Supporting Pharmacovigilance Signal Validation and Prioritization with Analyses of Routinely Collected Health Data: Lessons Learned from an EHDEN Network Study.

INTRODUCTION: Individual case reports are the main asset in pharmacovigilance signal management. Signal validation is the first stage after signal detection and aims to determine if there is sufficient evidence to justify further assessment. Throughout signal management, a prioritization of signals is continually made. Routinely collected health data can provide relevant contextual information but are primarily used at a later stage in pharmacoepidemiological studies to assess communicated signals. OBJECTIVE: The aim of this study was to examine the feasibility and utility of analysing routine health data from a multinational distributed network to support signal validation and prioritization and to reflect on key user requirements for these analyses to become an integral part of this process. METHODS: Statistical signal detection was performed in VigiBase, the WHO global database of individual case safety reports, targeting generic manufacturer drugs and 16 prespecified adverse events. During a 5-day study-a-thon, signal validation and prioritization were performed using information from VigiBase, regulatory documents and the scientific literature alongside descriptive analyses of routine health data from 10 partners of the European Health Data and Evidence Network (EHDEN). Databases included in the study were from the UK, Spain, Norway, the Netherlands and Serbia, capturing records from primary care and/or hospitals. RESULTS: Ninety-five statistical signals were subjected to signal validation, of which eight were considered for descriptive analyses in the routine health data. Design, execution and interpretation of results from these analyses took up to a few hours for each signal (of which 15-60 minutes were for execution) and informed decisions for five out of eight signals. The impact of insights from the routine health data varied and included possible alternative explanations, potential public health and clinical impact and feasibility of follow-up pharmacoepidemiological studies. Three signals were selected for signal assessment, two of these decisions were supported by insights from the routine health data. Standardization of analytical code, availability of adverse event phenotypes including bridges between different source vocabularies, and governance around the access and use of routine health data were identified as important aspects for future development. CONCLUSIONS: Analyses of routine health data from a distributed network to support signal validation and prioritization are feasible in the given time limits and can inform decision making. The cost-benefit of integrating these analyses at this stage of signal management requires further research.

Meta-Analysis of Circulating Cell-Free DNA's Role in the Prognosis of Pancreatic Cancer.

INTRODUCTION: The analysis of cell-free DNA (cfDNA) for genetic abnormalities is a promising new approach for the diagnosis and prognosis of pancreatic cancer patients. Insights into the molecular characteristics of pancreatic cancer may provide valuable information, leading to its earlier detection and the development of targeted therapies. MATERIAL AND METHODS: We conducted a systematic review and a meta-analysis of studies that reported cfDNA in pancreatic ductal adenocarcinoma (PDAC). The studies were considered eligible if they included patients with PDAC, if they had blood tests for cfDNA/ctDNA, and if they analyzed the prognostic value of cfDNA/ctDNA for patients' survival. The studies published before 22 October 2020 were identified through the PubMED, EMBASE, Web of Science and Cochrane Library databases. The assessed outcomes were the overall (OS) and progression-free survival (PFS), expressed as the log hazard ratio (HR) and standard error (SE). The summary of the HR effect size was estimated by pooling the individual trial results using the Review Manager, version 5.3, Cochrane Collaboration. The heterogeneity was assessed using the Cochran Q test and I2 statistic. RESULTS: In total, 48 studies were included in the qualitative review, while 44 were assessed in the quantitative synthesis, with the total number of patients included being 3524. Overall negative impacts of cfDNA and KRAS mutations on OS and PFS in PDAC (HR = 2.42, 95% CI: 1.95-2.99 and HR = 2.46, 95% CI: 2.01-3.00, respectively) were found. The subgroup analysis of the locally advanced and metastatic disease presented similar results (HR = 2.51, 95% CI: 1.90-3.31). In the studies assessing the pre-treatment presence of KRAS, there was a moderate to high degree of heterogeneity (I2 = 87% and I2 = 48%, for OS and PFS, respectively), which was remarkably decreased in the analysis of the studies measuring post-treatment KRAS (I2 = 24% and I2 = 0%, for OS and PFS, respectively). The patients who were KRAS positive before but KRAS negative after treatment had a better prognosis than the persistently KRAS-positive patients (HR = 5.30, 95% CI: 1.02-27.63). CONCLUSION: The assessment of KRAS mutation by liquid biopsy can be considered as an additional tool for the estimation of the disease course and outcome in PDAC patients.

