Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources.

Functional enrichment is the process of identifying implicated functional terms from a given input list of genes or proteins. In this article, we present Flame (v2.0), a web tool which offers a combinatorial approach through merging and visualizing results from widely used functional enrichment applications while also allowing various flexible input options. In this version, Flame utilizes the aGOtool, g: Profiler, WebGestalt, and Enrichr pipelines and presents their outputs separately or in combination following a visual analytics approach. For intuitive representations and easier interpretation, it uses interactive plots such as parameterizable networks, heatmaps, barcharts, and scatter plots. Users can also: (i) handle multiple protein/gene lists and analyse union and intersection sets simultaneously through interactive UpSet plots, (ii) automatically extract genes and proteins from free text through text-mining and Named Entity Recognition (NER) techniques, (iii) upload single nucleotide polymorphisms (SNPs) and extract their relative genes, or (iv) analyse multiple lists of differentially expressed proteins/genes after selecting them interactively from a parameterizable volcano plot. Compared to the previous version of 197 supported organisms, Flame (v2.0) currently allows enrichment for 14 436 organisms. AVAILABILITY AND IMPLEMENTATION: Web Application: http://flame.pavlopouloslab.info. Code: https://github.com/PavlopoulosLab/Flame. Docker: https://hub.docker.com/r/pavlopouloslab/flame.

In Silico Identification and Analysis of Proteins Containing the Phox Homology Phosphoinositide-Binding Domain in Kinetoplastea Protists: Evolutionary Conservation and Uniqueness of Phox-Homology-Domain-Containing Protein Architectures.

Kinetoplastea are free living and parasitic protists with unique features among Eukaryota. Pathogenic Kinetoplastea parasites (i.e., Trypanosoma and Leishmania spp.) undergo several developmental transitions essential for survival in their hosts. These transitions require membrane and cytoskeleton reorganizations that involve phosphoinositides (PIs). Phospholipids like PIs are key regulators of vital functions in all eukaryotes including signal transduction, protein transport and sorting, membrane trafficking, and cytoskeleton and membrane remodeling. A large repertoire of PI-metabolizing enzymes and PI-binding proteins/effectors carrying distinct PI-binding modules like the PX (phox homology) module could play significant roles in the life and virulence of pathogenic Kinetoplastea. The aim of this study was to retrieve the entire spectrum of Kinetoplastea protein sequences containing the PX module (PX-proteins), predict their structures, and identify in them evolutionary conserved and unique traits. Using a large array of bioinformatics tools, protein IDs from two searches (based on PFam's pHMM for PX domain (PF00787)) were combined, aligned, and utilized for the construction of a new Kinetoplastea_PX pHMM. This three-step search retrieved 170 PX-protein sequences. Structural domain configuration analysis identified PX, Pkinase, Lipocalin_5, and Vps5/BAR3-WASP domains and clustered them into five distinct subfamilies. Phylogenetic tree and domain architecture analysis showed that some domain architectures exist in proteomes of all Kinetoplastea spp., while others are genus-specific. Finally, amino acid conservation logos of the Kinetoplastea spp. and Homo sapiens PX domains revealed high evolutionary conservation in residues forming the critical structural motifs for PtdIns3P recognition. This study highlights the PX-Pkinase domain architecture as unique within Trypanosoma spp. and forms the basis for a targeted functional analysis of Kinetoplastea PX-proteins as putative targets for a rational design of anti-parasitic drugs.

Association of ITPA gene polymorphisms with adverse effects of AZA/6-MP administration: a systematic review and meta-analysis.

