed, the accumulations of osmiophilic material within the cytoplasm are reminiscent of lipofuschin, a characteristic of involuting or aging cells. These observations are consistent with a gradual shift from essentially histiotrophic nutrition of the human conceptus during the early first trimester to haemotrophic nutrition towards the start of the second trimester [3,20]. We previously reported that at 6 weeks gestational age the glandular epithelial cells closely resemble those during the luteal phase of the cycle, with large accumulations of glycogen within the apical portions of the cell [18,21]. In the normal menstrual cycle these accumulations disperse around days 23–24, but their persistence indicates that the corpus luteum of pregnancy maintains the glands in a highly active state during early gestation. The composition of the secretions from the uterine glands has been extensively investigated during the various phases of the menstrual cycle [22,23], but their contribution post-implantation has largely been ignored. The secretions are rich in carbohydrates, glycoproteins and, as demonstrated here, lipids. They therefore may provide an important source of nutrients for energy and elements for anabolic pathways within the feto-placental unit. The observation that glycodelin, a protein that is not expressed within placental tissues and so must be of maternal origin [24], enters the lysosomal digestive pathway within the syncytiotrophoblast supports this hypothesis. We have speculated that reliance on histiotroph during the period of organogenesis may protect the fetus from teratogenic damage by reactive oxygen species, for all mammalian embryos studied so far appear to rely heavily on anaerobic pathways during this period of development [25,26]. Once organogenesis is complete the oxygen concentration within the feto-placental unit rises as placental attachment and development occurs or, as in the case of the human, the maternal circulation to the placenta is fully established. Besides acting as a source of nutrients our results also demonstrate that the glands express a wide variety of growth factors and cytokines, and so may play an important role in regulating placental development as in other species. Receptors for EGF have been localised immunohistochemically to the cytotrophoblast cells in the earliest stages of pregnancy, and on the syncytiotrophoblast in later gestation [27,28]. This switch parallels the dual actions of EGF reported, for in the earliest samples of 4–5 weeks EGF stimulated cytotrophoblast proliferation, whereas at 6–12 weeks it stimulated secretion of human chorionic gonadotropin (HCG) and placental lactogen [29]. Similarly, receptors for LIF have been demonstrated on first trimester villous and extravillous trophoblast populations, and on villous endothelial cells [30]. Addition of LIF to purified extravillous trophoblast cells had no effect on proliferation or integrin expression, but did inhibit forskolin-induced HCG production by BeWo cells in a dose-dependent fashion [30,31]. Receptors for VEGF have also been identified on the villous and extravillous trophoblast populations, and on villous endothelial cells [32,33], whilst TGFβ3 can modulate trophoblast differentiation between the proliferative and invasive phenotype [34]. Histiotroph may therefore potentially play significant roles in regulating trophoblast proliferation and differentiation during early pregnancy, as well as modulating placental vascularization. Another group of proteins expressed by the glandular epithelium is that of transport carriers. TTP is a cytosolic protein first identified in the liver, but which has recently also been reported in the syncytiotrophoblast of the human placenta [35,36]. The high level of expression in the glandular epithelium suggests that histiotroph may be an important route for transfer of antioxidants during early pregnancy, increasing the defences of the feto-placental tissues against oxidative stress associated with onset of the maternal intraplacental circulation [3,37]. Lactoferrin is glycoprotein (molecular weight 82,400) traditionally associated with the transport of iron in breast milk. It was first identified immunohistochemically in the endometrial glandular epithelium and in their secretions, and although immunoreactivity was variable between specimens it was generally strongest during the late secretory phase of the cycle [38]. Here we identified significant staining only at the earliest gestational ages. The role of lactoferrin in the transport of iron is doubtful given the presence of transferrin receptors on the syncytiotrophoblast. Potentially, it may act as an antioxidant, for by forming stable complexes with free iron ions within the intervillous space it will reduce the possibility of generation of the highly toxic hydroxyl ion through the Fenton reaction [39,40]. It also possesses anti-microbial properties and so may contribute to the immune defences of the endometrium and early placenta [41]. Endometrial secretions may also modulate maternal immunological responses to the placental tissues. Thus glycodelin, which is released into the intervillous space, is immunosuppressive and functions as a direct T-cell inhibitor [9,42]. As gestation advances NK and stromal decidual cells migrate and come to lie closely approximated to the basal lamina of the glandular epithelium. The presence of NK cells within the glandular epithelium has been reported previously [43], and similar cells have been observed in an intraepithelial position in other species [44]. Whether the subepithelial cells we observed play a role in immune surveillance or support the epithelium in some other way is not clear at present, but the fact that they are immunopositive for EGF raises the possibility of paracrine signalling. Because insufficient decidualization could have an impact on implantation and placentation, evaluation of the endometrial morphology by ultrasound has generated a lot of clinical interest. An endometrial thickness of 8 mm or more is considered to be favourable for implantation in humans [45], although this remains controversial as other authors have not found an association between endometrial thickness and pregnancy achievement [46]. One reason for this may be the fact that endometrial growth is not an homegeneous process, and that a single measurement of the endometrial thickness may not reflect the entire endometrial development. Within this context evaluation of the total endometrial volume with 3-D ultrasound could be a more accurate way of evaluating endometrial development [47]. Adequate endometrial thickness seems to be directly linked to uterine vascularization, and women with a good endometrial thickness on ultrasound but a poor intra-endometrial blood flow tend to have a poor reproductive outcome [48]. Uterine perfusion appears to regulate endometrial receptivity, and a high uterine resistance to blood flow is associated with recurrent miscarriages [49]. The visualisation of the endometrial circulation with 3-D doppler ultrasound appears to be an efficient parameter in predicting implantation in IVF cycles [50]. Attempts to correlate functional activity of the glands with pregnancy outcome have also met with mixed success. Thus, whilst reduced concentrations of MUC-1, LIF and glycodelin in uterine flushings have been reported in women suffering recurrent miscarriages [51,52], expression of these markers within the endometrium shows no significant association [53]. Why the glands should regress while maternal progesterone concentrations remain high is not known, but it would seem reasonable to assume that the decline of histiotrophic nutrition and the onset of haemotrophic exchange are co-ordinated in some way. How this might be achieved in the human is unknown at present. Authors' contributions JH performed the tissue processing and colourimetric immunohistochemistry. TC-D performed the confocal immunofluoresence and dual-labelling. EJ performed the clinical procedures and collection of samples. GJB conceived the study and performed the electron microscopy and morphometric analysis. Acknowledgements The authors are grateful to WellBeing for financial support, and to Prof. M Seppälä and Dr D Kaempf-Rotzoll for their gifts of antibodies. Ms O Spasic-Boskovic and Ms J Powell provided excellent technical assistance. The confocal and electron microscopy was performed in the Multi-Imaging Centre of the School of Biological Sciences, which was established with grants from The Wellcome Trust. ==== Refs Hustin J Schaaps JP Echographic and anatomic studies of the maternotrophoblastic border during the first trimester of pregnancy Am J Obstet Gynecol 1987 157 162 168 3300349 Jaffe R Jauniaux E Hustin J Maternal circulation in the first-trimester human placenta-Myth or reality? Am J Obstet Gynecol 1997 176 695 705 9077631 Jauniaux E Watson AL Hempstock J Bao Y-P Skepper JN Burton GJ Onset of maternal arterial bloodflow and placental oxidative stress; a possible factor in human early pregnancy failure Am J Pathol 2000 157 2111 2122 11106583 Kliman HJ Uteroplacental blood flow. The story of decidulaisation, menstruation and trophoblast invasion Am J Pathol 2000 157 1759 1768 11106547 Amoroso EC Parkes AS Placentation In Marshall's Physiology of Reproduction 1952 2 3 London: Longmans Green and Co 127 311 Hamlett WC Koob T Hamlett WC Female reproductive system In Sharks, Skates and Rays: Biology of Elasmobranch Fishes 1999 Baltimore: The Johns Hopkins Univ Press 398 443 Wooding FBP Flint APF Lamming GE Placentation In Marshall's Physiology of Reproduction 1994 London: Chapman & Hall 233 460 Mossman HW Vertebrate fetal membranes:comparative ontogeny and morphology; evolution; phylogenetic significance; basic functions; research opportunities 1987 London: Macmillan Seppälä M Bohn H Tatarinov Y Glycodelins Tumour Biol 1998 19 213 220 9591048 10.1159/000030009 Carson DD Bagchi I Dey SK Enders AC Fazleabas AT Lessey BA Yoshinaga K Embryo implantation Dev Biol 2000 223 217 237 10882512 10.1006/dbio.2000.9767 Brayman M Thathiah A Carson DD MUC1: a multifunctional cell surface component of reproductive tissue epithelia Reprod Biol Endocrinol 2004 2 4 14711375 10.1186/1477-7827-2-4 Gray CA Taylor KM Ramsey WS Hill JR Bazer FW Bartol FF Spencer TE Endometrial glands are required for preimplantation conceptus elongation and survival Biol Reprod 2001 64 1608 1613 11369585 Lennard SN Gerstenberg C Allen WR Stewart F Expression of epidermal growth factor and its receptor in equine placental tissues J Reprod Fertil 1998 112 49 57 9538329 10.1530/jrf.0.1120049 Moore KL Persaud TVN The Developing Human. 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Saunders Larsen WJ Human Embryology 1997 New York: Churchill Livingstone Schaaps JP Hustin J In vivo aspect of the maternal-trophoblastic border during the first trimester of gestation Trophoblast Res 1988 3 39 48 Rodesch F Simon P Donner C Jauniaux E Oxygen measurements in endometrial and trophoblastic tissues during early pregnancy Obstet Gynecol 1992 80 283 285 1635745 Burton GJ Watson AL Hempstock J Skepper JN Jauniaux E Uterine glands provide histiotrophic nutrition for the human fetus during the first trimester of pregnancy J Clin Endocrinol Metab 2002 87 2954 2959 12050279 10.1210/jc.87.6.2954 Hadlock FP Shah YP Math DJK Lindsey JV Fetal crown-rump length: reevaluation of relation to menstrual age (5–18 weeks) with high-resolution real-time ultrasound Radiology 1992 182 501 505 1732970 Burton GJ Hempstock J Jauniaux E Nutrition of the human fetus during the first trimester–a review Placenta 2001 22 S70 77 11312634 Dockery P Li TC Rogers AW Cooke ID Lenton EA The ultrastructure of the glandular epithelium in the timed endometrial biopsy Hum Reprod 1988 3 826 834 3182973 Bell SC Secretory endometrial/decidual proteins and their function in early pregnancy J Reprod Fertil Suppl 1988 36 109 125 3057194 Beir-Hellwig K Sterzik K Bonn B Beir HM Contribution to the physiology and pathology of endometrial receptivity: the determination of protein patterns in human uterine secretions Hum Reprod 1989 4 115 120 2613870 Seppälä M Jukunen M Riitinen L Koistinen R Endometrial proteins: a reappraisal Hum Reprod 1992 7 31 38 1280281 New DAT Whole-embryo culture and the study of mammalian embryos during organogenesis Biological Rev 1978 53 81 122 Burton GJ Hempstock J Jauniaux E Oxygen, early embryonic metabolism and free radical-mediated embryopathies Reprod BioMed Online 2003 6 84 96 12626148 Ladines-Llave CA Maruo T Manalo AS Mochizuki M Cytologic localization of epidermal growth factor and its receptor in developing human placenta varies over the course of pregnancy Am J Obstet Gynecol 1991 165 1377 1382 1957866 Mühlhauser J Crescimanno C Kaufmann P Höfler H Zaccheo D Castellucci M Differentiation and proliferation patterns in human trophoblast revealed by c-erbB-2 oncogene product and EGF-R J Histochem Cytochem 1993 41 165 173 8093455 Maruo T Matsuo H Murata K Mochizuki M Gestational age-dependent dual action of epidermal growth factor on human placenta early in gestation J Clin Endocrinol Metab 1992 75 1362 1367 1430098 10.1210/jc.75.5.1362 Sharkey AM King A Clark DE Burrows TD Johki PP Charnock Jones DS Loke YW Smith SK Localization of leukaemia inhibitory factor and its receptor in human placenta thoughout pregnancy Biol Reprod 1999 60 355 364 9916002 Kojima K Kanzaki H Iwai M Hatayama H Fujimoto M Narukawa S Higuchi T Kaneko Y Mori T Fujita T Expression of leukaemia inhibitory factor (LIR) receptor in human placenta: a possible role for LIF in the growth and differentiation of trophoblasts Hum Reprod 1995 10 1907 1911 8583009 Cooper JC Sharkey AM McLaren J Charnock Jones DS Smith SK Localization of vascular endothelial growth factor and its receptor, flt, in human placenta and decidua by immunohistochemistry J Reprod Fertil 1995 105 205 213 8568762 10.1530/jrf.0.1050205 Charnock Jones DS Kaufmann P Mayhew TM Aspects of human fetoplacental vasculogenesis and angiogenesis. I. Molecular recognition Placenta 2004 25 103 113 14972443 10.1016/j.placenta.2003.10.004 Caniggia I Mostachfi H Winter J Gassmann M Lye SJ Kuliszewski M Post M Hypoxia-inducible factor-1 mediates the biological effects of oxygen on human trophoblast differentiation through TGFbeta(3) J Clin Invest 2000 105 577 587 10712429 Gordon MJ Campbell FM Dutta-Roy AK alpha-Tocopherol-binding protein in the cytosol of the human placenta Biochem Soc Trans 1996 24 202S 8736860 Kaempf-Rotzoll DE Horiguchi M Hashiguchi K Aoki J Tamai H Linderkamp O Arai H Human placental trophoblast cells express alpha-tocopherol transfer protein Placenta 2003 24 439 444 12744919 10.1053/plac.2002.0966 Hempstock J Jauniaux E Greenwold N Burton GJ The contribution of placental oxidative stress to early pregnancy failure Hum Pathol 2003 34 1265 1275 14691912 10.1016/j.humpath.2003.08.006 Masson PL Heremans JF Ferin J Presence of an iron-binding protein (lactoferrin) in the genital tract of the human female. 1. Its immunolocalization in the endometrium Fertil Steril 1968 19 679 689 4877677 Halliwell B Gutteridge JMC Free Radicals in Biology and Medicine 1999 Oxford: Oxford Science Publications Sandomirsky BP Galchenko SE Galchenko KS Antioxidant properties of lactoferrin from bovine colostrum before and after its lyophilization Cryo Letters 2003 24 275 280 14566387 King AE Critchley HOD Kelly RW Innate immune defences in the human endometrium Reprod Biol Endocrinol 2003 1 116 14641912 10.1186/1477-7827-1-116 Rachmilewitz J Riely GJ Tykocinski ML Placental protein 14 functions as a direct T-cell inhibitor Cell Immunol 1999 191 26 33 9918684 10.1006/cimm.1998.1408 King A Balendran N Wooding P Carter NP Loke YW CD3- leukocytes present in the human uterus during early placentation: phenotypic and morphological characterization of the CD56++ population Dev Immunol 1991 1 169 190 1726555 Perry JS Crombie PR Ultrastructure of the uterine glands of the pig J Anat 1982 134 339 350 7076558 Basir GS O WS So WW Ng EH Ho PC Evaluation of cycle-to-cycle variation of endometrial responsiveness using transvaginal sonography in women undergoing assisted reproduction Ultrasound Obstet Gynecol 2002 19 484 489 11982983 10.1046/j.1469-0705.2002.00685.x Kolibianakis EM Zikopoulos KA Fatemi HM Osmanagaoglu K Evenpoel J Van Steirteghem A Devroey P Endometrial thickness cannot predict ongoing pregnancy achievement in cycles stimulated with clomiphene citrate for intrauterine insemination Reprod Biomed Online 2004 8 115 118 14759299 Child TJ Sylvestre C Tan SL Endometrial volume and thickness measurements predict pituitary suppression and non-suppression during IVF Hum Reprod 2002 17 3110 3113 12456609 10.1093/humrep/17.12.3110 Yang JH Wu MY Chen CD Jiang MC Ho HN Yang YS Association of endometrial blood flow as determined by modified colour Doppler technique with subsequent outcome of in-vitro fertilization Hum Reprod 1999 14 1606 1610 10357984 10.1093/humrep/14.6.1606 Habara T Nakatsuka M Konishi H Asagiri K Noguchi S Kudo T Elevated blood flow resistance in uterine arteries of women with unexplained recurrent pregnancy loss Hum Reprod 2002 17 190 194 11756386 10.1093/humrep/17.1.190 Wu HM Chiang CH Huang HY Chao AS Wang HS Soong YK Detection of the subendometrial vascularization flow index by three-dimensional ultrasound may be useful for predicting the pregnancy rate for patients undergoing in vitro fertilization-embryo transfer Fertil Steril 2003 79 507 511 12620431 10.1016/S0015-0282(02)04698-8 Dalton CF Laird SM Estdale SE Saravelos HG Li TC Endometrial protein PP14 and CA-125 in recurrent miscarriage patients; correlation with pregnancy outcome Hum Reprod 1998 13 3197 3202 9853880 10.1093/humrep/13.11.3197 Mikolajczyk M Skrzypczak J Szymanowski K Wirstlein P The assessment of LIF in uterine flushing – a possible new diagnostic tool in states of impaired infertility Reprod Biol 2003 3 259 270 14688825 Tuckerman E Laird SM Stewart R Wells M Li TC Markers of endometrial function in women with unexplained recurrent pregnancy loss: a comparison between morphologically normal and retarded endometrium Hum Reprod 2004 19 196 205 14688182

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==== Front J Transl MedJournal of Translational Medicine1479-5876BioMed Central London 1479-5876-2-251526875910.1186/1479-5876-2-25ResearchTransient spleen enlargement in peripheral blood progenitor cell donors given G-CSF Stroncek David F [email protected] Kristin [email protected] Thomas [email protected] Angela [email protected] Susan F [email protected] Department of Transfusion Medicine, Warren G. Magnuson Clinical Center, National Institutes of Health, Bethesda, Maryland, USA2 Department of Diagnostic Radiology, Warren G. Magnuson Clinical Center, National Institutes of Health, Bethesda, Maryland, USA2004 21 7 2004 2 25 25 26 6 2004 21 7 2004 Copyright © 2004 Stroncek et al; licensee BioMed Central Ltd.2004Stroncek et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The administration of granulocyte colony-stimulating factor (G-CSF) to peripheral blood progenitor cell (PBPC) donors causes spleen length to increase, but the duration of enlargement is not known. Eighteen healthy subjects were given 10 μg/kg of G-CSF for 5 days and a PBSC concentrate was collected by apheresis. Ultrasound scans were used to assess craniocaudal spleen length before and after G-CSF administration. Mean spleen length increased from a baseline length of 10.7 ± 1.3 cm to 12.1 ± 1.2 cm on the apheresis day (p < 0.001). Ten days after apheresis, spleen length fell to 10.5 ± 1.2 cm and did not differ from baseline levels (p = 0.57), but in 3 subjects remained 0.5 cm greater than baseline length. Increases in spleen length in PBPC donors are transient and reversible. granulocyte colony-stimulating factorperipheral blood progenitor cellssplenomegalyspleen ==== Body Background Peripheral blood progenitor cell (PBPC) concentrates donors are routinely given granulocyte colony-stimulating factor (G-CSF) to increase the concentration of circulating PBPCs and hence the number of progenitors that can be collected by apheresis. Typically 10 to 16 μg/kg of G-CSF are given subcutaneously daily for 4 to 6 days prior to the collection [1-3]. The administration of G-CSF to healthy PBPC concentrates donors is very safe, but there have been four reports of spontaneous rupture of the spleen and splenectomy in healthy allogeneic PBPC donors given G-CSF [4-7]. While spontaneous rupture of the spleen in PBSC donors given G-CSF is rare, the administration of G-CSF for five days causes spleen length to increase in almost all healthy donors [8,9]. The increase in length is highly variable, but the mean increase is approximately 13%. Spleen length begins to return to baseline levels quickly, but it is not known how long it takes to return to baseline. In a previous study of 20 PBPC donors given 10 μg/kg of G-CSF for 5 days, we found that spleen length measured four days after the last dose of G-CSF was less than the length on the day of apheresis but greater than baseline values [8]. Since allogeneic PBPC donors may be at risk for splenic rupture while the spleen is enlarged, it is important to determine when spleen size returns to baseline levels. The purpose of this study was to determine if spleen length returns to baseline 10 days after G-CSF-mobilized PBPC concentrates are collected by apheresis from healthy subjects. Methods Study design All of the subjects were in good health and were donating G-CSF-mobilized PBPC concentrates for laboratory investigations. The donors were given 10 μg/kg of G-CSF (Filgrastim, Amgen, Thousand Oaks, CA) daily for 5 days, and a PBSC concentrate was collected approximately 2 hours after the last G-CSF dose was given. PBPC concentrates were collected with a CS3000 blood cell separator (Baxter Health Care Corporation, Round Lake, IL). Spleen length was evaluated by ultrasound examination three times: prior to the administration of the first dose of G-CSF, on the day of apheresis, and 10 or 11 days after apheresis. This study was approved by the Institutional Review Board of the National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland. Spleen length assessment Craniocaudal spleen length was assessed using ultrasound (Acuson Aspen Advanced, Siemens Medical Solutions Ultrasound Division, Mountain View, CA) with a sector transducer (Acuson, 4V1, 4.0 mHz frequency, Siemens Medical Solutions). The intra-observer error for measuring spleen length using ultrasound is 4.9 mm when healthy subjects are evaluated at separate settings [10]. Blood counts and chemistries Complete blood counts were performed with an automated cell counter (Cell Dyne 4000, Abbott Diagnostics, Santa Clara, CA). CD34+ cell counts were performed using a flow cytometer (Beckman Coulter, Miami, FL). Statistical analysis Spleen lengths measured before and after the G-CSF course were compared using 2-tailed paired t-tests. Spleen length changes were also compared among males and females and Caucasians and non-Caucasians using 2-tailed t-tests. The percent change in spleen length was compared with blood counts, CD34+ cell counts, and donor age using linear regression. Results and Discussion The median age of the 18 healthy subjects was 34 years old and ranged from 22 to 55 years of age. Eight of the subjects were male, 13 of the donors were Caucasian, 3 were African American, and 2 Asian. Apheresis day spleen length increased above baseline length in 17 of 18 donors (Figure 1). The mean spleen length increased from a baseline level of 10.7 ± 1.3 cm to 12.1 ± 1.2 cm on the day of apheresis (p < 0.001). The mean increase in length was 1.4 ± 0.8 cm or 13.1 ± 8.9%. Figure 1 Spleen length changes in healthy subjects donating G-CSF-mobilized PBPC concentrates. Eighteen subjects were given 10 μg/kg of G-CSF for 5 days and a PBPC concentrate was collected approximately 2 hours after the last dose of G-CSF. Spleen length was measured by ultrasound before G-CSF was given (day 1), immediately after the PBPC concentrate collection (day 5), and approximately 10 or 11 days after the collection (day 15 or 16). Spleen length was measured 10 or 11 days after apheresis in all 18 subjects. The mean spleen length 10 days after apheresis fell to 10.5 ± 1.2 cm and was less than the apheresis day spleen length (p < 0.001). There was no difference between the 10-day post-apheresis and pre-G-CSF spleen length (p = 0.57). The spleen length 10 days after apheresis was less than the apheresis day length in all 17 donors whose spleen length increased. However, the spleen length 10 days after apheresis remained more than 0.5 cm greater than baseline spleen length in 3 subjects (Table 1). All 3 were Caucasian and 2 were female. Their spleen length remained 6.7%, 11.0%, and 10.5% greater than baseline levels. Two of these subjects had relatively large increases in spleen length of 18.3% and 26.3%, but their spleen length fell considerably 10 days after apheresis. It is likely that the spleen returned to baseline length in these 2 subjects shortly after the third ultrasound was preformed. The other subject's spleen increased only 12.0% in length and 10 days after apheresis had changed little. It is not certain when this subject's spleen returned to baseline length. Table 1 Peripheral Blood Stem Cell Donors Whose Spleen Length 10 Days After Apheresis Remained More than 0.5 cm Greater than Baseline Length Spleen Length (cm) Donor Age (Yrs) Gender Race Baseline Apheresis Day 10 Days Post-Apheresis Enlargement 10 Days Post-Apheresis 7 23 Female Cauc 10.4 12.3 11.1 0.7 13 22 Female Cauc 10.0 11.2 11.1 1.1 14 54 Male Cauc 9.5 12.0 10.5 1.0 Cauc = Caucasian PBPC donors with the largest increase in spleen size may be at the greatest risk for spontaneous splenic rupture. In order to determine if subject age, gender, race, or post-G-CSF blood counts affected the magnitude of spleen enlargement, we assessed the relationship between these factors and percent change in spleen length in the 18 subjects in this study and 20 subjects studied previously [14]. There was no difference in the spleen length increase between males and females (12.3 ± 9.7% versus 14.8 ± 8.0%, p = 0.40) or between Caucasians and non-Caucasians (13.5 ± 8.4% versus 13.6 ± 10.4%; p = 0.98). Spleen length increase was not related to donor age (r = 0.13). In addition, spleen length increase was not related to preapheresis CD34+ (r = 0.04), WBC (r = 0.05), neutrophil (r = 0.07), lymphocyte (r = -0.14), monocyte (r = -0.04), and platelet counts (r = 0.19) or hemoglobin level (r = -0.04). Conclusions Healthy PBPC concentrate donors given G-CSF should be warned that their spleens will be enlarged for a brief time and that they may be at risk of splenic rupture. Most donors are likely at risk for splenic rupture only during the time of G-CSF administration and for about 10 days after the completion of the G-CSF course. Since splenic enlargement may persist for longer periods in some donor, until more data are available it may be worthwhile to counsel PBPC donors to avoid activities that could lead to abdominal and splenic trauma for 2 to 3 weeks after the last dose of G-CSF. Acknowledgments We thank the nurses of the Dowling Clinic, Department of Transfusion Medicine, for helping to recruit subjects and collecting the PBPC concentrates, and the staff of the Ultrasound Section, Diagnostic Radiology Department, at the Warren G. Magnuson Clinical Center, NIH, Bethesda, Maryland. ==== Refs Bensinger WI Weaver CH Appelbaum FR Rosley S Demirer T Sanders J Storb R Buchner CD Transplantation of allogeneic peripheral blood stem cells mobilized by recombinant human granulocyte colony-stimulating factor Blood 1995 85 1655 1658 7534140 Korbling M Prezepiorka D Huh YO Engel K van Besien S Giralt S Anderson B Kleine HD Seong D Desseroth AB Andreff M Champlin R Allogeneic blood stem cell transplantation for refractory leukemia and lymphoma: potential advantage of blood over marrow allografts Blood 1995 85 1659 1665 7888684 Schmitz N Dreger P Suttrop M Rohwedder EB Haferlach T Loffler II Hunter A Russel NH Primary transplantation of allogeneic peripheral blood progenitor cells mobilized by filgrastim (granulocyte colony stimulating factor) Blood 1995 85 1666 1672 7534141 Becker PS Wagle M Matous S Swanson RS Pihan G Lowry PA Stewart FM Heard SO Spontaneous splenic rupture following administration of granulocyte colony-stimulating factor (G-CSF): occurrence in an allogeneic donor of peripheral blood stem cells Biol Blood Marrow Transplant 1997 3 45 49 9209740 Falzetti F Aversa F Minelli O Tabilio A Spontaneous rupture of the spleen during peripheral blood stem-cell mobilisation in a healthy donor Lancet 1999 353 555 10028986 10.1016/S0140-6736(99)00268-8 Margolis D Dincer A Anderson L Moraski L Casper J Gottschall J Serious adverse events in parental donors undergoing peripheral blood progenitor cell mobilization: a trigger for changing the donation process [abstract] Blood 2001 98 178a Kroger N Renges H Kruger W Sonnenberg S Gutensohn K Dielschneider T Cortes-Dericks L Zander A Intermediate versus standard dose of G-CSF for stem cell mobilization in healthy donors for allogeneic transplantation [abstract] Bone Marrow Transplantation 2002 29 S17 Platzbecker U Prange-Krex G Bornhauser M Koch R Soucek S Aikele P Haack A Haag C Schuler U Berndt A Rutt C Ehninger G Holig K Spleen enlargement in healthy donors during G-CSF mobilization of PBPCs Transfusion 2001 41 184 189 11239220 10.1046/j.1537-2995.2001.41020184.x Stroncek D Shawker T Follmann D Leitman SF G-CSF-induced spleen size changes in peripheral blood progenitor cell donors Transfusion 2003 43 609 613 12702182 10.1046/j.1537-2995.2003.00384.x Lamb PM Lund A Kanagasabay RR Martin A Webb JA Reznek RH Spleen size. How well do linear ultrasound measurements correlated with three-dimensional CT volume assessments? Br J Radiol 2002 75 573 579 12145129

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J Transl Med. 2004 Jul 21; 2:25

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J Transl Med

10.1186/1479-5876-2-25

oa_comm

==== Front BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-981527122410.1186/1471-2105-5-98SoftwareIntegrated web service for improving alignment quality based on segments comparison Plewczynski Dariusz [email protected] Leszek [email protected] Yuzhen [email protected] Lukasz [email protected] Adam [email protected] Bioinformatics Laboratory, BioInfoBank Institute, Poznan, Poland2 Interdisciplinary Centre for Mathematical and Computational Modeling, University of Warsaw, Poland3 The Burnham Institute, La Jolla, USA4 Bioinformatics Core JCSG, University of California San Diego, La Jolla, USA2004 22 7 2004 5 98 98 30 3 2004 22 7 2004 Copyright © 2004 Plewczynski et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Defining blocks forming the global protein structure on the basis of local structural regularity is a very fruitful idea, extensively used in description, and prediction of structure from only sequence information. Over many years the secondary structure elements were used as available building blocks with great success. Specially prepared sets of possible structural motifs can be used to describe similarity between very distant, non-homologous proteins. The reason for utilizing the structural information in the description of proteins is straightforward. Structural comparison is able to detect approximately twice as many distant relationships as sequence comparison at the same error rate. Results Here we provide a new fragment library for Local Structure Segment (LSS) prediction called FRAGlib which is integrated with a previously described segment alignment algorithm SEA. A joined FRAGlib/SEA server provides easy access to both algorithms, allowing a one stop alignment service using a novel approach to protein sequence alignment based on a network matching approach. The FRAGlib used as secondary structure prediction achieves only 73% accuracy in Q3 measure, but when combined with the SEA alignment, it achieves a significant improvement in pairwise sequence alignment quality, as compared to previous SEA implementation and other public alignment algorithms. The FRAGlib algorithm takes ~2 min. to search over FRAGlib database for a typical query protein with 500 residues. The SEA service align two typical proteins within circa ~5 min. All supplementary materials (detailed results of all the benchmarks, the list of test proteins and the whole fragments library) are available for download on-line at . Conclusions The joined FRAGlib/SEA server will be a valuable tool both for molecular biologists working on protein sequence analysis and for bioinformaticians developing computational methods of structure prediction and alignment of proteins. Library of protein motifsProfile-profile sequence similarity (BLASTFFAS)Fragments library (FRAGlib)Predicted Local Structure Segments (PLSSs)Segment Alignment (SEA)Network matching problem ==== Body Background Protein structure is obviously modular, with similar structural segments, such as alpha helices and beta strands found in unrelated proteins. Such segments, identified from structure, are used extensively in description and analysis of protein structures [1,2]. Several groups have demonstrated that only a small library of segments is sufficient to rebuild experimental protein structures with high accuracy [3]. Predicted local structure segments (PLSS) are also used in structural prediction, starting from the nearest neighbor approach to secondary structure prediction [4-6]. This idea was later extended and lead to even more successful applications of PLSSs in ab initio structure prediction by Baker and colleagues, who developed a library of sequence-structure motifs called I-sites [7]. Those motifs are later assembled in a complete protein structure by a program ROSETTA [8]. Predicted local structure segments are also used in a novel protein alignment algorithm, based on the comparison of PLSSs for two proteins treated as networks and finding a common path through networks describing the two proteins [9]. The underlying idea in all those approaches is that because global folding constraints can override local preferences, the prediction of structure segments from local sequence is by necessity uncertain. Therefore, instead of trying to predict a correct local structure, all possible local solutions are identified and other constraints (folded structure in Rosetta, or compatible alignment in SEA) are used to identify a globally consistent solution. Prediction of local structure segments can be approached in two different ways. A first possibility, used in most nearest neighbor secondary structure algorithms, is to use a representative set of proteins with known structure as source of structure segments, but without any restrictions on a number or type of segments. In this approach, we don't make any assumptions about the compositions and distributions of segments in the library and this approach can be compared to unsupervised learning approach. In a second approach, used for instance in the I-site method, only segments from a specifically constructed fragment library are used in prediction, thus this approach is similar to supervised learning. Interestingly, some limited tests suggest that the former approach leads to lower prediction accuracy [10]. The same tests suggested the possibility that different segment libraries could lead to different prediction, and likely, some segment libraries would be better suited to some tasks. Following this observation, we have developed the FRAGlib – a fragment library specifically designed to complement a segment alignment SEA. SEA alignment algorithm was developed previously in our group [9] and originally used in conjunction with the I-site library. I-site library [7] was originally developed to be used in ab initio folding predictions and anecdotal evidence suggested that it may not be ideally suited for alignment purposes. In this note we describe a combined FRAGlib/SEA server and first benchmarking results of this method. Implementation Database of Short Fragments FRAGlib is based on the idea of developing a uniform coverage of all known types of local structural regularity with the distribution based on that observed in natural proteins. The collection of segments is constructed using representative set of proteins from the ASTRAL database [11,12]. For each protein in this set, each continuous segment with regular secondary structure, including the flanking residues on both sides, is added to the FRAGlib (see below for details). We do not utilize any further clustering algorithm so our database contains no-unique entries and it is redundant both in terms of structure and sequence information. Local structure is described by the SLSR (Symbolized Local Stuctures Representation) codes consisting of 11 symbols {HGEeBdbLlxc}, each representing a certain backbone dihedral (phi and psi) region [7,13]. Protein local structure is described as a string of local-structure symbols and a local structure segment is defined as a 5–17 amino acid fragment with constant local structural codes. Segments are then extended by two additional residues offset at the beginning, and at the end of a segment. We store all such segments with their sequence, SLSR style local structures representation codes and the homology profile [14,15], derived from that of their parent protein. The library is highly redundant, i.e. there are many segments with the same structural description, but each of the redundant fragments is coming from a different parent protein (or a different part of the same parent protein), therefore it has a different sequence and a different profile associated with it. FRAGlib prediction In a next step, FRAGlib segment library is used to assign local structure segments for a new protein (query) based only on sequence information using a variant of the FFAS profile-profile alignment algorithm [16]. A profile for the query protein is calculated following the FFAS protocol, then for all possible overlapping segments of length from 7 to 19 amino acids, their profiles are compared to those of the segments from the FRAGlib database and the score of each alignment is calculated using a FFAS-like scalar product of composition vectors at each position. Since the segments being compared have the same length, no dynamic programming alignment is necessary and the score calculation can be highly optimized. As the result of this procedure, each position in the query protein can be assigned to all of the possible LSSs in the database, each with a specific score (see Figure 1). Only reduced sets of predicted LSSs, rather arbitrarily limited to the first 20 highest scoring segments are kept for further analysis. This cut-off is the only free parameter of the method, and can be set by user using the Web interface of the server. The Q3 quality of the FRAGlib used as a secondary structure prediction algorithm (data not shown), with the prediction based on the single best scoring segment for each position is 73% on a standard secondary structure prediction benchmark. The Q3 gives percentage of residues predicted correctly as helix, strand, and coil or for all three conformational states. SEA Segment Alignment Approach to Protein Comparison The principal motivation to develop the FRAGlib segment prediction was to further improve the alignment quality for comparing distantly related proteins, which is one of the most important problems in practical application of comparative modeling and fold recognition [17]. To address this problem, we have previously developed a SEA algorithm, which compares the network of predicted local structure segments (PLSSs) for two proteins using the network matching approach. In a previous paper we have demonstrated that the SEA algorithm, using I-site server for PLSSs prediction and a simple sequence-sequence scoring for segment comparison resulted in alignments better than the FFAS profile-profile alignment algorithm and several other alignment tools. A full description of the SEA algorithm is available in the previous manuscript [9], so only a brief summary is presented here. Every residue in each of the proteins being aligned is described as a vertex in the graph. Two artificial vertices are added to the very beginning of each protein as a source vertex, and also at the end as a sink vertex. For each PLSS is described as an edge between the vertices representing its first and last positions. For some PLSS protocols, some parts of the protein may not be covered by any predicted segments, so virtual edges are added to all neighbor residues to form a complete, continuous network. Each assembly of connected PLSSs corresponds to a path in this network. In a next step, PLSSs networks of two proteins are compared by the SEA algorithm. For each pair of positions i and j, with position i coming form the first protein and position j from the second protein, all possible segments covering each of the positions must be considered in a combinatorial way and compared to get the optimal similarity score. It is not the sequences or secondary structures at two positions that are compared, but all segments that cover these two positions. This is the main feature of SEA that makes it different from standard sequence pair-wise alignments. The computational complexity of SEA is about O(NMC1C2), where C1 and C2 are the average numbers of segments that cover a position in each protein (the segment coverage). Detailed description of the SEA mathematical algorithm together with benchmarks results obtained using the I-site server calculated PLSSs network can be found elsewhere [9]. The integrated FRAGlib and SEA server is available at [18]. The FRAGlib database and segment prediction provides the PLSSs network for each aligned protein, and the SEA algorithm aligns the two networks. On Figure 2 we present the flowchart of the integrated web service. Preliminary benchmarks for the FRAGlib/SEA server and presented below. A full paper on the FRAGlib algorithm is in preparation. Results and Discussion We use here as a benchmark the database of 409 family-level similar pairs [19]. Each protein pair shares at least one similar domain as identified by SCOP [20]. Segments coming from the proteins of the same SCOP family as the proteins being compared were removed from the FRAGlib calculated PLSSs network. Further analysis of the SEA results also confirmed that the memorization is not a problem here, as all the SEA alignment are build predominantly from segments that are not locally optimal. To evaluate the improvement we use two measures of alignment quality: the classical root mean square deviation (RMSD) and the shift score [1]. The shift score measures misalignment between a predicted alignment of two proteins and the reference alignment. The shift score measure ranges from -ε(default as -0.2) to 1.0, where 1.0 means an identical alignment. RMSD is dependent on alignment length and the shift score is dependent on the reference alignment, so both measures are less than perfect in comparing alignments. In our case we use as the reference alignment provided by the CE structural method [21]. We chose the CE, which is available as a single file executable for various operating systems, as an example of purely structural alignment tool. It is a method for fast calculation of pairwise structure alignments, which aligns two proteins chains using characteristics of their local geometry as defined by vectors between Cα positions. Heuristics are used there in defining a set of optimal paths joining termed aligned fragment pairs with gaps as needed. The path with the best RMSD is subject to dynamic programming in order to achieve an optimal alignment. For specific families of proteins additional characteristics are used to weight the alignment. 'Table 1 [see Additional file 5]' compared the quality of the FRAGlib/SEA (identified as SEAF in the Table) alignment with that of the structural alignment prepared with the CE algorithm [21] and the SEA algorithm used with I-site segment prediction (SEAI), SEA algorithm used with the actual (not predicted) local structure segments (SEAT), local single predicted structures (SEAloc) and few other publicly available alignment tools. All the results other than the FRAGlib/SEA alignments, as well as alignment quality evaluation, were adopted from the original SEA manuscript [9]. The results presented in 'Table 1 [see Additional file 5]' show that SEAF significantly improves the alignment quality as compared to all other methods, including SEAI (SEA using I-site prediction), bringing it close to (and in the shift based quality measure actually improving on) the SEA algorithm using the actual structure segments. Conclusions The benchmarks show that SEA with FRAGlib (SEAF) integrated prediction service better incorporate diversities of local structure predictions over known methods. It produces also more accurate alignments in comparison to SEAI (based on the I-site library), or the SEA with single predicted structures (SEAloc). Comparing those sequence pairwise alignments we can observe that predicted local structure information seems to improve the alignment qualities. Alignments from SEA using FRAGlib method of describing diversities of local structure prediction have the same quality as alignments using true local structures derived from their known 3D structures SEAT. Availability and requirements An integrated SEA/FRAGlib server is available at [18]. Both components can be used separately, SEA alignment with arbitrary PLSSs and FRAGlib for other purposes than segment alignment, but the integrated server provides the complete alignment method for comparing pairs of protein sequences using a network matching algorithm. The fragments library prediction method (FRAGlib) is also available as the separate http server at [22]. The software is freely available to academics. Contact Dariusz Plewczynski [email protected] or Adam Godzik [email protected] for information on obtaining the local copy of a software. Authors' contributions DP designed, implemented, and evaluated the FRAGlib program. The benchmark dataset and programme for aligning two short sequence profiles were provided by LJ. The integration of FRAGlib predictions within SEA network alignment software together with benchmark evaluation of the SEA method was done by YY. AG was responsible for the overall project coordination. All authors have read and approved the final manuscript. Acknowledgments This work was supported by the USA grant ("SPAM" GM63208) and BioSapiens project within 6FP EU programme (LHSG-CT-2003-503265). Figures and Tables Figure 1 The FRAGlib fragments database is build from ASTRAL representative subset of SCOP database using 40% sequence similarity threshold (see right picture). We store the symbolized local structure representation codes of each fragment together with the homology sequence profile (see left picture). Both are dissected from the SLSR codes and homology profile of a parent protein. The string of SLSR codes representing the local structure of the Cα chain in the phi-phi space. We remove from the fragments database all identical in terms of both SLSR codes and sequence homology profile fragments. On the left picture we present the FRAGlib module for prediction of local structural segments using homology profile similarity and the fragments database. The Query protein is dissected into short parts (from 7 up to 19 residues long). For each part the similarity search is performed. Any member of the fragments database which is similar in terms of homology sequence profile similarity is added to the list of predicted structures for this short part of query protein. This list is then sorted and cut after arbitrary chosen 20th position. If the highest score of predicted fragments is below the user's cut-off value whole prediction is discarder. In the end some of parts of a query protein are covered by list of 20 fragments from the database. They are called the predicted local structural segments (PLSSs). Figure 2 We present here the flowchart of SEA/FRAGlib integrated Web service. The server is based on two modules: the FRAGlib prediction of LSSs and the SEA algorithm for building an alignment between two proteins using comparison of two networks of predicted segments for both of them. Table 1 General performance of classical methods for building alignments together with segment alignment algorithm incorporating different local structure diversities. CE SEAT SEAF SEAI SEAloc BLAST ALIGN FFAS Family (409 pairs) shift average 0.61 0.62 0.56 0.49 0.44 0.48 0.49 >0.9 73 84 69 47 51 60 43 >0.7 207 231 199 152 146 165 161 >0.5 282 277 260 215 197 228 227 RMS ≤ 3.0 257 95 82 82 63 77 54 40 ≤ 5.0 397 237 204 184 147 157 138 118 ≤ 8.0 408 294 269 248 231 196 206 194 all 409 345 409 404 366 232 372 409 len 1 0.84 1.14 1.08 0.87 0.56 0.99 1.18 Family-level benchmark for SEA algorithm using FRAGlib's prediction of LSSs (SEAF) is compared with SEAI (SEA algorithm using I-sites library), SEAT, SEAloc (local single predicted structures), and other classical tools: CE, BLAST, ALIGN and FFAS. The 'average' is the shift score averaged over all the alignments of the whole subset. The numbers of protein pairs with a shift score or RMSD larger than a certain cut-off value in the subset are listed in columns for each program. The counting based on RMSD requires the length of the alignment to be longer than half of its corresponding structural alignment. The 'all' stands for all the alignments with alignment length no shorter than half of the structural alignments. We use the CE for building reference alignments for shift score calculation, as an example of purely structural alignment tool. The 'len' stands for the average alignment length (predicted aligned position / aligned position in reference alignment from CE). We can see that our method provides very long alignments with relatively good overall score. The difference in the values between SEAT and SEAF is explained by different lengths of these alignments. ==== Refs Cline M Hughey R Karplus K Predicting reliable regions in protein sequence alignments Bioinformatics 2002 18 306 314 11847078 10.1093/bioinformatics/18.2.306 Fischer D Eisenberg D Protein fold recognition using sequence-derived predictions Protein Science 1996 5 947 955 8732766 Levitt M Gerstein M A unified statistical framework for sequence comparison and structure comparison Proc Natl Acad Sci 1998 95 5913 5920 9600892 10.1073/pnas.95.11.5913 Yi TM Lander ES Protein secondary structure prediction using nearest-neighbor methods J Mol Biol 1993 232 1117 1129 8371270 10.1006/jmbi.1993.1464 Rychlewski L Godzik A Secondary structure prediction using segment similarity Protein Engineering 1997 10 1143 1153 9488139 10.1093/protein/10.10.1143 Xu H Aurora R Rose GD White RH Identifying two ancient enzymes in archaea using predicted secondary structure alignment Nature Structural Biology 1999 6 750 754 10426953 10.1038/11525 Bystroff C Baker D Prediction of local structure in proteins using a library of sequence-structure motifs J Mol Biol 1998 281 565 577 9698570 10.1006/jmbi.1998.1943 Simons KT Bonneau R Ruczinski II Baker D Ab initio protein structure prediction of CASP III targets using ROSETTA Proteins 1999 37 171 176 10.1002/(SICI)1097-0134(1999)37:3+<171::AID-PROT21>3.3.CO;2-Q Ye Y Jaroszewski L Li W Godzik A A segment alignment approach to protein comparison Bioinformatics 2003 19 742 749 12691986 10.1093/bioinformatics/btg073 Godzik A unpublished personal communication 2003 Chandonia JM Walker NS Lo Conte L Koehl P Levitt M Brenner SE ASTRAL compendium enhancements Nucleic Acids Research 2002 30 260 263 11752310 10.1093/nar/30.1.260 Brenner SE Koehl P Levitt M The ASTRAL compendium for sequence and structure analysis Nucleic Acids Research 2000 28 254 256 10592239 10.1093/nar/28.1.254 I-sites/HMMSTR backbone angle regions Altschul SF Gish W Miller W Myers EW Lipman DJ Basic local alignment search tool J Mol Biol 1990 215 403 410 2231712 10.1006/jmbi.1990.9999 Altschul SF Madden TL Schäffer AA Zhang J Zhang Z Miller W Lipman DJ Gapped BLAST and PSI-BLAST: a new generation of protein database search programs Nucleic Acids Research 1997 25 3389 3402 9254694 10.1093/nar/25.17.3389 Rychlewski L Jaroszewski L Li W Godzik A Comparison of sequence profiles. Strategies for structural predictions using sequence information Protein Science 2000 9 232 241 10716175 Elofsson A A study on protein sequence alignment quality Proteins 2002 46 330 339 11835508 10.1002/prot.10043 SEgment Alignment (SEA) server (Protein pairwise alignment based on network matching algorithm) Jaroszewski L Li W Godzik A Improving the quality of twilight-zone alignments Protein Science 2001 9 1487 1496 Murzin AG Brenner SE Hubbard T Chothia C SCOP: a structural classification of proteins database for the investigation of sequences and structures J Mol Biol 1995 247 536 540 7723011 10.1006/jmbi.1995.0159 Shindyalov IN Bourne PE Protein structure alignment by incremental combinatorial extension (CE) of the optimal path Protein Engineering 1998 11 739 747 9796821 10.1093/protein/11.9.739 Fragments Library Tool using profile-profile alignments

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BMC Bioinformatics. 2004 Jul 22; 5:98

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BMC Bioinformatics

10.1186/1471-2105-5-98

oa_comm

==== Front BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-981527122410.1186/1471-2105-5-98SoftwareIntegrated web service for improving alignment quality based on segments comparison Plewczynski Dariusz [email protected] Leszek [email protected] Yuzhen [email protected] Lukasz [email protected] Adam [email protected] Bioinformatics Laboratory, BioInfoBank Institute, Poznan, Poland2 Interdisciplinary Centre for Mathematical and Computational Modeling, University of Warsaw, Poland3 The Burnham Institute, La Jolla, USA4 Bioinformatics Core JCSG, University of California San Diego, La Jolla, USA2004 22 7 2004 5 98 98 30 3 2004 22 7 2004 Copyright © 2004 Plewczynski et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Defining blocks forming the global protein structure on the basis of local structural regularity is a very fruitful idea, extensively used in description, and prediction of structure from only sequence information. Over many years the secondary structure elements were used as available building blocks with great success. Specially prepared sets of possible structural motifs can be used to describe similarity between very distant, non-homologous proteins. The reason for utilizing the structural information in the description of proteins is straightforward. Structural comparison is able to detect approximately twice as many distant relationships as sequence comparison at the same error rate. Results Here we provide a new fragment library for Local Structure Segment (LSS) prediction called FRAGlib which is integrated with a previously described segment alignment algorithm SEA. A joined FRAGlib/SEA server provides easy access to both algorithms, allowing a one stop alignment service using a novel approach to protein sequence alignment based on a network matching approach. The FRAGlib used as secondary structure prediction achieves only 73% accuracy in Q3 measure, but when combined with the SEA alignment, it achieves a significant improvement in pairwise sequence alignment quality, as compared to previous SEA implementation and other public alignment algorithms. The FRAGlib algorithm takes ~2 min. to search over FRAGlib database for a typical query protein with 500 residues. The SEA service align two typical proteins within circa ~5 min. All supplementary materials (detailed results of all the benchmarks, the list of test proteins and the whole fragments library) are available for download on-line at . Conclusions The joined FRAGlib/SEA server will be a valuable tool both for molecular biologists working on protein sequence analysis and for bioinformaticians developing computational methods of structure prediction and alignment of proteins. Library of protein motifsProfile-profile sequence similarity (BLASTFFAS)Fragments library (FRAGlib)Predicted Local Structure Segments (PLSSs)Segment Alignment (SEA)Network matching problem ==== Body Background Protein structure is obviously modular, with similar structural segments, such as alpha helices and beta strands found in unrelated proteins. Such segments, identified from structure, are used extensively in description and analysis of protein structures [1,2]. Several groups have demonstrated that only a small library of segments is sufficient to rebuild experimental protein structures with high accuracy [3]. Predicted local structure segments (PLSS) are also used in structural prediction, starting from the nearest neighbor approach to secondary structure prediction [4-6]. This idea was later extended and lead to even more successful applications of PLSSs in ab initio structure prediction by Baker and colleagues, who developed a library of sequence-structure motifs called I-sites [7]. Those motifs are later assembled in a complete protein structure by a program ROSETTA [8]. Predicted local structure segments are also used in a novel protein alignment algorithm, based on the comparison of PLSSs for two proteins treated as networks and finding a common path through networks describing the two proteins [9]. The underlying idea in all those approaches is that because global folding constraints can override local preferences, the prediction of structure segments from local sequence is by necessity uncertain. Therefore, instead of trying to predict a correct local structure, all possible local solutions are identified and other constraints (folded structure in Rosetta, or compatible alignment in SEA) are used to identify a globally consistent solution. Prediction of local structure segments can be approached in two different ways. A first possibility, used in most nearest neighbor secondary structure algorithms, is to use a representative set of proteins with known structure as source of structure segments, but without any restrictions on a number or type of segments. In this approach, we don't make any assumptions about the compositions and distributions of segments in the library and this approach can be compared to unsupervised learning approach. In a second approach, used for instance in the I-site method, only segments from a specifically constructed fragment library are used in prediction, thus this approach is similar to supervised learning. Interestingly, some limited tests suggest that the former approach leads to lower prediction accuracy [10]. The same tests suggested the possibility that different segment libraries could lead to different prediction, and likely, some segment libraries would be better suited to some tasks. Following this observation, we have developed the FRAGlib – a fragment library specifically designed to complement a segment alignment SEA. SEA alignment algorithm was developed previously in our group [9] and originally used in conjunction with the I-site library. I-site library [7] was originally developed to be used in ab initio folding predictions and anecdotal evidence suggested that it may not be ideally suited for alignment purposes. In this note we describe a combined FRAGlib/SEA server and first benchmarking results of this method. Implementation Database of Short Fragments FRAGlib is based on the idea of developing a uniform coverage of all known types of local structural regularity with the distribution based on that observed in natural proteins. The collection of segments is constructed using representative set of proteins from the ASTRAL database [11,12]. For each protein in this set, each continuous segment with regular secondary structure, including the flanking residues on both sides, is added to the FRAGlib (see below for details). We do not utilize any further clustering algorithm so our database contains no-unique entries and it is redundant both in terms of structure and sequence information. Local structure is described by the SLSR (Symbolized Local Stuctures Representation) codes consisting of 11 symbols {HGEeBdbLlxc}, each representing a certain backbone dihedral (phi and psi) region [7,13]. Protein local structure is described as a string of local-structure symbols and a local structure segment is defined as a 5–17 amino acid fragment with constant local structural codes. Segments are then extended by two additional residues offset at the beginning, and at the end of a segment. We store all such segments with their sequence, SLSR style local structures representation codes and the homology profile [14,15], derived from that of their parent protein. The library is highly redundant, i.e. there are many segments with the same structural description, but each of the redundant fragments is coming from a different parent protein (or a different part of the same parent protein), therefore it has a different sequence and a different profile associated with it. FRAGlib prediction In a next step, FRAGlib segment library is used to assign local structure segments for a new protein (query) based only on sequence information using a variant of the FFAS profile-profile alignment algorithm [16]. A profile for the query protein is calculated following the FFAS protocol, then for all possible overlapping segments of length from 7 to 19 amino acids, their profiles are compared to those of the segments from the FRAGlib database and the score of each alignment is calculated using a FFAS-like scalar product of composition vectors at each position. Since the segments being compared have the same length, no dynamic programming alignment is necessary and the score calculation can be highly optimized. As the result of this procedure, each position in the query protein can be assigned to all of the possible LSSs in the database, each with a specific score (see Figure 1). Only reduced sets of predicted LSSs, rather arbitrarily limited to the first 20 highest scoring segments are kept for further analysis. This cut-off is the only free parameter of the method, and can be set by user using the Web interface of the server. The Q3 quality of the FRAGlib used as a secondary structure prediction algorithm (data not shown), with the prediction based on the single best scoring segment for each position is 73% on a standard secondary structure prediction benchmark. The Q3 gives percentage of residues predicted correctly as helix, strand, and coil or for all three conformational states. SEA Segment Alignment Approach to Protein Comparison The principal motivation to develop the FRAGlib segment prediction was to further improve the alignment quality for comparing distantly related proteins, which is one of the most important problems in practical application of comparative modeling and fold recognition [17]. To address this problem, we have previously developed a SEA algorithm, which compares the network of predicted local structure segments (PLSSs) for two proteins using the network matching approach. In a previous paper we have demonstrated that the SEA algorithm, using I-site server for PLSSs prediction and a simple sequence-sequence scoring for segment comparison resulted in alignments better than the FFAS profile-profile alignment algorithm and several other alignment tools. A full description of the SEA algorithm is available in the previous manuscript [9], so only a brief summary is presented here. Every residue in each of the proteins being aligned is described as a vertex in the graph. Two artificial vertices are added to the very beginning of each protein as a source vertex, and also at the end as a sink vertex. For each PLSS is described as an edge between the vertices representing its first and last positions. For some PLSS protocols, some parts of the protein may not be covered by any predicted segments, so virtual edges are added to all neighbor residues to form a complete, continuous network. Each assembly of connected PLSSs corresponds to a path in this network. In a next step, PLSSs networks of two proteins are compared by the SEA algorithm. For each pair of positions i and j, with position i coming form the first protein and position j from the second protein, all possible segments covering each of the positions must be considered in a combinatorial way and compared to get the optimal similarity score. It is not the sequences or secondary structures at two positions that are compared, but all segments that cover these two positions. This is the main feature of SEA that makes it different from standard sequence pair-wise alignments. The computational complexity of SEA is about O(NMC1C2), where C1 and C2 are the average numbers of segments that cover a position in each protein (the segment coverage). Detailed description of the SEA mathematical algorithm together with benchmarks results obtained using the I-site server calculated PLSSs network can be found elsewhere [9]. The integrated FRAGlib and SEA server is available at [18]. The FRAGlib database and segment prediction provides the PLSSs network for each aligned protein, and the SEA algorithm aligns the two networks. On Figure 2 we present the flowchart of the integrated web service. Preliminary benchmarks for the FRAGlib/SEA server and presented below. A full paper on the FRAGlib algorithm is in preparation. Results and Discussion We use here as a benchmark the database of 409 family-level similar pairs [19]. Each protein pair shares at least one similar domain as identified by SCOP [20]. Segments coming from the proteins of the same SCOP family as the proteins being compared were removed from the FRAGlib calculated PLSSs network. Further analysis of the SEA results also confirmed that the memorization is not a problem here, as all the SEA alignment are build predominantly from segments that are not locally optimal. To evaluate the improvement we use two measures of alignment quality: the classical root mean square deviation (RMSD) and the shift score [1]. The shift score measures misalignment between a predicted alignment of two proteins and the reference alignment. The shift score measure ranges from -ε(default as -0.2) to 1.0, where 1.0 means an identical alignment. RMSD is dependent on alignment length and the shift score is dependent on the reference alignment, so both measures are less than perfect in comparing alignments. In our case we use as the reference alignment provided by the CE structural method [21]. We chose the CE, which is available as a single file executable for various operating systems, as an example of purely structural alignment tool. It is a method for fast calculation of pairwise structure alignments, which aligns two proteins chains using characteristics of their local geometry as defined by vectors between Cα positions. Heuristics are used there in defining a set of optimal paths joining termed aligned fragment pairs with gaps as needed. The path with the best RMSD is subject to dynamic programming in order to achieve an optimal alignment. For specific families of proteins additional characteristics are used to weight the alignment. 'Table 1 [see Additional file 5]' compared the quality of the FRAGlib/SEA (identified as SEAF in the Table) alignment with that of the structural alignment prepared with the CE algorithm [21] and the SEA algorithm used with I-site segment prediction (SEAI), SEA algorithm used with the actual (not predicted) local structure segments (SEAT), local single predicted structures (SEAloc) and few other publicly available alignment tools. All the results other than the FRAGlib/SEA alignments, as well as alignment quality evaluation, were adopted from the original SEA manuscript [9]. The results presented in 'Table 1 [see Additional file 5]' show that SEAF significantly improves the alignment quality as compared to all other methods, including SEAI (SEA using I-site prediction), bringing it close to (and in the shift based quality measure actually improving on) the SEA algorithm using the actual structure segments. Conclusions The benchmarks show that SEA with FRAGlib (SEAF) integrated prediction service better incorporate diversities of local structure predictions over known methods. It produces also more accurate alignments in comparison to SEAI (based on the I-site library), or the SEA with single predicted structures (SEAloc). Comparing those sequence pairwise alignments we can observe that predicted local structure information seems to improve the alignment qualities. Alignments from SEA using FRAGlib method of describing diversities of local structure prediction have the same quality as alignments using true local structures derived from their known 3D structures SEAT. Availability and requirements An integrated SEA/FRAGlib server is available at [18]. Both components can be used separately, SEA alignment with arbitrary PLSSs and FRAGlib for other purposes than segment alignment, but the integrated server provides the complete alignment method for comparing pairs of protein sequences using a network matching algorithm. The fragments library prediction method (FRAGlib) is also available as the separate http server at [22]. The software is freely available to academics. Contact Dariusz Plewczynski [email protected] or Adam Godzik [email protected] for information on obtaining the local copy of a software. Authors' contributions DP designed, implemented, and evaluated the FRAGlib program. The benchmark dataset and programme for aligning two short sequence profiles were provided by LJ. The integration of FRAGlib predictions within SEA network alignment software together with benchmark evaluation of the SEA method was done by YY. AG was responsible for the overall project coordination. All authors have read and approved the final manuscript. Acknowledgments This work was supported by the USA grant ("SPAM" GM63208) and BioSapiens project within 6FP EU programme (LHSG-CT-2003-503265). Figures and Tables Figure 1 The FRAGlib fragments database is build from ASTRAL representative subset of SCOP database using 40% sequence similarity threshold (see right picture). We store the symbolized local structure representation codes of each fragment together with the homology sequence profile (see left picture). Both are dissected from the SLSR codes and homology profile of a parent protein. The string of SLSR codes representing the local structure of the Cα chain in the phi-phi space. We remove from the fragments database all identical in terms of both SLSR codes and sequence homology profile fragments. On the left picture we present the FRAGlib module for prediction of local structural segments using homology profile similarity and the fragments database. The Query protein is dissected into short parts (from 7 up to 19 residues long). For each part the similarity search is performed. Any member of the fragments database which is similar in terms of homology sequence profile similarity is added to the list of predicted structures for this short part of query protein. This list is then sorted and cut after arbitrary chosen 20th position. If the highest score of predicted fragments is below the user's cut-off value whole prediction is discarder. In the end some of parts of a query protein are covered by list of 20 fragments from the database. They are called the predicted local structural segments (PLSSs). Figure 2 We present here the flowchart of SEA/FRAGlib integrated Web service. The server is based on two modules: the FRAGlib prediction of LSSs and the SEA algorithm for building an alignment between two proteins using comparison of two networks of predicted segments for both of them. Table 1 General performance of classical methods for building alignments together with segment alignment algorithm incorporating different local structure diversities. CE SEAT SEAF SEAI SEAloc BLAST ALIGN FFAS Family (409 pairs) shift average 0.61 0.62 0.56 0.49 0.44 0.48 0.49 >0.9 73 84 69 47 51 60 43 >0.7 207 231 199 152 146 165 161 >0.5 282 277 260 215 197 228 227 RMS ≤ 3.0 257 95 82 82 63 77 54 40 ≤ 5.0 397 237 204 184 147 157 138 118 ≤ 8.0 408 294 269 248 231 196 206 194 all 409 345 409 404 366 232 372 409 len 1 0.84 1.14 1.08 0.87 0.56 0.99 1.18 Family-level benchmark for SEA algorithm using FRAGlib's prediction of LSSs (SEAF) is compared with SEAI (SEA algorithm using I-sites library), SEAT, SEAloc (local single predicted structures), and other classical tools: CE, BLAST, ALIGN and FFAS. The 'average' is the shift score averaged over all the alignments of the whole subset. The numbers of protein pairs with a shift score or RMSD larger than a certain cut-off value in the subset are listed in columns for each program. The counting based on RMSD requires the length of the alignment to be longer than half of its corresponding structural alignment. The 'all' stands for all the alignments with alignment length no shorter than half of the structural alignments. We use the CE for building reference alignments for shift score calculation, as an example of purely structural alignment tool. The 'len' stands for the average alignment length (predicted aligned position / aligned position in reference alignment from CE). We can see that our method provides very long alignments with relatively good overall score. The difference in the values between SEAT and SEAF is explained by different lengths of these alignments. ==== Refs Cline M Hughey R Karplus K Predicting reliable regions in protein sequence alignments Bioinformatics 2002 18 306 314 11847078 10.1093/bioinformatics/18.2.306 Fischer D Eisenberg D Protein fold recognition using sequence-derived predictions Protein Science 1996 5 947 955 8732766 Levitt M Gerstein M A unified statistical framework for sequence comparison and structure comparison Proc Natl Acad Sci 1998 95 5913 5920 9600892 10.1073/pnas.95.11.5913 Yi TM Lander ES Protein secondary structure prediction using nearest-neighbor methods J Mol Biol 1993 232 1117 1129 8371270 10.1006/jmbi.1993.1464 Rychlewski L Godzik A Secondary structure prediction using segment similarity Protein Engineering 1997 10 1143 1153 9488139 10.1093/protein/10.10.1143 Xu H Aurora R Rose GD White RH Identifying two ancient enzymes in archaea using predicted secondary structure alignment Nature Structural Biology 1999 6 750 754 10426953 10.1038/11525 Bystroff C Baker D Prediction of local structure in proteins using a library of sequence-structure motifs J Mol Biol 1998 281 565 577 9698570 10.1006/jmbi.1998.1943 Simons KT Bonneau R Ruczinski II Baker D Ab initio protein structure prediction of CASP III targets using ROSETTA Proteins 1999 37 171 176 10.1002/(SICI)1097-0134(1999)37:3+<171::AID-PROT21>3.3.CO;2-Q Ye Y Jaroszewski L Li W Godzik A A segment alignment approach to protein comparison Bioinformatics 2003 19 742 749 12691986 10.1093/bioinformatics/btg073 Godzik A unpublished personal communication 2003 Chandonia JM Walker NS Lo Conte L Koehl P Levitt M Brenner SE ASTRAL compendium enhancements Nucleic Acids Research 2002 30 260 263 11752310 10.1093/nar/30.1.260 Brenner SE Koehl P Levitt M The ASTRAL compendium for sequence and structure analysis Nucleic Acids Research 2000 28 254 256 10592239 10.1093/nar/28.1.254 I-sites/HMMSTR backbone angle regions Altschul SF Gish W Miller W Myers EW Lipman DJ Basic local alignment search tool J Mol Biol 1990 215 403 410 2231712 10.1006/jmbi.1990.9999 Altschul SF Madden TL Schäffer AA Zhang J Zhang Z Miller W Lipman DJ Gapped BLAST and PSI-BLAST: a new generation of protein database search programs Nucleic Acids Research 1997 25 3389 3402 9254694 10.1093/nar/25.17.3389 Rychlewski L Jaroszewski L Li W Godzik A Comparison of sequence profiles. Strategies for structural predictions using sequence information Protein Science 2000 9 232 241 10716175 Elofsson A A study on protein sequence alignment quality Proteins 2002 46 330 339 11835508 10.1002/prot.10043 SEgment Alignment (SEA) server (Protein pairwise alignment based on network matching algorithm) Jaroszewski L Li W Godzik A Improving the quality of twilight-zone alignments Protein Science 2001 9 1487 1496 Murzin AG Brenner SE Hubbard T Chothia C SCOP: a structural classification of proteins database for the investigation of sequences and structures J Mol Biol 1995 247 536 540 7723011 10.1006/jmbi.1995.0159 Shindyalov IN Bourne PE Protein structure alignment by incremental combinatorial extension (CE) of the optimal path Protein Engineering 1998 11 739 747 9796821 10.1093/protein/11.9.739 Fragments Library Tool using profile-profile alignments

