Pantelis G Bagos

Abstract: We describe here OMPdb, which is currently the most complete and comprehensive collection of integral beta-barrel outer membrane proteins from Gram-negative bacteria. The database currently contains 69,354 proteins, which are classified into 85 families, based mainly on structural and functional criteria. Although OMPdb follows the annotation scheme of Pfam, many of the families included in the database were not previously described or annotated in other publicly available databases. There are also cross-references to other databases, references to the literature and annotation for sequence features, like transmembrane segments and signal peptides. Furthermore, via the web interface, the user can not only browse the available data, but submit advanced text searches and run BLAST queries against the database protein sequences or domain searches against the collection of profile Hidden Markov Models that represent each family's domain organization as well. The database is freely accessible for academic users at http://bioinformatics.biol.uoa.gr/OMPdb and we expect it to be useful for genome-wide analyses, comparative genomics as well as for providing training and test sets for predictive algorithms regarding transmembrane beta-barrels.

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Abstract: Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system.

Abstract: Phoenix 100 and Vitek 2 (operating with the current colorimetric cards) are commonly used in hospital laboratories for rapid identification of microorganisms. The present meta-analysis aims to evaluate and compare their performance on gram positive and gram negative bacteria. MEDLINE database was searched up to October 2010 for the retrieval of relevant articles. Pooled correct identification rates were derived from random-effects models, using the arcsine transformation. Separate analyses were conducted at the genus and species levels; subanalyses and meta-regression were undertaken to reveal meaningful system- and study- related modifiers. A total of 29 (6,635 isolates) and 19 (4,363 isolates) articles were eligible for Phoenix and colorimetric Vitek 2, respectively. No significant differences were observed between Phoenix and Vitek 2 either at the genus (97.70% versus 97.59%, p=0.919) or the species (92.51% versus 88.77%, p=0.149) level. Studies conducted with conventional comparator methods tended to report significantly better results compared to those employing molecular reference techniques. Speciation of S. aureus was significantly more accurate in comparison to CoNS by both Phoenix (99.78% versus 88.42%, p<0.00001) and Vitek 2 (98.22% versus 91.89%, p=0.043). Vitek 2 also reached higher correct identification rates for gram negative fermenters versus non-fermenters at the genus (99.60% versus 95.90%, p=0.004) and the species (97.42% versus 84.85%, p=0.003) level. In conclusion, the accuracy of both systems seems modified by underlying sample- and comparator method-related parameters. Future simultaneous assessment of the instruments against molecular comparator procedures may facilitate interpretation of the current observations.

Abstract: Many environmental and genetic factors have been implicated in the development of multiple sclerosis. However, the aetiology has not been clarified yet. Therefore, using a meta-analytic approach, we tried to probe the potential association between various cytokine gene polymorphisms and the occurrence of multiple sclerosis. A comprehensive literature search yielded 45 eligible studies, which involved 7379 cases and 8131 controls. Totally, the effect of eight polymorphisms, i.e. IL-1A C[-889]T, IL-1B C[-511]T, IL-1B C[3953]T, IL-4 C[33]T, IL-10 C[-819]T, IL-10 G[-1082]A, tumour necrosis factor-a (TNFA) G[-308]A and TNFA G[-238]A, was evaluated in a random-effects meta-analysis. There was no evidence of statistically significant association between the aforementioned polymorphisms and multiple sclerosis. Publication bias and heterogeneity were absent in most analyses. Within its limitations, the current literature-based meta-analysis does not indicate that specific polymorphic variations of genes encoding pro-inflammatory and anti-inflammatory cytokines affect susceptibility to multiple sclerosis.

Abstract: BACKGROUND: Meta-analysis is a popular methodology in several fields of medical research, including genetic association studies. However, the methods used for meta-analysis of association studies that report haplotypes have not been studied in detail. In this work, methods for performing meta-analysis of haplotype association studies are summarized, compared and presented in a unified framework along with an empirical evaluation of the literature. RESULTS: We present multivariate methods that use summary-based data as well as methods that use binary and count data in a generalized linear mixed model framework (logistic regression, multinomial regression and Poisson regression). The methods presented here avoid the inflation of the type I error rate that could be the result of the traditional approach of comparing a haplotype against the remaining ones, whereas, they can be fitted using standard software. Moreover, formal global tests are presented for assessing the statistical significance of the overall association. Although the methods presented here assume that the haplotypes are directly observed, they can be easily extended to allow for such an uncertainty by weighting the haplotypes by their probability. CONCLUSIONS: An empirical evaluation of the published literature and a comparison against the meta-analyses that use single nucleotide polymorphisms, suggests that the studies reporting meta-analysis of haplotypes contain approximately half of the included studies and produce significant results twice more often. We show that this excess of statistically significant results, stems from the sub-optimal method of analysis used and, in approximately half of the cases, the statistical significance is refuted if the data are properly re-analyzed. Illustrative examples of code are given in Stata and it is anticipated that the methods developed in this work will be widely applied in the meta-analysis of haplotype association studies.

Abstract: Pandemic A (H1N1) 2009 mortality rates varied widely from one country to another. Our aim was to identify potential socioeconomic determinants of pandemic mortality and explain between-country variation.

Abstract: Type 2 diabetes mellitus (T2DM) is a highly complicated metabolic disorder for which there is worldwide effort for the identification of susceptibility genes. Polymorphisms of the Apolipoprotein E (ApoE) gene are associated with plasma lipid and lipoprotein levels and influence cardiovascular risk. Since insulin resistance is known to be strongly associated with metabolic dyslipidemia, ApoE polymorphisms have been implicated in predisposition to diabetes but the results of the individual studies were inconclusive. We present here a meta-analysis of population-based case-control genetic-association studies relating ApoE polymorphisms and T2DM. We included in the analysis 30 studies, which reported data of ApoE genotypes in 5423 T2DM patients and 8197 healthy unrelated controls. Multivariate and univariate methods suggest a significant role played by the E2 allele, since carriers of the E2 allele were at elevated risk for T2DM (Odds Ratio=1.18, 95% CI: 1.02, 1.35). There was no evidence for publication bias or other small-study related bias or significant heterogeneity in the analyses. Cumulative meta-analysis revealed no trend of the effect estimates over time and influential analysis excluded the possibility of a single influential study. E2 allele of ApoE seems to be a moderate risk factor for T2DM. Meta-regression analysis provided some weak evidence that the risk conferred by E2 allele is mediated through altering serum lipid levels (Total Cholesterol, LDL and HDL). Further studies are needed in order to elucidate the metabolic mechanism of this association as well as to study its effects on larger populations.

Abstract: G-protein coupled receptors (GPCRs) are a major family of membrane receptors in eukaryotic cells. They play a crucial role in the communication of a cell with the environment. Ligands bind to GPCRs on the outside of the cell, activating them by causing a conformational change, and allowing them to bind to G-proteins. Through their interaction with G-proteins, several effector molecules are activated leading to many kinds of cellular and physiological responses. The great importance of GPCRs and their corresponding signal transduction pathways is indicated by the fact that they take part in many diverse disease processes and that a large part of efforts towards drug development today is focused on them. We present Human-gpDB, a database which currently holds information about 713 human GPCRs, 36 human G-proteins and 99 human effectors. The collection of information about the interactions between these molecules was done manually and the current version of Human-gpDB holds information for about 1663 connections between GPCRs and G-proteins and 1618 connections between G-proteins and effectors. Major advantages of Human-gpDB are the integration of several external data sources and the support of advanced visualization techniques. Human-gpDB is a simple, yet a powerful tool for researchers in the life sciences field as it integrates an up-to-date, carefully curated collection of human GPCRs, G-proteins, effectors and their interactions. The database may be a reference guide for medical and pharmaceutical research, especially in the areas of understanding human diseases and chemical and drug discovery. Database URLs: http://schneider.embl.de/human_gpdb; http://bioinformatics.biol.uoa.gr/human_gpdb/

Abstract: Meta-analyses of genetic association studies are usually performed using a single polymorphism at a time, even though in many cases the individual studies report results from partially overlapping sets of polymorphisms. We present here a multipoint (or multilocus) method for multivariate meta-analysis of published population-based case-control association studies. The method is derived by extending the general method for multivariate meta-analysis and allows for multivariate modelling of log(odds ratios (OR)) derived from several polymorphisms that are in linkage disequilibrium (LD). The method is presented in a genetic model-free approach, although it can also be used by assuming a genetic model of inheritance beforehand. Furthermore, the method is presented in a unified framework and is easily applied to both discrete outcomes (using the OR), as well as to meta-analyses of a continuous outcome (using the mean difference). The main innovation of the method is the analytical calculation of the within-studies covariances between estimates derived from linked polymorphisms. The only requirement is that of an external estimate for the degree of pairwise LD between the polymorphisms under study, which can be obtained from the same published studies, from the literature or from HapMap. Thus, the method is quite simple and fast, it can be extended to an arbitrary set of polymorphisms and can be fitted in nearly all statistical packages (Stata, R/Splus and SAS). Applications in two already published meta-analyses provide encouraging results concerning the robustness and the usefulness of the method and we expect that it would be widely used in the future.