The first insight into the genetic structure of the population of modern Serbia.

The complete understanding of the genomic contribution to complex traits, diseases, and response to treatments, as well as genomic medicine application to the well-being of all humans will be achieved through the global variome that encompasses fine-scale genetic diversity. Despite significant efforts in recent years, uneven representation still characterizes genomic resources and among the underrepresented European populations are the Western Balkans including the Serbian population. Our research addresses this gap and presents the first ever targeted sequencing dataset of variants in clinically relevant genes. By measuring population differentiation and applying the Principal Component and Admixture analysis we demonstrated that the Serbian population differs little from other European populations, yet we identified several novel and more frequent variants that appear as its unique genetic determinants. We explored thoroughly the functional impact of frequent variants and its correlation with the health burden of the population of Serbia based on a sample of 144 individuals. Our variants catalogue improves the understanding of genetics of modern Serbia, contributes to research on ancestry, and aids in improvements of well-being and health equity. In addition, this resource may also be applicable in neighboring regions and valuable in worldwide functional analyses of genetic variants in individuals of European descent.

Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies.

For the last couple of decades, there has been a significant growth in sequencing data, leading to an extraordinary increase in the number of gene variants. This places a challenge on the bioinformatics research community to develop and improve computational tools for functional annotation of new variants. Genes coding for epigenetic regulators have important roles in cancer pathogenesis and mutations in these genes show great potential as clinical biomarkers, especially in hematologic malignancies. Therefore, we developed a model that specifically focuses on these genes, with an assumption that it would outperform general models in predicting the functional effects of amino acid substitutions. EpiMut is a standalone software that implements a sequence based alignment-free method. We applied a two-step approach for generating sequence based features, relying on the biophysical and biochemical indices of amino acids and the Fourier Transform as a sequence transformation method. For each gene in the dataset, the machine learning algorithm-Naïve Bayes was used for building a model for prediction of the neutral or disease-related status of variants. EpiMut outperformed state-of-the-art tools used for comparison, PolyPhen-2, SIFT and SNAP2. Additionally, EpiMut showed the highest performance on the subset of variants positioned outside conserved functional domains of analysed proteins, which represents an important group of cancer-related variants. These results imply that EpiMut can be applied as a first choice tool in research of the impact of gene variants in epigenetic regulators, especially in the light of the biomarker role in hematologic malignancies. EpiMut is freely available at https://www.vin.bg.ac.rs/180/tools/epimut.php.

Tally-2.0: upgraded validator of tandem repeat detection in protein sequences.

MOTIVATION: Proteins containing tandem repeats (TRs) are abundant, frequently fold in elongated non-globular structures and perform vital functions. A number of computational tools have been developed to detect TRs in protein sequences. A blurred boundary between imperfect TR motifs and non-repetitive sequences gave rise to necessity to validate the detected TRs. RESULTS: Tally-2.0 is a scoring tool based on a machine learning (ML) approach, which allows to validate the results of TR detection. It was upgraded by using improved training datasets and additional ML features. Tally-2.0 performs at a level of 93% sensitivity, 83% specificity and an area under the receiver operating characteristic curve of 95%. AVAILABILITY AND IMPLEMENTATION: Tally-2.0 is available, as a web tool and as a standalone application published under Apache License 2.0, on the URL https://bioinfo.crbm.cnrs.fr/index.php? route=tools&tool=27. It is supported on Linux. Source code is available upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Deciphering Imidazoline Off-targets by Fishing in the Class A of GPCR field.