Azathioprine (AZA) and its metabolite, mercaptopurine (6-MP), are widely used immunosuppressant drugs. Polymorphisms in genes implicated in AZA/6-MP metabolism, reportedly, could account in part for their potential toxicity. In the present study we performed a systematic review and a meta-analysis, comprising 30 studies and 3582 individuals, to investigate the putative genetic association of two inosine triphosphatase (ITPA) polymorphisms with adverse effects in patients treated with AZA/6-MP. We found that rs1127354 is associated with neutropenia in general populations and in children (OR: 2.39, 95%CI: 1.97-2.90, and OR: 2.43, 95%CI: 2.12-2.79, respectively), and with all adverse effects tested herein in adult populations (OR: 2.12, 95%CI: 1.22-3.69). We also found that rs7270101 is associated with neutropenia and leucopenia in all-ages populations (OR: 2.93, 95%CI: 2.36-3.63, and OR: 2.82, 95%CI: 1.76-4.50, respectively) and with all adverse effects tested herein in children (OR: 1.74, 95%CI: 1.06-2.87). Stratification according to background disease, in combination with multiple comparisons corrections, verified neutropenia to be associated with both polymorphisms, in acute lymphoblastic leukemia (ALL) patients. These findings suggest that ITPA polymorphisms could be used as predictive biomarkers for adverse effects of thiopurine drugs to eliminate intolerance in ALL patients and clarify dosing in patients with different ITPA variants.

Drug genetic associations with COVID-19 manifestations: a data mining and network biology approach.

Available drugs have been used as an urgent attempt through clinical trials to minimize severe cases of hospitalizations with Coronavirus disease (COVID-19), however, there are limited data on common pharmacogenomics affecting concomitant medications response in patients with comorbidities. To identify the genomic determinants that influence COVID-19 susceptibility, we use a computational, statistical, and network biology approach to analyze relationships of ineffective concomitant medication with an adverse effect on patients. We statistically construct a pharmacogenetic/biomarker network with significant drug-gene interactions originating from gene-disease associations. Investigation of the predicted pharmacogenes encompassing the gene-disease-gene pharmacogenomics (PGx) network suggests that these genes could play a significant role in COVID-19 clinical manifestation due to their association with autoimmune, metabolic, neurological, cardiovascular, and degenerative disorders, some of which have been reported to be crucial comorbidities in a COVID-19 patient.

Staphylococcus aureus Carriage in Hidradenitis Suppurativa: Impact on Response to Adalimumab.

BACKGROUND: Several patients with hidradenitis suppurativa (HS) present flare-ups during treatment with adalimumab (ADA), the cause of which is not clear. ADA is the only FDA-approved biologic for the therapy of moderate-to-severe HS. A previous study of our group has shown that Staphylococcus aureus stimulation of whole blood affects the production of human ß-defensin 2 and modulates HS severity. It is, therefore, hypothesized, that carriage of S. aureus may drive HS flare-ups. OBJECTIVE: To explore the association between carriage of S. aureus and loss of response to ADA. PATIENTS AND METHODS: Among patients with moderate-to-severe HS without carriage of S. aureus at start of treatment with ADA, we investigated for carriage of S. aureus from the nares when flare-ups occurred. Flare-ups were pre-defined as at least 25% increase of inflammatory lesions (sum of inflammatory nodules and abscesses) from baseline. Samplings were also done after completion of 12 weeks of ADA treatment from all patients who did not present flare-ups. Clinical response to ADA was assessed by the HS Clinical Response score (HiSCR). RESULTS: Thirty-nine patients were studied; 24 with Hurley II stage HS and 15 with Hurley III stage HS. Twenty-nine patients achieved HiSCR after 12 weeks of treatment without any flare-ups; 10 patients had flare-ups and failed HiSCR. Three (10.3%) and 5 (50%) patients, respectively, had nasal carriage of S. aureus (odds ratio 8.67; 95% CI 1.54-48.49; p = 0.014). Among 32 patients reaching follow-up week 48, 20 patients achieved HiSCR and 12 had flare-ups leading to ADA failure; 2 (10%) and 5 (41.7%) patients, respectively, had positive culture for S. aureus (odds ratio 6.42; 95% CI 1.00-41.20; p = 0.05). CONCLUSION: Nasal carriage of S. aureus may be associated with loss of response to ADA. Findings need confirmation in larger series of patients.

Smoking prevalence among hospitalized COVID-19 patients and its association with disease severity and mortality: an expanded re-analysis of a recent publication.