15260887

PMC497041

CC BY

2021-01-04 16:32:49

no

BMC Med Res Methodol. 2004 Jul 19; 4:19

latin-1

BMC Med Res Methodol

10.1186/1471-2288-4-19

oa_comm

==== Front BMC Infect DisBMC Infectious Diseases1471-2334BioMed Central London 1471-2334-4-231527122310.1186/1471-2334-4-23Research ArticleCaveolin-2 associates with intracellular chlamydial inclusions independently of caveolin-1 Webley Wilmore C [email protected] Leonard C [email protected] Elizabeth S [email protected] Department of Microbiology, University of Massachusetts – Amherst, MA 01003, USA2004 22 7 2004 4 23 23 5 4 2004 22 7 2004 Copyright © 2004 Webley et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Lipid raft domains form in plasma membranes of eukaryotic cells by the tight packing of glycosphingolipids and cholesterol. Caveolae are invaginated structures that form in lipid raft domains when the protein caveolin-1 is expressed. The Chlamydiaceae are obligate intracellular bacterial pathogens that replicate entirely within inclusions that develop from the phagocytic vacuoles in which they enter. We recently found that host cell caveolin-1 is associated with the intracellular vacuoles and inclusions of some chlamydial strains and species, and that entry of those strains depends on intact lipid raft domains. Caveolin-2 is another member of the caveolin family of proteins that is present in caveolae, but of unknown function. Methods We utilized a caveolin-1 negative/caveolin-2 positive FRT cell line and laser confocal immunofluorescence techniques to visualize the colocalization of caveolin-2 with the chlamydial inclusions. Results We show here that in infected HeLa cells, caveolin-2, as well as caveolin-1, colocalizes with inclusions of C. pneumoniae (Cp), C. caviae (GPIC), and C. trachomatis serovars E, F and K. In addition, caveolin-2 also associates with C. trachomatis serovars A, B and C, although caveolin-1 did not colocalize with these organisms. Moreover, caveolin-2 appears to be specifically, or indirectly, associated with the pathogens at the inclusion membranes. Using caveolin-1 deficient FRT cells, we show that although caveolin-2 normally is not transported out of the Golgi in the absence of caveolin-1, it nevertheless colocalizes with chlamydial inclusions in these cells. However, our results also show that caveolin-2 did not colocalize with UV-irradiated Chlamydia in FRT cells, suggesting that in these caveolin-1 negative cells, pathogen viability and very likely pathogen gene expression are necessary for the acquisition of caveolin-2 from the Golgi. Conclusion Caveolin-2 associates with the chlamydial inclusion independently of caveolin-1. The function of caveolin-2, either in the uninfected cell or in the chlamydial developmental cycle, remains to be elucidated. Nevertheless, this second caveolin protein can now be added to the small number of host proteins that are associated with the inclusions of this obligate intracellular pathogen. ==== Body Background The Chlamydiaceae are gram-negative obligate intracellular bacterial pathogens that replicate entirely within membrane bound inclusions that develop from the phagocytic vacuoles in which they enter. By mechanisms not understood in detail, inclusions avoid fusing with endosomes or lysosomes that might destroy the pathogen [1-3]. Instead, the inclusions provide an intracellular niche in which the pathogen can survive and complete its developmental cycle. The initial pathogen-containing endocytic vacuoles co-associate with microtubules, and dynein, and traffic to a perinuclear region, ultimately residing at the microtubule organizing center and in close proximity to the Golgi [4,5]. Here, they intercept exocytic vesicles of the biosynthetic pathway that are derived from the Golgi and continue their developmental cycle [6-9]. There are two genera in the family of Chlamydiaceae [10]. The genus Chlamydia is comprised of the C. trachomatis species, which contains human pathogens, as well as the mouse and swine strains, C. muridarum and C. suis respectively, while the Chlamydophila genus is more diverse, consisting of six species. Of these, C. pneumoniae, and C. psittaci are relevant in human diseases. C. trachomatis is primarily a pathogen of humans, containing the trachoma biovariant (biovar), and the lymphogranuloma biovar (LGV). The mouse pneumonitis agent (MoPn), frequently used in vaccine work [11,12], is now classified separately as C. muridarumn [10]. Some serological variants (serovars) of the trachoma biovar primarily infect the urogenital tract (e.g., E, K, and F) and are the major cause of significant venereal disease in the United States [13]. Other, trachoma serovars primarily infect the conjunctiva (e.g., A, B, and C), causing trachoma, the major form of preventable blindness, worldwide [14]. The LGV biovar also causes chronic venereal disease, which differs clinically from the sexually transmitted disease caused by strains of the trachoma biovar. C. pneumoniae, another human pathogen, is now implicated in atherosclerosis and cardiac disease, as well as respiratory infection [15,16]. C. psittaci primarily infects birds, but can also cause disease in humans. The mechanism by which chlamydiae enter host cells is not well defined, largely because of conflicting reports that these organisms can enter by both clathrin-dependent, as well as clathrin-independent pathways. Recently, we examined the entry mechanisms of representative members of each of the above Chlamydia species and biovars. Our concern was whether their entry might be mediated by the cholesterol and sphingolipid enriched micro-domains in host cell plasma membranes known as lipid rafts [17]. Application of biochemical criteria and confocal microscopy demonstrated that five of the ten strains we examined indeed enter by a mechanism that is dependent on intact lipid rafts [18,19]. These strains included C. trachomatis serovars E, F, and K, C. pneumoniae (strain AR39), and C. caviae (Guinea Pig Inclusion Conjunctivitis strain; GPIC). In contrast , C. trachomatis serovars A, B, C, biovar LGV (L2 strain), and MoPn do not enter via lipid rafts. Caveolae are now considered to be a specialized kind of lipid raft. These 50–100 nm flask-shaped invaginations form in lipid rafts when the caveolae marker protein, caveolin-1, is expressed [reviewed in [20]]. In contrast, lipid rafts are ubiquitous entities that form regardless of caveolin-1 expression. The inclusion membranes of all of the chlamydial strains that entered via lipid rafts, as indicated by biochemical criteria, also appeared to acquire caveolin-1 at entry, and accumulated more of the protein during development in caveolin-1 expressing HeLa cells [18,19]. In contrast to the strains that entered by a raft-dependent process, none of the other strains examined acquired caveolin-1 at entry. The fact that entry of some chlamydial strains is dependent on lipid rafts in general, rather than on the more specialized caveolae, was demonstrated using Fischer Rat Thyroid (FRT) cells, which do not express caveolin-1 or form caveolae. All ten strains that we examined were able to enter the caveolin-1 negative FRT cells, but not if those cells were treated with pharmacological agents that specifically disrupt lipid rafts [19]. The Golgi is a likely source of most caveolin-1 on the inclusions of those strains entering via lipid rafts. This is suggested by the fact that caveolin-1 immunostaining of those inclusions actually increases as they enlarge and mature, and is consistent with reports that caveolin-1 first becomes associated with raft domains in the Golgi, and subsequently is transported to the plasma membrane on vesicles derived from the Golgi [21-23]. Moreover, chlamydial inclusions are known to intercept vesicles of the biosynthetic pathway that originate from the Golgi, from which they acquire sphingolipids and perhaps other nutrients as well[6,7]. However, since inclusions of all strains appear to acquire sphingolipids from the Golgi, whereas only a subset of strains acquires caveolin-1, there may be more than one sorting process and class of vesicles with which Chlamydia can interact. The caveolin gene family also contains caveolin-2 [24] which has 38% sequence identity with caveolin-1 and 58% similarity. Caveolin-1 and -2 are co-expressed in most cells, in which they form hetero-oligomeric complexes. These complexes form in the endoplasmic reticulum (ER), becoming associated with lipid rafts in the Golgi, from which they are transported to the plasma membrane. All of the putative functions of caveolae are thus far associated with caveolin-1, and the function of caveolin-2 is essentially unknown. Importantly, unlike caveolin-1, caveolin-2 can not induce caveolae to form in lipid rafts. An experimental finding that is particularly relevant to the current study is that caveolin-2 can not exit the Golgi in caveolin-1 negative FRT cells. However, it can exit when recombinant caveolin-1 is expressed [25,26]. Thus, formation of hetero-oligomeric complexes of caveolin-1 and -2 facilitates transport of caveolin-2 out of the Golgi and its sorting to the plasma membrane. In the present study, we ask whether caveolin-2, like caveolin-1, might be associated with inclusions formed by particular chlamydial strains. Unexpectedly, we found that in HeLa cells, caveolin-2 was associated with inclusions of strains that did not enter via lipid rafts and that did not acquire caveolin-1 either at entry or at later times. These strains included C. trachomatis serovars A, B, and C. In the same cells, inclusions of C. trachomatis serovars E, F, and K, C. pneumoniae (A39), and C. caviae (GPIC) were marked by both of the caveolin proteins. Each of these eight strains acquired caveolin-2 even in caveolin-1 negative FRT cells. In addition, the ability of each of these strains to acquire caveolin-2 in the FRT cells was dependent on pathogen gene expression. Thus, chlamydiae appear to activate or induce an additional pathway for the transport of caveolin-2 that is independent of caveolin-1. Inclusions of two strains, LGV (L2) and MoPn, were not marked by either caveolin-1 or -2. Methods Chlamydial strains C. pneumoniae AR39 (Cpn), C. caviae, Guinea Pig Inclusion Conjunctivitis (GPIC strain), C. trachomatis serovars A/Har-13, Har-36B, C/TW-3, E/VW-KX, F, K/VR887, mouse pneumonitis agent, (MoPn) and Lymphogranuloma venereum, (LGV 434) were grown in HeLa 229 cells without centrifuge assistance. Infectious elementary bodies (EBs) were purified by renografin (Squibb diagnostics, New Bronswick, NJ) density gradient centrifugation; alternatively lysates from infected cells were used to infect monolayers. Cell lines used HeLa 229 cells were obtained from the American Type Culture collection. Fischer rat thyroid (FRT) cells were a gift from Dr. Michael P. Lisanti (Albert Einstein College of Medicine, Bronx, NY). Cells were grown in minimum essential medium with insulin (IMEMZO, Irvine Scientific, Santa Ana, CA) with 5% fetal bovine serum (FBS, Atlanta Biologicals, Norcross, GA.). Immunostaining of Chlamydia infected cells HeLa 229 or FRT cells were grown to confluence on 12 mm coverslips in 24 well plates (Becton Dickinson Labware, Franklin Lakes, NJ). The cells were infected using the various chlamydial strains listed above at an MOI of 3.0 in complete cycloheximide overlay media (Bio-Whittaker, Walkersville, Md.) containing 10% FBS, 1X L-glutamine (CCOM) for 48 h at 37°C and 5% CO2. Coverslips with the cell monolayers were harvested, rinsed with phosphate buffered saline (PBS), fixed with 70% cold methanol, stored and subsequently immunostained following protocols similar to that described in detail previously [18,19]. Briefly, infected cells were immunostained with a guinea pig anti-chlamydia polyclonal antibody (Biomedia, Foster City, CA) and the monoclonal mouse anti-caveolin-2 antibody (Transduction Laboratories, Lexington, KY) for 1 h at 37°C. Following 4 washes with PBS, the bound antibodies were detected using a 1:50 dilution of TRITC-conjugated goat anti-guinea pig and FITC-conjugated goat anti-mouse secondary antibodies (Jackson Immuno Research, West Grove, PA). Following incubation for 1 h at RT and 4 rinses with (PBS), coverslips were mounted onto slides using Fluoromount-G (Southern Biotechnology Associates Inc., Birmingham, AL). Slides were examined at 600X using a Bio-Rad MRC-600 Laser Confocal Microscope system. Images were captured and as relevant, merged using the Confocal Assistant™ version 4.02 Image Processing Software. UV-treatment of EBs and infection of HeLa and FRT cells C. trachomatis serovars A, B, C, E, F, LGV, C. muridarum (MoPn), C. caviae (GPIC), and C. pneumoniae EB concentrates were exposed to short wavelength ultraviolet light for 1 h in a Biosafety hood. A portion of the EBs used was removed before UV treatment to serve as a control for initial viability. The EBs were then diluted in CCOM to an MOI of 4.0 per cell and used to infect HeLa 229 and FRT cells grown to confluence on 12 mm coverslips in 24 well plates. Plates were incubated for 2 h at 37°C and 5% CO2. After the 2 h incubation, the inoculum was removed and replaced with fresh CCOM; cells were then re-incubated for 34 h. At the end of the incubation period, the coverslips with cells were rinsed with sterile PBS and fixed with methanol for 10 minutes as detailed above. Following fixation, the cells were immunostained with a polyclonal guinea pig anti-chlamydia and mouse monoclonal anti-caveolin-2 antibodies as described above. FITC-conjugated goat anti-mouse and TRITC-conjugated goat anti-guinea pig secondary antibodies were used respectively to visualize the caveolin-2 and EBs within the cells. Slides were examined at 600X with a Laser Confocal Microscope system and images processed as described above. Western blot of purified EBs and anti-Caveolin-2 C. trachomatis serovar K and C. pneumoniae AR39 purified EBs were electrophoresed on a 4–12% NuPAGE gel (Invitrogen Life technologies, Carlsbad, CA) using MES running buffer. The separated proteins were transferred to a PVDF membrane at 25 V for 1 h using a 1X Novex® Tris Glycine buffer with 20% methanol in the XCell II™ Blot Module and a XCell Surelock™ Mini-Cell apparatus (Invitrogen). The blot was then rinsed with 0.1% BSA/PBS, blocked with 5% non-fat dry milk for 3 h, then rinsed and cut into two. One half was stained with a 1:200 dilution of mouse monoclonal anti-caveolin-2 antibody (Transduction Labs, Lexington, KY), while the second half was stained with a 1:100 dilution of a rabbit anti-Chlamydia for 2 h with shaking. The blot was then rinsed and a 1:1000 dilution of AP-conjugated goat anti-mouse secondary antibody added and incubated for 1 h at RT. Following several washes, a BCIP/NBT alkaline phosphatase substrate was added and the color allowed to develop. Results Caveolin-2 on chlamydial inclusions in caveolin-1 positive HeLa cells We asked whether caveolin-2 might be associated with the inclusions of each of the ten chlamydial strains and species that we had previously assessed for raft-mediated entry and for inclusion-associated caveolin-1 [19]. We began by looking at infections in caveolin-1 positive HeLa cells. Inclusions of C. trachomatis serovars E, F, and K, C. pneumoniae (A39), and C. caviae (GPIC), previously were seen to enter HeLa cells via lipid rafts, and their inclusions were marked by caveolin-1 in those cells (ibid). Immunostaining with caveolin-2 specific monoclonal antibodies demonstrated that in HeLa cells, inclusions of each of these strains also were marked by caveolin-2 (Figure 1, Table 1). As in the case of caveolin-1 [19], optical sections through the Z-axis clearly indicated the caveolin-2 as packets associated with the vacuolar membrane (Figure 2). The disappearance of the caveolin-2 staining as the depth of the optical sections increased, with no change in the anti-Chlamydia staining, confirms that the caveolin-2 proteins are associated with the vacuolar membranes of these inclusions and are not associated with the individual EBs and RBs inside of the chlamydial vacuole. This profile is similar to that seen when Z-sections were taken of the caveolin-1 staining. Importantly, immunostaining for caveolin-2 appears nonrandom on the inclusion membrane (Figure 2, Z section #1). Indeed, the series of merged optical sections shows that the caveolin-2 staining appears in apposition to those pathogen cells that are at the inclusion membrane (Figure 2, see Discussion). In contrast to the above strains, C. trachomatis serovars A, B, and C, do not enter via lipid rafts, and their inclusions do not acquire caveolin-1 in HeLa cells [19]. Nevertheless, in HeLa cells inclusions of each of these strains also acquired caveolin-2 (Figure 1, Table 1). In our earlier study, entry of strains LGV (L2) and MoPn was seen to be independent of lipid rafts, and their inclusions were not marked by caveolin-1. In the present study, their inclusions did not display caveolin-2 and therefore they did not accumulate this host protein during their development (Figure 1, Table 1). Caveolin-2 is present on chlamydial inclusions in caveolin-1 negative FRT cells Others have demonstrated that caveolin-2 is not transported out from the Golgi in caveolin-1 negative FRT cells, but that expression of a transfected caveolin-1 gene in those cells restores caveolin-2 transport [25,26]. These experimental findings imply that caveolin-2 transport is dependent on its association with caveolin-1. Thus, it was somewhat surprising that during development in HeLa cells, inclusions of C. trachomatis serovars A, B, and C acquired caveolin-2 without acquiring caveolin-1. To confirm that chlamydial inclusions indeed could acquire caveolin-2 independently of caveolin-1, we further examined infections using caveolin-1 negative FRT cells. Normally these cells do not express caveolin-1 or form caveolae, but do so when transfected with caveolin-1 cDNA [27-29]. Inclusions of C. trachomatis serovars A, B, C, E, F, and K, as well as C. pneumoniae, and C. caviae each were marked by caveolin-2 in the caveolin-1 negative FRT cells (Figure 3). This clearly confirms that trafficking of caveolin-2 to chlamydial inclusions does occur independently of caveolin-1. Note that earlier we demonstrated that among those we tested, MoPn and GPIC are the only strains that develop large inclusions in FRT cells, and confirmed that these cells indeed do not express caveolin-1 [19]. To confirm the specificity of the caveolin-2 antibody for the caveolin-2 protein in these cells, and to rule out the possibility of cross-reactivity of this antibody with the chlamydial EBs, we performed western blot on renografin gradient purified EBs from C. pneumoniae and C. trachomatis and stained the blot with anti-caveolin-2 antibodies (figure 4A). As seen in the representative blot, the caveolin-2 antibody does not cross-react with purified EBs. This is also true for anti-caveolin-1 antibodies (data not shown). The presence of chlamydial EBs on the blot was assessed by loading the same amount of EBs onto the second half of the gel in figure 4A and staining with a rabbit anti-Chlamydia serum (figure 4B). Note that there is adequate material on this stained blot, confirming that if the caveolin antibody was cross-reactive with EB material one would be able to see such a reaction. Acquisition of caveolin-2 by inclusions requires pathogen gene expression The apparent inability of caveolin-2 to traffic from the Golgi independently of caveolin-1 in uninfected cells [25,26] implies that in a manner yet unknown, the chlamydial inclusions might produce a factor that activates an additional sorting and transport process for caveolin-2. To test this hypothesis, we asked whether UV-inactivated EBs, the infectious form of the pathogen, might sequester caveolin-2 on their vacuoles in caveolin-1 negative FRT cells. Pathogen-containing vacuoles of each of the strains indeed were unable to acquire caveolin-2 when the EBs had been inactivated by UV irradiation prior to infection. This result therefore supports the premise that vacuole/inclusion acquisition of caveolin-2 is dependent on pathogen gene expression (Figure 5). In caveolin-1 positive HeLa cells, a minimal amount of caveolin-2 protein was observed to co-localize to vacuoles with UV-treated EBs (Figure 6, see Discussion). Discussion Experimental results presented here demonstrate that for a number of chlamydial strains and species, the intracellular inclusions indeed acquire caveolin-2 during the developmental cycle. These strains include C. trachomatis serovars A, B, C, E, F, and K, C. pneumoniae (A39), and GPIC (Table 1). C. trachomatis serovars E, F, and K, and the C. pneumoniae and C. caviae species of Chlamydophila previously were shown to enter host cells via lipid rafts. Their vacuoles/inclusions were demonstrated to acquire host cell caveolin-1 at entry and to accumulate it during later stages of infection [18,19]. Consequently, in host cells that express both caveolin proteins, inclusions of these strains display caveolin-2, as well as caveolin-1. However, in this same cell type, inclusions of serovars A, B, and C, are marked only by caveolin-2, a result implying that for chlamydial inclusions this acquisition can occur independently of caveolin-1. This implication is somewhat surprising since there is experimental evidence demonstrating that caveolin-2 usually can not exit from the Golgi and traffic to the plasma membrane in the absence of caveolin-1[25,26]. However, if recombinant caveolin-1 DNA is expressed by caveolin-1 negative cells, then caveolin-2 can transit from the Golgi and be delivered to the plasma membrane, as a hetero-oligomeric complex with caveolin-1[25,26,30,31]. To confirm that transport of caveolin-2 to chlamydial inclusions indeed might occur independently of caveolin-1, we asked whether chlamydial inclusions might similarly acquire caveolin-2 in FRT cells, a cell line that does not express caveolin-1 [27-29]. In our earlier report [19], we confirmed that FRT cells in fact do not express caveolin-1. Also, we demonstrated that whereas for some chlamydial strains, entry into those cells was dependent on intact lipid rafts (Table 1), in no case was entry dependent on caveolin-1 and caveolae. We show here that in the caveolin-1 negative FRT cells, inclusions of each of the above chlamydial strains in fact acquired caveolin-2. Moreover, since FRT cells do not contain caveolin-2 at the plasma membrane, pathogen-associated caveolin-2 could not have been acquired at entry. Rather, it had to derive from an intracellular source. Chlamydial inclusions thus acquired caveolin-2 despite the fact that in the absence of caveolin-1 this protein usually does not traffic from the Golgi. We therefore suggested that the pathogen might influence the host cell and actually induce a transport and sorting pathway that normally may not actively function in the uninfected cell. Consistent with this suggestion is the fact that for all strains and species tested in FRT cells, if the pathogens were inactivated by prior EB treatment with UV irradiation, none of the EB containing vesicles acquired caveolin-2. In contrast, in HeLa cells, there were low levels of caveolin-2 associated with vesicles containing UV-treated EBs. The source of these low levels of caveolin-2 on vesicles/inclusions in HeLa cells is not yet clear. Perhaps the EBs are acquiring minimal amounts of caveolin-2 from the membranes of these cells upon entry. This suggestion is supported by the fact that the vesicles of these UV irradiated EBs contain both caveolin-1 and -2 (data not shown); suggesting that the proteins might be co-localizing in hetero-oligomeric complexes. It is well known that Chlamydia-containing vacuoles and later, the developing inclusions, are able to intercept and fuse with exocytic vesicles of the biosynthetic pathway that originate from the Golgi [6,7]. These Golgi-derived vesicles provide the inclusions with sphingolipids and perhaps other key metabolites [7,32]. In addition, the ability of at least some chlamydial strains to intercept these Golgi-derived vesicles is dependent on pathogen viability and therefore likely on gene expression. Considering these points, and the fact that caveolin-1/2 hetero-oligomers traffic to the plasma membrane from the Golgi [25,26,30,31], it is reasonable to suggest that the previously described pathogen induced interception of Golgi-derived vesicles might account for the acquisition of caveolin-2 by inclusions, as seen in the current study. However, although studies by others indicate that all chlamydial strains appear to intercept vesicles of the biosynthetic pathway, not all strains acquire caveolin-2 and this fact provides a strong counter argument. Thus for example, in the current study LGV (L2) inclusions did not acquire caveolin-2 (Table 1), although they have been reported to intercept Golgi-derived vesicles of the biosynthetic pathway [7]. Furthermore, in the current study, acquisition of caveolin-2 by inclusions of some strains was independent of caveolin-1, while among other strains, inclusions acquired both caveolin proteins. Thus there may be more than one pathway by which inclusions acquire caveolin-2. Importantly, acquisition of caveolin-2 by inclusions of any particular strain did not correlate with entry by a raft-mediated pathway. In contrast, acquisition of caveolin-1 at early as well as late stages in pathogen development did correlate with a route of entry involving lipid raft microdomains. Together, these facts are consistent with the conclusion that acquisition of caveoloin-1 and -2 can represent independent and distinct processes. As expected, similar incubations with anti-caveolin-3 demonstrated this component was not present in any of these cells whether infected with Chlamydiae or not. To date, no chlamydial protein has been identified that might be secreted into the host cell cytosol to influence host intracellular trafficking. However, several chlamydial proteins, termed Incs, have been identified in the inclusion membrane [33-35]. Several of these Inc proteins have cytoplasmic domains, making them potential mediators for interactions with the host that might influence trafficking [36]. One of these proteins, IncG, interacts with host protein, 14-3-3β [37]. This particular interaction can not underlie the trafficking of caveolin-2 to the inclusions since GPIC (C. caviae) and C. pneumoniae (AR39) do not express IncG [37], although, as shown here, they do acquire caveolin-2. Furthermore, the reverse also is true. Thus, as demonstrated above, mouse pneumonitis strain, MoPn and LGV (L2) do not acquire caveolin-2, but they do express IncG [37]. There are, however, other Inc proteins and they may underlie the phenomena we have presented here. Despite their presence on chlamydial inclusions, it remains unclear what role, if any, caveolin proteins may play in the developmental cycle of Chlamydia. As noted above, all ten of the chlamydial strains and species we examined were able to enter caveolin-1 negative FRT cells [19]. Since these caveolin-1 negative cells do not express caveolin-2 at the plasma membrane [25,26], it would appear that for any of these strains neither of the caveolin proteins is necessary for entry per se. Although following internalization all ten strains and species remained viable in the FRT cells [19], the only strains able to generate large inclusions in the FRT cells were GPIC which acquires both caveolin-1 and -2 in caveolin-positive cells, and MoPn which acquires neither caveolin protein. Despite these latter two instances, our findings remain consistent with the possibility that caveolin-1 may yet play a post entry role in the development of at least some strains. The fact that successful MoPn development does occur in inclusions that acquire neither caveolin-1 nor -2 [19] (and current study) could mean that neither caveolin-1 nor -2 has a necessary role in the development of MoPn. The ability of GPIC to develop mature inclusions in FRT cells where it acquires only caveolin-2, despite acquiring both caveolin proteins in caveolin-1 positive cells, may imply that for GPIC caveolin-2 rather than caveolin-1 is a key for development. Likewise, inclusions of C. trachomatis serovars A, B, and C, whether developing in HeLa or FRT cells, display only caveolin-2, suggesting a potentially broader chlamydial requirement for the caveolin-2 protein during development. The series of merged optical sections demonstrating the association of caveolin-2 with the inclusions (Figure 2) implies that caveolin-2 is in apposition to pathogen cells that are located at the inclusion membrane. Nevertheless, the caveolin-2 is not in direct contact with the pathogens. Caveolin is not a transmembrane protein, a fact that can be deduced from the experimental finding that cell-surface biotinylation does not label caveolin proteins [38]. Thus, these membrane proteins are not accessible from the extracellular milieu, and this originally extracellular membrane surface, is topologically equivalent to the lumen of the chlamydial inclusion. Hence, whereas our findings imply a specific association between the pathogens and caveolin-2, this association appears to be indirect. The paucity of information concerning caveolin-2 does not enable us to suggest an identity for the linking molecule. Moreover, how this indirect association between caveolin-2 and the pathogen might relate to the protein's function in the chlamydial developmental cycle, or in its acquisition by the pathogen, is also not yet clear. Conclusions Among the Chlamydiae, depending on the serovar, or the species, one or both of the caveolin proteins may play an important role in the developmental cycles. Further studies of the interaction of the Chlamydiae with these enigmatic host cell proteins may well help to clarify caveolin functions in the host cell. Likewise, such studies may indicate factors influencing caveolin expression and trafficking from the Golgi, and elucidate their significance to the developmental cycle of these pathogens. The function of caveolin-2, either in the uninfected cell or in the chlamydial developmental cycle, remains to be elucidated. Nevertheless, this second caveolin protein can now be added to the small number of host proteins that are reported as associated with the inclusions of this obligate intracellular pathogen. Competing interests None Declared. Authors' contributions ES and LN conceived of the study and participated in its design and coordination. LN was instrumental in the design of the study and prepared the initial draft of the manuscript. WW carried out all the experimental work, took photographs, did statistical analyses where necessary, and participated in the design and coordination of the project. All authors contributed to the preparation of the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements We thank Michael P. Lisanti for providing wild type FRT cells and FRT cells that express caveolin-1, Judy Whittum-Hudson for providing C. trachomatis serovars C, E and LGV (L2), and Roger G. Rank for providing C. trachomatis (MoPn). This material was supported by the Cooperative State Research Extension, Education Service, U.S. Department of Agriculture, Massachusetts Agricultural Experiment Station, under Project. MAS00845. The University of Massachusetts Central Microscopy Facility is supported by a grant from the National Science Foundation (NSF BBS 8714235). Wilmore C. Webley was supported in part by a Fulbright Scholarship. Figures and Tables Figure 1 Localization of chlamydial inclusions and caveolin-2 in HeLa cells. HeLa 229 cells were infected with chlamydial strains for 48 h. Cells were fixed and double stained with a rabbit anti-Chlamydia and a mouse anti-caveolin-2 antibody. Inclusions of C. pneumoniae (AR39) Cpn, C. Psittaci (guinea pig inclusion conjunctivitis, GPIC strain), C. trachomatis serovars A/Har-13, Har36B, C/TW-3, K, (E/VW-KX and F not shown), are seen to co-localize with caveolin-2. Inclusions of C. trachomatis Mouse pnuemonitis agent (MoPn) and Lymphogranuloma venereum biovar (LGV 434) [not shown] do not colocalize with caveolin-2. Scale bar represents 25 μm and original magnification: 600X Figure 2 Optical Z-axis sections of caveolin-2 associated with chlamydial inclusion membranes. FRT cells were infected with C. trachomatis serovar K for 48 h. Cells were fixed with 10% cold methanol and double stained with a guinea pig anti-Chlamydia and a mouse anti-caveolin-2 antibody. The secondary antibodies were FITC-conjugated goat anti-mouse and TRITC-conjugated goat anti-guinea pig antibody. Slides were examined using a laser confocal microscope and optical Z-axis sections were taken at 0.5 μm depth and images merged using the Confocal Assistant™ version 4.02 Image Processing Software. Original magnification: 600X; the scale bar is 25 μm in length. Figure 3 Acquisition of caveolin-2 by inclusions in caveolin-1 negative FRT cells. (A) FRT cells were infected with chlamydial organisms for 48 h. C. pneumoniae, C. psittaci, and C. trachomatis serovars A, B, E, K (not shown), as well as serovars C and F colocalized with caveolin-2. MoPn and LGV (not shown), did not colocalize with caveolin-2 in these cells. Note that only two species, the mouse pneumonitis strain (MoPn) and GPIC produce large inclusions in these cells even after 48 h infection (scale bar is 25 μm). The small inclusions nonetheless release viable progeny into the culture supernatant which could be used to infect new cells. (B) Uninfected FRT-cells stained with anti-caveolin-2 antibody to demonstrate the localization of caveolin-2 protein in the cell. Note the caveolin-2 staining (C) close to the nucleus of the cell and that there is no caveolin in the cell membrane. (N), the nucleus of the cells and the scale bar represents 10 μm. Original magnification: 600X Figure 4 Anti-caveolin-2 antibodies do not cross-react with chlamydial EBs. C. trachomatis serovar K and C. pneumoniae AR39 EBs were purified by renografin gradient and separated on a 10–12% NuPAGE gel. The proteins were transferred to a PVDF membrane, (A) stained with a mouse monoclonal anti-caveolin-2 antibody and the reacting complex detected with an AP-conjugated goat-anti-mouse secondary antibody. Mw is the molecular weight marker, Con is an endothelial cell lysates for caveolin-2 control, Cpn is C. pneumoniae EBs and Ct represents C. trachomatis EBs. Note that there is staining in the control lane at 22 kDa (caveolin), while there is no staining in the chlamydial organism lanes. (B) control blot using the same quantity and preparation of C. trachomatis serovar K and Cpn EBs as above. The blot was stained with a rabbit anti-Chlamydia polyclonal antibody. Figure 5 Ultraviolet-treated EBs do not colocalize with caveolin-2 in caveolin-1 negative FRT cells. Chlamydial EBs were treated with short wavelength UV for 1 h and then used to infect FRT HeLa cells for 36 h. The cells were fixed and stained with a mouse anti-caveolin-2 and a guinea pig anti-Chlamydia antibody. FITC-conjugated goat anti-mouse as well as TRITC-conjugated goat anti-guinea pig secondary antibody was used to visualize the stained EBs. The representative confocal micrographs above show that UV-treated EBs did not colocalize with caveolin-2 in FRT cells. All of the Chlamydiaceae not shown had a similar morphology. Scale bar represents 15 μm Original magnification: 600X Figure 6 Ultraviolet-treated EBs colocalize with caveolin-2 in HeLa cells. Chlamydial EBs were treated with UV light for 1 h and then used to infect HeLa 229 HeLa cells for 36 h. The cells were fixed and stained with a mouse anti-caveolin-2 and a guinea pig anti-Chlamydia antibody. A FITC-conjugated goat anti-mouse antibody and a TRITC-conjugated goat anti-guinea pig secondary antibody were used to visualize the stained EBs. Unlike the case in FRT cells, UV-treated EBs colocalized with caveolin-2 in HeLa cells, which express both Caveolin-1 and Caveolin-2. The above confocal micrographs are representative of the morphology of all Chlamydiaceae that colocalize with caveolin-2 (C. pneumoniae, GPIC, and C. trachomatis serovars A, B, C, E, F and K). Original magnification: 600 X. Each scale bar represents 15 μm. Table 1 Comparative colocalization of the Chlamydiaceae with caveolin-1 and -2 Note that caveolin-2 colocalizes with all the inclusions that caveolin-1 colocalized with. In addition, caveolin-2 is seen associated with the inclusions of serovars, A, B and C, all ocular serovars, which did not colocalize with caveolin-1 Organisms Caveolin-1 Caveolin-2 Raft-mediated Entry C. pneumoniae (AR 39) + + Yes C. caviae (GPIC) + + Yes C. trachomatis (MoPn) - - No C. trachomatis (ser A) - + No C. trachomatis (36 B) - + No C. trachomatis (ser C) - + No C. trachomatis (ser E) + + Yes C. trachomatis (ser F) + + Yes C. trachomatis (ser K) + + Yes LGV (L2) - - NO (+) colocalization; (-) no colocalization ==== Refs Friis RR Interaction of L cells and Chlamydia psittaci: entry of the parasite and host responses to its development J Bacteriol 1972 110 706 721 4336694 Lawn AM Blyth WA Taverne J Interactions of TRIC agents with macrophages and BHK-21 cells observed by electron microscopy J Hyg (Lond) 1973 71 515 528 4518351 Wyrick PB Brownridge EA Growth of Chlamydia psittaci in macrophages Infect Immun 1978 19 1054 1060 565338 Clausen JD Christiansen G Holst HU Birkelund S Chlamydia trachomatis utilizes the host cell microtubule network during early events of infection Mol Microbiol 1997 25 441 449 9302007 10.1046/j.1365-2958.1997.4591832.x Campbell S Richmond SJ Yates PS The effect of Chlamydia trachomatis infection on the host cell cytoskeleton and membrane compartments J Gen Microbiol 1989 135 ( Pt 9) 2379 2386 2483409 Hackstadt T Scidmore MA Rockey DD Lipid metabolism in Chlamydia trachomatis-infected cells: directed trafficking of Golgi-derived sphingolipids to the chlamydial inclusion Proc Natl Acad Sci U S A 1995 92 4877 4881 7761416 Hackstadt T Rockey DD Heinzen RA Scidmore MA Chlamydia trachomatis interrupts an exocytic pathway to acquire endogenously synthesized sphingomyelin in transit from the Golgi apparatus to the plasma membrane Embo J 1996 15 964 977 8605892 Heinzen RA Scidmore MA Rockey DD Hackstadt T Differential interaction with endocytic and exocytic pathways distinguish parasitophorous vacuoles of Coxiella burnetii and Chlamydia trachomatis Infect Immun 1996 64 796 809 8641784 Scidmore MA Fischer ER Hackstadt T Sphingolipids and glycoproteins are differentially trafficked to the Chlamydia trachomatis inclusion J Cell Biol 1996 134 363 374 8707822 10.1083/jcb.134.2.363 Everett KD Bush RM Andersen AA Emended description of the order Chlamydiales, proposal of Parachlamydiaceae fam. nov. and Simkaniaceae fam. nov., each containing one monotypic genus, revised taxonomy of the family Chlamydiaceae, including a new genus and five new species, and standards for the identification of organisms Int J Syst Bacteriol 1999 49 Pt 2 415 440 10319462 Pal S Theodor I Peterson EM de la Maza LM Immunization with the Chlamydia trachomatis mouse pneumonitis major outer membrane protein can elicit a protective immune response against a genital challenge Infect Immun 2001 69 6240 6247 11553566 10.1128/IAI.69.10.6240-6247.2001 Morrison RP Caldwell HD Immunity to murine chlamydial genital infection Infect Immun 2002 70 2741 2751 12010958 10.1128/IAI.70.6.2741-2751.2002 Westrom L Joesoef R Reynolds G Hagdu A Thompson SE Pelvic inflammatory disease and fertility. A cohort study of 1,844 women with laparoscopically verified disease and 657 control women with normal laparoscopic results Sex Transm Dis 1992 19 185 192 1411832 Thylefors B A global initiative for the elimination of avoidable blindness Indian J Ophthalmol 1998 46 129 130 10085623 Byrne GI Kalayoglu MV Chlamydia pneumoniae and atherosclerosis: links to the disease process Am Heart J 1999 138 S488 90 10539855 Campbell LA Kuo CC Grayston JT Chlamydia pneumoniae and cardiovascular disease Emerg Infect Dis 1998 4 571 579 9866733 Simons K Ikonen E Functional rafts in cell membranes Nature 1997 387 569 572 9177342 10.1038/42408 Norkin LC Wolfrom SA Stuart ES Association of caveolin with Chlamydia trachomatis inclusions at early and late stages of infection Exp Cell Res 2001 266 229 238 11399051 10.1006/excr.2001.5202 Stuart ES Webley WC Norkin LC Lipid rafts, caveolae, caveolin-1, and entry by Chlamydiae into host cells Exp Cell Res 2003 287 67 78 12799183 10.1016/S0014-4827(03)00059-4 Matveev S Li X Everson W Smart EJ The role of caveolae and caveolin in vesicle-dependent and vesicle-independent trafficking Adv Drug Deliv Rev 2001 49 237 250 11551397 10.1016/S0169-409X(01)00138-7 Brown DA Rose JK Sorting of GPI-anchored proteins to glycolipid-enriched membrane subdomains during transport to the apical cell surface Cell 1992 68 533 544 1531449 10.1016/0092-8674(92)90189-J Dupree P Parton RG Raposo G Kurzchalia TV Simons K Caveolae and sorting in the trans-Golgi network of epithelial cells Embo J 1993 12 1597 1605 8385608 Lisanti MP Tang ZL Sargiacomo M Caveolin forms a hetero-oligomeric protein complex that interacts with an apical GPI-linked protein: implications for the biogenesis of caveolae J Cell Biol 1993 123 595 604 8227128 10.1083/jcb.123.3.595 Galbiati F Razani B Lisanti MP Emerging themes in lipid rafts and caveolae Cell 2001 106 403 411 11525727 10.1016/S0092-8674(01)00472-X Mora R Bonilha VL Marmorstein A Scherer PE Brown D Lisanti MP Rodriguez-Boulan E Caveolin-2 localizes to the golgi complex but redistributes to plasma membrane, caveolae, and rafts when co-expressed with caveolin-1 J Biol Chem 1999 274 25708 25717 10464308 10.1074/jbc.274.36.25708 Parolini I Sargiacomo M Galbiati F Rizzo G Grignani F Engelman JA Okamoto T Ikezu T Scherer PE Mora R Rodriguez-Boulan E Peschle C Lisanti MP Expression of caveolin-1 is required for the transport of caveolin-2 to the plasma membrane. Retention of caveolin-2 at the level of the golgi complex J Biol Chem 1999 274 25718 25725 10464309 10.1074/jbc.274.36.25718 Lipardi C Mora R Colomer V Paladino S Nitsch L Rodriguez-Boulan E Zurzolo C Caveolin transfection results in caveolae formation but not apical sorting of glycosylphosphatidylinositol (GPI)-anchored proteins in epithelial cells J Cell Biol 1998 140 617 626 9456321 10.1083/jcb.140.3.617 Sargiacomo M Sudol M Tang Z Lisanti MP Signal transducing molecules and glycosyl-phosphatidylinositol-linked proteins form a caveolin-rich insoluble complex in MDCK cells J Cell Biol 1993 122 789 807 8349730 10.1083/jcb.122.4.789 Zurzolo C van't Hof W van Meer G Rodriguez-Boulan E VIP21/caveolin, glycosphingolipid clusters and the sorting of glycosylphosphatidylinositol-anchored proteins in epithelial cells Embo J 1994 13 42 53 8306971 Das K Lewis RY Scherer PE Lisanti MP The membrane-spanning domains of caveolins-1 and -2 mediate the formation of caveolin hetero-oligomers. 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==== Front BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-381526523310.1186/1471-2407-4-38Research ArticleIrinotecan plus folinic acid/continuous 5-fluorouracil as simplified bimonthly FOLFIRI regimen for first-line therapy of metastatic colorectal cancer Teufel Andreas [email protected] Silke [email protected] Jürgen [email protected] Christiane [email protected] Herbert [email protected] Bernd [email protected] M [email protected] O [email protected]öhler Thomas [email protected] Peter R [email protected] Michael [email protected] Markus [email protected] Dept. of Internal Medicine I, Outpatient Unit for GI Cancer, Johannes Gutenberg University, Mainz, Germany2 Hospital Bad Ems, Outpatient Unit, Bad Ems, Germany3 General practice, Alzey, Germany4 General practice, Landau, Germany5 General practice, Kostheim, Germany2004 20 7 2004 4 38 38 13 4 2004 20 7 2004 Copyright © 2004 Teufel et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Combination therapy of irinotecan, folinic acid (FA) and 5-fluorouracil (5-FU) has been proven to be highly effective for the treatment of metastatic colorectal cancer. However, in light of safety and efficacy concerns, the best combination regimen for first-line therapy still needs to be defined. The current study reports on the bimonthly FOLFIRI protocol consisting of irinotecan with continuous FA/5-FU in five German outpatient clinics, with emphasis on the safety and efficiency, quality of life, management of delayed diarrhea, and secondary resection of regressive liver metastases. Methods A total of 35 patients were treated for metastatic colorectal cancer. All patients received first-line treatment according to the FOLFIRI regimen, consisting of irinotecan (180 mg/m2), L-FA (200 mg/m2) and 5-FU bolus (400 mg/m2) on day 1, followed by a 46-h continuous infusion 5-FU (2400 mg/m2). One cycle contained three fortnightly administrations. Staging was performed after 2 cycles. Dosage was reduced at any time if toxicity NCI CTC grade III/IV was observed. Chemotherapy was administered only to diarrhea-free patients. Results The FOLFIRI regimen was generally well tolerated. It was postponed for one-week in 51 of 415 applications (12.3%). Dose reduction was necessary in ten patients. Grade III/IV toxicity was rare, with diarrhea (14%), nausea/vomiting (12%), leucopenia (3%), neutropenia (9%) and mucositis (3%). The overall response rate was 31% (4 CR and 7 PR), with disease control in 74%. After primary chemotherapy, resection of liver metastases was achieved in three patients. In one patient, the CR was confirmed pathologically. Median progression-free and overall survival were seven and 17 months, respectively. Conclusions The FOLFIRI regimen proved to be safe and efficient. Outpatient treatment was well tolerated. Since downstaging was possible, combinations of irinotecan and continuous FA/5-FU should further be investigated in neoadjuvant protocols. irinotecan5-fluorouracilsafetymetastatic colorectal cancerneoadjuvant ==== Body Background Patients with advanced ColoRectal Cancer (CRC) have been demonstrated to benefit from chemotherapeutic treatment in terms of both quality and duration of life [1]. Of these treatments, fluoropyrimidines are the most often used and best investigated drugs [2,3]. 5-Fluorouracil (5-FU)-based chemotherapy, – usually biomodulated with folinic acid (FA) to increase its affinity for thymidylate synthase [4] -, was associated with an approximate doubling of the median survival compared to routine standard of care [5]. Additionally, recent phase III studies suggested that combinations of FA/5-FU with irinotecan or oxaliplatin resulted in improved response rates and prolonged survival [6-9]. These encouraging results prompted the use of combination therapy of irinotecan and FA/5-FU as a first line chemotherapy in CRC. Irinotecan (CPT-11), a potent inhibitor of the enzyme topoisomerase I, has demonstrated anti-tumorogenic activity in metastatic colorectal cancer, when used alone or in combinations with FA/5-FU, as adjuvant or palliative treatment (for review see 10 or 11). In randomized phase III clinical trials, second-line therapy with irinotecan significantly improved survival compared to supportive care [12] or to infusional FA/5-FU [13]. In the first-line setting of metastatic colorectal cancer, two randomized multi-center phase III clinical trials demonstrated synergistic activity of irinotecan with both bolus and infusional FA/5-FU regimens [6,7]. In both studies, combinations of irinotecan and FA/5-FU were superior to the control arms, irinotecan alone or FA/5-FU, specifically in regard to response rate, progression-free, and overall survival. However, the best regimen of irinotecan and FA/5-FU has yet to be defined. Altogether, irinotecan combined with continuous FA/5-FU infusions seemed to be superiorly active and less toxic than combination with FA/5-FU bolus regimens [6,7]. Recently, irinotecan was investigated in a bi-monthly protocol with bolus FA/5-FU and a continuous 48 h infusion 5-FU (simplified LV5-FU2 regimen; FOLFIRI) [14-16]. In a consecutive phase III clinical trial, a response rate of 56% was achieved with FOLFIRI, as compared to 39% achieved with the original Saltz protocol [6,14]. Furthermore, irinotecan schedules of weekly and of once every two or three weeks demonstrated similar efficacy and quality of life, as well as significantly lower incidences of severe diarrhea in patients with 5-FU-refractory, metastatic colorectal cancer [15-17]. In contrast to the irinotecan and bolus FA/5-FU regimen, which has attracted criticism due to unexpectedly high early death rates due to gastrointestinal toxicity and thromboembolic events observed in two subsequent trials [18,19], no increased 60-day mortality rate was found in two recent trials, each with continuous 5-FU treatment arms [14,17,20,21]. To date, little data is available regarding irinotecan combined with the simplified bi-monthly LV5-FU2 regimen as a first-line treatment in patients with metastatic colorectal cancer [14]. Therefore, we initiated this prospective open-label, multi-center phase IV clinical trial to evaluate its toxicity and efficacy in German outpatient clinics. We were especially interested in the safety of this regimen in an outpatient setting, with particular emphasis placed on the prompt and aggressive management of delayed diarrhea with loperamide, hospitalization and parenteral rehydration in case of refractory diarrhea lasting more than 48 hours [22,23]. Furthermore, all patients were closely monitored for the possibility of resection of liver metastases after successful response. Methods Accrual and eligibility After approval by the local ethical committee, patients were consecutively recruited from five German outpatient clinics (one university hospital, one community hospital, and three general practices). Eligibility requirements included (1) histologically documented adenocarcinoma of the colon or rectum and progressive measurable metastatic disease, (2) minimum life expectancy of three months, (3) Karnofsky performance status ≥ 60, (4) adequate hematologic, hepatic, and renal function, and (4) no prior chemotherapy for metastatic disease. Participants needed to be between 18 and 75 years of age. This study required that previous adjuvant 5-FU-based therapy with or without radiation therapy be completed at least 6 months prior to entry. Patients with CNS metastases, bowel obstruction, or ileus were excluded from the study. The study was approved by the ethics commission board responsible for all participating institutions. Prior to treatment, all patients gave written informed consent. Treatment and management of toxicity As previously described [14-16], treatment consisted of the bi-monthly combination of irinotecan 180 mg/qm given as a 90-min intravenous infusion, day (d)1, FA 200 mg/m2 d1, 5-FU bolus 400 mg/m2 d1, followed by 5-FU 46 h continuous infusion 2400 mg/m2 (simplified LV5-FU2 schedule). To prevent expected toxicities, patients were carefully informed of the potential risk of delayed diarrhea and neutropenia and the need for early intervention with loperamide [22] and metoclopramide, prophylactic antibiotics, or hospitalization and parenteral rehydration in case of refractory diarrhea lasting more than 48 hours. Patients with loperamide-resistant diarrhea defined as loose stools persisting for more than 24 hours despite adequate treatment with loperamide would receive a trial of the oral steroid budesonide (9 mg per day for a maximum of 4 days) [23]. Atropine was given for irinotecan-related cholinergic symptoms if needed [25]. Antiemetic treatment was performed using metoclopramide or HT5 antagonists in a sequential manor. The prophylactic use of colony-stimulating factors was not permitted. Treatment was continued until one of the following occurred: disease progression, unacceptable adverse effects, or withdrawal of consent by the patient. Assessments Primary measures of the study were the overall objective response rate (ORR, complete and partial responses), overall survival, and quality of life. Secondary measures included the disease control rate (ORR + stable disease), time to progression, and frequency and severity of toxicities. Quality of life was assessed after inclusion into the study and as often as possible during the course of treatment, using the EORTC QLQ-C30 (version 2) questionnaire [24]. Safety assessments and complete blood counts were performed weekly. Toxicity was graded according to National Cancer Institute Common Toxicity Criteria (NCI CTC). Toxicities not defined by NCI CTC criteria were classified as grade 0 (none), grade I (minor), grade II (moderate), grade III (severe), and grade IV (life-threatening). In case of any toxicity grade II, with the exception of hand-foot syndrome or alopecia, the next scheduled doses of irinotecan, folinic acid and 5-FU were delayed for a maximum of 1 week (or resolution of diarrhea for at least five days). In case of toxicity grade III/IV or if improvement from grade II to I (or resolution of diarrhea) was not achieved by two weeks, the following chemotherapy doses were reduced by 20 percent. If grade III/IV toxicity did not improve by 2 weeks, treatment was discontinued. Dose reductions were mandatory from the first cycle of chemotherapy in case of toxicity higher than grade II, and chemotherapy was resumed only after complete recovery from diarrhea. Tumor response was assessed according to World Health Organization (WHO) criteria. Tumor reassessment by the same imaging method used to establish baseline tumor measurement was generally performed after every two courses of therapy until progression. A complete response (CR) was defined as complete disappearance of evidence of cancer. A partial response (PR) was defined as a reduction in the sum of the products of the bi-perpendicular diameters of all measurable lesions by at least 50%. Progressive disease (PD) was defined as an increase in the sum of the products of the greatest bi-perpendicular diameters of all measurable lesions by at least 25% or the appearance of new lesions. Stable disease was defined as any reduction or increase in measurable lesions which did not meet the criteria for PR or PD. Confirmed objective responses were those for which a follow-up scan obtained at least four weeks later demonstrated the persistence of the response. The assessment of response and progression was based on investigator-reported measurements. Statistical analysis Statistical analysis including survival analysis according to Kaplan-Meier was performed with the SPSS software package. The deadline for data evaluation was March 15, 2004. Survival was measured from the time of diagnosis to the date of death or last follow-up. Progression-free survival was calculated from treatment onset to the time of progression, study withdrawal or death of any cause. Patients who received at least one dose of the treatment regimen were evaluated for toxicity, and patients who completed at least two chemotherapy cycles were evaluated for response. Results Between 10/2001 and 5/2003, 35 consecutive patients (25 male, 10 female) with metastatic colorectal cancer were enrolled into the study. The median age of these patients was 62 years, ranging from 38 to 73. Baseline characteristics of all patients are summarized in table 1. Most patients were in good overall physical condition, although 60% had at least two metastatic sites. All patients had undergone surgery prior to chemotherapeutic treatment. Six patients previously had received adjuvant chemotherapy, one of them in combination with radiation therapy. All patients were evaluated for toxicity, for response, and survival. The 35 patients received a total of 151 chemotherapy cycles (mean 4,3 per patient), consisting of 451 administrations. Overall, 51 (12,3 %) of all administrations had to be delayed for one week. During the complete study period, 19 patients had a delay of therapy for a median of nine days and in 10 patients (29%) a dose reduction was necessary at some point during the treatment period. The most common cause for discontinuation of study treatment was disease progression (18 patients, 51%). In case of discontinuation, 16 (46%) patients received a second line treatment with either oxaliplatin plus a FA/5-FU consisting regimen or an epidermal growth factor receptor antagonist. Hematologic toxicity was mild to moderate in the majority of patients (table 2). Only one patient (3 %) had a grade III/IV leucopenia, three patients (9 %) had a grade III neutropenia, and grade III or IV anemia or thrombocytopenia were not observed. The predominant non-hematologic toxicities were nausea/vomiting and delayed diarrhea, which affected a total of 21 (60%) and 10 (29%) patients, respectively. However, grade III/IV of these side effects were only observed in 4 (11%) and 2 (6%) patients, respectively (table 3). Other non-hematological toxicities were predominantly mild, including mucositis (I°, 5 patients, 14%), fever (I°/II°, 5 patients, 14%), cholinergic syndrome (I°, 2 patients, 6%), constipation (I°, 8 patients, 23%), alopecia (I°, 9 patients, 26%, II°, 1 patient, 3%), asthenia (I°, 2 patients, 6%). Regarding the irinotecan induced delayed diarrhea, 11 patients received at least one course of loperamide [22] and 1 patient received budesonide for loperamide refractory diarrhea [23]. In two patients treatment-related hospital admissions as a result of III/IV leucopoenia and grade III diarrhea were required. Other adverse events in three patients included a bowel obstruction due to local recurrence, unexplained vertigo, and renal failure due to urethral obstruction. Pulmonary embolism did not occur in any patients during treatment. With regard to response, four complete (CR) and seven partial responses were seen, and thus an overall response rate of 31% was observed (table 4). In addition, 15 patients (43%) had stable disease (disease control rate, 74%). Disease progression occurred in nine patients (26%). Resectability of metastases was achieved in three patients. In one patient CR, was pathologically confirmed. Median progression-free survival was seven months and overall survival was 17 months (95% confidence intervall: 9–25 months, figure 1). Quality of life data were obtained before and at least once during treatment from 13 patients [24]. The 13 patients evaluated for quality of life did not differ in their pattern of response to chemotherapy from the total population of all evaluated patients. Global health status improved slightly during treatment compared to pre-therapy values (figure 2). In addition, patients treated with the FOLFIRI regimen had a small increase in emotional and physical wellbeing compared to a previously reported cohort of untreated patients. No remarkable changes in the other items of the questionaire were seen during treatment, especially with regard to therapy-dependent symptoms such as nausea and vomiting, diarrhea and pain. Slightly increased nausea and fatigue were observed in our patients. However, a clear trend could not be concluded from our data. Discussion In the current phase IV study we evaluated toxicity and efficacy of the FOLFIRI combination of irinotecan with FA/5-FU as first-line chemotherapy for metastatic colorectal cancer. This combination was established within a phase I clinical trial with a recommended irinotecan dose of 180 mg/qm [15,16]. Irinotecan-containing regimens have been the most commonly used chemotherapy protocols for metastatic colorectal cancer in North America since the publications of Saltz et al. [3,6,7,12,17]. After unexpectedly high early death rates, due to gastrointestinal toxicity and thromboembolic events, which were reported in two subsequent trials due to gastrointestinal toxicity and thromboembolic events, the safety of the Saltz regimen became a subject of considerable debate [18,19]. However, in our previous experience with this regimen we did not observe any major complications [25]. Moreover, additional comprehensive data showed that combinations of irinotecan with continuous FA/5-FU, either weekly or bi-monthly, resulted in higher response rates and better survival. Therefore, we investigated the bi-monthly FOLFIRI schedule in an outpatient setting for its safety and clinical efficacy. In the majority of our patients the FOLFIRI regimen was well tolerated. Gastrointestinal toxicity or thrombembolic events were never fatal. Most hospitalizations were for prevention rather than treatment of life-threatening conditions. Delayed diarrhea, a well known side effect of irinotecan [22], was generally managed in the outpatient setting using loperamide, which was administered to approximately one third of the patients. Budesonide, which has demonstrated activity in loperamide-refractory diarrhea was required in only one of our patients (3%). Overall, we observed relatively low toxicity in our study, with NCI CTC grade III leucopenia amounting to 3%, diarrhea to 14% and nausea/vomiting to 11%. The toxicity observed in our study was lower than that reported by Douillard et al. in the pivotal European first-line trial in the patient group receiving weekly irinotecan (80 mg/qm), 24-h HD-5-FU (2300 mg/qm) preceded by 2-h FA 500 mg/qm [7]. In this patient group, grade III/IV toxicities were reported for leucopenia 20.4%, diarrhea 44.4% and nausea 7.4%. The lower toxicity in our study might be due to the lower per day doses of 5-FU (2400 mg/qm administered over 48 h) and L-FA (200 mg/qm) used. In the EORTC phase III study 40986, comparing first-line AIO schedule alone with irinotecan (80 mg/qm), FA 500 mg/qm and continuous FA/5-FU (2300 mg/qm), the 5-FU-dose had to be reduced to 2000 mg/qm because of initially high toxicity in an interim analysis [26]. In addition, lower toxicity in our study may have been more limited because of the early and rigorous dose reductions according to our protocol. Furthermore, we observed improved physical and emotional status and an increase in global health status during treatment [24]. This is in concordance with our previously reported data [25]. Tournigand et al. demonstrated an increase in weight of at least 5% in 35% and an improved physical status in 35% of the irinotecan/FA/5-FU treated patients, respectively [14]. Koehne et al. reported a significantly better quality of life in the irinotecan/FA/5-FU group compared to FA/5-FU [26]. The response rate achieved in our study (31%) was quite comperable tp previously published data [6,7,14,26]. In these studies, response rates were 40–56 % with time to progression (TTP) of 6–8 months. Tumor control (CR+PR+SD) was achieved in 74% of our patients, which is similar to other reports. Median progression-free and overall survival, was comparable, but slighty less than 8,5 and 21,5 months reported by Tournigand [14]. Three reasons may account for these differences in survival between the studies. The most important reason may be that a higher percentage of patients (21 patients, 60% compared to 41% and 10% reported by Tournigand and Saltz, respectively) in our study had two or more metastatic sites, indicating a larger tumor burden and consequently a worse prognosis regarding survival [6,14]. Second, our study included patients with carcinoma of the rectum (12 patients, 34% versus 15 %). And finally, as much as five (14%) of our patients had previously received adjuvant FA/5-FU-containing chemotherapy or radiotherapy compared with 10% of the patients in the other study [6]. It appears particularly noteworthy that after chemotherapy three of our patients achieved surgical resectability of their metastases. To our knowledge these results are the first ever reported to suggest a potential role for the FOLFIRI regimen in the neoadjuvant setting. Thus far, studies of regional chemotherapy for initially unresectable colorectal liver metastases could demonstrate some success with secondary curative surgery. In two recently published retrospective studies chronomodulated chemotherapy with oxaliplatin and FA/5-FU was used as neoadjuvant treatment for patients with unresectable colorectal liver metastases [27,28]. Therefore, combination regimens of irinotecan or oxaliplatin with FA/5-FU should be strongly considered as standard first-line chemotherapy for metastatic colorectal cancer. Through multidisciplinary efforts involving both surgeons and medical oncologists, it should be possible, to translate the antitumour activity of the new first-line regimens into long-term survival benefit for patients with initially unresectable colorectal liver metastases. Conclusions The FOLFIRI regimen, consisting of irinotecan with continuous FA/5-FU over 48 h, given on an outpatient basis was safe and well tolerated in our study. The rate of severe side effects was comparably low with this bi-monthly regimen. As tumor control was achieved in about 75% and downstaging of metastatic disease was possible in some cases, combinations of irinotecan plus continuous FA/5-FU should be further investigated in neoadjuvant protocols for initially unresectable liver metastases. List of abbreviations CRC Colorectal Cancer FOLFIRI chemotherapeutic regimen consisting of irinotecan combined with continuous FA/5-FU infusions FA Folinic Acid 5-FU 5-Fluorouracil HD-5-FU high dose 5-FU NCI CTC National Cancer Institute Common Toxicity Criteria CR Complete remission PR Partial response SD Stable disease PD Progressive disease TTP Time to progression OS Overall survival Author's contributions MM, SS and AT drafted the manuscript. MM and MH initiated the study. JS, CZ, HH, BA, MS, OK, T, PG and MH were involved in the patient's treatment as well as the documentation of response and side effects. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements This work represents parts of the MD thesis of S. Steinmann. The authors would like to thank A. Schindler for comments on the manuscript. Figures and Tables Figure 1 Overall survival. The time to progression (TTP) and overall survival (OS, in months) are plotted as Kaplan-Meier-curves. Overall survival is drawn as a continuous line, total progression free survival as a dotted curve. TTP and OS were 7 and 17 months, respectively. Figure 2 Quality of life assessed by the QLQ-C30 questionnaire. Quality of life was assessed immediately after inclusion into the study and at least once during the course of treatment, using the standardized EORTC QLQ-C30 questionnaire, version 2. Overall scores range from 0 to 100, divided into several subsets such as physical ability, emotional feelings, nausea/vomiting, fatigue, pain or diarrhea. Higher scores on global health status and physical functioning and lower scores on symptom scales and emotional assessment indicate a better quality of life. Table 1 Patient characteristics of all 35 patients with respect to primary tumor side, metastatic sites, number of metastatic sites and previous treatment as well as gender and age. Characteristics No. No. Patients 35 Gender, male/female 25/10 median age, years (range) 62 (38–75) Primary tumor site Colon 21 Rectum 12 Ileum 1 Metastatic sites Liver 26 Lung 12 Lymph nodes 6 Local relapse 3 other sites 3 Peritoneum 8 No. of metastatic sites 1 14 2 17 ≥3 4 Previous treatment surgery only 28 surgery+radiotherapy+adjuvant chemotherapy 1 surgery+adjuvant chemotherapy 5 Table 2 Hematologic toxicity. Number of patients affected with each side effect are listed in the corresponding rows. NCI CTC Grade 1 2 3 4 Leukopenia 7 1 0 1 Neutropenia 4 7 3 0 Anaemia 3 1 0 0 Table 3 Non-hematologic toxicities. Number of patients affected with each side effect are listed in the corresponding rows. NCI CTC Grade 1 2 3 4 Nausea/Vomiting 9 8 4 0 Acute diarrhea 3 0 2 1 Delayed diarrhea 4 4 1 1 Cholinergic syndrome 2 0 0 0 Fever 3 2 0 0 Mucositis 5 0 1 0 Obstipation 8 0 0 0 Asthenia 2 0 0 0 Alopezie 9 1 0 0 Table 4 Summary of the response of 35 evaluated patients, divided into CR, PR, SD and PD. Response No.(%) Complete Response 4 (11) Partial Response 7 (20) Overall Response Rate (CR+PR) 11 (31) Stable disease 15 (43) Tumor control rate (CR+PR+SD) 26 (74) Progressive disease 9 (26) ==== Refs Scheithauer W Rosen H Kornek GV Sebesta C Depisch D Randomised comparison of combination chemotherapy plus supportive care with supportive care alone in patients with metastatic colorectal cancer. Bmj 1993 306 752 755 7683942 Moertel CG Chemotherapy for colorectal cancer. 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==== Front BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-391526523210.1186/1471-2407-4-39Research ArticleIdentification of astrocytoma associated genes including cell surface markers Boon Kathy [email protected] Jennifer B [email protected] Charles G [email protected] Gregory J [email protected] Department of Neurosurgery, Mason F. Lord Bldg., Center Tower, 5th Floor, 5200 Eastern Avenue, Baltimore MD 21224, USA2 Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA2004 21 7 2004 4 39 39 7 4 2004 21 7 2004 Copyright © 2004 Boon et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Despite intense effort the treatment options for the invasive astrocytic tumors are still limited to surgery and radiation therapy, with chemotherapy showing little or no increase in survival. The generation of Serial Analysis of Gene Expression (SAGE) profiles is expected to aid in the identification of astrocytoma-associated genes and highly expressed cell surface genes as molecular therapeutic targets. SAGE tag counts can be easily added to public expression databases and quickly disseminated to research efforts worldwide. Methods We generated and analyzed the SAGE transcription profiles of 25 primary grade II, III and IV astrocytomas [1]. These profiles were produced as part of the Cancer Genome Anatomy Project's SAGE Genie [2], and were used in an in silico search for candidate therapeutic targets by comparing astrocytoma to normal brain transcription. Real-time PCR and immunohistochemistry were used for the validation of selected candidate target genes in 2 independent sets of primary tumors. Results A restricted set of tumor-associated genes was identified for each grade that included genes not previously associated with astrocytomas (e.g. VCAM1, SMOC1, and thymidylate synthetase), with a high percentage of cell surface genes. Two genes with available antibodies, Aquaporin 1 and Topoisomerase 2A, showed protein expression consistent with transcript level predictions. Conclusions This survey of transcription in malignant and normal brain tissues reveals a small subset of human genes that are activated in malignant astrocytomas. In addition to providing insights into pathway biology, we have revealed and quantified expression for a significant portion of cell surface and extra-cellular astrocytoma genes. SAGEastrocytomagene expressionbioinformatics and therapeutic targets. ==== Body Background Astrocytomas are the most frequent malignant primary brain tumors in adults. Clinically, this group of tumors can be divided into four World Health Organization (WHO) grades. Pilocytic astrocytomas (WHO grade I) are generally slow growing and non-infiltrative pediatric tumors, which are rarely fatal. For the infiltrating astrocytomas, survival decreases with increasing grade. Grade II astrocytomas patients survive an average of over 5 years, but survival drops to 3 years for anaplastic astrocytomas (grade III). Grade IV astrocytomas (glioblastoma multiforme or GBM) account for about half of all astrocytic tumors, with a median survival of less than a year. Effective treatment options for the invasive grade II to IV tumors are still limited to surgery and radiation therapy, with most chemotherapy regimens showing little or no increase in survival. Several recent gene expression profiling studies of human astrocytomas have been able to distinguish between various grades of astrocytomas and between astrocytomas and other human glial tumors, and to identify new molecular classes within histological grade [3-7]. This enhanced molecular classification based on expression patterns of genes and pathways holds promise for better diagnostic and prognostic tools. Candidate glioblastoma associated genes have also been identified using expression profiling [8-11]. While these studies in brain cancers have produced leads for potential therapeutic targets, a systematic and comprehensive evaluation of gene expression in malignant astrocytomas is not readily and freely available for the scientific community. In this study, we sought to create a public and comprehensive gene expression resource for astrocytomas, with the primary intention of aiding searches for new therapeutic targets in malignant astrocytomas. For this purpose we produced and analyzed in detail 25 gene expression profiles of primary astrocytic tumors (grade II, III and IV) using Serial Analysis of Gene Expression (SAGE) [12]. Complete expression profiles are posted for the scientific community at the CGAP SAGE Genie website [2]. The utility of the resource was validated by extensive comparisons of tumor with normal tissue. SAGE profiles on normal brain and other tissues created by the Cancer Genome Anatomy Project [8] were used to subtract out the genes normally expressed in adult brain, leaving a small and specific set of astrocytoma associated genes for each class, and revealing cell surface or extra-cellular matrix related genes highly expressed in the tumors when compared to their expression in normal tissue. A subset of the tumor-associated genes was validated in an independent set of tumors at both the transcript and protein level. In summary we have identified several novel tumor-associated markers in astrocytic tumors as well as various cell surface markers highly expressed in the most aggressive tumor types. Methods Tissue and RNA Astrocytic tumor samples from 21 adults and 4 children were obtained from the Duke Brain Tumor Bank. All samples were classified based on histology according to the World Heath Organization grading criteria. Pediatric normal cortex (15 months) was a gift of Dr. Rachel Myerowitz and normal pediatric cerebellum was from the Maryland Brain Bank. Normal adult cortex and cerebellum were rapid autopsy samples obtained from the Duke Alzheimer's Brain Bank. Total RNA from normal substantia nigra was obtained from Clontech (Palo Alto, CA). PolyA+ RNA from normal adult leukocytes was obtained from Stratagene (La Jolla, CA), as noted for each library's information at the SAGE Genie Website [2]. RNA integrity was confirmed by gel electrophoresis prior to SAGE library construction. SAGE libraries and informatics SAGE libraries from 25 selected astrocytomas: 8 grade II astrocytomas, 10 grade III anaplastic astrocytomas and 7 grade IV glioblastomas (primary GBM) were constructed using Nla III as the anchoring enzyme and BsmF I as the tagging enzyme using a micro-SAGE protocol. The SAGE library clones were partially arrayed at Lawrence Livermore National Laboratories and inserts were purified and sequenced at the BC Cancer Agency Genome Sciences Centre or arrayed and sequenced at Agencourt Bioscience Corporation. The SAGE 2000 software version 4.12 (available at ) was used to extract SAGE tags from the original sequence files, remove duplicate ditags, remove linker sequences, remove one base pair variations of linker sequences and tabulate the occurrence of each tag. Tag sequences, tag counts and gene associations were stored in a Microsoft Access relational database for subsequent selection of tags with a particular profile. A total of 2,605,122 tags were obtained with an average of 102,988 tags per library. Normal neural tissue tags included a total of 443,560 tags from normal brain [8,13]. These SAGE normal brain libraries have the following unique identifiers: cortex_B_BB542; cortex_B_pool6; thalamus_B_1; cerebellum_B_1; cerebellum_B_BB542; peds_cortex_B_H1571 and substantia_nigra_B_1. Tags totaling 48,039 were also included from normal leukocytes (SAGE_Leukocytes_normal_B_1). Detailed library information and tag counts for each tissue are located at CGAP's SAGE Genie [2]. Tag counts were normalized to 100,000 tags per library. Real-time PCR Total RNA extraction, cDNA synthesis and quantitative PCR were performed as previously described [1]. Gene expression levels were normalized to 3 genes; GAPDH, ribosomal protein RPS27 and low molecular mass ubiquinone-binding protein (QP_C). Both RPS27 and QP-C showed a relatively even expression level across the libraries as assed by SAGE analysis. Relative expression levels were calculated in comparison to the levels in nine normal neural tissues, including normal brain (3x), cerebellum (2x), thalamus, gray matter, caudate nucleus and pediatric cortex according to Saha et al., 2001 [14]. A list of the PCR primers used for each gene is available upon request. Immunohistochemistry Formalin-fixed 5-μm paraffin-embedded sections were stained with various antibodies using the biotin/streptavidin RTU Vectastain Universal Quick kit (Vector laboratories, Burlingame, CA) as previously described [1]. Shortly, sections were deparaffinized in HemoD and re-hydrated through descending alcohols. Endogenous peroxidase was quenched by incubating the slides in methanol/5% H2O2 at room temperature for 10 min. Non-enzymatic antigen retrieval was performed using the Antigen retrieval solution AR or Citra (Biogenex, San Ramon, CA) in combination with microwave treatment. Sections were then blocked in PBS/0.5% Triton X-100 containing 2.5% normal horse serum for at least 30 min at room temperature and incubated with primary antibody overnight at 4°C. The primary antibodies used were mouse monoclonal anti-aquaporin1, at a 1:60 dilution (Abcam Limited, Cambridgeshire, United Kingdom) and mouse monoclonal anti-Topoisomerase II Alpha used at a 1:30 dilution (Novocatra Laboratories, New Castle, United Kingdom). Sections were developed with a DAB (Sigma, St. Louis, MO) substrate, counterstained with hematoxylin and mounted with Cytoseal-60. Tissue micro arrays The tissue micro array used in these studies contained cores from 20 glioblastomas, 20 anaplastic astrocytomas, 20 infiltrating astrocytomas and 20 oligodendroglial lesions and was prepared according to methods described by Kononen and Kallioniemi [15]. Microscopic examination of the array confirmed that the appearance of the tumor tissue cores corresponded to that in the donor blocks. A neuropathologist (CGE) selected the tumor areas sampled in each case and examined the resulting arrays to ensure they accurately represented the donor cases. Results and Discussion SAGE gene expression profiles, selection and confirmation of tumor-associated genes This report describes the comprehensive generation of expression profiles of three astrocytic tumor grades based on Serial Analysis of Gene Expression (SAGE). The main goals were to identify genes not expressed in normal brain tissue and genes highly expressed in the more aggressive astrocytomas encoding cell surface or extra-cellular matrix related proteins that could be of potential therapeutic interest. We generated SAGE profiles on 8 infiltrating astrocytomas, 10 anaplastic astrocytomas and 7 glioblastoma samples. Combined with two glioblastoma profiles [8], previously deposited on SAGE Genie, this study analyzed 2,734,106 astrocytoma SAGE tags. On average we could distinguish over 27,000 unique tags in each tumor grade after excluding those with single counts (Table 1). Complete expression profiles and library information are posted for the scientific community at the Cancer Genome Anatomy Project (CGAP) SAGE Genie website , where the libraries can be downloaded or viewed online using SAGE Genie tools [2]. In order to identify tumor-associated genes, we sought highly expressed transcripts in each grade of astrocytoma that were not expressed in 7 normal brain tissues. This also helped control for contaminating normal cells within the tumor sample. Tags expressed in a normal leukocyte SAGE library were also included in the analysis so they could be subtracted to reduce the chances of identifying transcripts from white blood cells that frequently infiltrate these tumors. Initially we selected for tags with an average expression of at least 3 per 100,000 tags. Subsequently, we selected for tags expressed at less than 2 counts per 100,000 in each of the 8 normal libraries (7 normal neural tissues & one leukocyte), reducing the number of tags to less than 100 per tumor type (Table 1). We further narrowed down the list of candidates by including only those tag sequences that could be matched with a full-length cDNA sequence. From these lists of genes we selected respectively 8, 16 and 10 genes for real-time PCR analysis in an independent set of 14 to 17 grade II, grade III and grade IV primary tumors. Only 6 genes could not be confirmed by real-time PCR analysis, 3 of 8 (grade II selection) and 3 of 16 (grade III selection). Another 8 genes were confirmed in only 20 to 25 % of the tumors. Table 2 lists those genes with a 5-fold or more over-expression by real time PCR in at least 30% of the tumor samples from the corresponding grade, when compared to an average of the normal neural tissue expression. The results show that the in silico selection using SAGE profiles revealed tumor-associated genes that can be found in a different set of primary tumors implying a possible role for these genes in tumor development and increasing their value as putative therapeutic targets. The limited availability of high quality antibodies for the identified tumor-associated genes (Table 2) narrowed our study at the protein level to those genes that had previously been implicated in astrocytic tumors. Monoclonal antibodies for TOP2A and AQ1 were used to analyze the expression at the protein level in individual glioblastoma sections and in a tissue micro-array. Strong nuclear staining in 5 of 8 individual glioblastomas tested was found for TOP2A (Figure 1A,1C and 1D). The GBMs showed intensely staining cells with the percentage of positive cells varying between 3 and 10%. Similar results were obtained when staining a tissue micro-array containing a different set of 20 GBMs. In six cases we found 5 to 10% of the cells expressing TOP2A. The percentage of strong positive cells among the 20 anaplastic astrocytomas present on the tissue micro array was lower and estimated to be 2 – 3% in 10 cases. Normal neural brain tissues like cortex, white matter, spinal cord and hippocampus where negative for TOP2A (Figure 1B). Cytoplasmatic staining was observed when anti-aquaporin 1 monoclonal antibodies were used. Three of 5 individual GBM sections where positive, with examples shown in Figures 1F and 1G. These results emphasize that the high transcript levels of TOP2A and Aquaporin 1 correlate well with a high protein level in a third independent set of tumors, implying that this might also be the case for the other identified tumor-associated genes as listed in Table 2 and Table 3. Potential therapeutic targets in astrocytic tumors Encouraged by the confirmation of our initial in silico analysis for tumor-associated genes we formulated a slightly simplified approach to find cell surface, extra-cellular matrix and cell adhesion related genes. We selected for transcript tags with at least a ten fold over-expression when compared to the average expression level in normal neural brain tissues. Next we applied a filter that would include only those tags with an average expression of at least 5 or 10 counts per 100,000 tags in 30% or more of the tumors, respectively for anaplastic astrocytomas and glioblastomas. The generated lists of transcript tags were mapped to the corresponding gene using SAGE Genie, where after the gene ontology information (if available) was used as a final filter to identify membrane, cell surface and cell adhesion related genes (Table 3). Interestingly, almost 50% of the genes identified as highly expressed in both tumor types have not previously been implicated in astrocytomas and are potential new therapeutic targets. Intracellular proteins that contribute to the fusion of the vesicles with the plasma membrane during exocytosis include synaptosomal protein and vesicle-associated membrane proteins (VAMP). Both anaplastic astrocytomas and glioblastomas show high expression of VAPB and anaplastic astrocytomas express caveolin1 (Table 3). It has been shown that caveolae require intact VAMP for targeted transport in endothelial cells. Caveolae and associated proteins might be targeted in cancer as recently suggested [16]. One of the other genes expressed in common between the three astrocytomas is chitinase 3-like 2 (CHI3L2) or YKL-39 (Table 2). CHI3L2 is a chondrocyte growth related gene and is an antigen found in rheumatoid arthritis [17] and osteoarthritis and a possible immunotherapy target. Another commonly over-expressed gene is Neuromedin B. This neuropeptide has been implicated as an autocrine growth factor in lung cancer cells [18] that binds to a G protein-coupled receptor on the cell surface and might have a similar role in astrocytic tumors. It is tempting to speculate that a specific neuropeptide antagonist or neutralizing antibodies might reduce astrocytoma growth. Neuromedin B had previously been described as a GBM marker [9], and was included along with ABCC3 in the real-time PCR analysis as a positive control. Another relatively unknown gene is the recently characterized SMOC-1 [19], which was identified as a grade II and III tumor-associated gene (Table 2). This gene is related to SPARC/osteonectin, which was reported to participate in angiogenesis and tumor formation of human melanomas. Another extra cellular gene, Matrix Gla protein [20] had increased expression levels in higher-grade astrocytomas. MGP helps regulate the calcification of the extra cellular matrix [20]. Aquaporin 1 is an integral membrane protein important in the regulation of water transport in various epithelial and endothelial cell types [21]. The over-expression of AQP1 in human brain tumors was described in a limited array study of 4 Glioblastomas [11] and it has been suggested that the protein might play a role in brain tumor edema in a similar way as the closely related aquaporin-4 [22]. Although the specific role of AQP1 in brain tumors is still unknown, our demonstration that AQP1 is consistently expressed in GBM may prompt other studies. Thymidilate synthetase and Topoisomarese 2A were over-expressed in glioblastoma as well as in our previous study of medulloblastoma [1]. Considering the role of Top2A as a molecular target of various anticancer drugs, and its identification as a survival marker in astrocytomas [23], its over-expression at the protein level in multiple brain tumors and the development of TOP2A inhibitors [24] makes the molecular targeting of TOP2A worthy of further investigation. In summary we have identified a number of new tumor-associated genes for three different grades of astrocytic tumors, and helped re-confirm in a larger set of samples several previously known astrocytoma genes. Despite the high heterogeneity among gliomas, a small set of genes is consistently observed at high levels in more than a third of each grade of astrocytoma studied. Many other cell surface, extra-cellular matrix or cell adhesion genes have been identify as potential targets for cancer therapy in astrocytic tumors. Although the therapeutic value of these markers is speculative at this point, by integrating this data onto the commonly used gene expression resource, SAGE Genie, this data can be used as a standard to determine gene expression in astrocytomas. Further evaluation by in vitro and in vivo studies will be necessary to establish the role of these over-expressed genes in brain tumor development and progression. Authors' contributions KB performed computational analyses, generated experimental data, participated in the design of the study and drafted the manuscript. JBE generated experimental data. CBE and GJR participated in the study design and manuscript editing. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgments Funding was provided by the Ludwig Trust, a Cancer Genome Anatomy Project contract (NIH 23X- S073) and a Director's Challenge Grant (U01 CA88128). C.G. Eberhart is recipient of a Burroughs Wellcome Fund Career Award. G.J. Riggins is the recipient of the Irving J. Sherman, M.D. Research Professorship in Neurosurgery. Figures and Tables Figure 1 Representative immunostaining of paraffin sections with antibodies against Topoisomerase 2A and Aquaporin 1. (A) Upper panel shows two TOP2A positive cores from GBMs; lower panel shows two negative cores on the same tissue micro-array. (B) Upper panel from left to right: cortex and white matter; lower panel from left to right: spinal cord and hypocampus. All stained with anti-TOP2A antibodies. (C – D) Paraffin section of GBM showing nuclear staining of TOP2A; D shows a higher magnification and E is the corresponding negative control with mouse IgG1 and no primary antibodies. (F – G) Paraffin section of GBM showing cytoplasmatic staining with anti-aquaporin 1 antibodies; G shows a higher magnification and H is the negative control. Table 1 Summary of number of tag sequences. Group Total tags Unique tagsa Expressed tags per libraryb Expressed in tumors and not in neural normalsc Grade II 869,992 22,574 3,999 41 Grade III 1,026,620 26,445 3,866 55 Grade IV 798,451 33,541 3,518 76 aUnique tags with a tag count of 2 and higher. bAmount of tags with an expression level of 3 or more per 100,000 tags. cAmount of tags expressed in at least 20% of the tumors and with an expression level of 2 or less in the normal neural tissues and leukocytes. Table 2 Astrocytoma associated genes validated by real-time PCR. Tag sequence Symbol Gene/Ontology Mean tumora Mean normalb % SAGEc % RT-PCRd Low grade astrocytomas (grade II) TGGGATTCCC CHI3L2 Chitinase 3-like 2 / cartilage biogenesis, extra-cellular 10.8 0 25 57 ATCTGCTCGG NMB Neuromedin B / signal transduction, neuropeptide 9.1 0.1 38 57 GTCAGAACTT cDNA FLJ22528 / unknown 5.8 0.3 38 43 GTACGGAGAT VCAM1 Vascular cell adhesion molecule 1/ cell adhesion 4.4 0.2 50 29 Anaplastic astrocytomas (grade III) TGGGATTCCC CHI3L2 Chitinase 3-like 2 / cartilage biogenesis, extra-cellular 12.3 0 30 63 GTGCGGAGGA SAA1 Serum amyloid A1/ inflammatory response, lipid metabolism, extra-cellular 11.4 0 20 50 TTTGCACTTT SMOC1 Secreted modular calcium-binding 1/ calcium ion binding 6.0 0 50 50 CCATTGTGGA ADORA3 Adenosine A3 receptor / signal transduction, G-protein coupled receptor 4.1 0.1 60 50 ATCTGCTCGG NMB Neuromedin B/ signal transduction, neuropeptide 9.1 0.1 40 81 CGCGGCGGCG CRF C1q-related factor / function unknown 7.8 0.1 70 56 CCCAGTAAGA CSRP2 Cysteine and glycine-rich protein 2 / cell differentiation/growth, LIM domain protein 9.4 0.2 80 44 GTACGGAGAT VCAM1 Vascular cell adhesion molecule 1/ cell adhesion 3.0 0.2 20 69 Glioblastomas (grade IV) ACTGTCGCCA AQP1 aquaporin 1 / water transport, ion channel 5.8 0 22 53 ATGTAGAGTG TYMS thymidylate synthetase / pyrimidine metabolism 7.1 0 67 71 CTCAGCAATG TOP2A topoisomerase (DNA) II alpha (170 kD) / DNA metabolism, isomerase 4.2 0 44 88 GTCAACAGTA ABCC3 ATP-binding cassette, subfamily C, member 3/ multidrug resistance, transport protein 12.3 0 89 94 GTGCGGAGGA SAA1 serum amyloid A1/ inflammatory response, lipid metabolism, extra-cellular 14.3 0 44 53 TGGGATTCCC CHI3L2 chitinase 3-like 2 / cartilage biogenesis, extra-cellular 31.3 0 67 100 ATCTGCTCGG NMB Neuromedin B / signal transduction, neuropeptide 4.6 1 44 53 GTTTATGGAT MGP Matrix Gla protein /cartilage condensation, ossification, extra-cellular 7 2 56 35 All counts are normalized per 100,000 tags. aAverage tag count per individual tumor SAGE library. bAverage tag count per individual normal neural tissue SAGE library. cPercentage of tumor SAGE libraries with an expression of at least 5 or more counts. dPercentage of tumor samples in an independent panel of tumors with at least a 5-fold of more over-expression when compared to neural normal tissue samples in real-time PCR analysis. Table 3 Selected highly expressed genes in grade III and IV astrocytomas. Tag Sequence Tumora Inductionb Gene Gene Ontology Anaplastic astrocytomas (Grade III) AAGCCGAAGA 10 40 GFAP glial fibrillary acidic protein Major intermediate filament protein of mature astrocytes CTGGGGAGTG 8.1 22 VAPB VAMP associated protein B and C Type IV membrane protein in plasma and intracellular vesicle membranes GCAACAGCAA 80.9 19 SEC61G Sec61 gamma subunit Endoplasmatic reticulum membrane protein, translocation TTAATCTGAG 6.9 18 ARHGEF6 Rac/Cdc42 guanine nucleotide exchange factor (GEF) 6, intra-cellular ATCTTGTTAC 11 15 FN1 fibronectin 1 Extra-cellular matrix, cell adhesion, cell migration GGAACAAACA 11.9 14 CD24 Plasma membrane, small cell lung carcinoma cluster 4 antigen AGGGAGGGGC 32.9 14 GPX3 glutathione peroxidase 3 Extra-cellular, response to oxidative stress, glutathione metabolism GTATGGGCCC 26.8 13 CHI3L1 chitinase 3-like 1 Cartilage glycoprotein ACGAGGGGTG 71.4 12 BCAN, chondroitin sulfate proteoglycan Cell adhesion CACTTGAAAA 6.9 11 CALU calumenin Endoplasmatic reticulum, calcium ion binding GATAACTACA 6.7 11 IGFBP7 Extra-cellular, insulin-like growth factor binding protein 7 TCCTGTAAAG 6.8 11 CAV1 caveolin 1, 22 kDa Plasma membrane, caveolae protein, integrin-mediated celladhesion, vesicular trafficking GTCTTAAAGT 41.8 10 SOD2 Superoxide dismutase, mitochondrial, erythropoietin-mediated neuro protection through NF-kB Glioblastomas (grade IV) GTATGGGCCC 144.2 72 CHI3L1 chitinase 3-like 1 Cartilage glycoprotein GACCACCTTT 17.3 69 MFAP2 microfibrillar-associated protein 2 Major antigen of elastin-associated microfibrils ACCAAAAACC 115.8 49 COL1A1 collagen, type I, alpha 1 Extra-cellular matrix AGTGGTGGCT 14.8 39 FMOD fibromodulin Extra-cellular matrix, TGF beta receptor complex assembly CTGGGGAGTG 13.2 35 VAPB VAMP associated protein B and C Type IV membrane protein in plasma and intracellular vesicle membranes ACATTCTTTT 38.2 34 GPNMB glycoprotein nmb Transmembrane glycoprotein ATCTTGTTAC 21.1 28 FN1 fibronectin 1 Extra-cellular matrix, cell adhesion, cell migration GCAACAGCAA 97.9 23 SEC61G Sec61 gamma subunit Endoplasmatic reticulum membrane protein, translocation TCACCAAAAA 19.3 19 STAB1 stabilin 1 Cell adhesion, calcium ion binding CACTTGAAAA 11.6 19 CALU calumenin Endoplasmatic reticulum, calcium ion binding TTTTCAAATA 15.4 15 T1A-2 lung type-I Type I integral membrane glycoprotein GTCTTTCTTG 24.4 14 RAB13 Cell adhesion, member RAS oncogene family GGAAATGTCA 12.6 13 MMP2 matrix metalloproteinase Extra-cellular matrix, calcium ion binding GTACTAGTGT 23.8 12 CCL2 chemokine (C-C motif) ligand 2 Extra-cellular matrix, cell adhesion, signal transduction AAGCTGTATA 16.7 12 TNC tenascin C (hexabrachion) Extra-cellular matrix, cell adhesion GTGCTAAGCG 10.8 11 COL6A2 collagen, type VI, alpha 2 Extra-cellular matrix CATATCATTA 151.6 11 IGFBP7 Extra-cellular, insulin-like growth factor binding protein 7 AACACAGCCT 24.9 11 C4A complement component 4A Extra-cellular matrix, complement pathway Highly expressed genes were selected with at least a ten fold over-expression when compared to normal neural tissue and a minimal expression level of at least 5 or 10 counts per 100,000 tags (in respectively grade III and IV tumors) in 30% of the corresponding tumor type. aAverage tag count count per individaul tunor SAGE library. bAverage induction factor. ==== Refs Boon Kathy Edwards Jennifer B Siu I-Mei Olschner Deric Eberhart Charles G Marra Marco A Strausberg Robert L Riggins Gregory J. 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==== Front Biomed Eng OnlineBioMedical Engineering OnLine1475-925XBioMed Central London 1475-925X-3-251527121910.1186/1475-925X-3-25ResearchAn active electrode for biopotential recording from small localized bio-sources Valchinov Emil S [email protected] Nicolas E [email protected] Department of Medical Physics, University of Patras, Patras 26500, Greece2004 22 7 2004 3 25 25 2 6 2004 22 7 2004 Copyright © 2004 Valchinov and Pallikarakis; licensee BioMed Central Ltd.2004Valchinov and Pallikarakis; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Laser bio-stimulation is a well-established procedure in Medical Acupuncture. Nevertheless there is still a confusion as to whether it works or the effect is just placebo. Although a plethora of scientific papers published, showing positive clinical results, there is still a lack of objective scientific proofs about the bio-stimulation effect of lasers used in Acupuncture. The objective of this work was to design and build a body surface electrode and an amplifier for biopotential recording from acupuncture points, considered here as small localized bio-sources (SLB). The design is aimed for studying SLB potentials provoked by laser stimulus, in search for objective proofs of the bio-stimulation effect of lasers used in Medical Acupuncture. Methods The active electrode presented features a new adjustable anchoring system and fractionation of the biopotential amplifier between the electrode and the cabinet's location. The new adjustable electrode anchoring system is designed to reduce the electrode-skin contact impedance, its variation and motion artifacts. That is achieved by increasing the electrode-skin tension and decreasing its relative movement. Additionally the sensing element provides local constant skin stretching thus eliminating the contribution of the skin potential artifact. The electrode is attached to the skin by a double-sided adhesive pad, where the sensing element is a stainless steel, 4 mm in diameter. The fractionation of the biopotential amplifier is done by incorporating the amplifier's front-end op-amps at the electrodes, thus avoiding the use of extra buffers. The biopotential amplifier features two selectable modes of operation: semi-AC-mode with a -3 dB bandwidth of 0.32–1000 Hz and AC-mode with a bandwidth of 0.16–1000 Hz. Results The average measured DC electrode-skin contact impedance of the proposed electrode was 450 kΩ, with electrode tension of 0.3 kg/cm2 on an unprepared skin of the inner forearm. The peak-to-peak noise voltage measured at the amplifier output, with input terminals connected to common, was 10 mVp-p, or 2 μVp-p referred to the input. The common-mode rejection ratio of the amplifier was 96 dB at 50 Hz, measured with imbalanced electrodes' impedances. The prototype was also tested practically and sample records were obtained after a low intensity SLB laser stimulation. All measurements showed almost a complete absence of 50 Hz interference, although no electrolyte gel or skin preparation was applied. Conclusion The results showed that the new active electrode presented significantly reduced the electrode-skin impedance, its variation and motion artifact influences. This allowed SLB signals with relatively high quality to be recorded without skin preparation. The design offers low noise and major reduction in parts, size and power consumption. The active electrode specifications were found to be better or at least comparable to those of other existing designs. ==== Body Background The non-invasive nature of laser bio-stimulation have made lasers an attractive alternative in Medical Acupuncture at the last 25 years. Unfortunately there is still a confusion as to whether they work or their effect is just placebo. Although a plethora of scientific papers published, showing positive clinical results, there is still a lack of objective scientific proofs about the bio-stimulation effect of lasers used in Acupuncture. The objective of this work was to design and built a body surface electrode and an amplifier for biopotential recording from acupuncture points. The latter are considered here as small localized bio-sources (SLB). As discussed by other authors, SLB are small area body regions with specific electrical, physiological and anatomical properties (e.g. high density of gap junctions, relatively low impedance etc.) [1-4]. They appear to be highly sensitive to mechanical, thermal, electrical or electromagnetic stimulation and are found to take place from the epidermis (stratum granulosum) to a maximum depth of 2 cm [5-8]. The active electrode is aimed for studying SLB potentials provoked by laser stimulus, in search for objective proofs of the bio-stimulation effect of lasers used in Medical Acupuncture. Methods Electrode design The attempt to define the optimal parameters of the active electrode was based on a set of preliminary measurements performed in our laboratory. Anatomical, physiological and electrical characteristics of the signal source were considered. The SLB AC signal level, after stimulation, varied from subject to subject, but did not exceed 1 mV peak-to-peak (p-p). Additionally SLB occasionally manifested a high DC potential up to 200 mV, implying the use of differential amplifier with optional DC coupling and wide DC input range. The frequency band of the signal of interest was found to be in the range 0–200 Hz. The preliminary experiments showed that SLB potentials were best recorded with small pasteless electrodes although their contact impedance depends strongly on sweat gland secretion. The application of electrolytic gel resulted in significant reduction of the SLB signal amplitude, probably due to smoothing of the potential caused by saturation of the epidermis with electrolyte. Moreover, potentials between closely spaced SLB might be shortened by the application of excessive gel or large surface electrodes. An additional difficulty is that the SLB are often situated at convex or concave body surface areas where large flat electrodes could not be easily affixed. Skin abrasion with sandpaper is also not recommended since it can cause skin irritation and SLB potential changes. However, the use of small passive dry electrodes on an unprepared skin results in high electrode-skin impedance, motion artifacts, high power-line cable interference and noise. When the electrodes are DC coupled to the amplifier, a motion induced interfering signal appears at the amplifier input, mainly due to: • Electrokinetic effect – the disturbance of the double layer of charge at the electrode-skin interface causes variations of the DC polarization potential [9]. • Skin potential or skin stretch artifact – stretching of the skin causes a change of the potential of the barrier layer between the epidermis and the dermis [10]. • Variation in the electrode-skin contact resistance – caused by the amplifier input bias current and the current flowing due to the polarization potential. The complex electrochemical interactions that take place at the electrode-skin interface have been subject to much study in order an equivalent electrical model to be developed [10-12]. The simple but adequate electrical model used, is shown in Fig. 1, where Cd//Rd is the coupling impedance of the double layer at the electrode-skin interface, Ci//Ri is the amplifier input impedance, Rs is the minimum series contact resistance and Vpol is the DC polarization potential. Then the motion artifact signal at the amplifier input can be expressed as Figure 1 Equivalent electrical model of the electrode-skin interface. Vmot = ΔVpol + ΔVskin + (ΔRd + ΔRs) (VPOL / Ri + ib)     (1) where ΔVskin is the skin stretch artifact and ib is the amplifier input bias current. It was deduced that in order to keep the resistive interfering component less than 10 μV when DC coupling is employed and with both currents contributing equally to it, then ib<50 pA and Ri>1 GΩ [11]. If an AC coupling is used then the resistive component of the motion artifact is eliminated. For dry electrodes the motion artifacts are mainly caused by changes of the polarization potential and the contact impedance due to the poor electrolyte layer at the electrode-skin interface. Therefore a firm electrode-skin contact is of primary importance. Thus a new adjustable electrode anchoring system was designed for the purpose, as shown in Fig. 2. Turning the electrode cap clockwise pushes the sensing element against the skin. Thus electrode-skin pressure is increased, leading to reduction of the contact impedance and its variation. The electrode-skin relative movement is also reduced, making the noise contribution of the electrokinetic effect insignificant. Additionally, the sensing element provides constant skin stretching that lowers the contribution of the skin potential artifact. Turning the electrode cap counter-clockwise, releases the spring, which pushes back the sensing element, resulting in a lower electrode-skin pressure. Titanium, stainless steel and aluminum were considered as electrode sensing materials. Stainless steel was chosen because it is more commonly available than titanium and preferable to aluminum, which has been shown to have problems due to chemical response of its oxide to perspiration [13]. The use of stainless steel electrode material ensures low noise, minimal offset potentials and excellent DC stability suitable for low-drift DC measurements [14]. The sensing element is a 4 mm diameter, selected according to the average SLB size. Although the obtained contact impedances with the prototype electrode were relatively low for dry electrodes of that size, impedance matching at the electrode site was still needed to cope with the power-line cable interference. Figure 2 Dry active electrode with adjustable anchoring system. Basic amplifier circuit Electrodes with impedance matching at the sensing site are referred to as active electrodes and have been designed since 1960's [15-17]. The electronic part of these transducers mostly consists of a buffer amplifier, but some have been designed to need only two lead connection wire [18,19]. However, as the signal is not amplified, buffers introduce significant noise and a low noise amplifier is still needed at the front-end. In order to avoid this drawback we used a two-op-amp biopotential amplifier [20] shown in Fig. 3, where op-amps A0 and A1 were integrated at the electrodes (Fig. 4), instead of using extra buffers. This resulted in lower noise and less parts, at the expense of increased number of electrode leads. The amplifier is based on the two-op-amp instrumentation amplifier shown in Fig. 5. The output voltage of the basic two-op-amp amplifier is Figure 3 Schematic of the biopotential amplifier with active DC rejection/suppression. Figure 4 Simplified schematic of the biopotential amplifier with active electrodes. Figure 5 Schematic of the basic two-op-amp instrumentation amplifier. where Ad1(s) is the differential-mode (DM) gain and Ac1(s) is the common-mode (CM) gain of op-amp A1. If we take the usual definitions for the DM input signal, Vd=(E1-E0), and for the CM input signal, Vc=(E1 + E0)/2, then the output voltage can be also written as U1(s) = Ad(s)Vd + Ac(s)Vc     (3) It can be shown that the respective expressions for the DM gain Ad(s), and the CM gain Ac(s), are given by where , τ0 and τ1 are the respective DM open loop gains, and the time constants of the first poles of op-amps A0 and A1 (assumed to be internally compensated), and Ac1(s) is the CM gain of op-amp A1. It should be noted that the CM gain of op-amp A0 is omitted from (4) and (5), since its influence on both gains is insignificant. Considering (4) and (5), then the common-mode rejection ratio CMRR(s) is Assuming op-amps A0 and A1 are ideal then the only factor contributing to the CMRR is the mismatching of the resistors. Thus we can define a common-mode rejection ratio for the resistors, CMRRR. By taking 1/Ad0(s) = Ac(s) = 0 in (6) we obtain Therefore CMRRR(s) approaches infinity if the relevant impedances are chosen according to If the condition in (8) is fulfilled and op-amps A0 and A1 are ideal, then (4) simplifies to Considering (7), equation (6) can be written as where CMRRA1(s) is the CMRR of op-amp A1. Further if we assume that Z1, R2, R3 and ZE have tolerance t, then from (7) and (8) we can deduce that the worst case condition will be when Z1 = Z10(1-t), R2 = R20(1+t), R3 = R30(1-t) and ZE = ZE0(1+t) where Z10, R20, R30, and ZE0, are the respective nominal values. Equation (7) then can be written as where . This means that large differential gain is desirable since very small tolerance components are expensive. Therefore, considering (11), equation (10) can be written as The CMRRA1 has the form where ωr is the frequency where CMRRA1 has decreased by 3 dB and is usually between 100 Hz and 1 kHz. The open loop gain Ad0(s), also decreases at higher frequencies with a corner frequency ω0 = 1/τ0 which is usually lower than ωr, if A0 and A1 are of the same type. Therefore the CMRR is mainly determent at low frequency by the matching of the resistors and the DM gain, and at high frequencies by the open loop response of op-amp A0, rather than its CMRR. If we take the advantage of the fact implicit in (10), and achieve then theoretically the CMRR becomes infinite. In part this can be achieved by the use of a capacitor and resistor in parallel for the impedance Z1 (Fig. 3), and then trimming R2. Thus the need of low-tolerance components is eliminated. Therefore Z1(s) will have the form It has been shown [20] that a good approximation for the optimal value of the capacitor C1 is where GBPA0 is the gain bandwidth product of op-am A0. Trimming R2 is a good solution for achieving an ultra high CMRR for demanding application, however it is not practical since the trimmer has to be incorporated in the electrode. Alternatively, ZE or R3 can be trimmed, which however will alter also the amplifier DM gain. Considering equation (12) it can be shown that for application with relatively high DM gain and proper op-amp selection, both trimming and compensation (C1) can be omitted, without significantly degrading the CMRR. For example, if the usual 1% tolerance resistors are used and op-amps with CMRR of 100 dB and DM open loop gain of 120 dB at 50 Hz, then for an amplifier with DM gain of 5000, a CMRR of 96 dB can be achieved without trimming. In the amplifier circuit shown in Fig. 3, ZE is replaced with an active DC rejection/suppression circuit [20]. It includes an integrator (A2, Ri, Ci) and two potential dividers (R6, R5 and R4, R3). The amplifier can operate in AC-mode or in semi-AC-mode. The two modes are selectable by the switch S1: AC-mode with S1 open and semi-AC-mode with S1 closed. In AC-mode the DC signals are rejected, where in semi-AC-mode they are suppressed. If R6 = R6 *, R5 = R5 * and Ri = Ri *, then the respective expressions for the equivalent impedance ZE(s) for the two modes are given by where τi = RiCi is the time constant of the integrator, and τ2 are the respectively the DM open loop gain and the time constant of the first pole of op-amp A2. Whenever Ri>>R5 then k ≈ (R6/R5+1), which is true with the time constants and voltage gains, typical in biopotential recordings. For signals bellow the amplifier high-pass cut-off frequency, ZE(s) decreases due to the active DC rejection/suppression circuit. For DC signals equation (8) is maximally imbalanced and thus CMRRR(0) ≈ Ad(0). Since for biopotential amplifiers Ad(0) is much lower than CMRRA1(0) and Ad0(0), therefore CMRR(0) ≈ Ad(0), which represents the worst case. If we consider only the -3 dB bandwidth and assume that op-amp A2 is ideal, then (17) and (18) simplify to Therefore, in this case (9) can be written as which represents the mid band DM gain for both modes. After substituting (17) and (18) in (9), it can be shown that the respective DC differential gains for the two modes are given by where 2k is approximately the DC gain of the stopped integrator (A2, Ri, Ci, Ri *, R5 *, R6 *) in semi-AC-mode. Thus DC signals meet lower gain, in order to prevent saturation from large electrode offsets or other high DC potentials. The active electrodes' input resistances RiE0 and RiE1, are not equal due to the different closed loop gains of op-amps A0 and A1, and can be expressed as where RiA0 and RiA1 are the input resistances of op-amps A0 and A1. However, at higher frequencies, the electrodes' input impedances are much lower and about the same (assumed that A0 and A1 are of the same type), due to the op-amps' input and additional stray capacitance, being in parallel to the high op-amps' input resistance. The output noise spectral density for the -3 dB bandwidth is approximately the same for both modes and can be written as where en0, en1 and en2 are the respective voltage noises of op-amps A0, A1 and A2. Assuming E0 is connected to common (Fig. 3), then the amplifier transfer function H(s) is given by After substituting ZE(s) and Ad1(s) in (24), it can be shown that H(s) has three poles and two zeros for both modes. However, with the time constants and voltage gain used in the current application, one pole almost coincides with one zero. Therefore, H(s) can be approximated very well by a transfer function with two poles and one zero. The respective approximations for AC-mode and semi-AC-mode are given by The circuits described by the transfer functions HAC(s) and HsAC(s) are stable because all the poles are situated in the left half of the complex s-plane and there are no resonance effects as the poles are on the real s-axis. Practical amplifier circuit The schematic of a multichannel amplifier with active electrodes, built according to the design discussed is shown in Fig. 6. Each channel amplifies the signal between its input (E1...EN) and the reference input E0 (monopolar configuration). The output voltage of op-amp A2 is equal to the DC input voltage, multiplied by the ratio (R3 + R4)/R3 for both modes. The choice of the resistor ratio (R3 + R4)/R3 is a trade-off between DC input range and noise, since a low ratio enhances the noise contribution of op-amp A2. The ratio R4/R3 was chosen so that to allow a DC input voltage range of ± 370 mV, without saturating op-amp A2. The offset voltage at the amplifier output (U1) is the input offset voltage of op-amp A2, times the resistor ratio (R5 + R6)/R5. In case of high DM gain, the output offset voltage would become unacceptably high. Thus, op-amp A2 was selected for its ultra-low offset voltage of 1 μV, low noise and high CM input range to prevent latch-up. Moreover A2 is a low input bias current type, which allows the use of high value input resistances without producing large offset voltages between its inputs. Op-amp A0, was selected with a relatively high GBP, in order to confine its influence on the CMRR at higher frequencies. Combining high DM gain, with high GBP op-amps, allowed Z1 to be implemented only with its active part R1, as thus the CMRR at 50 Hz was not significantly degraded. As shown in (23), the equivalent input noise is mainly determent by the noise of op-amps A0 and A1. They were implemented with the low-noise CMOS op-amp LMV751 in a SOT23-5 package. Figure 6 Multichannel biopotential amplifier with active electrodes and DRL circuit. Because of the large integrator's time constant, the amplifier has a very slow response after overloads (≤ 10τi), caused by large signal disturbances. Thus a deblocking circuit was added at the cabinet's location, for temporary reduction of the time constant during overload [24]. It is controlled by the output voltage U1 through the low pass filter (R13, C2). The filter output controls two threshold triggers (A3, A4), which through D1,D2 control the MOS transistor T1, acting like a switch. When the output signal reaches its range limits (defined by R14, R15, R16), T1 opens and the new reduced time constant τi* = (R7//R11)C2, pulls the output signal to the zero level. This state is maintained for additional hundred milliseconds (R13C2) and then is switched back to its original value. The connection between the amplifier common and the signal source is implemented by a driven right-leg (DRL) circuit. The CM voltage at the output of A0 is reduced by a factor equal to the DRL circuit gain (ADRL = 314 at 50 Hz), which theoretically should give a 50 dB extra CMRR at 50 Hz. In addition, in case of a faulty op-amp, the DRL circuit will limit the maximum patient current to a safe level of 50 μA. Results The contact impedance of the proposed electrode, measured and averaged over five subjects, and its calculated model impedance are shown in Fig. 7. The values of the model elements were determent to give the closest agreement between the measured electrode-skin impedance and that of the model. The values are: Rs = 300 Ω, Rd = 450 kΩ and Cd = 3 nF. The measuring technique is described by Bergey et al. [21] and was performed five minutes after the application of the electrodes to allow their impedance to settle to a constant value [13]. The electrodes were applied with moderate tension of 0.3 kg/cm2 on an unprepared skin of the inner forearm. The impedance was lower when higher tension was applied or when sweat was present on the skin. The applied current density was 0.01 mA/cm2 and no current density impedance dependence was observed [22]. The electrode-skin impedance showed two-decade spread between different subjects, which was also reported in other works [23]. Figure 7 The average electrode-skin contact and the calculated model impedance against frequency. The plot shows also the minimum and the maximum data sets for five subjects. Simulations of the amplifier circuit were carried out using PSPICE. The op-amps used in the model were with gain-bandwidth product (GBP) of 5 MHz, DM open loop gain of 120 dB and CMRR of 100 dB. The integrator time constant and the resistor ratios of the feedback loop were: τi = RiCi = 1, R4/R3 = 6 and R6/R5 = 700. The amplifier frequency response plots, for both operating modes, are shown in Fig. 8. In semi-AC-mode the high-pass response is with 1st order pole at 0.32 Hz and zero at 0.2 mHz. In AC-mode the high-pass response is with 1st order pole at 0.16 Hz and zero at 0.16 μHz. The mid band DM gain is the same for both modes. Simulations were also carried out for the estimation of the power line interference due to induced displacement currents into the electrode leads. The increased stray capacitance between the power line and the amplifier, caused by the increased number of electrode leads, was taken into consideration in the simulation model. The results showed that the interference caused by the active electrode unshielded leads was insignificant. Figure 8 A plot of the amplifier frequency response. Table 1 shows the amplifier specifications, measured with battery powered prototype and test equipment. All the parameters were in close agreement with those of the simulations. The peak-to-peak noise voltage measured at the amplifier output, with input terminals connected to common, was 10 mVp-p, or 2 μVp-p when referred to the input. The CMRR of the amplifier was 96 dB at 50 Hz, measured with imbalanced electrode impedances (ΔZe = 47 kΩ). The maximum measured CMRR with DRL and a CM input signal of 4 Vp-p, was 126 dB at 50 Hz, where the output signal level was approximately equal to the amplifier output voltage noise. Table 1 Active electrode specifications Parameter semi-AC-mode AC-mode Bandwidth (-3 dB) 0.32–1000 Hz 0.16–1000 Hz DC gain 3.22 ≈ 0 AC mid band gain 74 dB Differential mode AC input range 0.005–1 mVp-p Differential mode DC input range ± 370 mV Common mode input range ± 2 V Input noise current 1 pArms @ 0.1–200 Hz Input bias current 1.5 pA Input impedance, Active Electrode 320 MΩ @ 50 Hz (1000 GΩ //10 pF) CMRR 96 dB @ 50 Hz Output offset 0.7 mV Input noise voltage 2 μVp-p (0.33 μVrms) @ 0.1–200 Hz Power consumption 11 mW @ one channel A sample record from the practical application of the active electrode, obtained after a low intensity SLB laser stimulation, is shown in Fig. 9. The amplifier was battery powered and optically isolated by linear optocouplers. The bandwidth was limited to 200 Hz, by 6th order low pass Bessel filter, and the signal was sampled with 1 kHz. The electrodes were connected with a high-density unshielded ribbon cable. One electrode (E1) was placed on SLB TH-23, near the eyebrow, where the reference (E0) was placed on the ear lobe. No electrolyte gel or skin preparation was applied. The measurements were performed in a typical laboratory room. All measurements showed almost complete absence of 50 Hz interference. The noise present in the signal is mainly compounded of artifacts from eye movements, electromyographic signals, and noise from the electrode-skin interface. Figure 9 Biopotential acquired in semi-AC-mode from SLB TH-23 after low intensity laser stimulation. Discussion The best solution for an active electrode would be to perform the entire analog signal processing at the electrode site. This could be achieved with a custom made integrated circuit, but the cost would be much higher. We found a good alternative in using SMD technology and integrating only the front-end of the amplifier into the electrode. The ultra high input resistance of the electrode is degraded at higher frequencies by the op-amp's input capacitance in parallel with the stray capacitance due to the electrode Printed Circuit Board (PCB). Nevertheless, combining an op-amp with low input capacitance and a proper PCB design, allowed a relatively high input impedance to be achieved at 50 Hz. That decreased the amplifier sensitivity to high electrode-skin impedance imbalances, by reducing the transformation of the CM interference signal into unwanted DM signal. Unfortunately, most data sheets do not properly specify op-amp's input capacitance, neither DM nor CM. The active electrode presented is not suitable for applications requiring a low differential gain and large signal bandwidth due to the decreasing CMRR at higher frequencies, if not properly compensated. On the other hand, below the high-pass cut-off frequency, the CMRR is degraded by the active feedback circuit, and reaches its minimum value for DC signals, equal to the DM gain. The circuit can accept high value input filter resistances, which will also limit the patient auxiliary current in case of fault condition of op-amps A0 and A1. Because of the limited electrode space, it is preferable that the front-end op-amps feature internal electrostatic discharge protection circuitry, rather than building an external one. Conclusions The new electrode anchoring system significantly reduced the electrode-skin impedance, its variation and motion artifact influences. The proposed amplifier fractionation resulted in lower noise and less parts. Moreover splitting the amplifier between the electrodes and the cabinet's location allowed the use of an automatic DC deblocking system and mode switching. The prototype tests showed that with the active electrode presented, SLB signals with relatively high quality could be recorded without skin preparation. The 50 Hz interference pickup by the electrode leads was practically eliminated. Because high electrode-skin impedances are tolerated, no electrolytic gel is needed. This allows fast application of the electrodes, minimizes patient discomfort and eliminates the risk of infection. With proper op-amps selection, the active electrode specifications were found to be better or at least comparable to those of other existing designs. The design offers low noise and major reduction in parts, size and power consumption. It is currently used in studying laser provoked SLB potentials and their propagation, aiming to gain a better insight into the bio-stimulation effect of lasers in Medical Acupuncture. Authors' contributions The authors contributed equally to this work ==== Refs Mashansky VF Markov UV Topography of the gap junctions in the human skin and their possible role in the non-neural signal transduction Arch Anat Histol Embryol 1983 84 53 60 Reichmanis M Marino AA Becker RO Electrical correlates of acupuncture points IEEE Trans Biomed Eng 1975 22 533 535 1184029 Reichmanis M Marino AA Becker RO Laplace plane analysis of transient impedance between acupuncture points Li-4 and Li-12 IEEE Trans Biomed Eng 1977 24 402 405 881215 Johng HM Cho JH Shin HS Soh KS Koo TH Choi SY Koo HS Park MS Frequency dependence of impedances at the acupuncture point Quze (PC3) IEEE Eng Med Biol Mag 2002 21 33 36 12012602 10.1109/MEMB.2002.1000183 Litscher G Effects of acupressure, manual acupuncture and Laserneedle acupuncture on EEG bispectral index and spectral edge frequency in healthy volunteers Eur J Anaesthesiol 2004 21 13 19 14768918 Litscher G Cerebral and peripheral effects of laser needle-stimulation Neurol Res 2003 25 722 728 14579790 10.1179/016164103101202237 Niboyet J Traite d'acupuncture 1979 Maisonneuve: Sainte-Ruffine Stux G Pomeranz B Basics of Acupuncture 1998 New York: Springer-Verlag Khan A Greatbatch W Ray CD Physiologic electrodes In Medical Engineering 1974 Chicago: Year Book Medical Publishers 1073 1082 Travis C Miller HA Nonlinear aspects of the bioelectrode-electrolyte interface In Biomedical electrode technology 1974 New York: Academic Press 143 159 Zipp P Ahrens H A model of bioelectrode motion artifact and reduction of artifact by amplifier input stage design, J Biomed Eng 1979 1 273 276 537352 Neuman MR Webster JG Biopotential electrodes In Medical Instrumentation, Application and Design 1995 2 New York: John Wiley and Sons 227 287 Searle A Kilrup L A direct comparison of wet, dry and insulating bioelectric recording electrodes Physiol Meas 2000 21 271 283 10847194 10.1088/0967-3334/21/2/307 Godin DT Parker PA Scott RN Noise characteristics of stainless-steel surface electrodes Med Biol Eng Comput 1991 29 585 590 1813753 Richardson PC Some new electrode techniques for long-term physiological monitoring Aerosp Med 1968 39 745 750 5705742 Ko WH Hynecek J Miller HA, Harrison DC Dry electrodes and electrode amplifiers In Biomedical electrode technology 1974 New York: Academic Press 169 181 MettingVanRijn AC Kuiper AP Dankers TE Grimbergen CA Low cost active electrode improves the resolution in biopotential recordings In Proceedings of the 18th Ann Int Conf IEEE Eng Med Biol, Amsterdam 1 101 102 31 October-3 November 1996 Hagemann B Luhede G Luczak H Improved active electrode for recording bioelectric signals in work physiology Eur J Appl Physiol Occup Physiol 1985 54 95 98 4018063 Padmadinata FZ Veerhoek JJ Van Dijk GJA Huijsing JH Microelectronic skin electrode Sensors and Actuators 1990 B1 491 494 MettingVanRijn AC Peper A Grimbergen CA Amplifiers for bioelectric events: a design with a minimal number of parts, Med Biol Eng Comput 1994 32 305 310 7934255 Bergey GE Squires RD Sipple WC Electrocardiogram recording with pasteless electrodes IEEE Trans Biomed Eng 1971 18 206 211 Geddes LA Electrodes and the measurement of bioelectric events 1972 New York: Wiley-Interscience Rosell J Colominas J Riu P Pallas-Areny R Webster JG Skin impedance from 1 Hz to 1 MHz IEEE Trans Biomed Eng 1988 35 649 651 3169817 10.1109/10.4599 MettingVanRijn AC Kuiper AP Honsbeek RH Speijer K Peper A DC rejection and deblocking in multichannel bioelectric recordings In Procceedings of the 17th Ann Int Conf IEEE Eng Med Biol, Montreal 2 1665 1666 20–23 September 1995

