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I. Romnes Faculty Fellow"],"award-info":[{"award-number":["H. I. Romnes Faculty Fellow"]}],"id":[{"id":"10.13039\/100001395","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100015691","name":"Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["R01 AI153356"],"award-info":[{"award-number":["R01 AI153356"]}],"id":[{"id":"10.13039\/100015691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosgenetics.org"],"crossmark-restriction":false},"short-container-title":["PLoS Genet"],"abstract":"<jats:p>\n                    Antifungal drug resistance is a major challenge in fungal infection management. Numerous genomic changes are known to contribute to acquired drug resistance in clinical isolates of specific pathogens, but whether they broadly explain natural resistance across entire lineages is unknown. We leveraged genomic, ecological, and phenotypic trait data from naturally sampled strains from nearly all known species in subphylum\n                    <jats:italic>Saccharomycotina<\/jats:italic>\n                    to examine the evolution of resistance to eight antifungal drugs. The phylogenetic distribution of drug resistance varied by drug; fluconazole resistance was widespread, while 5-fluorocytosine resistance was rare, except in\n                    <jats:italic>Lipomycetales<\/jats:italic>\n                    . A random forest algorithm trained on genomic data predicted drug-resistant yeasts with 54\u201375% accuracy. Fluconazole resistance was consistently predicted with the highest accuracy (75.2%). Furthermore, fluconazole resistance prediction accuracy was similar between models trained on genome-wide variation in the presence and number of InterPro protein annotations across\n                    <jats:italic>Saccharomycotina<\/jats:italic>\n                    (75.2%) and those trained on amino acid sequence alignment data of Erg11, a protein known to be involved in fluconazole resistance (74.3-74.9%). Interestingly, the top Erg11 residues for predicting fluconazole resistance across\n                    <jats:italic>Saccharomycotina<\/jats:italic>\n                    do not overlap with, are not spatially close to, and are less conserved than those previously linked to resistance in clinical isolates of\n                    <jats:italic>Candida albicans<\/jats:italic>\n                    .\n                    <jats:italic>In silico<\/jats:italic>\n                    deep mutational scanning of the\n                    <jats:italic>C. albicans<\/jats:italic>\n                    Erg11 protein reveals that amino acid variants implicated in clinical cases of resistance are almost universally destabilizing while variants in our most informative residues are energetically more neutral, explaining why the latter are much more common than the former in natural populations. Importantly, previous experimental analyses of\n                    <jats:italic>C. albicans<\/jats:italic>\n                    Erg11 have shown that amino acid variation in our most informative residues, despite having never been directly implicated in clinical cases, can directly contribute to resistance. Our results suggest that studies of natural resistance in yeast species never encountered in the clinic will yield a fuller understanding of antifungal drug resistance.\n                  <\/jats:p>","DOI":"10.1371\/journal.pgen.1012091","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T17:32:21Z","timestamp":1773768741000},"page":"e1012091","update-policy":"https:\/\/doi.org\/10.1371\/journal.pgen.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine learning identifies novel signatures of antifungal drug resistance in Saccharomycotina yeasts"],"prefix":"10.1371","volume":"22","author":[{"given":"Marie-Claire","family":"Harrison","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David C.","family":"Rinker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abigail 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Todd","family":"Hittinger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7248-6551","authenticated-orcid":true,"given":"Antonis","family":"Rokas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,3,17]]},"reference":[{"issue":"6694","key":"pgen.1012091.ref001","doi-asserted-by":"crossref","DOI":"10.1126\/science.adj4503","article-title":"Genomic factors shape carbon and nitrogen metabolic niche breadth across Saccharomycotina yeasts","volume":"384","author":"DA Opulente","year":"2024","journal-title":"Science"},{"issue":"1","key":"pgen.1012091.ref002","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1038\/s44259-023-00007-2","article-title":"Molecular mechanisms governing antifungal drug resistance","volume":"1","author":"Y Lee","year":"2023","journal-title":"NPJ Antimicrob 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