BACKGROUND: There is a lot of debate about the effects of smoking on COVID-19. A recent fixed-effects meta-analysis found smoking to be associated with disease severity among hospitalized patients, but other studies report an unusually low prevalence of smoking among hospitalized patients. The purpose of this study was to expand the analysis by calculating the prevalence odds ratio (POR) of smoking among hospitalized COVID-19 patients, while the association between smoking and disease severity and mortality was examined by random-effects meta-analyses considering the highly heterogeneous study populations. METHODS: The same studies as examined in the previous meta-analysis were analyzed (N = 22, 20 studies from China and 2 from USA). The POR relative to the expected smoking prevalence was calculated using gender and age-adjusted population smoking rates. Random-effects meta-analyses were used for all other associations. RESULTS: A total of 7162 patients were included, with 482 being smokers. The POR was 0.24 (95%CI 0.19-0.30). Unlike the original study, the association between smoking and disease severity was not statistically significant using random-effects meta-analysis (OR 1.40, 95%CI 0.98-1.98). In agreement with the original study, no statistically significant association was found between smoking and mortality (OR 1.86, 95%CI 0.88-3.94). CONCLUSION: An unusually low prevalence of smoking, approximately 1/4th the expected prevalence, was observed among hospitalized COVID-19 patients. Any association between smoking and COVID-19 severity cannot be generalized but should refer to the seemingly low proportion of smokers who develop severe COVID-19 that requires hospitalization. Smokers should be advised to quit due to long-term health risks, but pharmaceutical nicotine or other nicotinic cholinergic agonists should be explored as potential therapeutic options, based on a recently presented hypothesis.

Multiple outcome meta-analysis of gene-expression data in inflammatory bowel disease.

We performed a multivariate meta-analysis of microarray data in Crohn's disease (CD) and Ulcerative colitis (UC), which are the main forms of inflammatory bowel disease (IBD). They share similar symptoms but differ in the location and extent of inflammation and in complications. We identified 249 differentially expressed genes (DEGs) in CD and 38 in UC at a false discovery rate of 1%. 20 of the DEGs were common to both diseases. A multivariate test identified 260 DEGs associated with IBD, 53 of which were not found in any of the disorders. We identified important molecular pathways implicated in the pathogenesis of IBD, such as the JAK/STAT and interferon-gamma signaling pathways, genes involved in cell adhesion, apoptosis and carcinogenesis. Among others, BCAT1 and GZMB are interesting novel DEGs that deserve further investigation in experimental models. The method could also be useful to other cases of meta-analysis of gene expression data.

Landscape of Eukaryotic Transmembrane Beta Barrel Proteins.

Even though in the last few years several families of eukaryotic ß-barrel outer membrane proteins have been discovered, their computational characterization and their annotation in public databases are far from complete. The PFAM database includes only very few characteristic profiles for these families, and in most cases, the profile hidden Markov models (pHMMs) have been trained using prokaryotic and eukaryotic proteins together. Here, we present for the first time a comprehensive computational analysis of eukaryotic transmembrane ß-barrels. Twelve characteristic pHMMs were built, based on an extensive literature search, which can discriminate eukaryotic ß-barrels from other classes of proteins (globular and bacterial ß-barrel ones), as well as between mitochondrial and chloroplastic ones. We built eight novel profiles for the chloroplastic ß-barrel families that are not present in the PFAM database and also updated the profile for the MDM10 family (PF12519) in the PFAM database and divide the porin family (PF01459) into two separate families, namely, VDAC and TOM40.

Semi-supervised learning of Hidden Markov Models for biological sequence analysis.

MOTIVATION: Hidden Markov Models (HMMs) are probabilistic models widely used in applications in computational sequence analysis. HMMs are basically unsupervised models. However, in the most important applications, they are trained in a supervised manner. Training examples accompanied by labels corresponding to different classes are given as input and the set of parameters that maximize the joint probability of sequences and labels is estimated. A main problem with this approach is that, in the majority of the cases, labels are hard to find and thus the amount of training data is limited. On the other hand, there are plenty of unclassified (unlabeled) sequences deposited in the public databases that could potentially contribute to the training procedure. This approach is called semi-supervised learning and could be very helpful in many applications. RESULTS: We propose here, a method for semi-supervised learning of HMMs that can incorporate labeled, unlabeled and partially labeled data in a straightforward manner. The algorithm is based on a variant of the Expectation-Maximization (EM) algorithm, where the missing labels of the unlabeled or partially labeled data are considered as the missing data. We apply the algorithm to several biological problems, namely, for the prediction of transmembrane protein topology for alpha-helical and beta-barrel membrane proteins and for the prediction of archaeal signal peptides. The results are very promising, since the algorithms presented here can significantly improve the prediction performance of even the top-scoring classifiers. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

JUCHMME: a Java Utility for Class Hidden Markov Models and Extensions for biological sequence analysis.

