Data-driven identification of inherent features of eukaryotic stress-responsive genes

dc.contributor.authorLatorre Domenech, Pablo
dc.contributor.authorBöttcher, René
dc.contributor.authorNadal Ribelles, Mariona
dc.contributor.authorLi, Constance H.
dc.contributor.authorSolé Serra, Carme
dc.contributor.authorMartínez Cebrián, Gerard
dc.contributor.authorBoutros, Paul C.
dc.contributor.authorPosas, Francesc
dc.contributor.authorNadal Clanchet, Eulàlia De
dc.date.accessioned2025-04-22T09:24:35Z
dc.date.available2025-04-22T09:24:35Z
dc.date.issued2022-01-13
dc.date.updated2025-04-16T11:34:50Z
dc.description.abstractLiving organisms are continuously challenged by changes in their environment that can propagate to stresses at the cellular level, such as rapid changes in osmolarity or oxygen tension. To survive these sudden changes, cells have developed stress-responsive mechanisms that tune cellular processes. The response of Saccharomyces cerevisiae to osmostress includes a massive reprogramming of gene expression. Identifying the inherent features of stress-responsive genes is of significant interest for understanding the basic principles underlying the rewiring of gene expression upon stress. Here, we generated a comprehensive catalog of osmostress-responsive genes from 5 independent RNA-seq experiments. We explored 30 features of yeast genes and found that 25 (83%) were distinct in osmostress-responsive genes. We then identified 13 non-redundant minimal osmostress gene traits and used statistical modeling to rank the most stress-predictive features. Intriguingly, the most relevant features of osmostress-responsive genes are the number of transcription factors targeting them and gene conservation. Using data on HeLa samples, we showed that the same features that define yeast osmostress-responsive genes can predict osmostress-responsive genes in humans, but with changes in the rank-ordering of feature-importance. Our study provides a holistic understanding of the basic principles of the regulation of stress-responsive gene expression across eukaryotes.
dc.format.extent19 p.
dc.format.mimetypeapplication/pdf
dc.identifier.idimarina6545167
dc.identifier.issn2631-9268
dc.identifier.pmid35265837
dc.identifier.urihttps://hdl.handle.net/2445/220511
dc.language.isoeng
dc.publisherOxford University Press
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1093/nargab/lqac018
dc.relation.ispartofNar Genom Bioinform, 2022, vol. 4, num. 1
dc.relation.urihttps://doi.org/10.1093/nargab/lqac018
dc.rightscc-by-nc (c) Latorre Domenech, Pablo et al., 2022
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/es/*
dc.sourceArticles publicats en revistes (Institut de Recerca Biomèdica (IRB Barcelona))
dc.subject.classificationCèl·lules eucariotes
dc.subject.classificationEstrès (Fisiologia)
dc.subject.classificationExpressió gènica
dc.subject.otherEukaryotic cells
dc.subject.otherStress (Physiology)
dc.subject.otherGene expression
dc.titleData-driven identification of inherent features of eukaryotic stress-responsive genes
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion

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