Abstract: A growing body of literature suggests that statins may have a chemopreventive potential against melanoma through pleiotropic anti-inflammatory, immunomodulatory, and antiangiogenesis mechanisms. Our aim was to examine this association through a detailed meta-analysis of randomized controlled trials (RCTs). A comprehensive search for trials published up to June 2009 was performed, reviews of each study were conducted and data were abstracted. Prior to meta-analysis, the studies were evaluated for publication bias and heterogeneity. Pooled relative risk estimates (RR) and 95% confidence intervals (CIs) were calculated using the fixed- and the random-effects models. Subgroup and sensitivity analyses were also conducted. Sixteen RCTs of statins for cardiovascular outcomes, involving 62,568 individuals with a mean age of 60 years and an average follow-up of nearly 4.7 years, contributed to the analysis. We found no evidence of publication bias (P = 0.47) or heterogeneity among the studies (P = 0.25). Statin use did not significantly affect the risk of developing melanoma assuming either a fixed- (RR = 0.92, 95% CI: 0.67-1.26), or a random-effects model (RR = 0.92, 95% CI: 0.62-1.36). This neutral effect was further supported by the results of subgroup and sensitivity analyses. Our findings do not support a protective effect of statins against melanoma.

Abstract: BACKGROUND: There is considerable evidence that elevated plasma homocysteine levels are associated with a prothrombotic milieu, whereas activation of the coagulation cascade is an important component of the pathogenesis of sepsis. The protein C pathway has been reported to play a central role both in the propagation of sepsis and a hyperhomocysteinemia-induced hypercoagulable state. Our primary aim was to measure plasma homocysteine levels in mechanically ventilated patients with severe sepsis/septic shock and to assess the association of these levels with relevant clinical outcomes. METHODS: The study cohort included 102 mechanically ventilated patients with severe sepsis or septic shock. Demographics, comorbidities, clinical data and severity scores were recorded. Plasma homocysteine, vitamin B12, folate, creatinine, and protein C levels were measured in all study subjects upon enrollment, and genotyping for the C677T and A1298C polymorphisisms of the methylenetetrahydrofolate reductase (MTHFR) gene and for factor V Leiden (FVL) mutations was performed as well. The primary outcomes were mortality at 28 and 90 days; secondary outcomes included the number of days without renal or cardiovascular failure and the ventilator-free days during the study period. RESULTS: Homocysteine levels were not significantly associated with any primary or secondary outcomes in the multivariable analysis. In addition, a synergistic effect of homocysteine with protein C levels was not detected. CONCLUSION: Our data suggest that plasma homocysteine levels may not inform the prognosis of mechanically ventilated patients with severe sepsis/septic shock.

Abstract: ExTopoDB is a publicly accessible database of experimentally derived topological models of transmembrane proteins. It contains information collected from studies in the literature that report the use of biochemical methods for the determination of the topology of alpha-helical transmembrane proteins. Transmembrane protein topology is highly important in order to understand their function and ExTopoDB provides an up to date, complete and comprehensive dataset of experimentally determined topologies of alpha-helical transmembrane proteins. Topological information is combined with transmembrane topology prediction resulting in more reliable topological models. AVAILABILITY: http://bioinformatics.biol.uoa.gr/ExTopoDB.

Abstract: Meta-analysis is a quantitative approach for systematically combining the results of previous studies in order to arrive at conclusions about the body of research. It answers a specific research question, includes an explicit methodology section, employs strategies to minimize bias, yields objective findings and enables evidence-based decisions. In this review, we examine meta-analysis taking examples from the field of cancer chemoprevention, an innovative area of cancer research that focuses on the prevention of cancer through pharmacological, biologic, and nutritional interventions. In particular, we consider the practical steps involved in the conduct of a meta-analysis, illustrate the statistical techniques for the calculation of summary estimates, present the available methodology for detecting and minimizing bias and, finally, we discuss unresolved issues and future applications.

Abstract: Polycystic ovary syndrome (PCOS) is a heterogeneous condition with unknown aetiology which is considered to be the most common endocrine disorder in women of reproductive age. In this work we investigated the association of insulin receptor (IotaNSR) and insulin receptor substrates (IRSs) polymorphisms with the risk of developing PCOS. The meta-analysis of eleven studies (889 cases, 1303 controls) yielded a significant association for IRS-1 Gly972Arg (G972R) polymorphism concerning the GR vs. GG genotype (OR: 1.77, 95% CI: 1.28, 2.45), with no between-studies heterogeneity. Concerning IotaNSR His1058 C/T, the meta-analysis of eight studies (795 cases, 576 controls) found no significant evidence for association with PCOS (OR for the TT+CT vs. CC comparison equal to 1.28 with 95% CI: 0.88, 1.85) and a moderate between studies variability (I(2)=44.6%). No evidence for publication bias was found in these meta-analyses. Following a multivariate Mendelian randomization approach, the overall OR was unaffected but the overall mean difference of fasting insulin levels between carriers of GR and RR genotypes in controls was significant (2.18, 95% CI: 0.36, 4.01). These results suggest that IRS-1 Gly972Arg polymorphism is significantly associated with the risk of developing PCOS and that this association is primarily mediated by increasing the levels of fasting insulin. The particular polymorphism is located in a region nearby two phosphorylation sites that interact physically with INSR and PI 3-kinase and there is enough evidence from the literature suggesting that the Arg972 variant is associated with decreased PI 3-kinase activity and impaired insulin-stimulated signaling.

Abstract: AIM: A systematic review and a meta-analysis were conducted in order to investigate the potential association of Fcgamma receptor (FcgammaR) polymorphisms with susceptibility to aggressive and chronic periodontal disease. MATERIALS AND METHODS: A database search yielded a total of 17 studies involving 1685 cases and 1570 controls. Three polymorphisms were included in the meta-analysis: FcgammaRIIA H131R (rs1801274), FcgammaRIIIA F158V (rs396991) and FcgammaRIIIB NA1/NA2. Random-effect models were used in the analysis. Odds ratios (ORs) along with their 95% confidence intervals (CIs) were computed to compare the distribution of alleles and genotypes between cases and controls. RESULTS AND CONCLUSIONS: The FcgammaRIIIB NA1/NA2 polymorphism was associated with both aggressive (per-allele OR 2.005, 95% CI: 1.044, 3.851) and chronic periodontitis (recessive contrast NA2NA2 versus NA1NA1+NA1NA2 OR 1.397, 95% CI: 1.039, 1.878). The analysis showed weak evidence for association between the FcgammaRIIA H131R polymorphism and aggressive periodontitis in Asians (R versus H allele OR 1.579, 95% CI: 1.025, 2.432). On the contrary, no relationship was identified between FcgammaRIIIA F158V and periodontal disease. Accumulating evidence from basic research makes the suggested association between FcgammaRIIIB NA1/NA2 polymorphism and periodontitis biologically plausible. Further research, however, is needed in order to assess possible gene-gene or gene-environment interactions (i.e. with smoking).