SUMMARY: JUCHMME is an open-source software package designed to fit arbitrary custom Hidden Markov Models (HMMs) with a discrete alphabet of symbols. We incorporate a large collection of standard algorithms for HMMs as well as a number of extensions and evaluate the software on various biological problems. Importantly, the JUCHMME toolkit includes several additional features that allow for easy building and evaluation of custom HMMs, which could be a useful resource for the research community. AVAILABILITY AND IMPLEMENTATION: http://www.compgen.org/tools/juchmme, https://github.com/pbagos/juchmme. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Susceptibility to leishmaniasis is affected by host SLC11A1 gene polymorphisms: a systematic review and meta-analysis.

Leishmaniases are cutaneous, mucocutaneous, and visceral diseases affecting humans and domesticated animals mostly in the tropical and subtropical areas of the planet. Host genetics have been widely investigated for their role in developing various infectious diseases. The SLC11A1 gene has been reported to play a role in neutrophil function and is associated with susceptibility to infectious and inflammatory diseases such as tuberculosis or rheumatoid arthritis. In the present meta-analysis, we investigate the genetic association of SLC11A1 polymorphisms with susceptibility to leishmaniasis. Genotypes and other risk-related data were collected from 13 case-control and family-based studies (after literature search). Conventional random-effects meta-analysis was performed using STATA 13. To pool case-control and family-based data, the weighted Stouffer's method was also applied. Eight polymorphisms were investigated: rs2276631, rs3731865, rs3731864, rs17221959, rs201565523, rs2279015, rs17235409, and rs17235416. We found that rs17235409 (D543N) and rs17235416 (1729 + 55del4) are significantly associated with a risk for cutaneous leishmaniasis (CL), whereas rs17221959, rs2279015, and rs17235409 are associated with visceral leishmaniasis (VL). Our results suggest that polymorphisms in SLC11A1 affect susceptibility to CL and VL. These findings open new pathways in understanding macrophage response to Leishmania infection and the genetic factors predisposing to symptomatic CL or VL that can lead to the usage of predictive biomarkers in populations at risk.

GWAR: robust analysis and meta-analysis of genome-wide association studies.

MOTIVATION: In the context of genome-wide association studies (GWAS), there is a variety of statistical techniques in order to conduct the analysis, but, in most cases, the underlying genetic model is usually unknown. Under these circumstances, the classical Cochran-Armitage trend test (CATT) is suboptimal. Robust procedures that maximize the power and preserve the nominal type I error rate are preferable. Moreover, performing a meta-analysis using robust procedures is of great interest and has never been addressed in the past. The primary goal of this work is to implement several robust methods for analysis and meta-analysis in the statistical package Stata and subsequently to make the software available to the scientific community. RESULTS: The CATT under a recessive, additive and dominant model of inheritance as well as robust methods based on the Maximum Efficiency Robust Test statistic, the MAX statistic and the MIN2 were implemented in Stata. Concerning MAX and MIN2, we calculated their asymptotic null distributions relying on numerical integration resulting in a great gain in computational time without losing accuracy. All the aforementioned approaches were employed in a fixed or a random effects meta-analysis setting using summary data with weights equal to the reciprocal of the combined cases and controls. Overall, this is the first complete effort to implement procedures for analysis and meta-analysis in GWAS using Stata. AVAILABILITY AND IMPLEMENTATION: A Stata program and a web-server are freely available for academic users at http://www.compgen.org/tools/GWAR. CONTACT: pbagos@compgen.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Apolipoprotein E Polymorphism and Left Ventricular Failure in Beta-Thalassemia: A Multivariate Meta-Analysis.