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==== Front Biomed Eng OnlineBioMedical Engineering OnLine1475-925XBioMed Central London 1475-925X-3-261527293110.1186/1475-925X-3-26ResearchMulti-component based cross correlation beat detection in electrocardiogram analysis Last Thorsten [email protected] Chris D [email protected] Frank J [email protected] School of Electrical and Mechanical Engineering, Faculty of Engineering, University of Ulster at Jordanstown, Northern Ireland2 School of Computing and Mathematics, Faculty of Engineering, University of Ulster at Jordanstown, Northern Ireland2004 23 7 2004 3 26 26 19 2 2004 23 7 2004 Copyright © 2004 Last et al; licensee BioMed Central Ltd.2004Last et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background The first stage in computerised processing of the electrocardiogram is beat detection. This involves identifying all cardiac cycles and locating the position of the beginning and end of each of the identifiable waveform components. The accuracy at which beat detection is performed has significant impact on the overall classification performance, hence efforts are still being made to improve this process. Methods A new beat detection approach is proposed based on the fundamentals of cross correlation and compared with two benchmarking approaches of non-syntactic and cross correlation beat detection. The new approach can be considered to be a multi-component based variant of traditional cross correlation where each of the individual inter-wave components are sought in isolation as opposed to being sought in one complete process. Each of three techniques were compared based on their performance in detecting the P wave, QRS complex and T wave in addition to onset and offset markers for 3000 cardiac cycles. Results Results indicated that the approach of multi-component based cross correlation exceeded the performance of the two benchmarking techniques by firstly correctly detecting more cardiac cycles and secondly provided the most accurate marker insertion in 7 out of the 8 categories tested. Conclusion The main benefit of the multi-component based cross correlation algorithm is seen to be firstly its ability to successfully detect cardiac cycles and secondly the accurate insertion of the beat markers based on pre-defined values as opposed to performing individual gradient searches for wave onsets and offsets following fiducial point location. ==== Body Background Computerised classification of the electrocardiogram (ECG) is a complex and multi staged process. The overall goal is to determine if the patient is 'normal' and may remain untreated, or whether the patient exhibits any cardiac abnormalities requiring treatment. The classification of the ECG by computerised techniques has been an active area of research for more than 4 decades. A plethora of algorithmic techniques have been applied and developed [1,2] all with the common goal of enhancing the classification accuracy and becoming as reliable and successful as expert cardiologists. The process can be divided into a number of sequential stages. Pre-processing stages of Beat Detection and Feature Extraction/Selection provide suitable information from the recorded ECG in the form of a digitised feature vector [3]. This can be considered to describe the current morphology of the recorded signal, hence, following processing by the classification algorithm [1,2], a set of suggestive diagnostic statements can be produced. With a multi-stage computerised approach, the overall classification capabilities of the system are highly dependent on the early stages of processing, i.e., accurate detection of each ECG complex in all recorded leads. Hence the necessity of a reliable beat detection algorithm is of paramount importance. Regardless of the approach employed to analyse and classify the ECG signal, all require accurate detection of each QRS complex. Beat detection algorithms are designed with two main objectives. Firstly, the algorithm employed should provide reliable detection of each cardiac cycle. Secondly, the temporal location of the reference points should be described accurately. In general terms detection of each cardiac cycle involves the location of a fiducial point, usually taken as the peak amplitude of the R-wave or of the QRS complex. From this it is then possible to detect markers for the other interwave components (if present) and features of the ECG; QRS onset and offset, P onset and offset and T offset (Figure 1). Figure 1 P wave, QRS complex and T wave beat markers inserted into ECG recording. Two of the most commonly employed approaches to beat detection involve the use of established non-syntactic algorithms [3-5] or cross correlation (CC) algorithms [3,6]. A brief overview of each approach follows. Non-syntactic beat detection A non-syntactic approach to beat detection normally involves an algorithm based on two distinct steps; a pre-processor and a decision rule. The function of the pre-processing stage is to enhance the QRS complex and suppress all other components of the recorded signal i.e. P and T waves and noise artefacts. This is achieved through firstly applying a linear filter to extract the required frequencies, followed by a non-linear transformation with the goals of providing a single positive peak for each QRS complex. The output from the non-linear transformation is then processed by a thresholding function to indicate the presence or absence of a QRS complex. Following location of a reference point remaining inter-wave components can be located to the left (P wave) and the right (T wave) of the QRS complex. A number of non-linear transformations exist which can be applied and can occur in a number of different permutations. A commonly adopted approach is conceptualised in Figure 2. The primary stage of bandpass filtering (approximately 5–25 Hz) reduces the noise artefacts that may be present in the signal. Detection of the fiducial point need not necessarily occur on the original signal, but can be considered advantageous once some signal transformations have been applied, hence the inclusion of the differentiator, squaring function and moving window integrator. The differentiation stage acts as a highpass filter, exploiting the slope characteristics of the QRS complex. The squaring function is also favourable to the high-frequency components of the signal and serves the purpose for signal rectification. The integrator includes information relating to the duration of the QRS complex, which is recognised in electrocardiography as normally having the longest duration of any component in the ECG. QRS complexes cannot occur physiologically closer than 200 ms in succession. Therefore it is common practice once a QRS complex is detected, that a 200 ms refractory blanking period is initiated. This eliminates the condition where the same QRS complex is detected twice or even a T wave is mistaken for a QRS complex. (For a more detailed review of studies applying non-syntactic beat detection see [3].) Figure 2 Non-syntactic non-linear transformations transforming original ECG signal into a series of impulse like outputs. Cross Correlation based beat detection The CC function can be used to measure the similarities between two signals [7]. This process entails the computation of the sum of the products of corresponding pairs of points of two signals, within a specified time frame, or window. The process also takes into consideration any potential phase differences between the two signals via the inclusion of a lead or lag term. The formula for CC is represented as: where N represents the number of samples, j is the lag factor and x1 and x2 are the two signals. To normalise the results based on the number of sample points, the factor 1/N is introduced. When the value of r(12) is maximal, this is considered as the point of maximal similarity between the two waveforms. As the required amount of lagging between the two signals is initially unknown, various degrees of lags within the specified correlation interval must be performed. It is possible to use CC for the purposes of beat detection, by locating the point of maximal similarity between an ECG signal and a predefined template and hence identifying the temporal location of the QRS complex. It is also necessary for the CC algorithm to store a template or a reference signal of the ECG signal. The origin of the template may be from a variety of sources. It may be of an adaptive nature, whereby a section of the patient's recorded ECG is averaged and stored prior to the analysis. Alternatively, a mathematical model may be used or a collection of ECG recordings from a database used to produce a generic template. Many studies in the past have successfully reported the use of CC as a means of automated beat detection. Abboud et al. [6] used the CC function, calculated using the cross spectrum and fast Fourier transform algorithm for extrastoyle rejection and location of fiducial points. Variations to the CC algorithm have also been successfully reported. These were considered to be more computationally efficient as they do not require the intense multiplicative processes associated with CC, but may be based on for example weighted correlation of differences methods [8] and average magnitude cross difference methods [9]. Methods of CC have generally focused on the usage of one waveform as the basis for the template, for example the QRS complex. The algorithms of Abboud et al. [6] were adapted by Govrin et al. [10] to facilitate the location via CC of both the P waves and QRS complexes. Templates of both waves were used with unknown ECG traces to identify the individual components of each cardiac cycle. It is the aim of the current study to investigate the potential of adapting the general CC based approach as an accurate and reliable means for beat detection. This approach in the past has been shown to be successful and hence is investigated with the aims of further enhancement. Individual templates for P waves, QRS complexes and T waves are generated and the CC approach applied individually to identify each of these components as opposed to a one template CC based approach. Standard approaches have also been developed for benchmarking purposes. The structure of the remainder of the paper is as follows: Section 2 describes the new beat detection method based on the fundamentals of CC. In addition a description of the benchmarking methods developed are also presented. Section 3 describes the structure of the data set and presents the results and discussions for the beat detection algorithms. Final conclusions to the study are presented in Section 4. Methods The aims of the current study were to investigate the possibilities of developing a new approach to beat detection which would offer enhanced accuracy and reliability in comparison to established techniques. Two different approaches, one based on a standard non-syntactic approach and one based on a standard CC approach were developed for the purposes of benchmarking. Developments of both of these algorithms were based on existing published approaches. A new approach, based on a multi-component based CC algorithm was additionally developed. Details of the 3 beat detection approaches are as follows. Non-syntactic approach The first stage of the non-syntactic approach is the inclusion of a bandpass filter, centred at 17 Hz. The purpose of this is to isolate the predominant QRS energy and attenuate the low-frequency characteristics of the P and T waves, any baseline drift present and the higher frequencies associated with electromyographic noise and power line interference [11]. (The passband that maximises the QRS energy is approximately in the 5–25 Hz range.) An FIR bandpass filter was implemented with the difference equation expressed in Equation 2: where yi is the filtered output sequence, xi is the input ECG signal and bq represents the coefficients calculated with the Remez exchange design algorithm [12]. To produce the necessary stage of differentiation, a five-point derivative with the second term equal to zero (as represented in Equation 3) was employed. H(z) = 0.1(2 + z-1 - z-3 + 2z-4)     (3) This has been chosen as the function H(z) in Equation 3 behaves in a similar manner to a parabolic smoothing filter and does not amplify any high-frequency noise. The advantage of such a filter is that it goes to zero at half of the sampling frequency. The non-linear transformation is performed by the point-by-point squaring of the signal samples. This derivative approximates an ideal derivative in the dc through to 30 Hz frequency range. This is the necessary frequency range since all higher frequencies are significantly attenuated by the bandpass filter. Finally, the squared waveform is passed through a moving window integrator. A window integrator with the difference equation as presented in Equation 4 was employed. where N is the number of samples in the width of the moving window. The integrator sums the area under the squared waveform over an approximate 180 ms interval, advances one sample interval and integrates a new 180 ms window. To locate the fiducial point, the maximum of the QRS complex is required. Following application of the aforementioned processes to the recorded signal, the maximum amplitude of the resultant signal is sought. An initial threshold value based on 80% of the maximum amplitude located during an initial training period is used to locate potential QRS complexes. Within a 125 ms window, following exceeding this value, the maximum amplitude located is considered to be the fiducial point. A refractory blanking stage of 200 ms is employed prior to repeating the process for the next cardiac cycle. The markers for the onset and offset are inserted following gradient searches to the right and left of the fiducial point within a window of 200 ms on each side. A thresholding technique was exercised, whereby following the location of 6% of the gradient, for a duration of at least 25 ms, to the left and right of the fiducial point the start and end points were identified respectively. Prior to location of the T wave offset, the original signal was low-pass filtered at 12 Hz in an effort to maximise the frequency content of the T wave whilst suppressing the remaining inter-wave components. With knowledge of the end of the QRS-complex and the beginning of the next adjacent QRS-complex a value of the peak of the T wave, i.e. the maximum value within the identified window, was calculated. The T-offset is located based on a gradient descent thresholding search method. The condition of thresholding was set at 25% of the maximum gradient of the T wave, for a duration of at least 25 ms, to the right of its peak. Following filtering and smoothing, the P wave was sought in a window of 300 ms prior to the QRS onset. Similar to the T wave offset location, the maximum (or peak) is located within the defined window prior to the location of the onsets and offsets. The onset is located following detection of 75% of the maximum gradient, for a duration of at least 25 ms, to the left of the peak and the offset is located following detection of 75% of the maximum gradient, for a duration of at least 25 ms, to the right of the peak. CC approach The necessary templates required for the CC were generated during an initial training stage prior to any CC based analysis. During this stage templates required on an individual lead-per-lead basis were generated by averaging cardiac cycles in the initial section of the patient's ECG recording. This requirement for a training stage generally results in CC based approaches being more suitable for ambulatory processing situations, for example Holter recordings, as opposed to analysis of short term rest ECGs. Given that infinite signals are to be analysed, the CC function for a lag k can be defined as in Equation 5: where the correlation coefficient is defined as: where z can be set to either x or y. The CC approach is only used to detect the fiducial point. The signal is cross correlated with a complete PQRST template. The correlation function calculates a number of correlation coefficients and returns the position of the highest value. Providing this value is greater than a predefined threshold, a QRS is deemed detected. The threshold value can be changed for each signal and template, if required. This ensures adaptability to each individual patient's recording. The QRS onset and offset as well as the P wave onset and offset and T wave offset are detected with similar rule-based gradient search techniques as described in the non-syntactic approach in the previous section. The main difference being that the search windows in which the gradient searches are performed are dependent upon the distances specified in the template with which the ECG signal is cross correlated. Multi-component based CC algorithm With the proposed multi-component based CC detection method, separate templates for each interwave component are used. Each template consists of an individual component of the complete cardiac cycle, detailing accurate marker positions for the respective inter-wave component onset and offset. In the given approach, 3 templates were employed, one for the P wave, one for the QRS complex and one for the T wave (Figure 3). Figure 3 An example of templates used by the multi-component based CC approach for (a) P wave (b) QRS complex (c) T wave. The detection method is based purely on CC and is structured into a number of procedural steps as shown in Figure 4. The CC function employed in all steps is as described in Equation 5. The only difference is that for each interwave component sought, differing templates are used within the algorithm. The first step of the process is concerned with the location of the QRS complex. With this process, a QRS template is cross correlated with the ECG signal. The next step is the detection of the P wave, which is performed using the same CC function, but in this case, the template is representative of the patient's P wave. The last stage involves the detection of the T wave. In this process, a template, representative of the patient's T wave is cross correlated with the ECG signal using the same CC function as with the previous 2 steps. Figure 4 Basic structure of the multi-component based CC detection method using 3 separate templates. In each case the value indicated as being the point of maximum similarity, i.e. the highest correlation coefficient, between the ECG and the template, within the given correlation interval is compared with a pre-defined threshold. The threshold level, used to initially locate the peak amplitude can be varied in the algorithm if required and is established during a pre-learning phase. If the amplitude of the value exceeds the threshold, a waveform detection is considered as having occurred, otherwise the process is repeated with a new portion of the signal. Markers for the QRS onset, QRS offset, P wave onset and P wave offset, as well as the T wave offset form part of the templates used during the CC process. Hence as soon as the individual interwave components are detected, the markers are generated automatically based on the templates used with no requirement to perform any means of gradient based searching. Results and Discussion To examine the efficiency of the algorithms, excerpts from the already established QT database were employed [13]. This database has been designed specifically for the evaluation of algorithms which detect waveform boundaries in the ECG. The database has approximately 100 records, each record consisting of 15 minute excerpts of two-channel digitised ECGs. The recordings were chosen to include a broad variety of QRS and ST-T morphologies. The records within the QT database were chosen from the MIT-BIH database, the European ST-T database and several other ECG databases collected at Boston's Beth Israel Deaconess Medical Centre. For each record, a minimum of 30 beats have been manually annotated by clinical experts. For each annotated beat, the following markers have been inserted; P wave onset, P wave offset and P wave peak amplitude, QRS onset, QRS offset and QRS peak amplitude and T wave offset and T wave peak amplitude. For the purposes of the given study, each algorithm was exposed to 3000 beats from this database. Comparison of beat detection algorithms To quantify the accuracy of each of the three aforementioned beat detection algorithms in terms of their correct positioning of beat markers, measurements of mean error (me) and standard deviation (SD) of this error were used. The me value is used to determine how close the detector is to the annotated markers, with the SD value providing information relating to the stability of the detection criteria. For the purposes of validation, the database used for testing [13] has associated with it a set of tolerance values for each of the beat markers. These measures can be considered to be the minimum values that should be expected with any automatic algorithm. The accuracy with which the automated algorithms performed the detection was compared with manually annotated and clinical validated beat markers. Such comparisons with a gold standard and subsequently with other automated approaches operating on the same data sets adhere to recommended approaches of comparison of automated medical decision support systems [14]. Average values for the SD (Equation 6) of the me can be generated with the following equation: Where xid is the detected marker position for ECG trace i as identified by the algorithms used and xim is the original stored marker position from the database annotated by experts for ECG trace i. Table 1 shows the performance of each of the algorithms in detecting the 3000 cardiac cycles from the test set. Table 2 indicates the performance of each of the algorithms in comparison with the accepted tolerances for marker insertion. Table 1 Results of performance following exposure to 3000 cardiac cycles for each algorithm. Non-Syntactic CC Multi-component based CC Number of QRS detected out of possible 3000 2850 2799 2931 Table 2 Results of marker accuracy following exposure to 3000 cardiac cycles for each algorithm. Marker Tolerance SD ms Non-Syntactic SD ms CC SD ms Multi-component based CC SD ms P-onset 10.2 22.6 22.1 11.9 P amplitude 23.4 23.8 7.8 P-offset 12.7 16.7 19.7 11.6 QRS-onset 6.5 10.4 10.1 6.6 QRS amplitude 14.3 1.8 1.8 QRS-offset 11.6 12.8 13.1 6.9 T amplitude 19.2 21.4 8.2 T-offset 30.6 18.7 20.6 14.6 In terms of the overall accuracy in detecting cardiac cycles, as shown in Table 1, the multi-component based CC approach provided the best results. The advantages of considering only the QRS complex during the CC process offers an improvement in detection of each cardiac cycle. It can be considered that the PQRST wave poses a more complex situation to achieve an accurate measurement of maximum similarity during the correlation process than with a template which only represents the QRS portion of the ECG. Considering the accuracy with which the three algorithms were able to detect the correct position for the marker insertion as shown in Table 2 indicates that the multi-component based CC approach outperformed the two benchmarking algorithms in 7 out of the 8 marker insertion positions. In the remaining case (QRS amplitude) the multi-component based CC achieved a similar SD of 1.8 ms with the traditional CC approach. The non-syntactic and CC approaches performed to a similar level with the former providing superior results in 5 out of the 8 marker values. The SD values, however, only differ slightly. This can be considered to be the result of both techniques using a similar approach of gradient descent searching to identify onsets and offsets. The difference in the results can be attributed to the different manner in which each technique defines the search window within which the gradient searching is performed. The increased accuracy of marker insertion of the multi-component based CC approach can be attributed to a number of factors. The major factor being the avoidance of any gradient searching techniques for the marker positioning as is required by the two benchmarking approaches. As the multi-component based CC approach has pre-defined marker positions in-built as an inherent feature of its design, the ability to detect the fiducial point accurately is the most important process the algorithm initially undertakes. Following this process, markers are inserted based on the values stored within the templates used during the correlation process. Methods used in the two benchmarking techniques of gradient searches are prone to false detection of local gradients and noise still remaining in the signal under examination. Considering the accepted tolerances for the 5 markers as identified in Table 2, the multi-component based CC approach conformed to 4 of these (QRS onset marginally higher – 6.6 ms vs 6.5 ms) with the P onset lying outside of what was considered to be an acceptable range. Given the historical difficulty of P wave detection and accurate marker location reported by many studies [15], this is not initially considered to be a drawback of the algorithm, but more an indication of an area requiring further improvement of the approach. Overall the multi-component based CC approach outperformed the two benchmarking techniques in both accuracy of cardiac detection and marker insertions, however, 2 parameters within the algorithm were found to have a significant influence on the algorithm's performance; correlation interval and threshold parameter. Values for these parameters can be established during a training period. Correlation interval Results following the testing process indicated that care must be taken when selecting the appropriate correlation interval for CC based approaches. If this value is too large, interwave components of adjacent waves maybe considered during the correlation process. On the other hand, if the interval is too small, the templates used during the correlation process may not have the ability to discriminate between different desired portions of the underlying signal. Figure 5 shows the multi-component based CC approach processing an excerpt from one record with a correlation interval of 560 ms and 280 ms. The markers indicated at the top of the trace are those automatically inserted by the algorithm. The markers indicated at the bottom of the trace are those which have been inserted manually by clinical experts. Figure 5 Insertion of beat markers with a correlation interval of (a) 560 ms (b) 280 ms. Markers included on the bottom of the trace are those indicated and inserted by clinical experts. The notation used is as follows: t] represents the position of the markers for the t wave amplitude and t wave offset respectively; [N] represents the position of the markers for the QRS onset, peak and offset respectively, where N is used to represent a QRS complex; u] represents the position of the markers for the u wave amplitude and u wave offset respectively. Markers included on the top of the trace are those indicated following automated processing. The notation used is as follows: [Q R S] represents the position of the markers for the QRS onset, QRS peak and QRS offset respectively; T T] represents the position of the markers for the T wave peak and T wave offset respectively. In instances where no markers have been indicated, the algorithm has failed to correctly detect the waveform boundaries and peaks. In the first instance detection rates are low as the interval is too large, however, when the interval is reduced to 280 ms, the rate of detection of the markers for the algorithm increases significantly. Given that under normal conditions the duration of the QRS complex is in the region of 100 ms [16] and a second QRS complex cannot physiologically occur for a further 200 ms, this would suggest a value of the correlation interval in the region of 300 ms as a suitable choice. Threshold parameter The threshold parameter can be considered to be the minimum value for a correlation result to be considered as a true wave detection. For example, P waves can be considered to have relatively low energy content. If the threshold value is initially set at too large a value then it will not be possible for the point at which the CC function returns the point of maximum similarity to exceed this and subsequently indicate a waveform detection. Figure 6 shows the multi-component based CC algorithm with threshold values of 85% and 60% of training waveform peak amplitude averages. As can be seen it was found necessary that the final thresholding check following the CC considered a lower percentage of the average signal amplitude to ensure successful detection of the wave. Figure 6 Insertion of beat markers for threshold values of (a) 85% (b) 60% of training averages. Markers included on the bottom of the trace are those indicated and inserted by clinical experts. The notation used is as follows: t] represents the position of the markers for the t wave amplitude and t wave offset respectively; [N] represents the position of the markers for the QRS onset, peak and offset respectively, where N is used to represent a QRS complex; [p] represents the position of the markers for the p wave onset, amplitude and p wave offset respectively. Markers included on the top of the trace are those indicated following automated processing. The notation used is as follows: [Q R S] represents the position of the markers for the QRS onset, QRS peak and QRS offset respectively; T T] represents the position of the markers for the T wave peak and T wave offset respectively; [P P P] represents the position of the markers for the p wave onset, peak and offset respectively. In instances where no markers have been indicated, the algorithm has failed to correctly detect the waveform boundaries and peaks. The multi-component based CC approach is affected by both the correlation interval and threshold parameters of the algorithm, hence a training/tuning process is required. Each of the other two algorithms suffer from similar inherent algorithmic drawbacks and hence this is not considered to be a disadvantage of the approach provided it is taken into consideration during application of the algorithm. Abnormal recordings Although, as previously mentioned, the QT database has a large variety of abnormal ECG recordings, two specific examples are highlighted at this point to further compare the performance of the multi-component based CC approach and the non-syntactic based approach. In the first instance a recording where an inverted T wave is present is examined and in the second instance a recording similar to conditions exhibited by First Degree Heart Block is examined. Figure 7(a) shows the correct insertion of the markers for the multi-component based CC approach and Figure 7(b) shows the insertion of the markers with the non-syntactic based approach. As can be seen, comparing these two techniques, both have the ability to correctly detect the peak of the T wave, however, the multi-component based CC approach has the ability to detect in all instances the end of the T wave, which was not correctly detected in any of the cases analysed by the non-syntactic approach. The ability to know the shape and form of each component of the ECG waveform in advance has shown here the further benefits of the multi-component based CC approach and in addition showed its ability in instances of non-normal ECG recordings to perform successfully. Figure 7 Application of the multi-component based CC approach (a) and the non-syntactic based approach (b) to abnormal ECG waveforms. In this case, an inverted T wave is present. Markers included on the bottom of the trace are those indicated and inserted by clinical experts. The notation used is as follows: t] represents the position of the markers for the t wave amplitude and t wave offset respectively; [N] represents the position of the markers for the QRS onset, peak and offset respectively, where N is used to represent a QRS complex; [p] represents the position of the markers for the p wave onset, amplitude and p wave offset respectively. Markers included on the top of the trace are those indicated following automated processing. The notation used is as follows: [Q R S] represents the position of the markers for the QRS onset, QRS peak and QRS offset respectively; T T] represents the position of the markers for the T wave peak and T wave offset respectively; [P P P] represents the position of the markers for the p wave onset, peak and offset respectively. In instances where no markers have been indicated, the algorithm has failed to correctly detect the waveform boundaries and peaks. Figure 8(a) and 8(b) show the results of the multi-component based CC approach and the non-syntactic based approach respectively given the instance of First Degree Heart Block. As can be seen the multi-component based CC approach outperforms the non-syntactic based approach, with the ability to detect all elements of the QRS complex. The non-syntactic based approach has only the ability to detect the peak of the QRS complex. The complexity associated with the abnormality has deemed the algorithm unable to analyse any further interwave components. The benefits of matching the waveform with a template previously established for the person under examination offers the ability to specifically tailor to the recordings under investigation as opposed to generically processing the signal as with the non-syntactic approach. Figure 8 Application of the multi-component based CC approach (a) and the non-syntactic based approach (b) to abnormal ECG waveform. In this case First Degree Heart Block is present in the recording. Markers included on the bottom of the trace are those indicated and inserted by clinical experts. The notation used is as follows: t] represents the position of the markers for the t wave amplitude and t wave offset respectively; [N] represents the position of the markers for the QRS onset, peak and offset respectively, where N is used to represent a QRS complex; [p] represents the position of the markers for the p wave onset, amplitude and p wave offset respectively. Markers included on the top of the trace are those indicated following automated processing. The notation used is as follows: [Q R S] represents the position of the markers for the QRS onset, QRS peak and QRS offset respectively; T T] represents the position of the markers for the T wave peak and T wave offset respectively; [P P P] represents the position of the markers for the p wave onset, peak and offset respectively. In instances where no markers have been indicated, the algorithm has failed to correctly detect the waveform boundaries and peaks. Conclusions The accurate detection of the interwave components of the ECG can be considered to significantly effect the overall performance of the computerised classification process. Three approaches to beat detection were developed and extensively tested on 3000 cardiac cycles to assess their performance. Non-syntactic beat detection and CC algorithms were used as means of benchmarking algorithms. A new approach of multi-component based CC was proposed and results showed it to out perform the two benchmarking techniques in both accuracy of cardiac cycle detection and marker insertions. The multi-component approach identified beat markers based on 3 individual CC processes addressing the QRS complex, the P wave and the T wave individually. For each of these correlation steps, once a correlation match had been established, markers were inserted for onsets and offsets based on predefined values. It is proposed that the increase in performance of this approach in comparison with the benchmarks can be attributed to the lack of heuristic gradient searching required for marker insertion. In addition, the ability to match a portion of the ECG signal, namely the QRS, during CC as opposed to matching to the entire PQRST reduces the complexity of the overall process and subsequently enhances performance. Overall, the results have shown the benefits of employing a multi-component based CC approach. Further studies are currently underway to investigate the performance of the algorithms under instances of noise conditions and further variants of non-normal ECG recordings and in addition possible improvements to the stage of P wave detection. Authors' contributions TL carried out the development of the algorithms and performed the testing procedures. TL, CDN and FJO collaboratively designed the study. TL, CDN and FJO co-authored the paper. ==== Refs Kors JA van Bemmel JH Classification methods for computerized interpretation of the electrocardiogram Methods of Information in Medicine 1990 29 330 336 2233379 Nugent CD Webb JAC Black ND Wright GTH Electrocardiogram 2: Classification Automedica 1999 17 281 306 Nugent CD Webb JAC Wright GTH Black ND Electrocardiogram 1: Pre-processing prior to classification Automedica 1998 16 263 282 Ligtenberg A Kunt M A robust-digital QRS-detection algorithm for arrhythmia monitoring Computers and Biomedical Research 1993 16 273 286 6872535 10.1016/0010-4809(83)90027-7 Pan J Tompkins WJ A real-time QRS detection algorithm IEEE Transactions on Biomedical Engineering 1985 32 230 236 3997178 Abboud S Sadeh D The use of cross-correlation function for the alignment of ECG waveforms and rejection of extrasystoles Computers and Biomedical Research 1984 17 258 266 6203681 Ifeachor EC Jervis BW Digital Signal Processing: A Practical Approach 1993 Wokingham, England: Addison-Wesley Publishing Company Alperin N Sadeh D An improved method for on-line averaging and detecting of ecg waveforms Computers and Biomedical Research 1986 19 193 202 3709121 10.1016/0010-4809(86)90015-7 Lindecrantz KG Lilja H New software QRS detector algorithm suitable for real time applications with low signal to noise ratio Journal of Biomedical Engineering 1988 10 280 284 3392980 Govrin O Sadeh S Akselrod S Abboud S Cross-correlation techniques for arrhythmia detection using PR and PP intervals Computers and Biomedical Research 1985 18 37 45 3971704 10.1016/0010-4809(85)90005-9 Friesen GM Jannett TC Jadallah MA Yates SL Quint SR Nagle HT A comparison of the noise sensitivity of nine QRS detection algorithms IEEE Transactions on Biomedical Engineering 1990 37 85 98 2303275 10.1109/10.43620 Parks TW McClellan JH Chebyshev Approximation for the Design of Linear Phase Finite Impulse Response Digital Filters IEEE Trans Audio Electroacoust 1972 AU-20 195 199 10.1109/TAU.1972.1162381 Laguna P Mark RG Goldberger A Moody GB A database for the evaluation of algorithms for measurement of QT and other waveform intervals in the ecg Computers in Cardiology 1997 24 673 676 Smith AE Nugent CD McClean SI Neural networks as decision support systems: formal evaluation of inherent performance Artificial Intelligence in Medicine 2003 27 1 27 12473389 10.1016/S0933-3657(02)00088-X Freeman K Singh A P wave detection of ambulatory ECG Annual International Conference of the IEEE Engineering in Medicine and Biology Society 1991 13 647 648 Goldberger AL Goldberger E Clinical Electrocardiography, A Simplified Approach 1994 Baltimore: Mosby

