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Data from: The genetic architecture of biofilm formation in a clinical isolate of Saccharomyces cerevisiae

Cite this dataset

Granek, Joshua A.; Murray, Debra; Kayıkçı, Ömür; Magwene, Paul M. (2013). Data from: The genetic architecture of biofilm formation in a clinical isolate of Saccharomyces cerevisiae [Dataset]. Dryad.


Biofilms are microbial communities that form on surfaces. They are the primary form of microbial growth in nature and can have detrimental impacts on human health. Some strains of the budding yeast Saccharomyces cerevisiae form colony biofilms, and there is substantial variation in colony architecture between biofilm-forming strains. To identify the genetic basis of biofilm variation, we developed a novel version of quantitative trait locus mapping, which leverages cryptic variation in a clinical isolate of S. cerevisiae. We mapped 13 loci linked to heterogeneity in biofilm architecture and identified the gene most closely associated with each locus. Of these candidate genes, six are members of the cyclic AMP-protein kinase A pathway, an evolutionarily conserved cell signaling network. Principal among these is CYR1, which encodes the enzyme that catalyzes production of cAMP. Through a combination of gene expression measurements, cell signaling assays, and gene overexpression, we determined the functional effects of allelic variation at CYR1. We found that increased pathway activity resulting from protein coding and expression variation of CYR1 enhances the formation of colony biofilms. Four other candidate genes encode kinases and transcription factors that are targets of this pathway. The protein products of several of these genes together regulate expression of the sixth candidate, FLO11, which encodes a cell adhesion protein. Our results indicate that epistatic interactions between alleles with both positive and negative effects on cyclic AMP-protein kinase A signaling underlie much of the architectural variation we observe in colony biofilms. They are also among the first to demonstrate genetic variation acting at multiple levels of an integrated signaling and regulatory network. Based on these results, we propose a mechanistic model that relates genetic variation to gene network function and phenotypic outcomes.

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