We are attempting to elucidate the mechanism of apoptotic cell death induced by hypoxia in oral cancer cells. Since hypoxia can render solid tumors more resistant to radiation and chemotherapy, understanding the pathways involved in hypoxia-induced apoptosis of oral cancer cells would be of significant therapeutic value.Here we showed that oral cancer cells from primary tumor and lymph node metastasis undergo apoptosis after 24 to 48 h of hypoxia. During hypoxic growth, an increase in caspase-3 proteolytic activity was observed, accompanied by the cleavage of PARP (poly (ADP-ribose) polymerase) indicative of caspase activity. In addition, hypoxic stress also lead to activation of caspase-8, -9, and -10 but not -1, elicited the release of cytochrome C into the cytosol, and resulted in internucleosomal DNA fragmentation.These results show that hypoxia-induced apoptosis in oral carcinoma cell lines relies on both intrinsic  and extrinsic (cell death receptor mediated) pathways. This novel evidence will assist in designing more efficient combination chemotherapy approaches as promising strategy for the treatment of oral cancers. Oral cancer is one of the 10 most frequently occurring cancers worldwide, and its incidence in Europe and the United States ranges from 2% to 6% among all cancer patients [Hypoxia, a reduction in the level of tissue oxygen tension, occurs during acute and chronic vascular disease, pulmonary disease and cancer, and can lead to apoptotic or necrotic cell death ,4. Fast via two major pathways, involving either the release of cytochrome C from the mitochondria  [Apoptotic cell death is controlled by pro-apoptotic caspases, proteases that are synthesized as inactive precursors and activated by proteolytic processing . The apopathway)  or activpathway) . Upon trpathway) . Both papathway) ,11. The pathway) .via the adapter molecule FADD, cytoplasmic monomeric initiator caspase-8 to their surface receptors, resulting in dimerization and activation of caspase-8 [Death ligands such as TNF-α or CD95L recruit, aspase-8 ,14. Actiaspase-8 ,15. On taspase-8 . Defectsaspase-8 ,18. Thisaspase-8 ,20. Actiaspase-8 , and is aspase-8 .The low oxygen tension in hypoxic tumors is known to interfere with the efficacy of chemotherapy or radiotherapy. Also, hypoxia-induced apoptosis may impose a selection pressure favoring growth of more resistant tumor cells. However, the factors leading to hypoxia-induced apoptosis and their relative contribution to intrinsic and extrinsic apoptotic pathways are not well characterized. In the present study, we determined which factors in the mitochondria-dependent and -independent apoptosis pathways are activated in oral cancer cells. We observed that hypoxia-induced apoptotic cell death occurs through activation of caspase 8, but also cytochrome C release, caspase-9 activation, and results in caspase 3 processing, PARP cleavage, and DNA fragmentation. These results suggest that hypoxia-induced apoptosis in oral carcinomas cells relies on both intrinsic  and also extrinsic (cell death receptor mediated) pathways.2 levels can range from 3.1 to 8.7 % (24–66 mm Hg) [2 levels (hypoxic thresholds) characterize the upper limit of the hypoxic range below which activities and functions progressively become restricted. These levels can encompass O2 partial pressures from 35 mm Hg  to 0.02 mm Hg; below this level, cytochromes aa3 and c are no longer fully oxidized [Direct measurements of oxygen tensions in human tumors show a range of median oxygen tensions from 1.3 to 3.9 % (10–30 mm Hg), with readings recorded as low as 0.01 % (0.08 mm Hg) under severe hypoxia, whereas in normal tissues O6 mm Hg) . Severe oxidized . During oxidized . Thus, aTrypan Blue dye exclusion was used to quantitate the number of viable cells after 24 and 48 h of hypoxia. TUNEL assays were used to visualize apoptotic cells, and DNA fragmentation into oligonucleosome-sized fragments, indicative of apoptotic cellular death, was monitored by gel electrophoresis . The perThe effects of hypoxia treatment on activation of key caspases and PARP in the four cell lines 686Tu/Ln and 1386Tu/Ln was determined by Western blotting using antibodies that recognize both full-length and cleaved proteins Figures  and 5. GTo gain further insight into the role of mitochondria in this process, the extent of cytochrome C release under hypoxia was analyzed. Translocation of cytochrome C from the mitochondria to the cytosol was detected in all four cell lines after 24 h, and more pronounced after 48 h, of hypoxia Figures  and 5E. Since we observed that hypoxia activated caspase-3 in the oral carcinoma cells, we investigated the cleavage of the caspase-3 substrate PARP under hypoxic versus normal growth. Clearly, cleavage of PARP, as indicated by a decrease in the full-length 113 kD protein and appearance of the 85 kD cleaved PARP product, was prominent in hypoxic cells, whereas it was almost completely absent in normoxia cells Figures  and 5D. As caspases are early effectors for triggering apoptosis, assays to determine caspase enzymatic activities further substantiated our findings that hypoxia-induced apoptosis occurs through both intrinsic  and also extrinsic (cell death receptor mediated) pathways. We examined caspase proteolytic activities in cell extracts using fluorogenic peptide substrates specific for individual caspases. These substrates are conjugated with AFC or AMC and have aspartic acid residues at P1 positions, a requirement for caspase proteolysis. Cleavage of these substrates after the aspartic acid residue results in release of unbound AFC or AMC which can be monitored fluorometrically.Detergent extracts prepared from cells after exposure to hypoxia or normoxia for either 24 or 48 h were tested for caspase cleavage activities, and specific inhibitors for control measurements were used as described in Materials and Methods. N-Ac-DEVD-AFC is cleaved by caspase-3 and -7, but may also be cleaved by other caspases, N-Ac-YVAD-AFC is cleaved by caspase-1, N-Ac-IETD-AMC by caspase-8, N-Ac-LEHD-AFC by caspase-9, and N-Ac-AEVD-AFC by caspase-10. Activities of caspase-3, -8, -9 and -10 were clearly and consistently elevated during hypoxia treatment for up to 48 h compared to normoxic growth for the 686 Figure  and 7A.Overall, significant induction of hypoxia-mediated cleavage activities for the N-Ac-DEVD-AFC, N-Ac-IETD-AMC and N-Ac-LEHD-AFC substrates was detected in all four cell extracts, and induction of these activities correlated well with the levels observed for caspase-3, caspase-8 and caspase-9 protein expression and processing. Thus, induction of apoptosis was preceded by the activation of activator caspase-8, initiating receptor-mediated apoptosis, and caspase-9, initiating mitochondrial apoptosis, as well as the effector caspase-3.We investigated the effects of individual cell-permeable caspase inhibitors on caspase-3, -8 and -9 activities during hypoxic growth for 48 hours , leading to the activation of caspase-8 and caspase-3 ,36. ReceHypoxia-induced caspase-3 activation and DNA fragmentation have been described by others recently ,43, as wAlthough cleavage of procaspase-9 was evident as early as 20 h into hypoxia treatment, it is possible that its activation is mediated by other caspases at earlier time points. Currently, possible involvement of other Bcl-2 family of apoptosis regulating proteins (e.g. Bad, Bag, Bak, Bik, etc.) in hypoxia-induced activation of the mitochondrial caspase cascade cannot be ruled out. The key regulator of hypoxia-induced cellular response is believed to be hypoxia inducible factor 1 (HIF-1). For all cell lines used here, we observed recently that there were several-fold increases in HIF-1α expression during hypoxia compared to normoxia (Wickramasinghe N, Banerjee K, Nagaraj N, Vigneswaran N and Zacharias W, manuscript submitted). HIF-1 can initiate apoptosis by inducing pro-apoptotic proteins such as BNIP3 or NIX, which will inhibit Bcl anti-apoptotic activity. It can also cause stabilization of wild-type p53 tumor suppressor, an effect that is lost in cells with pre-existing p53 mutations ,47. On tIt is apparent that during hypoxia, an intricate balance exists between factors that induce or counteract apoptosis, or even stimulate proliferation. More detailed studies are needed to define the precise mechanism for hypoxia-induced cleavage of procaspase-9 and procaspase-8; however, our results clearly demonstrate involvement of both caspase-8 and caspase-9 in hypoxia-mediated cleavage of caspase-3 and PARP. Because caspase-3 is a critical mediator of apoptosis  and corrThe detailed temporal and spatial relationship of these events to other components of the apoptotic pathway including downstream caspases remain to be determined. Recently, the targeted elimination of oral squamous cell carcinoma cells by inducing apoptosis has emerged as a valued strategy to combat oral cancer . IncreasIn some of the caspase cleavage assays, slightly lower activities were found for the metastatic Ln cells compared to the corresponding primary Tu cells. Also, the