bioRxivMicrobiologyPreprint
The common evolution of fungicide persistence in cryptococcosis patients
Yuyan XieiD, Weixin KeiD, Xin FaniD, Heping XuiD, Wenzhao WangiD, Zhen Zeng, Na Zhang, Haoran Ma, Zhongjie TangiD, Huawei ZhuiD, Lei Jiang, Bo ZhuiD, Guohui Shi, Sihan Yang, Minling Yu, Feiyi LiuiD, Xixian WangiD, Shaojie LiiD, Jian XuiD, Koon Ho WongiD, Xinping XuiD, Cui Hua LiuiD, David S. PerliniD, Yingchun XuiD, Chong Li, , Linqi WangiD
Abstract
Fungicide persistence, the ability of dormant fungal cells to survive lethal drug exposure, undermines treatment efficacy, yet its clinical evolution remains largely unexplored. We assembled a nationwide collection of clinical Cryptococcus neoformans isolates from China and revealed extensive inter strain variation in amphotericin B (AmB) persistence. This variation can arise from common evolutionary events in patients, generating previously undescribed high persistence cryptococcal variants refractory to AmB clearance. Genome wide fitness landscape analysis shows that persistence associated mutations generally impose minimal fitness costs, facilitating persistence evolution even in fungistatic resistant and hypervirulent genetic backgrounds. Machine learning identified deficient ACO2 expression as a key predictor of high persistence clinical isolates. ACO2 deficiency reproduces the high persistence phenotype by promoting POPC accumulation, which competes with AmB for its target. Moreover, the clinical stage antifungal T-2307 effectively eliminates high persistence strains across diverse genetic backgrounds. Collectively, our findings unveil common evolution of fungicide persistence in cryptococcosis patients, representing a previously overlooked clinical concern.
- organism: human
- assay: whole-genome sequencing
- research focus: clustering
- research focus: differential expression
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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