Hepatocellular carcinoma (HCC) is one of the most worldwide frequent primary carcinomas resulting in the death of many cirrhotic patients. Unfortunately, the molecular mechanisms of this cancer are not well understood; therefore, we need a good model system to study HCC. The dog is recognized as a promising model for human medical research, namely compared with rodents. The objective of this study was to establish and characterize a spontaneous canine tumor cell line as a potential model for studies on HCC.Histomorphological, biochemical, molecular biological and quantitative assays were performed to characterize the canine HCC cell line that originated from a dog with a spontaneous liver tumor. Morphological investigations provided strong evidence for the hepatocytic and neoplastic nature of the cell line, while biochemical assays showed that they produced liver-specific enzymes. PCR analysis confirmed expression of ceruloplasmin, alpha-fetoprotein and serum albumin. Quantitative RT-PCR showed that the canine HCC cell line resembles human HCC based on the measurements of expression profiles of genes involved in cell proliferation and apoptosis.We have developed a novel, spontaneous tumor liver cell line of canine origin that has many characteristics of human HCC. Therefore, the canine HCC cell line might be an excellent model for comparative studies on the molecular pathogenesis of HCC. Hepatocellular carcinoma (HCC) is one of the most worldwide frequent primary tumors in man, with an estimated 564,000 new cases and almost as many deaths in 2000 . It almoHowever, the study of those mechanisms is hampered because the liver tissue of patients with HCC has only limited value and primary hepatocytes are difficult to maintain in culture. Furthermore, primary hepatocytes rapidly lose detoxifying P450 isoenzymes. In addition, and because of the heterogeneity of the molecular genetic changes that can lead to HCC across species, molecular genetic studies in animals have not yet provided a precise general model for the molecular pathogenesis of HCC in humans. The dog is a valuable model for human comparative studies, since it has a comparative life span and habitat and thus similar risk factors and its domestication started over 10,000 years ago [Here, we describe the establishment and morphological, immunohistochemical, biochemical, and molecular characterization of the first canine hepatocyte cell line derived from a spontaneous HCC of a dog. The objective of this study was to investigate whether this cell line could be used as a potential model for studies on human HCC. Therefore, we investigated whether this canine hepatocyte tumor cell line had features similar to human HCC with respect to mutations in the hepatocyte growth factor receptor (c-MET) gene and the differential gene expression of several oncogenes, proto-oncogenes and proteins involved in proliferation, apoptosis and cell survival.The primary neoplasm was histologically characterized by broad trabeculae, 2 – 6 cells in thickness, of well-differentiated hepatocytes and separated by sinusoidal structures lined by endothelium. The hepatocytes had uniform moderately sized nuclei and small nucleoli; mitotic figures were very rare. Regularly areas with marked steatosis or glycogen accumulation within the neoplastic hepatocytes were observed. Locally within this well differentiated tumor there was a carcinomatous area characterized by broad trabeculae of more basophilic cells with large nuclei, moderate anisokaryosis, usually one or more large nucleoli and 3–5 mitotic figures per high power field Figure . The nonDirectly after establishment of the initial cell suspension (June 19, 2002), the cells appeared pleiomorphic. After approximately 10 weeks of culturing, the cells formed clusters as rounded, vital cells, which are non-adherent. This characteristic has remained ever since. Freezing and re-culturing of the cell line had no effect on cell growth. A 1:10 splitting and medium refreshment of the culture by careful trypsinization once a week (a "passage") is optimal. The tryspinized cell clusters were further cultured with fresh DMEM culture medium. The cells rather grow in these smaller clusters and not as single cells.The cell clusters were collected, fixed and handled as described. As shown in Figure The activity of ALT, GLDH and AST was measured to investigate whether the cHCC cells produced liver characteristic enzymes. In order to compare the amount of hepatic enzymes produced by the cHCC cell line, those measurements were also performed for the widely used human hepatocyte cell line HepG2 (enzyme activity of HepG2 was set at 100%), and the commonly used canine kidney cell line MDCK. The results showed the cHCC cell line produced 25% of the highly liver-specific ALT compared to HepG2, whereas the MDCKs did not produce ALT at all Table . Of anotTo further examine whether the cell line truly consists of hepatocytes, we isolated RNA from the cHCC, made cDNA, and performed PCRs for the gene expression of hepatocyte markers. The cHCCs proved to be PCR-positive for canine serum albumin, alpha-fetoprotein and ceruloplasmin. All obtained products were sequence confirmed.Mutations in exons 15–21 of the c-MET gene have been described in human HCC. A PCR was therefore performed with primers based on this region of the c-MET gene. The products were analyzed and aligned with known canine and human c-MET sequences (Gen Bank Accession numbers AB118945 and NM_000245, respectively). At nucleotide position 4089, a thymine (T) instead of an adenine (A) was observed, which resulted in a serine in the cHCC cell line versus a threonine in healthy tissue at codon 1363 (T1363S) . Then, the dog was immediately euthanized.2 and 95% air under a humidified atmosphere in non-coated flasks. It was observed every day for any changes. The medium was refreshed twice a week.Immediately after resection, the liver samples were kept in DMEM culture medium supplemented with 10% fetal calf serum , penicillin and streptomycin  and were kept on ice. Under sterile conditions, liver samples of various areas were cut into small pieces (5 × 5 mm) and trypsinized with 30 ml of trypsin/EDTA  in a sterile Erlenmeyer flask placed on a stirring platform for 30 minutes. The cell suspensions were filtered with a 70 μm nylon filter . Erythrocytes were lysed from the filtered suspension. The remaining cell suspension was resuspended in DMEM supplemented with 10% FCS and P/S and was cultured at 37°C with 5% CO2 flask with the cHCC cell culture was harvested by transferring the entire contents of the flask to a tube, which was centrifuged for 10 minutes at 1,500 g. The supernatant was replaced by freshly made fixation fluid for 4 hours. For optimal immunohistochemical staining, four different fixatives were used: zinc sulfate formalin, Bouin, Carnoy, and 10% neutral buffered formalin. The fixated cell pellet was transferred to a foam leaf-protected plastic embedding cassette. After fixation, samples were manually dehydrated and embedded in paraffin. Sections (3 μm thick) were cut and stained with hematoxylin and eosin (HE). For immunohistochemical staining, paraffin sections were mounted on poly-L-lysine coated slides, post-fixed into ice-cold acetone fixation fluid for 10 minutes, air dried and stored at room temperature (RT) until use.After 3 weeks of culturing with medium refreshment only and no splitting of the culture, the content of a T80 cm2O2, in methanol, for 30 minutes at RT. After washing with PBS buffer containing 0.1%Tween-20, background staining was blocked by incubating the sections with normal goat serum (1:10 diluted in PBS), for 30 minutes. Sections were incubated overnight at 4°C with the primary antibody HepPar1 (clone OCH1E5, Dakocytomation, Glostrup, Denmark) diluted 1:50 in PBS. After washing in PBS-Tween, slides were incubated in DAKO EnVision™ + reagent, HRP-labeled (Dakocytomation,) for 45 minutes at RT. After washing in PBS buffer, sections were developed using 3,3-diaminobenzidine as chromogen, and counterstained with hematoxylin.For the detection of HepPar1, slides were deparaffinized, immersed in 10 mM Tris, 1 mM EDTA buffer (pH 9), heated in a microwave oven for 10 minutes for antigen retrieval, cooled down for 10 minutes at RT and washed in PBS buffer. Endogenous peroxidase activity was blocked by 0.3% HFor the detection of CK7, the slides were treated as described above but without antigen retrieval, and incubated overnight at 4°C with mouse anti-human CK7, clone OV-TL 12/30 (Dakocytomation), diluted 1:25 in PBS with 1% bovine serum albumin. For both HepPar1 and CK7, formalin-fixed paraffin-embedded canine liver and kidney tissue controls were incubated with and without the primary antibody. In contrast to the cell culture, in the liver tissue antigen retrieval for CK7 was necessary and, therefore, a 40-minute proteinase-K (Dakocytomation) digestion at RT was performed before blocking of the endogenous peroxidase, in methanol.2 flask with the cHCC cell culture was harvested, spun down for 5 minutes at 1,500 g, the cell pellet was washed in 10 ml PBS, centrifuged for 5 minutes at 1,500 g and the cells were resuspended in 1 ml PBS. Two hundred μl of the cell suspension were lysed and homogenized with a pestle in RIPA buffer containing 1% Igepal, 0.6 mM phenylmethylsulfonyl fluoride, 17 μg/ml aprotinine and 1 mM sodium orthovanadate , for 30 minutes on ice. Total protein concentrations were calculated using a Lowry-based assay .The content of a T80 cmFor the liver enzyme measurement, 800 μl of the cell suspension was centrifuged at 12,100 g for 5 minutes. The pellet was lysed in milliQ by vortexing, centrifuged again for 5 minutes at 12,100 g and the supernatant was analyzed in a Beckman Synchron CX7 analyzer. The following enzymes were measured: ALT, AST and GLDH. AST and ALT were measured at 37°C with the Tris-pyridoxal phosphate method with Beckman-Coulter reagent. GLDH was measured with Roche reagent. All samples were subjected to the external quality control mission of the Dutch Foundation for Quality Assessment in Medical Laboratories.2 flask) under standard culturing conditions, as described for the cHCC cell line.As comparison, the widely used human hepatoma cell line HepG2 (Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH, DSMZ, Germany) and MDCK canine kidney cell line (own collection) were used. These were grown to 80–100% confluency  according to the manufacturer's instructions. The RNA samples were treated with DNase-I (Qiagen RNase-free DNase kit). In total 3 μg of RNA was incubated with poly (dT) primers at 42°C for 45 min, in a 60 μl reaction, using the reverse transcription system (Promega Benelux, Leiden, The Netherlands).Taq polymerase (Invitrogen), 2 mM MgCl2 (Invitrogen) and 250 μM of each nucleotide (Promega Corporation, Madison, WI). The PCR conditions were: initial denaturation at 95°C for 4 min, followed by 40 cycles consisting of denaturation at 95°C for 1 minute, annealing at 60°C for 1 minute, elongation at 72°C for 1 min, and, finally, an elongation step at 72°C for 10 min. The PCR products were analyzed on a 1.5% agarose gel, and the DNA fragments were visualized with ethidium bromide. The primers used for these PCRs are depicted in Table To examine whether the cell line consisted of hepatocytes, we isolated total RNA and made cDNA as described above. PCRs were performed to investigate the gene expressions of hepatocyte  markers. All reactions were performed in a 50 μl volume with a thermal cycler (MJ Research Inc., Watertown, MA). Reaction mixtures contained 0.2 μM of each oligonucleotide primer (Isogen Life Science, Maarssen, The Netherlands), PCR buffer (Invitrogen Corporation, Carlsbad, CA), 2.5 U of Platinum To investigate mutations in the tyrosine kinase domain of c-MET, a PCR was performed with two overlapping primer sets for this domain Table , both reQuantitative gene expression measurements of the cHCC cell line were compared with a group of four healthy liver tissues. Liver biopsies from the healthy dogs, which included two Cairn terriers (breed of the donor dog), were obtained under local anesthesia with a 16G biopsy needle and immediately snap-frozen and stored at -70°C until further analysis.® green I, BMA, Rockland, ME) was performed in triplicate in a spectrofluorometric thermal cycler (iCycler®, BioRad). Per reaction, 1.67 μl of cDNA was used in a 50 μl volume containing 1 × manufacturer's buffer, 2 mM MgCl2, 0.5 × SYBR® green I, 200 μM dNTP's, 0.4 μM of each oligonucleotide primer, 1.25 units of AmpliTaq Gold (Applied Biosystems), on 96-well iCycler iQ plates (BioRad). Primers ; AST – aspartate aminotransferase; CK7 – cytokeratin 7; GLDH – glutamate-lactate dehydrogenase; HCC – hepatocellular carcinoma; HepPar1 – hepatocyte paraffin-1; HGF – hepatocyte growth factor; HPRT – hypoxanthine phosphoribosyl transferase; MDCK – Madin-Darby canine kidney cells; ODC – ornithine decarboxcylase; P27KIP – kinase inhibitor protein 27 kDa; PTEN – phosphatase and Tensin homolog deleted on chromosome TEN; SOCS3 – suppressors of cytokine signalling type 3; TGF-alpha – transforming growth factor alpha.SYB carried out the establishment, the biochemical and molecular characterization, and the mutations in c-Met study. BS carried out the quantitative RT-PCR, whereas JY and RK carried out the histomorphological part. TvdI performed the description of the initial tumor. SYB, HFE, TvdI, JR and LC participated in the study design and coordination of the study. All authors read and approved the final manuscript.


A 'spike-in' experiment for Affymetrix GeneChips is described that provides a defined dataset of 3,860 RNA species. A 'best route' combination of analysis methods is presented which allows detection of approximately 70% of true positives before reaching a 10% false discovery rate. As more methods are developed to analyze RNA-profiling data, assessing their performance using control datasets becomes increasingly important.We present a 'spike-in' experiment for Affymetrix GeneChips that provides a defined dataset of 3,860 RNA species, which we use to evaluate analysis options for identifying differentially expressed genes. The experimental design incorporates two novel features. First, to obtain accurate estimates of false-positive and false-negative rates, 100-200 RNAs are spiked in at each fold-change level of interest, ranging from 1.2 to 4-fold. Second, instead of using an uncharacterized background RNA sample, a set of 2,551 RNA species is used as the constant (1x) set, allowing us to know whether any given probe set is truly present or absent. Application of a large number of analysis methods to this dataset reveals clear variation in their ability to identify differentially expressed genes. False-negative and false-positive rates are minimized when the following options are chosen: subtracting nonspecific signal from the PM probe intensities; performing an intensity-dependent normalization at the probe set level; and incorporating a signal intensity-dependent standard deviation in the test statistic.A best-route combination of analysis methods is presented that allows detection of approximately 70% of true positives before reaching a 10% false-discovery rate. We highlight areas in need of improvement, including better estimate of false-discovery rates and decreased false-negative rates. Since their introduction in the mid 1990s , expressThe most useful control datasets to date for evaluating the effectiveness of analysis methods for Affymetrix arrays are cRNA spike-in datasets from Affymetrix and Gene Logic. The Affymetrix Latin square dataset  is a serHere we present a new control dataset for the purpose of evaluating methods for identifying differentially expressed genes (DEGs) between two sets of replicated hybridizations to Affymetrix GeneChips. This dataset has several features to facilitate the relative assessment of different analysis options. First, rather than containing a limited number of spiked-in cRNAs, the current dataset has 1309 individual cRNAs that differ by known relative concentrations between the spike-in and control samples. This large number of defined RNAs enables us to generate accurate estimates of false-negative and false-positive rates at each fold-change level. Second, the dataset includes low fold changes, beginning at only a 1.2-fold concentration difference. This is important, as small fold changes can be biologically relevant, yet are frequently overlooked in microarray datasets because of a lack of knowledge as to how reliably such small changes can be detected. Third, our dataset uses a defined background sample of 2,551 RNA species present at identical concentrations in both sets of microarrays, rather than a biological RNA sample of unknown composition. This background RNA population is sufficiently large for normalization purposes, yet also enables us to observe the distribution of truly nonspecific signal from probe sets which correspond to RNAs not present in the sample.versus average log signal intensity (A)), and if necessary, performing a normalization at the probe-set level to center this plot around M = 0; and third, choosing the best test statistic . The cRNAs were divided into two samples - 'constant' (C) and 'spike' (S) - and each sample was hybridized in triplicate to Affymetrix GeneChips . The S sample contains the same cRNAs as the C sample, except that selected groups of approximately 180 cRNAs each are present at a defined increased concentration compared to the C sample  clone sOur dataset design provides the rare knowledge of virtually all of the RNA sequences within a complex sample  of clones for which only partial sequence was available, and the possible rare mistakenly assigned or contaminated clone). We can therefore evaluate various absent/present call metrics on the basis of their ability to distinguish between the known present and absent RNAs. We investigate this issue at both the probe pair level and probe set level. For the probe pair level assessment, we first identify the probe pairs which we expect to show signal, and those which should not. We thus define two classes of probe pairs: first, perfect probe pairs, whose PM probe matches perfectly to a target RNA sequence, and neither PM nor MM probe matches to any other RNA in the sample with a BLAST E-value cutoff of 1 and word size of 7, and second, empty probe pairs, whose PM and MM probes do not match to any RNA sequence when using the same criteria.2(PM/MM), PM-MM, , and log2(PM) - to distinguish between perfect and empty probe pairs, by calculating receiver-operator characteristics (ROC) curves using the perfect probe pairs as true positives and the empty ones as true negatives. Each point on a curve depicts the specificity and sensitivity for RNA detection, when using a specific value of the corresponding metric as a cutoff for classifying probe sets as present or absent. Instead of depicting the false-positive rate (the fraction of true negatives that are detected as present) on the x-axis, which is customary for these types of graphs, we show the false-discovery rate (the fraction of detected probe sets which are true negatives), which distinguishes between the metrics more effectively for the top-scoring probe sets. Figure 2(PM/MM) and