Machine learning enables identification of an alternative yeast galactose utilization pathway

• 成果类型: Article
• 署名作者: Harrison, Marie-Claire; Ubbelohde, Emily J.; LaBella, Abigail L.; Opulente, Dana A.; Wolters, John F.; Zhou, Xiaofan; Shen, Xing-Xing; Groenewald, Marizeth; Hittinger, Chris Todd; Rokas, Antonis
• 署名单位: Vanderbilt University; Vanderbilt University; United States Department of Energy (DOE); University of Wisconsin System; University of Wisconsin Madison; University of North Carolina; University of North Carolina Charlotte; Villanova University; South China Agricultural University; Zhejiang University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-14438
• DOI: 10.1073/pnas.2315314121
• 发表日期: 2024-04-30
• 关键词: comparative genomics; xylose fermentation; genes; metabolism

摘要

How genomic differences contribute to phenotypic differences is a major question in biology. The recently characterized genomes, isolation environments, and qualitative species (nearly all known) in the yeast subphylum Saccharomycotina provide a powerful, yet complex, dataset for addressing this question. We used a random forest algorithm trained on these genomic, metabolic, and environmental data to predict growth on several carbon sources with high accuracy. Known structural genes involved in assimilation of these sources and presence/absence patterns of growth in other sources were important features contributing to prediction accuracy. By further examining growth on galactose, we found that it can be predicted with high accuracy from either genomic (92.2%) or growth data (82.6%) but not from isolation environment data (65.6%). Prediction accuracy was even higher (93.3%) when we combined genomic and growth data. After the GALactose utilization genes, the most important feature for predicting growth on galactose was growth on galactitol, raising the hypothesis that several species in two orders, Serinales and Pichiales (containing the emerging pathogen Candida auris and the genus Ogataea, respectively), have an alternative galactose utilization pathway because they lack the GAL genes. Growth and biochemical assays confirmed that several of these species utilize galactose through an alternative oxidoreductive D- galactose pathway, rather than the canonical GAL pathway. Machine learning approaches are powerful for investigating the evolution of the yeast genotype-phenotype map, and their application will uncover novel biology, even in well- studied traits.