Abstract: MOTIVATION: Computational prediction of signal peptides is of great importance in computational biology. In addition to the general secretory pathway (Sec), Bacteria, Archaea and chloroplasts possess another major pathway that utilizes the Twin-Arginine translocase (Tat), which recognizes longer and less hydrophobic signal peptides carrying a distinctive pattern of two consecutive Arginines (RR) in the n-region. A major functional differentiation between the Sec and Tat export pathways lies in the fact that the former translocates secreted proteins unfolded through a protein-conducting channel, whereas the latter translocates completely folded proteins using an unknown mechanism. The purpose of this work is to develop a novel method for predicting and discriminating Sec from Tat signal peptides at better accuracy. RESULTS: We report the development of a novel method, PRED-TAT, which is capable of discriminating Sec from Tat signal peptides and predicting their cleavage sites. The method is based on Hidden Markov Models and possesses a modular architecture suitable for both Sec and Tat signal peptides. On an independent test set of experimentally verified Tat signal peptides, PRED-TAT clearly outperforms the previously proposed methods TatP and TATFIND, whereas, when evaluated as a Sec signal peptide predictor compares favorably to top-scoring predictors such as SignalP and Phobius. The method is freely available for academic users at http://www.compgen.org/tools/PRED-TAT/. CONTACT: pbagos@ucg.gr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: It has been shown that the progress in the determination of membrane protein structure grows exponentially, with approximately the same growth rate as that of the water-soluble proteins. In order to investigate the effect of this, on the performance of prediction algorithms for both alpha-helical and beta-barrel membrane proteins, we conducted a prospective study based on historical records. We trained separate hidden Markov models with different sized training sets and evaluated their performance on topology prediction for the two classes of transmembrane proteins. We show that the existing top-scoring algorithms for predicting the transmembrane segments of alpha-helical membrane proteins perform slightly better than that of beta-barrel outer membrane proteins in all measures of accuracy. With the same rationale, a meta-analysis of the performance of the secondary structure prediction algorithms indicates that existing algorithmic techniques cannot be further improved by just adding more non-homologous sequences to the training sets. The upper limit for secondary structure prediction is estimated to be no more than 70% and 80% of correctly predicted residues for single sequence based methods and multiple sequence based ones, respectively. Therefore, we should concentrate our efforts on utilizing new techniques for the development of even better scoring predictors.

Abstract: OBJECTIVE: Cumulative meta-analysis allows the evaluation of a study's contribution to the combined effect of the preceding research. It accrues evidence, gradually adding studies one at a time and provides updated estimates along with confidence intervals whenever new evidence emerges. In many research areas, a temporal evolution of the effect size (ES) is present, leading to diminishing effects and would be advantageous to have methods capable of detecting it. STUDY DESIGN AND SETTING: We propose a simple regression-based approach for detecting trends in cumulative meta-analysis. We use the combined ES of studies published up to a particular time, as dependent variable and the rank of the published studies as independent variable, in a weighted linear regression to detect a possible trend over time. The correlation between successive ESs used in the regression, is dealt by introducing a first-order autoregressive coefficient using Generalized Least Squares. RESULTS: Application in several published meta-analyses of genetic association studies provides encouraging results, outperforming the commonly used method of comparing the results of first vs. subsequent studies. CONCLUSION: The particular method is intuitive, easily implemented and allows drawing conclusions based on formal statistical tests. A STATA command is available at http://bioinformatics.biol.uoa.gr/~pbagos/metatrend/.

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Abstract: Computational prediction of signal peptides (SPs) and their cleavage sites is of great importance in computational biology; however, currently there is no available method capable of predicting reliably the SPs of archaea, due to the limited amount of experimentally verified proteins with SPs. We performed an extensive literature search in order to identify archaeal proteins having experimentally verified SP and managed to find 69 such proteins, the largest number ever reported. A detailed analysis of these sequences revealed some unique features of the SPs of archaea, such as the unique amino acid composition of the hydrophobic region with a higher than expected occurrence of isoleucine, and a cleavage site resembling more the sequences of gram-positives with almost equal amounts of alanine and valine at the position-3 before the cleavage site and a dominant alanine at position-1, followed in abundance by serine and glycine. Using these proteins as a training set, we trained a hidden Markov model method that predicts the presence of the SPs and their cleavage sites and also discriminates such proteins from cytoplasmic and transmembrane ones. The method performs satisfactorily, yielding a 35-fold cross-validation procedure, a sensitivity of 100% and specificity 98.41% with the Matthews' correlation coefficient being equal to 0.964. This particular method is currently the only available method for the prediction of secretory SPs in archaea, and performs consistently and significantly better compared with other available predictors that were trained on sequences of eukaryotic or bacterial origin. Searching 48 completely sequenced archaeal genomes we identified 9437 putative SPs. The method, PRED-SIGNAL, and the results are freely available for academic users at http://bioinformatics.biol.uoa.gr/PRED-SIGNAL/ and we anticipate that it will be a valuable tool for the computational analysis of archaeal genomes.

Abstract: BACKGROUND: Several reports have shown that homocysteine promotes thrombosis by disturbing the procoagulant-anticoagulant balance, whereas alterations in coagulation and fibrinolysis have been suggested as important pathogenetic and prognostic determinants of mortality in acute lung injury (ALI)/acute respiratory distress syndrome (ARDS). The objective of the study was to evaluate the effect of plasma homocysteine levels on the outcomes of patients with ALI/ARDS. METHODS: Sixty-nine consecutive ventilated patients with ALI/ARDS were studied. Blood samples were drawn within 3 days of clinical recognition of ARDS. Measurement of plasma homocysteine, vitamin B12, folate, creatinine, protein C and plasminogen-activator inhibitor-1 antigen levels, and genotyping of the methylenetetrahydrofolate reductase gene C677T and A1298C polymorphisms were carried out. The primary outcomes were 28- and 90-day mortality, whereas secondary outcomes included nonpulmonary organ failure-free days, liberation from mechanical ventilation up to day 28, and ventilator-free days during the 28 days after enrollment. RESULTS: In the multivariable analysis, plasma homocysteine concentration adjusted for age, Acute Physiology and Chronic Health Evaluation II score, methylenetetrahydrofolate reductase C677T and A1298C polymorphisms, and levels of plasminogen-activator inhibitor-1 antigen, protein C, creatinine, vitamin B12, and folate was not found to affect significantly mortality at 28 and 90 days (P = 0.39 and P = 0.83, respectively), days without organ failure besides lungs (P = 0.38), the probability of being free from mechanical ventilation at day 28 (P = 0.63), and days without ventilation assistance (P = 0.73). CONCLUSION: Our data suggest that increased plasma homocysteine levels, either alone or in synergy with other thrombophilic risk factors, do not seem to adversely affect the prognosis in patients with ALI/ARDS.

Abstract: Polycystic ovary syndrome (PCOS) is a heterogeneous condition with unknown etiology and is considered to be the most common endocrine disorder in women of reproductive age. Two meta-analyses are presented here concerning the association of Plasminogen Activator Inhibitor 1 (PAI-1) 4G/5G polymorphism and the methylene-tetrahydrofolate reductase (MTHFR) C677T polymorphism with the risk of developing PCOS. Seven studies were included concerning PAI-1 (1538 cases, 710 controls) and six studies concerning MTHFR C677T (223 cases, 392 controls). Overall, a significant association was found for PAI-1, with the odds ratio (OR) for 4G carriers versus 5G homozygotes being equal to 1.600 (95% CI 1.052, 2.434) with strong evidence for dominant inheritance. There was however a large between-studies variability (I(2) = 67.3%). No evidence was found for association of MTHFR C677T polymorphism with PCOS (OR for the TT+CT versus CC comparison equal to 0.940 with 95% CI 0.561, 1.575). No evidence of publication bias was found in these meta-analyses. PAI-1 4G/5G polymorphism seems to be associated with the risk of developing PCOS. Further studies are needed in order to investigate the etiologic mechanism behind this association, as well as the interrelations with other components of the metabolic syndrome (hypertension, diabetes, etc.).