PM-MM, are the most successful at distinguishing perfect from empty probe pairs. This indicates that the PM signal alone is a less effective indicator of RNA presence, probably because the probe hybridization affinity is highly sequence-dependent. However, even with the more successful metrics, only about 60% of the perfect probe sets are detected before reaching a 10% false-discovery rate, indicating that there is still a high level of variability in probe pair sensitivity, even when using the MM signal to estimate the probe hybridization affinity.On the chip, which contains 195,994 probe pairs, there are 50,859 perfect probe pairs and 117,904 empty ones. Observation of the signal for these probe pairs Figure  clearly p-values from this test are used to generate the ROC curves in Figure 2(PM/MM), performs best. Therefore, the MM signals are important in generating accurate presence/absence calls. In our dataset, about 85% of the true positives could be detected before having a 10% false-discovery rate. The detection of perfect probe pairs is not improved when we include additional information from replicates. The 15% of probe sets which are called absent may represent truly absent RNAs, owing to failed transcription or labeling . However, as we do not have an independent measure of failed transcription for the individual cRNA sequences in the target sample, we cannot completely rule out the possibility that they are the result of non-responsive probes or a suboptimal absent/present metric that fails to score low-abundance cRNAs. Regardless, as non-responsive probes or missing target cRNAs should affect both the C and S chips identically, these factors should not limit the value of this dataset in making relative assessments of different analysis methods.When signals from the 14 probe pairs in each probe set are combined to create a composite absence/presence call, a much larger fraction of the spiked-in RNA species can be detected reliably. To obtain absent/present calls at the probe-set level, we perform the Wilcoxon signed rank test using each of the metrics listed above . The p-vPerfect Match [gcrma [The first task in analyzing Affymetrix microarrays is to combine the 14 PM and 14 MM probe intensities into a single number ('expression summary') which reflects the concentration of the probe set's target RNA species. Generating this value involves several discrete steps designed to subtract background levels, normalize signal intensities between arrays and correct for nonspecific hybridization. To compare the effectiveness of different analysis packages at each of these steps, we created multiple expression summary datasets using every possible (that is, compatible) combination of the options described below. Algorithms were chosen for their popularity with microarray researchers and their open-source availability, and were generated using the implementations found in the Bioconductor 'affy' package . Figure ct Match  and gcrmh [gcrma ). The coMAS background [RMA) algorithm [An estimate of the background signal, which is the signal due to nonspecific binding of fluorescent molecules or the autofluorescence of the chip surface, was generated using two possible metrics. The ckground  is calculgorithm  subtractConstant is a global adjustment by a constant value to equalize the chip-wide mean (or median) signal intensity between chips. Constantsubset is the same global adjustment but equalizing the mean intensity for only the probe sets with fold change equal to 1. Invariantset [Invariantsetsubset is the same as invariantset but the rank-invariant set is selected as a subset of the probe sets with fold change equal to 1. Loess normalization [Loesssubset normalization is the same as loess but using only the probe sets with fold change equal to 1. Quantile normalization [Quantilesubset normalization is the same as quantile but normalizes the spiked-in and non-spiked-in probe sets separately.The signal intensities are normalized between chips to allow comparisons between them. Because in our dataset, a large number of RNAs are increased in S versus C (and none are decreased), commonly used methods often result in apparent downregulation for spiked-in probe sets in the 1x change category. We thus added a set of modified normalization methods which used our knowledge of the 1x probe sets. The following different methods were applied. riantset  is a nonlization  is a nonlization  enforcessubtractmm). The second is the method used in MAS 5.0, in which negative values are avoided by estimating the nonspecific signal when the MM value exceeds its corresponding PM intensity [PM only (no correction). The subtractmm and MAS methods are compatible only with the MAS background correction method; that is, it does not make sense to combine these with RMA background correction.We chose three ways to adjust the PM signal intensities to account for nonspecific signal. The first is to subtract the corresponding MM probe signal (ntensity . The thiMAS 5.0); median polish (RMA); or the model-based Li-Wong expression index (dChip). Analyses including the subtractmm PM correction method require dealing with negative values when PM is less than MM, which occurs in about a third of the cases. Within Bioconductor, the Li-Wong estimator can handle negative values, but the other two metrics mostly output 'not applicable' (NA) for the probe set when any of the constituent probe pairs has negative PM - MM. The result for MAS and median polish is NA for about 85% of the probe sets on the chip. To study the consequence of losing so many probe sets, we modified one of these two metrics (median polish) to accept negative (PM - MM) (medianpolishna), and added this metric whenever subtractmm was used.The 14 probe intensity values were combined using one of the following robust estimators: Tukey-biweight  when the probe sets in the lowest quartile of signal intensity are filtered out. The expression summary datasets which involve correcting the PM signal by subtracting the MM signal (subtractmm) have the highest correlation coefficient, because low-intensity probe sets have been filtered out during processing, as described above. We therefore suggest that an important feature of a successful microarray analysis is to account for probe sets with low signal intensity, either by filtering them out or by using a signal-dependent metric for significance. Several ways of accomplishing such filtering are described below.For each of the 150 expression summary datasets that we generated, fold changes between the S and C samples were calculated and then compared with the actual fold changes. Most expression summary datasets show good correlation between the observed and actual fold changes  [t-statistic has a constant value added to the standard deviation. This constant 'fudge factor' is chosen to minimize the dependence of the t-statistic variance on standard deviation levels. The second is CyberT [basic (Student's) t-statistic. For CyberT and the basic t-test, we performed the tests on the expression summaries after log transformation, as well as on the raw data. As shown in the example ROC curve, the CyberT statistic outperforms the other statistics for the vast majority of expression summary datasets  does not effectively filter out these same false-positive probe sets . Upon further inspection, we observed that the SAM algorithm favors using large values for the constant fudge factor, so that the t-statistic depends more on the fold change value, than on the noise level. The basic t-statistic is prone to false positives resulting from artificially low standard deviations, owing to the limited number of replicates in a typical microarray experiment  outperforms several other methods. Because the CyberT statistic clearly performs the best, we use only this statistic to compare the options for the other steps in microarray analysis, below.Because a typical microarray experiment contains a large number of hypotheses here 14,010) and a limited number of replicates (in this case three), high false-positive rates are a common problem in identifying DEGs. An important factor in minimizing false positives is to incorporate an appropriate error model into the signal/noise metric. We compared three ys (SAM) , in whic,010 and s Figure . Inspecth Figure . As showy = 0 when necessary. Owing to the significant improvement seen when the second normalization is used, the subsequent figures , or using the MAS 5.0 correction method, is better than using uncorrected or RMA-corrected PM values (PM-only). The MAS 5.0 method performs the best because it does not create any negative values. This result is in apparent conflict with the conclusions of Irizarry et al. [et al. use a test statistic that takes the variance into account, PM-only and MM-corrected methods (MAS) have similar sensitivity/specificity  because PM-only does not try to correct for nonspecific hybridization in a probe-specific fashion. In contrast, for the Latin square datasets used in [PM-only works just as well as MM-corrected methods because the contribution of nonspecific hybridization is constant. Therefore, datasets which compare substantially different RNA samples (such as two different tissue types) should probably be processed using the MAS 5.0 method for PM correction.With respect to adjusting the PM probe intensity with an estimate of nonspecific signal, Figure median polish (RMA) and the Tukey Biweight methods (MAS 5.0) perform the best. Figure Figure We were concerned that some of our analyses might be confounded by a possible correlation between low fold change and low expression summary levels, which