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      Vaccinia virus hijacks ESCRT-mediated multivesicular body formation for virus egress

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          Abstract

          Poxvirus extracellular virions are critical for virus virulence. This study shows that multivesicular bodies serve as a major cellular source of membrane for their formation and spread.

          Abstract

          Poxvirus egress is a complex process whereby cytoplasmic single membrane–bound virions are wrapped in a cell-derived double membrane. These triple-membrane particles, termed intracellular enveloped virions (IEVs), are released from infected cells by fusion. Whereas the wrapping double membrane is thought to be derived from virus-modified trans-Golgi or early endosomal cisternae, the cellular factors that regulate virus wrapping remain largely undefined. To identify cell factors required for this process the prototypic poxvirus, vaccinia virus (VACV), was subjected to an RNAi screen directed against cellular membrane-trafficking proteins. Focusing on the endosomal sorting complexes required for transport (ESCRT), we demonstrate that ESCRT-III and VPS4 are required for packaging of virus into multivesicular bodies (MVBs). EM-based characterization of MVB-IEVs showed that they account for half of IEV production indicating that MVBs are a second major source of VACV wrapping membrane. These data support a model whereby, in addition to cisternae-based wrapping, VACV hijacks ESCRT-mediated MVB formation to facilitate virus egress and spread.

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          Most cited references55

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          STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

          Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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            Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

            DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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              Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

              Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
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                Author and article information

                Journal
                Life Sci Alliance
                Life Sci Alliance
                lsa
                lsa
                Life Science Alliance
                Life Science Alliance LLC
                2575-1077
                18 June 2021
                August 2021
                18 June 2021
                : 4
                : 8
                : e202000910
                Affiliations
                [1 ]Medical Research Council-Laboratory for Molecular Cell Biology, University College London, London, UK
                [2 ]Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK
                [3 ]Host-Toxoplasma Interaction Laboratory, The Francis Crick Institute, London, UK
                [4 ]Department of Infectious Diseases, King’s College London, London, UK
                [5 ]Department of Biochemistry, University of Utah, Salt Lake City, UT, USA
                Author notes
                Author information
                https://orcid.org/0000-0002-4745-0477
                https://orcid.org/0000-0003-0678-6510
                https://orcid.org/0000-0003-1466-9541
                Article
                LSA-2020-00910
                10.26508/lsa.202000910
                8321658
                34145027
                b168f54e-4d6a-4d13-8d99-2b3d486a895a
                © 2021 Huttunen et al.

                This article is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by/4.0/).

                History
                : 18 September 2020
                : 3 June 2021
                : 4 June 2021
                Funding
                Funded by: MRC Laboratory for Molecular Cell Biology;
                Award ID: MC_UU12018/7
                Award Recipient :
                Funded by: European Research Council;
                Award ID: 649101-UbiProPox
                Award Recipient :
                Funded by: MRC Laboratory for Molecular Cell Biology;
                Award ID: MC_U12266B
                Award Recipient :
                Funded by: Wellcome Trust;
                Award ID: WT102871MA
                Award Recipient :
                Funded by: NIH;
                Award ID: R37 AI 51174
                Award Recipient :
                Funded by: MRC-UCL University Unit;
                Award ID: MC_U12266B
                Award Recipient :
                Funded by: MRC Dementia Platform;
                Award ID: MR/M02492X/1
                Funded by: The Wellcome Trust;
                Funded by: Wellcome Trust Senior Research Fellowship;
                Award ID: 217202/Z/19/Z
                Award Recipient :
                Funded by: Cancer Research UK;
                Award ID: FC001076
                Award Recipient :
                Funded by: UK Medical Research Council;
                Award ID: FC001076
                Award Recipient :
                Funded by: Wellcome Trust;
                Award ID: FC001076
                Award Recipient :
                Categories
                Research Article
                Research Articles
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