Machine learning reveals genes impacting oxidative stress resistance across yeasts

Aranguiz, Katarina; Horianopoulos, Linda C.; Elkin, Logan; Abá, Kenia Segura; Jordahl, Drew; Overmyer, Katherine A.; Wrobel, Russell L.; Coon, Joshua J.; Shiu, Shin-Han; Rokas, Antonis; Hittinger, Chris Todd

doi:10.1038/s41467-025-60189-3

Machine learning reveals genes impacting oxidative stress resistance across yeasts

Journal Article · Tue Jul 01 00:00:00 EDT 2025 · Nature Communications

DOI:https://doi.org/10.1038/s41467-025-60189-3· OSTI ID:2570914

Aranguiz, Katarina ^[1]; ^[1]; ^[1]; ^[2]; ^[1]; Overmyer, Katherine A. ^[1]; ^[1]; ^[1]; ^[2]; ^[3]; ^[1]

Univ. of Wisconsin, Madison, WI (United States)
Michigan State Univ., East Lansing, MI (United States)
Vanderbilt Univ., Nashville, TN (United States)

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.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of Wisconsin, Madison, WI (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0018409

OSTI ID:: 2570914

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 16; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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