Based on the finding that a central antihypertensive agent with high affinity for I1-type imidazoline receptors - rilmenidine, shows cytotoxic effects on cultured cancer cell lines, it has been suggested that imidazoline receptors agonists might have a therapeutic potential in the cancer therapy. Nevertheless, potential rilmenidine side effects caused by activation of α-adrenoceptors, or other associated receptors and enzymes, might hinder its therapeutic benefits. Considering that human α-adrenoceptors belong to the rhodopsin-like class A of G-protein-coupled receptors (GPCRs) it is reasonable to assume that imidazolines might have the affinity for other receptors from the same class. Therefore, to investigate possible off-target effects of imidazoline ligands we have prepared a reverse docking protocol on class A GPCRs, using imidazoline ligands and their decoys. To verify our in silico results, three ligands with high scores and three ligands with low scores were tested for antagonistic activity on α2 - adrenoceptors.

Computational design and characterization of nanobody-derived peptides that stabilize the active conformation of the ß2-adrenergic receptor (ß2-AR).

This study aimed to design and functionally characterize peptide mimetics of the nanobody (Nb) related to the ß2-adrenergic receptor (ß2-AR) (nanobody-derived peptide, NDP). We postulated that the computationally derived and optimized complementarity-determining region 3 (CDR3) of Nb is sufficient for its interaction with receptor. Sequence-related Nb-families preferring the agonist-bound active conformation of ß2-AR were analysed using the informational spectrum method (ISM) and ß2-AR:NDP complexes studied using protein-peptide docking and molecular dynamics (MD) simulations in conjunction with metadynamics calculations of free energy binding. The selected NDP of Nb71, designated P3, was 17 amino acids long and included CDR3. Metadynamics calculations yielded a binding free energy for the ß2-AR:P3 complex of ΔG = (-7.23 ± 0.04) kcal/mol, or a Kd of (7.9 ± 0.5) µM, for T = 310 K. In vitro circular dichroism (CD) spectropolarimetry and microscale thermophoresis (MST) data provided additional evidence for P3 interaction with agonist-activated ß2-AR, which displayed ~10-fold higher affinity for P3 than the unstimulated receptor (MST-derived EC50 of 3.57 µM vs. 58.22 µM), while its ability to inhibit the agonist-induced interaction of ß2-AR with ß-arrestin 2 was less evident. In summary, theoretical and experimental evidence indicated that P3 preferentially binds agonist-activated ß2-AR.

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens.

BACKGROUND: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. RESULTS: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. CONCLUSION: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.

DiNGO: standalone application for Gene Ontology and Human Phenotype Ontology term enrichment analysis.

SUMMARY: Although various tools for Gene Ontology (GO) term enrichment analysis are available, there is still room for improvement. Hence, we present DiNGO, a standalone application based on an open source code from BiNGO, a widely-used application to assess the overrepresentation of GO categories. Besides facilitating GO term enrichment analyses, DiNGO has been developed to allow for convenient Human Phenotype Ontology (HPO) term overrepresentation investigation. This is an important contribution considering the increasing interest in HPO in scientific research and its potential in clinical settings. DiNGO supports gene/protein identifier conversion and an automatic updating of GO and HPO annotation resources. Finally, DiNGO can rapidly process a large amount of data due to its multithread design. AVAILABILITY AND IMPLEMENTATION: DiNGO is implemented in the JAVA language, and its source code, example datasets and instructions are available on GitHub: https://github.com/radoslav180/DiNGO. A pre-compiled jar file is available at: https://www.vin.bg.ac.rs/180/tools/DiNGO.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Automated feature engineering improves prediction of protein-protein interactions.