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==== Front Lipids Health DisLipids in Health and Disease1476-511XBioMed Central London 1476-511X-3-181527121810.1186/1476-511X-3-18ResearchLow fasting low high-density lipoprotein and postprandial lipemia Kolovou Genovefa D [email protected] Katherine K [email protected] Nektarios [email protected] Nikolaos [email protected] Konstandina [email protected] Eleftherios [email protected] Dennis V [email protected] 1st Cardiology Department, Onassis Cardiac Surgery Center, 17674, Athens, Greece2 Biochemistry Laboratory, Onassis Cardiac Surgery Center, 17674, Athens, Greece2004 23 7 2004 3 18 18 21 6 2004 23 7 2004 Copyright © 2004 Kolovou et al; licensee BioMed Central Ltd.2004Kolovou et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Low levels of high density lipoprotein (HDL) cholesterol and disturbed postprandial lipemia are associated with coronary heart disease. In the present study, we evaluated the variation of triglyceride (TG) postprandially in respect to serum HDL cholesterol levels. Results Fifty two Greek men were divided into 2 main groups: a) the low HDL group (HDL < 40 mg/dl), and b) the control group. Both groups were further matched according to fasting TG (matched-low HDL, and matched-control groups). The fasting TG concentrations were higher in the low HDL group compared to controls (p = 0.002). The low HDL group had significantly higher TG at 4, 6 and 8 h postprandially compared to the controls (p = 0.006, p = 0.002, and p < 0.001, respectively). The matched-low HDL group revealed higher TG only at 8 h postprandially (p = 0.017) compared to the matched-control group. ROC analysis showed that fasting TG ≥ 121 mg/dl have 100% sensitivity and 81% specificity for an abnormal TG response (auc = 0.962, p < 0.001). Conclusions The delayed TG clearance postprandially seems to result in low HDL cholesterol even in subjects with low fasting TG. The fasting TG > 121 mg/dl are predictable for abnormal response to fatty meal. low high-density lipoprotein cholesterolcoronary heart diseasepostprandial lipemiatriglyceride clearance. ==== Body Background The hypothesis that low levels of high density lipoprotein (HDL) cholesterol is associated with coronary heart disease (CHD), raised since the 1950s [1]. Fifty years later, it was well-established [2,3] as it has been excellently proved after a number of large prospective studies [4,5]. In the PROCAM Study [6] 45% of men and women who developed CHD had an HDL cholesterol lower than 35 mg/dl. In the Framingham Heart Study, total cholesterol levels did not provide a predictive value in identifying people at risk for CHD compared to cholesterol/HDL ratio [7]. Furthermore, a change in ratio is better predictor for successful CHD risk reduction than changes in total cholesterol levels. There is no longer any doubt that HDL cholesterol is a powerful independent inverse predictor of CHD. On the other hand, the long duration of the postprandial lipemia and repetition of meals during the daytime leads to important changes of lipoproteins postprandially. Studies have shown that disturbed postprandial lipemia is found in patients with CHD [8,9] and other conditions [10-12] related to an increased risk of cardiovascular disease. Many studies comparing patients with CHD and controls have shown that postprandial triglyceride (TG) levels were an independent predictor of CHD in multivariate analysis [8,13]. In the present study, we evaluated the variation of TG postprandially in respect to serum HDL cholesterol levels. The delayed TG clearance postprandially seems to result in low HDL cholesterol even in subjects with low fasting TG. Results All participants ingested their fatty meal and tolerated it well. Baseline characteristics (Table 1) Table 1 Clinical characteristics of the two main study groups (all low HDL patients and controls). All biochemical values were obtained in the fasting state and for TG 4,6,8 h postprandially. Characteristics Low HDL n = 29 Controls n = 23 P values Age (years) 45(13) 51(9) NS BMI (kg/m2) 26(2) 26(3) NS CHD -/+ 19/10 23/0 0.002 Hypertension-/+ 27/2 23/0 NS Smokers-/+ 17/12 23/0 <0.001 Diabetes mellitus 28/1 23/0 NS TC (mg/dl) 198(45) 194(38) NS HDL (mg/dl) 31(7) 53(19) <0.001 LDL (mg/dl) 141(39) 124(38) NS Apo A (mg/dl) 123(28) 153(42) 0.007 Apo B (mg/dl) 127(56) 126(30) NS Lp (a) (mg/dl) 25(25) 30(29) NS Glucose (mg/dl) 94(13) 90(11) NS TG0 (mg/dl) 128(54) 89(30) 0.002 TG4 (mg/dl) 207(102) 140(36) 0.006 TG6 (mg/dl) 210(112) 135(45) 0.002 TG8 (mg/dl) 192(92) 103(37) <0.001 AUC (mg/dl/h) 1459(631) 1007(253) <0.005 All values are presented as means (standard deviation). BMI: body mass index, CHD: coronary heart disease, TC: total cholesterol, HDL: high-density lipoprotein cholesterol, LDL: low-density lipoprotein cholesterol, Apo: apolipoprotein, Lp: lipoprotein, TG0: fasting plasma triglyceride concentration, TG4, TG6, TG8: plasma triglyceride concentration 4,6,8 h after the fat load, respectively, AUC: area under the curve. For TC, HDL and LDL, to convert from mg/dl to mmol/L divide by 38.7 For TG, to convert from mg/dl to mmol/L divide by 88.6 The clinical characteristics of the two main study groups (low HDL and controls) are shown in Table 1. Renal and liver function was normal as determined by measuring plasma creatinine, urea, uric acid, alanine aminotransferase, aspartate aminotransferase and γ-glutamate transferase. Therefore, there was no biochemical evidence of a fatty liver. Baseline glucose levels were similar in both groups. None of the patients fulfilled the criteria for the metabolic syndrome according to the National Cholesterol Education Program- Adults Treatment Panel III (NCEP ATP III) guidelines [14]. The plasma HDL cholesterol and apolipoprotein A were lower in low HDL group compared to controls (p < 0.001, and p = 0.007, respectively) by definition. The fasting TG (TG0) concentrations were higher in low HDL cholesterol group compared to controls (p = 0.002). Postprandial TG concentrations in the two main groups (low HDL subjects and controls) (Table 1, Figure 1) Figure 1 Schematic representation of TG-AUC of all groups in relation to time. The highest AUC values were observed in low HDL and low HDL-A groups which are characterized by low fasting HDL levels and the highest fasting TG levels compared to the other groups. The lowest AUC values were found in matched-low HDL, matched-controls, low HDL-N and control groups. These are characterized by lower fasting TG levels but with variable fasting HDL levels, ranging from 30(9) to 55(20) mg/dl. This means that the primary determinant of the magnitude of TG postprandial response is the fasting TG concentration. TG-AUC: triglyceride-area under the curve Low HDL-A: low HDL-Abnormal group Low HDL-N: low HDL-Normal group TG levels 4 h after the fatty meal (TG4) The low HDL subjects had a significantly higher (p < 0.006) TG level compared to the controls. TG levels 6 h after the fatty meal (TG6) The low HDL subjects had a significantly higher (p < 0.002) TG level compared to controls. TG levels 8 h after the fatty meal (TG8) The low HDL subjects had a significantly higher (p < 0.001) TG level compared to the controls. Glucose did not show any change postprandially. The above groups were subdivided in matched-low HDL and matched-control group (matched for low fasting TG levels) and in low HDL-Abnormal (low HDL-A) and low HDL-Normal (low HDL-N) groups (based on their TG postprandial response). Results of the matched low HDL subjects and matched controls (Table 2, Figure 1) Table 2 Clinical characteristics and biochemical parameters of the low HDL subjects matched to controls for fasting TG levels, and their response to a fatty meal. Characteristics Matched-low HDL n = 20 Matched-controls n = 20 P values Age (years) 47(13) 51(10) NS BMI (kg/m2) 26(2) 25(2) NS CHD -/+ 11/9 20/0 0.001 Hypertension-/+ 18/2 20/0 NS Smokers-/+ 13/7 20/0 0.004 TC (mg/dl) 187(50) 197(35) NS HDL (mg/dl 31(8) 55(20) <0.001 LDL (mg/dl) 136(43) 128(38) NS Apo A (mg/dl) 123(31) 158(43) 0.009 Apo B (mg/dl) 123(63) 132(27) NS Lp (a) (mg/dl) 29(27) 28(23) NS Glucose (mg/dl) 95(14) 90(11) NS TG0 (mg/dl) 100(31) 95(27) NS TG4 (mg/dl) 153(55) 140(36) NS TG6 (mg/dl) 172(105) 138(45) NS TG8 (mg/dl) 154(78) 105(38) 0.017 AUC (mg/dl/h) 1142(322) 1007(253) NS All values are presented as means (standard deviation). BMI: body mass index, CHD: coronary heart disease, TC: total cholesterol, HDL: high-density lipoprotein cholesterol, LDL: low-density lipoprotein cholesterol, Apo: apolipoprotein, Lp: lipoprotein, TG0: fasting plasma triglyceride concentration, TG4, TG6, TG8: plasma triglyceride concentration 4,6,8 h after the fat load, respectively, AUC: area under the curve. For TC, HDL and LDL, to convert from mg/dl to mmol/L divide by 38.7 For TG, to convert from mg/dl to mmol/L divide by 88.6 Subjects with low HDL cholesterol had higher TG concentration at 8 h postprandially (p = 0.017). Results of the low HDL subjects divided into those with (low HDL-N group) and without (low HDL-A group) a normal response to a fatty meal (Table 3, Figure 1) Table 3 Clinical characteristics and biochemical parameters of the low HDL subjects with an abnormal (low HDL-A) and normal response (low HDL-N) to a fatty meal. Characteristics low HDL-A n = 13 low HDL-N n = 16 P values Age (years) 42(14) 48(13) NS BMI (kg/m2) 26(2) 27(3) NS CHD-/+ 12/1 7/9 0.006 Hypertension-/+ 11/2 16/0 NS Smokers-/+ 6/7 11/5 NS TC (mg/dl) 221(16) 179(53) 0.007 HDL (mg/dl) 32(5) 30(9) NS LDL (mg/dl) 154(22) 130(46) NS Apo A (mg/dl) 131(18) 118(31) NS Apo B (mg/dl) 135(17) 122(68) NS Lp (a) (mg/dl) 10(7) 32(28) 0.012 Glucose (mg/dl) 92(11) 95(14) NS TG0 (mg/dl) 172(43) 92(29) <0.001 TG4 (mg/dl) 298(82) 135(35) <0.001 TG6 (mg/dl) 321(74) 127(35) <0.001 TG8 (mg/dl) 270(78) 129(37) < 0.001 AUC (mg/dl/h) 2074(381) 986(252) <0.012 All values are presented as means (standard deviation) BMI: body mass index, CHD: coronary heart disease, TC: total cholesterol, HDL: high-density lipoprotein cholesterol, LDL: low-density lipoprotein cholesterol, Apo: apolipoprotein, Lp: lipoprotein, TG0: fasting plasma triglyceride concentration, TG4, TG6, TG8: plasma triglyceride concentration 4,6,8 h after the fat load, respectively, AUC: area under the curve. For TC, HDL and LDL, to convert from mg/dl to mmol/L divide by 38.7 For TG, to convert from mg/dl to mmol/L divide by 88.6 Thirteen (45%) of the low HDL subjects (low HDL-A group) had an abnormal TG response to the fatty meal test compared to the controls. The other sixteen low HDL subjects (low HDL-N group) had a TG response to the fatty meal that was similar to that in the control subjects. The low HDL subjects with an abnormal fatty meal response (low HDL-A group) had higher plasma total cholesterol and lower lipoprotein (a) concentrations (p = 0.007, and p < 0.012, respectively). As expected, postprandial TG levels were higher in those classified as having an abnormal response. TG levels of all groups (low HDL, controls, matched low HDL, matched controls, low HDL-A, low HDL-N) in relation to time are schematically represented in Figure 1. Predictors of abnormal TG response In multivariate linear regression analysis, where the independent variables were age, body mass index (BMI), total cholesterol, HDL cholesterol, TG, lipoprotein (a), and areas under the curve (AUC) was the dependent variable, the TG0 was the only predictor of high AUC values (Coefficience B = 7.15, p = 0.016). ROC analysis showed that TG0 levels ≥ 121 mg/dl have 100% sensitivity and 81% specificity for an abnormal TG response (auc=0.962, p < 0.001). After we divided the low HDL men in low- or high- groups by using the ROC curve cut-off values of TG0, total cholesterol and lipoprotein (a), i.e. low/high TG0, low/high total cholesterol and low/high lipoprotein (a) groups, the only distinction that predicted an abnormal TG response was the high-TG0 group (p < 0.001) Discussion In the present study, we found that a slower clearance of TG-rich lipoproteins from circulation postprandially results in low fasting levels of HDL cholesterol. Additionally, fasting plasma TG concentration is the primary determinant of the magnitude of postprandial lipemia. Subjects with a low fasting HDL and low TG levels showed a delayed TG clearance at 8 h compared to those with a normal fasting HDL value. The increase in TGs (from 2 to 4 h) after meal consumption mainly reflects dietary TG absorption, whereas the return to fasting levels (from 6 to 9 h) is presumably a function of TG clearance [15]. In our study, the delayed response of TGs to the fatty meal in matched group for low fasting TG levels may be indicative for an HDL involvement. It has been proposed that elevated plasma TG concentrations promote the cholesterol ester exchange reactions mediated by cholesteryl ester transfer protein [16]. It is possible that in this transient hypertriglyceridemia, the HDL particles are TG-enriched via cholesteryl ester transfer protein mediated exchange with TG-rich lipoproteins. Such HDL-TG enriched particles are cleared more rapidly from the circulation [17] leading to low serum HDL cholesterol levels [18,19]. In subjects with initially low HDL concentrations and low fasting TG levels, it is possible that this reaction does not happen at all or happens in a less degree and the slower removal of TGs from the circulation could be explained by insufficient amount of HDL particles which are responsible for their clearance. The high TG and low HDL cholesterol phenotype has been frequently reported in "abdominal" obesity [20,21]. Obesity is associated with a range of metabolic abnormalities including fasting and postprandial dyslipidemia. This was clearly shown in the study of Couillard et al., whose study subjects were characterised by a BMI of 32.3 ± 4.5 kg/m2 [22]. Our results showed that even in slightly overweight subjects with a BMI of 26 ± 2 kg/m2, this phenotype may provoke similar metabolic abnormalities. The HDL formation is closely associated with TG catabolism [23] as mentioned above. The deleterious effect of the high TG and low HDL phenotype on the rate of postprandial TG clearance has been also shown in children (mean age 14 years old) [24]. Patsch et al., [25] have proposed that low HDL cholesterol levels could result from an impaired TG lipolysis, a condition that would favour an exaggerated postprandial lipemia, in subject with hypoalphalipoproteinemia. In our study, no difference was observed in the early postprandial TG response (absorption phase) between normolipidemic controls and men with low fasting HDL cholesterol and low fasting plasma TG concentration. In contrast, the group with low HDL cholesterol (31 ± 7 mg/dl) and variable values of fasting TG (53–248 mg/dl, mean value 128 ± 54 mg/dl) revealed a higher TG curve postprandially compared to healthy subjects with moderate HDL cholesterol (53 ± 19 mg/dl) and low fasting TG levels (89 ± 30 mg/dl). When both groups were matched for fasting TG levels, this curve-difference was diminished, although HDL cholesterol levels remained significantly different between the two groups. When subjects with low HDL cholesterol were subdivided based on their response (normal or abnormal) to fat loading, fasting TG concentration seemed to be the critical determinant of such response. Since there are no official guidelines to determine normal postprandial TG ranges, the characterization of a TG response as "normal" or "abnormal" after fat loading was based on this report and on previous reports of ours and others. The peak TG mean value in control subjects was 139 ± 41 mg/dl (at 6 h) [11], 176 ± 17 mg/dl (at 3.5 h) [26], 177 mg/dl [27] and 186 mg/dl [28]. Higher TG concentrations at 4 h were reported by others, approximately 213 mg/dl and 248 mg/dl [29-32]. Therefore, we defined an abnormal postprandial TG response to the fatty meal as any postprandial TG concentration (at 4, 6 or 8 h) higher than 219 mg/dl which was the highest TG concentration in any hour in any control individual. Subjects with abnormal postprandial lipemia had fasting TG concentration twice higher than those with normal postprandial lipemia and the TG concentration remained much higher through all tests. Additionally, the ROC analysis showed that TG0 level ≥ 121 mg/dl have 100% sensitivity and 81% specificity for an abnormal TG response. Couillard et al., [22] reported a significant association between the magnitude of the postprandial TG response and fasting plasma HDL cholesterol concentrations. However, the subjects included in their study showed a wider range of fasting TG concentrations (44–390 mg/dl) compared to ours (53–248 mg/dl). In the subgroup of isolated low fasting HDL cholesterol, fasting hypertriglyceridemia was a prerequisite in order to have an exaggerated postprandial TG response [22]. This finding was similar to ours; an abnormal response to the fatty meal was only dependent on fasting TG concentration. Cohen and co-workers [33] measured plasma TG and retinyl palmitate responses to different fat meals in endurance-trained men with a wide range of plasma HDL cholesterol concentrations (36–105 mg/dl). Their data indicated that the magnitude of postprandial lipemia is not primarily affected by the HDL cholesterol concentrations, which agrees to our results. However, they failed to show any correlation between HDL cholesterol levels and chylomicron remnant metabolism which is contradictory to our findings, although we did not directly measured chylomicron remnant particles. Postprandial hypertriglyceridemia is not a uniform abnormality. It pathophysiologic cause is not yet known. It is possible that the response to a fatty meal is gene dependent. It has been reported that a number of gene loci, such as these of apolipoprotein E, lipoprotein lipase, apolipoprotein CIII, apolipoprotein A1, apolipoprotein A4, cholesterol ester transfer protein are related to fat load response [34,35]. However, this gene polymorphism-dependence remains controversial, and it is more likely that the postprandial lipemia is a polygenic phenomenon, although the phenotype of the postprandial lipemia is probably one. This concept allowed us to study only the phenotypic manifestation of the postprandial lipemia, which would have immediate clinical implications. Our aim was to evaluate the postprandial response in men with isolated low fasting HDL cholesterol. Although it has been also described elsewhere that baseline TG levels impact on the postprandial response and that HDL cholesterol levels are predictors of this response, the novelty of this study lies on the fact that delayed postprandially TG clearance was observed in low HDL group and that emphasis should be given on the decrease of TG levels < 121 mg/dl, lower that those indicated by the NCEP ATP III guidelines. Conclusions The delayed TG clearance postprandially seems to result in low HDL cholesterol levels even in subjects with low fasting TG concentration. Moreover, fasting plasma TG levels appear to be the primary determinant of the magnitude of postprandial lipemia. The fasting TG levels higher than 121 mg/dl are predictable for abnormal response to a fatty meal. This should be taken into account for the management of patients with fasting low HDL cholesterol levels. Methods Subjects The study population consisted of 52 Greek men recruited from the Lipid Clinic of Onassis Cardiac Surgery Center, Athens, Greece. Heavy drinking, liver and renal disease, diabetes mellitus, metabolic syndrome, according to the NCEP ATP III guidelines [14], hypothyroidism and professional sport activity were exclusion criteria. No subject took lipid lowering drugs before entering the study. Only patients with CHD (defined by angiography) were taking soluble aspirin (100 mg) and isosorbide mononitrate (40 mg). No other patient or control subject was on medication. The study population was divided into 2 main categories: A: The low HDL group and the control group. 1. The low HDL group consisted of 29 men, mean age 45(13) years with low HDL cholesterol (< 40 mg/dl) according to NCEP ATP III guidelines [14]. 2. The control group consisted of 23 healthy men, mean age 51(9) years with no family history of premature atherosclerosis, diabetes mellitus, arterial hypertension or dyslipidemia. Their fasting TG levels were < 150 mg/dl, total cholesterol < 240 mg/dl and HDL cholesterol = 40 mg/dl. All subjects were never smokers. B: Matched groups according to fasting TG level. 1. The matched-low HDL group consisted of 20 men, mean age 47(13) years with low HDL cholesterol and low fasting TG levels [100(31) mg/dl]. 2. The matched controls consisted of 20 men, mean age 51(10) years with lasting TG levels 95(27) mg/dl. Fat-rich meal protocol and blood sample All patients were studied in the outpatient clinic between 8.00–9.00 am after 12 hours (h) overnight fast. The fatty meal was consumed within 20 min and plasma TG concentrations were measured before and 4, 6 and 8 h after the fat load. During this 8 h period, the participants did not eat; they could only drink water and they did not smoke. Blood samples were drawn at 8:00 am (before the meal), at 12:30 pm (4 h after the meal), at 2:30 pm (6 h after the meal), and at 4:30 pm (8 h after the meal). In all samples total cholesterol, TG, HDL, apolipoprotein A and B, and lipoprotein (a) were measured. BMI was calculated as weight divided by height (expressed in kg/m2). Fatty meal The fatty meal has been previously described [11]. Briefly, the fatty meal was a slight modification of that described by Patsch et al [8], consisting of 83.5% fat, 14.0 % carbohydrates and 2.5 % proteins. This was administered in a dose based on the patient's body surface area (350 g to 2 m2). Definition of abnormal postprandial response We defined postprandial hypertri-glyceridemia as any postprandial TG concentration (at 4, 6 or 8 h) higher than the highest TG concentration in any hour in any control individual; this TG value was 219 mg/dl. Determination of blood lipids and glucose Plasma total cholesterol, TG and HDL cholesterol were measured using enzymatic colorimetric methods on a Roche Integra Biochemical analyzer with commercially available kits (Roche). The serum low-density lipoprotein cholesterol levels were calculated using the Friedewald formula [36]. Apolipoprotein A, B and Lipoprotein (a) were measured by nephelometery (Nephelometer:BN-100, Behring, Germany). Blood glucose was measured by the hexokinase method with a Dade Behring reagent on a Dimension (Dade Behring) instrument. All samples were analyzed within 24 h. All participants gave their informed consent before the study. The ethical committee of the Onassis Cardiac Surgery Center approved the study. Statistical analysis Values of numerical characteristics were tested for normality and are presented as mean value with one standard deviation, when normally distributed, or as median and range, when they deviated from normality. A t-test for independent samples or a Mann Whitney U test was used for the comparison of numerical values between two groups and ANOVA or Kruskal-Wallis test for three groups, with Bonferoni correction for post hoc analysis. The comparison of clinical categorical variables was performed with the use of chi-square test. Multivariate linear regression analysis was performed to determine the predictors of elevated AUC levels, where age, BMI, total cholesterol, HDL cholesterol, fasting TG (TG0), and lipoprotein (a) were the independent variables tested. ROC curve statistics was used to estimate the cutoff value of baseline triglyceride, cholesterol and lipoprotein (a) levels (which they were significant univariate predictors of elevated TG levels postprandially) above which the low HDL men develop an abnormal TG response to fat load. Using those cut-off values, we created dichotomous variables for each one of the univariate predictors to test their significance in discriminating patients with abnormal TG response to fat load. The level of significance was set at p < 0.05. AUC for serial measurements of TG levels at baseline and after the fatty meal were calculated using the trapezoid rule. Authors' contributions GK conceived the study and participated in the development of the hypothesis, the study design and drafting of the manuscript. KA is a research associate who participated in the development of the hypothesis, study design and drafting of the manuscript. NP participated in data analysis and in the interpretation of the findings. NK is a physician who participated in the study design and recruitment of subjects, clinical evaluation and collection of blood samples. KS is a senior nurse who participated in the study design, in the recruitment of subjects and in blood sample collection. EP is a biochemist who participated in the laboratory part of the study. DC participated in the design of the study and its coordination. Acknowledgment We are grateful to Alexandra Valaora, dietician, for expertly managing the patients during the meal. ==== Refs Barr DP Russ EM Eder HA Protein-lipid relationships in human plasma Am J Med 1951 11 480 485 14885223 10.1016/0002-9343(51)90183-0 Yaari S Goldbourt Even-Zohar S Neufeld HN Associations of serum high density lipoprotein and total cholesterol with total, cardiovascular, and cancer mortality in a 7-year prospective study of 10 000 men Lancet 1981 1 1011 1015 6112410 10.1016/S0140-6736(81)92184-X Gordon DJ Probstfield JL Garrison RJ Neaton JD Castelli WP Knoke JD Jacobs DR JrBangdiwala S Tyroler HA High-density lipoprotein cholesterol and cardiovascular disease. 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583 8085689 Cohen JC Stray-Gundersen J Grundy SM Dissociation between postprandial lipemia and high density lipoprotein cholesterol concentrations in endurance-trained men Arterioscler Thromb 1991 11 838 843 2065037 Ooi TC Cousins M Ooi DS Steiner G Uffelman KD Nakajima K Simo IE Postprandial remnant-like lipoproteins in hypertriglyceridemia J Clin Endocrinol Metab 2001 86 3134 3142 11443178 10.1210/jc.86.7.3134 Ordovas JM Genetics, postprandial lipemia and obesity Nutr Metab Cardiovasc Dis 2001 11 118 133 11434189 Friedewald WT Levy RI Fredrickson DS Estimation of the concentration of the low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentifuge Clin Chem 1972 18 499 502 4337382

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10.1186/1476-511X-3-18

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==== Front Cardiovasc UltrasoundCardiovascular Ultrasound1476-7120BioMed Central London 1476-7120-2-91526876110.1186/1476-7120-2-9Case ReportEchocardiographic assessment and percutaneous closure of multiple atrial septal defects Mitchell Andrew RJ [email protected] Philip [email protected]öfer Jonas [email protected] Jonathan [email protected] Oliver JM [email protected] The Department of Cardiology, John Radcliffe Hospital, Oxford, OX3 9DU, United Kingdom2 The Department of Paediatric Cardiology, John Radcliffe Hospital, Oxford, OX3 9DU, United Kingdom2004 21 7 2004 2 9 9 1 4 2004 21 7 2004 Copyright © 2004 Mitchell et al; licensee BioMed Central Ltd.2004Mitchell et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Atrial septal defect closure is now routinely performed using a percutaneous approach under echocardiographic guidance. Centrally located, secundum defects are ideal for device closure but there is considerable morphological variation in size and location of the defects. A small proportion of atrial septal defects may have multiple fenestrations and these are often considered unsuitable for device closure. We report three cases of multiple atrial septal defects successfully closed with two Amplatzer septal occluders. Atrial septal defectsAmplatzer septal occluderechocardiography ==== Body Introduction Atrial septal defect (ASD) closure is now commonly performed using a transcatheter, percutaneous approach and with the Amplatzer septal occluder, large defects can be safely closed [1,2]. Device deployment requires a rim of atrial septal tissue surrounding the defect to allow effective capture of the septum by the occluder. The rim of tissue is also important to separate the septal occluder from important structures including the inferior vena cava, coronary sinus and the atrioventricular valves. The majority of patients require a single device for closure of the ASD but a small proportion of patients may have more than one defect in the atrial septum. This can be difficult to diagnose using transthoracic echocardiography (TTE) as abnormal colour flow obscures the origins of the shunt, particularly if the second defect is situated inferiorly. We report three cases of patients referred for ASD closures that were found to have multiple ASDs and the techniques used to close these defects. Case 1 A 34-year old woman was referred for consideration of percutaneous ASD closure. The ASD had been diagnosed when the patient was 12 years old and TTE had suggested that the right ventricle was dilating. At cardiac catheterisation there were mildly elevated right ventricular systolic pressures and a pulmonary to systemic flow ratio of over two. The secundum ASD was estimated to be 15 mm wide using TTE with aneurysmal formation of the interatrial septum. The patient was admitted for percutaneous ASD closure and underwent uncomplicated placement of a 17 mm Amplatzer septal occluder. Transesophageal echocardiography (TEE) during the procedure revealed the presence of a second ASD near the inferior vena cava and a small post-procedure shunt. The septal occluder did not completely cover both defects. Equivalent chest x-ray radiation dose (assuming a single posteroanterior projection chest x-ray is eight centi-Gray/cm2) was 400. Repeat TTE continued to demonstrate left to right shunting and the patient was readmitted for a further device closure six months later. At cardiac catheterisation, the second defect was identified low in the secundum septum between the fossa ovalis and the mouth of the coronary sinus. The defect was successfully closed with a 9 mm Amplatzer septal occluder with no evidence of obstructed flow in either the coronary sinus or the inferior vena cava. Equivalent chest x-ray radiation dose for the second procedure was 187. The patient remained well with no evidence of residual shunt six months following the procedure. Case 2 A 31-year old woman was found to have a secundum ASD during investigations for breathlessness. The defect was estimated to be 10 mm wide on TTE with evidence of right atrial and right ventricular dilatation. Left atrial size was normal. She was referred was further investigation and treatment. Cardiac catheterisation demonstrated a pulmonary to systemic flow shunt of four to one. Peri-procedure TEE revealed that there were two defects; one measuring 24 mm located inferiorly between the inferior vena cava and coronary sinus os with the second defect situated superiorly and measuring 30 mm. The inferior defect was closed using a 24 mm Amplatzer septal occluder. Equivalent chest x-ray radiation dose was 403. The patient was discharged the following day and readmitted three months later for closure of the superior defect. This was performed using a 30 mm Amplatzer septal occluder. The procedure was technical difficult as after deployment of the left sided disc, the device initially lay obliquely in the defect. The final position was satisfactory with no evidence of intra-cardiac shunting (figure 1). There was no interference between the two devices and mitral valve function remained normal. Equivalent chest x-ray radiation dose for the second procedure was 727. The patient remained well and at six month follow-up there was no evidence of residual shunt. Figure 1 Stored fluoroscopy following placement of the two Amplatzer septal occluders (ASOs). TEE – Transesophageal echocardiography probe. Case 3 A 30-year old woman was investigated for palpitations and a secundum ASD of approximately 20 mm was diagnosed. TEE suggested that the atrial septum was fenestrated with a small inferior rim of tissue and she was referred for device closure. At cardiac catheterisation it became clear that there were two principal defects, one in the fossa ovalis and the other situated inferior and posterior between the fossa ovalis and the coronary sinus. The superior hole was closed with a 16 mm Amplatzer septal occluder but failed to cover the inferior defect. A 15 mm Amplatzer septal occluder device was subsequently placed successfully across the inferior defect with a stable position (figure 2). Equivalent chest x-ray radiation dose for the procedure was 124. Subsequent follow-up revealed a small left to right shunt between the two septal occluders but further intervention was not considered necessary. Figure 2 Transesophageal echocardiography four-chamber image following deployment of the two Amplatzer septal occluders (ASO). LA – left atrium, LV – left ventricle, RA – right atrium, RV – right ventricle. Discussion There is considerable morphological variation of secundum-type ASDs. Podnar et al reported the echocardiographic findings of 190 patients with isolated secundum ASDs referred for device closure [3]. Twenty four per cent had centrally placed defects but the remaining 144 patients had morphological variations. A deficient superior anterior rim was seen in 42%, a deficient inferior posterior rim in 10%, perforated aneurysm of the interatrial septum was seen in 7.9% and 7.3% of patients had multiple septal defects. Experience of multiple ASDs closure using more than one Amplatzer septal occluder remains limited [4,5]. In the worldwide report of use of the Amplatzer septal occluder, 3460 patients received a single device but only 45 patients received two devices for multiple ASDs [1]. Cao et al reported a series of 22 patients who had two septal occluders implanted simultaneously for multiple ASDs [6]. Closure rate was 97.7% with one device embolisation. In closely positioned multiple defects the septal occluder should be implanted in the largest defect aiming to cover any smaller defects but in widely separated defects more than one device is required. Echocardiographic studies have suggested that patients with multiple ASDs should have a rim of tissue of more than seven millimetres between defects to allow the deployment of two septal occluders [6]. Continuous echocardiographic monitoring is required for device positioning. When TEE is used, patients usually require a general anaesthetic due to the prolonged oesophageal intubation. The development of intracardiac echocardiography now provides an alternative to TEE for device closure. Benefits include more detailed imaging, a reduced need for general anaesthesia, and reduced radiation exposure [7]. In particular, use of intracardiac echocardiography allows clearer visualisation of the inferior atrial septum. Three-dimensional echocardiography may allow more detailed assessment of multiple ASD anatomy and septal occluder positioning. One question that remains unclear is whether multiple septal occluders should be deployed simultaneously or implanted as staged procedures. Serious complications during single septal occluder implantation is a rare occurrence (less than 0.3% of cases) but it likely that simultaneous deployment will increase the procedure risk [1,6]. When implanting large devices (greater than 20 mm) with little septal separation it is favourable to deploy the larger device and bring the patient back for a further procedure once the device has stabilised. Conclusions There is considerable variation in atrial septal defect anatomy. A small proportion of patients with an ASD have more than one defect and these can be closed using conventional septal occluders under transoesophageal echocardiography guidance. The use of intracardiac echocardiography should allow more accurate device positioning, particularly defects located low in the atrial septum. ==== Refs Omeish A Hijazi ZM Transcatheter closure of atrial septal defects in children & adults using the Amplatzer Septal Occluder J Interv Cardiol 2001 14 37 44 12053325 Fischer G Stieh J Uebing A Hoffmann U Morf G Kramer HH Experience with transcatheter closure of secundum atrial septal defects using the Amplatzer septal occluder: a single centre study in 236 consecutive patients Heart 2003 89 199 204 12527678 10.1136/heart.89.2.199 Podnar T Martanovic P Gavora P Masura J Morphological variations of secundum-type atrial septal defects: feasibility for percutaneous closure using Amplatzer septal occluders Catheter Cardiovasc Interv 2001 53 386 91 11458420 10.1002/ccd.1187 Pedra CA Fontes-Pedra SR Esteves CA Assef J Fontes VF Hijazi ZM Multiple atrial septal defects and patent ductus arteriosus: successful outcome using two Amplatzer septal occluders and Gianturco coils Cathet Cardiovasc Diagn 1998 45 257 9 9829882 10.1002/(SICI)1097-0304(199811)45:3<257::AID-CCD8>3.0.CO;2-U Suarez J Medina A Pan M Romero M Segura J Pavlovic D Hernandez E Delgado A Caballero E Siles J Franco M Mesa D Lafuente M Transcatheter occlusion of complex atrial septal defects Catheter Cardiovasc Interv 2000 51 33 41 10973016 10.1002/1522-726X(200009)51:1<33::AID-CCD9>3.0.CO;2-5 Cao Q Radtke W Berger F Zhu W Hijazi ZM Transcatheter closure of multiple atrial septal defects. Initial results and value of two- and three-dimensional transoesophageal echocardiography Eur Heart J 2000 21 941 7 10806019 10.1053/euhj.1999.1909 Bartel T Konorza T Arjumand J Ebradlidze T Eggebrecht H Caspari G Neudorf U Erbel R Intracardiac echocardiography is superior to conventional monitoring for guiding device closure of interatrial communications Circulation 2003 107 795 7 12591745 10.1161/01.CIR.0000057547.00909.1C

15268761

PMC497050

CC BY

2021-01-04 16:38:28

no

Cardiovasc Ultrasound. 2004 Jul 21; 2:9

utf-8

Cardiovasc Ultrasound

10.1186/1476-7120-2-9

oa_comm

==== Front Cardiovasc UltrasoundCardiovascular Ultrasound1476-7120BioMed Central London 1476-7120-2-91526876110.1186/1476-7120-2-9Case ReportEchocardiographic assessment and percutaneous closure of multiple atrial septal defects Mitchell Andrew RJ [email protected] Philip [email protected]öfer Jonas [email protected] Jonathan [email protected] Oliver JM [email protected] The Department of Cardiology, John Radcliffe Hospital, Oxford, OX3 9DU, United Kingdom2 The Department of Paediatric Cardiology, John Radcliffe Hospital, Oxford, OX3 9DU, United Kingdom2004 21 7 2004 2 9 9 1 4 2004 21 7 2004 Copyright © 2004 Mitchell et al; licensee BioMed Central Ltd.2004Mitchell et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Atrial septal defect closure is now routinely performed using a percutaneous approach under echocardiographic guidance. Centrally located, secundum defects are ideal for device closure but there is considerable morphological variation in size and location of the defects. A small proportion of atrial septal defects may have multiple fenestrations and these are often considered unsuitable for device closure. We report three cases of multiple atrial septal defects successfully closed with two Amplatzer septal occluders. Atrial septal defectsAmplatzer septal occluderechocardiography ==== Body Introduction Atrial septal defect (ASD) closure is now commonly performed using a transcatheter, percutaneous approach and with the Amplatzer septal occluder, large defects can be safely closed [1,2]. Device deployment requires a rim of atrial septal tissue surrounding the defect to allow effective capture of the septum by the occluder. The rim of tissue is also important to separate the septal occluder from important structures including the inferior vena cava, coronary sinus and the atrioventricular valves. The majority of patients require a single device for closure of the ASD but a small proportion of patients may have more than one defect in the atrial septum. This can be difficult to diagnose using transthoracic echocardiography (TTE) as abnormal colour flow obscures the origins of the shunt, particularly if the second defect is situated inferiorly. We report three cases of patients referred for ASD closures that were found to have multiple ASDs and the techniques used to close these defects. Case 1 A 34-year old woman was referred for consideration of percutaneous ASD closure. The ASD had been diagnosed when the patient was 12 years old and TTE had suggested that the right ventricle was dilating. At cardiac catheterisation there were mildly elevated right ventricular systolic pressures and a pulmonary to systemic flow ratio of over two. The secundum ASD was estimated to be 15 mm wide using TTE with aneurysmal formation of the interatrial septum. The patient was admitted for percutaneous ASD closure and underwent uncomplicated placement of a 17 mm Amplatzer septal occluder. Transesophageal echocardiography (TEE) during the procedure revealed the presence of a second ASD near the inferior vena cava and a small post-procedure shunt. The septal occluder did not completely cover both defects. Equivalent chest x-ray radiation dose (assuming a single posteroanterior projection chest x-ray is eight centi-Gray/cm2) was 400. Repeat TTE continued to demonstrate left to right shunting and the patient was readmitted for a further device closure six months later. At cardiac catheterisation, the second defect was identified low in the secundum septum between the fossa ovalis and the mouth of the coronary sinus. The defect was successfully closed with a 9 mm Amplatzer septal occluder with no evidence of obstructed flow in either the coronary sinus or the inferior vena cava. Equivalent chest x-ray radiation dose for the second procedure was 187. The patient remained well with no evidence of residual shunt six months following the procedure. Case 2 A 31-year old woman was found to have a secundum ASD during investigations for breathlessness. The defect was estimated to be 10 mm wide on TTE with evidence of right atrial and right ventricular dilatation. Left atrial size was normal. She was referred was further investigation and treatment. Cardiac catheterisation demonstrated a pulmonary to systemic flow shunt of four to one. Peri-procedure TEE revealed that there were two defects; one measuring 24 mm located inferiorly between the inferior vena cava and coronary sinus os with the second defect situated superiorly and measuring 30 mm. The inferior defect was closed using a 24 mm Amplatzer septal occluder. Equivalent chest x-ray radiation dose was 403. The patient was discharged the following day and readmitted three months later for closure of the superior defect. This was performed using a 30 mm Amplatzer septal occluder. The procedure was technical difficult as after deployment of the left sided disc, the device initially lay obliquely in the defect. The final position was satisfactory with no evidence of intra-cardiac shunting (figure 1). There was no interference between the two devices and mitral valve function remained normal. Equivalent chest x-ray radiation dose for the second procedure was 727. The patient remained well and at six month follow-up there was no evidence of residual shunt. Figure 1 Stored fluoroscopy following placement of the two Amplatzer septal occluders (ASOs). TEE – Transesophageal echocardiography probe. Case 3 A 30-year old woman was investigated for palpitations and a secundum ASD of approximately 20 mm was diagnosed. TEE suggested that the atrial septum was fenestrated with a small inferior rim of tissue and she was referred for device closure. At cardiac catheterisation it became clear that there were two principal defects, one in the fossa ovalis and the other situated inferior and posterior between the fossa ovalis and the coronary sinus. The superior hole was closed with a 16 mm Amplatzer septal occluder but failed to cover the inferior defect. A 15 mm Amplatzer septal occluder device was subsequently placed successfully across the inferior defect with a stable position (figure 2). Equivalent chest x-ray radiation dose for the procedure was 124. Subsequent follow-up revealed a small left to right shunt between the two septal occluders but further intervention was not considered necessary. Figure 2 Transesophageal echocardiography four-chamber image following deployment of the two Amplatzer septal occluders (ASO). LA – left atrium, LV – left ventricle, RA – right atrium, RV – right ventricle. Discussion There is considerable morphological variation of secundum-type ASDs. Podnar et al reported the echocardiographic findings of 190 patients with isolated secundum ASDs referred for device closure [3]. Twenty four per cent had centrally placed defects but the remaining 144 patients had morphological variations. A deficient superior anterior rim was seen in 42%, a deficient inferior posterior rim in 10%, perforated aneurysm of the interatrial septum was seen in 7.9% and 7.3% of patients had multiple septal defects. Experience of multiple ASDs closure using more than one Amplatzer septal occluder remains limited [4,5]. In the worldwide report of use of the Amplatzer septal occluder, 3460 patients received a single device but only 45 patients received two devices for multiple ASDs [1]. Cao et al reported a series of 22 patients who had two septal occluders implanted simultaneously for multiple ASDs [6]. Closure rate was 97.7% with one device embolisation. In closely positioned multiple defects the septal occluder should be implanted in the largest defect aiming to cover any smaller defects but in widely separated defects more than one device is required. Echocardiographic studies have suggested that patients with multiple ASDs should have a rim of tissue of more than seven millimetres between defects to allow the deployment of two septal occluders [6]. Continuous echocardiographic monitoring is required for device positioning. When TEE is used, patients usually require a general anaesthetic due to the prolonged oesophageal intubation. The development of intracardiac echocardiography now provides an alternative to TEE for device closure. Benefits include more detailed imaging, a reduced need for general anaesthesia, and reduced radiation exposure [7]. In particular, use of intracardiac echocardiography allows clearer visualisation of the inferior atrial septum. Three-dimensional echocardiography may allow more detailed assessment of multiple ASD anatomy and septal occluder positioning. One question that remains unclear is whether multiple septal occluders should be deployed simultaneously or implanted as staged procedures. Serious complications during single septal occluder implantation is a rare occurrence (less than 0.3% of cases) but it likely that simultaneous deployment will increase the procedure risk [1,6]. When implanting large devices (greater than 20 mm) with little septal separation it is favourable to deploy the larger device and bring the patient back for a further procedure once the device has stabilised. Conclusions There is considerable variation in atrial septal defect anatomy. A small proportion of patients with an ASD have more than one defect and these can be closed using conventional septal occluders under transoesophageal echocardiography guidance. The use of intracardiac echocardiography should allow more accurate device positioning, particularly defects located low in the atrial septum. ==== Refs Omeish A Hijazi ZM Transcatheter closure of atrial septal defects in children & adults using the Amplatzer Septal Occluder J Interv Cardiol 2001 14 37 44 12053325 Fischer G Stieh J Uebing A Hoffmann U Morf G Kramer HH Experience with transcatheter closure of secundum atrial septal defects using the Amplatzer septal occluder: a single centre study in 236 consecutive patients Heart 2003 89 199 204 12527678 10.1136/heart.89.2.199 Podnar T Martanovic P Gavora P Masura J Morphological variations of secundum-type atrial septal defects: feasibility for percutaneous closure using Amplatzer septal occluders Catheter Cardiovasc Interv 2001 53 386 91 11458420 10.1002/ccd.1187 Pedra CA Fontes-Pedra SR Esteves CA Assef J Fontes VF Hijazi ZM Multiple atrial septal defects and patent ductus arteriosus: successful outcome using two Amplatzer septal occluders and Gianturco coils Cathet Cardiovasc Diagn 1998 45 257 9 9829882 10.1002/(SICI)1097-0304(199811)45:3<257::AID-CCD8>3.0.CO;2-U Suarez J Medina A Pan M Romero M Segura J Pavlovic D Hernandez E Delgado A Caballero E Siles J Franco M Mesa D Lafuente M Transcatheter occlusion of complex atrial septal defects Catheter Cardiovasc Interv 2000 51 33 41 10973016 10.1002/1522-726X(200009)51:1<33::AID-CCD9>3.0.CO;2-5 Cao Q Radtke W Berger F Zhu W Hijazi ZM Transcatheter closure of multiple atrial septal defects. Initial results and value of two- and three-dimensional transoesophageal echocardiography Eur Heart J 2000 21 941 7 10806019 10.1053/euhj.1999.1909 Bartel T Konorza T Arjumand J Ebradlidze T Eggebrecht H Caspari G Neudorf U Erbel R Intracardiac echocardiography is superior to conventional monitoring for guiding device closure of interatrial communications Circulation 2003 107 795 7 12591745 10.1161/01.CIR.0000057547.00909.1C

15272941

PMC497052

CC BY

2021-01-04 16:38:11

no

Health Qual Life Outcomes. 2004 Jul 23; 2:37

latin-1

Health Qual Life Outcomes

10.1186/1477-7525-2-37

oa_comm

==== Front Cardiovasc UltrasoundCardiovascular Ultrasound1476-7120BioMed Central London 1476-7120-2-91526876110.1186/1476-7120-2-9Case ReportEchocardiographic assessment and percutaneous closure of multiple atrial septal defects Mitchell Andrew RJ [email protected] Philip [email protected]öfer Jonas [email protected] Jonathan [email protected] Oliver JM [email protected] The Department of Cardiology, John Radcliffe Hospital, Oxford, OX3 9DU, United Kingdom2 The Department of Paediatric Cardiology, John Radcliffe Hospital, Oxford, OX3 9DU, United Kingdom2004 21 7 2004 2 9 9 1 4 2004 21 7 2004 Copyright © 2004 Mitchell et al; licensee BioMed Central Ltd.2004Mitchell et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Atrial septal defect closure is now routinely performed using a percutaneous approach under echocardiographic guidance. Centrally located, secundum defects are ideal for device closure but there is considerable morphological variation in size and location of the defects. A small proportion of atrial septal defects may have multiple fenestrations and these are often considered unsuitable for device closure. We report three cases of multiple atrial septal defects successfully closed with two Amplatzer septal occluders. Atrial septal defectsAmplatzer septal occluderechocardiography ==== Body Introduction Atrial septal defect (ASD) closure is now commonly performed using a transcatheter, percutaneous approach and with the Amplatzer septal occluder, large defects can be safely closed [1,2]. Device deployment requires a rim of atrial septal tissue surrounding the defect to allow effective capture of the septum by the occluder. The rim of tissue is also important to separate the septal occluder from important structures including the inferior vena cava, coronary sinus and the atrioventricular valves. The majority of patients require a single device for closure of the ASD but a small proportion of patients may have more than one defect in the atrial septum. This can be difficult to diagnose using transthoracic echocardiography (TTE) as abnormal colour flow obscures the origins of the shunt, particularly if the second defect is situated inferiorly. We report three cases of patients referred for ASD closures that were found to have multiple ASDs and the techniques used to close these defects. Case 1 A 34-year old woman was referred for consideration of percutaneous ASD closure. The ASD had been diagnosed when the patient was 12 years old and TTE had suggested that the right ventricle was dilating. At cardiac catheterisation there were mildly elevated right ventricular systolic pressures and a pulmonary to systemic flow ratio of over two. The secundum ASD was estimated to be 15 mm wide using TTE with aneurysmal formation of the interatrial septum. The patient was admitted for percutaneous ASD closure and underwent uncomplicated placement of a 17 mm Amplatzer septal occluder. Transesophageal echocardiography (TEE) during the procedure revealed the presence of a second ASD near the inferior vena cava and a small post-procedure shunt. The septal occluder did not completely cover both defects. Equivalent chest x-ray radiation dose (assuming a single posteroanterior projection chest x-ray is eight centi-Gray/cm2) was 400. Repeat TTE continued to demonstrate left to right shunting and the patient was readmitted for a further device closure six months later. At cardiac catheterisation, the second defect was identified low in the secundum septum between the fossa ovalis and the mouth of the coronary sinus. The defect was successfully closed with a 9 mm Amplatzer septal occluder with no evidence of obstructed flow in either the coronary sinus or the inferior vena cava. Equivalent chest x-ray radiation dose for the second procedure was 187. The patient remained well with no evidence of residual shunt six months following the procedure. Case 2 A 31-year old woman was found to have a secundum ASD during investigations for breathlessness. The defect was estimated to be 10 mm wide on TTE with evidence of right atrial and right ventricular dilatation. Left atrial size was normal. She was referred was further investigation and treatment. Cardiac catheterisation demonstrated a pulmonary to systemic flow shunt of four to one. Peri-procedure TEE revealed that there were two defects; one measuring 24 mm located inferiorly between the inferior vena cava and coronary sinus os with the second defect situated superiorly and measuring 30 mm. The inferior defect was closed using a 24 mm Amplatzer septal occluder. Equivalent chest x-ray radiation dose was 403. The patient was discharged the following day and readmitted three months later for closure of the superior defect. This was performed using a 30 mm Amplatzer septal occluder. The procedure was technical difficult as after deployment of the left sided disc, the device initially lay obliquely in the defect. The final position was satisfactory with no evidence of intra-cardiac shunting (figure 1). There was no interference between the two devices and mitral valve function remained normal. Equivalent chest x-ray radiation dose for the second procedure was 727. The patient remained well and at six month follow-up there was no evidence of residual shunt. Figure 1 Stored fluoroscopy following placement of the two Amplatzer septal occluders (ASOs). TEE – Transesophageal echocardiography probe. Case 3 A 30-year old woman was investigated for palpitations and a secundum ASD of approximately 20 mm was diagnosed. TEE suggested that the atrial septum was fenestrated with a small inferior rim of tissue and she was referred for device closure. At cardiac catheterisation it became clear that there were two principal defects, one in the fossa ovalis and the other situated inferior and posterior between the fossa ovalis and the coronary sinus. The superior hole was closed with a 16 mm Amplatzer septal occluder but failed to cover the inferior defect. A 15 mm Amplatzer septal occluder device was subsequently placed successfully across the inferior defect with a stable position (figure 2). Equivalent chest x-ray radiation dose for the procedure was 124. Subsequent follow-up revealed a small left to right shunt between the two septal occluders but further intervention was not considered necessary. Figure 2 Transesophageal echocardiography four-chamber image following deployment of the two Amplatzer septal occluders (ASO). LA – left atrium, LV – left ventricle, RA – right atrium, RV – right ventricle. Discussion There is considerable morphological variation of secundum-type ASDs. Podnar et al reported the echocardiographic findings of 190 patients with isolated secundum ASDs referred for device closure [3]. Twenty four per cent had centrally placed defects but the remaining 144 patients had morphological variations. A deficient superior anterior rim was seen in 42%, a deficient inferior posterior rim in 10%, perforated aneurysm of the interatrial septum was seen in 7.9% and 7.3% of patients had multiple septal defects. Experience of multiple ASDs closure using more than one Amplatzer septal occluder remains limited [4,5]. In the worldwide report of use of the Amplatzer septal occluder, 3460 patients received a single device but only 45 patients received two devices for multiple ASDs [1]. Cao et al reported a series of 22 patients who had two septal occluders implanted simultaneously for multiple ASDs [6]. Closure rate was 97.7% with one device embolisation. In closely positioned multiple defects the septal occluder should be implanted in the largest defect aiming to cover any smaller defects but in widely separated defects more than one device is required. Echocardiographic studies have suggested that patients with multiple ASDs should have a rim of tissue of more than seven millimetres between defects to allow the deployment of two septal occluders [6]. Continuous echocardiographic monitoring is required for device positioning. When TEE is used, patients usually require a general anaesthetic due to the prolonged oesophageal intubation. The development of intracardiac echocardiography now provides an alternative to TEE for device closure. Benefits include more detailed imaging, a reduced need for general anaesthesia, and reduced radiation exposure [7]. In particular, use of intracardiac echocardiography allows clearer visualisation of the inferior atrial septum. Three-dimensional echocardiography may allow more detailed assessment of multiple ASD anatomy and septal occluder positioning. One question that remains unclear is whether multiple septal occluders should be deployed simultaneously or implanted as staged procedures. Serious complications during single septal occluder implantation is a rare occurrence (less than 0.3% of cases) but it likely that simultaneous deployment will increase the procedure risk [1,6]. When implanting large devices (greater than 20 mm) with little septal separation it is favourable to deploy the larger device and bring the patient back for a further procedure once the device has stabilised. Conclusions There is considerable variation in atrial septal defect anatomy. A small proportion of patients with an ASD have more than one defect and these can be closed using conventional septal occluders under transoesophageal echocardiography guidance. The use of intracardiac echocardiography should allow more accurate device positioning, particularly defects located low in the atrial septum. ==== Refs Omeish A Hijazi ZM Transcatheter closure of atrial septal defects in children & adults using the Amplatzer Septal Occluder J Interv Cardiol 2001 14 37 44 12053325 Fischer G Stieh J Uebing A Hoffmann U Morf G Kramer HH Experience with transcatheter closure of secundum atrial septal defects using the Amplatzer septal occluder: a single centre study in 236 consecutive patients Heart 2003 89 199 204 12527678 10.1136/heart.89.2.199 Podnar T Martanovic P Gavora P Masura J Morphological variations of secundum-type atrial septal defects: feasibility for percutaneous closure using Amplatzer septal occluders Catheter Cardiovasc Interv 2001 53 386 91 11458420 10.1002/ccd.1187 Pedra CA Fontes-Pedra SR Esteves CA Assef J Fontes VF Hijazi ZM Multiple atrial septal defects and patent ductus arteriosus: successful outcome using two Amplatzer septal occluders and Gianturco coils Cathet Cardiovasc Diagn 1998 45 257 9 9829882 10.1002/(SICI)1097-0304(199811)45:3<257::AID-CCD8>3.0.CO;2-U Suarez J Medina A Pan M Romero M Segura J Pavlovic D Hernandez E Delgado A Caballero E Siles J Franco M Mesa D Lafuente M Transcatheter occlusion of complex atrial septal defects Catheter Cardiovasc Interv 2000 51 33 41 10973016 10.1002/1522-726X(200009)51:1<33::AID-CCD9>3.0.CO;2-5 Cao Q Radtke W Berger F Zhu W Hijazi ZM Transcatheter closure of multiple atrial septal defects. Initial results and value of two- and three-dimensional transoesophageal echocardiography Eur Heart J 2000 21 941 7 10806019 10.1053/euhj.1999.1909 Bartel T Konorza T Arjumand J Ebradlidze T Eggebrecht H Caspari G Neudorf U Erbel R Intracardiac echocardiography is superior to conventional monitoring for guiding device closure of interatrial communications Circulation 2003 107 795 7 12591745 10.1161/01.CIR.0000057547.00909.1C

