Systems-Biology Approaches to Discover Anti-Viral Effectors of the Human Innate Immune Response

Virus infections elicit an immediate innate response involving antiviral factors. The activities of some of these factors are, in turn, blocked by viral countermeasures. The ensuing battle between the host and the viruses is crucial for determining whether the virus establishes a foothold and/or induces adaptive immune responses. A comprehensive systems-level understanding of the repertoire of anti-viral effectors in the context of these immediate virus-host responses would provide significant advantages in devising novel strategies to interfere with the initial establishment of infections. Recent efforts to identify cellular factors in a comprehensive and unbiased manner, using genome-wide siRNA screens and other systems biology “omics” methodologies, have revealed several potential anti-viral effectors for viruses like Human immunodeficiency virus type 1 (HIV-1), Hepatitis C virus (HCV), West Nile virus (WNV), and influenza virus. This review describes the discovery of novel viral restriction factors and discusses how the integration of different methods in systems biology can be used to more comprehensively identify the intimate interactions of viruses and the cellular innate resistance

The RIKEN integrated database of mammals

The RIKEN integrated database of mammals (http://scinets.org/db/mammal) is the official undertaking to integrate its mammalian databases produced from multiple large-scale programs that have been promoted by the institute. The database integrates not only RIKEN’s original databases, such as FANTOM, the ENU mutagenesis program, the RIKEN Cerebellar Development Transcriptome Database and the Bioresource Database, but also imported data from public databases, such as Ensembl, MGI and biomedical ontologies. Our integrated database has been implemented on the infrastructure of publication medium for databases, termed SciNetS/SciNeS, or the Scientists’ Networking System, where the data and metadata are structured as a semantic web and are downloadable in various standardized formats. The top-level ontology-based implementation of mammal-related data directly integrates the representative knowledge and individual data records in existing databases to ensure advanced cross-database searches and reduced unevenness of the data management operations. Through the development of this database, we propose a novel methodology for the development of standardized comprehensive management of heterogeneous data sets in multiple databases to improve the sustainability, accessibility, utility and publicity of the data of biomedical information

RAPID: Resource of Asian Primary Immunodeficiency Diseases

Availability of a freely accessible, dynamic and integrated database for primary immunodeficiency diseases (PID) is important both for researchers as well as clinicians. To build a PID informational platform and also as a part of action to initiate a network of PID research in Asia, we have constructed a web-based compendium of molecular alterations in PID, named Resource of Asian Primary Immunodeficiency Diseases (RAPID), which is available as a worldwide web resource at http://rapid.rcai.riken.jp/. It hosts information on sequence variations and expression at the mRNA and protein levels of all genes reported to be involved in PID patients. The main objective of this database is to provide detailed information pertaining to genes and proteins involved in primary immunodeficiency diseases along with other relevant information about protein–protein interactions, mouse studies and microarray gene-expression profiles in various organs and cells of the immune system. RAPID also hosts a tool, mutation viewer, to predict deleterious and novel mutations and also to obtain mutation-based 3D structures for PID genes. Thus, information contained in this database should help physicians and other biomedical investigators to further investigate the role of these molecules in PID

Proteomics Databases and Websites

Information avalanche (overload or expansion) in various scientific fields is a novel issue turned out by a number of factors considered necessary to facilitate their record and registration. Though, the biological science and its diverse fields like proteomics are not immune of this event and even may be as the event’s herald. On the other hand, time as the most valued anxiety of human has encountered a huge mass of information. Therefore, in order to maintain access and ease the understanding of information in several fields some emprises have been prepared. Bioinformatics is an upshot of this anxiety and emprise. Interestingly, proteomics through studying proteins collection in alive things has covered a great portion of bioinformatics. Consequently, a noteworthy outlook on proteomics related databases (DBs) and websites not only can help investigators to face the upcoming archive of databases but also estimate the volume of the needed facilitates. Furthermore, enrichment of the DBs or related websites must be the priority of researchers. Herein, by covering the major proteomics related databases and websites, we have presented a comprehensive classification to simplify and clarify their understanding and applications

Improving anti-cancer therapies through a better identification and characterization of non-canonical MHC-I associated peptides