final appearance of nucleosomal DNA ladders is much more pronounced in both Tu cells than in their Ln counterparts. Although some of those differences were only minor, they are in line with previous studies which demonstrated much higher resistance of metastatic OSCC lines to TRAIL-induced cell death  and alsoIn summary, we have reported that hypoxia directs apoptosis through mitochondria and cell death receptor mediated signaling pathways in oral cancer cells. We believe that this is the first report on caspase-dependent mechanisms during hypoxia in human oral cancer cells. Exposure to hypoxia lead to the activation of procaspase-9, -8, -3, and -10, cytochrome C release from mitochondria, with subsequent cleavage of PARP by activated caspase 3, finally resulting in the induction of apoptosis. The detailed molecular and sequential mechanisms of such hypoxia-induced caspase activation leading to apoptosis need further investigation. However, the knowledge of the relevant signaling cascades participating in this process can provide important insights in the mechanisms of acquired apoptotic deficiencies during malignant progression in poorly oxygenated oral carcinomas. It is well established that poor oxygenation of solid tumors is associated with poor prognosis. This may not only be due to direct effects of hypoxia on the efficacy on certain tumor treatment modalities, but also due to the evolvement of resistant tumor cells during the ontogenesis of a tumor under hypoxic conditions . Our nov2. All protocols for the use of human cell lines in this work were approved by the Institutional Review Boards of The University of Louisville and the University of Texas at Houston.MDA-686Tu (686Tu) and MDA-686Ln (686Ln) cell lines were derived concurrently from the primary tumor and lymph node metastasis, respectively, of OSCC involving the left tonsillar fossa and posterior portion of the tongue in a 49 year old man (tumor stage T3N3B). MDA-1386Tu (1386Tu) and MDA-1386Ln (1386Ln) cell lines were obtained from the primary tumor and lymph node metastasis, respectively, of a 71 year old male patient with primary hypopharynx tumor (tumor stage T4N3B). All cell lines were generous gifts from Dr Peter Sacks, New York University, New York . The cel2 and 94 % nitrogen. The cell lines were maintained under hypoxic conditions for periods of 24 or 48 hours. Control cells were grown in normal oxygen conditions for the same duration. After incubation, media collection and cell harvesting were done immediately within 2–3 minutes to avoid adaptation of the cells to re-oxygenation.Hypoxic conditions were produced by placing logarithmic phase subconfluent monolayer cultures, grown on 100 mm dishes, in a modular incubator chamber and equilibrating for 30 minutes with humidified gas containing 1 % oxygen, 5 % CO4 cells/well) in 3 ml medium to 70 % confluence, then washed and treated for hypoxia in DMEM/F12 medium with 10 % fetal bovine serum. For viability counting, cultures containing both dead and live cells from each well were collected, centrifuged, and resuspended in 0.5 ml FBS-free DMEM/F12. An aliquot of 0.1 ml was taken and incubated with 0.1 ml of Trypan Blue dye (0.4 %) for 5 min. Both live (unstained) and dead (blue) cells were counted in triplicate measurements from randomly selected fields in a hemocytometer.Determination of cell viability was done by Trypan Blue dye exclusion assay. Cells were grown in six-well plates (2 × 10Cultured cells were rinsed with PBS, gently scraped into 1 ml of PBS, and centrifuged at 4,000 rpm for 3 min. The pellets were resuspended into RIPA buffer (10 mM Tris-HCl, pH 7.4, 150 mM NaCl, 1 % Triton X-100, 0.1 % SDS, and 1 mM EDTA) containing fresh protease inhibitors (0.5 mM phenylmethylsulfonyl fluoride, 10 μg/ml aprotinin, and 2 μg/ml of both leupeptin and pepstatin) . Then, cell extracts were sonicated (Model 550 Sonic Dismembrator, Fisher Scientific, Pittsburgh, PA) for 1 min (1.0 sec on/0.5 sec off pulses) and cell debris was removed by centrifugation. Proteins were quantified using the Bradford protein assay kit (Bio-Rad, Hercules, CA) and compared with a γ-globulin standard curve. Equal amounts of total proteins were separated on a SDS-polyacrylamide gel and transferred onto a nitrocellulose membrane by electroblotting overnight at 20 V. Membranes were blocked in TBS-T (10 mM Tris-HCL, 150 mM NaCl, 0.25 % Tween 20, pH 7.5) with 5 % fat-free powdered milk at room temperature for 1 h. After rinsing membranes in TBS-T, the following primary antibodies were used: rabbit polyclonal IgGs for caspase-3 (H-277), poly(ADP-ribose) polymerase (PARP) (H-250), caspase-8 (H-134), caspase-9 (H-170) , or mouse monoclonal β-actin antibody (Sigma, St. Louis, MO). After incubation overnight at 4°C or 1 h at room temperature, the membranes were washed four times, 10 min each, in TBS-T. Secondary antibodies used were either horseradish peroxidase-conjugated goat anti-rabbit IgG or goat anti-mouse IgG (ICN, Costa Mesa, CA), followed by five washes with TBS-T. Bands were detected using the enhanced chemiluminescence ECL substrate (Amersham Biosciences, Piscataway, NJ). For β-actin detection, previously probed membranes were soaked in stripping buffer (70 mM Tris-HCl, pH 6.8, 2 % SDS, 0.1 % β-mercaptoethanol) at 60°C for 30 min and incubation as above.After hypoxia treatment for 24 or 48-hours, treated and control cell cultures were rinsed once in cold PBS and collected in cold PBS by scraping. After centrifugation and removal of PBS, cell pellets were kept at -80°C until caspase assays were performed. The frozen pellets were resuspended in caspase lysis buffer (10 mM HEPES, pH 7.4, 2 mM EDTA, 0.1 % CHAPS) supplemented with protease inhibitors (5 mM dithiothreitol, 1 mM phenylmethylsulfonyl fluoride, 10 μg/ml pepstatin A, 10 μg/ml aprotinin, and 20 μg/ml leupeptin). Freeze-thaw cell lysis cycles were performed by alternatively transferring the samples from an ethanol/dry ice bath to a 37°C water bath five times. The supernatant was collected after 20 min of centrifugation at 12,000 rpm in a cold microcentrifuge. Assays were performed in caspase buffer (10 mM PIPES, pH 7.4, 2 mM EDTA, 0.1 % CHAPS, 5 mM dithiothreitol), to which 50 μM of substrate and 5 μl of protein extract were added to yield a final volume of 100 μl. Peptide substrates for caspase-3, N-acetyl-Asp-Glu-Val-Asp-AFC (DEVD-AFC), caspase-1, N-acetyl-Tyr-Val-Ala-Asp-AFC (YVAD-AFC), caspase-8, N-acetyl-Ile-Glu-Thr-Asp-AMC (IETD-AMC), caspase-9, N-acetyl-Leu-Glu-His-Asp-AFC (LEHD-AFC) (Biomol, Plymouth Meeting, PA) and caspase-10, N-acetyl-Ala-Glu-Val-Asp-AFC (AEVD-AFC) (Alexis, San Diego, CA) were dissolved in dimethyl sulfoxide. The respective specific inhibitors N-acetyl-Asp-Glu-Val-Asp-CHO (DEVD-CHO), N-acetyl-Tyr-Val-Ala-Asp-CHO (YVAD-CHO), N-acetyl-Ile-Glu-Thr-Asp-CHO (IETD-CHO), N-acetyl-Leu-Glu-His-Asp-CHO (LEHD-CHO) (Biomol), and N-acetyl-Ala-Glu-Val-Asp-CHO (AEVD-CHO) (Alexis, San Diego, CA) were used in control assay reactions. Assays were performed in black-wall, clear bottom plates using a Spectramax Gemini XS Microplate Spectrofluorometer (Molecular Devices); reading was at 500 nm after excitation at 405 nm for 7-amino-4-trifluoromethylcoumarin (AFC) and at 380 nm after excitation at 460 nm for 7-amino-4-methylcoumarin (AMC). The results were compared against AFC and AMC standard curves generated in parallel. Specific activity was expressed as units, with 1 unit defined as AFC or AMC release of 1 nMol/hour/μg protein.Cells were collected at the indicated times and washed once in ice cold PBS. Cell pellets were resuspended in cytosol extraction buffer, and cytosolic extracts were prepared by the method described previously . WesternOral cancer cells 1386 and 686 were exposed to hypoxia for 48 hours in the presence or absence of individual cell-permeable inhibitors for caspase-3 , caspase-8 , caspase-9 , or pan-caspase  , and processed for caspase activity assays as above.in situ TdT-mediated dUTP nick end labeling (TUNEL) assays using the In Situ Cell Death Detection Kit POD, and nucleosomal DNA fragments detected with the Apoptotic DNA Ladder Kit (both from Roche, Indianapolis, IN). DNA fragments were resolved on 2 % agarose gels for visualizations of apoptosis-indicative DNA ladders.Apoptotic cells were detected by AFC, 7-amino-4-trifluoromethylcoumarin; AMC, 7-amino 4-methyl coumarin; DAB, diaminobenzidine; DISC, death-inducing signaling complex; ECL, enhanced chemiluminescence; FADD, Fas-associated death domain protein; fmk, fluoromethylketone; PARP, poly (ADP-ribose) polymerase; TRAIL, TNF-α related apoptosis inducing ligand; TUNEL, TdT-mediated dUTP nick end labeling; z, benzyloxycarbonyl.NSN carried out the molecular and enzymatic studies and drafted the manuscript. NV participated in the design of the study, interpretation of collected data, and contributed to the manuscript preparation. WZ conceived and directed the study, contributed its design and coordination, and participated in the interpretation and final manuscript preparation. All authors read and approved the final manuscript.