15272937

PMC497053

CC BY

2021-01-04 16:37:48

no

Int J Behav Nutr Phys Act. 2004 Jul 23; 1:9

latin-1

Int J Behav Nutr Phys Act

10.1186/1479-5868-1-9

oa_comm

==== Front Respir ResRespiratory Research1465-99211465-993XBioMed Central 1465-9921-5-71528578810.1186/1465-9921-5-7ResearchSignificant receptor affinities of metabolites and a degradation product of mometasone furoate Valotis Anagnostis [email protected]ögger Petra [email protected] Institut für Pharmazie und Lebensmittelchemie, Bayerische Julius-Maximilians-Universität, Würzburg, Germany2004 22 7 2004 5 1 7 7 5 2 2004 22 7 2004 Copyright © 2004 Valotis and Högger; licensee BioMed Central Ltd.2004Valotis and Högger; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Mometasone furoate (MF) is a highly potent glucocorticoid used topically to treat inflammation in the lung, nose and on the skin. However, so far no information has been published on the human glucocorticoid receptor activity of the metabolites or degradation products of MF. We have now determined the relative receptor binding affinities of the known metabolite 6β-OH MF and the degradation product 9,11-epoxy MF to understand their possible contribution to undesirable systemic side effects. In competition experiments with human lung glucocorticoid receptors we have determined the relative receptor affinities (RRA) of these substances with reference to dexamethasone (RRA = 100). We have discovered that 6β-OH MF and 9,11-epoxy MF display RRAs of 206 ± 15 and 220 ± 22, respectively. This level of activity is similar to that of the clinically used inhaled corticosteroid flunisolide (RRA 180 ± 11). Furthermore we observed that 9,11-epoxy MF is a chemically reactive metabolite. In recovery experiments with human plasma and lung tissue we found a time dependent decrease in extractability of the compound. Hence, we provide data that might contribute to the understanding of the pharmacokinetics as well as the clinical effects of MF. ==== Body Introduction Mometasone furoate (MF) is a highly potent topical glucocorticoid for the treatment of asthma [1], allergic rhinitis [2] and various skin diseases [3]. The clinical efficacy of MF is comparable to that of fluticasone propionate [4]. Both compounds have a very high affinity to the human glucocorticoid receptor. With reference to dexamethasone, fluticasone propionate has an eighteen-fold higher relative receptor affinity (RRA) of 1800 [5,6], while MF displays a RRA of about 2200 [7]. These high receptor affinities as well as the administered doses, the absolute lung deposition and a prolonged retention time in the lung tissue contribute to the clinical success of both compounds. Besides the efficacy of a corticosteroid, safety issues have to be taken into consideration. For topically applied glucocorticoids, the high local anti-inflammatory activity should be paralleled by a low systemic exposure. Therefore, a prolonged redistribution from lung tissue into systemic circulation and a rapid and complete hepatic metabolism of the compounds to inactive derivatives are favorable. For MF, a very low systemic bioavailability of less than 1 % has been reported [8]. However, there have been discussions about the appropriate methodology and the validity of the conclusion has been questioned [9,10]. Indeed, the claimed low systemic bioavailability of MF would appear to be inconsistent with the considerable suppression of the hypothalamic-pituitary-adrenal (HPA) axis recorded in a clinical study [11,12]. Frequently, various researchers called attention to the formation of active MF metabolites that would account for undesirable systemic side effects [9,13]. In an early study by Isogai et al. more than ten different metabolites and related compounds of MF displayed varying binding affinities to the rat glucocorticoid receptor [14]. There had been, however, not much information on the extent and site of metabolite formation in humans. Recent studies now provided some of the required information [7,13,15,16]. In rat liver microsomes, 6β-hydroxy MF (6β-OH MF) was identified as the major metabolite [16]. This metabolite was also found after incubation of MF with human liver and intestine microsomes [13]. Additionally, the degradation product 9,11-epoxy MF was detected in plasma and urine. 9,11-epoxy MF is formed in aqueous solutions [15] indicating a general time- and pH-dependent instability of MF [7]. Recently, we discovered 9,11-epoxy MF in incubation mixtures of human lung tissue as well as in fresh human plasma [7]. We pointed out that this degradation product might form covalent adducts with proteins in follow-up reactions. Despite the recent discovery of the major metabolite 6β-OH MF and the abundant degradation product 9,11-epoxy MF it is still not clear whether these compounds retain any significant binding affinity to the human glucocorticoid receptor. In the present study we addressed this open question and we present some evidence that the degradation product might bind tightly, most possibly covalently, to protein structures in human lung tissue and plasma. Materials and Methods Chemicals and reagents Mometasone furoate (MF), 6-hydroxy mometasone furoate (6-OH MF), mometasone and 9,11-epoxy mometasone furoate (9,11-epoxy MF) were generous gifts from GlaxoSmithKline (Greenford, England). [3H]-Dexamethasone was obtained from Amersham (Freiburg, Germany). All other chemicals were obtained from Sigma-Aldrich-Chemie (Taufkirchen, Germany) or E. Merck (Darmstadt, Germany). Source and handling of human specimen Human lung tissue resection material was obtained from patients with bronchial carcinomas who gave informed consent. Cancer-free tissue was used for the experiments. None of the patients was treated with glucocorticoids for the last 4 weeks prior to surgery. Tissue samples were shock frozen in liquid nitrogen after resection and stored at -70°C until usage. To collect sufficient material for the experiments, tissue samples of three or more patients were pooled. Lung cytosol for receptor competition experiments was prepared as detailed in [6]. Plasma samples were obtained from healthy volunteers who gave informed consent. Samples were either used immediately or were shock frozen in liquid nitrogen and stored at -70°C until usage. Determination of relative receptor affinity by competition tests The competition experiments were performed according to the procedure described earlier [6]. The displacement of a constant concentration of [3H] labelled dexamethasone by various concentrations of 6-OH MF, mometasone and 9,11-epoxy MF was determined. Recovery of MF and 9,11-epoxy MF from human plasma, lung tissue and buffer MF or 9,11-epoxy MF, respectively, were added to human plasma, lung tissue suspension (0.5 g / 20 ml) or buffer (0.2 M phosphate buffer, pH 7.4) yielding an initial concentration of 0.3 μg/ml. Only glass lab ware was used for these experiments to exclude any non-specific binding effects of the highly lipophilic compounds to plastic material. Samples were incubated at 37°C in a shaking water bath. At designated time intervals samples of 1.0 ml were removed, subjected to a fluid extraction with diethylether and analyzed by HPLC. Sample preparation and HPLC conditions Samples were prepared and analyzed as described previously [7]. The HPLC system consisted of a Waters HPLC (Milford, MA) with a 1525 binary pump, a 717plus autosampler and 2487 dual wavelength absorbance detector set at the detection wavelength of 254 nm. Data collection and integration were accomplished using Breeze™ software version 3.2. Analysis was performed on a Symmetry C18 column (150 × 4.6 mm I.D., 5 μm particle size, Waters, MA). Results We determined the relative receptor affinities (RRAs) of 6β-OH MF, 9,11-epoxy MF and mometasone base by competition assays with reference to dexamethasone (RRA = 100). Both, the metabolite 6β-OH MF and the degradation product 9,11-epoxy MF displayed residual receptor binding affinities about twice as high as dexamethasone (Table 1). This level of activity is between that of the clinically used inhaled corticosteroids flunisolide (RRA 180 ± 11) and triamcinolone acetonide (RRA 361 ± 26) [5]. Mometasone which is formed by hydrolysis of the furoate ester, revealed an even higher RRA of almost 800. For comparison, the RRA of the parent compound MF is about 2200 [7]. Table 1 Relative receptor affinities of mometasone furoate (MF, data from [7]), its metabolites 6β-hydroxy mometasone furoate (6β-OH MF), mometasone and the major degradation product 9,11-epoxy mometasone furoate (9,11-epoxy MF) in relation to dexamethasone (Dexa). Values represent mean and mean deviation of the mean of n = 3 independent experiments. Compound Relative receptor affinity (RRA) Mean deviation of the mean MF 2244 ± 142 Dexa 100 ± 10 6β-OH MF 206 ± 15 9,11-epoxy MF 220 ± 22 Mometasone 781 ± 27 To investigate the putative reactivity of the degradation product 9,11-epoxy MF we monitored the recovery of MF and 9,11-epoxy MF from human plasma by organic solvent extraction (Fig. 1). The determination of recovery was limited to a period of three hours since MF is successively degraded to 9,11-epoxy MF [7]. The retrieval of 9,11-epoxy MF from human plasma decreased steadily and was clearly more pronounced than for MF. After three hours 9.14 ± 2.3 % of 9,11-epoxy MF was not recovered from plasma while 4.8 ± 1.4 % of MF was not extractable any more. Figure 1 Recovery of mometasone furoate (MF) and its degradation product 9,11-epoxy MF from incubation mixtures with human plasma over three hours. Each data point represents the mean and mean deviation of the mean of three experiments. The decrease in recovery of 9,11-epoxy MF from human lung tissue was even more evident (Fig. 2). While there was no change in the control incubation mixture comprising of buffer (pH 7.4) a pronounced and steady decrease in recovery rates of 9,11-epoxy MF was revealed. After three hours 16.61 ± 0.58 % of the degradation product was not extractable any more. No new peaks were observed in the HPLC to indicate a further degradation of 9,11-epoxy MF. Figure 2 Recovery of 9,11-epoxy MF from incubation mixtures with human lung tissue and buffer (control experiment) over three hours. Each data point represents the mean and mean deviation of the mean of three experiments. Discussion In the present study we have determined the relative receptor binding affinities of the mometasone furoate (MF) metabolite 6β-OH MF and its degradation product 9,11-epoxy MF to understand their possible contribution to undesirable systemic side effects. For the first time we provide data that both compounds are significantly active at the human glucocorticoid receptor with binding affinities twice as high as dexamethasone and similar to that of the clinically used inhaled corticosteroids flunisolide and triamcinolone acetonide [5]. Furthermore, our data demonstrate that the ubiquitous degradation product 9,11-epoxy MF undergoes follow-up reactions. Glucocorticoids currently used for topical application in asthma therapy all share the safety relevant property of extensive metabolism and formation of inactive metabolites. For MF, however, data was sparse so far. Though putative metabolites and degradation products with binding affinity to the rat glucocorticoid receptor have been previously suggested [14], it was not clear whether this might have any implications to humans. Potential human metabolites such as 6β-OH MF, mometasone or MF-epoxide have been proposed [8], but experimental evidence of in vivo formation of these compounds was still lacking. Studies of Teng et al. identified 6β-OH MF and 9,11-epoxy MF as candidate compounds that can indeed be formed in vivo either by hepatic metabolism or by simple degradation of MF [13,16]. We discovered that 9,11-epoxy MF is also formed in human lung tissue suspensions and plasma [7]. Usually hydroxylation at the 6β position results in inactivation of the corticosteroid. The 6-OH metabolite of various glucocorticoids displays little or no residual binding affinity to the receptor (e.g.) [17,18]. This, however, is different for MF with its 6β-OH metabolite exhibiting a relative receptor affinity of more than 200 (dexamethasone: 100). Obviously, the substitution pattern of the D-ring of MF confers such potent binding affinity that hydroxylation in 6β position does not result in complete inactivation of this corticosteroid. Notably, neither the RRA we determined for 6β-OH MF nor for mometasone are coherent with the binding results of the early studies with the rat glucocorticoid receptors [14]. This emphasizes the need for data derived from human receptor studies. The MF degradation product 9,11-epoxy MF also displays a significant receptor binding affinity with an RRA of about 200. This RRA is within the range that could be expected from the studies of Isogai et al. [14]. Since 9,11-epoxy MF is also formed in the lung tissue suspensions [7], it can be assumed that it contributes to the effects after inhalation of MF. It can, however, be predicted that this compound might be also responsible for undesired effects such as HPA axis suppression. Besides the significant residual receptor binding affinity of 9,11-epoxy MF we discovered that this compound undergoes follow-up reactions. After incubation with plasma clearly less of 9,11-epoxy MF compared to the parent compound MF was recovered by extraction with an organic solvent. This extraction procedure usually reliably retrieves all non-covalently bound substance from the incubation mixture. In human lung tissue, it was even more obvious that 9,11-epoxy MF was recovered completely from buffer, but not from the tissue suspension. About 17 % of 9,11-epoxy MF was "lost" after three hours of incubation. This observation cannot be explained by simple non-specific tissue binding since the tissue adsorption reaches equilibrium very quickly after about 20 min [7]. Also, the non-specifically bound compound would be still extractable by organic solvents. Generally, epoxides are chemically reactive molecules that tend to bind irreversibly to cellular macromolecules. If this were the case for 9,11-epoxy MF it would have two implications. Firstly, irreversibly bound 9,11-epoxy MF escapes detection and feigns a low bioavailability after inhalation. The fact that after inhalation of a single dose of tritium labelled MF only 88% (63–99 %) of total radioactivity was recovered over seven days in humans [8] seems to support this conclusion. Secondly, if 9,11-epoxy MF is indeed covalently bound to cellular macromolecules the adduct might lead to allergic reactions. Such reactions to corticosteroids for asthma therapy do occur occasionally [19]. However, it cannot be excluded that 9,11-epoxy MF is further degraded although we did not observe any new peaks that emerged in the HPLC chromatograms. The chromatographic conditions were chosen for rather lipophilic compounds, thus, if a further degradation product of 9,11-epoxy MF with pronounced hydrophilic character was formed, it might have escaped our attention. However, the possibility of covalent adduct formation of 9,11-epoxy MF should be further investigated. Conclusions In contrast to other inhaled corticosteroids MF generates an active metabolite, 6β-OH MF, in the liver. The degradation product 9,11-epoxy MF, which is formed in human lung tissue and plasma, exhibits significant receptor affinity as well. Additionally, we found that 9,11-epoxy MF undergoes follow-up reactions. Our data contribute to the understanding of how the claimed low bioavailability of MF parent compound after inhalation might still be accompanied by HPA axis suppression. Thus, our findings are consistent with both pharmacokinetic and clinical data. We strongly suggest a clinical trial that determines both efficacy and safety in parallel as well as all known metabolites and degradation products after application of MF. Authors' contributions AV carried out all experiments and the data analysis and participated in the design of the study. PH conceived of and designed the study and wrote the manuscript. All authors read and approved the final manuscript. Acknowledgements Parts of this study were supported by the Fonds der Chemischen Industrie (FCI). The authors would like to thank GlaxoSmithKline for the donation of mometasone furoate, mometasone, 6-hydroxy mometasone furoate and 9,11-epoxy mometasone furoate. ==== Refs Sharpe M Jarvis B Inhaled mometasone furoate: a review of its use in adults and adolescents with persistent asthma Drugs 2001 61 1325 1350 11511026 Trangsrud AJ Whitaker AL Small RE Intranasal corticosteroids for allergic rhinitis Pharmacotherapy 2002 22 1458 1467 12432972 10.1592/phco.22.16.1458.33692 Brazzini B Pimpinelli N New and established topical corticosteroids in dermatology: clinical pharmacology and therapeutic use Am J Clin Dermatol 2002 3 47 58 11817968 O'Connor B Bonnaud G Haahtela T Luna JM Querfurt H Wegener T Lutsky BN Dose-ranging study of mometasone furoate dry powder inhaler in the treatment of moderate persistent asthma using fluticasone propionate as an active comparator Ann Allergy Asthma Immunol 2001 86 397 404 11345282 Würthwein G Rehder S Rohdewald P Lipophilicity and receptor affinity of glucocorticoids Pharm Ztg Wiss 1992 4 161 167 Hogger P Rohdewald P Binding kinetics of fluticasone propionate to the human glucocorticoid receptor Steroids 1994 59 597 602 7878687 10.1016/0039-128X(94)90054-X Valotis A Neukam K Ehlert O Högger P Human receptor kinetics, tissue binding affinity and stability of mometasone fuorate J Pharm Sci 2004 93 1337 1350 15067709 10.1002/jps.20049 Affrime MB Cuss F Padhi D Wirth M Pai S Clement RP Lim J Kantesaria B Alton K Cayen MN Bioavailability and metabolism of mometasone furoate following administration by metered-dose and dry-powder inhalers in healthy human volunteers J Clin Pharmacol 2000 40 1227 1236 11075308 Derendorf H Daley-Yates PT Pierre LN Efthimiou J Systemic bioavailability of inhaled steroids: the importance of appropriate and comparable methodology Eur Respir J 2001 17 157 158 11307748 10.1183/09031936.01.17101570 Derendorf H Daley-Yates PT Pierre LN Efthimiou J Bioavailability and metabolism of mometasone furoate: pharmacology versus methodology J Clin Pharmacol 2002 42 383 387 11936562 10.1177/0091270002424003 Affrime MB Kosoglou T Thonoor CM Flannery BE Herron JM Mometasone furoate has minimal effects on the hypothalamic-pituitary-adrenal axis when delivered at high doses Chest 2000 118 1538 1546 11115437 10.1378/chest.118.6.1538 Lipworth BJ Mometasone furoate levels Chest 2001 120 1034 1035 11555548 10.1378/chest.120.3.1034 Teng XW Cutler DJ Davies NM Mometasone furoate degradation and metabolism in human biological fluids and tissues Biopharm Drug Dispos 2003 24 321 333 14595701 10.1002/bdd.362 Isogai M Shimizu H Esumi Y Terasawa T Okada T Sugeno K Binding affinities of mometasone furoate and related compounds including its metabolites for the glucocorticoid receptor of rat skin tissue J Steroid Biochem Mol Biol 1993 44 141 145 8439518 10.1016/0960-0760(93)90021-N Teng XW Cutler DC Davies NM Degradation kinetics of mometasone furoate in aqueous systems Int J Pharm 2003 259 129 141 12787642 10.1016/S0378-5173(03)00226-6 Teng XW Cutler DJ Davies NM Cutler DC Kinetics of metabolism and degradation of mometasone furoate in rat biological fluids and tissues J Pharm Pharmacol 2003 55 617 630 12831504 10.1211/002235703765344522 Hochhaus G Moellmann HW Binding affinities of rimexolone (ORG 6216), flunisolide and their putative metabolites for the glucocorticoid receptor of human synovial tissue Agents Actions 1990 30 377 380 2386110 Grogan WM Phillips VM Schuetz EG Guzelian PS Watlington CO Corticosterone 6 beta-hydroxylase in A6 epithelia: a steroid-inducible cytochrome P-450 Am J Physiol 1990 258 C480 488 2316635 Kilpio K Hannuksela M Corticosteroid allergy in asthma Allergy 2003 58 1131 1135 14616123

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==== Front BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-5-1011527475010.1186/1471-2105-5-101Research ArticleStructural characterization of genomes by large scale sequence-structure threading: application of reliability analysis in structural genomics Cherkasov Artem [email protected] Sui Shannan J [email protected] Robert C [email protected] Steven JM [email protected] Division of Infectious Diseases, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada2 Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia, Canada3 University of British Columbia Center for Disease Control, Vancouver, British Columbia, Canada4 Genome Sciences Centre, British Columbia Cancer Agency, Vancouver, British Columbia, Canada2004 26 7 2004 5 101 101 22 4 2004 26 7 2004 Copyright © 2004 Cherkasov et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background We establish that the occurrence of protein folds among genomes can be accurately described with a Weibull function. Systems which exhibit Weibull character can be interpreted with reliability theory commonly used in engineering analysis. For instance, Weibull distributions are widely used in reliability, maintainability and safety work to model time-to-failure of mechanical devices, mechanisms, building constructions and equipment. Results We have found that the Weibull function describes protein fold distribution within and among genomes more accurately than conventional power functions which have been used in a number of structural genomic studies reported to date. It has also been found that the Weibull reliability parameter β for protein fold distributions varies between genomes and may reflect differences in rates of gene duplication in evolutionary history of organisms. Conclusions The results of this work demonstrate that reliability analysis can provide useful insights and testable predictions in the fields of comparative and structural genomics. ==== Body Background Recent advances in networks theory have demonstrated a key role of uneven distributions occurring in many natural processes. It has been found that seemingly unrelated systems such as economic, professional, sexual and social networks, airline routing, power lines connections, language networks and internet hyperlinks all exhibit a power law decay of the cumulative distribution Px ≈ x-γ, where x is the number of links connected to each network node and γ is the value of the exponent typically varying in the range of 2–3 [1]. The heterogeneous architecture of scale-free networks imparts a robustness and error-tolerance from random perturbation and is often viewed as a possible common blueprint for naturally occurring large-scale networks. The critical role of the power law distribution has also been acknowledged in many areas of life sciences: metabolic and other cellular networks, proteins interaction maps, brain cellular organization, food and ecological webs all have been described as scale-free systems. It would be fair to say that the advances in the scale free network studies have revitalized the original Pareto's inequality law introduced more then a century ago [2]. The applicability of the scale free networks has been examined in numerous structural genomics studies. It has been proposed that the genomic occurrence of protein families, superfamilies and folds can follows an asymptotic power law: SDF(GO) = aGO-b     (1) , where SDF(GO) is survival distribution function of genomic occurrence GO of a certain protein family, superfamily and fold. These findings have laid the foundation for characterizing the evolution of the protein universe in terms of a growing scale-free system in which individual genes are represented as nodes of a propagating network [3-7]. In our previous work [9], we have used the large-scale sequence-structure threading to assign protein folds to 33 genomes from all three superkingdoms of life. It has been found that more then 60% of the studied eukaryotic, 68% of archaeal and 70% of bacterial proteomes could be assigned to defined protein folds by threading. The estimated results have been used to analyze the distribution of protein architectures, topologies and domains (or homologous superfamilies according to the CATH classification [8]). Thus, we have found that the frequencies of genomic occurrence of assigned protein domains (homologous superfamilies) and topologies can be described by a power function (1) with moderate accuracy. According to the formalism of network theory, such a power law representation of the cumulative distribution of node connections governs a scale-free character of the system [10]. At the same time we have noted that the values of the power exponent b estimated in the study generally fall below the 2–3 range typical for scale-free systems (analogous observations could also be noted in a number of similar investigations [3-5]). Table 1 (see Additional file 1) features the estimated parameters a and b along with the corresponding correlation coefficients r2 reflecting the goodness of fit of experimental data with the logarithmic linear plots (1) (Table 1 also reflects the total number of the analyzed ORF-s in each genome and the corresponding number of proteins for which the THREADER has confidently assigned certain fold). The established lowered values of the power exponent and modest accuracy of the power law dependences (1) encouraged us to seek alternative approaches to more accurately describe protein folds distributions. Results Weibull (reliability) analysis The Weibull distribution is a general-purpose statistical function defined within Extreme Value Theory [11] and widely used in reliability engineering to model material strength, durability of electronic and mechanical components or equipments. In the most common case the probability density distribution is described by a two-parameter Weibull distribution , where α is a scaling factor and β is a shape parameter also known as the slope [12]. The Weibull analysis operates on life data, i.e it utilizes time-to-failure (or time under the testing stress) to assess the reliability of a system and to forecast its stability through parameters of the characteristic life span α and shape β. A typical Weibull experiment is based on application of disruptive stress to multiple samples representative of the population until the tested objects achieve a state of failure and produce time-to-failure numbers. The corresponding time-to-failure values form heterogeneous Weibull distributions described by (2). Application of Weibull function to genomic analysis The distribution of protein folds in a genome can be viewed much like the behavior of a mechanical system under disruptive testing. It is feasible to stipulate that the increase of genomic abundance of any protein fold occurs under evolutionary pressure. Some folds are able to expand their genomic occurrence over a course of evolution others have higher probability to be lost through genetic drift and other random events, i.e. to fail. Considering these analogies, we anticipated that the Weibull logistic can provide some natural explanations for highly heterogeneous abundance of protein folds in genomes. To test this hypothesis we used two independent approaches to examine whether the genomic occurrence of protein topologies and domains can indeed be adequately described by the Weibull function. First of all, we employed the maximum likelihood (ML) method [13] to fit the survival distribution function SDF(x) of the genomic occurrences of protein topologies and homologous superfamilies into the Weibull dependence (2). The corresponding Weibull shape parameters have been established by solving the equation while the scaling factors have been calculated as . The ML method allowed very accurate description of the distribution of protein folds among the genomes. Figures 1a and 1b feature the survival distributions of CATH topologies and homologous superfamilies among all the studied genomes in combined (these experimental (observed) data curves are marked in red). On the same graphs we have plotted the SDF(GO) parameters reproduced within equation (2) through α and β values estimated by the ML approach. It is obvious that these computed blue curves labeled as 'Weibull analytical' resemble the experimental distributions (marked in red) very precisely. The corresponding α and β values estimated by the ML approach have been collected into Table 2 (see Additional file 2). The second way of examining applicability of the Weibull function (2) was based on notion that the double logarithmic transformation of the SDF(x) leads to the equation of a straight line: log(- log(SDF(x)) = β log(x) - β log(α)     (3) We performed the transformation (3) on the experimental SDF(GO) data to estimate the Weibull coefficients α and β and squared correlation coefficients r2 which all have also been collected into Table 2 (marked as 'Weibull by plotting'). The values of the estimated squared correlation coefficients r2 from Table 2 demonstrate very high accuracy of the linear dependences (3) established from the survival distributions of CATH folds in the studied genomes. These parameters also allow comparing the accuracy of double logarithmic dependences (3) with accuracy of simple logarithmic dependences derived from the power law model (1): log(SDF) = a - b*log(GO)     (4) As it has been mentioned earlier, the dependences (4) have been estimated for the SDF(GO) functions for individual genomes, superkingdoms and for the combined set of proteins. The comparison of r2 values from Table 2 and Table 1 established from the linear functions (3) and (4) respectively, reveals that for all studies cases (individual genomes, superkingdoms, total dependences) the statistical quality of Weibull dependences (3) is much better than of power law function (4). Figures 1a and 1b feature the Weibull distributions estimated by plotting (double logarithmic transformation) which reproduce the experimental SDF(x) curves with remarkable accuracy. Apparently, the distributions calculated from (3) (labeled as 'Weibull by plotting') are much closer to the experimental distributions than the power law curves (labeled as 'power law') computed within the conventional power function (1). Apparently, that the Weibull distributions established by the double logarithmic representations (4) (marked on Figure 1 'Weibull by plotting') are very close to those calculated by the ML method ('Weibull analytical'). It should be mentioned, however, that despite close resemblance between the Weibull distributions established by the analytical ML method and the 'double logarithmic' approach, the corresponding values of α and β parameters from Table 2 differ (due to the different data fitting algorithms employed by two methods) and the preference should, perhaps, be given to more stringent ML-derived data. Characteristic conditions for the Weibull distribution Although the estimated statistical criteria clearly demonstrate the suitability and superiority of a Weibull function over a power function in describing protein fold distributions, we decided to examine several additional criteria characteristic of the Weibull distribution. As it has been suggested by Romeu [14] there are four such characteristic properties immanent for the Weibull function. The double logarithmic plot of life data (also called 'a Weibull paper') should be linear As it can be seen from Table 1 the estimated r2 values from the columns marked as 'Weibull by plotting' are all contained within the range ~0.95–0.98 what demonstrates that the 'Weibull papers' do indeed describe protein folds distribution in the studied genomes with high accuracy. Figures 2a,2b feature the 'Weibull papers' for the distribution of protein topologies and domains among all the studied species and illustrate that deviations from linearity are very insignificant. The slope of the 'Weibull paper' is an alternative estimator of β The data from Table 2 demonstrate that the estimated slopes of the 'Weibull papers' are very close to the values of β derived by analytical maximum likelihood approach. The xβ transformation should yield an exponential distribution with mean αβ The genomic occurrences of protein topologies and domains in the genomes and superkingdoms have been transformed into GOβ distributions through the power factors β. The exponential character of the resulting distribution has been examined by several statistical tests and in all cases has been confirmed. The observed medians of the exponential distributions GOβ accumulated in Table 3 (see Additional file 3) demonstrate strong correlations with the calculated αβ values. Characteristic life α of the Weibull distribution lies approximately at the 63% of the population The values of the Weibull characteristic life at 63% of distributions have been calculated and collected in Table 3. It is obvious that these parameters closely match values α defined by plotting. Thus, all four specific criteria studied indicate that the genomic occurrence of protein topologies and domains can be characterized as true Weibull distributions. To support this notion further we have also considered another important property of the he Weibull distribution – the dependence of its median (MDN) from shape and scale parameters [13]: To assess the applicability of this condition, we calculated Weibull medians using sets of α and β parameters – estimated by graphical (double logarithmic transformation) and analytical (ML) approaches. The corresponding 'MDN Calctd' values have been collected into Table 3 along with the observed medians of the Weibull distributions (marked 'MDN Obsrvd'). The estimated high quality linear dependences between the theoretical and observed medians are present on figures 3a and 3b for topologies and domains distributions respectively. The graphics demonstrate that calculated and observed median values are virtually the same what unanimously confirms validity of the condition (5). Thus, multiple independent tests have demonstrated that occurrence of protein folds in genomes obey the Weibull distribution and therefore can be interpreted in terms of the reliability theory what can provide additional insight into folds evolution. Discussion Interpretation of the Weibull parameters The very fact that we were able to assign the Weibull character to the distributions of the CATH protein topologies and homologous superfamilies within genomes ultimately implies that parameters of genomic occurrence can be classified as extreme values. According to the Extreme Values Theory the Weibull distribution will successfully model life systems for which many competing similar failure processes are "racing" to failure and the first to reach it produces the observed failure time [15]. In regard to genomic occurrence this may suggest that protein folds increase their genomic occurrence in a competitive manner and that those folds having a greater potential to duplicate, will continue to duplicate at the cost of less abundant folds which may ultimately disappear from genome. On another hand, according to reliability theory a Weibull distribution with β > 1 characterizes a life system that increasingly deteriorates. If the shape parameter is smaller then unity (β <1), there is a reliability growth as the failure rate of the system decreases with time [14]. It is not clear at the moment, whether a reliability criterion is directly applicable to protein folds distributions. However, β does indeed describe the "skewdness" of the fold distribution, for example Caenorhabditis elegans has the lowest calculated value β among the studied organisms, whilst this genome has also been characterized for its recent expansion and duplication of several gene families [16]. Presumably, many of these folds are present at lower abundances in other genomes. It could be proposed that such a low β (according to the reliability theory characterizing the genome of C. elegans as the most stable amongst the studied) may reflect the fact that chances of loosing some lower abundant fold families are lower for C. elegans (considering that >70% of the translated ORFs C. elegans genome have been covered by the sequence-structure threading we have assumed that the recently duplicate genes are accordingly represented in the results). In this context, the reliability of a proteome can be viewed as its ability to maintain and expand its composition without loss of protein folds. We can speculate that life systems that enjoy evolutionary success will tend to minimize β <1 i.e. to have more balanced (less heterogeneous) folds representation in their genomes. The fact that most β values presented in Table 2 fall below the unity threshold demonstrates that, in general, the reliability of genome fold composition increases with time, i.e. less protein folds reach the failure state (termination of multiplication and, likely, following evolutionary extinction) as an organism evolves. Interestingly, little difference is observed has been found between the β shape parameters for topologies distributions across the three superkingdoms. All three linear dependences ln(- ln(SDF(GO))) ~ ln(GO) for Bacteria, Eukaryote and Archaea presented on Figures 4a,4b appear very similar. As it has been already mentioned above, it is difficult to decide at this point whether the observed Weibull character of protein folds distribution can be placed in a larger context. We can only speculate that protein folding preferences may lead to a greater abundance of favorable protein configurations and to extinction of those folds which are less favorable. Such selection may represent a mechanism of evolutionary quest for searching for better protein folds. In any case, the observed phenomenon illustrates the act of natural selection in determination of the protein fold repertoires and that the propagation of protein folds in a genome occurs in a competitive manner, i.e. more abundant folds tend to expand their genomic presence even further causing lesser abundant folds to extinct. It also remains to be seen whether some other properties of genomes and proteomes can also be described by the Weibull statistics. In our studies we plan to use the Weibull approach to examine other distributions such as genomic occurrence of transcriptional promoters and regulatory elements, levels of gene expression and occurrence of protein domains per gene, among others. Another possible development for the reliability analysis in structural genomics might be to investigate whether the standard libraries of proteins folds themselves can be adequately described by the Weibull function. As it has been stipulated, in the study we have used the CATH standard library of protein folds, which is one of the most accepted and used protein folds classifications. Ii is not unfeasible, that the representation of protein architectures, topologies, homologous superfamilies, etc in the CATH can be adequately described by the Weibull law. Thus, it has been previously demonstrated that another widely used folds library – SCOP does indeed obey the power low [4]. Such observations would not necessarily contradict the uneven character of the fold distributions in individual proteomes or superkingdoms as a given protein fold library should reflect the proportion of protein folds occurrence in nature. At the same time, we anticipate that the analysis of the standard fold libraries in terms of the Weibull distributions may bring an additional insight into the field and will be carried out in the near future. To summarize the current work, it is possible to conclude that the use of the Weibull distribution allows more accurate description of protein topologies and domains distributions within and among genomes than power function used in conventional structural genomic studies. In addition, we were able to establish the Extreme Values relationships for protein folds distributions to demonstrate that the protein fold repertoire of an organism most likely occurs as a result of the competition amongst folds. This may reflect a mechanism of natural selection searching for an optimal protein structures when more evolutionary favorable folds tend to populate the entire genomic space and cause the extinction of lesser favorable protein configurations. Conclusions Use of a Weibull function allows describing cumulative distribution of protein topologies and domains within individual genomes and superkingdoms with higher accuracy compared to the conventional power function used in the related studies. The developed approach may be applied to quantification of the distribution of different properties of genomes and can be particularly useful for assessing and comparing fold distributions between different organisms and possible impact of the "reliability" of organisms due to a higher redundancy in their fold composition. In general, the results of investigation demonstrate the feasibility and importance of using the reliability analysis to improve the bioinformatics analysis of proteomes. Methods Assignment of protein folds The prediction of the protein folds has been conducted using the THREADER2 program [17]. The CATH homologues superfamily representative has been assigned to a given protein sequence if the THREADER2 produced an output above 2.9 for the Z score for the threading energy. After a certain CATH entry has been assigned to a protein sequence, it has also been associated with the corresponding higher level CATH representations: class, architecture and topology. The translated protein sequences for 33 complete genomes downloaded from the NCBI and ENSEMBL databases have been processed in this manner. The threading computations have been paralleled for processing on a Beowulf cluster consisting of 52 dual processor blades (2 × 1 GHz, 1 G RAM). The automated control was implemented by our own PVM-supporting Perl scripts enabling to distribute and query the individual threading processes over multiple computer servers. Survival distribution calculation After the occurrences of distinct classes, architectures, topologies and homologue family representatives have been established within the individual genomes, superkingdoms and in total, the corresponding survival distributions have been computed. First of all, we have established the counts of protein architectures, topologies and domains (homologues families) with a given genomic occurrence GO. At the next step these numbers have been converted into the fractional values. After that the survival distribution functions SDF(x) have been computed for genomes, superfamilies and for the combined set of proteins. The SDF(GO) numbers have been calculated for each integer GO value in the range from 0 to the maximal GO estimated within the set (genome/superfamily/total). Statistical analysis The fitting of the SDF(GO)~GO functions has been conducted by the SAS 9.0 statistical package (SAS Inc.). The power law dependences SDF(GO) = aGO-b have been analyzed as a logarithmic transforms Log(SDF(GO) = a - b*GO where the fitting has been conducted for the linear function. The Weibull – like dependences SDF(GO) = exp(-aGOb) have been fitted using both non-linear approximation (by maximum likelihood method) and by the linear fitting of the double logarithmic transform: log(- log(SDF(x)) = β log(x) - β log(α) The calculation of median valued for the survival distribution has been done by the 'R-project' open source statistical package. Supplementary Material Additional File 1 Parameters of power – law dependences for the survival distribution of genomic occurrences SDF(GO) = a GOb. Click here for file Additional File 2 Parameters α, β and medians (calculated and observed) of Weibull distribution of survival functions of genome occurrences established by maximum likelihood and plotting methods. Click here for file Additional File 3 Statistical parameters for 'Weibull papers' for genomic occurrences of protein topologies and domains. Click here for file Acknowledgements The authors thank Dr. Boris Sobolev (Clinical Epidemiology and Statistics, UBC) for valuable inputs and help with statistical analysis of genomic data with the SAS. The work has been funded in part by the Functional Pathogenomics of Mucosal Immunity project, funded by Genome Prairie, Genome BC and their industry partner Inimex Pharmaceuticals and by the Vancouver Hospital and Health Sciences Centre research award for AC. RCB acknowledges support from the Canadian Institutes for Health Research (CIHR). Student SHS acknowledges the support of the CIHR/MSFHR Strategic Training Program in Bioinformatics . SJMJ is a Michael Smith Foundation for Health Research Scholar. Figures and Tables Figure 1 Observed and recalculated survival distribution functions of genomic occurrences of protein topologies (a) and domains (b) among all the studied genomes combined. Figure 2 Weibull plots for survival distributions of genomic occurrences of protein topologies (2) and domains (2) among the studied genomes. Figure 3 Observed vs. calculated medians of the Weibull distributions established by the maximum likelihood and plotting methods for protein topologies (a) and domains (b). Figure 4 Weibull distributions of protein topologies (a) and domains (b) among three superkingdoms (Archaea, Bacteria and Eukaryote). ==== Refs Barabasi A-L Linked: The New Science of Networks 2002 Perseus Publ; Cambridge, Mass Pareto V The New Theories of Economics. Journal of Political Economy 1897 5 485 502 10.1086/250454 Luscombe NM Qian J Zhang Z Johnson T Gerstein M The dominance of the population by a selected few: power-law behavior applied to a wide variety of genomic properties. Genome Biology 2002 3 0040.1 0040.7 10.1186/gb-2002-3-8-research0040 Koonin EV Wolf YI Karev GP The structure of the protein universe and genome evolution. Nature 2002 420 218 223 12432406 10.1038/nature01256 Qian J Luscombe NM Gerstein M Protein family and fold occurrence in genomes: power-law behaviour and evolutionary model. J Mol Biol 2001 313 673 81 11697896 10.1006/jmbi.2001.5079 Rzhetski A Gomez SM Birth of scale-free molecular networks and the number of distinct DNA and protein domains per genome. Bioinformatics 2001 17 988 996 11673244 10.1093/bioinformatics/17.10.988 Yanai I Camacho CJ DeLisi C Predictions of gene family distributions in microbial genomes: evolution by gene duplication and modification. Phys Rev Lett 2000 85 2641 2644 10978127 10.1103/PhysRevLett.85.2641 Orengo CA Michie AD Jones S Jones DT Swindells MB Thornton JM CATH-A Hierarchic Classification of Protein Domain Structures. Structure 1997 5 1093 1108 9309224 10.1016/S0969-2126(97)00260-8 Cherkasov A Jones SJM Structural characterization of genomes by threading. BMC Bioinformatics 2004 5 37 15061866 10.1186/1471-2105-5-37 Barabasi A-L Albert R Emergence of scaling in random networks. Science 1999 286 509 512 10521342 10.1126/science.286.5439.509 Coles S An Introduction to Statistical Modeling of Extreme Values 2001 London: Springer-Verlag Cox DR Oakes D Analysis of Survival Data 1984 London, New York: Chapman and Hall Wu S-J Estimation of the parameters of the Weibull distribution with progressively censored data. J Japan Stat Soc 2002 32 155 163 Romeu JL Empirical assessment of Weibull distribution. Selected Topics in Assurance Related Technologies 2003 10 1 6 Gumbel EJ Statistical Theory of Extreme Values and Some Practical Applications, in National Bureau of Standards Applied Mathematics Series 1954 33 Washington, D.C: U.S. Government Printing Office The C. elegans Sequencing Consortium Genome sequence of the nematode C. elegans: a platform for investigating biology. Science 1998 282 2012 2018 9851916 10.1126/science.282.5396.2012 Jones DT Taylor WR Thornton JM A new approach to protein fold recognition. Nature 1992 358 86 89 1614539 10.1038/358086a0

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BMC Bioinformatics. 2004 Jul 26; 5:101

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==== Front BMC Cardiovasc DisordBMC Cardiovascular Disorders1471-2261BioMed Central London 1471-2261-4-111526523510.1186/1471-2261-4-11Case ReportTreatment of mechanically-induced vasospasm of the carotid artery in a primate using intra-arterial verapamil: a technical case report Coon Alexander L [email protected] Geoffrey P [email protected] William J [email protected] Lei [email protected] Philip [email protected] Connolly E [email protected] Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, USA2 Department of Neurological Surgery, Columbia University, New York, NY, USA3 Department of Radiology, Columbia University, New York, NY, USA2004 21 7 2004 4 11 11 18 2 2004 21 7 2004 Copyright © 2004 Coon et al; licensee BioMed Central Ltd.2004Coon et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Despite improvements in the safety and efficacy of endovascular procedures, considerable morbidity may still be attributed to vasospasm. Vasospasm has proven amenable to pharmacological intervention such as nitrates, intravenous calcium channel blockers (CCBs), and intra-arterial papaverine, particularly in small vessels. However, few studies have focused on medium to large vessel spasm. Here we report the use of an intra-arterial CCB, verapamil, to treat flow-limiting mechanically-induced spasm of the common carotid artery (CCA) in a primate. We believe this to be the first such report of its kind. Case presentation As part of a study assessing the placement feasibility and safety of a catheter capable of delivering intra-arterial cerebroprotective therapy, a female 16 kg baboon prophylaxed with intravenous nitroglycerin underwent transfemoral CCA catheterization with a metallic 6-Fr catheter without signs of acute spasm. The protocol dictated that the catheter remain in the CCA for 12 hours. Upon completion of the protocol, arteriography revealed a marked decrease in CCA size (mean cross-sectional area reduction = 31.6 ± 1.9%) localized along the catheter length. Intra-arterial verapamil (2 mg/2cc) was injected and arteriography was performed 10 minutes later. Image analysis at 6 points along the CCA revealed a 21.0 ± 1.7% mean increase in vessel diameter along the length of the catheter corresponding to a 46.7 ± 4.0% mean increase in cross-sectional area. Mean systemic blood pressure did not deviate more than 10 mm Hg during the procedure. Conclusions Intraluminal CCBs like verapamil may constitute an effective endovascular treatment for mechanically-induced vasospasm in medium to large-sized vessels such as the CCA. ==== Body Background Rapid advancements in endovascular technology and techniques allow for treatment of an ever-increasing range of neurovascular diseases. Despite improvements in the safety and efficacy of these procedures, complications such as vasospasm, stroke, and perforation still occur [1]. Vasospasm, or contraction of smooth muscle fibers in the wall of a vessel, is a commonly recognized adverse event that may complicate an endovascular procedure by limiting distal blood flow. Vasospasm complicates many disease states, particularly those affecting small vessels. Recently, treatment of small-vessel vasospasm has proven amenable to pharmacological intervention. For example, in the treatment of cerebral artery spasm, intravenous nitrates [2], intravenous calcium channel blockers (CCBs) [3], and intra-arterial papaverine [4] and CCBs [5] have been shown to prevent or mitigate this small artery spasm. However, few studies have focused on the treatment of medium and large vessel spasm [6], and even fewer have taken aim at mechanically-induced vasospasm. This type of spasm, unlike subarachnoid hemorrhage-induced vasospasm, is not the result of inflammation [7] and a functional nitric oxide deficiency [8,9], but rather direct physical irritation of the endothelium. In this report, we demonstrate the use of an intra-arterial CCB, verapamil, to treat flow-limiting mechanically-induced spasm of the common carotid artery (CCA) in a non-human primate. We believe this to be the first such report of its kind. Case presentation As part of a study aiming to assess the placement feasibility and safety of a catheter capable of delivering intra-arterial cerebroprotective therapy, a 16 kg female baboon (Papio anubis) underwent carotid artery catheterization under general anesthesia. Since Papio anubis is regarded as vasospasm-prone (unpublished data), the animal was pre-treated with oral nimodipine (Nimotop, Bayer,1 mg/kg every 4 hours for 24 hours), and placed on a prophylactic infusion of intravenous nitroglycerin (200 mcg/hr) and heparin (100 units/hr). To place a 6 Fr (2 mm) treatment device in the 3–4 mm right CCA [10], the animal underwent transfemoral catheterization with a 7 Fr guiding catheter using Seldinger technique. Under single-plane fluoroscopic guidance, the guiding catheter was placed into the brachiocephalic artery (5–6 mm) and then advanced into the right CCA after prophylactic administration of 2 mg of intra-arterial verapamil (1 mg verapamil/cc normal saline). A proprietary 6 Fr metallic catheter was then passed through the guiding catheter. Once inside the CCA, the 6 Fr catheter was exposed by retraction of the 7 Fr guiding catheter. Control arteriography, performed by injection of non-ionic iodinated contrast material through the guiding catheter, revealed normal patency of the carotid artery without evidence of spasm or limitation of arterial flow. As part of the study protocol, this co-axial catheter system remained in the brachiocephalic vessels for 12 hours. Throughout this procedure, the animal was maintained under general anesthesia with a narcotic-nitrous mixture. Intravenous nitroglycerin infusion (200 mcg/hr) and physiological monitoring were continued. The guiding catheter was connected to a heparinized saline infusion (3 units heparin/cc normal saline at a rate of 30 cc/hour). Before removing the co-axial system at the conclusion of the experiment, carotid arteriography was performed to verify positioning of the catheter and patency of the vessels. These images revealed a decrease in vessel diameter localized to the length of artery where the 6 Fr catheter was positioned (Fig. 1A and 1C). Prior to further manipulation of the catheters, an additional bolus of intra-arterial verapamil (2 mg/ 2 cc normal saline) was instilled through the guiding catheter positioned in the brachiocephalic artery. After ten minutes, repeat carotid arteriography demonstrated a visible increase in vessel caliber, presumably due to a reduction in vasospasm (Fig. 1B and 1D). The diameter of the CCA was compared before and after verapamil administration at 6 equally-spaced points along the catheter. This revealed an increase in the mean CCA diameter from 2.85 ± 0.14 mm during spasm to 3.45 ± 0.18 mm post-verapamil administration (Figure 2). This corresponded to a 21.0 ± 1.7% mean increase in the vessel diameter post-verapamil injection, which represents a 46.7 ± 4.0% mean increase in cross-sectional area (Fig. 3). Review of continuous invasive blood pressure tracings demonstrated minimal systemic response to the intra-arterial administration of verapamil at this dosage; systemic blood pressure did not deviate more than 10 mm Hg systolic following instillation of verapamil. Figure 1 Anterior-posterior angiogram of right common carotid artery injection of a Papio anubis with a 6 Fr catheter in place both (A.) during vessel spasm on catheter, and (B.) 10 minutes after infusion of intraluminal verapamil (2 mg). Overlay images showing 6 Fr catheter position in CCA (gold) during spasm (C.) and after alleviation with verapamil (D.). Arrows (→) indicate tip of catheter. Figure 2 Image analysis at 6 paired positions (Lines A-F) along catheter in common carotid artery both (A.) during vessel spasm, and (B.) 10 minutes after intraluminal verapamil (2 mg) administration. (C.) Raw data table includes vessel diameter measurements both pre and post-verapamil injection. Figure 3 (A.) Bar graph depicting both the pre and post-verapamil mean vessel diameters from six positions along the length of the common carotid artery (CCA) (2.85 ± 0.14 mm and 3.45 ± 0.18 mm, respectively), and (B.) cross-sectional areas (6.41 ± 0.61 mm2 and 9.39 ± 1.0 mm2, respectively). Note the 46.7% increase in mean cross-sectional area after verapamil administration. At the conclusion of the procedure, the co-axial catheter system was removed. Anesthetics, heparin, and nitroglycerin infusions were discontinued. The animal was awakened from anesthesia uneventfully showing no signs of neurological impairment. MRI brain scan, including diffusion-weighted imaging at 36 hours, showed no evidence of cerebral infarction. Discussion Driven by technology and the ever-increasing need for minimally invasive treatment modalities, the number of endovascular procedures performed annually continues to rise. The increased number and variety of endovascular procedures have introduced new situations in which vasospasm may be encountered. The sheer size and complexity of large bore catheters and their delivery systems makes them more likely to induce spasm in the vessel in which they are utilized (medium and large caliber arteries). Thus, it is important to identify pharmacological agents that will relieve this vasospasm with minimal side effects. Spasm of arteries secondary to therapeutic medications or diagnostic instrumentation has long been acknowledged as a possible complication of interventional procedures. Vasospasm, in general, has been attributed to a variety of pharmacological stimuli ranging from cocaine [11] and alcohol [12], to L-thyroxine [13] and NSAIDs [14]. Vasospasm may also be attributed to mechanical irritation [15], as in the present study. In the past, treatment of mechanical spasm has simply been withdrawal of the offending catheter. A passive treatment such as this is often times undesirable, especially when the catheter system needs to remain in position, as in our experiment. There are several agents that have been shown to be effective in preventing and treating vasospasm, each of which has its limitations. Intravenous nitrates have been the mainstay of vasospasm prevention for endovascular procedures [16], but their cardiovascular and intracranial pressure (ICP) effects limit their acute use for vasospasm treatment [17]. Intra-arterial papaverine has been used either as monotherapy or as an adjunct to balloon angioplasty in subarachnoid hemorrhage-induced vasospasm of smaller cerebral vessels [4,18,19]. However, papaverine therapy is short-acting, has untoward side-effects, such as elevation of ICP, and its role in larger vessel spasm remains ill-defined [20]. Recently, novel intra-arterial agents, such as mannitol and amrinone, have been used to reverse acute carotid spasm [21] and cerebral vasospasm following subarachnoid hemorrhage [22], respectively. Further efforts are needed to identify, compare, and validate pharamacotherapies for medium to large vessel spasm. To reduce the tone of a muscular artery, a logical point of intervention is inhibition of calcium influx into smooth muscle cells. Voltage-sensitive CCBs, or 1, 4-dihydropyridines (such as verapamil, nifedipine, nimodipine, and amlodipine), function in this manner. Nimodipine improves outcomes after cerebral vasospasm secondary to subarachnoid hemorrhage [23]. Intraluminal administration of verapamil, in particular, has been used for both the pretreatment of vessels for endovascular procedures [16], as well as reversal of spasm in coronary grafts [6]. Recently, intra-arterial verapamil has also been reported to be safe and effective in the treatment of cerebral vasospasm [5]. Verapamil is well tolerated systemically, yet hypotension is the primary concern during administration. In this case report we document the use of a 2 mg intra-arterial verapamil injection into the CCA of a non-human primate to acutely reverse catheter-induced vasospasm. This use is unique for several reasons. Firstly, in contrast to the report by He et al. [6], in which intra-arterial verapamil was used to alleviate spasm of an internal thoracic artery graft, we used verapamil to treat a significantly larger caliber vessel. Secondly, the presence of elastic fibers in the larger carotid artery compared to the highly muscular internal thoracic artery represents a different functional architecture. Thirdly, He et al. [6] attributed their observed vasospasm to ionotrope therapy (dobutamine, dopamine, and epinephrine) that was instigated for post-operative hemodynamic support. The spasm that we observed, however, occurred in the presence of hemodynamic stability and was isolated to a segment of the carotid in close association with a metallic 6 Fr catheter, suggesting mechanical irritation as the etiology. This report serves as preliminary evidence for the utility of intra-arterial verapamil in large vessel vasospasm and not the conclusive study as its scope is limited by three issues. First, by not having a control (untreated) subject, it is impossible to say for sure that the observed mitigation of vasospasm is due to the intervention and not the natural history of the disease. Considering that effects were observed in the presence of an in situ metallic catheter, we believe strongly that the vasodilation was due to the verapamil. Second, the observed mitigation of vasospasm occurred during a continuous intravenous infusion of nitrates. It is conceivable that the synergistic effects of verapamil with these nitrates actually treated the vasospasm, and not the verapamil itself. Finally, this study, by its design, does not attempt to define the long-term durability of intra-arterial verapamil. Only through additional experimentation and use will the full utility of this agent as an intraluminal treatment for vasospasm be understood. Conclusions We describe the acute alleviation of in situ catheter-induced CCA vasospasm in a non-human primate by an intra-arterial infusion of verapamil (2 mg) without demonstrable complications. Although only an observational study in one subject, this report suggests that intra-arterial administration of verapamil may be an effective intervention for the treatment of mechanically-induced vasospasm in medium to large-sized muscular arteries and that further experimentation in this area is warranted. Competing interests None declared. Authors' contributions ALC, GPC, and WJM performed the surgical procedure, delivered the critical care to the animal, composed, and revised the manuscript. LF and PM performed the angiography studies. ESC conceived the study and oversaw its design and completion. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements The authors would like to thank Daniel Batista and Evan Ransom for their expert technical assistance. This study was funded by the Department of Neurological Surgery, Columbia University, New York, NY. ==== Refs Qureshi AI Luft AR Sharma M Guterman LR Hopkins LN Prevention and treatment of thromboembolic and ischemic complications associated with endovascular procedures: Part II--Clinical aspects and recommendations Neurosurgery 2000 46 1360 75; discussion 1375-6 10834641 Ito Y Isotani E Mizuno Y Azuma H Hirakawa K Effective improvement of the cerebral vasospasm after subarachnoid hemorrhage with low-dose nitroglycerin J Cardiovasc Pharmacol 2000 35 45 50 10630732 10.1097/00005344-200001000-00006 Yoshimura S Tsukahara T Hashimoto N Kazekawa K Kobayashi A Intra-arterial infusion of papaverine combined with intravenous administration of high-dose nicardipine for cerebral vasospasm Acta Neurochir (Wien) 1995 135 186 190 8748812 Kaku Y Yonekawa Y Tsukahara T Kazekawa K Superselective intra-arterial infusion of papaverine for the treatment of cerebral vasospasm after subarachnoid hemorrhage J Neurosurg 1992 77 842 847 1432124 Feng L Fitzsimmons BF Young WL Berman MF Lin E Aagaard BD Duong H Pile-Spellman J Intraarterially administered verapamil as adjunct therapy for cerebral vasospasm: safety and 2-year experience AJNR Am J Neuroradiol 2002 23 1284 1290 12223366 He GW Fan KY Chiu SW Chow WH Injection of vasodilators into arterial grafts through cardiac catheter to relieve spasm Ann Thorac Surg 2000 69 625 628 10735717 10.1016/S0003-4975(99)01341-7 Mocco J Mack WJ Kim GH Lozier AP Laufer I Kreiter KT Sciacca RR Solomon RA Mayer SA Connolly E. S., Jr. Rise in serum soluble intercellular adhesion molecule-1 levels with vasospasm following aneurysmal subarachnoid hemorrhage J Neurosurg 2002 97 537 541 12296636 Gabikian P Clatterbuck RE Eberhart CG Tyler BM Tierney TS Tamargo RJ Prevention of experimental cerebral vasospasm by intracranial delivery of a nitric oxide donor from a controlled-release polymer: toxicity and efficacy studies in rabbits and rats Stroke 2002 33 2681 2686 12411661 10.1161/01.STR.0000033931.62992.B1 McGirt MJ Lynch JR Parra A Sheng H Pearlstein RD Laskowitz DT Pelligrino DA Warner DS Simvastatin increases endothelial nitric oxide synthase and ameliorates cerebral vasospasm resulting from subarachnoid hemorrhage Stroke 2002 33 2950 2956 12468796 10.1161/01.STR.0000038986.68044.39 Mocco J Hoh DJ Nair MN Choudhri TF Mack WJ Laufer I Connolly E. S., Jr. The baboon (Papio anubis) extracranial carotid artery: an anatomical guide for endovascular experimentation BMC Cardiovasc Disord 2001 1 4 11747471 10.1186/1471-2261-1-4 Lange RA Cigarroa RG Yancy C. W., Jr. Willard JE Popma JJ Sills MN McBride W Kim AS Hillis LD Cocaine-induced coronary-artery vasoconstriction N Engl J Med 1989 321 1557 1562 2573838 Oda H Suzuki M Oniki T Kishi Y Numano F Alcohol and coronary spasm Angiology 1994 45 187 197 8129199 Hiasa Y Ishida T Aihara T Bando M Nakai Y Kataoka Y Mori H Acute myocardial infarction due to coronary spasm associated with L-thyroxine therapy Clin Cardiol 1989 12 161 163 2924444 Mori E Ikeda H Ueno T Kai H Haramaki N Hashino T Ichiki K Katoh A Eguchi H Ueyama T Imaizumi T Vasospastic angina induced by nonsteroidal anti-inflammatory drugs Clin Cardiol 1997 20 656 658 9220183 Ilia R Cafri C Jafari J Weinstein JM Abu-Ful A Battler A Prolonged catheter-induced coronary artery spasm mimicking fixed stenosis Cathet Cardiovasc Diagn 1997 41 170 173 9184291 10.1002/(SICI)1097-0304(199706)41:2<170::AID-CCD14>3.3.CO;2-E Fessler RD Wakhloo AK Lanzino G Guterman LR Hopkins LN Transradial approach for vertebral artery stenting: technical case report Neurosurgery 2000 46 1524 7; discussion 1527-8 10834658 Ghani GA Sung YF Weinstein MS Tindall GT Fleischer AS Effects of intravenous nitroglycerin on the intracranial pressure and volume pressure response J Neurosurg 1983 58 562 565 6402569 Kallmes DF Jensen ME Dion JE Infusing doubt into the efficacy of papaverine AJNR Am J Neuroradiol 1997 18 263 264 9111661 Elliott JP Newell DW Lam DJ Eskridge JM Douville CM Le Roux PD Lewis DH Mayberg MR Grady MS Winn HR Comparison of balloon angioplasty and papaverine infusion for the treatment of vasospasm following aneurysmal subarachnoid hemorrhage J Neurosurg 1998 88 277 284 9452236 Coskun E Papaverine and vasospasm J Neurosurg 2002 96 973 4; discussion 974 12005413 Fortin D Osztie E Neuwelt EA Iatrogenic arterial spasm relieved by intraarterial mannitol infusion AJNR Am J Neuroradiol 2000 21 968 970 10815679 Yoshida K Watanabe H Nakamura S Intraarterial injection of amrinone for vasospasm induced by subarachnoid hemorrhage AJNR Am J Neuroradiol 1997 18 492 496 9090409 Barker F. G., 2nd Ogilvy CS Efficacy of prophylactic nimodipine for delayed ischemic deficit after subarachnoid hemorrhage: a metaanalysis J Neurosurg 1996 84 405 414 8609551

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BMC Cardiovasc Disord. 2004 Jul 21; 4:11

