Harrison Marie-Claire, Rinker David C, LaBella Abigail L, Opulente Dana A, Wolters John F, Zhou Xiaofan, Shen Xing-Xing, Groenewald Marizeth, Hittinger Chris Todd, Rokas Antonis
Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA.
Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Kannapolis, NC 28081, USA & Center for Computational Intelligence to Predict Health and Environmental Risks (CIPHER), University of North Carolina at Charlotte, Charlotte, North Carolina, USA.
bioRxiv. 2025 May 10:2025.05.09.653161. doi: 10.1101/2025.05.09.653161.
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 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 . A random forest algorithm trained on genomic data predicted drug-resistant yeasts with 54-75% accuracy. In general, frequency of drug resistance correlated with prediction accuracy, with fluconazole resistance being consistently predicted with the highest accuracy (74.9%). Fluconazole resistance accuracy was similar between models trained on genome-wide variation in the presence and number of InterPro protein annotations across (74.9% accuracy) and those trained on amino acid sequence alignment data of Erg11, a protein known to be involved in fluconazole resistance (74.3-74.9% accuracy). Interestingly, the top Erg11 residues for predicting fluconazole resistance across do not overlap with, are not spatially close to, and are less conserved than those previously linked to resistance in clinical isolates of . deep mutational scanning of the Erg11 protein revealed 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 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.
抗真菌药物耐药性是真菌感染治疗中的一项重大挑战。已知许多基因组变化会导致特定病原体临床分离株产生获得性耐药性，但这些变化是否能广泛解释整个谱系的天然耐药性尚不清楚。我们利用来自亚门几乎所有已知物种的自然采样菌株的基因组、生态和表型特征数据，来研究对八种抗真菌药物的耐药性演变。耐药性的系统发育分布因药物而异；氟康唑耐药性广泛存在，而5-氟胞嘧啶耐药性则很罕见，除了在……。基于基因组数据训练的随机森林算法预测耐药酵母的准确率为54%-75%。一般来说，耐药性频率与预测准确率相关，氟康唑耐药性的预测准确率始终最高（74.9%）。在全基因组范围内基于InterPro蛋白质注释的存在和数量变化训练的模型（准确率为74.9%）与基于已知参与氟康唑耐药性的Erg11蛋白质氨基酸序列比对数据训练的模型（准确率为74.3%-74.9%）之间，氟康唑耐药性的预测准确率相似。有趣的是，在……中预测氟康唑耐药性的Erg11蛋白的关键残基与之前在……临床分离株中与耐药性相关的残基不重叠、在空间上不接近且保守性更低。对……的Erg11蛋白进行深度突变扫描发现，与临床耐药病例相关的氨基酸变体几乎普遍会使蛋白不稳定，而我们最具信息量的残基中的变体在能量上更中性，这解释了为什么后者在自然种群中比前者更常见。重要的是，之前对……的Erg11进行的实验分析表明，我们最具信息量的残基中的氨基酸变异，尽管从未直接与临床病例相关，但可以直接导致耐药性。我们的结果表明，对临床上从未遇到过的酵母物种的天然耐药性进行研究，将有助于更全面地了解抗真菌药物耐药性。