ABCC8 (sulphonylurea receptor), KCNJ11 (KIR6.2), SLC2A2 (GLUT2), HNF4A (HNF4α), and INS (insulin)—significantly altered disease risk, and in three genes, the risk allele, haplotype, or both had a biologically consistent effect on a relevant physiological trait in the QT study. We examined 35 genes predicted to have their major influence on insulin action, and three (9%)—INSR, PIK3R1, and SOS1—showed significant associations with diabetes. These results confirm the genetic complexity of Type 2 diabetes and provide evidence that common variants in genes influencing pancreatic β-cell function may make a significant contribution to the inherited component of this disease. This study additionally demonstrates that the systematic examination of panels of biological candidate genes in large, well-characterised populations can be an effective complement to positional cloning approaches. The absence of large single-gene effects and the detection of multiple small effects accentuate the need for the study of larger populations in order to reliably identify the size of effect we now expect for complex diseases.Type 2 diabetes is an increasingly common, serious metabolic disorder with a substantial inherited component. It is characterised by defects in both insulin secretion and action. Progress in identification of specific genetic variants predisposing to the disease has been limited. To complement ongoing positional cloning efforts, we have undertaken a large-scale candidate gene association study. We examined 152 SNPs in 71 candidate genes for association with diabetes status and related phenotypes in 2,134 Caucasians in a case-control study and an independent quantitative trait (QT) cohort in the United Kingdom. Polymorphisms in five of 15 genes (33%) encoding molecules known to primarily influence pancreatic β-cell function— The absence of large single gene effects and the detection of multiple small effects confirms the genetic complexity of type 2 diabetes and the need for even larger studies CAPN10; LocusLink ID 11132) has emerged from such studies as a new putative diabetogene  confers significant protection against the development of Type 2 diabetes (KCNJ11 (LocusLink ID 3767) in Type 2 diabetes predisposition  in 71 such genes and, using these, have examined their association with Type 2 diabetes and related intermediate phenotypes in Caucasian subjects from the United Kingdom.Candidate gene studies are based on selection of genes with a known or inferred biological function whose role makes it plausible that they may predispose to disease or the observed phenotype. These types of studies are similar to traditional epidemiological approaches in which an a priori hypothesis between exposure to a given factor (in this case, a genotype at a given locus) and disease is formulated. To date, most Type 2 diabetes candidate gene studies have largely lacked thoroughness and sensitivity, as they have tested a limited number of genes and variants in small populations or in populations that were poorly matched or phenotyped, frequently resulting in a lack of replication of the weak associations detected . Our strThe 152 SNPs were genotyped in a population-based cohort of 517 unrelated Caucasians in the United Kingdom with Type 2 diabetes and an equal number of controls with normal glycated haemoglobin (HbA1c) levels, individually matched to cases by age, sex, and geographical location. A second independent population was also genotyped for the same 152 SNPs. This consisted of 1,100 middle-aged Caucasian subjects in the United Kingdom who had been extensively and serially phenotyped for glucose tolerance and variables related to insulin secretion, insulin action, and adiposity. In the first stage of data analysis, all SNPs (and haplotypes when multiple SNPs were present at the same gene) were examined for their association with diabetes in the case-control study using multiple models of inheritance. In the second phase of analysis, all SNPs and haplotypes showing statistically significant association with diabetes status in the first phase were examined for association with glucose levels and other intermediate phenotypes in the quantitative trait (QT) study population. The intermediate phenotypes chosen for study were fasting and 2-h post-glucose load plasma glucose levels (measures of glucose tolerance), fasting insulin (a measure of insulin sensitivity), 30-min insulin incremental response (a measure of β-cell function), and body mass index (BMI) (a measure of adiposity). Power to detect an association is dependent on several factors: the frequency of the ‘predisposing' allele, genotype, or haplotype; the accepted false-positive or Type 1 error rate (α); and the odds ratio (OR) or effect size. Rarer alleles, genotypes, or haplotypes with small effects require larger sample sizes to attain the same power to detect an association, as compared to more frequent alleles or alleles with larger effects. At the time that we collected the populations and designed this study, our power calculations had shown that a sample size of 500 cases and 500 matched controls would have 80% power to detected effect sizes as small as 1.3–1.7 OR, depending on the frequency of the predisposing allele, with a 5% Type 1 error rate .. Twenty SNPs in 11 different genes showed statistically significant association with disease status (p < 0.05) under at least one model (SOS1 (LocusLink ID 6654), SLC2A2 (LocusLink ID 6514), PIK3R1 (LocusLink ID 5295), ABCC8 and KCNJ11 (LocusLink ID 6833), and INSR (LocusLink ID 3643). Of the 29 loci with multiple SNPs, only three—HNF4A (LocusLink ID 3172) INSR, and ABCC8–KCNJ11—showed significant association of particular haplotypes with disease status (HNF4A) was a haplotype significantly associated with disease risk (see below) when no significant association was seen with any individual SNP in that gene. ABCC8–KCNJ11, HNF4A, and INSR with intermediate phenotypes in the QT study. We now consider in more detail, the data for those genes where the strongest and most consistent effects were seen.ne model . The stre status ; Table 3KCNJ11 encodes the channel protein KIR6.2 and ABCC8 encodes an ATP-binding cassette (ABC) transporter-containing transmembrane protein (SUR1) that is thought to regulate the activity of the channel and that also contains the site to which sulphonylurea antidiabetic drugs bind. Three SNPs in KCNJ11 were associated with disease under multiple genetic models. The strongest statistical association in this gene was with a 3′-SNP   and associated with borderline significance with higher fasting glucose  under a recessive model for allele 2  see . In ABCCle 2 see . SNP87  and all three had a statistically significant association with disease status . The recent evidence from multiple studies and from meta-analysis for association of the E23K SNP with Type 2 diabetes, along with in vitro studies using cell lines expressing the E23K mutation showing an increased stimulation threshold of insulinsecretion , a rare disorder of glucose homeostasis characterised by up-regulated insulin secretion despite severe hypoglycaemia. In addition, evidence for association of tibility . In totaatus see . In our ecretion , suggestecretion .ABCC8 variants have been associated with Type 2 diabetes in multiple studies  that were significantly associated with diabetes were significantly associated with diabetes status with an OR of approximately 1.4–1.5 (see p = 0.0059) under a dominant model for the minor allele. In the reduction process prior to haplotype estimations , only one SNP (SNP21) contributed significantly to disease association. Therefore, haplotype reconstructions were not performed. In the QT study, all three disease-associated SNPs were also associated with lower levels of fasting plasma insulin. Rather surprisingly allele 2 (A) at T198, which was associated with increased disease risk, was associated with lower 2-h plasma glucose. No other significant associations with intermediate phenotypes were seen. –1.5 see . The mosSLC2A2 variants and Type 2 diabetes, and most have reported negative results. However, all studies have been small and were insufficiently powered to detect effects of modest size  fasting insulin is strongly influenced by insulin sensitivity and (2) the potential risk alleles were not associated with any impairment of insulin secretion in response to a glucose load. Finally, allele 2 (A) at T198, which associated with increased risk of diabetes in the case-control study, was associated with lower 2-h glucose in the QT study. Clearly, more detailed genetic mapping combined with functional studies  will be needed to identify the mechanism whereby variants in this gene influence diabetes risk.Multiple previous studies have sought evidence for association or linkage between est size . SLC2A2 HNF4A (the MODY1 gene) encodes an orphan hormone nuclear receptor that, together with TCF1 (LocusLink ID 6927), encoding HNF1α, TCF2 (LocusLink ID 6928), encoding HNF1β, and FOXA2 (LocusLink ID 3170), encoding HNF3β, constitutes part of a network of transcription factors controlling gene expression in pancreatic β-cells, liver, and other tissues. In β-cells, these transcription factors regulate expression of the insulin gene as well as genes encoding proteins involved in glucose transport and metabolism and in mitochondrial metabolism, all of which are linked to insulin secretion  gene encodes the hormone preproinsulin, which upon proteolytic cleavage generates mature insulin and C-peptide. We tested for association of a single SNP in the 3′-UTR (SNP72) of the insulin gene with disease status. This SNP was significantly associated with increased Type 2 diabetes risk under a recessive model for the T allele (OR 2.02, p = 0.0258) (see INS–VNTR) has been extensively studied and is proposed to exert pleiotropic effects on birth weight and diabetes susceptibility (INS in Type 2 diabetes predisposition has not been definitively established. The data for the single SNP we tested suggest that either the insulin gene or other loci in LD may be involved in Type 2 diabetes risk.The258) see . In the tibility . HoweverINSR locus of the seven SNPs genotyped, we detected a single intronic SNP (SNP131) that was significantly associated with increased disease risk  ; in the QT study, there was a nonsignificant trend for subjects carrying this haplotype to have increased values for fasting glucose (5.32 mmol/l, 95% CI [5.24, 5.39] versus 5.27 mmol/l, 95% CI [5.21,5.33] for haplotype B and 5.27 mmol/l, 95% CI [5.23, 5.32] for haplotype A), 2-h glucose (6.00 mmol/l, 95% CI [5.80, 6. 