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BMC Cardiovasc Disord

10.1186/1471-2261-4-11

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==== Front Respir ResRespiratory Research1465-99211465-993XBioMed Central 1465-9921-5-61528578910.1186/1465-9921-5-6ReviewStem cells and repair of lung injuries Neuringer Isabel P [email protected] Scott H [email protected] Assistant Professor, Division of Pulmonary and Critical Care Medicine and Cystic Fibrosis/Pulmonary Research and Treatment Center, The University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA2 Assistant Professor, Division of Pulmonary and Critical Care Medicine, Cystic Fibrosis/Pulmonary Research and Treatment Center and Department of Cellular and Molecular Physiology, The University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA2004 20 7 2004 5 1 6 6 30 1 2004 20 7 2004 Copyright © 2004 Neuringer and Randell; licensee BioMed Central Ltd.2004Neuringer and Randell; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Fueled by the promise of regenerative medicine, currently there is unprecedented interest in stem cells. Furthermore, there have been revolutionary, but somewhat controversial, advances in our understanding of stem cell biology. Stem cells likely play key roles in the repair of diverse lung injuries. However, due to very low rates of cellular proliferation in vivo in the normal steady state, cellular and architectural complexity of the respiratory tract, and the lack of an intensive research effort, lung stem cells remain poorly understood compared to those in other major organ systems. In the present review, we concisely explore the conceptual framework of stem cell biology and recent advances pertinent to the lungs. We illustrate lung diseases in which manipulation of stem cells may be physiologically significant and highlight the challenges facing stem cell-related therapy in the lung. lung hypoplasiarespiratory distress syndromechronic lung disease of prematuritypulmonary emphysemapulmonary fibrosisbronchiolitis obliteranscystic fibrosisasthmalung cancer ==== Body Introduction According to Greek mythology, the immortal Prometheus stole fire from the Gods as a gift for humankind. As punishment, he was shackled to a rock, whereupon each day for 30,000 years an eagle consumed as much of his liver as would regenerate. There is some debate whether the eagle ate his liver or heart, but what if the bird had a taste for lung? And what if Prometheus was a mere mortal? Analogous to Prometheus and the eagle, the ambient air-exposed lung is subject to an array of potentially damaging agents, including chemical oxidants and proteolytic enzymes. Presumably, daily oxidant and protease wear and tear on structural components such as elastin and collagen contributes to inevitable age-related declines in pulmonary function in normal individuals [1,2]. Acute and chronic lung disease, or its treatment with oxygen and positive pressure ventilation, may further damage lung tissue in excess of the capacity for orderly repair, resulting in characteristic pathologic changes including tissue destruction or fibrotic scarring [3-5]. But what determines the lungs' capacity for repair? Certainly, one factor must be the ability of stem cells to proliferate and differentiate to replace damaged cells and tissues. As discussed later in this review, the traditional view is that, during development, self-renewing tissues are imbued with resident, tissue-specific stem cells, so-called adult somatic stem cells. However, recent but highly controversial evidence suggests that stem cells from one type of tissue may generate cells typical of other organs. In this fashion, circulating cells derived from bone marrow may augment resident stem cells, and we comprehensively review such data from lung. Finally, there is great hope that embryonic stem cells, embryonic germ cells, or even adult somatic stem cells can be engineered as an unlimited source of cells to enhance organ-specific repair or replace lost tissues. Below, we concisely review stem cell biology, focusing on recent findings relevant to the lungs. Diseases in which alterations in stem cells contribute to lung dysfunction are discussed, as are the challenges facing the nascent field of pulmonary regenerative medicine. Embryonic and adult (somatic) stem cells For links to more in-depth information on general principles in stem cell biology, a comprehensive glossary, and the latest updates in this quick moving field, the reader is referred to the International Society for Stem Cell Biology . During embryonic development, the inner cell mass of the blastocyst forms three primary germ layers, which generate all fetal tissue lineages (reviewed in [6], illustrated in Figure 1, path 1). Embryonic stem cells (derived from the blastocyst inner cell mass), or embryonic germ cells (derived from the gonadal ridge), when cultured on embryonic mouse fibroblast feeder cell layers in the presence of a differentiation-suppressing cytokine (leukemia inhibitory factor), proliferate indefinitely and remain pluripotent. Manipulation of culture conditions can coax the cells to undergo differentiation characteristic of many tissue types (Figure 1, paths 2 and 3). Theoretically, pluripotent embryonic cells can serve as an unlimited resource for therapeutic applications [7,8]. Figure 1 Cell lineage determination during embryogenesis and generation of pluripotent embryonic cells. The three primary germ layers form during normal development (path 1). Embryonic stem cells from the inner cell mass (path 2) or embryonic germ cells from the gonadal ridge (path 3) can be cultured and manipulated to generate cells of all three lineages. General principles of tissue renewal by adult stem cells have been reviewed recently [9] and can be summarized as follows. The traditional view of cell lineages is that adult somatic stem cells maintain cell populations in adult tissues. The adult lung falls into the category in which cell proliferation is very low in the normal steady-state but can be induced dramatically by injury (see [10,11] for recent reviews of lung stem cells). The conditional nature of lung cell proliferation complicates the search for lung stem cells. Cell lineages are much better understood in continuously proliferating tissues such as the gut, skin and hematopoietic system (reviewed in [12-14], respectively). The long-standing view, developed from these other organs, is that stem cells reside in well-protected, innervated, and vascularized niches that provide cues regulating cell fate decisions such as proliferation, migration, and differentiation [15]. Adult stem cells are capable of abundant self-renewal and can also generate the specific cell lineages within the tissue compartment (Figure 2). Proportional to tissue needs, stem cells may undergo asymmetric cell division, in which they generate one stem cell and a committed progenitor. The capacity for self-renewal decreases progressively as committed progenitors differentiate. The wisdom of the body is to conserve stem cells. They cycle infrequently and the majority of cell replacement is accomplished by committed progenitors within the so-called transiently amplifying compartment. Eventually, individual cells become incapable of further cell division. In tissues, there are specific temporal and spatial hierarchic relationships between stem cells in their niches and their differentiated progeny. Within this axis, cell proliferation, migration, differentiation, function, death, and removal are tightly regulated to maintain tissue homeostasis. Figure 2 Traditional view of cell lineage in adult renewing tissues. Organ-specific (somatic) stem cells generate characteristic cell types through a linear set of commitment and differentiation steps. Arrow thickness represents self-renewal potential. Cell compartments in the lung and functional integration In the architecturally complex lung, cells of multiple germinal lineages interact both during morphogenesis and to maintain adult lung structure. Even within derivatives of a single germ layer, cells become subdivided into separate cell lineage "zones". For example, the endoderm generates least four distinct epithelial regions, each with a different cellular composition (Figure 3). Additional cell types, including airway smooth muscle, fibroblasts, and the vasculature, are derived from mesoderm. Airway and alveolar architecture, and in turn, function, result from interaction among epithelium, smooth muscle, fibroblasts, and vascular cells, all within an elaborate structural matrix of connective tissue. The complexity of even this oversimplified view, which omits pulmonary neuroepithelial cells and bodies, innervation, and classical hematopoietically-derived cells such as dendritic cells, mast cells, and macrophages, has hindered identification of lung stem cells and patterns of cell migration during tissue renewal. Nevertheless, the prevailing view is that airway basal and Clara cells and alveolar type II cells serve as epithelial progenitors [11,16-19]. Cell lineages in the mesodermal compartments remain less well understood. Figure 3 Stem cell compartments in the lungs. The endoderm-derived epithelium can be subdivided into at least 4 types whereas smooth muscle, fibroblasts, and vascular cells are derived from mesoderm. The coordinated interaction of multiple cell types, including alveolar epithelium, interstitial fibroblasts, myofibroblasts and pulmonary endothelium, is necessary to form alveolar septa. Stem cell plasticity and the lung Recent studies challenge the view that tissues are maintained solely by organ-specific stem cells. There is evidence that adult stem cells from a variety of sources can generate not only their own lineages, but those of other tissues, sometimes crossing barriers of embryonic derivation previously thought impenetrable [20,21,8]. There are a few controversial reports that adult stem cells from outside the bone marrow may reconstitute the hematopoietic system, but most of the evidence flows in the other direction- namely, that cells from the bone marrow can generate diverse non-hematopoietic cell types. Both experimental studies in animals and human clinical studies, summarized in Table 1, provide evidence for, and against, circulatory delivery of lung progenitor cells. While bone marrow-derived cells, such as alveolar macrophages, dendritic cells, mast cells, and lymphocytes, normally migrate to the lung, the surprise in the recent literature is that under certain circumstances circulating cells can apparently generate lung resident cells, including epithelial, endothelial, and myofibroblast cells. The technical approach towards identification of these cells is often technically challenging and involves co-localization of a donor cell marker, for example, the Y chromosome, in sex-mismatched transplantation, or a genetically engineered marker in mouse experiments, and proteins characteristic of the differentiated cell type in the lung, for example, keratin in epithelial cells or collagen in fibroblasts. As discussed below, the results are highly variable and often contradictory, depending on factors including the starting cell population, the methods for marker detection, and the amount of injury to the lung. Table 1 Evidence for, and against, circulating progenitor cell generation of non-hematopoietic lung cell types. Study Type Disease or Model Tissue of Origin Lung Cell Type Formed / Frequency Method of Detection Ref. Animal, in-vivo BMT MSC Undefined mesenchymal cells / occasional PCR for collagen gene marker [30] Animal, in-vivo Bleomycin fibrosis MSC Type I pneumocytes / rare β galactosidase protein [23] Animal, in-vivo BMT HSC enrichment Type II pneumocytes / up to 20%, bronchial epithelium / 4% Y chromosome FISH, surfactant B mRNA [31] Animal, in-vivo Radiation pneumonitis Whole bone marrow Type II pneumocytes, bronchial epithelium / up to 20% of type II cells Y chromosome FISH, surfactant B mRNA [25] Animal, in-vivo BMT Whole bone marrow/EGFP retrovirus Type II pneumocytes / 1–7% EGFP, keratin immunostain, surfactant protein B FISH [33] Animal, in-vivo BMT and parabiotic animals HSC Hematopoietic chimerism but exceedingly rare lung cell types EGFP [32] Animal, in-vivo Bleomycin fibrosis MSC Type II pneumocytes / ~1% Y chromosome FISH [22] Animal, in-vivo Radiation fibrosis MSC or whole bone marrow Fibroblasts / common EGFP, Y chromosome FISH, vimentin immunostain [26] Animal, in-vivo BMT Bone marrow, EGFP labeled Fibroblasts, Type I pneumocyte / occasional to rare Flow cytometry [34] Animal, in-vitro and in-vivo Hypoxia-induced pulmonary hypertension Circulating BM-derived c-kit positive c-kit positive cells in pulmonary artery vessel wall; In hypoxia, circulating cells generate endothelial and smooth muscle cells in-vitro Flow cytometry and immunohistochemistry [27] Animal, in-vivo Ablative radiation and elastase induced emphysema GFP + fetal liver Alveolar epithelium and endothelium; frequency not reported but increased by G-CSF and retinoic acid Immunohistochemistry for CD45-, GFP+ cells [28] Animal, in-vivo Bleomycin fibrosis Whole marrow GFP+ GFP+ type I collagen expressing Flow cytometry and immunohistochemistry, RT-PCR [24] Human, in-vitro Heat shock in cell culture MSC and SAEC Cell fusion / common Immunostaining, microarray [39] Animal, in-vivo Human, in-vivo OVA-sensitized mouse model Allergen – sensitized asthmatics CD34 positive, collagen I expressing fibrocytes CD34 positive, collagen I expressing fibrocytes Myofibroblasts / ? Myofibroblasts / ? CD34-positive, collagen I, α-smooth muscle actin CD34-positive, collagen I, α-smooth muscle actin [29] Human, in-vivo Human heart and lung transplant Sex-mismatched donor lung or heart No lung cell types of recipient origin X and Y chromosome FISH, antibody stain for hematopoeitic cells [36] Human, in-vivo Human lung transplant Human BMT Sex-mismatched donor lung Sex-mismatched donor bone marrow Bronchial epithelium, type II pneumocytes, glands of recipient origin / 9 – 24% No lung cell types of donor origin Y chromosome FISH, short tandem repeat PCR Y chromosome FISH, short tandem repeat PCR [35] Human, in-vivo Human BMT Sex-mismatched donor bone marrow Lung epithelium and endothelium of donor origin / up to 43% X and Y chromosome FISH, keratin and PECAM immunostain [38] Human, in-vivo Human BMT Sex-mismatched donor bone marrow No nasal epithelium of donor origin Y chromosome FISH, cytokeratin immunostain [37] BMT = bone marrow transplant (with prior ablation), MSC = mesenchymal stem cells (bone marrow stromal cells, adherent bone marrow cells), EGFP = enhanced green fluorescent protein, HSC = hematopoietic stem cells, FISH = fluoresence in situ hybridization, SAEC = small airway epithelial cells Transplantation studies in mice can be performed using whole donor bone marrow, the fraction that adheres in culture, termed marrow stromal cells (MSC), or preparations enriched for hematopoietic stem cells (HSC). Whole body irradiation, which may injure lung tissue, is typically used to deplete the host bone marrow. Importantly, lung injury apparently enhances engraftment into lung [22-29]. Whole bone marrow, MSC, or HSC have all been reported to reconstitute lung parenchymal cells. MSC transplantation resulted in collagen I expressing donor cells in the lung [30], and in the presence of bleomycin injury, MSC reportedly generated type I [23] or type II pneumocytes [22]. Transplantation with HSCs yielded up to 20% donor-derived pneumocytes and 4% bronchial epithelial cells [31]. However, other investigators have identified only hematopoeitic chimerism by HSCs [32]. Whole bone marrow infusion generated type II pneumocytes [33], or fibroblasts and type I pneumocytes [34]. Radiation pneumonitis augmented whole bone marrow generation of type II pneumocytes and bronchial epithelial cells [25] or fibroblasts [26]. Bleomycin lung injury enhanced formation of type I collagen-producing cells [24] from whole bone marrow, whereas elastase-induced emphysema stimulated formation of alveolar epithelium and endothelium [28]. Lung injury alone, without bone marrow transplantation, may promote stem cell migration. For example, in the ovalbumin model of asthma, circulating fibrocytes were recruited into bronchial tissue [29], and in a bovine model of hypoxic pulmonary hypertension, cells capable of generating endothelial and smooth muscle cells in vitro were found in the circulation [27]. Sex-mismatched lung and bone marrow transplantation in humans provides a natural model for analysis of donor and recipient cell behavior. Bronchial epithelial and gland cells and type II pneumocytes of host origin were reported in one study of lung allografts [35], but not another [36]. After bone marrow transplantation, epithelial cells of donor origin were not detected in the nasal passages [37]. Similar to lung allografts, following bone marrow transplantation, epithelium and endothelium of donor origin were found in one study [38], but not another [35]. Many questions remain unanswered. The mechanism whereby cells assume lung cell phenotypes remains uncertain. Several studies have demonstrated that cell fusion occurs both in vitro and in vivo, which likely explains why some of the cells contain both donor and lung cell markers [see [39] for a study of fusion of MSCs and lung epithelium and [40,41] for recent reviews]. Alternatively, cells may reprogram in the lung environment- a concept termed "transdifferentiation", which is defined as the ability of a particular cell from one tissue type to differentiate into a cell type characteristic of another tissue. It has been suggested that many of the events previously attributed to transdifferentiation may actually represent cell fusions, particularly due to the influx of fusion-prone myeloid cells into damaged tissues from the repopulated bone marrow [40]. New, more stringent, criteria have been put forth for demonstration of transdifferentiation [41]. Bone marrow harbors a generalized pluripotent stem cell [42] and the bone marrow cell responsible for lung engraftment has not been identified with certainty. It is possible that rare transdifferentiation events represent migration of a pluripotent bone marrow cell type resembling an embryonic stem or embryonic germ cell still harbored in the adult bone marrow. It remains unknown whether bone marrow cells must transit through an intermediate compartment prior to lung colonization (Figure 4) or whether circulating stem cells can be mobilized from sources other than bone marrow. It is important to note that bone marrow derived cells of typical hematopoietic lineage, chimeric cells created by fusion, or lung cells generated by transdifferentiation may all play a role in lung repair by promoting the local production of stem cells or reparative function of lung-specific cell types. A compelling study suggests that mesenchymal stem cells from bleomycin-resistant mice can mitigate the pro-fibrotic effects of bleomycin in sensitive mice [22], while another study suggests that bone marrow cells actively contribute to the formation of fibrotic tissue [24]. Mitigating or exacerbating roles for bone marrow derived cells in lung repair or fibrosis are not mutually exclusive. The important concepts of whether the lungs' capacity for repair is dependent on circulating cells, and whether exogenously delivered cells can enhance resistance to injury or promote healing, remain unanswered and controversial. Figure 4 Evolving view of cell lineages in the lungs. The functional significance of circulating cells towards lung cell maintenance or tissue repair remains unknown, as does the precise mechanism whereby circulating cells generate lung cell types. Lung "stem cell" diseases Major lung diseases likely involving stem cells and the cellular targets for stem cell therapy are summarized in Table 2. These may be broadly categorized whether they involve stem cell deficiency, hyper-proliferation or possibly, a combination of both. For example, impaired pulmonary endothelial and/or epithelial barrier function may contribute to the pathophysiology of adult respiratory distress syndrome. Mobilization of endogenous endothelial or epithelial stem/progenitor cells or delivery of adult somatic stem cells, embryonic stem cells, or embryonic germ cells may theoretically improve barrier function, supporting the notion of treating a "stem cell deficiency". Similarly, toxic, viral or alloimmune destruction of the bronchiolar epithelium suggests stem cell deficiency in bronchiolitis obliterans. However, fibrotic reactions and scarring in response to epithelial injury can be viewed as fibroblast "stem cell hyper-proliferation". The general concept is that augmentation of stem cells may minimize lung injury, augment repair, or possibly regenerate lost tissue. However, one must also consider that inhibiting excessive growth of stem cells may be a valid therapeutic goal when hyper-proliferation contributes to disease pathophysiology, as in fibrosis, smooth muscle hyperplasia or lung cancer. Table 2 Major lung diseases potentially treatable by stem cell manipulation. Disease Category Injured, Depleted, or Deranged Cellular Compartment* Therapeutic Goals Congenital lung hypoplasia Chronic lung disease of prematurity Pulmonary emphysema Alveolar epithelium, Interstitial fibroblast, Capillary endothelium, Generate alveolar septa Restore complex three dimensional structure Neonatal RDS Adult RDS Alveolar epithelium, Capillary endothelium Enhance surfactant production Reinforce endothelial and epithelial barriers Pulmonary fibrosis Alveolar epithelium, Interstitial fibroblast Prevent alveolar epithelial loss Inhibit fibroblast proliferation Asthma Airway epithelium, Myofibroblasts, Airway smooth muscle Create an anti-inflammatory environment Inhibit airway wall remodeling Inhibit smooth muscle hypertrophy and hyperplasia Cystic fibrosis Airway epithelium Deliver functional CFTR Bronchiolitis obliterans Airway epithelium Reinforce the epithelium against toxic, viral or immunologic injury Lung cancer Epithelium Detection, monitoring or treatment based on molecular regulation of stem cell proliferation and differentiation RDS = respiratory distress syndrome, CFTR= cystic fibrosis transmembrane conductance regulator *Each cell type listed in this column is affected in all of the specific conditions listed in the left hand column Challenges for lung regenerative medicine What are the realistic prospects for beneficial stem cell therapy of the lung? First, we must conclusively identify lung diseases/cases/timing in which cell and tissue damage occurs in excess of the capacity for timely endogenous repair. Second, we must establish standardized sources of relevant stem/progenitor cells and methods for their delivery to the appropriate lung sub-compartment. Once delivered, therapeutic cells must home to microscopic sites of need and integrate to serve a beneficial function. There is clearly potential for adverse effects, as exemplified by the propensity of embryonic stem cells to form teratomas when implanted in vivo [43]. Major lung diseases potentially addressable by stem cell therapy may pose unique challenges. Reversal of lung developmental anomalies resulting in hypoplasia, or repair of chronic lung disease of prematurity and advanced pulmonary emphysema in adults, will require neogenesis of alveolar septa in which the endogenous "tissue blueprint" never developed, or was completely destroyed. Until we gain a much better understanding of lung tissue morphogenesis, we must rely on stem cells intrinsically "knowing" where to go and "how" to recreate alveolar septal architecture to ultimately restore higher order complex three dimensional relationships amongst alveoli, airways, and vessels. Stem cell therapy to cure cystic fibrosis will require heterologous, or gene corrected autologous, stem cells to colonize the airway, proliferate, and differentiate into columnar cells covering a significant portion of the airway lumen. However, most evidence thus far suggests that cells from the circulation may generate isolated, single airway basal cells. Stem cell therapy to mitigate respiratory distress syndrome (RDS) will require cells capable of restoring alveolar endothelial and epithelial function in the face of evolving injury. Whereas injury is thought to promote stem cell recruitment, the relevant question is whether it can occur quickly enough to meaningfully reverse acute, widespread cellular dysfunction typical of RDS. Conclusion Provocative, but controversial, recent evidence suggests that circulating stem cells may home to the lung. There is great excitement and hope that exogenous and/or mobilized endogenous stem cells may be harnessed to prevent or treat acute and chronic lung diseases and even regenerate abnormally developed or lost tissue. Our understanding of lung stem cells and the regulation of lung morphogenesis is still rudimentary, and the complex, integrated function of multiple cell types underlying normal lung structure and function poses unique challenges. Thus, the therapeutic prospects for stem cell therapy in lungs appear more distant than in some other organs. This realization should stimulate meaningful new studies from the lung research community. Unlike the mythical hero Prometheus, patients with lung disease cannot wait 30,000 years! Competing interests None declared. 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==== Front BMC MicrobiolBMC Microbiology1471-2180BioMed Central London 1471-2180-4-301527474710.1186/1471-2180-4-30Research ArticleIdentification and characterization of a nontypeable Haemophilus influenzae putative toxin-antitoxin locus Daines Dayle A [email protected] Justin [email protected] Arnold L [email protected] Nonproliferation, Arms Control, and International Security Directorate, Lawrence Livermore National Laboratory, L-501, 7000 East Avenue, Livermore CA 94550-9698, USA2 Bacterial Pathogenesis Program, Seattle Biomedical Research Institute, 307 Westlake Avenue North, Suite 500, Seattle, WA 98109-5219, USA2004 26 7 2004 4 30 30 15 5 2004 26 7 2004 Copyright © 2004 Daines et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Certain strains of an obligate parasite of the human upper respiratory tract, nontypeable Haemophilus influenzae (NTHi), can cause invasive diseases such as septicemia and meningitis, as well as chronic mucosal infections such as otitis media. To do this, the organism must invade and survive within both epithelial and endothelial cells. We have identified a facilitator of NTHi survival inside human cells, virulence-associated protein D (vapDHi, encoded by gene HI0450). Both vapDHi and a flanking gene, HI0451, exhibit the genetic and physical characteristics of a toxin/antitoxin (TA) locus, with VapDHi serving as the toxin moiety and HI0451 as the antitoxin. We propose the name VapXHi for the HI0451 antitoxin protein. Originally identified on plasmids, TA loci have been found on the chromosomes of a number of bacterial pathogens, and have been implicated in the control of translation during stressful conditions. Translation arrest would enhance survival within human cells and facilitate persistent or chronic mucosal infections. Results Isogenic mutants in vapDHi were attenuated for survival inside human respiratory epithelial cells (NCI-H292) and human brain microvascular endothelial cells (HBMEC), the in vitro models of mucosal infection and the blood-brain barrier, respectively. Transcomplementation with a vapDHi allele restored wild-type NTHi survival within both cell lines. A PCR survey of 59 H. influenzae strains isolated from various anatomical sites determined the presence of a vapDHiallele in 100% of strains. Two isoforms of the gene were identified in this population; one that was 91 residues in length, and another that was truncated to 45 amino acids due to an in-frame deletion. The truncated allele failed to transcomplement the NTHi vapDHi survival defect in HBMEC. Subunits of full-length VapDHi homodimerized, but subunits of the truncated protein did not. However, truncated protein subunits did interact with full-length subunits, and this interaction resulted in a dominant-negative phenotype. Although Escherichia coli does not contain a homologue of either vapDHi or vapXHi, overexpression of the VapDHi toxin in trans resulted in E. coli cell growth arrest. This arrest could be rescued by providing the VapXHi antitoxin on a compatible plasmid. Conclusion We conclude that vapDHi and vapXHi may constitute a H. influenzae TA locus that functions to enhance NTHi survival within human epithelial and endothelial cells. ==== Body Background Culturable Haemophilus influenzae are acquired in the nasopharynx shortly after birth, and are thought to persist throughout life. H. influenzae adheres to and penetrates into and between cultured human respiratory epithelial cells, a mechanism that may contribute to its persistence in chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF) patients [1,2]. H. influenzae can be found in the respiratory tracts of these patients even after they have undergone antibiotic treatment [3]. As well, COPD sputum cultures can be sterile, while H. influenzae can still be isolated from the subepithelial matrix [4]. Finally, we have found in a recent in vivo study that H. influenzae can persist in a human bronchiolar xenograft for at least three weeks [5]. This suggests that the organism can survive and persist in protected biological compartment(s). The ability of H. influenzae to survive antibiotic treatment and reappear when growth is favorable may be responsible for the reseeding of the middle ear observed in chronic otitis media. Often, middle ear fluid from children presenting with otitis media with effusion are sterile when cultured, but PCR analysis of the fluid has determined the presence of H. influenzae [6]. Further, RT-PCR studies of this sterile fluid have shown the presence of bacterial mRNA, confirming that the bacteria are alive and persisting in a viable but nonreplicative state [7]. Persistence was investigated in vitro using a NTHi strain that was susceptible to β-lactam antibiotics. This strain was allowed to invade a human respiratory epithelial cell monolayer for 24 hours, which was subsequently treated with a 4 hour incubation in 10 × MIC concentrations of the β-lactam antibiotics ampicillin, imipenem, cefuroxim, amoxycillin/clavulanic acid, or cephalothin. The antibiotics killed all the extracellular bacteria, but none of the intra- or paracellular bacteria, suggesting that the organism was not replicating inside or between the epithelial cells [8]. Non-replicating bacteria are not susceptible to the cidal action of β-lactam and aminoglycoside antibiotics. During a study aimed at identifying genes associated with virulence in pathogenic strains of the Gram-negative, strict anaerobe Dichelobacter nodosus, the causative agent of ovine footrot, Katz et al. [9] reported the discovery of a novel area of the chromosome that hybridized to all virulent strains tested, but to only 23% of the avirulent strains studied. They designated the four genes found on this fragment as vapA-D, for virulence-associated proteins. Homologues of these genes appear on the chromosomes and plasmids of a number of pathogenic microorganisms, including Neisseria gonorrhoeae, Helicobacter pylori, Reimerella anatipestifer and Actinobacillus actinomycetemcomitans. The chromosome of H. influenzae strain Rd KW20 (hereinafter referred to as strain Rd) contains vapA, vapBC, and vapD homologues, with one pair, vapBC, in duplicate. The genome organization of the vap genes in H. influenzae differs from that of D. nodosus, in that vapAHi (HI1250) is preceded by a conserved hypothetical protein, HI1251, and both genes are likely transcribed as an operon. As well, vapDHi is flanked by a gene encoding another conserved hypothetical protein, HI0451 (which we have named vapXHi), again in an apparent operon configuration (8 nucleotides separate HI0450 and HI0451). To determine if the vap homologues played a role in the persistence of NTHi, we chose vapDHi (HI0450) for further study, since this protein was found in a proteomic survey to be expressed in the soluble fraction of strain Rd [10]. VapDHi is 40% identical and 67% similar to the Dichelobacter VapD and belongs to the Cluster of Orthologous Groups (COG) 3309 and Pfam 04605, termed the "N-terminal conserved domain of VapD". Results Mutation of vapDHi results in attenuated survival in human endothelial cells When the Rd vapDHi mutant strain was used to invade the in vitro model of the blood-brain barrier, human brain microvascular endothelial cells (HBMEC) in 12-well plates, the amount of gentamicin-resistant bacteria recovered from the monolayer after three hours declined to approximately 60% of wild type levels, an average of 2.2 × 103 CFU/ml for the wild-type strain Rd versus 1.3 × 103 CFU/ml for the Rd vapDHi mutant (n = 3 (number of independent assays performed in at least duplicate); P < 0.05, Student's t test). No significant difference was observed between the wild type and vapDHi mutant in adherence to the human cell monolayers: the average number of cell-associated bacteria (both adherent and invaded) recovered for strain Rd was 1.5 × 105 CFU/ml and 1.8 × 105 CFU/ml for the Rd vapDHi mutant (n = 3; P > 0.05). To determine if the phenotype of attenuated survival observed in the Rd vapDHi mutant was a general phenomenon and not restricted to strain Rd, another isogenic pair was constructed and analyzed using a different strain, R3001. R3001 is a bronchoalveolar lavage isolate from a pediatric cystic fibrosis patient, and is considered invasive since it came from a normally sterile site [5]. The average number of gentamicin-resistant bacteria recovered from HBMEC monolayers was 1.2 × 105 CFU/ml for the parent strain R3001 versus 7.1 × 104 CFU/ml for the R3001 vapDHi mutant (n = 3; P < 0.05). Although the absolute numbers of bacteria recovered were higher with strain R3001 than with Rd (as is often observed with invasive isolates), the attenuation of survival inside the HBMEC monolayer of ≤ 60% observed in the strain with a vapDHi mutation was maintained. There was no significant difference between the wild-type R3001 and the R3001 vapDHi mutant in adherence to the monolayer: the average numbers of cell-associated bacteria recovered for strain R3001 were 1.7 × 107 CFU/ml versus 1.3 × 107 CFU/ml for R3001 vapDHi (n = 4; P > 0.05). Unlike Rd, strain R3001 carries the high molecular weight (HMW) adhesins, which may account for its more efficient adherence to the HBMEC monolayer [11]. No significant difference in the growth rates of either of the vapDHi mutants versus their cognate parent strains were observed, whether grown in bacteriological media (sBHI broth) or on HBMEC or NCI-H292 monolayers (data not shown). Mutation of vapDHi results in diminished long-term survival inside human respiratory epithelial cells To determine if the vapDHi mutation would affect the ability of H. influenzae to survive inside human respiratory epithelial cells over a longer period of time, 18-hour invasion assays were performed using NCI-H292 cells. The number of gentamicin-resistant bacteria recovered from the NCI-H292 monolayer after 18 hours for the parent strain Rd was an average of 6.4 × 104 CFU/ml versus 3.2 × 104 CFU/ml for the Rd vapDHi mutant (n = 3; P < 0.05). This represents a 50% reduction in survival of the vapDHi mutant within epithelial cells as compared to the parent strain, more attenuation than was seen for the three hour assays. Transcomplementation of Rd vapDHi The vapDHi locus from strain R3001 was cloned into the mobilizable broad host range plasmid pDD515, creating pDD564, and conjugally transferred into the Rd vapDHi mutant (Table 1). The plasmid pDD515 is a derivative of the IncQ plasmid RSF1010 and has an approximate copy number of 12 per chromosome in H. influenzae [12]. The survival inside HBMEC of strain Rd (pDD515) was within 5% of strain Rd without the vector in identical assays (data not shown). Carrying a vapDHi locus in trans restored wild-type survival of Rd vapDHi (pDD564) within HBMEC monolayers. The amount of gentamicin-resistant bacteria recovered from the endothelial cell monolayer after a three hour invasion assay was an average of 8.0 × 102 CFU/ml for Rd (pDD515), the vector control, and 7.6 × 102 CFU/ml for the mutant strain that carried the wild-type vapDHi allele in trans, Rd vapDHi (pDD564) (n = 3; P > 0.05), indicating that there was no significant difference in the survival inside HBMEC monolayers of the wild type strain versus the transcomplemented strain. These data confirm that the phenotype of attenuated survival was due to the interruption in vapDHi and not to polar effects. The R3001 vapDHi mutant mirrored the survival defect seen with the Rd vapDHi mutant, at approximately 60% of wild-type R3001 levels. However, attempts to conjugate the mobilizable broad host range plasmid carrying the vapDHi locus, pDD564, into strain R3001 for transcomplementation studies failed repeatedly. This clinical isolate likely has a plasmid or an origin of replication of the same incompatibility group incorporated into its chromosome and therefore will not maintain the broad host range plasmid for transcomplementation [13]. Reverse-transcriptase PCR of bacteria from human cell monolayers To determine if the vapDHi locus was transcribed during contact with a human cell monolayer, total RNA was isolated from the wild-type strain Rd recovered after 18 hours on HBMEC or NCI-H292 monolayers and was used as the template for RT-PCR. Figure 1 shows the 153 bp band amplified with the vapDHi-specific primers, with a molecular weight marker in lane A. The template for lane B is Rd RNA from HBMEC endothelial cell monolayers, lane C is its cognate negative control, with no reverse transcriptase added to the RNA prior to PCR amplification. Lane D shows the results using Rd RNA from NCI-H292 epithelial cell monolayers; lane E is its cognate negative control. The vapDHilocus is transcribed when strain Rd is in contact with either human epithelial or endothelial cell monolayers. PCR survey of vapDHi In order to estimate the carriage rate of vapDHi among the highly heterologous NTHi strains, a PCR survey of 59 commensal and disease-associated strains was undertaken (Table 2). The vapHI primer set was used. In Rd, these primers amplify a 769 bp PCR product that includes the full-length vapDHi gene. Purified chromosomal DNA preparations from 53 randomly-chosen NTHi strains and one each of the six capsular serotypes of H. influenzae (types a through f) from the American Type Culture Collection (ATCC) reference strains described in Table 2 were subjected to PCR with the vapHI forward and reverse primers. The NTHi strains included nasopharyngeal, blood, CSF, middle ear, tracheal aspirate, and sputum isolates. A PCR product was amplified in 100% of the strains. All of the ATCC encapsulated reference strains and Rd displayed a full-length vapDHiallele. Ninety-three percent of the nasopharyngeal strains carried a full-length allele, as did 71% of the blood and CSF isolates, and 50% of the middle ear, tracheal aspirate, and sputum isolates. Overall, only ten strains of the 59 included in the study displayed a truncated gene. Sequencing of the truncated vapDHi allele To study the truncated vapDHi in more detail, five out of the ten alleles that represented the smaller isoform of vapDHi from the PCR survey were sequenced on both strands. It was found that, in all cases, the gene had undergone a deletion event that had left the protein in frame, but missing 46 amino acids from the interior of the protein, resulting in a 45 amino acid protein rather than the full-length 91 amino acid protein (Figure 2). This corresponds to the loss of Rd genome coordinates 473123 to 473263. In addition, all of the smaller alleles had an aspartate residue inserted at position #7 as compared to Rd, which has a leucine at that position. The significance of this is unclear, as the full-length R3001 vapDHi allele, which did transcomplement the Rd vapDHi mutant, also has an aspartic acid inserted at the same position, resulting in a 91 amino acid protein. Interestingly, the VapD homologues from N. gonorrhoeae, H. pylori and A. actinomycetemcomitans, as well as VapD in D. nodosus, all have an aspartate at that position. Rd appears to be the only H. influenzae strain studied which lacks that particular residue. Full-length VapDHi homodimerizes Using an E. coli-based protein-protein interaction system that is dependent upon the DNA-binding domain (DBD) of LexA, homodimerization of identical protein subunits can be quantitated [14,15]. In this system, protein-protein interactions result in a LexA dimer that is active as a repressor, and consequently, the beta-galactosidase activity of the reporter strain (SU101) diminishes. Full-length VapDHi from strain R3001 was ligated to the DBD of LexA in plasmid pDD559 and the clones were analyzed on MacConkey agar with lactose. If there was no homodimerization of the LexA::VapDHi fusion protein, the colonies appeared red on MacConkey agar, as the native level of beta-galactosidase expression in the reporter strain was not inhibited. If the subunits interacted, the colonies appeared pale on MacConkey, as the engineered LexA operator controlling the lacZ reporter gene had been repressed by a homodimer of the LexA fusion protein. This repression was quantitated by beta-galactosidase activity assays. Each measurement is the mean of at least three experiments performed in triplicate. It was found that VapDHi interacted strongly with itself. The beta-galactosidase activity of the reporter strain SU101 carrying the vector control (pSR658) was an average of 975 (± 29) Miller units, and the activity of SU101 with the LexA::VapDHi fusion (pDD559) was an average of 16 (± 1) Miller units, indicating strong interaction. Full-length VapDHi forms homodimers in vivo. This protein may also form higher-order multimers, since this would result in a number of dimeric forms being available to act as a repressor of lacZ transcription in the reporter strain. Truncated VapDHi does not homodimerize, but interacts with full-length VapDHi Homodimerization assays with the small allele revealed that the subunits of the truncated VapDHi did not interact efficiently. The vector control for the homodimerization assays SU101 (pSR658) yielded 1490 (± 31) Miller units, and SU101 carrying the LexA fusion to the truncated VapDHi protein from strain R2866 (pDD577) displayed 1357 (± 54) Miller units of beta-galactosidase activity, showing little repression in this system. Since the wild-type VapDHi subunits homodimerized strongly but the truncated subunits did not, the truncated subunit and the full-length subunit were examined for interaction in heterodimerization assays. In the reporter strain SU202, a LexA operator with a mutated half-site was engineered upstream of the lacZ gene. This strain was then transformed with two compatible plasmids, one that carried a fusion of the truncated VapDHi with a wild-type LexA DBD, and one that carried the full-length VapDHi fusion to a mutated LexA DBD that only recognized the mutated LexA operator half-site in SU202 [15]. If a heterodimer of a truncated subunit and a full-length subunit was formed, a functional LexA repressor could recognize the hybrid operator and repress transcription of lacZ. It was determined that the truncated and full-length subunits could interact with each other. The vector control for the heterodimerization assays (SU202 with pSR658 and pSR659) yielded 1855 ± 196 Miller units, and SU202 carrying the LexA fusion to the truncated VapDHi protein from strain R2866, pDD577, coupled with the mutated LexA DBD fusion to the full-length VapDHi protein from strain R3001, pDD561, resulted in 792 ± 19 Miller units of beta-galactosidase activity, showing that the truncated subunit did heterodimerize with the full-length VapDHi subunit. Truncated VapDHi does not transcomplement the mutant and has a dominant-negative effect in the wild-type strain To investigate whether the truncated VapDHi protein could transcomplement a mutation in the full-length gene, the truncated locus from strain R2866 was cloned into the mobilizable broad host range vector pDD515, creating pDD594, and conjugally transferred into the Rd vapDHi mutant strain. Three-hour survival assays using HBMEC monolayers were performed, and the number of gentamicin-resistant bacteria recovered from the monolayer was an average of 5.6 × 102 CFU/ml for Rd (pDD515), the vector control, versus 1.9 × 102 CFU/ml for Rd vapDHi(pDD594) (n = 3; P < 0.005). Survival in HBMEC by the mutant strain did not increase to wild-type levels, as was the case with the Rd vapDHi mutant transcomplemented with a full-length vapDHi allele on pDD564. Since subunits of the full-length VapDHi and the truncated protein interacted, the plasmid pDD594 carrying the truncated allele was conjugally transferred into wild-type strain Rd to determine whether expression of the small protein would interfere with the function of the wild-type VapDHi protein. The strain was used in three-hour assays of HBMEC monolayers, and it was found that Rd (pDD594) was attenuated in human cell survival as compared to Rd (pDD515), the wild-type strain carrying the vector without an insert. The average number of gentamicin-resistant bacteria recovered from the monolayer for Rd (pDD515), the vector control, was 5.7 × 102 CFU/ml versus 1.8 × 102 CFU/ml for Rd (pDD594) (n = 3; P < 0.005). The in trans expression of a truncated vapDHi allele in the wild-type strain Rd resulted in a dominant-negative effect on survival within HBMEC monolayers. Expression of vapDHi in Escherichia coli DH5α results in cell growth arrest To test the hypothesis that VapDHi constituted the toxin, and that VapXHi encoded the antitoxin portion of a TA locus, both proteins were expressed in an E. coli background. E. coli does not contain a homologue of either protein. Initially, the vapDHi gene, HI0450, was cloned into the pTrcHisA vector (Invitrogen, Carlsbad, CA). This resulted in vapDHi being under the control of the strong Ptrc promoter, which is repressed for the most part until induced by IPTG. This plasmid was designated pDD560. Both the vector control, DH5α (pTrcHisA), and DH5α (pDD560) were grown to mid-log phase in LB broth with 100 μg/ml ampicillin and aliquots were spread on LB agar plates with 100 μg/ml ampicillin and 0.1 mM IPTG. Strain DH5α (pTrcHisA) grew on the plates, but DH5α (pDD560) did not, indicating that induction and overexpression of vapDHi was toxic to E. coli. The putative antitoxin VapXHi, was then cloned into the pTrcHisA vector. No growth disruption occurred in DH5α with the overexpression of VapXHi alone. The vapXHi gene plus the lacIq gene were then subcloned into the broad host range mobilizable plasmid, pDD515, resulting in pDD672. This strategy allowed the DH5α test strain to carry two compatible plasmids, one which encoded the vapDHi gene (pDD560) and the other expressing the vapXHi antitoxin gene (pDD672). Both genes were under the control of a Ptrc promoter and were therefore both repressed until induced by IPTG. When both genes were induced with 0.1 mM IPTG on LB agar plates that contained 100 μg/ml ampicillin and 10 μg/ml chloramphenicol in strain DH5α (pDD560, pDD672), growth was restored. This indicated that the concurrent expression of the vapXHi antitoxin with the vapDHi toxin ameliorated the cell growth arrest observed with expression of vapDHi alone, and that vapXHi was necessary for this rescue. Discussion Mutation of the vapDHi allele in strains Rd and R3001 resulted in attenuation of survival within both HBMEC and NCI-H292 monolayers, suggesting that in H. influenzae, the presence of a functional VapDHi facilitates persistence within epithelial and endothelial cells. Mutants invaded and survived in human cells at ≤ 60% of wild-type levels. Although relatively modest, this level of attenuation has been observed during the mutational analysis of other Haemophilus virulence factors, such as opacity-associated protein A as well as the high molecular weight (HMW) proteins [11,16]. H. influenzae survival within human cells is multifactorial, and our data indicate that VapDHi contributes to this process. However, strain Rd contains three other vap genes (vapAHi, vapBHi, and vapCHi), and it is possible that these Haemophilus vap genes act synergistically, such that multiple mutations may result in a more attenuated survival phenotype. Indeed, a recent study has determined that a chromosomally-located homologue of the VapBC locus acts as a toxin-antitoxin module in the spirochete Leptospira interrogans [17]. It would be interesting to characterize a Haemophilus strain with mutations in all the vap genes. Neither of the vapDHi mutants displayed differences in adherence to the monolayers compared to the parent strain, so the defect occurred after binding and affected the organism's ability to persist inside or between cells. Interestingly, the vapDHi mutants were not attenuated in growth rate when compared to the parent strains, either in bacteriological media or on the surface of human cell monolayers. The observed survival attenuation of the mutants could be transcomplemented with a full-length allele from a clinical isolate, R3001, demonstrating that the phenotype was due to the mutation in vapDHi and not polar effects. A truncated allele from another clinical isolate, R2866, did not transcomplement the Rd vapDHi strain, indicating that the truncated protein was not functional in vivo. RT-PCR analysis confirmed that the full-length vapDHi locus in Rd was transcribed during contact with both epithelial and endothelial cells. VapDHi has also been identified in the soluble fraction of strain Rd grown in bacteriological media [10]. It remains to be seen if the transcription of this locus increases upon contact with human cells. Results of a PCR survey on 59 randomly-chosen strains showed that nearly all of the genetically highly heterologous NTHi commensal isolates surveyed (93%) carried a full-length vapDHi allele on their chromosomes, suggesting that maintenance of a functional vapDHi gene was beneficial to survival in this niche (Table 2). Of the invasive strains isolated from the blood or cerebrospinal fluid, 71% retained a full-length allele. Fifty percent of the isolates from sputum, tracheal aspirates, and the middle ear carried the full-length allele. Finally, all the encapsulated strains tested contained a full-length allele. It must be noted, however, that this analysis was not an exhaustive study, since a limited number of strains were included. Many clinical NTHi strains have previously been shown to express various virulence factors that enhance adherence and invasion into human cells which are not found in the sequenced Rd strain [11,18-20]. The strains identified in this PCR study that lack a functional vapDHi allele probably compensate for its loss with genes that are not found in Rd, and these "extra genes" may well include other TA loci. The calculated molecular mass of the VapDHi protein in Rd is approximately 10 kilodaltons, and small bacterial proteins often form multimers. Full-length VapDHi subunits exhibited strong homodimerization in a LexA-based protein-protein interaction system, and this may indicate that the subunits form higher-order multimers such as homotrimers or homotetramers in vivo. However, the protein encoded by the truncated allele did not homodimerize in the same system, further evidence of its loss of function. Interestingly, the truncated subunit did interact with full-length subunits in heterodimerization assays. Further evidence of this heterodimerization in vivo was that the expression of the truncated subunit in the wild-type strain resulted in a dominant-negative effect on survival within HBMEC monolayers, the levels of which mimicked the attenuation observed with the vapDHi mutation. This was likely due to truncated subunits forming hybrid complexes with full-length subunits and interfering with their structure and/or function, resulting in the observed dominant-negative phenotype. The activities of only a few toxins encoded by TA loci have been elucidated thus far. Two specific targets of plasmid-encoded toxins have been identified: CcdB of the F plasmid and ParE of plasmid RK2 inhibit DNA gyrase, and Kid of plasmid R1 was previously thought to interact with DnaB helicase, but has recently been shown to cleave cellular mRNA [21-23]. The target and function of a toxin from a chromosomally-encoded TA locus (relBE) was determined to be cleavage of mRNA in the ribosomal A site [24]. Strain Rd contains relBE homologues (HI0710 and HI0711) as well as a homologue of higA (host inhibition of growth antidote protein) from plasmid Rts1. The higBA TA locus is unusual in that the toxin gene (higB) exists upstream of the antidote protein (higA). Interestingly, VapAHi of strain Rd is 29% identical and 53% similar to HigA. While the data acquired in this study suggests that VapDHi and VapXHi form a toxin-antitoxin pair, it is unusual to find homologues of VapXHi only in H. influenzae and the gonococcal plasmid. H. influenzae strains R2846 and R2866 both have a truncated vapDHi toxin gene, and possess a vapXHi which is 100% identical to that gene in strain Rd. Their respective genomes can be searched at . Interestingly, there are complete genome sequences available for two isolates of H. pylori, N. meningitidis and Haemophilus somnus, but only one strain of each has a homologue of vapDHi. Thus, several features of the vapDHi/vapXHi gene pair are unusual for a toxin-antitoxin locus. Conclusions Persistence of NTHi is important in the progression of disease caused by this organism. Many investigators have previously reported the discovery of a number of virulence factors associated with adherence, invasion and survival of NTHi inside human cells [1,3,4,16,18,25]. Here we report a locus that is also involved in the pathogenesis of nontypeable H. influenzae. Further studies are required to fully characterize the mechanism of VapDHi function and to define its role in the modulation of NTHi persistence in human cells. Methods Bacterial strains, media and reagents All H. influenzae strains used are listed in Table 2. H. influenzae was grown on chocolate agar (36 g Difco GC medium, 10 g hemoglobin, 10 ml Difco Supplement B (Becton Dickinson, Sparks, MD), 5,000 Units bacitracin per liter) or supplemented BHI (sBHI) broth or agar (37 g brain heart infusion media ± 15 g Bacto agar per liter (Remel, Lenexa, KS) with 10 μg/ml β-NAD, 10 μg/ml heme-histidine, and 5 Units/ml bacitracin). Strains containing the TSTE cassette [26] were grown on media with 15 μg/ml ribostamycin sulfate (CalBioChem, San Diego, CA). Bacteria were diluted for plating with PBS-G (phosphate-buffered saline (pH 7.0) with 0.1% gelatin). Escherichia coli strains used were DH5α, to clone fragments of NTHi DNA; DD12, as the host strain in conjugations; and SU101 or SU202 as the reporter strains for the homodimerization and heterodimerization assays, respectively [14,15]. T4 bacteriophage was obtained from the American Type Culture Collection (ATCC #11303). Antibiotics and other chemicals were from Sigma-Aldrich (St. Louis, MO). Restriction enzymes, deoxyribonucleotides, T4 DNA polymerase, and T4 DNA ligase were from Promega (Madison, WI). Enzymes and other reagents for PCR were from Eppendorf Scientific (Westbury, NY) and Bioline (Canton, MA). Enzymes and reagents for reverse transcriptase PCR (MasterAmp RT-PCR Kit) were from Epicentre Technologies (Madison, WI). Oligonucleotide primers were synthesized by Integrated DNA Technologies (Coralville, IA). DNA sequencing was performed by the University of Missouri DNA Core Facility (Columbia, MO), Davis Sequencing, LLC (Davis, CA), and the DNA Core Facility at Seattle Biomedical Research Institute (Seattle, WA). Plasmids were isolated using the Wizard SV Plus Plasmid Miniprep kit, PCR products and restriction digests were purified using the Wizard PCR Prep kit, and total bacterial RNA was isolated using the SV Total RNA Isolation System (Promega, Madison, WI). Plasmids and conjugations For transcomplementation, the 276 bp R3001 vapDHi allele, along with 269 bp upstream and 227 bp downstream, was PCR-amplified using primers vapHI forward 5'-TATGTCTAGACAGTCGCTTCATAAGC-3' and vapHI reverse 5'-CCATTCTAGATTTGAGGTTAAATATGG-3'. Both primers included a XbaI site (underlined) and amplified Rd genome coordinates 472803 to 473572. The product was sequenced and cloned both into the XbaI site of pBluescript SK+ (creating plasmid pDD562) and into the NheI site of pDD515, a mobilizable broad-host range vector that could be conjugally transferred into and stably maintained in H. influenzae [12], creating plasmid pDD564 (Table 1). Plasmid pDD564 was used for transcomplementation of Rd vapDHi. The same primers were used to PCR amplify the 135 bp truncated vapDHi allele from strain R2866, the product of which was cloned into the NheI site of pDD515, creating pDD594. The insert was sequenced, then the plasmid was conjugally transferred into both Rd vapDHi and wild-type Rd. Conjugations were carried out as previously described [12]. For allelic exchange, the plasmid pDD563 was constructed, which consisted of pDD562 with an interruption of vapDHi by an aminoglycoside phosphotransferase gene (aph (3')II). Specifically, the 2184 bp BamHI fragment from pTSTE [26], which had been rendered blunt-ended with mung bean nuclease, was inserted into the BsaBI site of vapDHi. For the homodimerization assays, the plasmids pDD559 and pDD577 were derived from pSR658 and carried the NTHi strain R3001 vapDHi allele or the NTHi strain R2866 vapDHiallele fused in-frame to the wild-type LexA DNA-binding domain, respectively [27]. For the heterodimerization assays, the plasmid pDD561 derived from pSR659 was constructed, which consisted of the R3001 vapDHi allele fused in-frame to the mutated LexA DNA-binding domain. Mutation of vapDHi The vapDHi genes in Rd and strain R3001 were disrupted by allelic exchange. Briefly, strains Rd and R3001 were made competent using the M-IV media technique [28] and pDD563 linearized with XmnI was used to transform each strain. Transformants were selected on chocolate agar supplemented with 15 μg/ml ribostamycin sulfate (CalBioChem, San Diego, CA). The insertion in vapDHi was confirmed by Southern blotting using a digoxygenin-labeled denatured PCR fragment of vapDHi as the probe. The orientation of the aminoglycoside phosphotransferase cassette was determined by PCR using a primer that originated inside the aph (3')II gene and another that flanked vapDHi. The resistance gene was found to be transcribed in the opposite orientation of vapDHi in both strains. Cell culture Human brain microvascular endothelial cells (HBMECs) were a gift from K. S. Kim [29]. Cells were passaged in collagen-1 coated T-25 flasks and monolayers for invasion assays were grown in 12-well collagen-1 coated BioCoat plates (Becton Dickinson, Bedford, MA). HBMEC media contained 760 ml RPMI 1640 with 25 mM HEPES and 2 mM L-glutamine, 100 ml heat-inactivated fetal calf serum, 10 ml each of 200 mM L-glutamine, 100× MEM non-essential amino acid solution, 100× MEM vitamin solution, 100 mM MEM sodium pyruvate solution (Gibco, Grand Island, NY), and 100 ml heat-inactivated NuSerum V (Becton Dickinson, Bedford, MA) per liter. Media was changed every two days and cells were passaged every 3–5 days. Monolayers were seeded at a density of ~2.0 × 105 cells per well and used 48 to 72 hours after seeding. NCI-H292 human respiratory epithelial cells (ATCC catalogue # CRL-1848) were passaged in collagen-1 coated T-25 flasks and monolayers for invasion assays were grown to confluency in 12-well collagen-1 coated BioCoat plates (Becton Dickinson, Bedford, MA). NCI-H292 media consisted of 870 ml RPMI 1640 medium with 25 mM HEPES and 2 mM L-glutamine, 10 ml of 100 mM MEM sodium pyruvate solution, 10 ml of 7.5% w/v sodium bicarbonate solution (Gibco, Grand Island, NY), 10 ml of 450 mg/ml filter-sterilized glucose solution, and 100 ml heat-inactivated fetal calf serum per liter. As above, media was changed every two days and cells were passaged every 3–5 days. Monolayers were seeded at a density of ~2.5 × 105 cells per well and used 72 to 96 hours after seeding. Invasion and survival assays Gentamicin-resistance invasion and survival assays were performed on HBMEC and NCI-H292 monolayers as previously described [5]. Briefly, the inoculum used was 1.0 – 5.0 × 106 CFU of H. influenzae in a volume of 1 ml per well of a 12-well plate (an MOI of ≤ 10:1). After a 3 or 18 hour incubation in an atmosphere of 5% CO2 at 37°C, each monolayer was extensively washed with Dulbecco's PBS and 1.5 ml of media containing 100 μg/ml gentamicin was added to each well. Following a subsequent one hour incubation in the antibiotic, the wells were again washed extensively, harvested with 1% saponin, diluted in PBS-G and plated on chocolate or sBHI agar for viable intracellular CFU/ml. To quantitate total cell-associated bacteria (both intracellular and adherent), wells were also harvested and plated after the first wash and prior to gentamicin addition. Methods for statistical analysis Statistical analyses were performed using the statistical analysis functions of Microsoft Excel (Microsoft Office 1997). For most comparisons of data, the Student's t-test was used and P-values of <0.05 were considered to indicate statistically significant differences. Reverse transcriptase PCR (RT-PCR) Total RNA was isolated using the SV Total RNA isolation system (Promega, Madison, WI) from the wild type strain Rd recovered from the media of 18-hour invasions of either HBMEC (endothelial) or NCI-H292 (epithelial) monolayers. Standard procedures were used, with the modification that two separate DNAseI incubations were performed instead of the single one recommended with the kit. RT-PCR using the MasterAmp RT-PCR kit was then performed as per the manufacturer's instructions (Epicentre Technologies, Madison, WI). Negative controls of no reverse transcriptase added to the RNA followed by traditional PCR using Biolase DNA polymerase (Bioline, Madison, WI) were used to ensure that both RNA preparations were free of contaminating DNA. The primers for RT-PCR, 450 RT for (5'-CAGGCTTATACAGACATTGG-3') and 450 RT rev (5'-TCGTACCGACTGAGAAATCC-3') amplified a 153 bp internal portion of the vapDHi cDNA. Protein-protein interaction assays The vapDHi alleles from strains R3001 (full-length) and R2866 (truncated) were amplified by PCR and fused in-frame to the LexA DNA-binding domain (DBD) in pSR658, resulting in pDD559 and pDD577, respectively, and used to transform the reporter strain SU101 for homodimerization assays [27]. Briefly, strain SU101 carries a lacZ gene controlled by a wild-type LexA operator site [14]. If a homodimer of two LexA DBD fusions was formed, the complex could bind to the LexA operator region and shut down transcription of lacZ, resulting in diminished levels of beta-galactosidase. The vapDHi allele from strain R3001 was also fused in-frame to the mutated LexA DNA-binding domain in pSR659, creating pDD561. The compatible plasmids pDD561 (full length vapDHi) and pDD577 (truncated vapDHi) were both used to transform the reporter strain SU202 for heterodimerization assays. Strain SU202 [14] also has a lacZ gene controlled by a LexA operator, but this operator site is engineered such that only a mutated LexA DBD subunit (coded on pSR659) can bind to one half-site, while a wild-type LexA DBD subunit (coded on pSR658) can bind to the other half-site. Consequently, only a heterodimer composed of one mutated LexA DBD fusion subunit and one wild-type LexA DBD fusion subunit could bind to this engineered site and decrease transcription of lacZ in SU202. Three independent beta-galactosidase assays were carried out in triplicate as previously described [27]. Author's contributions DAD conceived of the study, carried out the protein-protein interaction and molecular genetics work, and drafted the manuscript. JJ carried out the intracellular survival experiments. ALS supported the study and participated in its design and coordination. All authors read and approved the final manuscript. Acknowledgements The authors thank K.S. Kim for his kind gift of human brain microvascular endothelial cells and D. Nguyen for his technical assistance. This work was supported by NIH grants AI 07276 and AI 44002. Figures and Tables Figure 1 Agarose gel of RT-PCR products. Reverse-transcriptase PCR (RT-PCR) of total RNA isolated from wild-type strain Rd after 18 hours of contact with either HBMEC (human endothelial cell) or NCI-H292 (human epithelial cell) monolayers. Lane A: Molecular weight marker (Promega 1 Kb ladder); lane B: RNA from Rd on HBMEC monolayers; lane C: Negative control for lane B (no reverse transcriptase added prior to PCR); lane D: RNA from Rd on NCI-H292 monolayers; lane E: Negative control for lane D (no reverse transcriptase added prior to PCR). Figure 2 Multiple sequence alignment of truncated VapDHi proteins. Wild type full-length VapDHi protein sequence from strain Rd is included for comparison. R296: middle ear isolate; R3122: blood isolate; R2751: sputum isolate; R2866: blood isolate; R3270: nasopharynx isolate. Table 1 Plasmids and E. coli strains used in this study. Plasmid Description Reference pBluescript Cloning vector, ApR Stratagene pDD515 RSF1010 ori; a mobilizable broad host range vector, CmR 12 pDD559 pSR658 with vapDHi in frame with wild-type LexA DNA-binding domain (DBD) This work pDD560 pTrcHisA with vapDHi This work pDD561 pSR659 with vapDHi in frame with mutant LexA DBD This work pDD562 pBluescript with vapDHi region This work pDD563 pDD652 with an aph(3')II gene interrupting vapDHi This work pDD564 pDD515 with a full-length vapDHi region for transcomplementation This work pDD577 pSR658 with a truncated vapDHi in frame with the wild-type LexA DBD This work pDD594 pDD515 with a truncated vapDHi region for transcomplementation This work pDD672 pDD515 with the vapXHi antitoxin for rescue This work pSR658 Wild-type LexA DBD fusion vector, TcR 15 pSR659 Mutant LexA DBD fusion vector, ApR 15 pTrcHisA Cloning vector, ApR Invitrogen Strain Description Source DD12 Conjugal host strain 12 DH5α Cloning strain Promega SU101 Homodimerization reporter strain 14 SU202 Heterodimerization reporter strain 14 Table 2 H. influenzae strains used in this study. Strain* Description Type vapD Hi allele † Prevalence R538 ATCC #9795 b WT 100% R539 ATCC #9006 a WT R540 ATCC #9007 c WT R541 ATCC #9008 d WT R542 ATCC #8142 e WT R543 ATCC #9833 f WT R652, R3460*, R3539*, R3540*, R3541*, R3542* Rd NTHi WT Strain Anatomical site ‡ Type vapD Hi allele Prevalence C378, C483, C1591, C1607, R1624, R1625, R1627, R1632, R2754, R3254, R3256, R3258, R3259, R3262, R3264, R3265, R3266, R3267, R3268, R3269, R3271, R3273, R3274, R3276. R3277, R3282, R3283, R3285 Nasopharynx NTHi WT 93% (28/30) R3257, R3270 TV 7% R2752, R3001, R3027, R3157, R3543* Sputum/Tracheal Aspirate/Ear NTHi WT 50% (4/8) R296, R2751, R2846, R3151 TV 50% C432, R228, R2777, R3168, R3252, R3278, R3279, R3280, R3330, R3331 Blood/CSF NTHi WT 71% (10/14) R2866, R3122, R3164, R3169 TV 29% *: Asterisk denotes derivative strains, i.e. R3460 is strain R652 carrying the vector pDD515. The marked strains are not included in computing the incidence of the wild-type vapDHi allele versus the truncated allele. Independent strains are not marked with an asterisk. †: Type of vapDHi allele: WT = wild type (91 amino acids); TV = truncated version (45 amino acids). ‡: Site from which strains were isolated. ==== Refs van Schilfgaarde M van Alphen L Eijk P Everts V Dankert J Paracytosis of Haemophilus influenzae through cell layers of NCI-H292 lung epithelial cells Infect Immun 1995 63 4729 4737 7591129 Ketterer MR Shao JQ Hornick DB Buscher B Bandi VK Apicella MA Infection of primary human bronchial epithelial cells by Haemophilus influenzae : macropinocytosis as a mechanism of airway epithelial cell entry Infect Immun 1999 67 4161 4170 10417188 Möller LVM Regelink AG Grasselier H Dankert-Roelse JE Dankert J van Alphen L Multiple Haemophilus influenzae strains and strain variants coexist in the respiratory tract of patients with cystic fibrosis J Infect Dis 1995 172 1388 1392 7594685 van Alphen L Jansen HM Dankert J Virulence factors in the colonization and persistence of bacteria in the airways Am J Respir Crit Care Med 1995 151 2094 2100 7767563 Daines DA Cohn LA Coleman HN Kim KS Smith AL Haemophilus influenzae Rd KW20 has virulence properties J Med Microbiol 2003 52 277 282 12676864 10.1099/jmm.0.05025-0 Ueyama T Kurono Y Shirabe K Takeshita M Mogi G High incidence of Haemophilus influenzae in nasopharyngeal secretions and middle ear effusions as detected by PCR J Clin Microbiol 1995 33 1835 1838 7665655 Rayner MG Zhang Y Gorry MC Chen Y Post JC Ehrlich GD Evidence of bacterial metabolic activity in culture-negative otitis media with effusion JAMA 1998 279 296 299 9450714 10.1001/jama.279.4.296 van Schilfgaarde M Eijk P Regelink A van Ulsen P Everts V Dankert J van Alphen L Haemophilus influenzae localized in epithelial cell layers is shielded from antibiotics and antibody-mediated bactericidal activity Microbial Pathog 1999 26 249 262 10.1006/mpat.1998.0269 Katz ME Strugnell RA Rood JI Molecular characterization of a genomic region associated with virulence in Dichelobacter nododsus Infect Immun 1992 60 4586 4592 1398971 Langen H Takács B Evers S Berndt P Lahm H-W Wipf B Gray C Fountoulakis M Two-dimensional map of the proteome of Haemophilus influenzae Electrophoresis 2000 21 411 429 10675023 10.1002/(SICI)1522-2683(20000101)21:2<411::AID-ELPS411>3.3.CO;2-W Noel GJ Barenkamp SJ St Geme JW IIIHaining WM Mosser DM High-molecular-weight surface-exposed proteins of Haemophilus influenzae mediate binding to macrophages J Infect Dis 1994 169 425 429 8106776 Daines DA Smith AL Design and construction of a Haemophilus influenzae conjugal expression system Gene 2001 281 95 102 11750131 10.1016/S0378-1119(01)00788-0 Dimopoulou ID Russell JE Mohd-Zain Z Herbert R Crook DW Site-specific recombination with the chromosomal tRNA(Leu) gene by the large conjugative Haemophilus resistance plasmid Antimicrob Agents Chemother 2002 46 1602 1603 11959612 10.1128/AAC.46.5.1602-1603.2002 Dmitrova M Younes-Cauet G Oertel-Buchheit P Porte D Schnarr M Granger-Schnarr M A new LexA-based genetic system for monitoring and analyzing protein heterodimerization in Escherichia coli Mol Gen Genet 1998 257 205 212 9491079 10.1007/s004380050640 Daines DA Silver RP Evidence for multimerization of Neu proteins involved in polysialic acid synthesis in Escherichia coli K1 using improved LexA-based vectors J Bacteriol 2000 182 5267 5270 10960115 10.1128/JB.182.18.5267-5270.2000 Prasadarao NV Lysenko E Wass CA Kim KS Weiser JN Opacity-associated protein A contributes to the binding of Haemophilus influenzae to Chang epithelial cells Infect Immun 1999 67 4153 4160 10417187 Zhang YX Guo XK Wu C Bi B Ren SX Wu CF Zhao GP Characterization of a novel toxin-antitoxin module, VapBC, encoded by Leptospira interrogans chromosome Cell Res 2004 14 208 216 15225414 Noel GJ Barenkamp SJ St Geme JW IIIHaining WM Mosser DM High-molecular-weight surface-exposed proteins of Haemophilus influenzae mediate binding to macrophages J Infect Dis 1994 169 425 429 8106776 Hardy GG Tudor SM St Geme JW III Herbert MA, Hood DW, Moxon ER The pathogenesis of disease due to nontypeable Haemophilus influenzae. In Haemophilus influenzae Protocols 2003 Humana Press, Totowa, NJ 1 28 Munson RS JrHarrison A Gillaspy A Ray WC Carson M Armbruster D Gipson J Gipson M Johnson L Lewis L Dyer DW Bakaletz LO Partial analysis of the genomes of two nontypeable Haemophilus influenzae otitis media isolates Infect Immun 2004 72 3002 3010 15102813 10.1128/IAI.72.5.3002-3010.2004 Gerdes K Toxin-antitoxin modules may regulate synthesis of macromolecules during nutritional stress J Bacteriol 2000 182 561 572 10633087 10.1128/JB.182.3.561-572.2000 Zielenkiewicz U Ceglowski P Mechanisms of plasmid stable maintenance with special focus on plasmid addiction systems Acta Biochimica Polonica 2001 48 1003 1023 11995964 Zhang J Zhang Y Ling Z Suzuki M Inouye M Interference of mRNA function by sequence-specific endoribonuclease PemK J Biol Chem 2004 20 20678 20684 10.1074/jbc.M314284200 Pederson K Zavialov AV Pavlov MY Elf J Gerdes K Ehrenberg M The bacterial toxin RelE displays codon-specific cleavage of mRNAs in the ribosomal A site Cell 2003 112 131 140 12526800 10.1016/S0092-8674(02)01248-5 Murphy TF Apicella MA Nontypeable Haemophilus influenzae : a review of clinical aspects, surface antigens, and the human immune response to infection Rev Infect Dis 1987 9 1 15 3547567 Sharetzsky C Edlind TD LiPuma JJ Stull TL A novel approach to insertional mutagenesis of Haemophilus influenzae J Bacteriol 1991 173 517 524 Daines DA Granger-Schnarr M Dimitrova M Silver RP Use of a LexA-based system to identify protein-protein interactions in vivo Methods Enzymol 2002 358 153 161 12474385 10.1016/S0076-6879(02)58087-3 Herriott RM Meyer EM Vogt M Defined non-growth media for stage II development of competence in Haemophilus influenzae J Bacteriol 1970 101 517 524 5308771 Stins MF Badger J Kim KS Bacterial invasion and transcytosis in transfected human brain microvascular endothelial cells Microbial Pathog 2001 30 19 28 10.1006/mpat.2000.0406

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BMC Microbiol. 2004 Jul 26; 4:30