could affect the interpretations of Figure perfect match and gcrma) to the control dataset. With respect to detecting the true DEGs, these two models perform reasonably well, although slightly less well than the MAS 5.0 PM correction method. When we consider only the low signal DEGs , gcrma outperforms perfect match, and is similar in effectiveness to the top analysis option combinations.Models dependent on probe sequence provide a promising route to improving the accuracy of nonspecific signal measures. Here, we applied two different models , which is the fraction of false positives within a list of genes exceeding a given statistical cutoff. We used our control dataset to compare the actual q-values for the 10 optimal expression summary datasets with q-value estimates from the permutation method implemented in SAM. As shown in Figure q-value calculations using each of the top ten datasets underestimate the actual q-value for a given cutoff. We attempted to reduce the contribution of biases inherent in any given data-processing step by combining the results from the top 10 expression summary datasets. The goal is to pinpoint those genes that are called significant regardless of small changes in the analysis protocol . To identify these 'robustly significant' genes, we created a combined statistic from the top 10 datasets depicted in Figure q-value estimate than any of the individual datasets. However, there is still considerable difference between the estimated and actual q-values. For example, if we estimate q = 0.05, the corresponding CyberT statistic has an actual q = 0.18, and if we estimate q = 0.1, then the actual q = 0.3. Therefore, until more accurate methods for estimating the false-discovery rate are developed, we recommend that a conservative choice of false-discovery rate cutoff be used (for example < 1%) to prevent actual numbers of false-positive DEG calls (that is, the true, rather than estimated, FDR) from being too high.We have identified a set of analysis choices that optimally ranks genes according to significance of differential expression. To decide how many of the top genes to investigate further in follow-up experiments, it would be useful to have accurate estimates of the false-discovery rate (FDR or q) increases from 10% to 30%. Taking an upper acceptable bound for q as 10%, the maximum sensitivity obtained is about 71%. Thus, under the best-performing analysis scheme, roughly 380 (29%) of the 1,309 DEGs are not detected as being differentially expressed, with the number of false positives equaling about 105. At q = 2%, sensitivity reduces to around 60%, meaning that more than 520 DEGs are missed, albeit with fewer than 20 false positives.As the identities and relative concentrations of each of the RNAs in the experiment were known, we were able to assess directly the sensitivity and specificity obtained by the best-performing methods. Examination of the ROC curves in Figure q = 10%, sensitivity is increased to 93% when only cRNAs that differ by twofold or more are considered as DEGs . The reduction in sensitivity is almost wholly due to the low-fold-change genes: less than 50% of DEGs with fold change 1.5, and none of the DEGs with fold change 1.2, are detected at q = 10% , this is likely to prove a very real limitation to detecting DEGs. In cases where pure cell populations can be obtained, for example by laser capture microdissection, the numbers of cells are often small and RNA needs to undergo amplification in order to have enough for hybridization. Here, non-linearities in RNA amplification might also lead to observed fold changes that fall below the twofold level. We used three microarray replicates for this study, as this is frequently the number chosen by experimentalists because of cost and limiting amounts of RNA. One possible extension of this work would be to examine how many replicates are necessary for reliable detection of DEGs at a given fold change level.We have compared a number of popular analysis options for the purpose of identifying differentially expressed genes using an Affymetrix GeneChip control dataset. Clear differences in sensitivity and specificity were observed among the analysis method choices. By trying all possible combinations of options, we could see that choices at some steps of analysis are more critical than at others; for example, the normalization methods that we considered perform similarly, whereas the choice of the PM adjustment method can strongly influence the accuracy of the results. On the basis of our observations, we have chosen a best route for finding DEGs Figure . As any Drosophila Gene Collection release 1.0 cDNA clones [Drosophila arrays (DrosGenome1) using standard Affymetrix protocols. We chose to hybridize each replicate chip from an aliquot of a single C (or S) sample, resulting in technical replication; thus this dataset does not address the noise introduced by the labeling and mixing steps. The clones comprising each pool can be found in Additional data file 8, and the resulting Affymetrix chip intensity files (.CEL) files are available in Additional data files 6-7.PCR products from A clones  were genA clones . Each PCThe total amount of labeled cRNA that was added to each chip (approximately 18 μg) was comparable to a typical Affymetrix experiment (20 μg). Although we do not know the individual RNA concentrations, we estimate that these span the average RNA concentration in a biological GeneChip experiment. Our biological RNA samples typically result in about 40% of the probe sets on the DrosGenome1 chip called present, so the mean amount of individual RNA is 20 μg/(14,010 × 0.40) = 0.003 μg/RNA. In the C chips, the average concentration of individual RNAs in the different pools range from 0.0008 to 0.007 μg/RNA, so the concentrations are roughly similar to those in a typical experiment. We note, however, that there is no way to ensure that the concentration distribution is truly reflective of a real RNA distribution. This is especially true with respect to the low end of the range, as it is usually unknown how many of the absent genes on an array are truly absent versus weakly expressed and thus poorly detected by the analysis algorithms used. Therefore, our analysis possibly favors methods that perform best when applied to highly expressed genes.et al. [t-statistic variant: for the SAM statistic, false discovery rates from the authors' Excel Add-in software was used, whereas for the CyberT and basic t-statistics, the Bioconductor false-discovery rate implementation was applied, which includes an extra step to enforce monotonicity of the ROC curve. In our experience, this extra step does not qualitatively alter the results. All scripts generated in this study are available for use [All of the analysis was performed using the statistical program R , includiet al. ,32. In aet al. , MAS 5.0et al. , Perfectet al. ,21 and Set al.  executab for use ,28.n datasets and Cij, Sij are the logged signals for a given probe set in the jth C and S chips, respectively, in dataset i. The mean signal (for this probe set) for the C chips in dataset i is:Say we have  is the number of C chips in dataset i; similarly, the mean signal for the S chips in dataset i is:where The mean fold change over all datasets is:i is based on the CyberT estimate:The modified standard deviation for the C chips in dataset const is the weight for the contribution of the average standard deviation  for probe sets with the same average signal intensity as Cij. The modified standard deviation for the S chips in dataset i (sd.Si) is defined analogously. The pooled variance over all 10 datasets is defined as:where The variance between the 10 datasets is defined as:Then the combined statistic was chosen to be:2 fold change) vs A  plot for the comparison of two biological samples. Additional data file Additional data is available with the online version of this article. Additional data file contains a figure and explanatory legend showing the degree of overlap between two lists of differentially expressed genes. Additional data file A figure and explanatory legend showing the degree of overlap between two lists of differentially expressed genesClick here for additional data fileAll analysis option combinations used to generate the expression summary datasets in this studyClick here for additional data fileA plot of observed vs actual spiked-in fold changes at the probe levelClick here for additional data file2 fold change) vs A  plot for the comparison of two biological samplesAn example of asymmetric M , Affymetrix *.CEL files for the C chips in this datasetClick here for additional data fileA Zip archive containing plain text files (in Affymetrix CEL format), Affymetrix *.CEL files for the S chips in this datasetClick here for additional data fileDetailed information for the individual DGC clones used in this studyClick here for additional data file