20] versus 5.78 mmol/l, 95% CI [5.61, 5.96] for haplotype B and 5.87 mmol/l, 95% CI [5.75, 5.99] for haplotype A), and fasting insulin (41.8 pmol/l, 95% CI [39.2, 44.5] versus 40.4 pmol/l, 95% CI [38.4, 42.5] for haplotype B and 41.3 pmol/l, 95% CI [39.6, 43.1] for haplotype A)  see . In the e 2) see . Haplotye 2) see C for INSe A) see . A role  variant , 1999b,  variant . In thisPIK3R1, encoding the p85α regulatory subunit of the phosphatidylinositol 3-kinase, is a logical candidate gene for involvement in the development of Type 2 diabetes owing to its role in insulin signal transduction. An intronic variant, SNP42, was associated with increased disease risk under two genetic models  encodes a guanine nucleotide exchange factor that functions in the transduction of signals that control cell growth and differentiation. We analysed two SNPs for association with disease status, a nonsynonymous SNP (N1011S) and an intronic variant (SNP8). While the nonsynonymous S1011 variant, which was very rare , did not associate with disease status, the intronic SNP was highly significantly associated with decreased disease risk (OR 0.58, p = 0.0032)  see , despitePPARGC1 (LocusLink ID 10891) encodes a transcriptional coactivator of nuclear receptors with a critical role in regulating multiple aspects of energy metabolism, including adaptive thermogenesis (PPARGC1 SNP30 (Thr528Thr), which was associated with decreased disease risk (see p = 0.0295) (see ogenesis , mitochoogenesis , fatty aogenesis , the conogenesis , and theogenesis . PPARGC1risk see , was ratrisk see . In thisrisk see , but notrisk see , and hasrisk see , obesityrisk see , and mearisk see . In our 295) see . The opp295) see  may indiPYY . An intronic variant, IVS3+68, showed a significant association with increased Type 2 diabetes risk under two genetic models   see , but no ecretion , which le by 33% . AlthougABCC9 (LocusLink ID 10060) and LIPC (LocusLink ID 3990), single SNPs of modest significance were associated with disease status in the case control and therefore are not discussed further here.In the genes PPARG Pro12Ala Pro allele has previously been shown to confer susceptibility to Type 2 diabetes, with the Ala allele providing a decreased risk (p = 0.2269). The lower limit of the 95% CI for the protective effect of the Ala allele (OR 1.08, 95% CI [0.82,1.42], p = 0.583) is still consistent with the results of the meta-analysis by We were unable to confirm some associations observed in other studies. The sed risk . Our ressed risk , but theENPP1 (LocusLink ID 5167) gene (commonly known as PC-1) the K121Q polymorphism has variably been found to be both associated with increased Type 2 diabetes risk . PPP1R3A , which encodes the muscle-specific regulatory subunit of PP1, has been investigated as a potential diabetogene. Evidence for a role of the PPP1R3A D905Y polymorphism in Type 2 diabetes risk has also been conflicting (p = 0.0110) in this population  in the genes that we have considered, as well as ones that we have not, may exist and have effects equal to or greater than those we have demonstrated. In addition, this study is not intended to be an ‘exclusion study,' as many issues that relate to coverage of any given gene, environmental risk factors, and power in our populations do not allow us to definitively assert that negative findings correspond to genes that truly do not play a role in Type 2 diabetes predisposition.The power of our study to detect small effects in uncommon variants was low. Evidence from many recent studies now suggests that in Type 2 diabetes the effects are likely in the range of OR 1.15–1.5. It is clear that much larger studies than that reported here are required for such effects , in partPPARG example, it is likely that meta-analysis of several studies will be required to narrow the CIs around the point estimates of association seen in any single study. This will be especially important when the association is weak, as it is for Pro12Ala, because few individual case-control studies, including the one reported here, are currently powered to detect very small increases in risk. It is, however, important that such meta-analyses include all studies of variants examined, rather than only those that are individually published, to avoid publication bias.Although we have not undertaken a formal replication of the case-control study, we have used a complimentary QT population to examine the association of the variants studied with continuously distributed measures of glucose tolerance, insulin secretion, and insulin action. This provides different information to a replication case-control study, as it may identify pathophysiological mechanisms by which the association with diabetes arises. We are cautious about putting forward particular variants as established ‘diabetogenes' and enthusiastically invite researchers to examine these candidate variants in their own particular populations. Indeed, as the genetic architecture of Type 2 diabetes may vary between populations, it is critical that such variants are examined in multiple diverse ethnic groups. As with the Pro12Ala The associations we describe are highly biologically plausible and in many of the genes are supported by associations with multiple SNPs at the same locus. These include genes affecting both insulin secretion and insulin action. Given the importance of both insulin resistance and defective β-cell function to the pathogenesis of Type 2 diabetes, it is intriguing that we have found a disproportionate representation of genes affecting pancreatic β-cell function among those that were found to be associated with diabetes risk. This contrasts with the impact of known environmental factors and their correlates , all of which are thought to have their major influence on diabetes risk through impairment of insulin action. While it would be premature to put forward any definitive model for the causation of Type 2 diabetes, it is tempting to speculate that the ‘insulin resistance' component of the disease may have a substantial environmental influence modulated by polygenic effects, some of which may relate to molecules identified in this and other studies. On the other hand, the ability of the pancreatic β-cell to continue to secrete sufficient insulin to maintain life-long normoglycaemia may be more profoundly influenced by genetic factors, some of which are reported herein. It will be critical to examine the functional consequences of such variants, a task that will be particularly challenging when it comes to genes influencing human β-cell function, as it is entirely possible that this disproportionate representation of β-cell genes may be a reflection of our success in choosing diabetes genes in each of the candidate genes in the major groupings.systematic examination of panels of biological candidate genes in large, well-characterised populations may usefully complement positional approaches to the identification of allelic variants conferring susceptibility to complex polygenic disease. The detection of multiple small gene effects accentuates the need for larger populations in order to reliably identify the types of effects (OR 1.15–1.5) we now expect for complex diseases.The success of the approach presented here, although necessarily limited in scope, suggests that theSNP discovery was performed by a high-fSSCP-based analysis, as previously described . Genomic. HbA1c was assayed using high performance liquid chromatography on a BioRad Diamat 33 , according to the method of The Cambridgeshire Case-Control Population  consists QT study population is a collection of 1,100 samples collected for the Ely Study, a prospective population-based cohort study of the aetiology and pathogenesis of Type 2 diabetes and related metabolic disorders  method described by Chen et al.(1999). In short, PEP-amplified DNA samples were PCR-amplified in 8 μl reactions with primers flanking the variant site; unincorporated dNTP and remaining unused primer were degraded by exonuclease I and shrimp alkaline phosphatase at 37°C for 45 min before the enzymes were heat-inactivated at 95°C for 15 min. At the end of the reaction, the samples were held at 4°C. Single base primer extension reactions were performed as previously described , and all2 ‘goodness-of-fit' test. The disequilibrium coefficient for the controls (2r) was calculated  or Stata 7.0 (Stata Corporation, College Station, Texas, United States) statistical programs, unless otherwise stated. Agreement with Hardy–Weinberg equilibrium was tested using a χlculated .p value greater than 0.1 was removed from the model. The genotypes were assumed as having additive effects. p values for the additive effect are for the test for a linear trend across the genotypes, which were coded as 0 = 11, 1 = 12, 2 = 22. Where the subset consisted of more than one polymorphism within a gene, haplotype analysis was performed. To avoid possible errors due to either genotyping or the estimation process, only haplotypes that had a frequency greater than 5% were considered for further analysis. Haplotype frequencies were estimated using maximum-likelihood methods. A log-linear model embedded with the expectation-maximization algorithm was fitted to a frequency table . Haplotypes with a frequency greater than 5% were the same as those reconstructed in the case-control study. Only haplotypes that had a frequency greater than 5% and individuals that had at least one marker typed were considered for analysis. As currently haplotype analysis software cannot handle repeated measurements, the average of two measurements was used for further analysis. Associations of haplotypes (adjusted for age and sex) with the QTs were determined by cluster-weighted regression analysis, thereby taking into account nonindependent multiple observations from an individual  was used for further analysis. The subset of SNPs identified in the case-control study was used. In separate dominant, additive, and recessive models, adjusting for age and sex, genotype differences in these measurements were modelled using the General Linear Model procedure in the statistical package SAS. For each individual, a list of possible haplotypes and their probabilities was obtained using Snphap software .Click here for additional data file.Table S2(174 KB DOC).Click here for additional data file.The LocusLink accession numbers discussed in this paper are 3170, 3172, 3630, 3643, 3767, 3990, 5167, 5295, 5468, 5506, 5697, 6514, 6654, 6833, 6927, 6928, 10060, 10891, and 11132.