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BMC Microbiol

10.1186/1471-2180-4-30

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==== Front BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-5-241526876510.1186/1471-2202-5-24Research ArticleAttentional influences on functional mapping of speech sounds in human auditory cortex Obleser Jonas [email protected] Thomas [email protected] Carsten [email protected] Department of Psychology, University of Konstanz, Germany2 Department of Linguistics, University of Konstanz, Germany3 Department of Psychiatry and Psychotherapy, School of Medicine, University of Aachen, German2004 21 7 2004 5 24 24 17 2 2004 21 7 2004 Copyright © 2004 Obleser et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background The speech signal contains both information about phonological features such as place of articulation and non-phonological features such as speaker identity. These are different aspects of the 'what'-processing stream (speaker vs. speech content), and here we show that they can be further segregated as they may occur in parallel but within different neural substrates. Subjects listened to two different vowels, each spoken by two different speakers. During one block, they were asked to identify a given vowel irrespectively of the speaker (phonological categorization), while during the other block the speaker had to be identified irrespectively of the vowel (speaker categorization). Auditory evoked fields were recorded using 148-channel magnetoencephalography (MEG), and magnetic source imaging was obtained for 17 subjects. Results During phonological categorization, a vowel-dependent difference of N100m source location perpendicular to the main tonotopic gradient replicated previous findings. In speaker categorization, the relative mapping of vowels remained unchanged but sources were shifted towards more posterior and more superior locations. Conclusions These results imply that the N100m reflects the extraction of abstract invariants from the speech signal. This part of the processing is accomplished in auditory areas anterior to AI, which are part of the auditory 'what' system. This network seems to include spatially separable modules for identifying the phonological information and for associating it with a particular speaker that are activated in synchrony but within different regions, suggesting that the 'what' processing can be more adequately modeled by a stream of parallel stages. The relative activation of the parallel processing stages can be modulated by attentional or task demands. ==== Body Background This study explores attentional modulation within the 'what'-stream of the auditory modality during phoneme processing. Knowledge of speech sound representation in the auditory domain is still sparse. However, parallels to the extensively studied visual modality and also to the somatosensory domain are becoming evident. For example, columnar mapping of several stimulus properties (as known from the visual cortex) has been revealed in human and animal research: acoustic parameters like spectral bandwidth, periodicity, stimulus intensity [1,2] or – for human speech sounds – distance between spectral peaks [3,4] appear to be mapped perpendicularly to the main cochleotopic gradient. Recently, a segregation of a ventral 'what' and a dorsal 'where' stream – as long established in the visual system [5] – has also been proposed for the auditory system. This conclusion was based on neuroanatomical and functional studies in macaques [6-8] and has been substantiated in humans [9,10]. Given these parallels between sensory domains and the increasing preference for complex stimuli along the auditory central pathway, more complex topologies such as language-specific maps in auditory cortex are also plausible, and evidence for individually ordered mapping of speech sounds is growing [11-15] (for speech-specific vocalizations in animals see [8,16]). More specifically, data from our lab imply map dimensions along phonological features which build the basic components of speech sounds: In Obleser et al. [15], responses to DORSAL vowels (which are articulated with the back of the tongue and which exhibit a small distance between spectral peaks, i.e., small F1-F2 distance) were located more posterior in auditory association cortex than responses to CORONAL vowels (which are articulated with the tip of the tongue and which exhibit a large distance between spectral peaks, i.e., larger F1-F2 distance), and a topographical shift between these classes of vowels even when embedded in non-words has been reported [15,17]. Research has long been tackling the question of attention and attentional top-down modulation that may tune cortical neurons and with it functional maps in a context-specific manner: In the visual domain, a top-down influence on receptive fields of areas as basic as VI has been shown [18,19], and in the somatosensory domain Ergenzinger and colleagues reported that drastic changes in functional maps can be experimentally induced even on a thalamic level [20]. The thalamic homuncular representation of a monkey's hand becomes blurred and distorted when top-down modulation from somatosensory cortex is blocked neurochemically within the cortex. These results emphasize the possibility of attention-dependent modulation of maps, a topic exemplified in a somatosensory MEG mapping study by Braun and colleagues [21]: In a somatosensory stimulation with small brushes moving back and forth across the digit tips, subjects either attended the movement of single brushes on single digits and reported the movement direction or they attended and reported the global direction of all brushes on all five digits. Magnetic source imaging of the somatosensory evoked field revealed a typical homuncular representation of the single digits spread along the post central gyrus only in the condition where the focus of attention was on single digits rather than on the hand as a whole. In the latter condition, top-down attentional demands temporarily seemed to blur the single digit mapping. For the developing field of speech sound mapping, top-down influences of attentional demands on functional organization at the different stages in the processing streams have not been sufficiently studied. Nevertheless, it becomes a central issue when the functional architecture of the effortless and robust perception of speech shall be understood. It is common to study speech perception either in passive oddball paradigms [22,23] where the subject's attention is deliberately forced to a movie or to reading a book, or in passive listening conditions where no attentional control is experimentally induced (e.g. [24,25]), or in active target detection tasks where the attention is commonly focused on the phonological content of the speech material [14,15,26]. We analyzed the magnetic N100 (N100m) response to two vowels [o] and [ø], both produced by a male and a female speaker. Subject's attention was either on the vowel or on the speaker difference, in a counterbalanced order. How would a controlled shift of attention from specific phonological features of speech to features of speaker identity affect the speech sound mapping in timing and topography of the brain response? Two concurrent outcomes are conceivable here: First, from the numerous parallels between the auditory and other sensory domains, one might expect a blurring of differences of the phonological map in auditory cortex when features such as the speaker identity rather than phonological differences are attended over minutes. Second, phonological processing could be the default process needed in all speech-listening situations and should therefore activate phonological feature maps irrespectively of attentional demands. We would then expect that the separate mapping of DORSAL and CORONAL vowels described previously [15] is unaffected by an attentional focus on speaker identity. However, a shift of activational patterns as an entity would reveal more about the staging of parallel processing in the flow of the 'what' stream. Results In 21 of 22 subjects, a clear waveform deflection around 100 ms post vowel onset was observed (Fig. 2) in all conditions over both hemispheres and sensor space parameters peak latency and amplitude were obtained. Satisfying and physiologically plausible dipole fits (see methods) in both hemispheres could be obtained in 17 subjects and were subjected to statistical analysis. N100m latency, amplitude and source strength Analysis of the N100m root mean square (RMS) peak latency revealed foremost a main effect of vowel (F1,20 = 44.8, p < .0001, Fig. 2), whereby the DORSAL vowel [o] consistently elicited N100m peaks 5 ms later than the CORONAL vowel [ø]. In sensor space, an enhancement of RMS peak amplitude for the [ø] vowel by 10 fT (Fig. 2) almost attained significance (F1,20 = 4.12, p < .06). However, the effect was significant in source space that is not influenced by varying head-to-sensor positions: The [ø] dipole source strength, an estimate for the amount of massed neuronal activity, was larger for the [ø] vowel than for the [o] by 25 % or 6 nAm (F1,16 = 9.36, p < .01). No hemispheric differences in signal power between vowel categories or tasks were apparent. N100m source location and orientation In agreement with previous findings with a more comprehensive set of vowels [15], the vowel categories [o] and [ø] elicited statistically different centers of activity along the anterior-posterior axis (F1,16 = 7.73, p < .01), that is, the auditory processing in the DORSAL vowel [o] was reflected by a more posterior ECD location (Fig. 3). A difference in source configuration was also evident from a more superior position of the [o] source (F1,16 = 12.28, p < .01), a more vertical orientation (F1,16 = 5.81, p < .05) than the [ø] source, and from an angular difference between the two vowel categories in the sagittal plane (i.e. the [o] source was located more posterior and inferior, F1,16 = 10.91, p < .01) and in the axial plane (i.e. the [o] source was also located more posterior and lateral, F1,16 = 6.82, p < .05, relative to the [ø] source). None of these effects showed an interaction with hemisphere, but data gained further validity as the right-hemispheric sources were all located more posterior (F1,16 = 8.88, p < .01), more inferior (F1,16 = 4.27, p < .06) and were tilted more vertically (F1,16 = 14.29, p < .01) than their left-hemispheric counterpart. Such a difference is to be expected from previously reported N100 asymmetries between cerebral hemispheres [27-30]. The relative mapping of phonological features of the speech signal [14,15] was not affected by the task-induced shifts of attention. However, shifts of subjects' attentional focus from phonological categorization to identification of the speaker's voice shifted vowel sources as a whole to more posterior and superior locations within the supratemporal plane. Statistically, the speaker categorization task produced more superior (F1,16 = 4.72, p < .05) and marginally more posterior (F1,16 = 3.36, p < .10) ECD locations, which was also evident by an angular displacement in the sagittal plane (F1,16 = 4.6, p < .05). The effect seemed to be driven by changes in the left hemisphere but the task × hemisphere interaction never attained significance (all F < 1). When brain responses were analyzed separately for stimuli spoken by male and female speaker, which yielded satisfying dipole solutions only in 12 subjects, the most striking finding was a consistent speaker × task interaction of the dipole location in both the sagittal plane (F1,11 = 10.83, p < .01) and the axial plane (F1,11 = 7.16, p < .03). That is, subjects' attentional focus slightly affected the relative displacement of male and female voice-evoked brain responses: In both the sagittal plane and the axial plane, a significant 4° difference emerged in the phonological categorization task (both p < .05), which vanished in the speaker categorization task. In contrast, as reported above, no such task influence was evident in the relative position of vowel-evoked brain responses. Performance Overall target detection rate was 94.1 %, false alarms occurred in 5.5% of all trials. Responses of the 17 subjects whose brain responses were subjected to magnetic source imaging were analyzed in detail: The phonological categorization task (93.2 ± 3.0 % correct, 4.9 ± 2.2 % false alarms, M ± SEM) and the speaker categorization task (95.0 ± 2.9 % correct, 6.2 ± 3.2 % false alarms) did not differ significantly (one-way repeated measures ANOVAs, all F < 1). Discussion This study was set up to explore potential influences of the attentional focus on the mapping of speech sounds within the auditory cortex. With subject's attention either on the phonological differences or on the speaker difference between vowel stimuli, we mapped the auditory evoked N100m and localized its sources that fitted well with a single dipole per hemisphere. All responses were located in the perisylvian region. Furthermore, the relative distribution of sources indicated an interesting pattern. As hypothesized and expected from previous studies, the fundamental location difference between the sources of the DORSAL vowel [o] source and the CORONAL vowel [ø] [15,17] could be replicated under both attentional conditions. In contrast, the corresponding difference between speaker-dependent sources was subject to task influences. That is, a shift of subjects' attention to a non-phonological acoustic feature, the speaker identity, did not blur the spatial segregation within the speech sound map. In contrast, the [ø] and [o] generators were slightly displaced towards more posterior and more superior locations when subjects focused on speaker identity. In most situations, a listener may automatically extract the phonological invariants from the speech signal in order to access lexical information, for example the meaning of the information inherent in speech. Speaker-dependent features such as pitch and periodicity should not play a crucial role in this phonological decoding process. This is what we mimicked by asking our subjects to detect a certain vowel in a stream of varying speech sounds. However, in cocktail-party-like situations there is the additional demand to attend acoustic properties of certain speech streams or speakers, and we implemented it by asking our subjects to detect a certain voice in a stream of varying speakers. Speaker identification comprises an important but not necessarily orthogonal process to phonological decoding in speech perception: areas in the upper bank of the superior temporal sulcus (STS) have been identified previously [31] to be voice-selective (as opposed to other environmental sounds), and in many situations the selective tracking of one voice amongst others is a prerequisite for decoding the phonological content of this speaker's utterances. The displacement of dipolar sources seen here may mirror the involvement of additional cortical areas, such as the voice-specialized part in the STS [31] or pitch-specialized areas in the primary auditory cortex. An additional STS activation would most likely elicit an inferior shift of the dipole sources during speaker categorization. However, a shift into the opposite direction was obtained. This might indicate that the contribution of the voice-specialized part of the STS around 100 ms post-stimulus onset is small compared to other additional cortical areas, such as pitch-specialized areas in the primary auditory cortex. It is now well-established that a finegrained analysis of the speech signal takes place mainly in anterior parts of the supratemporal gyrus [17,32-34], thereby anterior of primary auditory areas. Consequently, the activity shift towards more posterior sites we observed in the speaker categorization task strongly argues for an additional involvement of these primary auditory areas. Unfortunately, we cannot dissociate speaker identification processes from pitch processing in the current study. However, pitch differences are among the primary cues dissociating male and female voices, and a clear involvement of auditory core areas in pitch processing has been shown in a recent MEG study focusing on pitch detection mechanisms [35]. Conclusions Data presented here suggest that the systematic mapping of speech sounds within the auditory cortex is robust under changing attentional demands and not tied to phonological awareness. However, the general shift of activity when a non-phonological speaker categorization must be accomplished shows that speech sound representations are modulated in their locations in a context-dependent manner. Situational demands obviously influence the differential but time-synchronous involvement of specialized neuronal assemblies that contribute to speech sound decoding in a top-down fashion. Hence, the spectrally high-resolving analysis of the incoming speech stream is performed at the same time but in different locations, i.e. in a different mix of cell assemblies than the analysis of speaker-dependent features (such as pitch, periodicity, or other features inherent to voice quality). Further spatially high-resolution brain imaging studies are needed to quantify as to which extent voice-selective areas in the upper bank of the STS [31] become involved when speaker categorization is accomplished. For the time being, this study increases our understanding of speech sound processing, as it replicates previous findings of an orderly mapping of phonological vowel features and as it shows that changing attentional foci affect the absolute but not the relative distribution of vowel-evoked activity within the auditory cortex. Methods Subjects 22 subjects (11 females, mean age 24.3 ± 4 years, M ± SD) participated in the procedure. All subjects were monolingual native speakers of German. Only right-handers as ascertained by the Edinburgh Handedness Questionnaire [36] were included. Subjects gave written informed consent and were paid €10 for their participation. Experimental design In an auditory target detection task, subjects listened to randomized sequences of four German natural vowel exemplars: The DORSAL rounded vowel [o] in two exemplars, in one spoken by a male voice and in the other by a female voice, and the CORONAL rounded vowel [ø], also produced by both voices (Fig. 1). 200 ms long vowels free of formant transitions were cut out of spoken words, digitized with a 10 kHz sampling rate and faded with 50 ms Gaussian on- and offset ramps. Table 1 summarizes exact pitch and formant frequencies of the four exemplars. Prior to the measurement, individual hearing thresholds were determined for both ears and all four vowel exemplars. Stimuli were presented binaurally with at least 50 dB SL (respective to the vowel exemplar which showed the weakest sensation level, if any differences between exemplars occurred) via a non-magnetic echo-free stimulus delivery system with almost linear frequency characteristic in the critical range of 200–4000 Hz. In a test sequence, subjects repeated vowels aloud and recognized all stimuli correctly, i.e. they distinguished between both vowel categories and voices without difficulty. Binaural loudness was slightly re-adjusted where necessary to ensure perception in the head midline. In the actual measurement, vowel exemplars were presented in two randomized sequences with equal probability and a randomized stimulus onset asynchrony of 1.6 – 2 s. All subjects performed – in a counterbalanced order – two different tasks during these two sequences: In a task A (hereafter called phonological categorization), subjects had to press a button with their right index finger whenever a given vowel ([o] or [ø], counterbalanced across subjects) occurred, irrespective of the speaking voice. In a task B (hereafter called speaker categorization), subjects had to press a button whenever a given voice (the male or the female voice, counterbalanced across subjects) uttered a vowel, irrespective of the uttered vowel category. Fig.1 (lower panel) which clarifies and visualizes the task. That is, in the phonological categorization task, subject's attention was focused on a categorical distinction between speech sounds, [o] or [ø], which closely resembles the tasks applied in most brain imaging studies testing active speech sound processing (e.g. [14,15,37]) – a process ubiquitously taking place when decoding running speech. In contrast, the speaker categorization task was intended to shift subject's attention to more general and more basic acoustic properties of the material [31] presented to accomplish speaker distinction. Data reduction and statistical analyses Data acquisition and analysis, including source modeling, closely followed the procedure described in [15]: Auditory magnetic fields were recorded using a whole head neuromagnetometer (MAGNES 2500, 4D Neuroimaging, San Diego) in a magnetically shielded room (Vaccumschmelze, Hanau, Germany). Epochs of 800 ms duration (including a 200 ms pre-trigger baseline) were recorded with a bandwidth from 0.1 to 200 Hz and a 687.17 Hz sampling rate. If the peak-to-peak amplitude exceeded 3.5 pT in one of the channels or the co-registered EOG signal was larger than 100 μV, epochs were rejected. Button-presses did not affect the auditory evoked field topography in the N100m time range. We analyzed up to 150 artifact-free vowel responses that remained for both vowel categories [o] and [ø] after off-line noise correction, and averaged them separately for vowel category but across speaker voice. Splitting up vowel conditions into male and female speaker sub-conditions was not possible due to a resulting small number of averages. However, we also performed separate averages and analyses of male and female speaker across vowel categories. In any case, the resulting averages thus contained brain responses to two acoustically variant exemplars which makes results more comparable to our previous studies [15,17]. A 20 Hz lowpass filter (Butterworth 12 dB/oct, zero phase shift) was subsequently applied to the averages. The N100m component was defined as the prominent waveform deflection in the time range between 90 and 160 ms (Fig. 2). Isofield contour plots of the magnetic field distribution were visually inspected to ensure that N100m and not P50 m or P200 m were analyzed. N100m peak latency was defined as the sampling point in this latency range by which the first derivative of the Root Mean Square (RMS) amplitude reached its minimum and second derivative was smaller than zero. RMS was calculated across 34 magnetometer channels selected to include the field extrema over the left and the right hemisphere, respectively. Prior to statistical analyses, all brain response latencies were corrected for a constant sound conductance delay of 19 ms in the delivery system. Using the same sets of channels, an equivalent current dipole (ECD) in a spherical volume conductor (fitted to the shape of the regional head surface) was modeled at every sampling point separately for the left and the right hemisphere [38]. The N100m source parameters were determined as the median of 5 successive ECD solutions in the rising slope of the N100m. The resulting ECD solution represents the center of gravity for the massed and synchronized neuronal activity. To be included in this calculation, single ECD solutions had to meet the following criteria: (i) Goodness of fit greater than .90, (ii) ECD location larger than 1.5 cm in medial-lateral direction from the center of the brain and 3–8 cm in superior direction, measured from the connecting line of the pre-auricular points. Statistical analysis of dependent variables N100m peak latency, amplitude and N100m source generator strength, location and orientation focused on 2 × 2 × 2 repeated measures analysis of variance with repeated factors hemisphere (left vs. right), vowel ([o] vs. [ø]) and task (attend phonology vs. attend speaker). As source location displacements do not appear exactly and exclusively along the Cartesian axes of the source space (cf. [21]), we additionally calculated differences in the polar angle Φ and the azimuth angle θ which here describe angular displacements in the sagittal and the axial plane, respectively. Authors' contributions J.O., T.E. and C.E. conceived the experiment and drafted the manuscript. J.O. and C.E. prepared the exact experimental setup. J.O. supervised data acquisition, and performed all data and statistical analyses. All authors read and approved the final manuscript. Acknowledgments Research was supported by German Science Foundation. Sonja Schumacher and Barbara Awiszus helped collect and analyze the data. We thank three anonymous reviewers for their helpful comments on the manuscript. Figures and Tables Figure 1 Upper panel: Illustration of the F1-F2 formant space for the vowel tokens used. Lower panel: Illustration of the stimulation paradigm and of the two tasks which all subjects performed. Attention was either focused on vowel category changes (Task A) or on changes in the voice speaking (Task B). Arrows indicate required button presses. Figure 2 Grand average (N = 21) of root mean squared amplitudes for all conditions over time separately for left (upper panel) and right hemisphere (lower panel). N100m is clearly the most prominent waveform deflection, and the repeatedly reported N100m time lag between coronal vowel [ø] (black) and dorsal vowel [o] (gray) is also obvious. Figure 3 Mean two-dimensional source space locations and orientations separately for the left and the right hemisphere (posterior-anterior on abscissa, inferior-superior on ordinate) are shown. Results of the phonological categorization task are shown in open source symbols, results of the speaker categorization task in filled source symbols. Please note that in both conditions the [ø] source (circle symbols) is more inferior and anterior than the [o] source (diamond symbols). Table 1 Formant Frequency Overview. Pitch (F0), formant frequencies (F1, F2, F3) and -distance (F2-F1) for the vowels used. Vowel voice F0 (Hz) F1 (Hz) F2 (Hz) F3 (Hz) F2-F1 (Hz) [o] male 123 317 516 2601 199 [ø] male 123 318 1357 1980 1039 [o] female 223 390 904 2871 514 [ø] female 223 417 1731 2627 1314 ==== Refs Schreiner CE Read HL Sutter ML Modular organization of frequency integration in primary auditory cortex Annu Rev Neurosci 2000 23 501 529 10845073 10.1146/annurev.neuro.23.1.501 Langner G Sams M Heil P Schulze H Frequency and periodicity are represented in orthogonal maps in the human auditory cortex: evidence from magnetoencephalography J Comp Physiol [A] 1997 181 665 676 9449825 10.1007/s003590050148 Ohl FW Scheich H Orderly cortical representation of vowels based on formant interaction Proc Natl Acad Sci U S A 1997 94 9440 9444 9256501 10.1073/pnas.94.17.9440 Diesch E Luce T Topographic and temporal indices of vowel spectral envelope extraction in the human auditory cortex J Cogn Neurosci 2000 12 878 893 11054929 10.1162/089892900562480 Ungerleider LG Mishkin M Macko KA Object vision and 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Auditory Evoked Magnetic Field Codes Place of Articulation in Timing and Topography around 100 ms Post Syllable Onset Neuroimage 2003 20 1839 1847 14642493 10.1016/j.neuroimage.2003.07.019 Treue S Neural correlates of attention in primate visual cortex Trends Neurosci 2001 24 295 300 11311383 10.1016/S0166-2236(00)01814-2 Engel AK Fries P Singer W Dynamic predictions: oscillations and synchrony in top-down processing Nat Rev Neurosci 2001 2 704 716 11584308 10.1038/35094565 Ergenzinger ER Glasier MM Hahm JO Pons TP Cortically induced thalamic plasticity in the primate somatosensory system Nat Neurosci 1998 1 226 229 10195147 10.1038/673 Braun C Haug M Wiech K Birbaumer N Elbert T Roberts LE Functional Organization of Primary Somatosensory Cortex Depends on the Focus of Attention Neuroimage 2002 17 1451 1458 12414284 10.1006/nimg.2002.1277 Naatanen R The perception of speech sounds by the human brain as reflected by the mismatch negativity (MMN) and its magnetic equivalent (MMNm) 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and functional organization of speech perception Trends Neurosci 2003 26 100 107 12536133 10.1016/S0166-2236(02)00037-1 Eulitz C Obleser J Lahiri A Intra-subject replication of brain magnetic activity during the processing of speech sounds Brain Res Cogn Brain Res 2004 19 82 91 14972361 10.1016/j.cogbrainres.2003.11.004 Krumbholz K Patterson RD Seither-Preisler A Lammertmann C Lutkenhoner B Neuromagnetic evidence for a pitch processing center in Heschl's gyrus Cereb Cortex 2003 13 765 772 12816892 10.1093/cercor/13.7.765 Oldfield RC The assessment and analysis of handedness: the Edinburgh inventory Neuropsychologia 1971 9 97 113 5146491 10.1016/0028-3932(71)90067-4 Poeppel D Phillips C Yellin E Rowley HA Roberts TP Marantz A Processing of vowels in supratemporal auditory cortex Neurosci Lett 1997 221 145 148 9121685 10.1016/S0304-3940(97)13325-0 Sarvas J Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem Phys Med Biol 1987 32 11 22 3823129 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==== Front BMC PharmacolBMC Pharmacology1471-2210BioMed Central London 1471-2210-4-121526523610.1186/1471-2210-4-12Research ArticleA protease activated receptor-2 (PAR-2) activating peptide, tc-LIGRLO-NH2, induces protease release from mast cells: role in TNF degradation Alshurafa Hashem N [email protected] Grant R [email protected] John L [email protected] Morley D [email protected] Befus A [email protected] Harissios [email protected] Glaxo-Heritage Asthma Research Laboratory, Pulmonary Research Group, Department of Medicine, Room 550A HMRC, University of Alberta, Edmonton, AB, Canada, T6G 2S22 Department of Pharmacology & Therapeutics University of Calgary 3330 Hospital Drive NW Calgary AB, Canada T2N 4N12004 20 7 2004 4 12 12 11 11 2003 20 7 2004 Copyright © 2004 Alshurafa et al; licensee BioMed Central Ltd.2004Alshurafa et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Mast cell (MC)-derived serine proteases have been implicated in a variety of inflammatory processes. We have previously shown that rat peritoneal MC (PMC) express mRNA for protease activated receptor 2 (PAR-2), a G-coupled receptor activated by trypsin-like proteases. Recent evidence also suggests that MC-induced inflammation can be mediated through PAR. Therefore, we hypothesized that specific PAR-2 agonist peptides (PAR-2ap) induce protease release from PMC. Results Western blot analysis of PMC supernatants revealed that a PAR-2ap, tc-LIGRLO (10 μM), stimulated the release of rat MC protease (RMCP)-1, RMCP-5 and carboxypeptidase-A. The release was evident by 20 min but further increased up to 8 h. To study the biological effects of protease release we tested supernatants from tc-LIGRLO, tc-OLRGIL (inactive control peptide) and antigen-activated PMC for proteolytic activity by seeding with TNF (150 pg/ml), incubating for 8 h at 37°C, and measuring TNF remaining in the supernatants. Supernatants from tc-LIGRLO-stimulated PMC degraded 44 % of seeded TNF (n = 5). Moreover, this TNF proteolysis was dependent on the concentration of tc-LIGRLO used to stimulate PMC, and was significantly inhibited (94 %) by soybean trypsin inhibitor. Antigen and tc-OLRGIL induced no significant release of such proteolytic activity. Conclusions These data indicate that a PAR-2ap induces the release of proteases from mast cells, which may degrade extracellular cytokines and other substrates thus modulating the inflammatory response. ==== Body Background Protease activated receptor-2 (PAR-2) has been identified on a variety of cell types including eosinophils [1], neutrophils [2], neurons and smooth muscle cells [3]. It can be activated by a variety of serine proteases including MC tryptase [4], pancreatic trypsin [5], and coagulation factors [6] to induce inflammatory, mitogenic and chemotactic functions. Serine proteases cleave PAR-2 at a specific site in the extracellular NH2-terminus unmasking a new NH2-terminus (tethered ligand) and changing the conformation of the receptor to allow the tethered ligand to interact with the activation site on the 2nd extracellular loop of the receptor. Peptides that are similar in sequence to the tethered ligand domains of PAR-2, such as SLIGRL-NH2 (SLI) or tc-LIGRLO-NH2 (tc-LIG) are able to interact directly with the activation site and act as potent agonists [7]. A growing number of studies have identified a role for PAR-2 in inflammation. There is a delayed onset of inflammation in PAR-2 knock out mice [8], and PAR-2-activating peptides (PAR-2ap) stimulate leukocyte rolling, adherence, and recruitment in rat mesenteric postcapillary venules [9]. Furthermore, PAR-2 activation of human airway epithelial cells mediates the release of the eosinophil survival-promoting factor GM-CSF and matrix metalloproteases [10,11]. The ability of serine proteases to activate MC and the observation that MC express PAR-2 [12,13], suggest that PAR-2-induced proinflammatory functions in vivo could be MC-mediated. The administration of PAR-2ap or trypsin into the rat hind paw enhanced vascular permeability and caused edema formation, which can be abolished by repeated pre-treatment with compound 48/80, known to deplete the MC of its granular content [14]. By contrast, Vergnolle et al., 1999 [15] showed that edema induced by injection of PAR-2ap was only slightly reduced in rats pre-treated with compound 48/80, and the pre-treatment of rats with cromolyn, a MC stabilizer, had no effects on PAR-2ap induced inflammation of the paw. These studies showed that the administration of PAR-2ap induces an acute inflammatory response characterized by persistent edema and granulocyte infiltration, but the involvement of MC in these responses requires further investigation. Activation of PAR-2 on the surface of mast cells could act as part of an autocrine and paracrine positive feedback loop through the release of serine proteases that could activate further PAR-2 on mast cells or other neighboring cells. Therefore, we investigated the direct effects of PAR-2ap on the release of serine proteases from purified PMC and the effects of these released proteases on extracellular protein degradation. In particular we studied the release of rat mast cell protease-1 (RMCP-1), RMCP-5 and carboxypeptidase A (CPA). Results The PAR-2ap, tc-LIG, induces release of RMCP-1, RMCP-5 and CPA from PMC To identify proteases released by mast cells following PAR-2ap stimulation we activated PMC with tc-LIG (10 μM), and analyzed the supernatants for various mast cell proteases by western blotting, using antisera against the amino-terminal sequences of RMCP-5 and MC-CPA and an antiserum against RMCP-1 protein. In supernatants from tc-LIG-treated PMC one band for RMCP-1 (30 kDa), two bands for RMCP-5 (34 and 35 kDa), and three bands for CPA (40, 41 and 42 kDa) were detected (Fig. 1A). The PAR-2ap tc-LIG induced most of the protease release in the first 20 min. However, proteases accumulated in the conditioned media up to 8 hr (Fig 1B). The release of all three proteases was dose-dependent and was detectable in supernatants of PMC stimulated with tc-LIG at concentrations 0.1 μM and higher (Fig 1C). PMC activation with 48/80 (0.5 mg/ml) induced the release of all three proteases in similar levels to 0.5 μM of tc-LIG (Fig. 1C). Figure 1 Release of RMCP-1, -5 and CPA from PMC following activation with tc-LIG (PAR-2ap), compound 48/80 and Ag. (A) Supernatants from tc-LIG (10 μM), Ag (10 We/mL) and sham-treated (spon) mast cells were concentrated (10 ×) and Western blot analysis preformed for CPA, RMCP-1 and RMCP-5. Left panel shows Coomassie blue staining of the same gel and right panel Western blot with normal rabbit serum as a negative control. (B) Release of RMCP-1, RMCP-5 and CPA following 20 min and 8 h activation of PMC with tc-LIG (10 μM). (C) Dose response for the release of RMCP-1, -5 and CPA by tc-LIG-stimulated or compound 48/80-stimulated PMC. In all cases representative blots from three experiments with similar results are shown. Nippostrongylus brasiliensis antigen (Nippo Ag) (10 We/ml) induced no detectable release of any of the three proteases studied (Fig 1A). Nippo Ag-activated cells under these conditions released 10 ± 2% of β-hex; level similar with that released by 0.1 μM of tc-LIG (12 ± 2%), which however, was associated with protease release (Fig 1C). This amount of β-hex release was the highest we could obtain following PMC activation with Nippo Ag, under the conditions used in our experiments. PAR-2ap-induced proteolytic activity released from mast cells degrades TNF On further comparing Fcε RI with tc-LIG-induced PMC activation we noted that Fcε RI-mediated activation induced TNF release while tc-LIG-mediated activation did not induce significant TNF release following PMC activation for up to 8 hr (Fig 2). One possible hypothesis to explain this effect was that TNF released by tc-LIG activated PMC was degraded by some of the proteases we showed to be released from mast cells at the same time. Therefore, we examined the ability of supernatants of PAR-2ap-activated PMC to degrade extracellular proteins. We used a bioassay for released protease activity, employing TNF as the cytokine to be degraded. PMC were incubated with no activators (sham treatment), tc-LIG (10 μM), tc-OLR (10 μM) or compound 48/80 (0.5 μg/ml) for 20 min or 8 hr at 37°C and the supernatants were collected. These supernatants or media were then seeded with 150 pg/ml of rat recombinant TNF and incubated for an additional 8 hr. At the end of the incubation TNF was measured by ELISA and the proteolytic activity was calculated as % degraded TNF (as discussed in the methods section). Proteolytic activity in the supernatants of sham-treated cells was subtracted from that in the supernatants of activated PMC. Supernatants from sham-treated PMC showed significant loss of seeded TNF (17 ± 7 % at 20 min and 22 ± 5 % at 8 hr) as compared to media. At both 20 min and 8 hr of treatment supernatants from tc-LIG-treated MC (10-0.1 μM) showed a greater loss of seeded TNF compared to supernatants of sham-treated MC (p < 0.05), suggesting tc-LIG-mediated activation induced the release of proteolytic activity. Proteolytic activity, following subtraction of spontaneous proteolytic activity released, was 44 ± 5 % at 8 hr and 30 ± 4 % at 20 min following PMC activation with 10 μM of tc-LIG (Fig. 3A and 3B respectively). Supernatants from tc-OLR- or Nippo Ag-treated cells showed no significant loss of TNF over that which occurred in sham-treated cells (Fig. 3A and 3B). Figure 2 TNF release from PMC (1 × 106 cells) after 8 hr incubation with PAR-2ap (tc-LIG, 10 μM), PAR-2cp (tc-OLR, 10 μM) and Ag (10 We/ml). (Mean ± SEM, n = 4). Star indicates statistically significant difference from spontaneous (p < 0.05, n = 4). Figure 3 PAR-2ap, PAR-2cp compound 48/80 and Ag-mediated release of proteolytic activity from mast cells. (A) Supernatants from PMC treated with tc-LIG (10 μM), tc-OLR (10 μM), or Ag (10 We/mL) for 8 hr were incubated with 150 pg/mL of TNF. Proteolytic activity was calculated as % degraded TNF according to the formula given in Methods section. Values in the graph indicate proteolytic activity after the subtraction of activity released by sham-treated cells (17 ± 7 %). (B) TNF-degrading proteolytic activity released from PMC by various doses of tc-LIG (20 min). Values in the graph indicate proteolytic activity after the subtraction of activity released by sham-treated cells (22 ± 5 %). (C) TNF-degrading proteolytic activity released by SLI (PAR2-ap, 40 μM), LSI (PAR2-cp, 40 μM) and tc-LIG (10 μM) treated PMC (20 min). Values in the graph indicate proteolytic activity after the subtraction of activity released by sham-treated cells (23 ± 7 %). Values are shown as "mean ± SEM" (n = 3–5). Star indicates statistically significant difference from spontaneous (p < 0.05). We also examined the ability of another PAR-2ap, SLI and its PAR-2cp, LSI, to release proteolytic activity from PMC (Fig. 3C). A small but significant increase in proteolytic activity over spontaneous release was induced by SLI (40 μM, 7 ± 1 %, p < 0.05). However, net SLI-mediated proteolytic activity released was not significantly different than that released by the inactive control peptide, LSI (40 μM, 7 ± 5 %). To study whether tc-LIG-mediated TNF proteolytic activity was a result of serine protease activity, the supernatants were mixed with the broad spectrum serine protease inhibitor, SBTI (1 mg/ml) before seeding with TNF. SBTI inhibited TNF loss from the supernatants of tc-LIG (10 μM) stimulated PMC by 82% (Fig. 4), confirming that tc-LIG-induced loss of TNF was by the proteolytic activity of serine proteases. Figure 4 Effect of SBTI on the proteolytic activity in supernatants of tc-LIG-stimulated mast cells. Supernatants from PMC stimulated by tc-LIG, tc-OLR, and Ag for 8 hr were incubated with or without SBTI (1 mg/mL) before 150 pg/mL of TNF was added and % degradation calculated. Values indicate proteolytic activity after the subtraction of spontaneous release (17 % ± 7). Star indicates statistically significant difference from spontaneous (p < 0.05, n = 4–5). (Mean ± SEM). Discussion We have previously shown that PMC express PAR-2 mRNA, that can be regulated by cytokines and PAR-2ap [13]. We have also shown that RMCP-1, RMCP-5 and CPA are stored in PMC and are the most prominent proteases produced by PMC [16]. In the present study we demonstrated that tc-LIG, a PAR-2ap, induces the release of RMCP-1, RMCP-5 and CPA from mast cells. Compound 48/80 induced comparable release of proteases form PMC, but FcεRI-mediated activation did not. We also showed that these, and possibly other proteases released at the same time from PMC, are capable of degrading TNF. In this study, we provided the first direct evidence for serine protease release from PMC measured by Western blot analysis of the supernatants, in addition to proteolytic activity assays. The sizes of released RMCP-1 (~30 kDa), RMCP-5 (2 close bands, ~34 kDa) and CPA (3 close bands, ~41 kDa) are similar to the sizes of the stored forms of these proteases that we published previously [16]. The different bands for RMCP-5 and CPA are likely due to differential glycosylation, as has been shown before [16]. PAR-2 ap have been shown to release proteases from gastric pepsinogen secreting cells [17] and from epithelial cells [10]. A recent report showed release of tryptase from human colon mast cells following PAR-2ap-mediated activation [18]. In that case the concentration of PAR-2ap needed was higher than in our experiments and the effect was similar with FcεRI-mediated activation, while in our experiments proteases were released by PAR-2ap but not with Nippo Ag. Previous studies have shown that activation of MC to release protease activity may be induced by a variety of agents both in vivo and in vitro. The release of RMCP-2 by rat mucosal mast cells has been reported to be induced by antigen challenge in parasitic infections, and during anaphylaxis [19-21]. The release of RMCP-2 mouse counterpart, MMCP-1, can be increased during parasitic infections [22]. Furthermore, rat CPA can be released by 48/80, Ca2+ ionophore and antigen activation of PMC [23]. In our experiments FcεRI-mediated PMC stimulation did not release detectable levels of RMCP-1, RMCP-5 or CPA, or any proteolytic activity with the ability to degrade TNF. It is possible that FcεRI-mediated activation induces low levels of protease release which is undetectable by Western blotting. Furthermore, the lack of demonstrable proteolytic activity in the supernatants does not necessarily indicate that proteases are not released since it may be due to an FcεRI-mediated simultaneous release of protease inhibitors stored in the MC. Indeed, MC produce and release secretory leukocyte protease inhibitor (a chymase inhibitor) and latexin (a CPA inhibitor) [23,24]. Finally, FcεRI-mediated activation may selectively release proteases different from the ones released by 48/80 or PAR-2ap. Given that RMCP-1 and RMCP-5 are present in the supernatants of tc-LIG-stimulated MC, it is likely that these proteases are involved in the TNF degradation. However, antibodies to RMCP-1 inhibited TNF degradation by supernatants of sham treated cells but did not affect the additional degradation of supernatants from tc-LIG activated MC (unpublished observation). We cannot rule out that PAR-2ap activated PMC release other proteases, including the tryptases RMCP-6 and RMCP-7 [25], which may contribute to TNF-degradation. It is also interesting that chymases and CPA, which are released in parallel, have synergistic effects [26]. Other proteases from leukocytes are known to be able to degrade TNF. These include cathepsin G [27], neutrophil elastase [28,29] and by proteases released from bacteria [39]. The same proteases can also degrade other cytokines, such as endothelin [31,32], lymphotoxin [27] and IFNγ [30]. Our data further suggest that MC may regulate TNF function by releasing proteases that can directly degrade this cytokine. Given that both TNF and serine proteases are stored and released from MC, our present findings suggest an important mechanism by which MC may regulate TNF function in vivo. It may be that such proteolytic activity directed against TNF, and possibly other cytokines, is an important anti-inflammatory function for mast cell serine proteases. The expression of PAR-2 by mast cells and the involvement of MC in PAR-2-mediated inflammation has been controversial. In vitro, MC tryptase can stimulate histamine release by human tonsillar [33] and guinea pig [34] MC, but not from foreskin mast cells. The tryptase inhibitor APC366 inhibits IgE-dependent MC activation, and also inhibits calcium ionophore-induced histamine release [33]. Tryptase-mediated bronchoconstriction in sheep is histamine mediated [35], indicating that tryptase induces lung MC activation. PAR-2 has been identified on human [12] and rat [13] mast cells. MC have been implicated in rat paw oedema caused by PAR-2ap or trypsin administration [14], but other reports failed to confirm this observation [15]. Taken together these reports strongly suggest a role for tryptase and possibly PAR-2 in MC activation. In a previous study we have shown that only one of two PAR-2ap (tc-LIG) activates β-hex release from PMC [13]. The other peptide, SLI, although it is a potent and selective PAR-2 agonist, was unable to induce release of β-hex as had also been shown before [36], although others showed that higher concentrations of SLI can induce the release of β-hex and to a greater extend histamine from rat PMC [37]. However, our study was the only one to use tc-LIG. In this study again only tc-LIG induced the release of proteases from PMC. SLI induced slightly increased release of proteolytic activity compared to sham treated cells but this release was not significantly different than the release induced by the control peptide LSI. We have previously shown that SLI is sensitive to proteases and its effects on MC increases in the presence of amastatin, an aminopeptidase inhibitor [13]. In contrast tc-LIG possesses a trans-cinnamoyl group, which acts to stabilize the peptide and prevent its degradation by aminopeptidases. It is unlikely that the different sensitivity to proteases can explain fully the difference between the effects of the two PAR-2ap peptides. It is also unlikely that the trans-cinnamoyl modification on tc-LIG is solely responsible for tc-LIG-mediated activation of MC, because it is also present on the reverse sequence peptide tc-OLR, but does not have the same effects with tc-LIG on protease release, as shown in this study, or in the release of β-hex, as we showed before. Compound 48/80 along with other cationic compounds can activate MC by directly interacting with a pertussis toxin sensitive component [38]. Our previous work suggested that tc-LIG may activate MC through a 48/80-like mechanism, but appears to also possess a second mechanism of signalling that is distinct from that of 48/80 [13]. Thus, we cannot rule out the possibility that tc-LIG-mediated release of proteolytic activity may be mediated in part through a 48/80-like mechanism. Indeed, 48/80 induced similar levels of proteolytic activity and protease release to tc-LIG. Recently, a new receptor activated by the PAR-2 activating peptide tc-LIGRLO-NH2 has been identified pharmacologically in murine vascular smooth muscle [39]. In that case, tc-LIG induced vasoconstriction, while the other PAR-2 activating peptide, SLI, did not have similar effects. The structure or the exact function of this receptor is not known. In our case also tc-LIG had a significant effect on protease release from mast cells while SLI had a very small effect. These data suggest that mast cells may express the same receptor as the one identified pharmacologically in smooth muscle cells. Conclusions Our study provides evidence that a PAR-2ap, tc-LIG, activates MC to release proteases and proteolytic activity that could potentially have both pro- and anti-inflammatory functions. We further showed that these proteases may degrade extracellular proteins and affect the inflammatory environment in areas of mast cell activation. Although the presence and function of PAR-2 on MC is still controversial, our findings indicate that PAR-2 may be part of an autocrine loop. PAR-2 activation leads to the release of serine proteases which in turn may further activate more PAR-2 receptors on mast cells and also on other cells. Methods Reagents Compound 48/80, 4-methylumbelliferyl-N-acetyl-β-D-glucosaminide (β-hexosaminidase (β-hex) substrate) and soybean trypsin inhibitor (SBTI) were purchased from Sigma Chemical Co. (St. Louis, MO). PAR-2ap and PAR-2 control peptides (PAR-2cp) were synthesized by the Peptide Synthesis Facility, Faculty of Medicine, University of Calgary. These peptides were determined to be ≥ 95 % pure by mass spectrometry and HPLC. Polyclonal RMCP-5 and CPA antibodies were produced and characterized as described previously [16]. Briefly, RMCP-5 (15 amino acids) and CPA (12 amino acids) NH2-terminal sequences were synthesized at Zymogenetics Inc, Seattle, WA, and used to immunize rabbits to develop specific polyclonal anti-protease antibodies. Professor H. Miller, Edinburgh, Scotland, kindly provided rabbit antibody to RMCP-1. Animal sensitization Outbred male Sprague-Dawley rats (weight 250–500 g) were purchased from Charles River Canada Inc., (St. Constant, Quebec). Rats were maintained in an isolation room with filter-topped cages to minimize unwanted infections. For the experiments where MC were activated through their IgE receptor, rats were sensitized to Nippostrongylus brasiliensis, by a single subcutaneous injection of 3000 third-stage larvae in 0.5 mL of saline as described previously [40]. The experimental protocol was approved by the University of Alberta Animal Care Committee in accordance with the guidelines of the Canadian Council on Animal Care. Harvesting and enrichment of peritoneal mast cells Fifteen mL of ice-cold Hepes-buffered (10 mM, pH 7.3) Tyrodes buffer supplemented with 0.1% BSA was injected into the peritoneal cavity of each rat for the isolation of PMC. MC in peritoneal lavage fluids were enriched by centrifugation through a discontinuous density gradient of Percoll, as described previously [41]. Recovered MC were >95 % pure. Cell viability was >97 %. Mast cell activation After isolation and enrichment, PMC were rested in RPMI (Invitrogen, Burlington, Ontario) supplemented with 5% FBS for 2 hr at 37°C. After incubation, the cells were washed twice by centrifugation (150 g) and resuspended in RPMI at 1 × 106 cells/mL. Cells were placed in 1.5 mL Eppendorf tubes or in 48 well plates, incubated at 37°C for 10 min, and then the same volume of pre-warmed (37°C) PAR-2ap or controls in complete RPMI were added, to give a final cell concentration of 0.5 × 106 cells/mL. The cells were incubated for different times (20 min to 8 hr) depending on the experiment. To measure spontaneous release of mediators by PMC, cells were mixed with media alone. As positive controls, either compound 48/80 (0.5 μg/mL) or Nippostrongylus brasiliensis Antigen (1–100 worm equivalents (WE)/mL [40]) were mixed with cells under the same conditions. After incubation, tubes were placed on ice for 10 min and then centrifuged (150 g) to separate supernatant from cells. The supernatants were collected in tubes and the same volume of fresh media was added to the pellets, which were then resuspended. Cell viability was assessed at different times. Cell pellets and supernatants were stored at -70°C until assayed for their content of cytokines or proteolytic activity. SDS-PAGE and western blot analysis Supernatants were concentrated (10×) using Centricon (YM-10) centrifugal filter devices (Millipore, Bedford, MA). For Western blot analysis, proteins were transferred electrophoretically (25 V, 35 min) to a polyvinylidene difluoride (PVDF) membrane (Bio-Rad Laboratories, Mississauga, ON) using the Semi-Dry Trans Blot System. The membranes were blocked in Tris-buffered saline containing 0.02% Tween, 5% w/vmilk (Bio-Rad Laboratories) and 5 % v/vgoat serum (Invitrogen) for 1 hr. The membranes were probed with 1/1000 dilution of anti-RMCP-1, 1/600 anti-CPA, 1/5000 anti-RMCP-5 and then incubated with donkey anti-rabbit IgG HRP-conjugated antibody (1:5000). Protein bands were detected by enhanced chemiluminescence using ECL Western blotting detection system (Amersham Pharmacia Biotech, Quebec, Canada). β-hexosaminidase (β-hex) assay β-hex was measured in the supernatants and cell pellets, as described [42]. Results are expressed as β-hex released as a percent of total β-hex (pellet + supernatant). Values shown have been corrected for the spontaneous β-hex release. Proteolytic activity assay and protease inhibition assay To measure release of proteolytic activity, supernatants from stimulated PMC (containing secreted TNF) were transferred to a 96-well plate. After 2 min incubation, exogenous TNF or medium was added to the supernatants and mixed to give a final concentration of 150 pg/ml. Plates were incubated at 37°C for 8 hr and then TNF content was measured by ELISA. Percent TNF proteolysis was calculated by the following formula: % TNF degraded = 1 - (TNF recovered / (rat recombinant TNF seeded + measured TNF release) × 100) For protease inhibition experiments SBTI (1 mg/ml) was added to the supernatants before seeding with TNF. The supernatants were then processed as above and used to measure TNF degradation. TNF measurements Supernatants from activated PMC were analysed for TNF using a rat TNF ELISA kit (Endogen, Woburn, MA), according to manufacturer's instruction. The sensitivity of the TNF assay was < 10 pg/ml. To exclude the possibility that proteases contained in PMC supernatants interfere with ELISA determination of TNF we incubated the TNF antibody coated wells with PMC supernatants washed them and then added specified amounts of TNF for determination. Pre-incubation with PMC supernatants did not affect the ability to measure TNF, indicating that the proteases in PMC supernatants do not degrade the antibodies of the assay. Statistics All values are given as mean ± standard error of mean (SEM) for the numbers of experiments noted and statistical analyses were performed using the Student's t-test and ANOVA. Abbreviations β-hex β-hexosaminidase CPA carboxypeptidase-A LSI LSIGRL-NH2 (PAR2-cp) MC mast cell PAR protease-activated receptor PAR-ap protease-activated receptor-agonist peptide PAR-cp protease-activated receptor-control peptide PMC peritoneal mast cells RMCP-1,5 rat mast cell protease-1, 5 SBTI soybean trypsin inhibitor SLI SLIGRL-NH2 (PAR2-ap) tc-LIG trans-cinnamoyl-LIGRLO-NH2 tc-OLR trans-cinnamoyl-OLRGIL-NH2 Nippo Ag Nippostrongylus brasiliensis Antigen WE worm equivalent. Authors' contributions HNA carried out the majority of the experiments presented and drafted the manuscript. This work was part of his MSc thesis. GRS assisted with mast cell isolation and some of the activation experiments and participated in the experimental design. JLW participated in the design of the study. MDH participated in the design of the study. ADB participated in the design of the study, supervised the work shown and made substantial contributions in manuscript preparation. HV participated in the design of the study and contributed in the preparation of the final manuscript. All authors read and approved the final manuscript. Acknowledgments The technical advice of Dr. Paige Lacy is greatly appreciated. Ms. Lynelle Watt provided skilled secretarial support. The authors acknowledge the financial support from the Canadian Institutes of Health Research (CIHR) (grants to ADB, MDH and HV), the Canadian Lung Association/Medical Research Council of Canada/GlaxoWellcome (fellowship to GRS), and the Alberta Lung Association (studentship award to HNA). ==== Refs Miike S McWilliam AS Kita H Trypsin induces activation and inflammatory mediator release from human eosinophils through protease-activated receptor-2 J Immunol 2001 167 6615 6622 11714832 Howells GL Macey MG Chinni C Hou L Fox MT Harriott P Stone SR Proteinase-activated receptor-2: expression by human neutrophils J Cell Sci 1997 110 881 887 9133675 D'Andrea MR Derian CK Leturcq D Baker SM Brunmark A Ling P Darrow AL Santulli RJ Brass LF Andrade-Gordon P Characterization of protease-activated receptor-2 immunoreactivity in normal human tissues J Histochem Cytochem 1998 46 157 164 9446822 Mirza H Schmidt VA Derian CK Jesty J Bahou WF Mitogenic responses mediated through the proteinase-activated receptor- 2 are induced by expressed forms of mast cell alpha- or beta-tryptases Blood 1997 90 3914 3922 9354658 Nguyen TD Moody MW Steinhoff M Okolo C Koh DS Bunnett NW Trypsin activates pancreatic duct epithelial cell ion channels through proteinase-activated receptor-2 J Clin Invest 1999 103 261 269 9916138 Camerer E Huang W Coughlin SR Tissue factor- and factor X-dependent activation of protease-activated receptor 2 by factor VIIa Proc Natl Acad Sci USA 2000 97 5255 5260 10805786 10.1073/pnas.97.10.5255 Vu TK Wheaton VI Hung DT Charo E Coughlin SR Domains specifying thrombin-receptor interaction Nature 1991 353 674 677 1717851 10.1038/353674a0 Lindner JR Kahn ML Coughlin SR Sambrano GR Schauble E Bernstein D Foy D Hafezi-Moghadam A Ley K Delayed onset of inflammation in protease-activated receptor-2- deficient mice J Immunol 2000 165 6504 6510 11086091 Vergnolle N Proteinase-activated receptor-2-activating peptides induce leukocyte rolling, adhesion, and extravasation in vivo J Immunol 1999 163 5064 5069 10528212 Vliagoftis H Schwingshackl A Milne CD Duszyk M Hollenberg MD Wallace JL Befus AD Moqbel R Proteinase-activated receptor-2-mediated matrix metalloproteinase-9 release from airway epithelial cells J Allergy Clin Immunol 2000 106 537 545 10984375 10.1067/mai.2000.109058 Vliagoftis H Befus AD Hollenberg MD Moqbel R Airway epithelial cells release eosinophil survival-promoting factors (GM-CSF) after stimulation of proteinase-activated receptor 2 J Allergy Clin Immunol 2001 107 679 685 11295658 10.1067/mai.2001.114245 D'Andrea MR Rogahn CJ Andrade-Gordon P Localization of protease-activated receptors-1 and -2 in human mast cells: indications for an amplified mast cell degranulation cascade Biotech Histochem 2000 75 85 90 10941511 Stenton GR Nohara O Déry RE Vliagoftis H Gilchrist M Johri A Wallace JL Hollenberg MD Moqbel R Befus AD Proteinase-activated receptor (PAR)-1 and -2 agonists induce mediator release from mast cells by pathways distinct from PAR-1 and PAR-2 J Pharmacol Exp Ther 2002 302 466 474 12130703 10.1124/jpet.302.2.466 Kawabata A Kuroda R Minami T Kataoka K Taneda M Increased vascular permeability by a specific agonist of protease- activated receptor-2 in rat hindpaw Br J Pharmacol 1998 125 419 422 9806321 Vergnolle N Hollenberg MD Sharkey KA Wallace JL Characterization of the inflammatory response to proteinase-activated receptor-2 (PAR2)-activating peptides in the rat paw Br J Pharmacol 1999 127 1083 1090 10455252 Befus AD Chin B Pick J Evans S Osborn S Forstrom J Proteinases of rat mast cells. Peritoneal but not intestinal mucosal mast cells express mast cell proteinase 5 and carboxypeptidase A J Immunol 1995 155 4406 4411 7594601 Fiorucci S Distrutti E Federici B Palazzetti B Baldoni M Morelli A Cirino G PAR-2 modulates pepsinogen secretion from gastric-isolated chief cells Am J Physiol Gastrointest Liver Physiol 2003 285 G611 G620 12748062 He SH He YS Xie H Activation of human colon mast cells through proteinase activated receptor-2 World J Gastroenterol 2004 10 327 331 14760751 Cummins AG Kenny AL Duncombe VM Bolin TD Davis AE The effect of protein deficiency on systemic release of rat mucosal mast cell protease II during Nippostrongylus brasiliensis infection and following systemic anaphylaxis Immunol Cell Biol 1987 65 357 363 3479391 Miller HR Wright SH Knight PA Thornton EM A novel function for transforming growth factor-beta1: upregulation of the expression and the IgE-independent extracellular release of a mucosal mast cell granule-specific beta-chymase, mouse mast cell protease-1 Blood 1999 93 3473 3486 10233900 Scudamore CL Thornton EM McMillan L Newlands GF Miller HR Release of the mucosal mast cell granule chymase, rat mast cell protease-II, during anaphylaxis is associated with the rapid development of paracellular permeability to macromolecules in rat jejunum J Exp Med 1995 182 1871 1881 7500033 10.1084/jem.182.6.1871 McLauchlan PE Roberts HC Loxton NJ Wastling JM Newlands GF Chappell LH Mucosal mast cell responses and release of mast cell protease-I in infections of mice with Hymenolepis diminuta and H. microstoma: modulation by cyclosporin A Parasite Immunol 1999 21 151 161 10205795 10.1046/j.1365-3024.1999.00214.x Uratani Y Takiguchi-Hayashi K Miyasaka N Sato M Jin M Arimatsu Y Latexin, a carboxypeptidase A inhibitor, is expressed in rat peritoneal mast cells and is associated with granular structures distinct from secretory granules and lysosomes Biochem J 2000 346 817 826 10698712 10.1042/0264-6021:3460817 Pemberton AD Huntley JF Miller HR Differential inhibition of mast cell chymases by secretory leukocyte protease inhibitor Biochim Biophys Acta 1998 1379 29 34 9468329 10.1016/S0304-4165(97)00078-0 Lützelschwab C Pejler G Aveskogh M Hellman L Secretory granule proteases in rat mast cells. 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Sequential degradation of apolipoprotein B by granule chymase and carboxypeptidase A J Biol Chem 1986 261 16067 16072 3536921 Scuderi P Nez PA Duerr ML Wong BJ Valdez CM Cathepsin-G and leukocyte elastase inactivate human tumor necrosis factor and lymphotoxin Cell Immunol 1991 135 299 313 2036673 van Kessel KP van Strijp JA Verhoef J Inactivation of recombinant human tumor necrosis factor-alpha by proteolytic enzymes released from stimulated human neutrophils J Immunol 1991 147 3862 3868 1940372 Nortier J Vandenabeele P Noel E Bosseloir Y Goldman M Deschodt-Lanckman M Enzymatic degradation of tumor necrosis factor by activated human neutrophils: role of elastase Life Sci 1991 49 1879 1886 1745103 10.1016/0024-3205(91)90288-M Parmely M Gale A Clabaugh M Horvat R Zhou WW Proteolytic inactivation of cytokines by Pseudomonas aeruginosa Infect Immun 1990 58 3009 3014 2117578 Sessa WC Kaw S Zembowicz A Anggard E Hecker M Vane JR Human polymorphonuclear leukocytes generate and degrade endothelin-1 by two distinct neutral proteases J Cardiovasc Pharmacol 1991 17 S34 S38 1725374 Patrignani P Del Maschio A Bazzoni G Daffonchio L Hernandez A Modica R Montesanti L Volpi D Patrono C Dejana E Inactivation of endothelin by polymorphonuclear leukocyte-derived lytic enzymes Blood 1991 78 2715 2720 1824263 He S Gaca MD Walls AF A role for tryptase in the activation of human mast cells: modulation of histamine release by tryptase and inhibitors of tryptase J Pharmacol Exp Ther 1998 286 289 297 9655871 He S Walls AF Human mast cell tryptase: a stimulus of microvascular leakage and mast cell activation Eur J Pharmacol 1997 328 89 97 9203574 10.1016/S0014-2999(97)83033-6 Molinari JF Scuri M Moore WR Clark J Tanaka R Abraham WM Inhaled tryptase causes bronchoconstriction in sheep via histamine release Am J Respir Crit Care Med 1996 154 649 653 8810600 Nishikawa H Kawabata A Kuroda R Nishida M Kawai K Characterization of protease-activated receptors in rat peritoneal mast cells Jpn J Pharmacol 2000 82 74 77 10874593 10.1254/jjp.82.74 Dugina TN Kiseleva EV Glusa E Strukova SM Activation of mast cells induced by agonists of proteinase-activated receptors under normal conditions and during acute inflammation in rats Eur J Pharmacol 2003 471 141 147 12818702 10.1016/S0014-2999(03)01752-7 Befus AD Mowat C Gilchrist M Hu J Solomon S Bateman A Neutrophil defensins induce histamine secretion from mast cells: mechanisms of action J Immunol 1999 163 947 953 10395691 McGuire JJ Dai J Andrade-Gordon P Triggle CR Hollenberg MD Proteinase-activated receptor-2 (PAR2): vascular effects of a PAR2-derived activating peptide via a receptor different than PAR2 J Pharmacol Exp Ther 2002 303 985 992 12438518 10.1124/jpet.102.040352 Befus AD Johnston N Bienenstock J Nippostrongylus brasiliensis: mast cells and histamine levels in tissues of infected and normal rats Exp Parasitol 1979 48 1 8 456459 10.1016/0014-4894(79)90048-1 Bissonnette EY Befus AD Inhibition of mast cell-mediated cytotoxicity by IFN-alpha/beta and – gamma J Immunol 1990 145 3385 3390 1700008 Schwartz LB Austen KF Enzymes of the mast cell granule J Invest Dermato 1980 74 349 10.1111/1523-1747.ep12543620

