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      Network of Interactions between ZIKA Virus Non-Structural Proteins and Human Host Proteins

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          Abstract

          The Zika virus (ZIKV) is a mosquito-borne Flavivirus and can be transmitted through an infected mosquito bite or through human-to-human interaction by sexual activity, blood transfusion, breastfeeding, or perinatal exposure. After the 2015–2016 outbreak in Brazil, a strong link between ZIKV infection and microcephaly emerged. ZIKV specifically targets human neural progenitor cells, suggesting that proteins encoded by ZIKV bind and inactivate host cell proteins, leading to microcephaly. Here, we present a systematic annotation of interactions between human proteins and the seven non-structural ZIKV proteins corresponding to a Brazilian isolate. The interaction network was generated by combining tandem-affinity purification followed by mass spectrometry with yeast two-hybrid screens. We identified 150 human proteins, involved in distinct biological processes, as interactors to ZIKV non-structural proteins. Our interacting network is composed of proteins that have been previously associated with microcephaly in human genetic disorders and/or animal models. Further, we show that the protein inhibitor of activated STAT1 (PIAS1) interacts with NS5 and modulates its stability. This study builds on previously published interacting networks of ZIKV and genes related to autosomal recessive primary microcephaly to generate a catalog of human cellular targets of ZIKV proteins implicated in processes related to microcephaly in humans. Collectively, these data can be used as a resource for future characterization of ZIKV infection biology and help create a basis for the discovery of drugs that may disrupt the interaction and reduce the health damage to the fetus.

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          Most cited references40

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          A comprehensive two-hybrid analysis to explore the yeast protein interactome.

          Protein-protein interactions play crucial roles in the execution of various biological functions. Accordingly, their comprehensive description would contribute considerably to the functional interpretation of fully sequenced genomes, which are flooded with novel genes of unpredictable functions. We previously developed a system to examine two-hybrid interactions in all possible combinations between the approximately 6,000 proteins of the budding yeast Saccharomyces cerevisiae. Here we have completed the comprehensive analysis using this system to identify 4,549 two-hybrid interactions among 3,278 proteins. Unexpectedly, these data do not largely overlap with those obtained by the other project [Uetz, P., et al. (2000) Nature (London) 403, 623-627] and hence have substantially expanded our knowledge on the protein interaction space or interactome of the yeast. Cumulative connection of these binary interactions generates a single huge network linking the vast majority of the proteins. Bioinformatics-aided selection of biologically relevant interactions highlights various intriguing subnetworks. They include, for instance, the one that had successfully foreseen the involvement of a novel protein in spindle pole body function as well as the one that may uncover a hitherto unidentified multiprotein complex potentially participating in the process of vesicular transport. Our data would thus significantly expand and improve the protein interaction map for the exploration of genome functions that eventually leads to thorough understanding of the cell as a molecular system.
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            The CRAPome: a Contaminant Repository for Affinity Purification Mass Spectrometry Data

            Affinity purification coupled with mass spectrometry (AP-MS) is now a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (e.g. proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. While the standard approach is to identify nonspecific interactions using one or more negative controls, most small-scale AP-MS studies do not capture a complete, accurate background protein set. Fortunately, negative controls are largely bait-independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the Contaminant Repository for Affinity Purification (the CRAPome) and describe the use of this resource to score protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely available online at www.crapome.org.
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              Detecting overlapping protein complexes in protein-protein interaction networks.

              We introduce clustering with overlapping neighborhood expansion (ClusterONE), a method for detecting potentially overlapping protein complexes from protein-protein interaction data. ClusterONE-derived complexes for several yeast data sets showed better correspondence with reference complexes in the Munich Information Center for Protein Sequence (MIPS) catalog and complexes derived from the Saccharomyces Genome Database (SGD) than the results of seven popular methods. The results also showed a high extent of functional homogeneity.
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                Author and article information

                Journal
                Cells
                Cells
                cells
                Cells
                MDPI
                2073-4409
                08 January 2020
                January 2020
                : 9
                : 1
                : 153
                Affiliations
                [1 ]Cancer Epidemiology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; golubeva.3@ 123456osu.edu (V.A.G.); thales.cn@ 123456gmail.com (T.C.N.); xueli.li@ 123456moffitt.org (X.L.);
                [2 ]Divisão de Pesquisa Clínica, Instituto Nacional de Câncer, Rio de Janeiro 20230-130, Brazil; gregoriis@ 123456gmail.com (G.d.G.); Kurtz@ 123456inca.gov.br (G.S.-K.)
                [3 ]Departamento de Bioquímica, Instituto de Química, Federal University of Rio de Janeiro, Rio de Janeiro 21941-909, Brazil; rdmesquita@ 123456iq.ufrj.br
                [4 ]Cancer Biology PhD Program, University of South Florida, Tampa, FL 33612, USA
                [5 ]Institute of Biomedical Science, Federal University of Rio de Janeiro, Rio de Janeiro 20230-130, Brazil; ppgarcez@ 123456gmail.com
                [6 ]Proteomics and Metabolomics Core, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; victoria.izumi@ 123456moffitt.org
                [7 ]Chemical Biology and Molecular Medicine Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA; john.koomen@ 123456moffitt.org
                [8 ]Instituto Federal do Rio de Janeiro-IFRJ, Rio de Janeiro 20270-021, Brazil
                Author notes
                [* ]Correspondence: marcelo.carvalho@ 123456ifrj.edu.br (M.A.C.); alvaro.monteiro@ 123456moffitt.org (A.N.A.M.); Tel.: +55-21-2566-7774 (M.A.C.); +813-7456321 (A.N.A.M.)
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0001-5430-3507
                https://orcid.org/0000-0002-1171-1030
                https://orcid.org/0000-0002-9107-1335
                https://orcid.org/0000-0002-1115-8319
                https://orcid.org/0000-0002-7053-0053
                https://orcid.org/0000-0002-8448-4801
                Article
                cells-09-00153
                10.3390/cells9010153
                7016862
                31936331
                925c9d18-b1ce-43c5-a225-82b0c7e693a7
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 03 September 2019
                : 01 January 2020
                Categories
                Article

                zikv,protein–protein interaction,non-structural viral proteins,network

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