In silico reconstruction of such ancestral protein sequences facilitates our understanding of evolutionary processes, protein classification and biological function. Additionally, reconstructed ancestral protein sequences could serve to fill in sequence space thus aiding remote homology inference.Modern-day proteins were selected during long evolutionary history as descendants of ancient life forms. We developed ANCESCON, a package for distance-based phylogenetic inference and reconstruction of ancestral protein sequences that takes into account the observed variation of evolutionary rates between positions that more precisely describes the evolution of protein families. To improve the accuracy of evolutionary distance estimation and ancestral sequence reconstruction, two approaches are proposed to estimate position-specific evolutionary rates. Comparisons show that at large evolutionary distances our method gives more accurate ancestral sequence reconstruction than PAML, PHYLIP and PAUP*. We apply the reconstructed ancestral sequences to homology inference and functional site prediction. We show that the usage of hypothetical ancestors together with the present day sequences improves profile-based sequence similarity searches; and that ancestral sequence reconstruction methods can be used to predict positions with functional specificity..As a computational tool to reconstruct ancestral protein sequences from a given multiple sequence alignment, ANCESCON shows high accuracy in tests and helps detection of remote homologs and prediction of functional sites. ANCESCON is freely available for non-commercial use. Pre-compiled versions for several platforms can be downloaded from  Present-day protein sequences can be used to reconstruct ancestral sequences based on a model of sequence evolution. Such knowledge about ancestral sequences is helpful for understanding the evolutionary processes as well as the functional aspects of a protein family. Existing methods of ancestral sequence reconstruction can be divided into two main categories: Maximum Parsimony (MP) methods ,2 and MaAll reconstruction methods require a phylogenetic tree inferred from a given alignment. The quality of the tree is crucial for the reliability of reconstruction. A number of methods exist for phylogenetic inference, such as maximum likelihood , distancαAB, relies only on a multiple sequence alignment and a general model of amino acid exchange. The other one is a maximum likelihood method (αML), which requires a tree. In our implementation, we incorporate αAB or αML in the joint and marginal reconstruction algorithms [αAB is also used in the Maximum Likelihood estimation of evolutionary distances [Overwhelming evidence exists for substitution rate variation across sites -15. For gorithms ,5. αAB iistances  for treeWe implement a method of evolutionary simulation that introduces site-specific rate variations in a natural way by imposing structural and functional constraints . We showπ) are usually estimated from the target set of sequences or from large databases of protein families. Background amino acid frequencies estimated from a small dataset tend to have bias, while amino acid frequencies from large databases may not be suitable for the specific protein family under analysis. Here, we propose a ML method to optimize the amino acid frequency vector π. The optimized π vector can give significant improvement over the likelihood of a alignment.Background (or equilibrium) amino acid frequencies  to reconstruct ancestral protein sequences considering rate variation among sites. Rate factors can be estimated either by an empirical method or by a maximum likelihood method. Consideration of rate variation among sites not only improves evolutionary distance estimation, but also gives more accurate ancestral sequence reconstruction. Ancestral sequences are used to improve profile-based sequence similarity searches. We also propose a new approach to predict positions with functional specificity based on the reconstruction of ancestral sequences.Z-score model introduce rate variation across sites in a natural way by incorporating structural and functional constraints specific for a protein family [α at a site equals the number of recorded substitutions at that site divided by the average substitution number for all sites. To reduce sampling variance, an average observed α vector is calculated from 100 simulations.Evolutionary simulations based on a n family . The simαAB vector was calculated according to equation (11) (for details see Methods). An average αAB vector was derived from 100 simulations. Correlation coefficient between the average αAB vector and the average observed α vector was high (data not shown). However, we found that for large observed α values, the corresponding αAB values were smaller. A constant β was introduced to correct this underestimation in equation (11).For the alignment consisting of all the leaf node sequences generated by the simulation process, an αi is Alignment-Based rate factor at site i. K is the number of sites in a given alignment. Ci is the value assigned to site i (for details see Methods).Here, β value by fitting the average αAB vector and average observed α vector to y = x line. Alignments for three different protein families (trypsin, carboxypeptidase and pdz domain) gave a good empirical estimation for β of about 1.3. The relation between this corrected average αAB vector and average observed α vector is shown in Figure We optimized the αML vector for each alignment generated from the simulation (for details see Methods). The average αML vector shows good correlation with the average observed α vector  based on sequence conservation in an empirical way. The αAB is compared and calibrated using the observed α as standards. Our method of estimating αML is similar to the one proposed by Nielson [αAB to eliminate outliers with very large αML estimates (for details see Methods).Rate variation across sites can be modeled by assuming that the rate factors follow a certain type of statistical distribution. Gamma distribution ,27 and i Nielson . One proαAB or setting α equal to 1.0 (equation (16)). Evolutionary distances were severely underestimated (average underestimation: 0.894) without considering rate variation among sites  [π vectors did not converge to exactly the same point, but they had a high correlation with each other  and the difference of logarithm likelihood function values was small (less than 0.1%). The logarithm likelihood of the alignment, using optimized π vector, increased slightly, but significantly  . Two aliπ vector is time consuming. The running time for reconstruction with or without optimizing π vector is 14,902 seconds and 213 seconds for SH2 alignment (44 sequences), respectively, on a Dell PowerEdge 8450 server (CPU 700MHz, RAM 8G) , suggesting that the information content of the reconstruction is low.For the second simulation method, we introduced rate heterogeneity across sites with structural and functional constraints . For theda) the same for all trees and equal to the tree of pii1  (da = 4.23). If da was too small (e.g. 0.5), the reconstruction accuracy was always close to 1 for all reconstruction methods used. The value da = 4.23 was large enough to generate diverse sequences to differentiate 4 different ancestral sequence reconstruction methods.The second simulation method was also used to test ANCESCON along with the reconstruction programs from PAML , PHYLIP αML), Alignment-Based rate factors (αAB) and no rate factors . Different parameters were also used for the reconstruction programs from PAML and PHYLIP to find their best reconstructions. For PAML, reconstruction was tested with parameter α (rate factor) estimated from alignment and without α. For PHYLIP, 4 different parameter settings were tried, which were combinations of with/without α estimated from alignment by PAML and with/without branch length dwelling in input tree topology. For PAUP*, default settings were used.For ANCESCON we had 3 different parameter settings, which included site-specific rate factors estimated by maximum likelihood method -fold  proteinsN sequences, we had four different methods, "BEST", "SECOND BEST", "SHUFFLE" and "RANDOM", to generate another N-1 sequences (for details see Methods). For each combined alignment (2N-1 sequences), PSI-BLAST [Given an alignment with SI-BLAST  searchesN sequences). For each type of the four combined alignments, we checked hits not found by the native alignments. New hits were verified to be true positives or false positives by running PSI-BLAST or HMMER [The benchmark experiment was PSI-BLAST seeded with the native alignment  from the Pfam database (version 7.3)  were selOur method intends to identify those sites with high similarity within individual sub-trees and high variation among sub-trees. These sites are likely to contribute to functional specificity. Based on a tree partition and the reconstructions at the cutting nodes (details see Methods), we have developed a measure called specificity score (equation (27)). We expect that both highly variable sites and highly conserved sites tend to score low in our method. Ten top-ranking sites were selected as our predicted functional sites for each family. For comparison, we also implemented a simple conservation (SC) method , the evoFigure Another example is the family of adenylyl kinases. Our method identified 3 residues within 5 Å to the ligand while the other 3 methods identified more such residues, most of which are in highly conserved positions such as the catalytic residues. Highly conserved residues, however, are not selected by our method since our measure is designed to emphasize on sites contributing to specificity. Figure α), namely Alignment-Based rate factor (αAB) and rate factor α estimated by maximum likelihood (αML). Consideration of rate variation among sites can alleviate the underestimation of evolutionary distances. Accuracy of ancestral sequence reconstruction by our method is higher than that of PAML, PHYLIP and PAUP* when the given alignment contains more diverse sequences. We show that reconstructed ancestral sequences help to improve detection of distant homologs and prediction of functional sites with specificity.We developed a package (ANCESCON) to reconstruct ancestral protein sequences that takes into account the variation of substitution rates among sites. Two methods were proposed to estimate site-specific evolutionary rates . The transition probability matrix of 20 amino acids is written as P(t), which can be calculated asFor all models discussed in this paper, we assume all sites in an alignment evolve independently and according to a homogeneous, stationary and time reversible Markov process. The probability of an amino acid P(t) = exp(Qt)     (2)Q is the rate matrix. The non-diagonal elements qij are the instantaneous rates of change from amino acid i to amino acid j and diagonal elements qii are such that each matrix row sums up to 0. Q can be calculated by:Here, Q = S* diag(π)     (3)S is the matrix of amino acid exchangeability parameters [πi is the equilibrium frequency for amino acid i. Time reversibility implies that S is a symmetric matrix. In our program, the S matrix is taken from Whelan and Goldman [π vector is estimated from the given alignment.rameters . πi is t Goldman  and the Q can be decomposed into eigenvalues (λi) and eigenvectors (ui).U = (u1, ..., u20)     (5)Pij(t) can be calculated using the following equation,The likelihood function  for an e is the equilibrium frequency of the amino acid at the node A.  is the transition probability from the amino acid at node A to the amino acid at node B after an evolutionary distance dAB.Here, i has a rate factor αi [Considering that each site actor αi ,18, we ht in equation (6) can be expressed as:t = α·d     (9)d is the evolutionary distance and α is rate factor. The following restriction on the vector α holds:K is the number of sites.Here, α (αAB) and Maximum Likelihood-estimated rate factor α (αML).Our program supports two methods to estimate a rate factor for each site: Alignment-Based