Abstract: Methods for multivariate meta-analysis of genetic association studies are reviewed, summarized and presented in a unified framework. Modifications of standard models are described in detail in order to be applied in genetic association studies. The model based on summary data is uniformly defined for both discrete and continuous outcomes and analytical expressions for the covariance of the two jointly modeled outcomes are derived for both cases. The models based on the binary nature of the data are fitted using both prospective and retrospective likelihood. Furthermore, formal tests for assessing the genetic model of inheritance are developed based on standard normal theory. The general model is compared to the recently proposed genetic model-free bivariate approach (either using summary or binary data), and it is clearly shown that the estimates provided by this approach are nearly identical to the estimates derived by the general bivariate model using the aforementioned tests for the genetic model. The methods developed here as well as the tests, are easily implemented in all major statistical packages, escaping the need of self written software. The methods are applied in several already published meta-analyses of genetic association studies (with both discrete and continuous outcomes) and the results are compared against the widely used univariate approach as well as against the genetic model free approaches. Illustrative examples of code in Stata are given in the appendix. It is anticipated that the methods developed in this work will be widely applied in the meta-analysis of genetic association studies.

Abstract: SUMMARY: gpDB is a publicly accessible, relational database, containing information about G-proteins, G-protein coupled receptors (GPCRs) and effectors, as well as information concerning known interactions between these molecules. The sequences are classified according to a hierarchy of different classes, families and subfamilies based on literature search. The main innovation besides the classification of G-proteins, GPCRs and effectors is the relational model of the database, describing the known coupling specificity of GPCRs to their respective alpha subunits of G-proteins, and also the specific interaction between G-proteins and their effectors, a unique feature not available in any other database. AVAILABILITY: http://bioinformatics.biol.uoa.gr/gpDB CONTACT: shamodr@biol.uoa.gr SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: AIM: We conducted a systematic review and a meta-analysis, in order to investigate the potential association of cytokine gene polymorphisms with either aggressive or chronic periodontal disease. MATERIAL AND METHODS: A comprehensive literature search was performed. We retrieved a total of 53 studies summarizing information about 4178 cases and 4590 controls. Six polymorphisms were included in our meta-analysis which are the following: IL-1A G[4845]T, IL-1A C[-889]T, IL-1B C[3953/4]T, IL-1B T[-511]C, IL-6 G[-174]C and TNFA G[-308]A. Random effect methods were used for the analysis. We calculated the specific odds ratios along with their 95% confidence intervals to compare the distribution of alleles and genotypes between cases and controls. RESULTS AND CONCLUSIONS: Using random effect methods we found statistically significant association of IL-1A C[-889]T and IL-1B C[3953/4]T polymorphisms with chronic periodontal disease without any evidence of publication bias or significant statistical heterogeneity. A weak positive association was also found concerning IL-1B T[-511]C and chronic periodontal disease. No association was found for all the cytokines examined as far as the aggressive form of the disease is concerned. Future studies may contribute to the investigation of the potential multigenetic predisposition of the disease and reinforce our findings.

Abstract: Surface proteins in Gram-positive bacteria are frequently implicated in virulence. We have focused on a group of extracellular cell wall-attached proteins (CWPs), containing an LPXTG motif for cleavage and covalent coupling to peptidoglycan by sortase enzymes. A hidden Markov model (HMM) approach for predicting the LPXTG-anchored cell wall proteins of Gram-positive bacteria was developed and compared against existing methods. The HMM model is parsimonious in terms of the number of freely estimated parameters, and it has proved to be very sensitive and specific in a training set of 55 experimentally verified LPXTG-anchored cell wall proteins as well as in reliable data sets of globular and transmembrane proteins. In order to identify such proteins in Gram-positive bacteria, a comprehensive analysis of 94 completely sequenced genomes has been performed. We identified, in total, 860 LPXTG-anchored cell wall proteins, a number that is significantly higher compared to those obtained by other available methods. Of these proteins, 237 are hypothetical proteins according to the annotation of SwissProt, and 88 had no homologs in the SwissProt database--this might be evidence that they are members of newly identified families of CWPs. The prediction tool, the database with the proteins identified in the genomes, and supplementary material are available online at http://bioinformatics.biol.uoa.gr/CW-PRED/.

Abstract: We present a Hidden Markov Model method for the prediction of lipoprotein signal peptides of Gram-positive bacteria, trained on a set of 67 experimentally verified lipoproteins. The method outperforms LipoP and the methods based on regular expression patterns, in various data sets containing experimentally characterized lipoproteins, secretory proteins, proteins with an N-terminal TM segment and cytoplasmic proteins. The method is also very sensitive and specific in the detection of secretory signal peptides and in terms of overall accuracy outperforms even SignalP, which is the top-scoring method for the prediction of signal peptides. PRED-LIPO is freely available at http://bioinformatics.biol.uoa.gr/PRED-LIPO/, and we anticipate that it will be a valuable tool for the experimentalists studying secreted proteins and lipoproteins from Gram-positive bacteria.

Abstract: Plasminogen activator inhibitor (PAI-1), is the central component of the fibrinolytic system. A deletion/insertion (4G/5G) polymorphism in the promoter region of the PAI-1 gene has been correlated with levels of plasma PAI-1. The 4G allele is associated with higher levels of PAI-1, and might increase the risk for intravascular thrombosis. However, the contribution of this genetic variant to the risk for thrombosis, both arterial and venous, has not been clearly established. A broad spectrum of findings regarding the effect of the 4G allele on thrombotic risk in different target organs has been reported. Our aim is to summarize the variable influence of this polymorphism on thrombotic events in different tissues or organs and explain the underlying mechanisms accounting for these differences.

Abstract: OBJECTIVES: The C825T single nucleotide polymorphism of the G-protein beta3 (GNB3) has been implicated in susceptibility to essential hypertension, through the expression of an alternatively spliced truncated variant. In an effort to clarify earlier inconclusive results, we performed a meta-analysis of population-based case-control genetic association studies. METHODS: Random-effects methods were applied on summary data in order to combine the results of the individual studies. RESULTS: We identified in total 34 studies, including 14,094 hypertensive cases and 17,760 controls. The TT versus CC + CT contrast yielded an overall odds ratio (OR) of 1.08 [95% confidence interval (CI): 1.01, 1.15], the contrast of TT + CT versus CC, an OR of 1.17 (95% CI: 1.06, 1.29), whereas that of the T allele versus C allele yielded a non-significant OR of 1.05 (95% CI: 0.98, 1.13). There was moderate evidence for a publication bias in the latter two contrasts, which was eliminated after excluding studies not in Hardy-Weinberg equilibrium and those performed on non-normal populations (those with a diagnosis of diabetes, obesity and myocardial infarction). Subgroup analyses revealed that non-significant estimates arose from studies on Asian populations, as opposed to the Caucasian ones. Furthermore, the frequency of the T allele was lower in Caucasians and these populations were found to inhabit higher latitudes. CONCLUSIONS: The findings are in agreement with a recently proposed causal model for systolic blood pressure, which correlates it with the T allele and the absolute latitude. Further studies are needed in order to fully address questions about the aetiological mechanism of the particular association, as well as to study the effect in populations of African descent.

Abstract: Alcohol abuse is a major cause of liver cirrhosis as well as chronic liver disease. The aim of the present study was to investigate the possible correlation, between liver dysfunction biological markers and vitamin B12, with interleukin-6, in the serum of alcohol-dependent individuals without liver disease (AWLD). In a sample of 43 alcohol abusing/dependent subjects (33 males and 10 females) treated on an inpatient basis according to a standard detoxification protocol, the serum activities of the hepatic enzymes (ASAT, ALAT, gamma-GT), as well as the concentration of B12 and IL-6, were determined on admission. A strong positive correlation has been observed between IL-6 and B12, ASAT, ALAT, and gamma-GT at the beginning of the detoxification period. The results confirmed that in alcohol-dependent individuals, the median serum concentration of IL-6, before the beginning of the treatment, had a significant positive correlation with the liver dysfunction biological markers and B12. In conclusion, IL-6 might be used as an additional diagnostic marker for the degree of liver dysfunction in alcohol dependent individuals.