Factor analysis is one of the most used statistical techniques to analyze the inter-relationships among symptoms reported by Gulf War veterans. The objective of this study was to apply factor analyses to binary symptom data from the UK study of Gulf War illness and the US Air Force study of Gulf War veterans, and to compare the symptom domains derived from the distinct samples.UK veterans of the 1991 Gulf War (n = 3,454), individuals deployed to Bosnia on U.N. peacekeeping operations (n = 1,979) and Gulf War-era servicemen (n = 2,577) who were not deployed to the Gulf were surveyed in 1997–1998, and US 1991 Gulf War veterans from four Air Force units (n = 1,163) were surveyed in 1995 to collect health characteristics including symptoms. Each sample was randomly split in half for exploratory and confirmatory dichotomous factor analyses with promax oblique rotation.Four correlated factors were identified in each of the samples. Three factors  overlapped considerably across the UK cohorts. The Gastrointestinal/Urogenital factor in the UK Gulf cohort was noticeably different from the Gastrointestinal factor identified from the Bosnia and Era cohorts. Symptoms from Gulf War UK and U.S cohorts yielded similar Gastrointestinal, Respiratory and Mood-Cognition factors, despite differences in symptom inventories between the two surveys. A Musculoskeletal factor was only elicited from the US Gulf sample.Findings of this report are consistent with those from other factor analysis studies that identified similar symptom dimensions between Gulf and non-Gulf War veterans, except that the Gastrointestinal factor in Gulf veterans included other symptom types. Correlations among factors raise the question as to whether there is a general illness, even if not unique to Gulf veterans, representing the common pathway underlying the identified factors. Hierarchical factor analysis models may be useful to address this issue. Reports that veterans of the 1991 Gulf War were suffering from unexplained signs and symptoms started to appear as early as one year after the conflict . Factor Several studies have applied factor analysis  to examine and compare the inter-relationships among symptoms reported by veterans -13. In gWhen symptoms are measured on a continuous scale normally distributed, linear factor analysis  can be The objective of this report was to apply dichotomous factor analyses to binary symptom data from two studies, i.e., the UK study of Gulf War illness ,17-19 anThe data used in this report came from two sources: the UK Study of Gulf War illness ,17-19 thDuring the past month, have you suffered from any of the following symptoms?") [current health problems [In the UK study, we considered the analysis of 50 symptoms that occurred in the preceding month ("ptoms?") ,17,18 . Exploratory dichotomous factor analyses  were perThe confirmatory dichotomous model  specifieOf the 3,454 veterans of the Gulf War, 93.3% were men, 75% were married or living with a partner, 92.5% had regular military status when they were deployed to the Gulf. Their average age was 34.4 years (standard deviation = 6.8). The Bosnia cohort had 1,979 veterans with average age of 29.3 years (standard deviation = 6.7) including 89.4% men, 57.5% married or living with a partner, and 91.1% with regular military status when deployed to Bosnia. The Era cohort included 2,577 veterans (92.7% men, 75.3% married or living with a partner, average age of 35.3 years (standard deviation = 7.2) and 48.8% regular military status).The US Gulf sample included 1,163 veterans who were 94.0% men, 74.9% married or living with a partner, with an average age of 37.9 years (standard deviation = 8.4).The most common symptoms across all UK groups were feeling unrefreshed after sleep, irritability, headaches, fatigue, sleeping difficulties, forgetfulness, loss of concentration, joint stiffness and flatulence/burping Table . The preThe exploratory sample consisted of 1,783 persons. The scree plot suggested 1 major factor, but it was not clear how many additional factors should be investigated (data available from authors). We removed the first eigenvalue from the plot, to better determine how much each additional factor contributed to the variance, and decided to examine 2 to 5-factor solutions. Although all solutions indicated a good fit between data and model (RMSEA ≤ 0.06), the 2-, and 3- factor models yielded non-interpretable factors, and the 5-factor solution could not be confirmed. Thus, we tested the 4-factor exploratory solution in the confirmatory sample that included 1,671 subjects. We specified the number of factors to be 4, the factor variances to be 1 and the leading symptoms of the first (pain on passing urine), second (loss of concentration), third (unable to breathe deeply enough), and fourth (tingling in fingers and arms) factors. Table The Bosnia Cohort exploratory sample included 1,008 subjects and the confirmatory included 971. A 4-factor solution with 32 symptoms was confirmed . Results for all 1,979 subjects are displayed in Table Although the constructs identified from symptoms reported by Bosnia veterans were similar to those confirmed in the Gulf War veteran sample, we were unable to confirm the Gulf War confirmatory 4-factor solution in the Bosnia sample. Some symptoms from the Gulf cohort Gastrointestinal/Urogenital factor loaded in several Bosnia cohort factors, yielding a structure that was difficult to interpret.There were 1,325 observations in the exploratory and 1,252 in the confirmatory samples. A confirmatory 4-factor model with 26 symptoms and all 2,577 subjects is displayed in Table The overlap of symptom composition across samples was remarkable for the Respiratory, Peripheral Nervous and Mood-Cognition factors Table . The facThe exploratory sample consisted of 590 subjects and the confirmatory included 573. A 4-factor solution with 26 symptoms was confirmed and results for all 1,163 subjects are displayed in Table We could not directly compare the factor structures because the symptom inventories were so different. For example, 3 peripheral nervous symptoms were asked from the UK veterans (Tingling in fingers and arms, Numbness or tingling in fingers or toes, Tingling in legs and arms) while the US study included only 1 (numbness or tingling in fingers or toes). Thus, a separate factor could not be derived from the US study. Nevertheless, both UK and US data yielded similar constructs, namely a mixed gastrointestinal factor, a mood-cognition factor, and a respiratory-related factor. The main difference was that the musculoskeletal construct could not be confirmed in the UK Gulf cohort, and it represented a separate factor in the US sample Tables  and 5.The objective of this report was to identify and compare syndromes among 4 samples collected from UK ,17-19 anWe identified and confirmed at most 4 correlated factors in each of the samples. Three of the four constructs  overlapped considerably across the UK cohorts. These factors were identical to those derived in a linear factor analysis of these data . HoweverIn addition, despite differences in study designs, methods of data collection, military populations and symptom inventories between the UK and US studies of Gulf War veterans, Gastrointestinal, Respiratory and Mood-cognition factors were identified in both UK and US studies. Of note, although joint pain and joint stiffness were measured in both UK and US samples of Gulf War veterans, a Musculoskeletal factor was only elicited as a separate factor from the US data.In general, findings of this report were consistent with those from other studies that used factor analysis of symptoms to compare symptom patterns between Gulf and non-Gulf War veterans ,9-11. HoIn this report, we encountered difficulties confirming the dichotomous factor structures, or reproducing a factor structure in another sample, because many symptoms were rare, which created numerical problems. On the other hand, failure to reproduce factor structures across samples may also be due to different symptom distributions in the samples being considered, as was the case of the UK Gulf and Bosnia cohorts. We also acknowledge the difficulties in analyzing self-report symptom data. However, since the UK and US studies were independent of the military and confidential, we do not believe there was a reason form service personnel to exaggerate symptoms in order to gain compensation or eligibility for veterans.Finally, it must be noted that, in each of the UK or US cohorts, factors were moderately or highly correlated. Correlated factors are complex to interpret because it is difficult to separate their independent effects . This fiIn conclusion, considerable progress has been made in defining medically unexplained illness associated with deployment to the 1991 Gulf War. Our results from independent studies conducted in the UK and US confirmed occurrence of an illness comprised of 4 correlated groups of symptoms (factors) in deployed military personnel from both countries. Similar illness occurred in troops who did not participate in the Gulf War , so we believe that this pattern of symptoms is not unique to Gulf War service nor does it represent a unique illness or "Gulf War syndrome." In fact, similar illnesses to those affecting Gulf War veterans have been noted among veterans of US Civil War  and BritNone declared.RN conceived of this analysis was responsible for its execution and had primary responsibility for the manuscript; KI was instrumental in the conception and design of the UK veterans' study and had primary responsibility for its analysis; collaborated in analysis and interpretation of the present data and writing the manuscript; SW was Principal Investigator for the UK veterans' study, Collaborated in the concept of the present study and collaborated in analysis, interpretation and the manuscript; CU collaborated in the UK veteran's study and collaborated in analysis and interpretation of data and drafting the manuscript for this study; LH collaborated in the UK veteran's study and collaborated in analysis and interpretation of data and drafting the manuscript for this study; WCR was Principal Investigator of the US Gulf War study, conceived the idea for the present study, served as Principal Investigator for the present study and collaborated in all aspects of data interpretation and writing the manuscript.