rate factor αAB is empirical and based on the observation that the substitution rate at a site is correlated with the conservation of the site, which, in turn, is correlated with the average transition probability among the amino acids at that site. Conserved sites are dominated by highly similar amino acids and thus have high average transition probabilities among the amino acids. The algorithm to calculate αAB is as follows:The estimation of t equal to 1.0 and use equation (6) to calculate a transition probability matrix P for 20 amino acids. Equation, , is used to compute a symmetric matrix P'.1. Set , where  is the number of non-gapped amino acid pairs in site i and the denominator is the sum over the transition probabilities between all amino acid pairs (j,k) at a site i.2. Calculate the average transition probability for each site and take the reciprocal: Ci is set to 0 to make it consistent with the Maximum Likelihood estimation.3. For invariant sites, αAB, so that equation (10) holds.4. Equation (11) is used to calculate αML factors by maximizing the likelihood (equation (8)) for each site:If an evolutionary tree is assumed for the alignment, we can estimate the αML at those sites can be very large, as has been previously noticed [α values. An empirical method is used to reduce the values of αML outliers, guided by the αAB values. a Z-score of the ratio of αML to αAB is calculated for each site except invariant sites:If some sites are highly variable, the  noticed . We cons is the ratio of  to  for site i;  is the number of sites excluding the invariant sites. If Zi is greater than 3, it is reduced to 3 by decreasing the value of . We repeat this procedure until no Zi for any site i is greater than 3. After removing the outliers, we scale the  values so that equation (10) holds.Here, π, one derived directly from the given alignment (Alignment-Based π or πAB) and the other estimated by Maximum Likelihood (πML). The likelihood for the entire alignment is a function of π with 19 variables. A continuous minimization method by simulated annealing [π, with the objective function being the logarithm likelihood of the alignment. The simulated annealing is computationally intensive and is the major reason for the long CPU time given in Table Two methods are implemented to estimate the equilibrium frequency vector nnealing  is used d can be written as:A Maximum Likelihood approach is used to estimate the evolutionary distances among sequences, either considering rate variation across sites or not. The logarithm likelihood for replacing one protein sequence (A) with another protein sequence (B) after an evolutionary distance  is the equilibrium frequency for the amino acid at site j in sequence A.  is the transition probability from amino acid at site j in sequence A to amino acid at site j in sequence B after an evolutionary distance α j·d. αj is 1 if all sites are assumed to evolve at the same rate; otherwise the αAB at site j is used for αj.Here, An estimate of the evolutionary distance between two sequences is obtained by maximizing the likelihood function of equation (15):Equation (16) can be solved by the bisection root-finding method .After the distance matrix is calculated, the "Weighbor" method, i.e. weighted neighbor joining, is used to infer an evolutionary tree .Two methods are implemented to reconstruct ancestral sequences. One is a marginal reconstruction method , and theP(Ar|{Al}T), which is the conditional probability of amino acid Ar at the root, given leaf node amino acid set {Al} and a tree T. Since time reversibility is assumed, any internal node can serve as a root. Using Bayes' theorem, we have:We calculate P(Ar) is used here instead of P(Ar|T) because the frequency of the root amino acid Ar, i.e. πr, does not depend on tree T. P({Al}|ArT) is the conditional probability of the known amino acids at the leaf nodes, given T and Ar. P({Al}|T) does not depend on Ar, so it is calculated as a normalization constant for P(Ar|{Al},T) terms over all 20 possible values of Ar to make the sum equal to 1.Here, P({Al}|ArT) can be expanded as:For Figure  is the transition probability from amino acid at node A to amino acid at node B after an evolutionary distance dAB. Equation (18) can be calculated using a recursive method suggested by Felsenstein [Here, quation 1 can be cIf rate factors are used in the reconstruction of the root sequence, we have:αi could be either αAB or αML at site i. P(AC,AD,AE,AF | AA,T)i is the conditional probability P(AC,AD,AE,AF | AA,T) at site i.Here, Ai} that maximize the conditional probability of this amino acid combination, given the leaf node amino acid set {Al} and a tree T, P({Ai}|{Al},T). Using the Bayes' theorem, we have:The objective of a joint reconstruction method is to find the combination of amino acids for an internal node set {P({Al}|T) is the same for all amino acid combination at internal node set {Ai} this problem becomes finding the maximum of P({Al}|{Ai},T) *P({Ai}).Because The details of a fast algorithm to solve equation (20) can be found in Pupko et al. . We alsoDue to difficulties with the probabilistic models of gaps, a simplified empirical approach is used to alleviate the problem. We assume that gaps are "supersede" letters. Gaps are considered for each site independently. If a leaf node has a gap instead of an amino acid at a site, this node will be deleted from the tree for this site. After dealing with leaves, we check all internal nodes for children. If an internal node has no children or only one child due to the leaf removal because of gaps, it will be removed from the tree and a gap will be assumed as its reconstructed state.π and the S matrix. Given the length of a branch from a parent node to one of its child nodes and the amino acid for the parent node, we simulated the substitution process to generate an amino acid for the child node based on the transition probabilities that were calculated using equation (6). For the arbitrarily selected tree shown in Figure Two methods of simulating amino acid substitution process were used to test the reliability of reconstruction, rate factors and evolutionary distance estimation. The first simulation method was based on a homogeneous time reversible Markov model. The parameters from Whelan and Goldman  were choZ-score model, introduced rate variation across sites by using structural and functional information for a specific protein family [Z-score simulations under structural and functional constraints: pdz domain (Protein DataBank (PDB) ID: 1g9o) [d, the child sequence was obtained after l*d accepted substitutions starting from the parent sequence, where l is protein sequence length. Simulations of the substitution process were repeated 100 times. For each site, the number of accepted substitutions was recorded and averaged over 100 simulations. Rate factors (observed α), representing site mutability, were calculated from these average substitution numbers, such that the average of rate factors is 1 (equation (10)). 100 simulated alignments were used to test the rate factor estimators (αAB and αML), distance calculation methods and ancestral sequence reconstruction.The second simulation method, based on a n family . We seleD: 1g9o) , trypsinD: 1g9o)  and carbD: 1g9o) . Given aβ-barrel structure. In the SCOP (Structure Classification of Proteins) database (version 1.55) [38 OB (Oligonucleotide/oligosaccharide binding)-fold  proteinson 1.55) , there aon 1.55)  for homoN sequences, ancestral sequences for the N-1 internal nodes were reconstructed. The idea is to test whether adding more sequences to a native alignment can help homology detection. Four types of combined alignments were generated, adding different sets of N-1 sequences to the native alignment. In the first case, the added sequence at each internal node consisted of amino acids with the largest probability at each position. In the second case, the added sequences were made up of amino acids with the second largest probability. In the third case, we shuffled the native alignment at each position while keeping the gap pattern as in the native alignment. After shuffling, we added N-1 sequences resulted from the shuffling to the native alignment. In the fourth case, N-1 random sequences were generated with the overall amino acid frequencies of the native alignment. These four methods are named "BEST", "SECOND BEST", "SHUFFLE" and "RANDOM", respectively.For each alignment with Our objective is to find sites that are well conserved within each sub-tree, but show high variability between different sub-trees. These sites are likely to contribute to functional specificity ,45,46.Multiple sequence alignments of ten protein families were chosen from the Pfam database (version 7.3) . These f1. For each family, there is a template sequence with known structure. The sequences, which cover less than 75% of the non-gapped positions in the template sequence with amino acids, were considered to be fragments and discarded.2. A sequence identity matrix was calculated for the remaining sequences. A single linkage clustering was done to form sequence groups at sequence identity threshold 0.8. For each group, we chose the longest sequence as a representative, discarding other members. This step reduced redundancy in the dataset.3. An average sequence identity was calculated for the remaining sequences. We used this average identity as a threshold for complete linkage clustering to form new sequence groups. Four groups with the largest sequence numbers were chosen to form our new alignment. Any group with the same number of sequences as the fourth group was also included in the new alignment. The purpose of this step is to keep the major sequence subgroups of a family while leaving out highly divergent sequences that might be deleterious for tree inference.The "Weighbor" method gives an unrooted tree. For our purpose of predicting functional sites, we need to find a point on the tree that serves as the root. We used a least-squares modification of the midpoint rooting procedure to define the root .dr, then the distance between adjacent layers is dr/5  , so that the distance between i and j is exactly dr/5. Each cutting node has only one ancestral cutting node neighbor.3. Node j and its ancestral cutting node neighbor i, i.e. anc(j), at site m is defined as:The dissimilarity score for cutting node  and  are the reconstructed probabilities of amino acid A at cutting node j and its ancestral cutting node neighbor i(anc(j)), respectively., K = 1,...,5     (22)Let  is the average dissimilarity score for layer K . NK is the number of cutting nodes in layer K.Here, The specificity score is defined as: reflects the difference of amino acid compositions among the major sub-trees defined by the first layer.  to  reflect the average difference of amino acid compositions within each sub-tree. If the amino acids are highly conserved within each sub-tree but show variability among the sub-trees,  to  are small and  is large, leading to a large value of Sm. We set Sm to 0 for invariant sites. We sort the sites by their specificity scores and choose the 10 top scoring sites as our predicted functional sites. Those predicted functional sites that lie within 5 Å from the ligand(s) are considered to be true positives.Z-score sequence design [Z-score design tends to weaken the conservation caused by function, functionally conserved sites tend to have a large conservation difference between the native alignment and the alignment of designed sequences. We chose 10 top ranking sites sorted by conservation difference as predictions by CD.We compared our method with three other methods for prediction of functional sites. The first method (Simple Conservation or SC) is based on sequence conservation. Highly conserved sites are considered to be functional. For each family, we sorted the sites by positional conservation  and chose design . The basNVG conceived and initiated the study. All authors took part in developing methods and designing experiments. WC wrote the source code and JP analyzed the data. All authors read and approved the final manuscripts.