Abstract: INTRODUCTION: Platelet adhesion to fibrillar collagen via the membrane glycoprotein (GP) Ia/IIa (alpha2beta1), is a crucial event in the pathogenesis of arterial occlusive disorders. The C807T single nucleotide polymorphism of the integrin, alpha 2 (ITGA2) gene has been shown to correlate with the platelet GPIa/IIa density. Consequently, subjects with the 807T allele, who express the highest receptor density, might have an increased potential of platelet adhesion and, hence an increased risk of cerebrovascular disease. However, the research findings remain controversial. MATERIALS AND METHODS: A comprehensive electronic search was carried out up until November 2005 and 7 independent studies with a maximum of 774 cases and 1074 controls were analyzed using random effects models. RESULTS: The pooled frequency of the T allele was 36.33% in cases and 37.01% in controls. The T versus the C allele contrast gave an OR of 1.11 (95% confidence interval=0.827-1.499). All the other comparisons failed to show any significant result. Age, sex, and cardiovascular risk factors were included as covariates into a meta-regression model without a significant finding. CONCLUSIONS: This meta-analysis do not support an association between the C807T polymorphism of ITGA2 gene and stroke, but given the significant between study heterogeneity and the small number of studies, the summary effect should be interpreted carefully.

Abstract: BACKGROUND: The platelet-collagen receptor, glycoprotein (GP) la/lla plays a crucial role in the adhesion of platelets to fibrillar collagen, an event contributing to the pathogenesis of thrombosis. The C807T polymorphism of the GPla gene is considered a genetic marker of the platelet GPla/lla density. The importance of the GPla gene C807T polymorphism as a genetic risk factor for coronary artery disease (CAD) remains controversial. To assess this association, we performed a meta-analysis of published data. METHODS: A comprehensive meta-analysis of 19 studies, with a total sample of 13835 subjects using random effects models. RESULTS: The C versus T allele contrast gave an OR of 0.998 with 95% Cl 0.937-1.064. Similarly, comparing the C homozygotes with the T homozygotes, the CC genotype versus the others and the TT genotype versus the rest, no evidence of any gene-disease association was obtained. Furthermore, the meta-regression analysis did not disclose any variable that could modify the role of this polymorphism in the development of CAD. CONCLUSION: Our findings support the view that C807T polymorphism of the GPla gene is not a significant risk factor for CAD, either alone or in combination with other major cardiovascular risk factors.

Abstract: The effect of the 675 insertion/deletion (4G/5G) polymorphism of plasminogen activator inhibitor-1 (PAI-1) gene on the risk of venous thromboembolism (VTE) remains controversial. In this study, we performed a meta-analysis of published data regarding this issue. A comprehensive electronic search was carried out up until September 2006. A total of 22 articles were included in the analysis that was performed using random effects models. Eighteen papers, concerning patients without another known risk factor, comprised 2,644 cases and 3,739 controls. The alleles contrast (4G vs. 5G allele) yielded a statistically significant odds ratio (OR) of 1.153 (95% confidence interval [CI]: 1.068-1.246). In a sub-analysis of five studies that included 256 cases with another genetic risk factor and 147 controls, the combined per-allele OR was still significant (OR: 1.833,95% CI: 1.325-2.536). On the contrary, the analysis of five studies regarding cases with a non-genetic risk factor for VTE (antiphospholipid antibody syndrome, Behcet disease) provided insignificant results in all aspects. There was no evidence for heterogeneity and publication bias in all analyses. Based on our findings, the 4G allele appears to increase the risk of venous thrombosis, particularly in subjects with other genetic thrombophilic defects. Recommendation for detection of this polymorphism in evaluating thrombophilia in such patients might be considered.

Abstract: This study investigated the hypothesis that the insertion/deletion (4G/5G) polymorphism of the plasminogen activator inhibitor type-1 gene affects the risk for ischemic stroke, since results concerning this association have been controversial. We therefore performed a meta-analysis of published data regarding this issue. A comprehensive electronic search was carried out until January 2006. The analysis was performed using random-effects models and meta-regression. Eighteen eligible studies were retrieved (15 case-control studies and three cohort studies). The case-control studies included 3104 cases and 4870 control individuals concerning the contrast of 4G/4G versus remaining genotypes. The 4G pooled allele frequencies in cases and controls were 54.21 and 54.75%, respectively. Overall, the per-allele odds ratio of the 4G allele was 0.98 (95% confidence interval, 0.858-1.121). Regarding genotypes, we derived nonsignificant odds ratios in all contrasts. The subanalysis including the three studies with a prospective design in the 4G/4G versus 5G/5G contrast derived a significant result (relative risk, 0.523; 95% confidence interval, 0.353-0.775), but the estimated effect size was insignificant when cohort and case-control studies were analyzed together (relative risk, 0.848; 95% confidence interval, 0.662-1.087). We failed to demonstrate a significant association between the 4G/5G polymorphism and ischemic stroke under basal conditions. Determination of plasminogen activator inhibitor type-1 function seems of much higher clinical value than determination of the 4G/5G polymorphism. The effect of this genotype on risk of ischemic stroke in acute stressful diseases and the role of cohort studies in genetic epidemiology, however, warrant further investigation.

Abstract: We conducted a meta-analysis concerning the association of cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) gene polymorphisms with the risk of developing multiple sclerosis (MS). We identified 18 eligible studies summarizing information about 3375 MS cases and 2930 healthy controls. Two polymorphisms were of interest: the exon 1 +49 A/G polymorphism (in 18 studies) and the promoter-318 C/T polymorphism (in 10 studies). Using random-effects methods we found no evidence for association of the various contrasts of genotypes (or allele frequencies) with the disease. There was significant between-studies heterogeneity that could not be explained by the ethnicity of the populations studied or by other summary measures (gender, disease course, latitude). The major finding of the meta-analysis, apart from the lack of an overall association, consists of detecting a significant time trend of the OR for the contrast of GA versus GG+AA genotypes of the exon 1 +49 A/G polymorphism. In particular, using cumulative meta-analysis we found that the large number of conflicting results on the subject was triggered by the early appearance of a highly significant published result (a study that indicated a significant association of the genotype with the disease).

Abstract: We propose here a simple and robust approach for meta-analysis of molecular association studies. Making use of the binary structure of the data, and by treating the genotypes as independent variables in a logistic regression, we apply a simple and commonly used methodology that performs satisfactorily, being at the same time very flexible. We present simple tests for detecting heterogeneity and we describe a random effects extension of the method in order to allow for between studies heterogeneity. We derive also simple tests for assessing the most plausible genetic model of inheritance, and its between-studies heterogeneity as well as adjusting for covariates. The methodology introduced here is easily extended in cases with polytomous or continuous outcomes as well as in cases with more than two alleles. We apply the methodology in several published meta-analyses of genetic association studies with very encouraging results. The main advantages of the proposed methodology is its flexibility and the ease of use, while at the same time covers almost every aspect of a meta-analysis providing overall estimates without the need of multiple comparisons. We anticipate that this simple method would be used in the future in meta-analyses of genetic association studies. A STATA command performing all the available computations is available at http://bioinformatics.biol.uoa.gr/~pbagos/metagen/.