Apolipoprotein E (ApoE) is potentially a genetic risk factor for the development of left ventricular failure (LVF), the main cause of death in beta-thalassemia homozygotes. In the present study, we synthesize the results of independent studies examining the effect of ApoE on LVF development in thalassemic patients through a meta-analytic approach. However, all studies report more than one outcome, as patients are classified into three groups according to the severity of the symptoms and the genetic polymorphism. Thus, a multivariate meta-analytic method that addresses simultaneously multiple exposures and multiple comparison groups was developed. Four individual studies were included in the meta-analysis involving 613 beta-thalassemic patients and 664 controls. The proposed method that takes into account the correlation of log odds ratios (log(ORs)), revealed a statistically significant overall association (P-value = 0.009), mainly attributed to the contrast of E4 versus E3 allele for patients with evidence (OR: 2.32, 95% CI: 1.19, 4.53) or patients with clinical and echocardiographic findings (OR: 3.34, 95% CI: 1.78, 6.26) of LVF. This study suggests that E4 is a genetic risk factor for LVF in beta-thalassemia major. The presented multivariate approach can be applied in several fields of research.

PRED-TMBB2: improved topology prediction and detection of beta-barrel outer membrane proteins.

MOTIVATION: The PRED-TMBB method is based on Hidden Markov Models and is capable of predicting the topology of beta-barrel outer membrane proteins and discriminate them from water-soluble ones. Here, we present an updated version of the method, PRED-TMBB2, with several newly developed features that improve its performance. The inclusion of a properly defined end state allows for better modeling of the beta-barrel domain, while different emission probabilities for the adjacent residues in strands are used to incorporate knowledge concerning the asymmetric amino acid distribution occurring there. Furthermore, the training was performed using newly developed algorithms in order to optimize the labels of the training sequences. Moreover, the method is retrained on a larger, non-redundant dataset which includes recently solved structures, and a newly developed decoding method was added to the already available options. Finally, the method now allows the incorporation of evolutionary information in the form of multiple sequence alignments. RESULTS: The results of a strict cross-validation procedure show that PRED-TMBB2 with homology information performs significantly better compared to other available prediction methods. It yields 76% in correct topology predictions and outperforms the best available predictor by 7%, with an overall SOV of 0.9. Regarding detection of beta-barrel proteins, PRED-TMBB2, using just the query sequence as input, achieves an MCC value of 0.92, outperforming even predictors designed for this task and are much slower. AVAILABILITY AND IMPLEMENTATION: The method, along with all datasets used, is freely available for academic users at http://www.compgen.org/tools/PRED-TMBB2 CONTACT: pbagos@compgen.org.

The impact of pneumatic tube system on routine laboratory parameters: a systematic review and meta-analysis.

BACKGROUND: Pneumatic tube system (PTS) is a widely used method of transporting blood samples in hospitals. The aim of this study was to evaluate the effects of the PTS transport in certain routine laboratory parameters as it has been implicated with hemolysis. METHODS: A systematic review and a meta-analysis were conducted. PubMed and Scopus databases were searched (up until November 2016) to identify prospective studies evaluating the impact of PTS transport in hematological, biochemical and coagulation measurements. The random-effects model was used in the meta-analysis utilizing the mean difference (MD). Heterogeneity was quantitatively assessed using the Cohran's Q and the I2 index. Subgroup analysis, meta-regression analysis, sensitivity analysis, cumulative meta-analysis and assessment of publication bias were performed for all outcomes. RESULTS: From a total of 282 studies identified by the searching procedure, 24 were finally included in the meta-analysis. The meta-analysis yielded statistically significant results for potassium (K) [MD=0.04 mmol/L; 95% confidence interval (CI)=0.015-0.065; p=0.002], lactate dehydrogenase (LDH) (MD=10.343 U/L; 95% CI=6.132-14.554; p<10-4) and aspartate aminotransferase (AST) (MD=1.023 IU/L; 95% CI=0.344-1.702; p=0.003). Subgroup analysis and random-effects meta-regression analysis according to the speed and distance of the samples traveled via the PTS revealed that there is relation between the rate and the distance of PTS with the measurements of K, LDH, white blood cells and red blood cells. CONCLUSIONS: This meta-analysis suggests that PTS may be associated with alterations in K, LDH and AST measurements. Although these findings may not have any significant clinical effect on laboratory results, it is wise that each hospital validates their PTS.

A multivariate method for meta-analysis and comparison of diagnostic tests.

