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dc.contributor.authorHong, Jiayin
dc.contributor.authorPalme, Julius
dc.contributor.authorHua, Bo
dc.contributor.authorSpringer, Michael
dc.date.accessioned2021-11-11T00:30:06Z
dc.date.available2021-11-11T00:30:06Z
dc.date.issued2021-09
dc.identifier.issn1553-734X
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330541
dc.description.abstractQuantitative traits are measurable phenotypes that show continuous variation over a wide phenotypic range. Enormous effort has recently been put into determining the genetic influences on a variety of quantitative traits with mixed success. We identified a quantitative trait in a tractable model system, the GAL pathway in yeast, which controls the uptake and metabolism of the sugar galactose. GAL pathway activation depends both on galactose concentration and on the concentrations of competing, preferred sugars such as glucose. Natural yeast isolates show substantial variation in the behavior of the pathway. All studied yeast strains exhibit bimodal responses relative to external galactose concentration, i.e. a set of galactose concentrations existed at which both GAL-induced and GAL-repressed subpopulations were observed. However, these concentrations differed in different strains. We built a mechanistic model of the GAL pathway and identified parameters that are plausible candidates for capturing the phenotypic features of a set of strains including standard lab strains, natural variants, and mutants. In silico perturbation of these parameters identified variation in the intracellular galactose sensor, Gal3p, the negative feedback node within the GAL regulatory network, Gal80p, and the hexose transporters, HXT, as the main sources of the bimodal range variation. We were able to switch the phenotype of individual yeast strains in silico by tuning parameters related to these three elements. Determining the basis for these behavioral differences may give insight into how the GAL pathway processes information, and into the evolution of nutrient metabolism preferences in different strains. More generally, our method of identifying the key parameters that explain phenotypic variation in this system should be generally applicable to other quantitative traits.
dc.format.mediumElectronic-eCollection
dc.languageeng
dc.publisherPublic Library of Science (PLoS)
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleComputational analysis of GAL pathway pinpoints mechanisms underlying natural variation.
dc.typeArticle
prism.issueIdentifier9
prism.publicationDate2021
prism.publicationNamePLoS Comput Biol
prism.startingPagee1008691
prism.volume17
dc.identifier.doi10.17863/CAM.77985
dcterms.dateAccepted2021-08-17
rioxxterms.versionofrecord10.1371/journal.pcbi.1008691
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2021-09-27
dc.contributor.orcidHong, Jiayin [0000-0002-2212-558X]
dc.contributor.orcidPalme, Julius [0000-0001-5897-1334]
dc.contributor.orcidHua, Bo [0000-0001-9313-4670]
dc.contributor.orcidSpringer, Michael [0000-0002-3970-6380]
dc.identifier.eissn1553-7358
rioxxterms.typeJournal Article/Review
cam.issuedOnline2021-09-27


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International