Over the last decade, various machine learning (ML) and statistical approaches for protein-protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understanding of life. Efficient ML approaches require informative and non-redundant features. In this paper, we introduce novel types of expert-crafted sequence, evolutionary and graph features and apply automatic feature engineering to further expand feature space to improve predictive modeling. The two-step automatic feature-engineering process encompasses the hybrid method for feature generation and unsupervised feature selection, followed by supervised feature selection through a genetic algorithm (GA). The optimization of both steps allows the feature-engineering procedure to operate on a large transformed feature space with no considerable computational cost and to efficiently provide newly engineered features. Based on GA and correlation filtering, we developed a stacking algorithm GA-STACK for automatic ensembling of different ML algorithms to improve prediction performance. We introduced a unified method, HP-GAS, for the prediction of human PPIs, which incorporates GA-STACK and rests on both expert-crafted and 40% of newly engineered features. The extensive cross validation and comparison with the state-of-the-art methods showed that HP-GAS represents currently the most efficient method for proteome-wide forecasting of protein interactions, with prediction efficacy of 0.93 AUC and 0.85 accuracy. We implemented the HP-GAS method as a free standalone application which is a time-efficient and easy-to-use tool. HP-GAS software with supplementary data can be downloaded from: http://www.vinca.rs/180/tools/HP-GAS.php .

Virtual Screen for Repurposing of Drugs for Candidate Influenza a M2 Ion-Channel Inhibitors.

Influenza A virus (IAV) matrix protein 2 (M2), an ion channel, is crucial for virus infection, and therefore, an important anti-influenza drug target. Adamantanes, also known as M2 channel blockers, are one of the two classes of Food and Drug Administration-approved anti-influenza drugs, although their use was discontinued due to prevalent drug resistance. Fast emergence of resistance to current anti-influenza drugs have raised an urgent need for developing new anti-influenza drugs against resistant forms of circulating viruses. Here we propose a simple theoretical criterion for fast virtual screening of molecular libraries for candidate anti-influenza ion channel inhibitors both for wild type and adamantane-resistant influenza A viruses. After in silico screening of drug space using the EIIP/AQVN filter and further filtering of drugs by ligand based virtual screening and molecular docking we propose the best candidate drugs as potential dual inhibitors of wild type and adamantane-resistant influenza A viruses. Finally, guanethidine, the best ranked drug selected from ligand-based virtual screening, was experimentally tested. The experimental results show measurable anti-influenza activity of guanethidine in cell culture.

Functional characterization of ß2-adrenergic and insulin receptor heteromers.

This study aimed to functionally characterize ß2-adrenergic (ß2AR) and insulin receptor (IR) heteromers in regard to ß-arrestin 2 (ßarr2) recruitment and cAMP signaling and to examine the involvement of the cytoplasmic portion of the IR ß chain in heteromerization with ß2AR. Evidence for ß2AR:IR:ßarr2 complex formation and the specificity of the IR:ßarr2 interaction was first provided by bioinfomatics analysis. Receptor-heteromer investigation technology (HIT) then provided functional evidence of ß2AR:IR heterodimerization by showing isoproterenol-induced but not insulin-induced GFP2-ßarr2 recruitment to the ß2AR:IR complex; the IR:ßarr2 interaction was found to only be constitutive. The constitutive IR:ßarr2 BRET signal (BRETconst) was significantly smaller in cells coexpressing IR-RLuc8 and a GFP2-ßarr2 1-185 mutant lacking the proposed IR binding domain. ß2AR:IR heteromerization also influenced the pharmacological phenotype of ß2AR, i.e., its efficacy in recruiting ßarr2 and activating cAMP signaling. Evidence suggesting involvement of the cytoplasmic portion of the IR ß chain in the interaction with ß2AR was provided by BRET2 saturation and HIT assays using an IR 1-1271 stop mutant lacking the IR C-terminal tail region. For the complex consisting of IR 1-1271-RLuc8:ß2AR-GFP2, saturation was not reached, most likely reflecting random collisions between IR 1-1271 and ß2AR. Furthermore, in the HIT assay, no substantial agonist-induced increase in the BRET2 signal was detected that would be indicative of ßarr2 recruitment to the IR 1-1271:ß2AR heteromer. Complementary 3D visualization of ß2AR:IR provided supporting evidence for stability of the heterotetramer complex and identified amino acid residues involved in ß2AR:IR heteromerization. This article is part of the Special Issue entitled 'Receptor heteromers and their allosteric receptor-receptor interactions'.

Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes.

