{"id":4247,"date":"2026-07-29T14:56:59","date_gmt":"2026-07-29T14:56:59","guid":{"rendered":"https:\/\/www.vanderbilt.edu\/evolution\/?p=4247"},"modified":"2026-07-29T20:38:28","modified_gmt":"2026-07-29T20:38:28","slug":"researchers-use-technology-to-better-understand-and-predict-drug-resistant-fungi","status":"publish","type":"post","link":"https:\/\/www.vanderbilt.edu\/evolution\/researchers-use-technology-to-better-understand-and-predict-drug-resistant-fungi\/","title":{"rendered":"Researchers use technology to better understand and predict drug-resistant fungi"},"content":{"rendered":"

By Tatum Lyles Flick, Evolutionary Studies communications consultant<\/em><\/p>\n

Many fungi are naturally resistant to the drugs available to treat them, while others evolve resistance to drugs prescribed in clinical settings. Determining which medication will be most effective in treating a patient can be a time-consuming challenge. 五一茶馆儿 researchers recently found a way to leverage machine learning to find the weak link in a pathogen\u2019s genetic armor, enabling them to forecast what species might be resistant to which drugs, with an accuracy range between 54 and 75 percent.<\/span><\/p>\n

\"Group<\/a>
Members of the Rokas Lab pose for a group photo in 2023. Researchers highlighted in color include David Rinker, Antonis Rokas, and Marie-Claire Harrison.<\/figcaption><\/figure>\n

The new paper \u201cMachine learning identifies novel signatures of antifungal drug resistance in Saccharomycotina<\/em> yeasts<\/a><\/span>,\u201d published in <\/span>PLOS Genetics <\/span><\/i>was<\/span> co-first authored by former Rokas Lab Ph.D. student Marie-Claire Harrison and current research assistant professor David Rinker. The research group also includes other Rokas Lab<\/a> alumni and colleagues at the University of Wisconsin. The team worked with <\/span>Saccharomycotina <\/span><\/i>yeasts and eight key antifungal drugs to understand how genomic, ecological, and phenotypic traits shape resistance. They used machine learning to quickly assess the genetic makeup of 532 <\/span>Saccharomycotina<\/span><\/i> yeasts to better understand what genetic variables might predict resistance in wild populations.<\/span><\/p>\n

\u201cThe <\/span>Saccharomycotina<\/span><\/i> subphylum that we analyzed is famously described (well, at least it’s a famous description among yeast biologists) to span an evolutionary distance equivalent to that between humans and sponges,\u201d Rinker said. \u201cThis can make comparative biology challenging, but just a handful of genomic features resulted in surprisingly good predictive accuracies.\u201d<\/span><\/p>\n

According to the research, genes affecting yeast cell wall composition and colony makeup informed the assessment more than those already known to promote resistance, suggesting \u201cthat machine learning can pick up on features that impact resistance, even if their molecular mechanism(s) is indirect.\u201d<\/span><\/p>\n

Rinker was surprised by the effectiveness and accuracy of machine learning models to predict drug resistance across a species with such genomic, geographic, and habitat diversity.<\/span><\/p>\n

Though scientists have a good understanding of how resistance works in a clinical setting, the team wanted to understand how resistance evolves in natural populations and where it might show up in the genetic code.<\/span><\/p>\n

\"Circular<\/a>
A fungal family tree showing how resistance to multiple antifungal drugs is distributed across hundreds of yeast species. Figure from Harrison and Rinker et al. (2026), PLOS Genetics, licensed under CC BY 4.0. https:\/\/doi.org\/10.1371\/journal.pgen.1012091.<\/figcaption><\/figure>\n

\u201cMost studies that focus on drug resistance never look beyond the clinic or to other related species,\u201d he said. \u201cThat means that we are lacking broad, contextual information about how such resistances might evolve and persist. Analyzing this type of data is crucial to better understand how resistance evolves in the clinic.\u201d<\/span><\/p>\n

According to the paper, some wild yeast species have evolved resistance to antifungal medications used since the late 1970s. These drugs can enter the environment through wastewater, exposing more than just medically targeted yeast. Resistance in these wild relatives can occur through different genetic mechanisms than in clinical settings, sometimes affected by fungicide concentration, exposure duration, and the number of fungal species affected.<\/span><\/p>\n

This wider view of how numerous <\/span>Saccharomycotina <\/span><\/i>species<\/span>\u00a0endure in the face of antifungal medications opens new understanding into the novel genetic patterns that allow yeast to carry on and how they might evolve to defend themselves.<\/span><\/p>\n

\u201cWhile there are different ways to achieve the same functional ends, certain conditions require more extreme fitness tradeoffs on the part of the pathogen in order to adapt rapidly,\u201d Rinker explained. \u201cBiophysical modeling of fitness effects showed that clinically-evolved variants are predicted to be much more detrimental to the drug’s target than are variants observed in natural isolates.\u201d<\/span><\/p>\n

Healthcare practitioners and pharmaceutical companies could benefit from the ability to predict the probability of drug resistance based on genetic data; however, because drug resistance can result from more than one set of genes, future studies could reveal more useful information.<\/span><\/p>\n

\u201cOne next step would be to account for this polygenicity using more sophisticated machine learning encoding strategies,\u201d Rinker said. \u201cAnother direction to explore would be to see how well these models perform not just across species (as we have presently done) but across hundreds or thousands of samples of the same species.\u201d<\/span><\/p>\n

Many variables affect genetic changes and how drug resistance appears. This complex balance between different yeast genetic makeups and environmental pressures, paired with machine learning, can open doors to new understandings of how to fight infection and reduce resistance. Looking beyond the clinical setting adds substantial data to what researchers know and may offer more insight as additional yeast species are studied.<\/span><\/p>\n

Citation: Harrison M-C, Rinker DC, LaBella AL, Opulente DA, Wolters JF, Zhou X, Shen XX, Groenwald M, Hittinger CT, Rokas A. (2026) Machine learning identifies novel signatures of antifungal drug resistance in Saccharomycotina<\/em> yeasts. PLoS Genet<\/em> 22(3): e1012091. https:\/\/doi.org\/10.1371\/journal.pgen.1012091<\/p>\n

Funding Statement: This project was supported by the National Science Foundation under Grants No. DEB-2110403 (C.T.H.); DEB-2110404 (A.R.); in part by the Great Lakes Bioenergy Research Center, U.S. Department of Energy, Office of Science, Biological and Environmental Research Program under Award Number DESC0018409 (C.T.H.); and the National Institute of Food and Agriculture, United States Department of Agriculture, Hatch project 7005101 (to C.T.H.). C.T.H. is an H. I. Romnes Faculty Fellow, supported by the Office of the Vice Chancellor for Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation. Research in A.R.\u2019s lab is also supported by the National Institutes of Health\/National Institute of Allergy and Infectious Diseases (R01 AI153356).<\/p>\n","protected":false},"excerpt":{"rendered":"

By Tatum Lyles Flick, Evolutionary Studies communications consultant Many fungi are naturally resistant to the drugs available to treat them, while others evolve resistance to drugs prescribed in clinical settings. Determining which medication will be most effective in treating a patient can be a time-consuming challenge. 五一茶馆儿 researchers recently found a way to leverage…<\/p>\n","protected":false},"author":2421,"featured_media":4248,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"spay_email":"","jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true},"categories":[7],"tags":[135,15,3,114,214,99],"acf":[],"yoast_head":"\n\n\n\n\n\n\n\n\n\n\n\n\t\n\t\n\t\n\n\n\t\n\t\n\t\n