We present here an extension of the classic bivariate random effects meta-analysis for the log-transformed sensitivity and specificity that can be applied for two or more diagnostic tests. The advantage of this method is that a closed-form expression is derived for the calculation of the within-studies covariances. The method allows the direct calculation of sensitivity and specificity, as well as, the diagnostic odds ratio, the area under curve and the parameters of the summary receiver operator's characteristic curve, along with the means for a formal comparison of these quantities for different tests. There is no need for individual patient data or the simultaneous evaluation of both diagnostic tests in all studies. The method is simple and fast; it can be extended for several diagnostic tests and can be fitted in nearly all statistical packages. The method was evaluated in simulations and applied in a meta-analysis for the comparison of anti-cyclic citrullinated peptide antibody and rheumatoid factor for discriminating patients with rheumatoid arthritis, with encouraging results. Simulations suggest that the method is robust and more powerful compared with the standard bivariate approach that ignores the correlation between tests. Copyright © 2016 John Wiley & Sons, Ltd.

HMMpTM: improving transmembrane protein topology prediction using phosphorylation and glycosylation site prediction.

During the last two decades a large number of computational methods have been developed for predicting transmembrane protein topology. Current predictors rely on topogenic signals in the protein sequence, such as the distribution of positively charged residues in extra-membrane loops and the existence of N-terminal signals. However, phosphorylation and glycosylation are post-translational modifications (PTMs) that occur in a compartment-specific manner and therefore the presence of a phosphorylation or glycosylation site in a transmembrane protein provides topological information. We examine the combination of phosphorylation and glycosylation site prediction with transmembrane protein topology prediction. We report the development of a Hidden Markov Model based method, capable of predicting the topology of transmembrane proteins and the existence of kinase specific phosphorylation and N/O-linked glycosylation sites along the protein sequence. Our method integrates a novel feature in transmembrane protein topology prediction, which results in improved performance for topology prediction and reliable prediction of phosphorylation and glycosylation sites. The method is freely available at http://bioinformatics.biol.uoa.gr/HMMpTM.

Key challenges for the creation and maintenance of specialist protein resources.

As the volume of data relating to proteins increases, researchers rely more and more on the analysis of published data, thus increasing the importance of good access to these data that vary from the supplemental material of individual articles, all the way to major reference databases with professional staff and long-term funding. Specialist protein resources fill an important middle ground, providing interactive web interfaces to their databases for a focused topic or family of proteins, using specialized approaches that are not feasible in the major reference databases. Many are labors of love, run by a single lab with little or no dedicated funding and there are many challenges to building and maintaining them. This perspective arose from a meeting of several specialist protein resources and major reference databases held at the Wellcome Trust Genome Campus (Cambridge, UK) on August 11 and 12, 2014. During this meeting some common key challenges involved in creating and maintaining such resources were discussed, along with various approaches to address them. In laying out these challenges, we aim to inform users about how these issues impact our resources and illustrate ways in which our working together could enhance their accuracy, currency, and overall value.

Polymorphisms of the CD24 Gene Are Associated with Risk of Multiple Sclerosis: A Meta-Analysis.

CD24 is a cell-surface protein mainly expressed in cells of the immune and central nervous system (CNS), cells that play a critical role in the development of multiple sclerosis (MS). In the current study, we investigated four polymorphisms of the CD24 gene regarding their associations with MS. To this end, univariate and multivariate meta-analysis were applied along with modifications to include data from family-trios so as to increase the robustness of the meta-analysis. We found that the polymorphism 226 C>T (Ala57Val) of the CD24 gene is associated with MS according to the recessive mode of inheritance (odds ratio = 1.75; 95% CI: 1.09, 2.81). Moreover, the 1527-1528 TG>del polymorphism is inversely associated with MS according to the dominant mode of inheritance (odds ratio = 0.57; 95% CI 0.39, 0.83). Conversely, the 1056 A>G and 1626 A>G polymorphisms were not found to be associated with MS. We conclude that the CD24 226 C>T polymorphism increases the risk of MS, while the 1527-1528 TG>del polymorphism seems to have a protective role against MS, suggesting that these two polymorphisms can be used as predictive biomarkers for MS development.