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==== Front BMC CancerBMC Cancer1471-2407BioMed Central London 1471-2407-4-401527293310.1186/1471-2407-4-40Research ArticleStimulation of MMP-11 (stromelysin-3) expression in mouse fibroblasts by cytokines, collagen and co-culture with human breast cancer cell lines Selvey Saxon [email protected] Larisa M [email protected] Erik W [email protected] Klaus I [email protected] Michael G [email protected] Lyn R [email protected] Genomics Research Center, School of Heath Science, Griffith University, Gold Coast, Australia2 Stem Cell and Tissue Repair Laboratory, Institute of Molecular and Cell Biology, Singapore3 VBCRC Invasion and Metastasis Unit, St. Vincent's Institute of Medical Research and Department of Surgery, University of Melbourne, Melbourne, Australia4 John Curtin School of Medical Research, Australian National University, Canberra, Australia5 Faculty of Health Science and Medicine, Bond University, Gold Coast, Australia2004 25 7 2004 4 40 40 28 3 2004 25 7 2004 Copyright © 2004 Selvey et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Matrix metalloproteinases (MMPs) are central to degradation of the extracellular matrix and basement membrane during both normal and carcinogenic tissue remodeling. MT1-MMP (MMP-14) and stromelysin-3 (MMP-11) are two members of the MMP family of proteolytic enzymes that have been specifically implicated in breast cancer progression. Expressed in stromal fibroblasts adjacent to epithelial tumour cells, the mechanism of MT1-MMP and MMP-11 induction remains unknown. Methods To investigate possible mechanisms of induction, we examined the effects of a number of plausible regulatory agents and treatments that may physiologically influence MMP expression during tumour progression. Thus NIH3T3 and primary mouse embryonic fibroblasts (MEFs) were: a) treated with the cytokines IL-1β, IL-2, IL-6, IL-8 and TGF-β for 3, 6, 12, 24, and 48 hours; b) grown on collagens I, IV and V; c) treated with fibronectin, con-A and matrigel; and d) co-cultured with a range of HBC (human breast cancer) cell lines of varied invasive and metastatic potential. Results Competitive quantitative RT-PCR indicated that MMP-11 expression was stimulated to a level greater than 100%, by 48 hour treatments of IL-1β, IL-2, TGF-β, fibronectin and collagen V. No other substantial changes in expression of MMP-11 or MT1-MMP in either tested fibroblast culture, under any treatment conditions, were observed. Conclusion We have demonstrated significant MMP-11 stimulation in mouse fibroblasts using cytokines, matrix constituents and HBC cell lines, and also some inhibition of MT1-MMP. Our data suggest that the regulation of these genes in the complex stromal-epithelial interactions that occur in human breast carcinoma, is influenced by several mechanisms. ==== Body Background Integral to both normal and pathological tissue remodeling, the matrix metalloproteinase (MMP) family of proteolytic enzymes collectively degrades laminin, collagen, gelatin, and other protein components of the extracellular matrix [1,2]. MMP expression allows degradation of the basement membrane, an event essential to the process of tumour metastasis [3]. The induction of specific MMPs such as MT1-MMP and MMP-11 have been shown to correlate with tumour invasiveness and resultant metastasis in several human cancers, including breast carcinoma [4-7]. Thus, gene expression analysis of these two MMPs may have clinical application in breast cancer diagnosis, management and therapy. Little is known about the mechanisms underlying MT1-MMP and MMP-11 induction. Previous studies have revealed induction of MT1-MMP in human fibroblasts grown on interstitial collagen (collagen I), which is abundant around tumours and during wound healing [8]. Furthermore, MMP-11 has been shown to be induced by retinoic acid [9]. This study aimed to investigate possible regulatory agents and treatments inducing MT1-MMP and/or MMP-11 expression. Specifically, MMP induction was measured in NIH3T3 and primary mouse embryonic fibroblasts (MEFs) subjected to each of the following potentially regulatory agents: collagen I, IV and V, concanavalin-A, fibronectin, and several exogenous cytokines. As induction by such agents could be transitory, treatment times were varied from 3, 6, 12, 24 and 48 hours, and were immediately followed by RNA extraction. As collagen I has been reported to induce MT1-MMP expression, detection of such induction by quantitative RT-PCR would provide an ideal basis for testing further regulatory agents. Collagen IV and V appeared to be ideal candidates for further analysis of MMP induction as they are present in the basement membrane and would therefore be released specifically during early tumour invasion and metastasis, which coincides with MT1-MMP and MMP-11 expression in vivo. Type IV collagen has been shown to mediate pro-MMP-2 activation in HT1080 cells, a human fibrosarcoma cell line, without inducing either a transcriptional modulation of MMP-2 or MT1-MMP expression nor any alteration of MT1-MMP protein synthesis or processing [10]. Concanavalin-A is a plant lectin reported to stimulate activation of MMP-2 [11,12]. Fibronectin encourages cell adhesion and HT1080 cells cultured on fibronectin have exhibited elevated levels of MT1-MMP protein [13-15]. We investigated the potential role of the following cytokines, interleukin (IL)-1β, IL-2, IL-6, IL-8 and transforming growth factor (TGF)-β in MT1-MMP and MMP-11 regulation. All of these cytokines are reportedly released by neoplastic cells and are capable of affecting transcription in fibroblasts [16-19]. IL-2 is also released by tumour infiltrating lymphocytes (TILs) and IL-6 is released by tumour associated macrophages (TAMs), TILs and fibroblasts [16,20-22]. Moreover, MT1-MMP and MMP-11 have both been reported to be induced in human fibroblasts treated with conditioned medium from MDA-MB-231, a highly invasive mammary tumour cell line [23]. Thus, we investigated the induction of MT1-MMP and MMP-11 in NIH3T3 and MEFs co-cultured with a range of HBC (human breast cancer) cell lines of varied invasive and metastatic potential. This allowed direct in situ interaction of MMPs, growth factors and other potentially regulatory agents between the fibroblasts and HBC cells. Methods Cell lines NIH3T3 culture was obtained from the ATCC, while MEF cultures were constructed at the John Curtin School of Medical Research, Australian National University. Briefly, embryos were removed from 13-day pregnant mice. Following removal of the liver and head, the remaining tissue was placed in PBS (1× pH 7.4, Sigma-Aldrich). Tissue was homogenised using an 18 guage syringe (Terumo) in PBS, plated in 25 cm2 tissue culture flasks (Costar) DMEM (Dulbecco's modified Eagles medium) with 10% FCS (Invitrogen) and allowed to attach for 6–12 hrs at 37°C, 5% CO2 prior to use. HBC cell lines were originally obtained from the ATCC and maintained by The Lombardi Cancer Center. ML-20 cells were derived from The Lombardi Cancer Center MCF-7 cells by clonal selection after transfection with a CMV-driven expression plasmid encoding bacterial β-galactosidase. Collagen coats and fibronectin treatment Collagen coats and fibronectin treatments were prepared on non-coated tissue culture plates as follows: prior to seeding, triplicate sets of 2 ml 24 well plates (Costar) were overlaid with 150 μl of collagen I, IV and V (Sigma, 1 mg/ml) solutions. For the fibronectin treatments, 15 μg of fibronectin (Promega), equivalent to 5 μg/cm2 of flask area were added to each well in 2 ml 24 well plates (Costar). The wells were then incubated at 37°C for 1 hour to allow the collagen coats and fibronectin to set in the wells. Excess solution was aspirated from all wells before seeding with 105 cells from either NIH3T3, or MEF cultures in 1 ml DMEM - 10% fetal calf serum (Invitrogen). Treated cultures were then incubated at two time-points, 24 hours and 48 hours at 37°C, 5% CO2, before undergoing RNA extraction. Con-A treatment Non-coated 2 ml 24 well tissue culture plates (Costar) were seeded with either NIH3T3 or MEF at 105 cells in 1 mL Dulbecco's modified Eagles medium (DMEM) - 10% fetal calf serum (Invitrogen). Treatments consisted of 25 μg Con-A added to the 1 ml of DMEM in each well. Treated cultures were then incubated at two time-points, 24 hours and 48 hours at 37°C, 5% CO2, before undergoing RNA extraction. Cytokine treatments 2 ml 24 well plates (Costar) were seeded with 105 cells from either NIH3T3 or primary mouse fibroblast cultures (passage 5) in 1 mL Dulbecco's modified Eagles medium (DMEM) - 10% fetal calf serum (Invitrogen). Cultures were incubated at 37°C, 5% CO2 for 24 hours prior to cytokine treatment, thus allowing complete attachment. All treatments were separate and consisted of final solutions of 100 units/ml IL-1β, IL-2, IL-6, 0.1 units/ml of IL-8 and 1 ng/ml pan-TGF-β (Sigma-Aldrich) in cultures grown in triplicate. Co-cultures 30 mm 6 well plates (Costar) were seeded with 106 cells of either NIH3T3 or MEFs (passage 5), which were each allowed to attach overnight in 1 mL Dulbecco's modified Eagles medium (DMEM) - 10% fetal calf serum (Invitrogen). The DMEM was then aspirated and 24 mm Transwell Porous Cell Culture Inserts (Costar) were seeded with 105 cells of either MDA-MB-231, MDA-MB-435, MDA-MB-436, MCF-7s or ML-20 epithelial tumour cultures in 2 ml of RPMI medium (Trace Biosystems) - 10% fetal calf serum. Use of the porous inserts meant that co-cultures were not in direct physical contact, but shared growth media at all times. These HBC cultures exhibit different levels of invasive and metastatic potential (see Table 1). Each co-culture then underwent 48 hour incubation at 37°C, 5% CO2, before RNA extraction. RNA extraction and quantification Total RNA was extracted from the fibroblasts using an RNeasy Mini Kit (QIAGEN) as previously described [24]. Total RNA was then quantified at OD260 using a QuantaGene spectrophotometer (The Australian Chromatography Company, Sydney, NSW, Australia). Oligonucleotide primers All 3' primers were labeled with the TET dye phosphoramidite and were commercially obtained (Applied Biosystems, Brisbane, QLD, Australia). Oligonucleotides for the target genes, MT1-MMP and MMP-11, were generated using Amplify 2.1 analysis software in conjunction with published cDNA sequences [6,25]. The oligonucleotide sequences for the housekeeping gene 18S rRNA were supplied by Ambion. All sequences utilised are outlined in Table 2. Constructing cDNA competitive templates cDNA competitive templates for the target genes, MT1-MMP and MMP-11 were made by PCR amplification using purpose built 47-mer oligonucleotides as described [26]. The 47-mers consisted of a 23 bp 5' primer complementary to target sequence either 28 bp, for MT1-MMP, or 20 bp, for MMP-11, further 5' than the remaining 24 bp of the 47-mer, which consisted of the normal 5' primer sequence for PCR. Hence, a PCR product generated from these 5' 47-mers in combination with the normal 3' primers, produced fragments shorter than when using the normal 5' primer, as these deletions were left out during sequence extension complementary to the 47-mer. Both 5' and 3' ends of the competitive template remain complementary to normal primers and therefore undergo further complementary-copying during additional PCR in a manner identical to the target sequences, except that the missing nucleotide fragments remain absent. (Table 2) Constructing mRNA competitive templates The cDNA competitive templates generated by PCR with the 47-mers described above were each ligated into pGem-T Easy Vectors (Promega, Annandale, NSW, Australia). JM109 (Promega) competent cells were then transformed and grown overnight. Plasmid DNA was extracted using a Magic Miniprep kit (Promega) and linearised by nuclease digest. Mimic RNA was generated from the "mimic" plasmids by transcription of the insert with T7 RNA polymerase (Promega). The post-transcription mixture then underwent RQ1 DNase (Promega) digestion; the remaining RNA was then purified using QIAGEN RNeasy Minikit, RNA competitive template was then resuspended in DEPC-treated H2O and stored at -70°C. To ensure that amplification would not occur as a result of residual DNA contamination, control samples were run in triplicate without the reverse transcriptase thermal cycle. No amplification products were visualised on resultant agarose gels. RT-PCR Single tube, one step RT-PCR was performed as previously described [24] for all samples using an ABI 480 thermal cycler in 25 μl volume containing: 24 ng total RNA, 1–2 μl of RNA competitive template, being equivalent to 24 ng of control total RNA, 200 mM dNTPs (Promega), 5× RT-PCR buffer [300 mM Tris-HCl, pH 8.3; 2.5 mM DTT; 250 mM KCl; 0.5% Triton X-100 (Evergreen Scientific); 30 μm EDTA; 7.5 mM MgCl2], 0.4 μM each primer, 1 unit Taq polymerase (Perkin Elmer ABI), 1.2 units AMV reverse transcriptase (Promega) and 900 ng tRNA (Boehringer Mannheim)]. Thermal cycling was as follows: 30 mins 50°C (reverse transcription), then 2 mins 95°C, followed by 35 cycles of 30 sec 95°C, 30 sec 65°C, 45 sec 72°C, followed by a final incubation of 5 mins at 72°C. GeneScan 3' primers for MT1-MMP, MMP-11 and 18S rRNA were labeled with TET fluorescent dye, thus allowing RT-PCR samples to be analysed by capillary electrophoresis using a 310 Genetic Analyzer with GeneScan software (ABI) as described previously [24]. Using laser technology to excite the fluorescently labeled primers, samples were sized to within one base pair and the amount of amplification product of any given size, determined by fluorescence peak area. Ratio calculations The GeneScan values for fluorescence peak area of the target gene transcripts and the control transcripts produced the target:control ratio. All graphed values represent averages from triplicate samples and were adjusted to produce a control value of 1.00. These target:control ratio values were used to plot time series graphs of all combined data for each gene. Data on the time series graphs was then normalised to 18S rRNA expression, as measured in the same respective cultures. The 0.5× control and 2.0× control are the resultant target:control ratios produced using half and double the amount of total RNA used in the control sample, respectively. Results Induction treatments MT1-MMP: 3, 6, 12 and 24 hour treatments Competitive quantitative RT-PCR using capillary electrophoresis and GeneScan analysis revealed no substantial changes in expression of MT1-MMP in either NIH3T3, or MEF cultures under any treatment conditions carried out over 3, 6, 12, and 24 hours (Tables 3 and 4). Although as indicated in Table 3 there are some minor initial variations, when the normalised to 18S rRNA no substantial changes are observed. The data presented are derived from GeneScan values for fluorescence peak area of the target gene transcripts and the control transcripts, thus producing the target:control ratio. All values represent averages from samples run in triplicate and have been adjusted to produce a control value of 1.00. Results for these time periods display only minor variations and generally fall between 0.5× control and 2.0× control expression levels. MT1-MMP: 48 hour treatments Induction Initial analyses of the 48 hour IL-1β, IL-2 and fibronectin treatments indicated consistently basal MT1-MMP expression levels (Table 5). Yet data for these treatments do indicate induction when normalized to the unexpectedly low level of 18S rRNA expression (Tables 7 and 8). However, 18S rRNA expression is monitored primarily to double check for substantial anomalies in mRNA quantification when positive results are found, rather than as a precise means of normalization for results which appear consistent. Furthermore, competitive RT-PCR of MT1-MMP may be considered more accurate than non-competitive differential RT-PCR of 18S rRNA. Initial competitive RT-PCR of MT1-MMP expression did produce consistently unchanged results for these treatments, while expression of 18S rRNA was found to display a relatively high level of variation in this treatment group. Specifically, the level of 18S rRNA expression exhibited in the control was high, therefore causing all other treatments to appear relatively low, which in turn caused an artificial elevation of all treatment data values during normalization. Hence, a consistent group result for competitive RT-PCR of MT1-MMP should not be discarded in favour of a single set of unusually efficient differential RT-PCRs of 18S rRNA control samples. In our opinion, MT1-MMP expression should be considered unaffected by IL-1β, IL-2 and fibronectin treatments during this time schedule. Inhibition Results indicated substantial inhibition of MT1-MMP expression by collagen I and collagen IV (Tables 5 and 6). Expression values for these treatments remained well below the 0.5× control expression level even after normalization (Tables 7 and 8), despite this procedure raising the values in a way that may have been to some degree artificial, as described above. MMP-11: 3, 6, 12 and 24 hour treatments Competitive quantitative RT-PCR using capillary electrophoresis and GeneScan analysis revealed no substantial changes in expression of MMP-11 in either fibroblast culture under any treatment conditions carried out over 3, 6, 12 and 24 hours (Tables 3, 4 and 5). Results for these time periods display only minor variations and generally fall between 0.5× control and 2.0× control expression levels. The use of an internal competitor to obtain increased specificity of target:template detection demonstrate data for these time periods and is highly consistent. Such consistency within groups of treatments carried out simultaneously (i.e: over an individual time schedule) further suggests that the minor variations from control expression levels which are present, may be due to amplification anomalies rather than individual differences in gene expression caused by specific treatments. MMP-11: 48 hour treatment At the 48 hour time point, MMP-11 expression appeared to be stimulated at least 2 fold by IL-1β, TGF-β, fibronectin and collagen V. Results indicate induction of MMP-11 by these agents independently of normalization to 18S rRNA expression (Tables 7 and 8), which further enhanced these expression values. IL-2 and IL-6 appeared to produce a very mild induction of MMP-11 without normalization to 18S rRNA, and only appeared to produce a greater than 2 fold induction when undergoing such normalization. The data suggesting that MMP-11 expression is induced by IL-1β, TGF-β, fibronectin and collagen V is highly persuasive given that these are the only data that demonstrate considerable within-group variation. The standard deviation for the 48 hour MMP-11 treatments was 47.6 while the second greatest standard deviation value was 40.5 for the 24 hour MMP-11 treatments. The average for all other MMP-11 groups was 17.3. This increase over the average variation is suggestive of differences in initial transcript levels for individual treatments. Furthermore, the fact that MT1-MMP expression levels (non-normalised) remained consistently normal for this treatment, effectively acting as an additional unregulated control, lends further credit to the likelihood of specific regulation of MMP-11 after 48 hours of treatment. There are also mild trends toward up regulation for the IL-2 and IL-6 treated samples. Co-culture with HBC cell lines Initial results demonstrate no induction or inhibition of either MT1-MMP or MMP-11 in mouse fibroblasts by co-culture with any of the HBC cell lines (Table 9). Results are highly consistent and all treatment values fall between 0.5× control and 2.0× control. Moreover, minor variations from control expression values observed for each co-culture are almost identical, between the two target genes. Hence, the mild expression elevations seen with ML-20 and MDA-MB-436 for both MT1-MMP and MMP-11 are in our opinion, artifactual. Discussion This study investigated the effects of a number of possible regulatory agents on the expression of two members of the MMP gene family for their role in human breast cancer metastases. NIH3T3 and MEF cultures were treated with cytokines, collagens, fibronectin, Con-A or matrigel, and co-cultured with various HBC cell lines. The expression of MT1-MMP and MMP-11 was then determined by competitive RT-PCR using capillary electrophoresis and GeneScan technology. Only MMP-11 expression at 48 hours was affected by treatment with IL-1β, TGF-β, fibronectin and collagen V, in the tested fibroblasts cultures. During tumour progression, the release of exogenous cytokines by neoplastic cells and immunological cells may cause a stimulatory effect on adjacent stromal fibroblasts, resulting in matrix metalloproteinase (MMP) induction. Past studies indicate that some MMPs undergo regulatory changes under the influence of cytokines [18,27]. Another recent study reported the induction of MMP-11 expression in primary cultured human fibroblasts over a 48 hour period by IL-6, IGF-2, EGF and PDGR-BB, but not by IL-1β or TNF-β [28]. Similar to the MT1-MMP results presented in the present study, MT1-MMP gene expression was reportedly not affected by insulin-like growth factor (IGF)-2, epidermal growth factor (EGF), platelet derived growth factor (PDGR)-BB, IL-6, TNF-β or IL-1β [28]. Any influence exerted on MT1-MMP by cytokines may be of particular interest because of its specific interaction with MMP-2, an MMP with a broad substrate specificity. Hence, further analysis of candidate stimulatory agents is required to elucidate possible mechanisms by which MT1-MMP expression is stimulated. Our study did however, find evidence for an inhibitory effect from both collagen I and collagen IV on MT1-MMP expression. Curiously, there is evidence suggesting that collagen I actually stimulates MT1-MMP mRNA production in fibroblasts and activates MMP-2 in a variety of cells over a longer time period [8,29]. However, past studies on rat smooth-muscle cell culture support findings that collagen IV exhibits MT1-MMP inhibitory effects [12]. Despite reports of MDA-MB-231 conditioned media inducing MT1-MMP expression [23] in human fibroblasts, co-culture between MEFs and MDA-MB-231, did not produce induction of either MT1-MMP or MMP-11 in this study. This finding is likely to be due to species-specificity, since mouse mammary stromal cells are not capable of supporting normal human mammary reorganization in vivo [30]. Although the co-cultures carried out here did not allow cell-cell contact, the previous study also involved no cell-cell contact, but rather treatment with pre-conditioned media (application of isolated medium from one cell population following in vitro expansion to an another isolated cell population). Another possible explanation is that the basal level of expression of MT1-MMP and MMP-11 in cultured murine fibroblasts is elevated, thus rendering them refractory to exogenous regulatory agents. We are currently investigating this possibility. Conclusion This study examined the effects of a number of regulatory agents and treatments on the induction of MMP-11 and MT1-MMP gene expression, factors that may influence breast cancer tumour progression. Stimulation of MMP-11 was demonstrated after treatments by both cytokines (IL-1β, TGF-β) and by matrix constituents (collagen IV and fibronectin), whilst MT1-MMP was inhibited by matrix constituents (collagen I and fibronectin). The results presented suggest that several mechanisms may be involved in MMP-11 and MT1-MMP regulation as part of the complex epithelial-stromal interactions that occur within human breast carcinomas. Competing interests None declared. Authors contributions S.S. performed the molecular genetic and in vitro studies, designed PCR primers and drafted the manuscript. L.M.H. contributed to the manuscript design and finalisation. E.W.T. contributed toward the design of the study. K.I.M. contributed toward the design and performance of competitive PCRs. M.G.I. participated in the conception and design of the study. L.R.G. participated in the conception and design of the study and its coordination. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements S.S. was supported by an Australian Postgraduate Study Award. This work was in part funded by the Kathleen Cunningham Breast Cancer Foundation. The authors wish to thank Dr Rod Lea for assistance with statistical analyses. Figures and Tables Table 1 Invasive and metastatic potential of HBC cell lines [31]. Cell Line Estrogen Receptor Status Vimentin Expression Invasiveness MDA-MB-231 - + +++ MDA-MB-435 - + ++ MDA-MB-436 - + ++ MCF-7 ++ - + NCI-adr - + ++ MCF-7stv + - + ML-20 + - + Table 2 Oligonucleode primer sequences. Gene 5' sequence 3' sequence Standard amplicon Competitor sequence (standard 5' primer plus) Competitor amplicon MT1-MMP gACAgTACACCCTTTgATggTgAA gATCgTTAgAATgTTCCAggCCTA 197 bp CAgGCCCCAATATTggAggggAT 169 (-28 bp) MMP-11 CCTTCCAggATgCTgAgggCTAT ATgACAgCATggTCgTTCCTACAA 330 bp TCTCTACTggAggTTTgATCCCgT 310 (-20 bp) 18s rRNA TCAAGAACgAAAgTCggAgg ggACATCTAAgggCATCACA 490 bp N/A N/A Table 3 MT1-MMP, MMP-11 and 18S rRNA Target:Control ratios for 3, 6 and 12 hour treatments of NIH3T3 cells. Treatment Target:Control Ratio 3 Hours 6 Hours 12 Hours MT1-MMP ± SEM MMP-11 ± SEM 18S rRNA ± SEM MT1-MMP ± SEM MMP-11 ± SEM 18S rRNA ± SEM MT1-MMP ± SEM MMP-11 ± SEM 18S rRNA ± SEM Control 1.00 ± 0.08 1.00 ± 0.10 1.00 ± 0.04 1.00 ± 0.08 1.00 ± 0.10 1.00 ± 0.04 1.00 ± 0.08 1.00 ± 0.08 1.00 ± 0.02 TGF-β 0.49 ± 0.08 1.27 ± 0.06 1.27 ± 0.08 1.21 ± 0.09 0.82 ± 0.08 1.53 ± 0.10 - 0.74 ± 0.10 1.22 ± 0.04 IL-1β 0.63 ± 0.06 0.96 ± 0.09 0.89 ± 0.04 1.25 ± 0.04 0.69 ± 0.02 1.09 ± 0.04 1.86 ± 0.02 0.87 ± 0.04 0.98 ± 0.02 IL-2 0.61 ± 0.14 0.96 ± 0.13 0.89 ± 0.05 0.63 ± 0.04 0.74 ± 0.02 1.16 ± 0.03 1.39 ± 0.04 0.72 ± 0.06 0.87 ± 0.02 IL-6 0.55 ± 0.02 0.88 ± 0.02 0.89 ± 0.04 0.90 ± 0.02 0.78 ± 0.04 1.03 ± 0.02 1.77 ± 0.04 0.66 ± 0.02 1.33 ± 0.04 IL-8 0.42 ± 0.02 1.04 ± 0.02 0.87 ± 0.02 0.87 ± 0.02 0.69 ± 0.02 1.06 ± 0.02 1.97 ± 0.04 0.70 ± 0.02 0.89 ± 0.02 Table 4 MT1-MMP, MMP-11 and 18S rRNA Target:Control ratios for 3, 6 and 12 hour treatments of MEF cells. Treatment Target:Control Ratio 3 Hours 6 Hours 12 Hours MT1-MMP ± SEM MMP-11 ± SEM 18S rRNA ± SEM MT1-MMP ± SEM MMP-11 ± SEM 18S rRNA ± SEM MT1-MMP ± SEM MMP-11 ± SEM 18S rRNA ± SEM Control 1.00 ± 0.06 1.00 ± 0.14 1.00 ± 0.04 1.00 ± 0.08 1.00 ± 0.10 1.00 ± 0.04 1.00 ± 0.08 1.00 ± 0.08 1.00 ± 0.02 TGF-β 0.52 ± 0.06 1.14 ± 0.08 1.35 ± 0.14 0.96 ± 0.08 1.14 ± 0.06 1.35 ± 0.16 0.80 ± 0.04 0.95 ± 0.08 1.34 ± 0.12 IL-1β 0.58 ± 0.06 0.74 ± 0.04 1.10 ± 0.10 1.12 ± 0.10 0.74 ± 0.06 1.24 ± 0.08 0.70 ± 0.04 0.75 ± 0.08 1.10 ± 0.08 IL-2 0.75 ± 0.14 0.90 ± 0.08 0.94 ± 0.06 0.59 ± 0.02 0.68 ± 0.05 0.95 ± 0.06 0.70 ± 0.04 0.75 ± 0.04 0.90 ± 0.02 IL-6 0.56 ± 0.02 0.90 ± 0.02 0.75 ± 0.06 0.85 ± 0.05 0.78 ± 0.08 1.15 ± 0.07 0.64 ± 0.02 0.74 ± 0.06 1.20 ± 0.02 IL-8 0.45 ± 0.03 0.96 ± 0.04 0.90 ± 0.02 0.75 ± 0.05 0.74 ± 0.05 1.00 ± 0.08 0.92 ± 0.04 0.76 ± 0.03 0.92 ± 0.02 Table 5 MT1-MMP, MMP-11 and 18S rRNA Target:Control ratios for 24 and 48 hour treatments of NIH3T3 cells. Treatment Target:Control Ratio 24 Hour 48 Hour MT1-MMP ± SEM MMP-11 ± SEM 18S rRNA ± SEM MT1-MMP ± SEM MMP-11 ± SEM 18S rRNA ± SEM Control 1.00 ± 0.02 1.00 ± 0.02 1.00 ± 0.02 1.00 ± 0.08 1.00 ± 0.04 1.00 ± 0.05 Collagen I 1.00 ± 0.08 1.35 ± 0.10 0.90 ± 0.08 0.59 ± 0.06 1.27 ± 0.08 0.96 ± 0.04 Collagen IV 0.60 ± 0.06 0.68 ± 0.08 0.58 ± 0.06 0.35 ± 0.10 0.71 ± 0.10 0.60 ± 0.05 Collagen V 1.10 ± 0.10 1.65 ± 0.08 0.60 ± 0.06 0.96 ± .004 1.91 ± 0.08 0.56 ± 0.08 Con-A 1.00 ± 0.14 1.35 ± 0.12 0.78 ± 0.08 0.82 ± 0.10 1.41 ± 0.14 0.80 ± 0.02 TGF-β 0.95 ± 0.06 0.56 ± 0.06 0.60 ± 0.02 0.70 ± 0.12 1.67 ± 0.14 0.56 ± 0.10 Fibronectin 1.18 ± 0.12 0.72 ± 0.08 0.58 ± 0.02 0.95 ± 0.32 2.16 ± 0.24 0.52 ± 0.01 IL-1β 1.14 ± 0.02 0.78 ± 0.04 0.55 ± 0.02 1.09 ± 0.18 2.20 ± 0.24 0.48 ± 0.02 IL-2 1.33 ± 0.20 0.70 ± 0.08 0.50 ± 0.02 1.01 ± 0.02 1.57 ± 0.16 0.48 ± 0.02 IL-6 0.80 ± 0.04 0.74 ± 0.04 0.65 ± 0.02 0.75 ± 0.06 1.27 ± 0.08 0.68 ± 0.04 Table 6 MT1-MMP, MMP-11 and 18S rRNA Target:Control ratios for 24 and 48 hour treatments of MEF cells. Treatment Target:Control Ratio 24 Hour 48 Hour MT1-MMP ± SEM MMP-11 ± SEM 18S rRNA ± SEM MT1-MMP ± SEM MMP-11 ± SEM 18S rRNA ± SEM Control 1.00 ± 0.02 1.00 ± 0.02 1.00 ± 0.02 1.00 ± 0.08 1.00 ± 0.04 1.00 ± 0.05 Collagen I 1.00 ± 0.08 1.38 ± 0.18 0.90 ± 0.04 0.84 ± 0.06 1.20 ± 0.10 0.94 ± 0.04 Collagen IV 1.10 ± 0.06 1.65 ± 0.08 0.58 ± 0.06 1.00 ± 0.08 1.45 ± 0.12 0.60 ± 0.05 Collagen V 1.04 ± 0.10 1.65 ± 0.08 0.60 ± 0.06 0.90 ± .004 1.80 ± 0.10 0.56 ± 0.02 Con-A 1.20 ± 0.14 0.66 ± 0.12 0.78 ± 0.08 0.95 ± 0.08 0.85 ± 0.10 0.80 ± 0.04 TGF-β 0.90 ± 0.06 0.56 ± 0.06 0.60 ± 0.02 0.84 ± 0.08 0.75 ± 0.05 0.64 ± 0.08 Fibronectin 1.07 ± 0.12 0.71 ± 0.08 0.58 ± 0.02 0.95 ± 0.32 0.95 ± 0.07 0.64 ± 0.04 IL-1β 1.34 ± 0.02 0.65 ± 0.04 0.55 ± 0.02 1.45 ± 0.16 1.86 ± 0.20 0.55 ± 0.03 IL-2 0.88 ± 0.20 0.66 ± 0.08 0.50 ± 0.02 0.96 ± 0.04 1.50 ± 0.10 0.56 ± 0.06 IL-6 1.03 ± 0.04 0.72 ± 0.04 0.65 ± 0.02 0.75 ± 0.06 1.15 ± 0.12 0.60 ± 0.02 Table 7 MT1-MMP and MMP-11 Target:Control ratios for 24 and 48 hour treatments normalised to 18S rRNA of NIH3T3 cells. Treatment Target:Control Ratio 24 Hour 48 Hour MT1-MMP ± SEM MMP-11 ± SEM MT1-MMP ± SEM MMP-11 ± SEM Control 1.00 ± 0.02 1.00 ± 0.02 1.00 ± 0.08 1.00 ± 0.04 Collagen I 1.32 ± 0.08 1.35 ± 0.10 1.32 ± 0.08 1.27 ± 0.08 Collagen IV 1.26 ± 0.06 0.68 ± 0.08 1.26 ± 0.06 0.71 ± 0.10 Collagen V 1.60 ± 0.08 1.65 ± 0.08 3.55 ± 0.35 1.91 ± 0.08 Con-A 1.00 ± 0.02 1.35 ± 0.12 1.00 ± 0.02 1.41 ± 0.14 TGF-β 0.62 ± 0.04 0.56 ± 0.06 2.95 ± 0.25 1.67 ± 0.14 Fibronectin 0.75 ± 0.08 0.72 ± 0.08 4.65 ± 0.38 2.16 ± 0.24 IL-1β 0.72 ± 0.10 0.78 ± 0.04 4.32 ± 0.28 2.20 ± 0.24 IL-2 0.68 ± 0.08 0.70 ± 0.08 3.36 ± 0.30 1.57 ± 0.16 IL-6 0.70 ± 0.04 0.74 ± 0.04 1.90 ± 0.14 1.27 ± 0.08 Table 8 MT1-MMP and MMP-11 Target:Control ratios for 24 and 48 hour treatments normalised to 18S rRNA of NIH3T3 cells of MEF cells. Treatment Target:Control Ratio 24 Hour 48 Hour MT1-MMP ± SEM MMP-11 ± SEM MT1-MMP ± SEM MMP-11 ± SEM Control 1.00 ± 0.02 1.00 ± 0.02 1.00 ± 0.08 1.00 ± 0.04 Collagen I 1.28 ± 0.04 1.30 ± 0.6 1.28 ± 0.08 1.32 ± 0.04 Collagen IV 1.35 ± 0.05 0.76 ± 0.04 1.35 ± 0.05 0.68 ± 0.02 Collagen V 1.54 ± 0.02 1.45 ± 0.10 2.85 ± 0.35 1.68 ± 0.04 Con-A 1.00 ± 0.02 1.26 ± 0.06 1.00 ± 0.02 1.35 ± 0.05 TGF-β 0.75 ± 0.05 0.65 ± 0.03 2.60 ± 0.18 1.44 ± 0.12 Fibronectin 0.85 ± 0.05 0.90 ± 0.04 4.20 ± 0.26 1.86 ± 0.18 IL-1β 0.68 ± 0.08 0.70 ± 0.04 4.00 ± 0.20 1.95 ± 0.17 IL-2 0.74 ± 0.06 0.68 ± 0.02 3.14 ± 0.26 1.44 ± 0.10 IL-6 0.70 ± 0.02 0.80 ± 0.05 1.74 ± 0.12 1.15 ± 0.07 Table 9 MT1-MMP and MMP-11 Target:Control ratios for co-cultures with HBC cell lines normalised to 18S rRNA of NIH3T3 cells and MEF cells. Treatment Target:Control Ratio NIH3T3 MEF MT1-MMP ± SEM MMP-11 ± SEM MT1-MMP ± SEM MMP-11 ± SEM 0.5 × Control 0.73 ± 0.08 0.40 ± 0.13 0.65 ± 0.10 0.38 ± 0.10 2.0 × Control 1.37 ± 0.18 1.48 ± 0.37 1.24 ± 0.08 1.55 ± 0.23 Control 1.00 ± 0.06 1.00 ± 0.09 1.00 ± 0.06 1.00 ± 0.09 MDA-MB-231 0.81 ± 0.08 0.71 ± 0.18 0.87 ± 0.09 0.65 ± 0.13 MDA-MB-436 1.04 ± 0.02 0.76 ± 0.11 1.14 ± 0.08 0.70 ± 0.08 MCF-7 0.82 ± 0.09 0.64 ± 0.31 0.86 ± 0.10 0.72 ± 0.26 MDA-MB-435 0.99 ± 0.11 0.65 ± 0.12 1.00 ± 0.13 0.62 ± 0.08 ML-20 1.22 ± 0.12 1.21 ± 0.27 1.16 ± 0.06 1.33 ± 0.15 ==== Refs Sternlicht MD Werb Z How matrix metalloproteinases regulate cell behavior Annu Rev Cell Dev Biol 2001 17 463 516 11687497 10.1146/annurev.cellbio.17.1.463 McCawley LJ Matrisian LM Matrix metalloproteinases: multifunctional contributors to tumor progression Mol Med Today 2000 6 149 56 10740253 10.1016/S1357-4310(00)01686-5 Liotta LA Kleinerman J Catanzaro P Rynbrandt D Degradation of basement membrane by murine tumor cells J Natl Cancer Inst 1977 58 1427 31 192901 Basset P Bellocq JP Wolf C Stoll I Hutin P Limacher JM Podhajcer OL Chenard MP Rio MC Chambon P A novel metalloproteinase gene specifically expressed in stromal cells of breast carcinomas Nature 1990 348 699 704 1701851 10.1038/348699a0 Sato H Okada Y Seiki M Membrane-type matrix metalloproteinases (MT-MMPs) in cell invasion Thromb Haemost 1997 78 497 500 9198203 Okada A Bellocq JP Rouyer N Chenard MP Rio MC Chambon P Basset P Membrane-type matrix metalloproteinase (MT-MMP) gene is expressed in stromal cells of human colon, breast, and head and neck carcinomas Proc Natl Acad Sci U S A 1995 92 2730 4 7708715 Polette M Clavel C Birembaut P De Clerck YA Localization by in situ hybridization of mRNAs encoding stromelysin 3 and tissue inhibitors of metallo-proteinases TIMP-1 and TIMP-2 in human head and neck carcinomas Pathol Res Pract 1993 189 1052 7 8302724 Gilles C Polette M Seiki M Birembaut P Thompson EW Implication of collagen type I-induced membrane-type 1-matrix metalloproteinase expression and matrix metalloproteinase-2 activation in the metastatic progression of breast carcinoma Lab Invest 1997 76 651 60 9166284 Guerin E Ludwig MG Basset P Anglard P Stromelysin-3 induction and interstitial collagenase repression by retinoic acid. 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10.1186/1471-2407-4-40

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==== Front BMC Public HealthBMC Public Health1471-2458BioMed Central London 1471-2458-4-301527474510.1186/1471-2458-4-30Research ArticleGeographic correlation between deprivation and risk of meningococcal disease: an ecological study Williams Christopher J [email protected] Lorna J [email protected] Iain R [email protected] Paul R [email protected] East & North Hertfordshire Health Protection Unit, Welwyn Garden City, Hertfordshire AL8 6JL, United Kingdom2 Health Protection Agency East of England, Cambridge CB2 2SR, United Kingdom3 School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom4 School of Medicine, Health Policy and Practice, University of East Anglia, Norwich, United Kingdom2004 26 7 2004 4 30 30 16 2 2004 26 7 2004 Copyright © 2004 Williams et al; licensee BioMed Central Ltd.2004Williams et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Meningitis caused by Neisseria meningitidis is a serious infection which is most common in young children and adolescents. This study investigated the relationships between the incidence and age distribution of meningococcal disease, and socioeconomic environment. Methods An ecological design was used, including mapping using a Geographical Information System (GIS) at census ward level. Results Incidence of meningococcal disease was highest in the most deprived wards, with a relative risk of 1.97 (1.55 – 2.51). Mapping revealed geographical coincidence of deprivation and meningococcal disease, particularly in urban areas. Two-thirds of the increased incidence was due to cases in the under fives. Conclusions The results suggest that area deprivation is a risk factor for meningococcal disease, and that its effects are seen most in young children. MeningitismeningococcalMeningococcal infectionsCommunicable diseaseGeographyMapsSocioeconomic factors. ==== Body Background Infections caused by Neisseria meningitidis are still an important cause of morbidity and mortality in the United Kingdom, with 1548 cases notified in England and Wales in 2001[1], an incidence rate of 3.2 cases per 100,000 per year, and a case fatality rate of around 10%[2]. The infection is most common in infants and adolescents, with peaks of incidence at 0–5 years and 15–19 years[2]. Outbreaks cause high degrees of anxiety in local populations, though 95% of cases are sporadic[2]. It is not clear how the bacterium spreads through the population in time and space, or what determinants are most important in each area. Suggested environmental risk factors for meningococcal infection include passive smoking[3], overcrowding[4], and weather conditions[5,6]. It is accepted that many infectious diseases preferentially affect the most disadvantaged in society. This has been discussed at historical[7], global[8], and national[9] levels, but new geomapping techniques have shown that it holds true when comparing small areas such as postal or electoral districts. The environmental and social factors conferred upon an individual through their residence in a particular area may be as important as the individual 'risk factors' where communicable disease is concerned[10]. Mapping of socioeconomic status and certain infectious diseases, in particular sexually transmitted infections [10-13], has shown a relationship between area socioeconomic indicators and disease incidence. Geographical Information Systems (GIS) techniques are able to both test hypotheses about the geographical distribution of disease and to display environmental characteristics and disease incidence in a clear and interpretable way. This study was prompted by an impression, gained from the practice of a consultant in communicable disease control (CCDC), that infant cases of meningococcal disease came from families of low socioeconomic status, whilst older cases tended to be drawn from families of higher socioeconomic status. Studies in northeast Thames[14], southwest England[15], and Wales[16] have shown an increase risk of meningococcal disease in more deprived areas, particularly in the under five age group. This study aims to test these hypotheses concerning age, area socioeconomic status and meningococcal incidence, using a larger sample size and a Geographical Information System (GIS). GIS methods are used to display the information on deprivation and disease incidence in an informative way, enabling the viewer to formulate new hypotheses about disease transmission in the region. Methods The study was ecological in design, and used census-derived area data, map data, and individual case data as described below. The geographical unit of analysis was the 1991 census ward, and the study population was the entire Eastern region population of the UK (1999 estimate 5.3 million). The Eastern region of England is made up from the counties of Hertfordshire and Essex (both adjoining Greater London to the south), Suffolk and Norfolk along the east coast, Cambridgeshire centrally and Bedfordshire to the north. Much of the area is rural, although there are several medium-sized urban areas in Essex and Hertfordshire, with links to the capital. Away from the London area, the major urban centres include Cambridge and Norwich (both with large university populations), the ports of Ipswich and Harwich, and Peterborough and Luton. Data sources Data on cases came from the Eastern region Communicable Disease Surveillance Centre (CDSC Eastern) regional database of enhanced surveillance of meningococcal disease[17]. To be included in the surveillance data, a case had to fit the Public Health Laboratory Service (PHLS) case definitions[18] at a local level, and also had to have been included in the monthly returns sent to the regional level. The study used full postcode and age band information on all cases from 1999 and 2000, a subset of the enhanced surveillance data. Linked information on cases, such as the serogroup or date of notification, was not requested for this study. This also meant that clusters could not be excluded from the analysis. The regional population data were projections for 1999 based on 1991 census data, obtained from the compendium of clinical and health indicators 2001[19]. This source gave age-banded population data at 'synthetic ward' level (single or aggregated census wards, producing a population of over 5000). Each synthetic ward population was shared equally between all its constituent census wards. The deprivation index used was the Townsend score, which combines local measures of unemployment, car ownership, overcrowding, and housing tenure[20]. This measure was used as it has already been widely used in similar literature[11,14,15,21], does not include potential confounders such as percentage of under fives, and was available at census ward 1991 level. A higher Townsend score indicates a more deprived area. Ward level scores based on 1991 census data were obtained from the Manchester Information and Associated Services (MIMAS)[22]. The shape files used in the MapInfo (© MapInfo Corporation) and EpiMap2000 (in Epi Info™, Centres for Disease Control and Prevention) maps were digitised ward boundaries from the 1991 census, obtained from the EDINA (Edinburgh data and information access) UKBORDERS service[23]. Vector-based files of census ward boundaries in the counties of Bedfordshire, Cambridgeshire, Essex, Hertfordshire, Norfolk and Suffolk, which make up the UK Eastern region, were downloaded in combined form. Ward population density was calculated using the ward areas contained in the map files, and the population estimates described above. Ethical approval Ethical approval was obtained from the West Hertfordshire health authority local research ethics committee. An application form was submitted, along with a copy of the project protocol, and written approval was returned. Analyses Cases were mapped to census wards using their postcode georeference (NHS postcode database). Microsoft Access was used to assign the deprivation scores, to divide and manipulate ward data, and to link the geographical and attribute files. The age distribution of cases and incidences was calculated using Microsoft Excel, which was also used to create the charts. Statistical testing was performed using StatsDirect (©StatsDirect Ltd), apart from the relative risk confidence intervals, which were calculated using Microsoft Excel[24]. Poisson confidence intervals for ward and deprivation group incidence rates were calculated by using the number of cases and the total person-years at risk (twice the ward or deprivation group population). Chi-squared tests for trend were performed for the successive incidence rates across the deprivation groups. The variation in the age distributions of incidence and case counts was compared using the non-parametric Friedman and Mann-Whitney tests respectively. Two Poisson regression models was constructed using StatsDirect, with incidence as the dependent variable and Townsend score and population density, and Townsend score alone, as predictors. The maps were produced with MapInfo, using the "range" function to colour each ward in shades depending on the level of deprivation or incidence of disease. The superimposed maps used a magnified version of the deprivation map in figure 3, and a stick figure to represent the magnitude of ward disease incidence. Results Case data A total of 773 cases were reported to the CDSC (Eastern) enhanced meningococcal database during 1999 and 2000. Of these, 524 had some postcode details and 499 had the full postcode. 458 cases had a ward assigned, and, of these, 451 had a Townsend score. These data losses were due to postcodes not being recorded (the major factor) or incorrect, discrepancies within the NHS postcode file, and incomplete ward deprivation data (this only relates to the 524 – 451 cases, not the bulk 773-524 cases). Further analysis is therefore restricted to the 451 cases with Townsend score. Where incidence rates are given, they will generally be underestimates of the true incidence (on average 58% of the true value), due to the loss of case data. It was assumed that the data losses were random with respect to the variables of interest. The age structure was well preserved despite the loss of around 40% of the initial cases. Figure 1 compares cases included in the study (451) with the numbers expected if losses were uniform across the age groups. Chi squared testing confirmed that the losses were not related to age group (P = 0.8529). When broken down into the eight Health Authorities supplying case data, the percentage of cases that included postcode information was roughly similar, with around 2/3 of cases being postcoded, with the exception of one authority where only 38.6% of cases were postcoded. However, this authority only contributed 7% of the cases. Figure 1 Case losses*: Comparison of numbers of cases included in analysis, by age group, with expected values if losses were equal across all age bands *Sources Cases: Confirmed and probable cases of invasive meningococcal disease included in analysis, enhanced surveillance data from CDSC eastern Of the 1184 wards included in the analysis, 325 (27%) had at least one case of meningococcal disease in the two-year period. In these wards, the maximum number of cases was 6, and the median 1. Incidence of meningococcal disease The overall incidence for 1999 and 2000 was 7.4 cases per 100,000 per year. Within the wards containing more than one case, the median incidence was 12.9 per 100,000 per year, and ranged from 3.7 to 60.0. Given the small numbers involved, Poisson confidence intervals for these incidences are wide. In a high incidence ward (41.7 cases per 100,0000 per year), the confidence interval was 11.4 to 106.8 cases per 100,000 per year. In a low incidence ward (4.0 cases per 100,000 per year), the confidence interval was from 0.1 to 22.5 cases per 100,000 per year. The ward incidences will be underestimates due to the loss of cases described above. Figure 1 also shows the age distribution of the cases. This bimodal distribution shows that the peak incidence is in children under 5, with a second peak in the 15–19 group. Figure 2 Age-specific incidence rates*: Comparison of cases in each Townsend score group (thirds, 1151 wards) *Sources Cases: Confirmed and probable cases of invasive meningococcal disease included in analysis, enhanced surveillance data from CDSC eastern Denominator: 1999 population estimates, compendium of clinical and health indicators 2001, adjusted from synthetic wards (see methods) Map files : 1991 Census digitised boundary data Townsend scores: 1991 Census area statistics Incidence and deprivation Table 1 shows the summary figures for the wards analysed. The mean incidence of meningococcal disease in the most deprived wards is twice that in the least deprived wards (5.9 versus 3.0), with a relative risk of 1.97 (1.55 – 2.51). The intermediate wards have an overall incidence similar to that in the least deprived wards. A chi-squared test for trend shows a significant variation between the rates in each group (chi squared for linear trend = 39.0, p < 0.0001). Table 1 Meningococcal disease in the eastern region, 1999 and 2000: Incidence rates after division into thirds by ward Townsend score Least deprived wards Intermediate wards Most deprived wards Number of wards a 384 383 384 Range of ward Townsend scores -6.26 to -2.19 -2.2 to-0.33 0.34 to 8.44 Number of cases b (all ages) 88 97 267 Total population c (all ages) 1,473,272 1,506,359 2,271,294 Incidence [corrected] d(cases /100,000 /yr) 3.0 [5.1] (2.4 – 3.7)e 3.2 [5.5] (2.6 – 3.9) 5.9 [10.1) (5.2 – 6.6) Incidence relative risk compared to least deprived wards [CI] 1.00 1.07 [0.80–1.43] 1.97 [1.55–2.51] (a) Census wards 1991, divided by Townsend index, from 1991 census (b) All cases of meningococcal disease (confirmed and probable) from enhanced surveillance data, CDSC eastern, included in analysis (c) Population estimates for 1999, Compendium of Health and Clinical indicators 2001, adjusted as in methods (d) Corrected to allow for the loss of 42% of cases. Corrected incidence = uncorrected/0.583. Assumes losses are equal across deprivation groups (e) Poisson confidence intervals – see methods Poisson regression using population density and Townsend score as predictors revealed population density to be a non-significant contributor to the variation in incidence (p = 0.086). A second model, setting incidence against Townsend score alone, suggested that ward incidence rises by 12% (9 – 16%) for every unit increase in deprivation score. Age distribution and deprivation The age-specific incidence rates are shown in figure 2. The most striking feature is the large excess of cases in the under ones and one to fours in the most deprived wards. The incidence is 1.9 times higher for the most deprived under fives (under one and one to four groups combined). The increased incidence in the under five age group accounts for 68% of the difference in overall incidence between the most and least deprived wards. The incidences in the 16 age groups used varied significantly between the three deprivation groups (Friedman test, p < 0.0001). Figure 3 Ward deprivation in the Eastern region, by Townsend score* *Sources Map files : 1991 Census digitised boundary data Townsend scores: 1991 Census area statistics Software: MapInfo© Professional The Poisson confidence intervals for these incidences are narrower than for individual wards. Two were calculated: 21.3 to 35.1 for an incidence of 27.6 per 100,000 per year, and 0.3 to 4.9 for a lower incidence of 1.7 per 100,000 per year. Mapping deprivation and meningococcal disease Figure 3 shows ward deprivation by Townsend score using MapInfo. The wards are divided into eight bands by their ward score. Many of the most deprived wards correspond to urban areas, though there was a broad area of greater deprivation in the north of the region. Figure 4 shows a map of meningococcal incidence by ward in the region, with colours coded by six incidence ranges. Areas of high incidence often coincide with urban regions, though some rural areas also have high rates of disease. These include some of the deprived rural areas, including the area in north Norfolk noted above. Figure 4 Eastern Region, 1999 and 2000: Incidence of meningococcal disease by census ward 1991* *Sources Cases: All cases of meningococcal disease (confirmed and probable) collected by CDSC Eastern for enhanced surveillance & included in analysis, 1999 and 2000 Denominator: 1999 population estimates from Compendium of Health and Clinical Indicators 2000, adjusted for true ward (see methods) Map files : 1991 Census digitised boundary data Software: MapInfo© Professional Figure 5 shows incidence rates superimposed on the deprivation map from figure 3, and magnified to show local detail in the Hertfordshire/Essex region. It shows the relationship of incidence to deprivation, with high incidence wards being clustered within and around the 'foci' of deprivation. This is particularly marked in Harlow, represented by the cluster in the lower central part of the map. Figure 5 Meningococcal incidence and deprivation superimposed, Hertfordshire and west Essex* *Sources Map files : 1991 Census digitised boundary data Townsend scores: 1991 Census area statistics Software: MapInfo© professional Cases: confirmed and probable cases of invasive meningococcal disease included in analysis, enhanced surveillance data from CDSC eastern Denominator: 1999 population estimates, compendium of clinical and health indicators 2001, adjusted for true ward (see methods) Discussion The results for the regional surveillance data support the theory that meningococcal disease is associated with socioeconomic deprivation. Compared to the Welsh study[16] this study included more cases (451 vs. 295) over a wider area, with a total population of 5.3 million. Also, both deprivation and incidence of meningococcal disease are mapped using GIS, showing the spatial relationship of disease foci to areas of deprivation, both urban and rural. The study, in common with that from southwest England[15], also shows that this relationship holds in an area containing many rural wards, despite the problems associated with deprivation indices in rural areas[25,26]. The analyses of the age distribution of cases and incidences suggest that there is variation between the deprivation groups. There was a significant difference between the age-specific incidence rates between the groups, and a median age difference of five years between the most and least deprived groups (non-significant). The relative risk of disease in the most deprived wards compared to the least deprived (1.97, CI 1.55 – 2.51), is greater than that observed in the northeast Thames study[14] (odds ratio 1.51 for N. meningitidis), and the southwest England study[15] (relative risk 1.76, between upper and lower quartiles), but less than that found in the Welsh study[16] (relative risk 2.4 between upper and lower quintiles). The incidence of other infections, such as infectious intestinal disease, has also been shown to vary with area socioeconomic conditions (relative risk 2.41, quintiles) [27]. All but two (25–29 years, 35–39 years) age groups experienced higher age-specific rates in the most deprived group, but the highest case numbers were seen in the under 5 age group. The increased incidence in the under fives in the most deprived group, compared to the least deprived, accounted for 68% of the total difference in age-specific incidence. This suggests that children under five are more vulnerable to meningococcal disease in the most deprived areas. The continuation of a similar pattern when case counts, percentage and age-specific incidence were considered improves the robustness of this conclusion. Several environmental factors might contribute to the increased risk of childhood meningococcal disease in more deprived areas. Childcare arrangements, necessitated by either personal or environmental circumstances, might expose a child in a more deprived area to more potential carriers. Smoking is also known to be more common in people from disadvantaged backgrounds and a number of studies have identified passive smoking as risk factors for both nasopharyngeal carriage and meningococcal disease [3,28-30]. An area with a relatively greater number of under fives might have a higher overall incidence rate, simply because there are more susceptible individuals. Analysis of the percentage age distribution of the populations in each deprivation group does not reveal any marked variations. There is a slight excess of 20–29 year olds in the most deprived wards compared to the other wards, but not an excess of under fives. Mapping of the incidence rates and deprivation indices by ward definitely added value to the routine surveillance data, emphasising the focal nature of disease, and the relationship of these foci to areas of higher deprivation. Mapping incidence at ward level, rather than pinpointing individual cases as in the Welsh study[16], both considers the population at risk, and avoids potential problems with case confidentiality. The maps also suggest that, in the Eastern region, invasive meningococcal disease is largely an urban problem. Although the higher population density of urban areas might explain this, the regression model suggested that the variation was better explained by ward deprivation (term for population density non-significant, p = 0.0866). Some of the highest rates were seen in deprived urban parts of Essex. Several rural wards containing cases corresponded to more deprived areas, particularly in North Norfolk. Figure 5 shows the relationship of incidence to deprivation in close-up, and also that disease is not confined to the more deprived areas. This is an ecological study, so the associations shown may not be valid at an individual level. Population density might still be a factor, as the regression is subject to some autocorrelation, the population variable occurring in the incidence (dependent) and population density (predictor) terms. A separate analysis, of urban and rural areas, might show whether this is the case. The paucity of cases in rural areas might make this difficult unless several more years' data were included. The north east Thames study[14] did, however, show the same relationship in an area of high population density, as have other urban studies of pertussis[31] and sexually transmitted infections[12,21]. Geographical bias in postcoded data might have contributed toward the results, as the health authority with the lowest rate of postcode inclusion, and therefore the greatest loss of data, includes many of the more affluent parts of the region. The cases supplied by this authority only accounted for 7.4% of all cases and 4.4% of postcoded cases, so this potential bias is unlikely to have had a major effect. Routine childhood immunisation with meningitis C conjugate vaccine started in November 1999[2], so should have had an effect on the year 2000 cases. The subset of enhanced surveillance data used for this study did not include the date of notification or the serogroup involved, so no comment can be made on the influence of the vaccine or of serogroup on the results in this paper. A breakdown of the serogroups was available for the years as a whole; of those where the group was known, 60% of cases were group B and 33% group C. Patterns of transmission may change as group C infections decline, and this might be seen with routine mapping of case data. Conclusions Mapping of deprivation indices and meningococcal cases is a useful tool in the analysis of routine surveillance data. Mapping of incidence rates revealed an association between areas of high incidence and areas of higher deprivation by Townsend score. High incidence and deprivation often coincided in urban areas. Mapping of deprivation indices also reveals areas of rural deprivation, such as the coastal band in north Norfolk. Yearly mapping of routine surveillance data can help to target control strategies for meningococcal disease locally. Analytic studies would be helpful in elucidating the mechanisms by which socioeconomic conditions influence the risk of meningococcal disease in the region. Along with the study described here, knowledge gained from such investigations could inform the work on health inequalities, and try to reduce such inequalities through health promotion and community infection control. Competing interests None declared. Authors' contributions CJW obtained and analysed the case and geographical data, performed the statistical analyses and mapping, and wrote the text of the paper. PH suggested the idea for the study and helped develop the methods by which the question was addressed. LW provided the data from the enhanced meningococcal surveillance database and advised on data quality and sources. IL provided expertise in geographical information systems and postcode geography. LW, PH and IL all advised on the design of the study, and the analysis and interpretation of the results. All authors contributed to drafting the paper and have read and approved the final draft. Pre-publication history The pre-publication history for this paper can be accessed here: Acknowledgements We would like to thank the following people for their help and support: Dr John Powles; Mr Brian Shipway; Mr Andrew Chronias; Mrs Anne Sowden; Dr Lorna Milne; Dr Margaret Eames; Dr Tom Morgan; Mr Edmund Tiddeman; Ms Aphrodite Niggebrugge; Dr Mark Kroese and all those who contribute data to the CDSC (Eastern) regional enhanced surveillance database. Census output is Crown copyright and is reproduced with the permission of the Controller of HMSO and the Queen's Printer for Scotland. This work is based on data provided with the support of the ESRC and JISC and uses boundary material, which is copyright of the Crown and the ED-LINE Consortium. ==== Refs Agency Health Protection Annual notifications of infectious disease 2002 Health Protection Agency website 2003 Agency Health Protection Background information on meningitis Health Protection Agency website 2003 Kriz P Bobak M Kriz B Parental smoking, socioeconomic factors, and risk of invasive meningococcal disease in children: a population based case-control study Archives of Disease in Childhood 2000 83 117 121 10906015 10.1136/adc.83.2.117 Schwartz B Moore PS Broome CV Global epidemiology of meningococcal disease. Clinical Microbiology Reviews 1989 2 Suppl S118 S124 2655881 Tzeng YL Stephens DS Epidemiology and pathogenesis of Neisseria meningitidis. Microbes & Infection 2000 2 687 700 10884620 10.1016/S1286-4579(00)00356-7 Collier CG Weather conditions prior to major outbreaks of meningococcal meningitis in the United Kingdom International Journal of Biometeorology 1992 36 18 29 1582720 Mckeown T The modern rise of population 1976 London Gwatkin DR Guillot M Heuveline P The burden of disease among the global poor. Lancet 1999 354 586 589 10470717 10.1016/S0140-6736(99)02108-X Sichieri R de Lolio CA Correia VR Everhart JE Geographical patterns of proportionate mortality for the most common causes of death in Brazil Revista de Saude Publica 1992 26 424 430 1342534 Kilmarx P Zaida AA Thomas JC Nakashima AK Louis ME Flock ML Peterman TA Sociodemographic factors and the variation in syphilis rates among US counties, 1984 through 1993: An ecological analysis American Journal of Public Health 1997 87 1937 1943 9431280 Low N Daker-White G Barlow D Pozniak AL Gonorrhoea in inner London: results of a cross sectional study BMJ 1997 314 1719 9185497 Shahmanesh M Gayed S Ashcroft M Smith R Roopnarainsingh R Dunn J Ross J Geomapping of chlamydia and gonorrhoea in Birmingham Sex Transm Infect 2000 76 268 272 11026881 10.1136/sti.76.4.268 Lacey C-JN Merrick DW Bensley DC Fairley I Analysis of the sociodemography of gonorrhoea in Leeds, 1989-93 BMJ 1997 314 1715 1718 9185496 Jones IR Urwin G Feldman RA Banatvala N Social deprivation and bacterial meningitis in north east Thames region: three year study using small area statistics BMJ 1997 314 794 795 9080999 Stuart JM Middleton N Gunnell DJ Socioeconomic inequality and meningococcal disease Communicable Disease and Public Health 2002 5 327 328 12564252 Fone DL Harries JM Lester N Nehaul L Meningococcal disease and social deprivation: a small area geographical study in Gwent, UK Epidemiology & Infection 2003 130 53 58 12613745 10.1017/S095026880200794X Davison KL Crowcroft NS Ramsay M N. Begg Kaczmarski EB J.M. Stuart White JM Orr H Enhanced surveillance scheme for suspected meningococcal disease in five regional health authorities in England: 1998 Communicable Disease and Public Health 2002 5 205 212 12434690 Forum Public Health Laboratory Service Meningococcus Guidelines for public health management of meningococcal disease in the UK Communicable Disease and Public Health 2002 5 187 204 12434689 Health Department of Compendium of clinical and health indicators 2001 2001 Townsend P Phillimore P Beattie A Health and deprivation: inequality and the North 1988 London: Croom Helm Winter AJ Sriskandabalan P Wade AA Cummins C Barker P Sociodemography of genital Chlamydia trachomatis in Coventry, UK, 1992-6 Sex Transm Infect 2000 76 103 109 10858711 10.1136/sti.76.2.103 unit Census dissemination Office for National Statistics 199 Census : Small area statistics and Local base statistics 1993 ESRC/JISC Census Programme, Census Data Support Unit, University of Manchester (UKBORDERS) Census geography data unit Ordnance survey 1991 Census: Digitised Boundary Data (Great Britain) 1991 ESRC/JISC Census Programme, Census support unit, University of Edinburgh Gardner MJ D. Altman Calculating confidence intervals for relative risks, odds ratios, and standardised ratios and rates Statistics with Confidence 1989 50 Haynes R Gale S Deprivation and poor health in rural areas: inequalities hidden by averages Health and Place 2000 6 275 11027953 10.1016/S1353-8292(00)00009-5 Barnett S.,et al. A multilevel analysis of the effects of rurality and social deprivation on premature limiting long term illness Journal of Epidemiology and Community Health 2001 55 44 11112950 10.1136/jech.55.1.44 Olowokure B Hawker J Weinberg J Gill N Sufi F Deprivation and hospital admission for infectious intestinal diseases Lancet 1999 353 807 808 10459964 10.1016/S0140-6736(99)00611-X Stuart JM Cartwright KA Dawson JA Rickard J Noah ND Risk factors for meningococcal disease: a case control study in south west England Community Medicine 1988 10 139 146 3243066 Stanwell-Smith RE Stuart JM Hughes AO Robinson PM Griffin MN K. Carwright Smoking, the environment and meningococcal disease: a case control study Epidemiology and Infection 1994 112 315 328 8150006 Fitzpatrick PE Salmon RL Hunter PR Roberts RJ Palmer SR Risk factors for carriage of Neisseria meningitidis during an outbreak in Wales Emerging Infectious Diseases 2000 6 Siegel C Davidson A Kafadar K Norris JM Todd J Steiner J Geographic analysis of pertussis infection in an urban area: a tool for health services planning American Journal of Public Health 1997 87 2022 2026 9431296

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==== Front BMC Musculoskelet DisordBMC Musculoskeletal Disorders1471-2474BioMed Central London 1471-2474-5-231528203210.1186/1471-2474-5-23Research ArticleShoulder posture and median nerve sliding Julius Andrea [email protected] Rebecca [email protected] Andrew [email protected] Bruce [email protected] Department of Physiology, University College London, Gower Street, London, UK2004 28 7 2004 5 23 23 18 3 2004 28 7 2004 Copyright © 2004 Julius et al; licensee BioMed Central Ltd.This is an open-access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background Patients with upper limb pain often have a slumped sitting position and poor shoulder posture. Pain could be due to poor posture causing mechanical changes (stretch; local pressure) that in turn affect the function of major limb nerves (e.g. median nerve). This study examines (1) whether the individual components of slumped sitting (forward head position, trunk flexion and shoulder protraction) cause median nerve stretch and (2) whether shoulder protraction restricts normal nerve movements. Methods Longitudinal nerve movement was measured using frame-by-frame cross-correlation analysis from high frequency ultrasound images during individual components of slumped sitting. The effects of protraction on nerve movement through the shoulder region were investigated by examining nerve movement in the arm in response to contralateral neck side flexion. Results Neither moving the head forward or trunk flexion caused significant movement of the median nerve. In contrast, 4.3 mm of movement, adding 0.7% strain, occurred in the forearm during shoulder protraction. A delay in movement at the start of protraction and straightening of the nerve trunk provided evidence of unloading with the shoulder flexed and elbow extended and the scapulothoracic joint in neutral. There was a 60% reduction in nerve movement in the arm during contralateral neck side flexion when the shoulder was protracted compared to scapulothoracic neutral. Conclusion Slumped sitting is unlikely to increase nerve strain sufficient to cause changes to nerve function. However, shoulder protraction may place the median nerve at risk of injury, since nerve movement is reduced through the shoulder region when the shoulder is protracted and other joints are moved. Both altered nerve dynamics in response to moving other joints and local changes to blood supply may adversely affect nerve function and increase the risk of developing upper quadrant pain. ==== Body Background Non-specific arm pain (NSAP), often called repetitive strain injury, describes the common problem of upper limb pain and functional impairment without objective physical findings. The contributing factors to the development of NSAP are not fully understood but ergonomic guidelines commonly suggest that good upper body posture protects against NSAP (e.g. [1]). In a study of 485 NSAP patients, shoulder protraction and forward head position were reported in a majority of patients (78% and 71% respectively) [2]. Poor upper body posture (e.g. rounded shoulders, head forward) has also been reported to increase the incidence of neck and shoulder pain [3]. The possible mechanisms leading to pain in patients with postural malalignment have not been examined in any detail. The painful symptoms often associated with NSAP suggest a minor neuropathy involving at least in part the median nerve [4]. Together, shoulder protraction, forward head position and flexion of the trunk form the main components of slumped sitting. The present study examines the effects of each of these components on longitudinal sliding of the median nerve using high frequency ultrasound imaging in asymptomatic normal subjects. In addition, the effects of sustained protraction on nerve sliding through the shoulder region are examined. Methods Ultrasound imaging Longitudinal nerve movement was measured using high frequency ultrasound imaging, as previously described by Dilley et al. [5,6]. A Diasus ultrasound system (Dynamic Imaging, Livingston, Scotland, UK) was used to collect sequences of ultrasound images at 10 frames/second for 50 to 70 seconds, running at 10–22 MHz and using a 26 mm linear transducer. A cross-correlation algorithm was used to determine relative movement between adjacent frames in sequences of images [5]. The maximum correlation coefficient (r) was calculated for each pixel shift determining the relative movement between frames. To account for probe movement the same method was employed on deep stationary structures (eg. bone or interosseous membrane) and the result subtracted from the nerve excursion values. Subject details Fourteen healthy subjects, 5 male and 9 female, aged 25–38 years (mean = 32 years) were screened to exclude upper limb or cervical spine pathologies, rheumatological or neurological conditions. In each subject the nerve bed length was estimated, from the C6 spinous process to the tip of the index finger (mean = 97.0 cm (SD, 5.9)), and used to normalise the ultrasound transducer position between individuals. All measurements were taken from the right upper limb only. Set-up and procedure The median nerve was imaged in longitudinal section in the forearm during forward head position, trunk flexion and protraction and in the forearm and upper arm during contralateral neck side flexion (CNSF). Each movement was repeated three times, including some reverse trials. Forward head position Each subject (n = 8) was imaged in the proximal forearm whilst positioned upright on a chair fitted with a back and head support, hips and knees at 90° flexion, and the trunk fixed with Velcro strapping. The right upper limb was strapped to a Perspex plate in 90° flexion and 20° abduction at the glenohumeral joint, with the elbow fully extended, 45° forearm supination, and the wrist, hand and fingers in neutral. An active forward head position movement was performed, which included lower cervical spine flexion and upper cervical spine extension. Trunk flexion Each subject (n = 8) was imaged in the proximal forearm whilst positioned upright on a chair, with hips and knees at 90 degrees flexion. The right upper limb was positioned as for the forward head position trials. The subject was taught to actively flex their trunk whilst posteriorly tilting their pelvis. Protraction Each subject (n = 13) was imaged at two locations in the forearm and positioned as for the forward head position. The distal upper arm was imaged in three of the 13 subjects. For each trial the shoulder girdle was passively protracted from neutral (i.e. the scapulothoracic joint in neutral) by sliding the Perspex plate supporting the arm on an adjustable table. In three of these subjects, additional data was also obtained during ultrasound imaging. A potentiometer attached using strong thread to the acromion process allowed measurement of the amount of protraction. Protraction data was captured on to a PC and synchronised offline to the recorded ultrasound sequence. In four subjects, good quality images of the median nerve within the upper arm could be obtained. In the majority of subjects, it was difficult to acquire good quality images because of dense tissue overlying the nerve that reduced the image quality. From these images, nerve trunk bowing was measured in the distal upper arm with the shoulder girdle in the neutral and protracted positions. The maximum deviation of the nerve from a straight line across single ultrasound frames was measured offline in both positions and the difference used as a measure of additional bowing. Repeat trials were averaged. Contralateral neck side flexion Each subject (n = 11) was imaged in the distal forearm and distal upper arm, whilst lying supine with the right upper limb abducted to 90° at the glenohumeral joint. Ninety degree abduction at the glenohumeral joint rather than 90° glenohumeral flexion was used, so that the present data could be related to previous work [6] which has shown that median nerve movements can be reliably measured with the glenohumeral joint abducted to 90°. The examined limb was fixed to a Perspex plate using Velcro strapping with the elbow extended, forearm supinated and the wrist, hand and fingers in neutral. The head was supported on a movable plate, with the centre of rotation positioned at the C7 spinous process. In each subject, the neck was passively moved to 35° CNSF with (a) the scapulothoracic joint in neutral (i.e. relaxed lying in supine) and (b) in full protraction. This movement was repeated several times. In four subjects, a potentiometer attached to the plate allowed continuous measurement of the angle of CNSF. Joint angle data was captured on to a PC and synchronised offline to the recorded ultrasound sequence. Subject movement measurements For each procedure the range of movement was determined from pictures obtained using a digital camera. Changes in joint angle and distance for each movement were determined from skin surface markers and measured using either CorelDraw (Kodak Digital Science, USA) or "tpsDig" (F. James Rohlf, Department of Ecology and Evolution, State University of New York). Measurements for the individual components of slumped sitting are summarised in figure 1. The posterior-anterior shift of the acromion was used as a measure of protraction during CNSF. Strain calculations Strain is defined by the difference in the amount of elongation that occurs at two points along a nerve divided by the distance between these two points. In practice strain was determined by using regression lines fitted to plots of nerve movement against the distance along the arm. Note that the strain estimates represent the additional strain produced by the movement rather than the total nerve strain. Statistical analysis Comparisons of nerve movement and strain in scapulothoracic neutral and protraction during CNSF were performed using paired t-tests. Results Median nerve movements in the arm in response to components of slumped posture Forward head position Moving the head forward while maintaining the shoulder and trunk position was tested in 8 subjects. This movement produced no detectable median nerve excursion in the forearm, the average trend being a movement of 0.1 mm (SEM, 0.02) occurring in a proximal direction. The repeat measure variability within subjects was very low, with a standard deviation ranging from 0–0.2 mm (mean = 0.1 mm). The mean change in the angle of lower cervical spine flexion and upper cervical spine extension was 23.6° (SD, 2.8) and 2.9° (SD, 1.9) respectively. Trunk flexion Trunk flexion also produced minimal median nerve excursion with a mean over 8 subjects of 0.1 mm (SEM, 0.1) proximal movement. The mean change in the angle of trunk flexion was 19.7° (SD, 4.7). Shoulder protraction In 13 subjects the median nerve moved in a proximal direction during shoulder protraction with more movement at proximal locations (mean in forearm = 3.5 mm (SEM, 0.3), mean in upper arm = 5.9 mm (SEM, 0.6)) (figure 2 [see additional file 1 for ultrasound sequence of median nerve sliding in the forearm]). The mean extent of scapular anterior translation was 38.3 mm (SD, 13). The additional strain on the median nerve was 0.7% (SEM, 0.3), given by the slope of the regression of nerve movement against distance along the arm (Figure 2). Median nerve excursion was measured in 3 subjects with simultaneous measurement of protraction. The results revealed an initial delay of 6.5–33.0 mm (mean = 17.0 mm; equivalent to 15.8–34.0% (mean = 23.7%) of the total protraction) before significant nerve movement occurred (see figure 3). After the initial delay, nerve movement was proportional to the extent of protraction. In three of four subjects, nerve bowing was observed in the upper arm with the shoulder girdle in the neutral test position. The maximum nerve course deviation from a straight line with the shoulder girdle in neutral compared to protraction was approximately 0.5 mm in all three subjects over the length of the ultrasound transducer, (26 mm). The nerve straightened during protraction (figure 4). Median nerve movement in the arm in response to contralateral neck side flexion (CNSF) with or without shoulder protraction In 11 subjects the median nerve moved in a proximal direction during 35° CNSF when the scapulothoracic joint was in neutral, as reported previously [6]. The movement increased at the more proximal location (scapulathoracic neutral, mean in upper arm = 2.3 mm (SEM, 0.2) and forearm = 1.5 mm (SEM, 0.2)). With the shoulder protracted, there was a 60% reduction in nerve movement in both upper arm and forearm locations (p < 0.05 for both locations) (mean in upper arm = 0.9 mm (SEM, 0.2) and forearm = 0.6 mm (SEM, 0.1)) (figure 5). The mean extent of protraction was 48.0 mm (SEM, 4.3). The additional strain on the median nerve was 0.3% (SEM, 0.1) in scapulothoracic neutral. There was a significant reduction in strain in protraction (0.1% (SEM, 0.1); p < 0.05, paired t-test). Median nerve excursion was measured in 4 subjects with simultaneous measurement of CNSF. The results revealed no obvious delay in the onset of nerve movement in protraction compared to scapulothoracic neutral. Despite less movement in protraction, the pattern of nerve movement mimicked that observed in scapulothoracic neutral (figure 6). Nine of 11 subjects reported paraesthesia in the distribution of the median nerve dermatone once the shoulder girdle was sustained in protraction. The onset of symptoms ranged from 1 to 4 minutes. Symptoms disappeared when the shoulder was repositioned in scapulothoracic neutral. Discussion Direct effects of the components of slumped sitting on median nerve movement Nerves are designed to slide and stretch to accommodate joint movement. Using the method of Dilley et al. [5,6], median nerve sliding was examined during the individual components of slumped sitting. Both forward head position and trunk flexion produced only minimal nerve movement in the forearm. The only examined component to produce substantial nerve movement was shoulder protraction. The median nerve strain in the forearm with protraction was 0.7%, which was well below the limits that cause changes to nerve function (reviewed in Grewel et al. [7]). As the shoulder girdle is protracted there is a delay in nerve movement, which is followed by a steady increase in nerve excursion. During this initial toe region the median nerve appears bowed in the upper arm. The nerve trunk appears to straighten as the range of protraction progresses. It therefore seems that with the upper limb in scapulothoracic neutral and the glenohumeral joint in 90° flexion, the median nerve is unloaded. If this is the case, the strain value of 0.7% will represent the total strain. Effects of protraction on the transmission of median nerve movement through the shoulder region The results for CNSF provide evidence for a possible restriction within the shoulder region during shoulder protraction. With the shoulder protracted there was a 60% reduction in the transmission of nerve movement through the upper limb. Consistent with a reduction in movement, there was also significantly less strain in the forearm. The possibility that the nerve becomes unloaded when the shoulder is protracted is unlikely since it had been found that protraction itself causes some median nerve stretch. In addition, there was no obvious delay in nerve movement in response to CNSF (Figure 6). The evidence for a restriction is consistent with previous suggestions that shoulder protraction may cause a neurovascular impingement within the shoulder region resulting in pain [2,8]. This suggestion was further supported by the experience of paraesthesia within the median nerve distribution during sustained protraction in 82% of subjects. These symptoms indicate the presence of a vascular restriction, which in turn affects neural function. Scapular protraction is a complicated movement, often resulting in the combined movement of numerous other structures within the shoulder girdle, including anterior displacement of the head of the humerus. It is therefore difficult to establish the precise cause of a neurovascular entrapment. Shortening of pectoralis minor and the downward displacement of the coracoid process might affect sliding of the cords of the brachial plexus. Alternatively, elevation of the first rib during full protraction (due to its soft tissue attachments with surrounding structures) might reduce the space between the clavicle and the first rib, restricting nerve sliding. Clinical significance The components of slumped sitting (i.e. forward head position, trunk flexion and protraction) are associated with poor posture [2,9-11], and are often adopted by office workers. Shoulder protraction is the only component of this posture to tension the median nerve, although the level of nerve strain in the forearm with the shoulder at 90° flexion and elbow extension, is not sufficiently high to result in direct neural injury. Problems are more likely to result from local effects of shoulder protraction on the chords of the brachial plexus. The present study shows that protraction restricts nerve sliding through the shoulder region. Most subjects also experienced paraesthesia when maintaining shoulder protraction plus elbow extension and shoulder abduction. Therefore, sustained shoulder protraction may place the median nerve at enhanced risk of injury and possibly cause a vascular compromise. This may in turn explain for the trend that a high number of NSAP patients have poor shoulder posture. (e.g. [2]). Conclusions The direct effects of slumped sitting on median nerve strain are not sufficient to alter nerve function. However, shoulder protraction does appear to restrict nerve sliding, and prolonged protraction leads to pareasthesias. Competing interests None declared. Authors' contributions AJ and RL participated in the study design, data collection, analysis and manuscript preparation. AD participated in the study design, analysis and manuscript preparation. BL participated in the study design and manuscript preparation. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Supplementary Material Additional File 1 Median nerve sliding in the forearm during protraction. Ultrasound sequence of median nerve sliding in the distal forearm during protraction. The subject was imaged with the limb in 90° flexion and 20° abduction at the glenohumeral joint and elbow neutral. The median nerve can be seen to slide in a proximal direction as the shoulder is protracted. In a repeat of the sequence the nerve movement is tracked using cross-correlation analysis (yellow plus sign). The total nerve movement was 4.70 mm. Click here for file Acknowledgements The authors would like to thank Dr Jane Greening for her helpful comments on the text. Figures and Tables Figure 1 Summary of subject movement measurements. (a) Lower cervical spine flexion: the change in angle from a line extending from C7 to the tragus of the ear (T) and a vertical line through C7. (b) Upper cervical spine extension: the change in angle from a line extending from the tragus to the mid forehead (MF) and a vertical line through the tragus. (c) Protraction: the change in distance of the acromion (A) along the horizontal axis. (d) Trunk flexion: the change in angle from two lines extending from L1 to the acromion (A) and L1 to the greater trochanter (GT). Figure 2 Individual nerve excursion values at sites in the upper arm and forearm for thirteen subjects produced by protraction. Each point is the average of three individual trials. Distance along the arm has been expressed as percentage of total distance from C6 spinous process to the tip of the index finger. A regression line has been fitted to the data. Figure 3 Nerve movements in forearm plotted against protraction (expressed as a percent of the total movement) (n = 3). Total protraction movements ranged from 40 to 95 mm. Each curve is a single trial from one of 3 subjects. Note the initial delay in nerve movement. Figure 4 Bowing of the median nerve. Ultrasound images of the median nerve in the distal upper arm (upper) with the shoulder girdle in neutral and (lower) protracted. Note substantial bowing with the shoulder girdle in the neutral compared to protracted position. Bar = 10 mm. Figure 5 Individual nerve excursion values (mm) in response to CNSF for eleven subjects in scapulothoracic neutral and in protraction. Each point is the average of three individual trials. Distance along the arm has been expressed as percentage of total distance from C6 spinous process to the tip of the index finger. A regression line has been fitted to the data. Figure 6 Nerve movements in the forearm (upper) and upper arm (lower) plotted against neck angle with the shoulder in scapulothoracic neutral and protraction. Each data point is the average of 4 subjects. Note the absence of a delay in nerve movement in protraction compared to scapulothoracic neutral. Error bars = SEM. ==== Refs Working with VDUs, Health and Safety Executive, London, Pascarelli EF Hsu YP Understanding work-related upper extremity disorders: clinical findings in 485 computer users, musicians, and others. J Occup Rehabil 2001 11 1 21 11706773 10.1023/A:1016647923501 Griegel-Morris P Larson K Mueller-Klaus K Oatis CA Incidence of common postural abnormalities in the cervical, shoulder, and thoracic regions and their association with pain in two age groups of healthy subjects. Phys Ther 1992 72 425 431 1589462 Greening J Lynn B Leary R Sensory and autonomic function in the hands of patients with non-specific arm pain (NSAP) and asymptomatic office workers. 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Am J Ind Med 2002 41 236 249 11920967 10.1002/ajim.10067 Buckle PW Devereux JJ The nature of work-related neck and upper limb musculoskeletal disorders. Appl Ergon 2002 33 207 217 12164505 10.1016/S0003-6870(02)00014-5 Szeto GP Straker L Raine S A field comparison of neck and shoulder postures in symptomatic and asymptomatic office workers. Appl Ergon 2002 33 75 84 11831210 10.1016/S0003-6870(01)00043-6

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BMC Musculoskelet Disord. 2004 Jul 28; 5:23

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BMC Musculoskelet Disord

10.1186/1471-2474-5-23

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