Abstract: We conducted a meta-analysis of studies comparing the presence of Chlamydia pneumoniae (Cpn) between multiple sclerosis (MS) patients and other neurological diseases patients or healthy controls. We identified 26 studies with 1332 MS patients and 1464 controls. Using random-effects methods, MS patients were found more likely to have detectable levels of Cpn DNA (OR = 3.216; 95% CI: 1.204, 8.585) in their cerebrospinal fluid, and intrathecally synthesized immunoglobulins (OR = 3.842; 95% CI: 1.317, 11.212), compared to other patients with neurological diseases. There is no evidence for increased levels of serum immunoglobulins (OR = 1.068; 95% CI: 0.745, 1.530), even though this result is confounded by the presence of studies using normal subjects as controls. Similarly, there is no evidence for association of immunoglobulins against Cpn in the cerebrospinal fluid (OR = 3.815; 95% CI: 0.715, 20.369). Up to 59.7% of the between-studies variability could be explained by the inappropriate matching of cases and controls for gender. In random-effects meta-regressions, adjusting for the confounding effect of gender differences results in stronger and statistically significant associations of MS with detectable levels of Cpn DNA, intrathecally synthesized immunoglobulins and immunoglobulins in the cerebrospinal fluid. Even though the presence of Cpn is clearly more likely in MS patients, these findings are insufficient to establish an etiologic relation.

Abstract: The location of the membrane lipid bilayer relative to a transmembrane protein structure is important in protein engineering. Since it is not present on the determined structures, it is essential to automatically define the membrane embedded protein region in order to test mutation effects or to design potential drugs. beta-Barrel transmembrane proteins, present in nature as outer membrane proteins (OMPs), comprise one of the two transmembrane protein fold classes. Lately, the number of their determined structures has increased and this enables the implementation and evaluation of structure-based annotation methods and their more comprehensive study. In this paper, we propose two new algorithms for (i) the geometric modelling of beta-barrels and (ii) the detection of the transmembrane region of a beta-barrel transmembrane protein. The geometric modelling algorithm combines a non-linear least square minimization method and a genetic algorithm in order to find the characteristics (axis, radius) of a shape with axial symmetry which best models a beta-barrel. The transmembrane region is detected by profiling the external residues of the beta-barrel along its axis in terms of hydrophobicity and existence of aromatic and charged residues. TbB-Tool implements these algorithms and is available in . A non-redundant set of 22 OMPs is used in order to evaluate the algorithms implemented and the results are very satisfying. In addition, we quantify the abundance of all amino acids and the average hydrophobicity for external and internal beta-stranded residues along the axis of beta-barrel, thus confirming and extending other researchers' results.

Abstract: The ability to predict the subcellular localization of a protein from its sequence is of great importance, as it provides information about the protein's function. We present a computational tool, PredSL, which utilizes neural networks, Markov chains, profile hidden Markov models, and scoring matrices for the prediction of the subcellular localization of proteins in eukaryotic cells from the N-terminal amino acid sequence. It aims to classify proteins into five groups: chloroplast, thylakoid, mitochondrion, secretory pathway, and "other". When tested in a five-fold cross-validation procedure, PredSL demonstrates 86.7% and 87.1% overall accuracy for the plant and non-plant datasets, respectively. Compared with TargetP, which is the most widely used method to date, and LumenP, the results of PredSL are comparable in most cases. When tested on the experimentally verified proteins of the Saccharomyces cerevisiae genome, PredSL performs comparably if not better than any available algorithm for the same task. Furthermore, PredSL is the only method capable for the prediction of these subcellular localizations that is available as a stand-alone application through the URL:http://bioinformatics.biol.uoa.gr/PredSL/.

Abstract: BACKGROUND: Alcohol-induced changes in thyroid function may contribute to the development of mood disorders such as depression and anxiety that almost invariably coexist in alcohol-dependent individuals. The aim of the present study was to investigate the severity of liver dysfunction and thyroid activity in correlation with anxiety and depressive-like symptomatology before and after a detoxification period. PATIENTS AND METHODS: In a sample of 100 alcohol-abusing/dependent subjects treated on an in-patient basis according to a standard detoxification protocol, measurements of the serum levels of hepatic enzymes (ASAT, ALAT, gammaGT) and thyroid hormones (T3, T4, TSH) as well as measures of anxiety, depression and global functioning were obtained at baseline and at weekly intervals over the period of 4-5 weeks. RESULTS: After completion of the alcohol detoxification, most measurements returned to normal levels and correlations were observed between the levels of hepatic enzymes and thyroid hormones. Additionally, a significant correlation was obtained between the levels of thyroid hormones and the mood status scales. CONCLUSION: Our results indicated a dysfunction of the hypothalamic-pituitary-thyroid axis in alcohol dependence with possible implications in the diagnosis and treatment of mood disorders associated with alcohol abuse.

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Abstract: BACKGROUND: Hidden Markov Models (HMMs) have been extensively used in computational molecular biology, for modelling protein and nucleic acid sequences. In many applications, such as transmembrane protein topology prediction, the incorporation of limited amount of information regarding the topology, arising from biochemical experiments, has been proved a very useful strategy that increased remarkably the performance of even the top-scoring methods. However, no clear and formal explanation of the algorithms that retains the probabilistic interpretation of the models has been presented so far in the literature. RESULTS: We present here, a simple method that allows incorporation of prior topological information concerning the sequences at hand, while at the same time the HMMs retain their full probabilistic interpretation in terms of conditional probabilities. We present modifications to the standard Forward and Backward algorithms of HMMs and we also show explicitly, how reliable predictions may arise by these modifications, using all the algorithms currently available for decoding HMMs. A similar procedure may be used in the training procedure, aiming at optimizing the labels of the HMM's classes, especially in cases such as transmembrane proteins where the labels of the membrane-spanning segments are inherently misplaced. We present an application of this approach developing a method to predict the transmembrane regions of alpha-helical membrane proteins, trained on crystallographically solved data. We show that this method compares well against already established algorithms presented in the literature, and it is extremely useful in practical applications. CONCLUSION: The algorithms presented here, are easily implemented in any kind of a Hidden Markov Model, whereas the prediction method (HMM-TM) is freely available for academic users at http://bioinformatics.biol.uoa.gr/HMM-TM, offering the most advanced decoding options currently available.

Abstract: BACKGROUND: G- Protein coupled receptors (GPCRs) comprise the largest group of eukaryotic cell surface receptors with great pharmacological interest. A broad range of native ligands interact and activate GPCRs, leading to signal transduction within cells. Most of these responses are mediated through the interaction of GPCRs with heterotrimeric GTP-binding proteins (G-proteins). Due to the information explosion in biological sequence databases, the development of software algorithms that could predict properties of GPCRs is important. Experimental data reported in the literature suggest that heterotrimeric G-proteins interact with parts of the activated receptor at the transmembrane helix-intracellular loop interface. Utilizing this information and membrane topology information, we have developed an intensive exploratory approach to generate a refined library of statistical models (Hidden Markov Models) that predict the coupling preference of GPCRs to heterotrimeric G-proteins. The method predicts the coupling preferences of GPCRs to Gs, Gi/o and Gq/11, but not G12/13 subfamilies. RESULTS: Using a dataset of 282 GPCR sequences of known coupling preference to G-proteins and adopting a five-fold cross-validation procedure, the method yielded an 89.7% correct classification rate. In a validation set comprised of all receptor sequences that are species homologues to GPCRs with known coupling preferences, excluding the sequences used to train the models, our method yields a correct classification rate of 91.0%. Furthermore, promiscuous coupling properties were correctly predicted for 6 of the 24 GPCRs that are known to interact with more than one subfamily of G-proteins. CONCLUSION: Our method demonstrates high correct classification rate. Unlike previously published methods performing the same task, it does not require any transmembrane topology prediction in a preceding step. A web-server for the prediction of GPCRs coupling specificity to G-proteins available for non-commercial users is located at http://bioinformatics.biol.uoa.gr/PRED-COUPLE.

