Environmental and genetic determinants of colony morphology in yeast.
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2010-01-22
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Nutrient stresses trigger a variety of developmental switches in the budding yeast Saccharomyces cerevisiae. One of the least understood of such responses is the development of complex colony morphology, characterized by intricate, organized, and strain-specific patterns of colony growth and architecture. The genetic bases of this phenotype and the key environmental signals involved in its induction have heretofore remained poorly understood. By surveying multiple strain backgrounds and a large number of growth conditions, we show that limitation for fermentable carbon sources coupled with a rich nitrogen source is the primary trigger for the colony morphology response in budding yeast. Using knockout mutants and transposon-mediated mutagenesis, we demonstrate that two key signaling networks regulating this response are the filamentous growth MAP kinase cascade and the Ras-cAMP-PKA pathway. We further show synergistic epistasis between Rim15, a kinase involved in integration of nutrient signals, and other genes in these pathways. Ploidy, mating-type, and genotype-by-environment interactions also appear to play a role in the controlling colony morphology. Our study highlights the high degree of network reuse in this model eukaryote; yeast use the same core signaling pathways in multiple contexts to integrate information about environmental and physiological states and generate diverse developmental outputs.
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Granek, JA, and PM Magwene (2010). Environmental and genetic determinants of colony morphology in yeast. PLoS Genet, 6(1). p. e1000823. 10.1371/journal.pgen.1000823 Retrieved from https://hdl.handle.net/10161/4461.
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Joshua Aaron Granek
We have broad interests in using microbial genomics to understand how microbes interact with each other and their hosts. This interest includes the roles played by both beneficial and harmful bacteria, fungi, and viruses and how they interact with the immune system. We study single microbes and microbial communities, primarily using high-throughput sequencing methods. We have a particular interest in developing new experimental and analytical methods that leverage the power of high-throughput sequencing. We are also interested in using deep learning in microbiology research.
Paul Mitaari Magwene
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