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Ivanov S, Lagunin A, Filimonov D, Tarasova O. Network-Based Analysis of OMICs Data to Understand the HIV-Host Interaction. Front Microbiol 2020; 11:1314. [PMID: 32625189 PMCID: PMC7311653 DOI: 10.3389/fmicb.2020.01314] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 05/25/2020] [Indexed: 12/22/2022] Open
Abstract
The interaction of human immunodeficiency virus with human cells is responsible for all stages of the viral life cycle, from the infection of CD4+ cells to reverse transcription, integration, and the assembly of new viral particles. To date, a large amount of OMICs data as well as information from functional genomics screenings regarding the HIV–host interaction has been accumulated in the literature and in public databases. We processed databases containing HIV–host interactions and found 2910 HIV-1-human protein-protein interactions, mostly related to viral group M subtype B, 137 interactions between human and HIV-1 coding and non-coding RNAs, essential for viral lifecycle and cell defense mechanisms, 232 transcriptomics, 27 proteomics, and 34 epigenomics HIV-related experiments. Numerous studies regarding network-based analysis of corresponding OMICs data have been published in recent years. We overview various types of molecular networks, which can be created using OMICs data, including HIV–human protein–protein interaction networks, co-expression networks, gene regulatory and signaling networks, and approaches for the analysis of their topology and dynamics. The network-based analysis can be used to determine the critical pathways and key proteins involved in the HIV life cycle, cellular and immune responses to infection, viral escape from host defense mechanisms, and mechanisms mediating different susceptibility of humans to infection. The proteins and pathways identified in these studies represent a basis for developing new anti-HIV therapeutic strategies such as new drugs preventing infection of CD4+ cells and viral replication, effective vaccines, “shock and kill” and “block and lock” approaches to cure latent infection.
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Affiliation(s)
- Sergey Ivanov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia.,Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Alexey Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia.,Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Dmitry Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Olga Tarasova
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
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Dirmeier S, Dächert C, van Hemert M, Tas A, Ogando NS, van Kuppeveld F, Bartenschlager R, Kaderali L, Binder M, Beerenwinkel N. Host factor prioritization for pan-viral genetic perturbation screens using random intercept models and network propagation. PLoS Comput Biol 2020; 16:e1007587. [PMID: 32040506 PMCID: PMC7034926 DOI: 10.1371/journal.pcbi.1007587] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 02/21/2020] [Accepted: 12/05/2019] [Indexed: 12/16/2022] Open
Abstract
Genetic perturbation screens using RNA interference (RNAi) have been conducted successfully to identify host factors that are essential for the life cycle of bacteria or viruses. So far, most published studies identified host factors primarily for single pathogens. Furthermore, often only a small subset of genes, e.g., genes encoding kinases, have been targeted. Identification of host factors on a pan-pathogen level, i.e., genes that are crucial for the replication of a diverse group of pathogens has received relatively little attention, despite the fact that such common host factors would be highly relevant, for instance, for devising broad-spectrum anti-pathogenic drugs. Here, we present a novel two-stage procedure for the identification of host factors involved in the replication of different viruses using a combination of random effects models and Markov random walks on a functional interaction network. We first infer candidate genes by jointly analyzing multiple perturbations screens while at the same time adjusting for high variance inherent in these screens. Subsequently the inferred estimates are spread across a network of functional interactions thereby allowing for the analysis of missing genes in the biological studies, smoothing the effect sizes of previously found host factors, and considering a priori pathway information defined over edges of the network. We applied the procedure to RNAi screening data of four different positive-sense single-stranded RNA viruses, Hepatitis C virus, Chikungunya virus, Dengue virus and Severe acute respiratory syndrome coronavirus, and detected novel host factors, including UBC, PLCG1, and DYRK1B, which are predicted to significantly impact the replication cycles of these viruses. We validated the detected host factors experimentally using pharmacological inhibition and an additional siRNA screen and found that some of the predicted host factors indeed influence the replication of these pathogens.