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.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  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 ACTN4 mutations cause a form of focal segmental glomerulosclerosis (FSGS) (In humans, s (FSGS) . This les (FSGS) .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 . α-ActinACTN4-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 .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 . These fActn4+/+, Actn4K228E/+, and Actn4K228E/K228E mice with either wild-type GFP–α-actinin-4 or K228E mutant GFP–α-actinin-4  to generate litters with wild-type, heterozygous, and Actn4K228E/K228E mice. We genotyped mice by testing for the presence or absence of an engineered silent EarI site as described previously  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 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 We observed decreased mutant α-actinin expression in an immortalized knockin mouse fibroblast homozygous for the broblast D. To helActn4K228/K228E mice, as well as Actn4K228/+ and wild-type littermates. In Actn4K228/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 Actn4K228/K228E kidneys examined by electron microscopy had abnormalities in podocyte structure. Typically, these consisted of focal areas of foot process effacement . 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 , heterozyogtes for either a null or K228E allele (Actn4+/– and Actn4K228E/+), and homozoygotes for either a null or K228E allele (Actn4–/– and Actn4K228E/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 Actn4K228E/+ mice, while both groups of homozygous mutant mice (Actn4–/– and Actn4K228E/K228E) had significantly greater—and similar—degrees of proteinuria  renal failure in adulthood.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 . Mice laThese 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 . The secWe 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 . These pACTN4 mutations become increasingly susceptible to minor insults. We note also that mice, unlike humans, express α-actinin-1 in podocytes .We note that, as shown in utations . AlthougActn4 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 Actn4K228E/+ mice appear normal. By contrast, we observe clear glomerular phenotypes in both Actn4–/– and Actn4K228E/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 Actn4K228E/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.As indicated in 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 . This su2. 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 (Mouse podocytes were kindly provided by Dr. Peter Mundel (Albert Einstein College of Medicine, Bronx, New York, United States) and cultured as described previously . In brieeviously . FibroblFibroblasts 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.We mutated a wild-type pBluescript-GFP-ACTN4 clone using a QuickChange kit  to create clones harboring each of three disease-associated mutations (K228E, T232I, S235P) . These mSucrose 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.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  or 0.125% DMSO alone with cold methionine.Immortalized mouse fibroblasts were incubated in methionine-free MEM medium containing 10% FCS  and 25 mM HEPES for 20 min at 37°C. [For injection and imaging, cells were cultured on MatTek Corporation (Ashland, Massachusetts, United States) 35 mm coverslip dishes in F12 media  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  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  Lambda 10–2 filter wheel image acquisition. Device control and postacquisition processing were done with Isee Imaging Software .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.Actn4 expression (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 .Click here for additional data file.Video S2(11.8 MB MOV).Click here for additional data file.Video S3(10.9 MB MOV).Click here for additional data file.ACTN4; also LocusLink ID 81) and NM_021895 and NP_068695 . OMIM numbers are 603278 (FSGS-1) and 604638 (ACTN4).GenBank accession numbers for genes and proteins discussed in this paper are NM_004924 and NP_004915 (human


A major challenge of sensory systems neuroscience is to quantify brain activity underlying perceptual experiences and to explain this activity as the outcome of elemental neuronal response properties. Rats make extremely fine discriminations of texture by “whisking” their vibrissae across an object's surface, yet the neuronal coding underlying texture sensations remains unknown. Measuring whisker vibrations during active whisking across surfaces, we found that each texture results in a unique “kinetic signature” defined by the temporal profile of whisker velocity. We presented these texture-induced vibrations as stimuli while recording responses of first-order sensory neurons and neurons in the whisker area of cerebral cortex. Each texture is encoded by a distinctive, temporally precise firing pattern. To look for the neuronal coding properties that give rise to texture-specific firing patterns, we delivered horizontal and vertical whisker movements that varied randomly in time (“white noise”) and found that the response probabilities of first-order neurons and cortical neurons vary systematically according to whisker speed and direction. We applied the velocity-tuned spike probabilities derived from white noise to the sequence of velocity features in the texture to construct a simulated texture response. The close match between the simulated and real responses indicates that texture coding originates in the selectivity of neurons to elemental kinetic events. Rats move their whiskers rhythmically across surfaces to sense their environment. Textured surfaces induce distinct patterns of vibration in the whiskers that are encoded by central neural activity One goal of sensory systems neuroscience is to understand how the representations of complex, natural stimuli arise from the basic response properties of neurons. The present experiments explore the representation of textures in the rat somatosensory system. Rats have texture discrimination capacities rivaling those of humans . In ratsThere have been no reports concerning the cortical or subcortical neuronal activity generated by whisking along irregular surfaces, and the differences in activity associated with two surfaces remain unknown . HoweverTo answer these questions, the model must be challenged under conditions where the sensory input is precisely controlled and yet resembles what occurs during natural tactile behavior. Guided by this strategy, in anesthetized rats we produced whisker movements across textures while measuring vibrations of the whisker shaft. We then played back the identical vibrations to other rats, and measured the neuronal activity at two stages of the sensory pathway—the first-order neurons that innervate the sensory receptors, and the barrel cortex neurons, which are the first site of cortical integration. Texture discrimination depends on the integrity of the cortical barrels . By measn = 3), we electrically stimulated cranial nerve VII, generating 8-Hz whisking movements [The experimental strategy  was to covements ,18 that ovements . MeanwhiH) and vertical (VV) velocity. For VH, whisker protraction (forward movement) is positive and whisker retraction (backward movement) negative. For VV, upward movement is positive and downward movement negative. To better visualize the time-varying frequency content of the velocity profiles, the velocity spectrograms are also plotted  consists of six first-order neuron recordings, five cortical cluster recordings, and seven “paired” recordings—simultaneous first-order neuron and cortical cluster. The principal result is that time-varying neuronal activity in the trigeminal ganglion and cortex captured the kinetic features of the texture-induced vibrations. From the same texture library given in Two barrel cortex neuron clusters were recorded simultaneously with the first-order neuron, allowing direct comparison of different stations along the sensory pathway. The selectivity of first-order neurons and cortical clusters for the direction of whisker movement is described in more detail in Xω (referred to in previous publications as Af). The generalization of Xω to the natural, texture-induced vibration is 〈|X(ω,τ)|ω〉Ω,Τ, a quantity known as “equivalent noise level”  and the first two P280 whisks (0 to 250 ms) of trial 50 is given in In The remarkable response locking of first-order neurons to stimulus features is further highlighted in From these observations we conclude that, under our experimental conditions, the trial-to-trial response variability of first-order neurons is caused exclusively by stimulus jitter, whereas that of cortical neurons results mainly from variations across time in sensory integration, and must emerge at some integration site between the trigeminal ganglion and cortex. A question of current interest is whether the variability in cortical responses results from noise and imprecision in neuronal integration , or elseH and VV varied randomly across time. Responses to noise stimuli allowed us to quantify velocity sensitivity and then to generate simulated spike trains based on the sequence of velocity events in the actual texture-induced vibrations. Finally, comparison between simulated and observed responses reveals the extent to which the responses to natural stimuli are explained by neuronal selectivity to velocity features: If simulated responses closely resemble real responses, we can conclude that neurons are in fact operating on natural texture stimuli according to their tuning to elemental kinetic events.We hypothesize that the firing patterns of first-order and cortical neurons during presentation of textures can be explained by their extraction of elemental features from the complex input signal, and that these elemental features are bursts of high velocity. To test this directly, we presented a “white noise” stimulus in which the two stimulus features VH and VV (white circles) across 5 ms of filtered white noise.Typically, neuronal tuning curves are mapped out using an “unbiased” stimulus—a stimulus that avoids the temporal correlations present in natural stimuli. The first step, therefore, was to map out how first-order and cortical neurons encode whisker kinetic features