The PedsQL™ Measurement Model was designed to measure health-related quality of life (HRQOL) in children and adolescents. The PedsQL™ 4.0 Generic Core Scales were developed to be integrated with the PedsQL™ Disease-Specific Modules. The newly developed PedsQL™ Family Impact Module was designed to measure the impact of pediatric chronic health conditions on parents and the family. The PedsQL™ Family Impact Module measures parent self-reported physical, emotional, social, and cognitive functioning, communication, and worry. The Module also measures parent-reported family daily activities and family relationships.The 36-item PedsQL™ Family Impact Module was administered to 23 families of medically fragile children with complex chronic health conditions who either resided in a long-term care convalescent hospital or resided at home with their families.Internal consistency reliability was demonstrated for the PedsQL™ Family Impact Module Total Scale Score (α = 0.97), Parent HRQOL Summary Score (α = 0.96), Family Functioning Summary Score (α = 0.90), and Module Scales (average α = 0.90, range = 0.82 – 0.97). The PedsQL™ Family Impact Module distinguished between families with children in a long-term care facility and families whose children resided at home.The results demonstrate the preliminary reliability and validity of the PedsQL™ Family Impact Module in families with children with complex chronic health conditions. The PedsQL™ Family Impact Module will be further field tested to determine the measurement properties of this new instrument with other pediatric chronic health conditions. Pediatric health-related quality of life (HRQOL) is increasingly acknowledged as an important health outcome measure in clinical trials and health services research and evaluation ,2. AdditAlthough there are a number of well-developed generic measures of family functioning, such as the Family Environment Scale , instrumAlthough these two well-developed measures existed when we conceptualized the PedsQL™ Family Impact Module, after an analysis of the items and scales of the existing instruments, we felt that a PedsQL™ Family Impact Module would make a significant contribution to the literature by creating a multidimensional instrument that could stand alone, or be easily integrated into the PedsQL™ Measurement Model . The PedIn this context, the PedsQL™ Family Impact Module was developed and initially field tested in families with medically fragile children with complex chronic medical conditions as part of our evaluation of the healing environment of a Children's Convalescent Hospital . In ordeThis study investigates the preliminary reliability and validity of the PedsQL™ Family Impact Module in medically fragile children with complex chronic health conditions. We hypothesized that the PedsQL™ Family Impact Module would distinguish between families in which the child resided at home versus those whose child resided in a long-term care facility based on the extant literature on pediatric chronic health conditions and the impact on parents and families ,6.Participants were the parents of 23 medically fragile pediatric patients with complex chronic health conditions, such as severe cerebral palsy and birth defects. Participants from the Children's Convalescent Hospital (CCH) were parents of 12 pediatric patients who were residents of this long-term care facility. For each CCH family, the family member who completed the PedsQL™ Family Impact Module was the resident's mother. Participants from the REACH program  were the parents of 11 pediatric patients. For each REACH family except one, the family member who completed the PedsQL™ was the patient's mother.The 36-item PedsQL™ Family Impact Module Scales encompass 6 scales measuring parent self-reported functioning: 1) Physical Functioning (6 items), 2) Emotional Functioning (5 items), 3) Social Functioning (4 items), 4) Cognitive Functioning (5 items), 5) Communication (3 items), 6) Worry (5 items), and 2 scales measuring parent-reported family functioning; 7) Daily Activities (3 items) and 8) Family Relationships (5 items). Items and scales were developed through focus groups, cognitive interviews and pre-testing measurement development protocols ,11, and The PedsQL™ Family Impact Module was developed as a parent-report instrument. A 5-point response scale is utilized . Items are reverse-scored and linearly transformed to a 0–100 scale (0 = 100, 1 = 75, 2 = 50, 3 = 25, 4 = 0), so that higher scores indicate better functioning (less negative impact). Scale Scores are computed as the sum of the items divided by the number of items answered (this accounts for missing data). If more than 50% of the items in the scale are missing, the Scale Score is not computed . AlthougThe PedsQL Family Impact Module Total Scale Score is the sum of all 36 items divided by the number of items answered. The Parent HRQOL Summary Score (20 items) is computed as the sum of the items divided by the number of items answered in the Physical, Emotional, Social, and Cognitive Functioning Scales. The Family Functioning Summary Score (8 items) is computed as the sum of the items divided by the number of items answered in the Daily Activities and Family Relationships Scales.The PedsQL™ Family Impact Module was mailed to families whose children were residents at the CCH and outpatients in the REACH program, along with a self-addressed stamped envelope in which to return the survey to the research team. A letter was included in the packet explaining the study, the confidentiality with which their data would be treated, and that the healthcare staff would not see this information. The protocol was approved by the Institutional Review Board at Children's Hospital and Health Center, San Diego.Scale internal consistency reliability was determined by calculating Cronbach's coefficient alpha . Scales Construct validity for the PedsQL™ Family Impact Module was determined utilizing the known-groups method. The known-groups method compares scale scores across groups known or expected to differ in the construct being investigated. In this study, PedsQL™ Family Impact Module scores in groups differing in residence of the child  were computed ,29, usinTable Internal consistency reliability alpha coefficients for the PedsQL™ Family Impact Module Scales are presented in Table Table This study presents the preliminary reliability and validity of the newly developed PedsQL™ Family Impact Module. All internal consistency reliabilities exceeded the recommended minimum alpha coefficient standard of 0.70 for group comparisons, with most scales approaching or exceeding an alpha of 0.90, recommended for individual patient analysis .The PedsQL™ Family Impact Module Scales performed as hypothesized utilizing the known-groups method. Where statistically significant differences existed between families with children at the CCH and REACH, REACH families were lower functioning, generally confirming the hypothesis that families whose medically fragile children live in a residential facility are higher functioning than those whose children live in the home.The present findings have certain limitations. Information on nonparticipants and an accurate response rate were not available, which may limit the generalizability of the findings. The generalizability of the findings is further limited by the small sample size and the selection of medically fragile children with complex chronic medical conditions. Whether the instrument would perform well in groups of children with other chronic health conditions is a matter of empirical inquiry. Given that instrument validation is an iterative process and consistent with this paradigm, the PedsQL™ Family Impact Module will be further field tested in other pediatric chronic health conditions with larger populations of children.The study demonstrates the preliminary reliability and validity of the PedsQL™ Family Impact Module, an instrument designed to assess the impact of pediatric chronic health conditions on parents' HRQOL and family functioning. As predicted, families of children with medically fragile conditions who resided in a children's convalescent hospital were higher functioning than families of similar children who resided at home.HRQOL Health-Related Quality of LifePedsQL™ Pediatric Quality of Life Inventory™JWV and PD conceptualized the rationale and design of the study. JWV designed the instrument and drafted the manuscript. PED participated in the instrument design and coordination of initial data collection. SAS performed the statistical analysis and participated in study coordination, instrument development, and data collection. TMB participated in study conceptualization and design, instrument development, and data collection. All authors read and approved the final manuscript.


Several cell lines and primary cultures benefit from the use of positively charged extracellular matrix proteins or polymers that enhance their ability to attach to culture plates. Polyethyleneimine is a positively charged polymer that has gained recent attention as a transfection reagent. A less known use of this cationic polymer as an attachment factor was explored with several cell lines.Polyethyleneimine compared favorably to traditional attachment factors such as collagen and polylysine. PC-12 and HEK-293 cells plated on dishes coated with polyethyleneimine showed a homogeneous distribution of cells in the plate, demonstrating strong cell adhesion that survived washing procedures. The polymer could also be used to enhance the adherence and allow axonal outgrowth from zebrafish retinal explants. The effects of this coating agent on the transfection of loosely attaching cell lines were studied. Pre-coating with polyethyleneimine had the effect of enhancing the transfection yield in procedures using lipofection reagents.Polyethyleneimine is an effective attachment factor for weakly anchoring cell lines and primary cells. Its use in lipofection protocols makes the procedures more reliable and increases the yield of expressed products with commonly used cell lines such as PC-12 and HEK-293 cells. Molecular cell biology experimentation often requires the culture of primary cells or immortalized cell lines. The most common substratum used in cell culture consists of a plastic dish that offers a negatively charged surface. A drawback of this technology is that some anchorage-dependent cell types do not produce sufficient amounts of positively charged extracellular matrix proteins, adhering only weakly to the plastic substratum. Pre-coating of the plastic surface with extracellular matrix proteins such as collagen, fibronectin, laminin, etc., usually enhances the attachment of these cell types ,2. SynthPolyethyleneimine (PEI) is an organic polymer that has a high density of amino groups that can be protonated. At physiological pH, the polycation is very effective in binding to DNA and can mediate the transfection of eukaryotic cells . The oriThis report presents a comparison of PEI with other traditional coating agents as attachment factors for several cell lines and primary cultures of retina tissue. Additionally, an enhancement of the transfection yield of weakly anchoring cell lines is shown by the combination of PEI coating with lipofection.PC-12 cells are used as a model of neuronal differentiation in the laboratory, as treatment of these cells with nerve growth factor (NGF) induces neurite extension and the expression of biochemical markers of the sympathetic neuronal phenotype . They grin vitro by the accelerated extension of long neurites. However, this phenomenon requires the use of extracellular matrix or attachment factors (for example collagen or PDL coating), as the explants show very low affinity for the uncoated plastic surface. Experiments were conducted to observe if PEI could act as an attachment factor conducive to axonal outgrowth from zebrafish retinal explants. Retinal explants from control zebrafish eyes were able to attach to PEI-pretreated culture dishes . Plates pretreated with the diverse attachment factors were compared with plates that received no pretreatment (untreated). Experiments were performed in triplicate, and the averages of dye retained in the plates that received each treatment were calculated. Those averages were normalized to the average obtained with the PEI pretreatment, which was assigned the arbitrary value of 100.0% in all the experiments. The results from representative experiments with the 3 cell lines are shown in Figure To provide an indication of the variation among experiments, the averages ± standard deviations of the relative cell counts obtained in the independent experiments were calculated for each cell line and treatment . The comparative results for the other treatments are indicated below. For the PC-12 cells, the relative counts were 81.5% ± 8.8% for collagen-pretreated wells (n = 4), 93.9% ± 21.2% for PDL-pretreated wells (n = 4), and 52.1% ± 13.7% for untreated wells (n = 4). For the HEK-293 cells, the relative counts