Machine learning reveals genes impacting oxidative stress resistance across yeasts
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-60189-3 |
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| _version_ | 1849402105008226304 |
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| author | 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 Chris Todd Hittinger |
| author_facet | 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 Chris Todd Hittinger |
| author_sort | Katarina Aranguiz |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e0bdd512fc2a48858658baa51973478e |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-e0bdd512fc2a48858658baa51973478e2025-08-20T03:37:37ZengNature PortfolioNature Communications2041-17232025-07-0116111510.1038/s41467-025-60189-3Machine learning reveals genes impacting oxidative stress resistance across yeastsKatarina Aranguiz0Linda C. Horianopoulos1Logan Elkin2Kenia Segura Abá3Drew Jordahl4Katherine A. Overmyer5Russell L. Wrobel6Joshua J. Coon7Shin-Han Shiu8Antonis Rokas9Chris Todd Hittinger10DOE Great Lakes Bioenergy Research Center, University of Wisconsin-MadisonDOE Great Lakes Bioenergy Research Center, University of Wisconsin-MadisonDOE Great Lakes Bioenergy Research Center, University of Wisconsin-MadisonDOE Great Lakes Bioenergy Research Center, Michigan State UniversityCellular and Molecular Biology Graduate Program, University of Wisconsin-MadisonDOE Great Lakes Bioenergy Research Center, University of Wisconsin-MadisonDOE Great Lakes Bioenergy Research Center, University of Wisconsin-MadisonDOE Great Lakes Bioenergy Research Center, University of Wisconsin-MadisonDOE Great Lakes Bioenergy Research Center, Michigan State UniversityDepartment of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt UniversityDOE Great Lakes Bioenergy Research Center, University of Wisconsin-MadisonAbstract 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.https://doi.org/10.1038/s41467-025-60189-3 |
| spellingShingle | 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 Chris Todd Hittinger Machine learning reveals genes impacting oxidative stress resistance across yeasts Nature Communications |
| title | Machine learning reveals genes impacting oxidative stress resistance across yeasts |
| title_full | Machine learning reveals genes impacting oxidative stress resistance across yeasts |
| title_fullStr | Machine learning reveals genes impacting oxidative stress resistance across yeasts |
| title_full_unstemmed | Machine learning reveals genes impacting oxidative stress resistance across yeasts |
| title_short | Machine learning reveals genes impacting oxidative stress resistance across yeasts |
| title_sort | machine learning reveals genes impacting oxidative stress resistance across yeasts |
| url | https://doi.org/10.1038/s41467-025-60189-3 |
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