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Abstract: BACKGROUND: Prediction of the transmembrane strands and topology of beta-barrel outer membrane proteins is of interest in current bioinformatics research. Several methods have been applied so far for this task, utilizing different algorithmic techniques and a number of freely available predictors exist. The methods can be grossly divided to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we compare the different available methods for topology prediction of beta-barrel outer membrane proteins. We evaluate their performance on a non-redundant dataset of 20 beta-barrel outer membrane proteins of gram-negative bacteria, with structures known at atomic resolution. Also, we describe, for the first time, an effective way to combine the individual predictors, at will, to a single consensus prediction method. RESULTS: We assess the statistical significance of the performance of each prediction scheme and conclude that Hidden Markov Model based methods, HMM-B2TMR, ProfTMB and PRED-TMBB, are currently the best predictors, according to either the per-residue accuracy, the segments overlap measure (SOV) or the total number of proteins with correctly predicted topologies in the test set. Furthermore, we show that the available predictors perform better when only transmembrane beta-barrel domains are used for prediction, rather than the precursor full-length sequences, even though the HMM-based predictors are not influenced significantly. The consensus prediction method performs significantly better than each individual available predictor, since it increases the accuracy up to 4% regarding SOV and up to 15% in correctly predicted topologies. CONCLUSIONS: The consensus prediction method described in this work, optimizes the predicted topology with a dynamic programming algorithm and is implemented in a web-based application freely available to non-commercial users at http://bioinformatics.biol.uoa.gr/ConBBPRED.

Abstract: MOTIVATION: G-protein coupled receptors are a major class of eukaryotic cell-surface receptors. A very important aspect of their function is the specific interaction (coupling) with members of four G-protein families. A single GPCR may interact with members of more than one G-protein families (promiscuous coupling). To date all published methods that predict the coupling specificity of GPCRs are restricted to three main coupling groups G(i/o), G(q/11) and G(s), not including G(12/13)-coupled or other promiscuous receptors. RESULTS: We present a method that combines hidden Markov models and a feed-forward artificial neural network to overcome these limitations, while producing the most accurate predictions currently available. Using an up-to-date curated dataset, our method yields a 94% correct classification rate in a 5-fold cross-validation test. The method predicts also promiscuous coupling preferences, including coupling to G(12/13), whereas unlike other methods avoids overpredictions (false positives) when non-GPCR sequences are encountered. AVAILABILITY: A webserver for academic users is available at http://bioinformatics.biol.uoa.gr/PRED-COUPLE2

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Abstract: BACKGROUND: Inflammation is frequently elevated, and seems to be episodic in hemodialysis (HD) patients. Whether, its episodic character is due to the temporal variability, in periods free of clinical events, of the inflammatory indices or due, to the acute phase response induced by common inflammatory stimuli, has not been investigated yet in a longitudinal study. This study explores inflammation forms, characteristics and causes which are probably related to the high cardiovascular disease (CVD) morbidity in HD patients. METHODS: In 37 HD patients, high-sensitivity C-reactive protein (hs-CRP), serum amyloid A (SAA) and interleukin-6 (IL-6) were weekly measured for 16 consecutive weeks. Inflammatory clinical events, in the week before every measurement, were recorded. Repeated measures ANOVA were applied for statistical analysis. RESULTS: Fifty-one of 533 patient-weeks were positive for a clinical event. Mean +/- SD (range) hs-CRP was 7.01 +/- 16.06 (0.2-169) mg/l for all the weeks of the study, 38.25 +/- 39.35 (2.1-169) mg/l for the weeks with clinical events and 3.70 +/- 3.86 (0.2-26.1) mg/l for the weeks free of events. Variations for SAA and IL-6 were similar. 'Clinical events' strongly influenced acute-phase proteins and IL-6 levels. The effect of the factor 'time' (as assessed by inflammatory indices variation in weekly repeated measurements) was significant for all the 3 indices measured, independently of the factor 'clinical events'. CONCLUSIONS: In periods free of clinical events, microinflammation characterizes HD patients and fluctuates in time. Inflammation due to common clinical events is added, periodically, to this microinflammation. The high level persistent microinflammation as well as the superimposed--due to clinical events--inflammation could be related to the CVD in these patients.

Abstract: The vast cell-surface receptor family of G-protein coupled receptors (GPCRs) is the focus of both academic and pharmaceutical research due to their key role in cell physiology along with their amenability to drug intervention. As the data flow rate from the various genome and proteome projects continues to grow, so does the need for fast, automated and reliable screening for new members of the various GPCR families. PRED-GPCR is a free Internet service for GPCR recognition and classification at the family level. A submitted sequence or set of sequences, is queried against the PRED-GPCR library, housing 265 signature profile HMMs corresponding to 67 well-characterized GPCR families. Users query the server through a web interface and results are presented in HTML output format. The server returns all single-motif matches along with the combined results for the corresponding families. The service is available online since October 2003 at http://bioinformatics.biol.uoa.gr/PRED-GPCR.

Abstract: The 'TransMembrane protein Re-Presentation in 2-Dimensions' (TMRPres2D) tool, automates the creation of uniform, two-dimensional, high analysis graphical images/models of alpha-helical or beta-barrel transmembrane proteins. Protein sequence data and structural information may be acquired from public protein knowledge bases, emanate from prediction algorithms, or even be defined by the user. Several important biological and physical sequence attributes can be embedded in the graphical representation.

Abstract: BACKGROUND: Systemic microinflammation is correlated with atherosclerosis. It needs a reliable assessment. This study explores the temporal variations of three inflammatory indexes [C-reactive protein (CRP), serum amyloid A (SAA) and interleukin-6 (IL-6)] in a period free of clinical events and tests the reliability of their multiple measurements for the assessment of microinflammation in haemodialysis (HD) patients, a population at high risk of atherosclerotic cardiovascular disease. METHODS: For 4 months, serum CRP, SAA and IL-6 were measured in 29 HD patients during the weeks they were free of inflammatory clinical events (> or =12 measurements for each index in every patient). The components of the variance as well as the reliability of two to five measurements for each index, aimed at assessing microinflammation precisely, were computed. RESULTS: The median (interquartile range) of CRP was 2.3 (0.9-4.9) mg/l, of SAA 3.7 (2.1-9.3) mg/l and of IL-6 4.4 (2.2-7.7) pg/ml. Patients were approximately equally distributed between three groups of low, intermediate and high variability for each index. The contribution of intraindividual (biological) variation to the total of variance was 71.3%, 69.3% and 86.7% for CRP, SAA and IL-6, respectively (higher than in all other similar studies in healthy populations). Using two measurements, the estimated reliability was 57-68% for CRP in two-thirds of the patients (comparable with that found in healthy subjects) and 57% for SAA and IL-6 in only one-third of the patients. Increasing the number of measurements up to five did not change the reliability. CONCLUSIONS: Individual factors significantly influence the levels of inflammatory indexes in HD patients in periods free of inflammatory clinical events. The mean of two weekly CRP measurements, but not of SAA or IL-6, seems to assess microinflammation in most patients with a sufficient reliability.

Abstract: BACKGROUND: G protein-coupled receptors (GPCRs) transduce signals from extracellular space into the cell, through their interaction with G proteins, which act as switches forming hetero-trimers composed of different subunits (alpha,beta,gamma). The alpha subunit of the G protein is responsible for the recognition of a given GPCR. Whereas specialised resources for GPCRs, and other groups of receptors, are already available, currently, there is no publicly available database focusing on G Proteins and containing information about their coupling specificity with their respective receptors. DESCRIPTION: gpDB is a publicly accessible G proteins/GPCRs relational database. Including species homologs, the database contains detailed information for 418 G protein monomers (272 Galpha, 87 Gbeta and 59 Ggamma) and 2782 GPCRs sequences belonging to families with known coupling to G proteins. The GPCRs and the G proteins are classified according to a hierarchy of different classes, families and sub-families, based on extensive literature searchs. The main innovation besides the classification of both G proteins and GPCRs is the relational model of the database, describing the known coupling specificity of the GPCRs to their respective alpha subunit of G proteins, a unique feature not available in any other database. There is full sequence information with cross-references to publicly available databases, references to the literature concerning the coupling specificity and the dimerization of GPCRs and the user may submit advanced queries for text search. Furthermore, we provide a pattern search tool, an interface for running BLAST against the database and interconnectivity with PRED-TMR, PRED-GPCR and TMRPres2D. CONCLUSIONS: The database will be very useful, for both experimentalists and bioinformaticians, for the study of G protein/GPCR interactions and for future development of predictive algorithms. It is available for academics, via a web browser at the URL: http://bioinformatics.biol.uoa.gr/gpDB.

