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Linking cell dynamics with gene coexpression networks to characterize key events in chronic virus infections

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dc.contributor.author Pedragosa Marín, Mireia, 1988-
dc.contributor.author Riera Domínguez, María Graciela, 1987-
dc.contributor.author Casella, Valentina, 1991-
dc.contributor.author Esteve-Codina, Anna
dc.contributor.author Steuerman, Yael
dc.contributor.author Seth, Celina
dc.contributor.author Bocharov, Gennady A.
dc.contributor.author Heath, Simon
dc.contributor.author Gat-Viks, Irit
dc.contributor.author Argilaguet Marqués, Jordi, 1977-
dc.contributor.author Meyerhans, Andreas
dc.date.accessioned 2019-07-30T07:42:39Z
dc.date.available 2019-07-30T07:42:39Z
dc.date.issued 2019
dc.identifier.citation Pedragosa M, Riera G, Casella V, Esteve-Codina A, Steuerman Y, Seth C et al. Linking cell dynamics with gene coexpression networks to characterize key events in chronic virus infections. Front Immunol. 2019 May 3;10:1002. DOI: 10.3389/fimmu.2019.01002
dc.identifier.issn 1664-3224
dc.identifier.uri http://hdl.handle.net/10230/42202
dc.description.abstract The host immune response against infection requires the coordinated action of many diverse cell subsets that dynamically adapt to a pathogen threat. Due to the complexity of such a response, most immunological studies have focused on a few genes, proteins, or cell types. With the development of "omic"-technologies and computational analysis methods, attempts to analyze and understand complex system dynamics are now feasible. However, the decomposition of transcriptomic data sets generated from complete organs remains a major challenge. Here, we combined Weighted Gene Coexpression Network Analysis (WGCNA) and Digital Cell Quantifier (DCQ) to analyze time-resolved mouse splenic transcriptomes in acute and chronic Lymphocytic Choriomeningitis Virus (LCMV) infections. This enabled us to generate hypotheses about complex immune functioning after a virus-induced perturbation. This strategy was validated by successfully predicting several known immune phenomena, such as effector cytotoxic T lymphocyte (CTL) expansion and exhaustion. Furthermore, we predicted and subsequently verified experimentally macrophage-CD8 T cell cooperativity and the participation of virus-specific CD8+ T cells with an early effector transcriptome profile in the host adaptation to chronic infection. Thus, the linking of gene expression changes with immune cell kinetics provides novel insights into the complex immune processes within infected tissues.
dc.description.sponsorship This work is supported by a grant from the Spanish Ministry of Economy, Industry and Competitiveness and FEDER grant no. SAF2016-75505-R (AEI/MINEICO/FEDER, UE) and the María de Maeztu Programme for Units of Excellence in R&D (MDM-2014-0370). GB and AM are also supported by the Russian Science Foundation (grant 18-11-00171). AE-C and SH are also supported by Instituto de Salud Carlos III (ISCIII) grant from the Spanish Ministry of Economy, Industry and Competitiveness and FEDER grant no. PT17/0009/0019. IG-V is supported by European Research Council (637885)
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Frontiers Media
dc.relation.ispartof Frontiers in Immunology. 2019 May 3;10:1002
dc.rights © 2019 Pedragosa, Riera, Casella, Esteve-Codina, Steuerman, Seth, Bocharov, Heath, Gat-Viks, Argilaguet andMeyerhans. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY)
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.subject.other Genètica
dc.subject.other Virus
dc.subject.other Infecció
dc.subject.other Malalties cròniques
dc.title Linking cell dynamics with gene coexpression networks to characterize key events in chronic virus infections
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.3389/fimmu.2019.01002
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/637885
dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/SAF2016-75505-R
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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