when these features are extracted from the context of the natural stimulus. We applied a stimulus that varied randomly in velocity—Gaussian velocity noise—and therefore was not constrained by the velocity patterns present in texture trajectories see . Figure 5,R9A . To construct the neuron's “tuning curve” in finer detail, velocity space was subdivided into 20 angular and 10 radial segments. The neuron emitted spikes with increasing probability as velocity increased, but only for restricted directions, preferring high speeds that combined retraction  and upward movement . For the simultaneously recorded cortical neuron cluster, spike probabilities in the 5–20-ms poststimulus interval are given in For the first-order neuron Zurvan, spike probabilities in the 1–2-ms postevent interval are given in Can the neurons' responses to complex, natural stimuli be explained as the outcome of these elemental tuning properties? To find out, we projected whisk velocity trajectories upon the white noise-derived tuning curves. One whisk  on P280 sandpaper is depicted on both tuning curves. The first observation is that the intersection of the velocity trajectory with the tuning curves explains why the first-order neuron was selective for whisker retraction while the cortical cluster was directionally nonselective see  and 5.P(t) and a corresponding raster plot for that trial. The 100-trial raster for one run of the simulation was summated to form a PSTH. The close match between the simulated PSTHs (black) and the real PSTHs  precise information about the kinetic features transmitted to the follicle. One kinetic feature is the “equivalent noise level”: Spike counts per whisk both for first-order neurons and for cortical neurons are proportional to the equivalent noise level of the texture-induced vibration. Thus, when texture vibrations differ in this quantity, neuronal spike counts also differ and thereby carry information that could, by itself, separate the textures. By the same token, when texture vibrations have similar equivalent noise levels, spike counts per whisk appear not to carry sufficient information. The second kinetic feature, then, is the temporal sequence of velocity events—distinctive velocity profiles induce distinctive temporal patterns in the spike trains with spike alignment of better than 0.2 ms in the first-order neurons and a few ms in the cortex.The stimulus playback method used here might not produce the identical input to the sensory receptor as occurs during active whisking . The optA classical approach to investigating sensory coding is to map the relationship between well-controlled artificial sensory stimuli and evoked neuronal activity. This can provide a complete description of neuronal feature extraction properties ,25, but In principle, one can bridge the gap between artificial and natural stimuli by (i) measuring neuronal activity during ecologically relevant stimuli, stimuli that are collected from an animal's normal interaction with the environment, (ii) constructing tuning curves under artificial stimulation , and (iii) applying the tuning curves to the natural stimuli to test whether they account for the observed response. Because artificial stimuli only partially cover dimensions of stimulus space present in natural stimuli, and because of neuronal nonlinearity, this procedure typically provides neuronal simulations that match real neuronal output with a correlation of less than 0.5 ,29,30. YAs an alternative to the temporal model outlined here, a spatial model for texture coding has recently been proposed. It begins from the observation that whisker length varies systematically across the anterior-posterior dimension of the rat's snout . When stThe resonance frequency hypothesis predicts that rats would fail to discriminate between surfaces using just a single whisker, yet they have been shown to perform texture discriminations after progressive clippings down to one or two whiskers . Our resExperiments were conducted in accordance with NIH and institutional standards for the care and use of animals in research. Subjects were ten adult male 250–350-g Wistar rats. In one set of anesthetized rats (urethane, 1.5 g/kg), “electrical whisking” ,18 was gH = 0), avoiding the introduction of any position or velocity discontinuity. A free whisk trial always separated two successive texture trials. A block was composed of five different texture trials (t1–5) with free whisk trials (fw) interspersed, e.g., fw-t3-fw-t5-fw-t1-fw-t2-fw-t4. Before stimulus delivery, signals were low-pass filtered at 500 Hz. All data were manipulated in MATLAB software (http://www.mathworks.com).For each texture, a 50-s continuous record was extracted and sliced into 100 trials of 500 ms, each trial composed of two-dimensional position signals across four 125-ms whisks. For free whisks, a 250-s record was sliced into 500 unique trials. The stimulus set was constructed by splicing trials together at the point of maximum retraction (Vx(t) and the whisker velocity profile asWe sought to quantify the kinetic features that characterized each texture-induced vibration. We refer to the whisker trajectory in one dimension as n)V| also corresponds to the spectrogram of the trajectory in position.Note that Xω [Xω to the texture-induced vibration. X becomes a time-varying spectrum X(ω,t)Previous studies have shown that, when the stimulus set consists of sinusoidal whisker movements, the spike count per stimulus for cortical neurons is proportional to the product of the sinusoid's amplitude and frequency, Xω ,12. To tT is the entire time domain and Ω is the entire domain of frequency ω.where Xω across all time intervals and all values of ω.This quantity—known as the “equivalent noise level”—represents the average amplitude of white noise velocity that dissipates the same average power as the signal of interest. It serves to characterize the entire kinetic signature by a single quantity, equivalent to mean value of the product H and VV. nτ, the separately measured spectrograms for the two dimensions. The “velocity spectrogram” column in Our experiments measured velocity in two dimensions, Vhttp://www.fh-co.com) into the right trigeminal ganglion according to stereotaxic coordinates. Cortical recordings were obtained by inserting a 100 microelectrode array  to a depth of 700–1,000 μm in the left barrel cortex [In a second set of urethane-anesthetized rats, neuronal recordings were made simultaneously from two sites. First-order neurons were recorded by advancing a single electrode  in the first-order-only recordings were C3, E1, D6, E6, and γ (twice). The principal whiskers in the cortex-only recordings were A1, B4, E5, and E3 (twice). The principal whiskers in the paired first-order neuron-cortex recordings were δ (twice), E3 (twice), C2 (twice), and E4.Texture stimuli were delivered to a single whisker using a motor constructed from two orthogonal pairs of parallel piezoelectric wafers driven independently by horizontal and vertical signals . The whiH and VV independently from a Gaussian distribution 7,634 times per second. The stimulus was then low-pass filtered (Chebyshev type II) at 500 Hz so as to not exceed the physical capacities of the piezoelectric wafer stimulator . The noise stimulus was presented for 10 min after conclusion of the texture stimuli.We selected V1–8A) and ten radial sectors (1–10R). At each time point, angle corresponds to the direction of whisker movement, while radial distance corresponds to instantaneous speed. The positions of the radial boundaries were chosen to make each segment contain an equal number of instantaneous velocity events: Because velocity had a Gaussian distribution with a mean of zero, high-velocity events were less common, and consequently the radial boundaries were increasingly widely spaced as distance from zero increased.We adopted a method of “forward correlation” between stimulus and response where, for all occurrences of a particular stimulus event, the ensuing neuronal spike trains were averaged to construct a response probability profile for that event. This required subdividing velocity space into discrete segments. In A,R) during the whisk gave an ensuing spike probability profile. To simulate a spike train, a spike was generated in each time bin t (size 0.13 ms) with probability P(t), given by the average of the overlying spike probabilities associated with velocity events in the time window before t . After completion of the simulation, P(t) was normalized so that the overall simulated spike count matched that in the real data; this normalization did not affect the temporal profile of the simulated PSTH.To test whether the complex, texture-induced spike patterns followed directly from the tuning curves, a more elaborate analysis was necessary. Each instantaneous velocity  Trajectories of two whisks  recorded during contact with sandpaper P280.(B) To test playback, a whisker was inserted in the piezoelectric motor guide tube and whisker displacements were measured by the optical sensor placed adjacent to the insertion point of the whisker. The trace shows recordings of the playback of the same two whisks of part A.(C) Magnified view of the traces indicated in the rectangle in (A) and (B).(161 KB PDF).Click here for additional data file.Video S1http://www.redlake.com). Rat EW3, whisker C3.Each optic sensor channel consisted of a pair of light tubes (one is a light source, the other leads to a photodiode). The two channels were mounted normal to each other in a metallic ring. Electrical stimulation of cranial nerve VII with 1-V pulses of 100 μs at 200 Hz for 60 ms produced whisker protraction that was followed by a passive 65-ms whisker retraction. The film shows four 125-ms free whisks in the air (8-Hz whisking), consisting of protraction (right to left) and retraction (left to right). Recorded at 500 frames per second with a Motion Scope 500 digital camera .Click here for additional data file.Video S2The motor consists of two pairs of piezoelectric wafers with axes meeting at a mobile joint. The whisker to be stimulated was placed inside the metal tube, which is orthogonal to the joint. The film shows playback of four 125-ms free whisks in the air, consisting of protraction (left to right) and retraction (right to left). Recorded at 500 frames per second.(9.0 MB MOV).Click here for additional data file.