Abstract: BACKGROUND: Integral membrane proteins constitute about 20-30% of all proteins in the fully sequenced genomes. They come in two structural classes, the alpha-helical and the beta-barrel membrane proteins, demonstrating different physicochemical characteristics, structure and localization. While transmembrane segment prediction for the alpha-helical integral membrane proteins appears to be an easy task nowadays, the same is much more difficult for the beta-barrel membrane proteins. We developed a method, based on a Hidden Markov Model, capable of predicting the transmembrane beta-strands of the outer membrane proteins of gram-negative bacteria, and discriminating those from water-soluble proteins in large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of correct predictions rather than the likelihood of the sequences. RESULTS: The training has been performed on a non-redundant database of 14 outer membrane proteins with structures known at atomic resolution; it has been tested with a jacknife procedure, yielding a per residue accuracy of 84.2% and a correlation coefficient of 0.72, whereas for the self-consistency test the per residue accuracy was 88.1% and the correlation coefficient 0.824. The total number of correctly predicted topologies is 10 out of 14 in the self-consistency test, and 9 out of 14 in the jacknife. Furthermore, the model is capable of discriminating outer membrane from water-soluble proteins in large-scale applications, with a success rate of 88.8% and 89.2% for the correct classification of outer membrane and water-soluble proteins respectively, the highest rates obtained in the literature. That test has been performed independently on a set of known outer membrane proteins with low sequence identity with each other and also with the proteins of the training set. CONCLUSION: Based on the above, we developed a strategy, that enabled us to screen the entire proteome of E. coli for outer membrane proteins. The results were satisfactory, thus the method presented here appears to be suitable for screening entire proteomes for the discovery of novel outer membrane proteins. A web interface available for non-commercial users is located at: http://bioinformatics.biol.uoa.gr/PRED-TMBB, and it is the only freely available HMM-based predictor for beta-barrel outer membrane protein topology.

Abstract: The beta-barrel outer membrane proteins constitute one of the two known structural classes of membrane proteins. Whereas there are several different web-based predictors for alpha-helical membrane proteins, currently there is no freely available prediction method for beta-barrel membrane proteins, at least with an acceptable level of accuracy. We present here a web server (PRED-TMBB, http://bioinformatics.biol.uoa.gr/PRED-TMBB) which is capable of predicting the transmembrane strands and the topology of beta-barrel outer membrane proteins of Gram-negative bacteria. The method is based on a Hidden Markov Model, trained according to the Conditional Maximum Likelihood criterion. The model was retrained and the training set now includes 16 non-homologous outer membrane proteins with structures known at atomic resolution. The user may submit one sequence at a time and has the option of choosing between three different decoding methods. The server reports the predicted topology of a given protein, a score indicating the probability of the protein being an outer membrane beta-barrel protein, posterior probabilities for the transmembrane strand prediction and a graphical representation of the assumed position of the transmembrane strands with respect to the lipid bilayer.

Abstract: BACKGROUND: Chlamydia pneumoniae (Cp) induces the production of cytokines and adhesion molecules in infected host eukaryotic cells. The causes for pro-inflammatory cytokine and adhesion molecule increase in hemodialysis (HD) patients have not been fully elucidated. The possibility that, in this particularly atherosclerotic population, Cp, a microorganism implicated in the infectious-based inflammatory hypothesis of atherosclerosis' is also responsible for these molecules' increase is assessed in this study. METHODS: In 130 stable HD patients, serum interleukin-1 beta (IL-1), interleukin-6, tumor necrosis factor alpha, interleukin-10, L-selectin, E-selectin, intercellular adhesion molecule-1, and vascular cell adhesion molecule-1 (VCAM-1) levels were determined. Cp presence was identified by inoculation of the patient's peripheral blood mononuclear cells (PBMCs) in Hep-2 cell lines and subsequent polymerase chain reaction (PCR) in DNA extracted from cell cultures, as well as by determination of serum IgG antibodies against Cp (IgGCp). RESULTS: Patients, positive or negative for IgGCp, had no statistically significant differences in all molecules measured. Patients with viable Cp in PBMCs had higher serum levels of IL-1 and soluble VCAM-1 than negative ones for IgGCp (IL-1 6.87 +/- 7.35 vs. 2.34 +/- 1.47 pg/mL; P = 0.0009 and VCAM-1 1647.16 +/- 513.64 vs. 1162.14 +/- 546.83 ng/mL; P = 0.0115, respectively). Viable Cp in PBMCs remained a significant predictor factor for IL-1 and VCAM-1 in statistical analysis, when patients' characteristics and dialysis conditions were also evaluated. CONCLUSIONS: Our results showed that some serum cytokine and adhesion molecule increase in HD patients could be attributed to viable Cp presence in PBMCs. These findings support the Cp-based inflammatory atherogenous hypothesis and add a better understanding of these molecules' increase in HD patients.

Abstract: G-protein coupled receptors (GPCRs) constitute a broad class of cell-surface receptors, including several functionally distinct families, that play a key role in cellular signalling and regulation of basic physiological processes. GPCRs are the focus of a significant amount of current pharmaceutical research since they interact with more than 50% of prescription drugs, whereas they still comprise the best potential targets for drug design. Taking into account the excess of data derived by genome sequencing projects, the use of computational tools for automated characterization of novel GPCRs is imperative. Typical computational strategies for identifying and classifying GPCRs involve sequence similarity searches (e.g. BLAST) coupled with pattern database analysis (e.g. PROSITE, BLOCKS). The diagnostic method presented here is based on a probabilistic approach that exploits highly discriminative profile Hidden Markov Models, excised from low entropy regions of multiple sequence alignments, to derive potent family signatures. For a given query, a P-value is obtained, combining individual hits derived from the same family. Hence a best-guess family membership is depicted, allowing GPCRs' classification at a family level, solely using primary structure information. A web-based version of the application is freely available at URL: http:/bioinformatics.biol.uoa.gr/PRED-GPCR.

Abstract: GPCRs comprise a wide and diverse class of eukaryotic transmembrane proteins with well-established pharmacological significance. As a consequence of recent genome projects, there is a wealth of information at the sequence level that lacks any functional annotation. These receptors, often quoted as orphan GPCRs, could potentially lead to novel drug targets. However, typical experiments that aim at elucidating their function are hampered by the lack of knowledge on their selective coupling partners at the interior of the cell, the G-proteins. Up-to-date, computational efforts to predict properties of GPCRs have been focused mainly on the ligand-binding specificity, while the aspect of coupling has been less studied. Here, we present the main motivations, drawbacks, and results from the application of bioinformatics techniques to predict the coupling specificity of GPCRs to G-proteins, and discuss the application of the most successful methods in both experimental works that focus on a single receptor and large-scale genome annotation studies.

Abstract: ß-barrel outer membrane proteins constitute the second and less well-studied class of transmembrane proteins. They are present exclusively in the outer membrane of Gram-negative bacteria and presumably in the outer membrane of mitochondria and chloroplasts. During the last few years, remarkable advances have been made towards an understanding of their functional and structural features. It is now wellknown that ß-barrels are performing a large variety of biologically important functions for the bacterial cell. Such functions include acting as specific or non-specific channels, receptors for various compounds, enzymes, translocation channels, structural proteins, and adhesion proteins. All these functional roles are of great importance for the survival of the bacterial cell under various environmental conditions or for the pathogenic properties expressed by these organisms. This chapter reviews the currently available literature regarding the structure and function of bacterial outer membrane proteins. We emphasize the functional diversity expressed by a common structural motif such as the ß-barrel, and we provide evidence from the current literature for dozens of newly discovered families of transmembrane ß-barrels.

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