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Affiliation(s)
- Simon Dirmeier
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Christopher Dächert
- Research Group “Dynamics of Early Viral Infection and the Innate Antiviral Response” (division F170), German Cancer Research Center, Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Martijn van Hemert
- Department of Medical Microbiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ali Tas
- Department of Medical Microbiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Natacha S. Ogando
- Department of Medical Microbiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Frank van Kuppeveld
- Virology Division, Department of Infectious Diseases and Immunology, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Ralf Bartenschlager
- Department for Infectious Diseases, Molecular Virology, Heidelberg University, Heidelberg, Germany
- Division Virus-Associated Carcinogenesis, German Cancer Research Center, Heidelberg, Germany
| | - Lars Kaderali
- University Medicine Greifswald, Institute of Bioinformatics, Greifswald, Germany
| | - Marco Binder
- Research Group “Dynamics of Early Viral Infection and the Innate Antiviral Response” (division F170), German Cancer Research Center, Heidelberg, Germany
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Basel, Switzerland
- * E-mail:
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Robinson S, Nevalainen J, Pinna G, Campalans A, Radicella JP, Guyon L. Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields. Bioinformatics 2018; 33:i170-i179. [PMID: 28881978 PMCID: PMC5870666 DOI: 10.1093/bioinformatics/btx244] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Motivation Incorporating gene interaction data into the identification of ‘hit’ genes in genomic experiments is a well-established approach leveraging the ‘guilt by association’ assumption to obtain a network based hit list of functionally related genes. We aim to develop a method to allow for multivariate gene scores and multiple hit labels in order to extend the analysis of genomic screening data within such an approach. Results We propose a Markov random field-based method to achieve our aim and show that the particular advantages of our method compared with those currently used lead to new insights in previously analysed data as well as for our own motivating data. Our method additionally achieves the best performance in an independent simulation experiment. The real data applications we consider comprise of a survival analysis and differential expression experiment and a cell-based RNA interference functional screen. Availability and implementation We provide all of the data and code related to the results in the paper. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sean Robinson
- CEA, BIG, Biologie à Grande Echelle, F-38054 Grenoble, France.,Université Grenoble-Alpes, F-38000 Grenoble, France.,INSERM, U1038, F-38054 Grenoble, France.,Department of Mathematics and Statistics, University of Turku, Turku, Finland.,Industrial Biotechnology, VTT Technical Research Centre of Finland, Turku, Finland
| | - Jaakko Nevalainen
- Department of Mathematics and Statistics, University of Turku, Turku, Finland.,School of Health Sciences, University of Tampere, Tampere, Finland
| | - Guillaume Pinna
- Plateforme ARN Interférence (PArI), DSV/ISVFJ/SBIGEM/UMR 9198 I2BC, CEA Saclay, Gif-sur-Yvette, France
| | - Anna Campalans
- Institute of Molecular and Cellular Radiobiology, CEA, Fontenay-aux-Roses, France.,INSERM, U967, Fontenay-aux-Roses, France.,Université Paris Diderot, U967, Fontenay-aux-Roses, France.,Université Paris Sud, U967, Fontenay-aux-Roses, France
| | - J Pablo Radicella
- Institute of Molecular and Cellular Radiobiology, CEA, Fontenay-aux-Roses, France.,INSERM, U967, Fontenay-aux-Roses, France.,Université Paris Diderot, U967, Fontenay-aux-Roses, France.,Université Paris Sud, U967, Fontenay-aux-Roses, France
| | - Laurent Guyon
- CEA, BIG, Biologie à Grande Echelle, F-38054 Grenoble, France.,Université Grenoble-Alpes, F-38000 Grenoble, France.,INSERM, U1038, F-38054 Grenoble, France
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Rioualen C, Da Costa Q, Chetrit B, Charafe-Jauffret E, Ginestier C, Bidaut G. HTS-Net: An integrated regulome-interactome approach for establishing network regulation models in high-throughput screenings. PLoS One 2017; 12:e0185400. [PMID: 28949986 PMCID: PMC5614607 DOI: 10.1371/journal.pone.0185400] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 09/12/2017] [Indexed: 12/28/2022] Open
Abstract
High-throughput RNAi screenings (HTS) allow quantifying the impact of the deletion of each gene in any particular function, from virus-host interactions to cell differentiation. However, there has been less development for functional analysis tools dedicated to RNAi analyses. HTS-Net, a network-based analysis program, was developed to identify gene regulatory modules impacted in high-throughput screenings, by integrating transcription factors-target genes interaction data (regulome) and protein-protein interaction networks (interactome) on top of screening z-scores. HTS-Net produces exhaustive HTML reports for results navigation and exploration. HTS-Net is a new pipeline for RNA interference screening analyses that proves better performance than simple gene rankings by z-scores, by re-prioritizing genes and replacing them in their biological context, as shown by the three studies that we reanalyzed. Formatted input data for the three studied datasets, source code and web site for testing the system are available from the companion web site at http://htsnet.marseille.inserm.fr/. We also compared our program with existing algorithms (CARD and hotnet2).
