Machine learning reveals genes impacting oxidative stress resistance across yeasts

Katarina Aranguiz, Linda C. Horianopoulos, Logan Elkin, Kenia Segura Abá, Drew Jordahl, Katherine A. Overmyer, Russell L. Wrobel, Joshua J. Coon, Shin-Han Shiu, Antonis Rokas and Chris Todd Hittinger ()
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Katarina Aranguiz: University of Wisconsin-Madison
Linda C. Horianopoulos: University of Wisconsin-Madison
Logan Elkin: University of Wisconsin-Madison
Kenia Segura Abá: Michigan State University
Drew Jordahl: University of Wisconsin-Madison
Katherine A. Overmyer: University of Wisconsin-Madison
Russell L. Wrobel: University of Wisconsin-Madison
Joshua J. Coon: University of Wisconsin-Madison
Shin-Han Shiu: Michigan State University
Antonis Rokas: Vanderbilt University
Chris Todd Hittinger: University of Wisconsin-Madison

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract 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 characterize the variation in resistance to the ROS-inducing compound tert-butyl hydroperoxide across the ancient yeast subphylum Saccharomycotina and use machine learning (ML) to identify gene families whose sizes are predictive of ROS resistance. The most predictive features are enriched in gene families related to cell wall organization and include two reductase gene families. We estimate the quantitative contributions of features to each species’ classification to guide experimental validation and show that overexpression of the old yellow enzyme (OYE) reductase increases ROS resistance in Kluyveromyces lactis, while Saccharomyces cerevisiae mutants lacking multiple mannosyltransferase-encoding genes are 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.

Date: 2025
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DOI: 10.1038/s41467-025-60189-3

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