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Efficient escape from local optima in a highly rugged fitness landscape by evolving RNA virus populations

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Efficient escape from local optima in a highly rugged fitness landscape by evolving RNA virus populations

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dc.contributor.author Cervera-Benet, Héctor es_ES
dc.contributor.author Lalic, Jasna es_ES
dc.contributor.author Elena Fito, Santiago Fco es_ES
dc.date.accessioned 2017-06-21T07:25:34Z
dc.date.available 2017-06-21T07:25:34Z
dc.date.issued 2016-07-17
dc.identifier.issn 0962-8452
dc.identifier.uri http://hdl.handle.net/10251/83328
dc.description.abstract [EN] Predicting viral evolution has proven to be a particularly difficult task, mainly owing to our incomplete knowledge of some of the fundamental principles that drive it. Recently, valuable information has been provided about mutation and recombination rates, the role of genetic drift and the distribution of mutational, epistatic and pleiotropic fitness effects. However, information about the topography of virus' adaptive landscapes is still scarce, and to our knowledge no data has been reported so far on how its ruggedness may condition virus' evolvability. Here, we show that populations of an RNA virus move efficiently on a rugged landscape and scape from the basin of attraction of a local optimum. We have evolved a set of Tobacco etch virus genotypes located at increasing distances from a local adaptive optimum in a highly rugged fitness landscape, and we observed that few evolved lineages remained trapped in the local optimum, while many others explored distant regions of the landscape. Most of the diversification in fitness among the evolved lineages was explained by adaptation, while historical contingency and chance events contribution was less important. Our results demonstrate that the ruggedness of adaptive landscapes is not an impediment for RNA viruses to efficiently explore remote parts of it. es_ES
dc.description.sponsorship This project was funded by grant nos. BFU2012-30805 and BFU2015-65037P from the Spanish Ministry of Economy and Competitiveness (MINECO), PROMETEOII/2014/021 from Generalitat Valenciana and EvoEvo (ICT610427) from the European Commission 7th Framework Program to S.F.E. H.C. was supported by contract BES2013-065595 from MINECO. J.L. was supported by a JAE-pre contract from CSIC. en_EN
dc.language Inglés es_ES
dc.publisher Royal Society, The es_ES
dc.relation MINECO/BFU2012-30805 es_ES
dc.relation GV/PROMETEO II 2014/021 es_ES
dc.relation MINECO/BFU2015-65037P
dc.relation MINECO/BES 2013-065595
dc.relation.ispartof Proceedings of the Royal Society B: Biological Sciences es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Adaptive landscapes es_ES
dc.subject Adaptation es_ES
dc.subject Contingency es_ES
dc.subject Experimental evolution es_ES
dc.subject Stochastic scape es_ES
dc.subject Virus evolution es_ES
dc.title Efficient escape from local optima in a highly rugged fitness landscape by evolving RNA virus populations es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1098/rspb.2016.0984
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/610427/EU es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario Mixto de Biología Molecular y Celular de Plantas - Institut Universitari Mixt de Biologia Molecular i Cel·lular de Plantes es_ES
dc.description.bibliographicCitation Cervera-Benet, H.; Lalic, J.; Elena Fito, SF. (2016). Efficient escape from local optima in a highly rugged fitness landscape by evolving RNA virus populations. Proceedings of the Royal Society B: Biological Sciences. 283:1-8. doi:10.1098/rspb.2016.0984 es_ES
dc.description.accrualMethod Senia es_ES
dc.relation.publisherversion http://doi.org/10.1098/rspb.2016.0984 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 8 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 283 es_ES
dc.relation.senia 323448 es_ES
dc.identifier.eissn 1471-2954
dc.identifier.pmid 27534955 en_EN
dc.identifier.pmcid PMC5013765 en_EN
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Economía y Competitividad (MINECO)
dc.contributor.funder Generalitat Valenciana (GV)


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