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Affiliation(s)
- Claire Rioualen
- Aix-Marseille Univ, Marseille, France
- Inserm, U1068, Centre de Recherche en Cancérologie de Marseille, Marseille, France
- Institut Paoli-Calmettes, Marseille, France
- CNRS, UMR7258, Centre de Recherche en Cancérologie de Marseille, Marseille, France
| | - Quentin Da Costa
- Aix-Marseille Univ, Marseille, France
- Inserm, U1068, Centre de Recherche en Cancérologie de Marseille, Marseille, France
- Institut Paoli-Calmettes, Marseille, France
- CNRS, UMR7258, Centre de Recherche en Cancérologie de Marseille, Marseille, France
| | - Bernard Chetrit
- Aix-Marseille Univ, Marseille, France
- Inserm, U1068, Centre de Recherche en Cancérologie de Marseille, Marseille, France
- Institut Paoli-Calmettes, Marseille, France
- CNRS, UMR7258, Centre de Recherche en Cancérologie de Marseille, Marseille, France
| | - Emmanuelle Charafe-Jauffret
- Aix-Marseille Univ, Marseille, France
- Inserm, U1068, Centre de Recherche en Cancérologie de Marseille, Marseille, France
- Institut Paoli-Calmettes, Marseille, France
- CNRS, UMR7258, Centre de Recherche en Cancérologie de Marseille, Marseille, France
| | - Christophe Ginestier
- Aix-Marseille Univ, Marseille, France
- Inserm, U1068, Centre de Recherche en Cancérologie de Marseille, Marseille, France
- Institut Paoli-Calmettes, Marseille, France
- CNRS, UMR7258, Centre de Recherche en Cancérologie de Marseille, Marseille, France
| | - Ghislain Bidaut
- Aix-Marseille Univ, Marseille, France
- Inserm, U1068, Centre de Recherche en Cancérologie de Marseille, Marseille, France
- Institut Paoli-Calmettes, Marseille, France
- CNRS, UMR7258, Centre de Recherche en Cancérologie de Marseille, Marseille, France
- * E-mail:
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Gambling with Flu: "All in" to Maximize Reward. Cell Host Microbe 2016; 18:643-5. [PMID: 26651939 DOI: 10.1016/j.chom.2015.11.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
In this issue of Cell Host & Microbe, Tripathi et al. (2015) report an in-depth meta-analysis of eight influenza virus siRNA screens combined with viral-host protein interactome data. The integration of the different omics datasets highlights candidate genes and pathways for further investigation and potential therapeutic targeting in the future.
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Colpitts CC, El-Saghire H, Pochet N, Schuster C, Baumert TF. High-throughput approaches to unravel hepatitis C virus-host interactions. Virus Res 2015; 218:18-24. [PMID: 26410623 DOI: 10.1016/j.virusres.2015.09.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Revised: 09/18/2015] [Accepted: 09/22/2015] [Indexed: 02/07/2023]
Abstract
Hepatitis C virus (HCV) remains a major global health burden, with more than 130 million individuals chronically infected and at risk for the development of hepatocellular carcinoma (HCC). The recent clinical licensing of direct-acting antivirals enables viral cure. However, limited access to therapy and treatment failure in patient subgroups warrants a continuing effort to develop complementary antiviral strategies. Furthermore, once fibrosis is established, curing HCV infection does not eliminate the risk for HCC. High-throughput approaches and screens have enabled the investigation of virus-host interactions on a genome-wide scale. Gain- and loss-of-function screens have identified essential host-dependency factors in the HCV viral life cycle, such as host cell entry factors or regulatory factors for viral replication and assembly. Network analyses of systems-scale data sets provided a comprehensive view of the cellular state following HCV infection, thus improving our understanding of the virus-induced responses of the target cell. Interactome, metabolomics and gene expression studies identified dysregulated cellular processes potentially contributing to HCV pathogenesis and HCC. Drug screens using chemical libraries led to the discovery of novel antivirals. Here, we review the contribution of high-throughput approaches for the investigation of virus-host interactions, viral pathogenesis and drug discovery.
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Affiliation(s)
- Che C Colpitts
- Inserm, U1110, Institut de Recherche sur les Maladies Virales et Hépatiques, 67000 Strasbourg, France; Université de Strasbourg, 67000 Strasbourg, France
| | - Hussein El-Saghire
- Inserm, U1110, Institut de Recherche sur les Maladies Virales et Hépatiques, 67000 Strasbourg, France; Université de Strasbourg, 67000 Strasbourg, France
| | - Nathalie Pochet
- Program in Translational NeuroPsychiatric Genomics, Brigham and Women's Hospital, Harvard Medical School, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Catherine Schuster
- Inserm, U1110, Institut de Recherche sur les Maladies Virales et Hépatiques, 67000 Strasbourg, France; Université de Strasbourg, 67000 Strasbourg, France
| | - Thomas F Baumert
- Inserm, U1110, Institut de Recherche sur les Maladies Virales et Hépatiques, 67000 Strasbourg, France; Université de Strasbourg, 67000 Strasbourg, France; Institut Hospitalo-Universitaire, PôleHépato-digestif, HôpitauxUniversitaires de Strasbourg, 67000 Strasbourg, France.
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