Increasing evidence of non-canonical protein translation has sparked interest in their identification and characterization for use in immunotherapy. In addition, recent studies on the repertoire of major histocompatibility complex class I (MHC-I) associated peptides (MAPs or immunopeptidome), have suggested that MAPs derived from these translations are potential targets for cancer immunotherapy. Therefore, the aim of this study was to assess the impact of these MAPs in cancer by developing methods to facilitate their identification and their validation as potential targets for immunotherapy. To facilitate the identification of non-canonical proteins, we developed Ribo-db, a proteogenomic approach that combines RNA sequencing, ribosome profiling and mass spectrometry. This approach enables the generation of specific databases aimed at including protein diversity. The use of Ribo-db to analyze diffuse large B-cell lymphoma (DLBCL) samples revealed that approximately 10% of MAPs were derived from non-canonical proteins. These proteins had distinct properties compared to those derived from canonical proteins. They had shorter lengths and lower stability, but greater efficiency in generating MAPs. Importantly, we found limited overlap between the non-canonical proteins detected in the immunopeptidome and those detected in the whole proteome suggesting the existence of two distinct non-canonical protein repertoires. Knowing that non-canonical MAPs can be effective targets for cancer immunotherapy, we developed BamQuery, a tool to assess their expression in tissues to determine whether they can be used in a vaccine. BamQuery aims to predict the probability of MHC-I presentation of each peptide in different tissues based on its RNA expression. Using BamQuery, we found that previously identified tumor antigens (TA) would be highly expressed in healthy tissues, making them poor candidates for immunotherapy. In addition, we also identified highly potential immunotherapeutic targets in DLBCL that were derived from non-canonical translations. These targets showed promising as they were poorly expressed in normal tissues but highly expressed and shared in tumor samples. Thus, BamQuery proved to be a useful tool for identifying and prioritizing potential immunotherapeutic targets. Overall, our research indicated that non-canonical regions of the genome increase the diversity of MAPs that can be recognized by T cells. Furthermore, the expression of MAPs in tissues can be used as a predictor of their presentation to MHC I to identify reliable targets for immunotherapy, for which BamQuery is an effective tool.Les preuves de plus en plus nombreuses de la traduction des protéines non canonique ont suscité l'intérêt pour leur identification et leur caractérisation en vue de leur utilisation dans les immunothérapies. En outre, des études récentes sur le répertoire des peptides associés au complexe majeur d'histocompatibilité de classe I (CMH-I, connus sous le nom de MAPs ou immunopeptidome), ont suggéré que les MAPs dérivés de ces traductions sont des cibles potentielles pour l'immunothérapie du cancer. L'objectif de cette étude était donc d'évaluer l'impact de ces MAP dans le cancer en développant des méthodes pour faciliter leur identification et leur validation en tant que cibles potentielles pour l'immunothérapie. Afin de faciliter l'identification des protéines non canoniques, nous avons développé Ribodb, une approche protéogénomique qui combine le séquençage de l'ARN, le profilage ribosomal et la spectrométrie de masse. Cette approche permet de générer des bases de données spécifiques visant à inclure la diversité des protéines. Notre analyse avec Ribo-db d'échantillons de lymphome diffus à grandes cellules B (DLBCL) a révélé qu'environ 10% des MAP étaient dérivés de protéines non canoniques. Ces protéines avaient des propriétés distinctes par rapport à celles dérivées de protéines canoniques. Elles étaient plus courtes et avaient une stabilité plus faible, mais une plus grande efficacité dans la génération de MAPs. Fait important, nous avons constaté un chevauchement limité entre les protéines non canoniques détectées dans l'immunopeptidome et celles détectées dans le proteome entier, ce qui suggère l'existence de deux répertoires distincts de protéines non canoniques. Sachant que les MAP non canoniques peuvent être des cibles efficaces pour l'immunothérapie du cancer, nous avons développé BamQuery, un outil permettant d'évaluer leur expression dans les tissus afin de déterminer s'ils peuvent être utilisés dans un vaccin. BamQuery vise à prédire la probabilité de présentation au CMH-I de chaque MAP dans différents tissus sur la base de son expression ARN. En utilisant BamQuery, nous avons découvert que des antigènes tumoraux (TA) précédemment identifiés seraient fortement exprimés dans les tissus sains, ce qui en fait de mauvais candidats pour l'immunothérapie. En outre, nous avons également ii identifié des cibles immunothérapeutiques très potentielles dans DLBCL qui étaient dérivées de traductions non canoniques. Ces cibles se sont révélées prometteuses car elles étaient peu exprimées dans les tissus normaux mais fortement exprimées et partagées dans les échantillons tumoraux. Ainsi, BamQuery s'est avéré être un outil utile pour identifier et hiérarchiser les cibles immunothérapeutiques potentielles. Dans l'ensemble, nos recherches ont indiqué que les régions non canonique du génome augmentent la diversité des MAPs qui peuvent être reconnues par les cellules T. De plus, l'expression des MAPs dans les tissus peut être utilisée comme un prédicteur de leur présentation au CMH I afin d'identifier des cibles fiables pour l'immunothérapie, ce pour quoi BamQuery est un outil efficace