were 16.3% ± 12.7% for collagen-pretreated wells (n = 3), 99.6% ± 2.5% for PDL-pretreated wells (n = 3), and 9.0% ± 4.1% for untreated wells (n = 3). In the experiments with MYS cells, the average of the relative counts with collagen-pretreated wells was 92.7% ± 16.2% (n = 3), 71.1% ± 6.7% for PDL-pretreated wells (n = 3), and 78.4% ± 11.5% for untreated wells (n = 3). Statistical analysis indicates that the enhancement of adhesion of both PC-12 and HEK-293 cells to PEI-pretreated dishes versus untreated plastic is significant , while it is not statistically significant for MYS cells (p > 0.05).5 HEK-293 cells and 1.5 × 106 PC-12 cells). The results indicate that PEI worked well as an attachment factor over a wide range of cell numbers for both PC-12 and HEK-293 cells. Lower cell numbers could not be tested reliably because they were close to the limit of sensitivity of the neutral red assay. The results shown are representative of at least 4 independent experiments performed with each cell line. Figure 2 incubator for 3 days, with a medium change performed every 24 h. The performance of PEI in these plates was then compared with plates treated with PEI using the standard protocol described for previous experiments. The results show relative cell counts normalized to the average of the absorbances obtained with the standard PEI-treated plates, which was given the arbitrary value of 100.0%. The results indicate that the PEI coating remains stable on the surface of the plastic dish for at least 10 days of refrigeration, and that it is not removed by incubation at 37°C in medium or even by repeated medium changes. Results are representative of 4 independent experiments performed in triplicate.To further characterize the properties of PEI as an attachment factor for weakly anchoring cells, experiments were carried out to analyze the dosage of PEI that can provide optimal cell anchoring, the range of cell numbers that can benefit from the presence of PEI, and the stability of PEI as attachment factor. Figure Transfection of eukaryotic cells by lipofection involves several steps, media additions and replacements, which can take a toll in weakly anchoring cells. Even if care is practiced to avoid cell loses, the weakly anchored cells may be less efficient in the uptake of the transfection complexes. Since PEI pretreatment of cell culture dishes increased the strength of attachment of cells to such surfaces, it was hypothesized that the attachment factor may have a positive effect in transfection yields obtained by lipofection. The three cell lines described above were used to test this hypothesis utilizing the coating agents previously examined. Transfections were performed with a plasmid encoding the reporter enzyme β-galactosidase. The transfection yield was monitored by determination of reporter enzyme activity in lysates from the transfected cells. At least two independent assays in triplicate were performed with each cell line. Representative plots are shown in Figure Compiling the results from all the experiments performed, the average fold-induction (± standard deviation) observed in the transfection yield of PC-12 cells anchored to PEI as compared with cells on untreated wells was 2.4-fold , while the enhancement with HEK-293 cells was 6.3-fold . t-test statistical analysis indicates that both increases are significant (p < 0.05). No significant transfection enhancement was observed in the experiments with MYS cells, with an average fold-change of 1.0-fold  by the pretreatment with PEI .4 to 3.2 × 105 cells per well. When larger numbers of HEK-293 cells were utilized the results were more variable, suggesting that when these cells are near confluency they may be able to form monolayers that attach more firmly to the plate, however further experiments are necessary to study this hypothesis. The effects of PEI and the other cationic attachment factors on PC-12 and HEK-293 cells suggest that these cells have a limited capacity to produce an effective extracellular matrix to allow them to attach to the plastic surface, resulting in weak anchoring to the dishes on their own. Cationic polymers such as PEI and PDL seem to work very effectively to coat the surface of the dish and substitute for the absence of the extracellular matrix components. On the other hand, fibroblast cells (such as the MYS cells) are very efficient producing extracellular matrix proteins, which allows them to attach firmly to the culture dishes, therefore no benefit was observed by the use of attachment factors with these cells , but this is not so for protocols with HEK-293 cells. PEI was very effective in improving lipofection protocols with both cell lines. The firm attachment afforded by PEI allowed transfections with cationic lipids (lipofection) to provide higher yields and more consistent results.2 atmosphere in a tissue culture incubator (NuAire). Zebrafish retinal explants were maintained in L-15 complete medium: L15 medium (Sigma) supplemented with 1% fetal bovine serum (Hyclone), 100 u/ml penicillin, 100 μg/ml of streptomycin and 2 mM glutamax . The explants were incubated at 28°C at normal atmospheric CO2 concentrations.PC-12 (rat pheochromocytoma, ATCC CRL-1721) cells were cultured with RPMI complete medium: RPMI medium (Invitrogen) supplemented with 10% horse serum and 5% fetal bovine serum (both from Hyclone), 100 u/ml of penicillin, 100 μg/ml of streptomycin, 10 mM HEPES, 2 mM glutamax and 1 mM sodium pyruvate . HEK-293 (293, human embryonic kidney, ATCC CRL-1573) and MYS (MYS-Cl-2-BCF1, mouse yolk sac, ATCC CRL-9292) cells were cultured in DMEM complete medium: DMEM medium (Invitrogen) supplemented with 10% fetal bovine serum (Hyclone), 100 u/ml of penicillin, 100 μg/ml of streptomycin and 2 mM glutamax . Cell lines were maintained at 37°C in a 5% CO6 cells) was added to each well of the MW12 and the plate was incubated at 37°C in a 5% CO2 atmosphere for 48 h. The cells were then examined under a phase contrast Olympus-CK40 microscope and photomicrographs were obtained with a Pixera Penguin 150 CL camera. The experiment was performed in triplicate and pictures were taken at the center, near the edge and midway between center and edge of the plate, to evaluate the distribution of cells on the wells.For the culture and microscopic observation of PC-12 cells, the wells of a multiwell-12 tissue culture dish  were pretreated for 20 min with 100–200 μl of the different solutions of attachment factors: PEI-800 kDa, from Fluka, at 25 μg/ml in 150 mM NaCl; PDL, from ICN, at 100 μg/ml in deionized water; bovine dermal collagen, from ICN, 3 mg/ml aqueous solution. Usually, each column of wells in the MW12 was pretreated with one of the attachment factors, while the last column was left untreated. After removal of the different attachment factor solutions, 1 ml of a PC-12 cell suspension  was performed retro-orbitally with anesthetized zebrafish 10 days prior to explantation, using protocols previously described ,25. Cont5 PC-12 cells and cultured with RPMI complete medium as previously described. After 24 h, the medium was replaced by RPMI medium with low serum , which was supplemented with NGF (Sigma) at 100 ng/ml to induce differentiation. The culture medium was replaced by new RPMI low serum medium with 100 ng/ml of NGF every two days. At different times during the differentiation process , photomicrographs were obtained as previously described.The wells of a MW12 were pretreated with PEI as described above. The wells were then seeded with 2.5 × 106 cells per well for the experiments with PC-12 cells, 1.5 × 105 cells for the experiments with HEK-293 cells, or 3 × 104 cells for the experiments with MYS cells. The MW12 dishes were incubated at 37°C in a 5% CO2 atmosphere for 48 h in the case of PC-12 cells, or 24 h for HEK-293 and MYS cells. In all the experiments, the wells were washed 4 times with phosphate buffered saline (PBS) to test the strength of anchorage to the substratum. To estimate the number of cells remaining after the multiple washings, a procedure that utilizes the vital dye neutral red was employed [Pretreatment of MW12 dishes with the attachment factors was performed as described above. PC-12, HEK-293 and MYS cells were counted using a Neubauer hemocytometer, and the MW12 plates were seeded with 1 × 10employed . BrieflyTwo-sample (unpaired) t-test analyses were performed with a Microsoft Excel worksheet designed for this purpose. The worksheet calculates the t-factor by comparing 2 independent sets of data and estimates the probability (p) of obtaining that result assuming that the two samples came from the same population  based on the Student's t-distribution (two-tailed). The averages of the two samples from the same experiment were considered statistically different whenever p was lower than 0.01. For comparisons of the independent experiments, one-sample t-test analyses were utilized to estimate the p values and assess the statistical significance of the difference observed between the PEI-treated wells and the untreated controls. The effect of PEI was considered significant whenever p was lower than 0.05.5 HEK-293 cells were seeded onto the dishes and incubated at 37°C for 24 h. Untreated wells were seeded in the same manner as controls.For the determination of optimal concentrations of PEI for the pretreatment procedure, a series of solutions of PEI were prepared so that their final concentrations of PEI polymer (w/v) were 0.025, 0.25, 2.5, 25 and 250 μg/ml. They were used to coat MW12 tissue culture dishes as previously described for the 25 μg/ml solution. To test the efficacy of coating of the solutions, 2 × 10The dishes were then subjected to the multiple washing procedure described above and the number of cells remaining in the dishes was measured by the neutral red dye procedure as described previously. All experiments were performed in triplicate and the relative cell counts were calculated by normalization to the values obtained with 25 μg/ml of PEI, which was assigned a value of 100.0%.4, 8 × 104, 1.6 × 105 and 3.2 × 105) or PC-12 cells (2.5 × 105, 5 × 105, 1 × 106 and 1.5 × 106). Control untreated MW12 culture dishes were seeded with the same numbers of cells in parallel with the treated plates. After 24 h for the HEK-293 cells or 48 h for the PC-12 cells, the strength of anchoring of the cells to the dishes was measured as described before and relative cell numbers were determined . All experiments were performed in triplicate.The wells of MW12 culture dishes were pretreated with 25 μg/ml of PEI as described above. The wells were then seeded with various numbers of either HEK-293 cells . The strength of attachment of cells to these dishes was determined as mentioned before, and relative cell counts were calculated by normalization to the value obtained with the standard PEI-treated wells, which was assigned arbitrarily a value of 100.0%. All experiments were performed in triplicate.To analyze the stability of the PEI attachment factor onto the plastic surface of the tissue culture dish, two tests were performed. One MW12 dish was coated with 25 μg/ml of PEI as previously described, then the PEI solution was replaced by PBS and the dish was kept at 4°C for 10 days. A second MW12 dish was coated with 25 μg/ml of PEI, then the PEI solution was replaced by DMEM complete medium and the dish was kept at 37°C in the CO2 atmosphere for 48 h for the PC-12 cells, or 24 h for the HEK-293 and MYS cells, the transfection yield was determined by measuring β-galactosidase activity in cell extracts [2HPO4, 36 mM NaH2PO4, 2 mM MgCl2 and 100 mM β-mercaptoethanol). The reaction mixture was incubated at 37°C until development of yellow coloration was apparent . Reactions were stopped by the addition of 500 μl of 1 M Na2CO3. The relative levels of β-galactosidase activity were obtained by determination of the absorbance at 420 nm in a spectrophotometer (Beckman DU500). Parallel assays with cells transfected with pcDNA3 plasmid (Invitrogen) were conducted as negative controls in every experiment, and subtracted from the experimental absorbances to correct for endogenous activities. The experiments were conducted in triplicate. Absorbances were normalized to the values obtained with PEI-pretreated wells.MW12 culture dishes were pretreated and seeded with cells as indicated above for the cell number measurements. The cells were subjected to lipofection with 1 μg per well of the plasmid pcDNA3-βgal  and Lipoextracts . The celThe authors declare that they have no competing interests.A.R.V. performed the experiments comparing the various attachment factors. S.G. and K.L.V.P. performed the PEI dosage, cell number and PEI stability analysis experiments, as well as the NGF differentiation experiments with PC-12 cells. M.J. participated in the experiments with MYS cells. R.P.B. and M.G.G. collaborated as Principal Investigators in this project, participating in the design of the experiments and the writing of the manuscript. R.P.B. performed the experiments with zebrafish retinal explants.