Regulation of gene expression—deciding how much of what proteins are produced in the cell—is controlled by a myriad of different molecules. One type of naturally occurring regulatory molecule is small interfering RNA (siRNA), which selectively disrupts the production of a protein it is programmed to recognize, a process called RNA interference. These short stretches of nucleotides combine with other cellular proteins to form an RNA-induced silencing complex, called RISC, which locates and destroys a targeted messenger RNA—the molecule that carries a protein recipe from the nucleus to the site of production in the cytoplasm. While RNA interference has been widely exploited by researchers as a tool to knock out gene expression and infer the function of missing proteins, very little is known about the mechanisms behind this regulatory process.Recently, biologists have discovered hundreds of other short pieces of regulatory RNA, called microRNAs, in both plants and animals. Like siRNA, they also affect gene expression, through similar, possibly even identical RISC molecules. Animal microRNAs, however, target messenger RNA at a different stage in protein production. Though researchers have determined the sequences of these microRNAs, uncovering their function—that is, which protein they interrupt and, in turn, what the interrupted protein does—has progressed slowly and sporadically without any decisive tool to study them. Only four animal microRNAs have known biological functions, despite the intense level of work going on in this field.O-methyl oligonucleotide, whose sequence mirrors the targeted siRNA or microRNA, could bind and inhibit their function, allowing researchers an unprecedented glimpse at the regulatory roles and mechanisms behind RNA interference.In this issue, György Hutvágner and colleagues report a rapid and reliable method for knocking out both siRNAs and microRNAs and thereby exploring their functions. The authors found that a short stretch of nucleotides, called a 2′-The authors first tested their oligonucleotide design against an siRNA known to interfere with production of the firefly protein, luciferase—this luminescent protein is often used as a “reporter,” lighting up when cells successfully produce the protein. Any interference means the glow is gone. Using extracts from fruitfly embryos as the test-bed, the researchers mixed in the luciferase-associated siRNA and the sequence-specific oligonucleotide. What holds these two molecules together is complementary base-pairing, the same force that holds two molecules of DNA together. As predicted, the oligonucleotide inhibited RISC activity—it could no longer silence the production of luciferase.Because the authors could easily control the concentration of both the siRNA and the oligonucleotide inhibitor in these fly extract experiments, they were able to answer several questions about how these two molecules interact. They found that adding greater and greater concentrations of siRNA molecules did not result in equally great numbers of RISC; the process became saturated, indicating that a protein in the RISC assembly pathway limits production. Furthermore, the authors saw a marked 1:1 relationship between the concentration of the oligonucleotide and the concentration of RISC, indicating that each inhibitor binds to one RISC molecule in order to inactivate, a binding that appears to be irreversible. The results also showed that, though RISC molecules bind to the inhibitor through complementary base-pairing, a very different and more complex interaction is used by RISC molecules to find and bind their natural interference targets.The authors then went on to use the luciferase siRNA to test the function of their oligonucleotide inhibitor in cultured human cells, which had been engineered to contain the luciferase gene. This in vivo experiment, using living and metabolizing cells, showed results similar to those with fruitfly extracts. But the real test for these inhibitors was to use them in a whole animal against a previously identified microRNA where the outcome of its inactivation was already known.let-7, which blocks the production of the protein Lin-41 and is important for proper developmental timing in roundworm larvae. Larvae injected with the oligonucleotide had the exact features of a let-7 deficient worm, showing that the inhibitor did indeed block this microRNA's function. The authors also used the oligonucleotides to provide evidence that two proteins, previously suggested to be involved with let-7, were directly associated with its interfering activity.Hutvágner and colleagues constructed an oligonucleotide inhibitor based on the sequence of a microRNA called Using the technique described here, scientists could make rapid headway toward uncovering the biological functions of hundreds of microRNAs, their accessory RISC proteins, and even the proteins and genes they are programmed to interrupt. Furthermore, finding that RISC production is saturable could have significant implications for genetic studies that use RNA interference to uncover the function of sequenced, but unknown, genes; knowing the minimum required concentration of siRNA, researchers can avoid a buildup and any unwanted cell activity that goes along with it.


The current understanding of organizer formation and neural induction in vertebrate embryos is discussed In what is usually referred to as the most famous experiment in embryology, Xenopus laevis. In the past few years, however, work in other vertebrate and nonvertebrate chordate systems has come to play an important role in the field and has shed light on generalities and differences among chordates. If the present primer uses Xenopus to illustrate the process, it is because it accompanies an article in this issue of PLoS Biology dealing with neural development in this species  and Nodal factors  and also of WNT  the fibroblast growth factor (FGF) signaling pathway plays a major role in this process, and (ii) neural specification starts well before gastrulation and thus before the formation and function of the organizer. Studies on the role of FGF in early  tissues . And whi tissues , in view tissues , and the tissues  to turn FGF does not seem to behave as a classical organizer-derived neural inducer, however. Maternal FGF mRNA and protein appear to be widely distributed in the early embryo, and at least one FGF family member is expressed primarily in the animal, pre-ectodermal region during blastula stages . A detaiAn exciting recent study shows that exposure of the epiblast (ectoderm) to FGF induces, after a time delay, a transcription factor named Churchill. Churchill expression inhibits cell ingression leading to mesoderm formation; the cells remaining in the epiblast assume a neural fate . The timXenopus, it was long known that the animal region or pre-ectoderm is not uniform or naïve, in that the dorsal, organizer-proximal region is predisposed towards a neural fate  region  region . They shignaling .Xenopus, as in other vertebrates, takes place before gastrulation and does not require organizer signals; this is not to say that full differentiation and patterning of the nervous system could be achieved without organizer participation.The formation of the vertebrate nervous system thus depends on multiple signaling pathways, such as the FGF, BMP, and WNT signaling cascades, that interact in complex ways e.g., . In cont