Understanding the response to endurance exercise using a systems biology approach: combining blood metabolomics, transcriptomics and miRNomics in horses

BACKGROUND: Endurance exercise in horses requires adaptive processes involving physiological, biochemical, and cognitive-behavioral responses in an attempt to regain homeostasis. We hypothesized that the identification of the relationships between blood metabolome, transcriptome, and miRNome during endurance exercise in horses could provide significant insights into the molecular response to endurance exercise. For this reason, the serum metabolome and whole-blood transcriptome and miRNome data were obtained from ten horses before and after a 160 km endurance competition.[br/] RESULTS: We obtained a global regulatory network based on 11 unique metabolites, 263 metabolic genes and 5 miRNAs whose expression was significantly altered at T1 (post- endurance competition) relative to T0 (baseline, pre-endurance competition). This network provided new insights into the cross talk between the distinct molecular pathways (e.g. energy and oxygen sensing, oxidative stress, and inflammation) that were not detectable when analyzing single metabolites or transcripts alone. Single metabolites and transcripts were carrying out multiple roles and thus sharing several biochemical pathways. Using a regulatory impact factor metric analysis, this regulatory network was further confirmed at the transcription factor and miRNA levels. In an extended cohort of 31 independent animals, multiple factor analysis confirmed the strong associations between lactate, methylene derivatives, miR-21-5p, miR-16-5p, let-7 family and genes that coded proteins involved in metabolic reactions primarily related to energy, ubiquitin proteasome and lipopolysaccharide immune responses after the endurance competition. Multiple factor analysis also identified potential biomarkers at T0 for an increased likelihood for failure to finish an endurance competition.[br/] CONCLUSIONS: To the best of our knowledge, the present study is the first to provide a comprehensive and integrated overview of the metabolome, transcriptome, and miRNome co-regulatory networks that may have a key role in regulating the metabolic and immune response to endurance exercise in horses

Die Integration von Multiskalen- und Multi-Omik-Daten zur Erforschung von Wirt-Pathogen-Interaktionen am Beispiel von pathogenen Pilzen

The ongoing development and improvement of novel measurement techniques for scientific research result in a huge amount of available data coming from hetero- geneous sources. Amongst others, these sources comprise diverse temporal and spatial scales including different omics levels. The integration of such multiscale and multi-omics data enables a comprehensive understanding of the complexity and dynamics of biological systems and their processes. However, due to the biologically and methodically induced data heterogeneity, the integration process is a well-known challenge in nowadays life science. Applying several computational integration approaches, the present doctoral thesis aimed at gaining new insights into the field of infection biology regarding host- pathogen interactions. In this context, the focus was on fungal pathogens causing a variety of local and systemic infections. Based on current examples of research, on the one hand, several well-established approaches for the analysis of multiscale and multi- omics data have been presented. On the other hand, the novel ModuleDiscoverer approach was introduced to identify regulatory modules in protein-protein interac- tion networks. It has been shown that ModuleDiscoverer effectively supports the integration of multi-omics data and, in addition, allows the detection of potential key factors that cannot be detected by other classical approaches. This thesis provides deeper insights into the complex relationships and dynamics of biological systems and, thus, represents an important contribution to the investigation of host-pathogen interactions. Due to the interactions complexity and the limitations of the currently available knowledge databases as well as the bioinformatic tools, further research is necessary to gain a comprehensive understanding of the complexity of biological systems

Identifying antimicrobial peptides in genomes using machine learning

Legana Fingerhut used machine learning to improve predictions of antimicrobial peptides (AMPs) from protein sequences. Her associated framework was the first to specifically address the problem of identifying AMPs from whole-genome data. Her work leads to improved workflows for identifying novel AMPs which advances our understanding of the innate immune system

A bioinformatics potpourri

© 2018 The Author(s). The 16th International Conference on Bioinformatics (InCoB) was held at Tsinghua University, Shenzhen from September 20 to 22, 2017. The annual conference of the Asia-Pacific Bioinformatics Network featured six keynotes, two invited talks, a panel discussion on big data driven bioinformatics and precision medicine, and 66 oral presentations of accepted research articles or posters. Fifty-seven articles comprising a topic assortment of algorithms, biomolecular networks, cancer and disease informatics, drug-target interactions and drug efficacy, gene regulation and expression, imaging, immunoinformatics, metagenomics, next generation sequencing for genomics and transcriptomics, ontologies, post-translational modification, and structural bioinformatics are the subject of this editorial for the InCoB2017 supplement issues in BMC Genomics, BMC Bioinformatics, BMC Systems Biology and BMC Medical Genomics. New Delhi will be the location of InCoB2018, scheduled for September 26-28, 2018