MassIVE MSV000096915

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Machine learning reveals genes impacting oxidative stress resistance across yeasts

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Description

Reactive oxygen species (ROS) are highly reactive molecules encountered by yeasts during routine metabolism and during interactions with other organisms, including host infection. Here, we characterized the variation in resistance to ROS across the ancient yeast subphylum Saccharomycotina and used machine learning (ML) to identify gene families whose sizes were predictive of ROS resistance. The most predictive features were enriched in gene families related to cell wall organization and included two reductase gene families. We estimated the quantitative contributions of features to each species' classification to guide experimental validation and showed that overexpression of the old yellow enzyme (OYE) reductase increased ROS resistance in Kluyveromyces lactis, while Saccharomyces cerevisiae mutants lacking multiple mannosyltransferase-encoding genes were hypersensitive to ROS. Altogether, this work provides a framework for how ML can uncover genetic mechanisms underlying trait variation across diverse species and inform trait manipulation for clinical and biotechnological applications. [doi:10.25345/C5WH2DS6P] [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: Yeast ; Proteomics ; Artificial Intelligece ; AI ; Kluyveromyces lactis ; Saccharomyces cerevisiae ; Data Independent Acquisition ; Machine Learning ; Oxidative Stress ; Reactive Oxygen Species ; DatasetType:Proteomics

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Principal Investigators:
(in alphabetical order) 		Joshua Coon, University of Wisconsin - Madison, United States
Submitting User: coonlabs
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