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The fitness landscape of the codon space across environments

2018-08-20Preprint Open access
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dc.contributor.authorFragata, Inês
dc.contributor.authorMatuszewski, Sebastian
dc.contributor.authorSchmitz, Mark A.
dc.contributor.authorBataillon, Thomas
dc.contributor.authorJensen, Jeffrey D.
dc.contributor.authorBank, Claudia
dc.date.accessioned2018-09-10T13:43:58Z
dc.date.available2019-08-01T00:30:09Z
dc.date.issued2018-08-20
dc.descriptionThe deposited article version is the Epub Ahead of Print version of the article, posted online 22 August 2018, provided by Biorxiv. It hasn´t been submitted to peer-review.pt_PT
dc.descriptionThis deposit is composed by the main article. The supplementary materials can be accessed through the following link: https://www.biorxiv.org/content/early/2018/06/27/252395.figures-onlypt_PT
dc.description.abstractFitness landscapes map the relationship between genotypes and fitness. However, most fitness landscape studies ignore the genetic architecture imposed by the codon table and thereby neglect the potential role of synonymous mutations. To quantify the fitness effects of synonymous mutations and their potential impact on adaptation on a fitness landscape, we use a new software based on Bayesian Monte Carlo Markov Chain methods and re-estimate selection coefficients of all possible codon mutations across 9 amino acid positions in Saccharomyces cerevisiae Hsp90 across 6 environments. We quantify the distribution of fitness effects of synonymous mutations and show that it is dominated by many mutations of small or no effect and few mutations of larger effect. We then compare the shape of the codon fitness landscape across amino acid positions and environments, and quantify how the consideration of synonymous fitness effects changes the evolutionary dynamics on these fitness landscapes. Together these results highlight a possible role of synonymous mutations in adaptation and indicate the potential mis-inference when they are neglected in fitness landscape studies.pt_PT
dc.description.sponsorshipThis work was supported by Fundação Calouste Gulbenkian and an ERC Starting Grant to JDJ. I. Fragata was supported by a postdoctoral fellowship from FCT (Fundação para a Ciência e a Tecnologia) within the project JPIAMR/0001/2016.pt_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
dc.identifier.citationInês Fragata, Sebastian Matuszweski, Mark A. Schmitz, Thomas Bataillon, Jeffrey D. Jensen, Claudia Bank bioRxiv 252395; doi: https://doi.org/10.1101/252395 Now published in Heredity doi: 10.1038/s41437-018-0125-7pt_PT
dc.identifier.doi10.1038/s41437-018-0125-7pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.7/893
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherNature Publishing Grouppt_PT
dc.relationPREPARE - Predicting the Persistence of Resistance Across Environments
dc.relation.publisherversionhttps://www.nature.com/articles/s41437-018-0125-7pt_PT
dc.subjectgenetic architecturept_PT
dc.subjectBayesian Monte Carlo Markov Chainpt_PT
dc.subjectfitness landscapept_PT
dc.subjectmutationspt_PT
dc.titleThe fitness landscape of the codon space across environmentspt_PT
dc.typepreprint
dspace.entity.typePublication
oaire.awardTitlePREPARE - Predicting the Persistence of Resistance Across Environments
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/JPIAMR%2F0001%2F2016/PT
oaire.citation.endPage33pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleHereditypt_PT
oaire.fundingStream3599-PPCDT
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.embargofctThe deposited article has 12 months of embargo period in terms of access, because of the journal's policies and the publisher's copyright policy, which require a period of 6 months of embargo after the released date of the publication. Once the article hasn't been published yet, the library service has defined an embargo period superior than 6 months for this upload, as a security measure for avoiding irregularities.pt_PT
rcaap.rightsembargoedAccesspt_PT
rcaap.typepreprintpt_PT
relation.isProjectOfPublicationa6719d1e-cf5a-4870-b601-1ce475ba9546
relation.isProjectOfPublication.latestForDiscoverya6719d1e-cf5a-4870-b601-1ce475ba9546

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