BACKGROUND: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. OBJECTIVE: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. METHODS: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. RESULTS: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. CONCLUSION: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein-protein complexes for experimental studies.

Genetic Markers for Coronary Artery Disease.

Coronary artery disease (CAD) and myocardial infarction (MI) are recognized as leading causes of mortality in developed countries. Although typically associated with behavioral risk factors, such as smoking, sedentary lifestyle, and poor dietary habits, such vascular phenotypes have also long been recognized as being related to genetic background. We review the currently available data concerning genetic markers for CAD in English and non-English articles with English abstracts published between 2003 and 2018. As genetic testing is increasingly available, it may be possible to identify adequate genetic markers representing the risk profile and to use them in a clinical setting.

IDPpi: Protein-Protein Interaction Analyses of Human Intrinsically Disordered Proteins.

Intrinsically disordered proteins (IDPs) are characterized by the lack of a fixed tertiary structure and are involved in the regulation of key biological processes via binding to multiple protein partners. IDPs are malleable, adapting to structurally different partners, and this flexibility stems from features encoded in the primary structure. The assumption that universal sequence information will facilitate coverage of the sparse zones of the human interactome motivated us to explore the possibility of predicting protein-protein interactions (PPIs) that involve IDPs based on sequence characteristics. We developed a method that relies on features of the interacting and non-interacting protein pairs and utilizes machine learning to classify and predict IDP PPIs. Consideration of both sequence determinants specific for conformational organizations and the multiplicity of IDP interactions in the training phase ensured a reliable approach that is superior to current state-of-the-art methods. By applying a strict evaluation procedure, we confirm that our method predicts interactions of the IDP of interest even on the proteome-scale. This service is provided as a web tool to expedite the discovery of new interactions and IDP functions with enhanced efficiency.

Ibuprofen as a template molecule for drug design against Ebola virus.

The Ebola virus outbreak in West Africa 2015 and Congo 2017, point out an urgent need for development of drugs against this important pathogen. Previously, by repurposing virtual screening of 6438 drugs from DrugBank, ibuprofen was selected as a possible inhibitor of the Ebola virus infection. The results of an additional docking analysis as well as experimental results showing measurable anti-Ebola effect of ibuprofen in cell culture suggest ibuprofen as a promising molecular template for the development of drugs for treatment of the infection by Ebola virus.

TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation.

The TRI_tool, a sequence-based web tool for prediction of protein interactions in the human transcriptional regulation, is intended for biomedical investigators who work on understanding the regulation of gene expression. It has an improved predictive performance due to the training on updated, human specific, experimentally validated datasets. The TRI_tool is designed to test up to 100 potential interactions with no time delay and to report both probabilities and binarized predictions. AVAILABILITY AND IMPLEMENTATION: http://www.vin.bg.ac.rs/180/tools/tfpred.php CONTACT: vladaper@vinca.rs; nevenav@vinca.rsSupplementary information: Supplementary data are available at Bioinformatics online.

DisProt 7.0: a major update of the database of disordered proteins.

The Database of Protein Disorder (DisProt, URL: www.disprot.org) has been significantly updated and upgraded since its last major renewal in 2007. The current release holds information on more than 800 entries of IDPs/IDRs, i.e. intrinsically disordered proteins or regions that exist and function without a well-defined three-dimensional structure. We have re-curated previous entries to purge DisProt from conflicting cases, and also upgraded the functional classification scheme to reflect continuous advance in the field in the past 10 years or so. We define IDPs as proteins that are disordered along their entire sequence, i.e. entirely lack structural elements, and IDRs as regions that are at least five consecutive residues without well-defined structure. We base our assessment of disorder strictly on experimental evidence, such as X-ray crystallography and nuclear magnetic resonance (primary techniques) and a broad range of other experimental approaches (secondary techniques). Confident and ambiguous annotations are highlighted separately. DisProt 7.0 presents classified knowledge regarding the experimental characterization and functional annotations of IDPs/IDRs, and is intended to provide an invaluable resource for the research community for a better understanding structural disorder and for developing better computational tools for studying disordered proteins.