Fan Xin, Chen Lei, Chen Min, Zhang Na, Chang Hong, He Mingjie, Shen Zhenghao, Zhang Lanyue, Ding Hao, Xie Yuyan, Huang Yemei, Ke Weixin, Xiao Meng, Zang Xuelei, Xu Heping, Fang Wenxia, Li Shaojie, Cao Cunwei, Xu Yingchun, Shan Shiguang, Wu Wenjuan, Chen Changbin, Xue Xinying, Wang Linqi
Department of Infectious Diseases and Clinical Microbiology, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China.
Beijing Research Center for Respiratory Infectious Diseases, Beijing 100020, China.
Innovation (Camb). 2024 Jul 31;5(5):100681. doi: 10.1016/j.xinn.2024.100681. eCollection 2024 Sep 9.
Strains from the species complex (CGSC) have caused the Pacific Northwest cryptococcosis outbreak, the largest cluster of life-threatening fungal infections in otherwise healthy human hosts known to date. In this study, we utilized a pan-phenome-based method to assess the fitness outcomes of CGSC strains under 31 stress conditions, providing a comprehensive overview of 2,821 phenotype-strain associations within this pathogenic clade. Phenotypic clustering analysis revealed a strong correlation between distinct types of stress phenotypes in a subset of CGSC strains, suggesting that shared determinants coordinate their adaptations to various stresses. Notably, a specific group of strains, including the outbreak isolates, exhibited a remarkable ability to adapt to all three of the most commonly used antifungal drugs for treating cryptococcosis (amphotericin B, 5-fluorocytosine, and fluconazole). By integrating pan-genomic and pan-transcriptomic analyses, we identified previously unrecognized genes that play crucial roles in conferring multidrug resistance in an outbreak strain with high multidrug adaptation. From these genes, we identified biomarkers that enable the accurate prediction of highly multidrug-adapted CGSC strains, achieving maximum accuracy and area under the curve (AUC) of 0.79 and 0.86, respectively, using machine learning algorithms. Overall, we developed a pan-omic approach to identify cryptococcal multidrug resistance determinants and predict highly multidrug-adapted CGSC strains that may pose significant clinical concern.
来自该物种复合体（CGSC）的菌株引发了太平洋西北地区的隐球菌病疫情，这是迄今为止已知的在原本健康的人类宿主中最大的一组危及生命的真菌感染群。在本研究中，我们采用了一种基于泛表型组的方法来评估CGSC菌株在31种应激条件下的适应性结果，全面概述了该致病进化枝内的2821种表型 - 菌株关联。表型聚类分析揭示了CGSC菌株子集中不同类型应激表型之间的强相关性，表明共同的决定因素协调了它们对各种应激的适应。值得注意的是，包括疫情分离株在内的一组特定菌株表现出对治疗隐球菌病最常用的三种抗真菌药物（两性霉素B、5-氟胞嘧啶和氟康唑）的显著适应能力。通过整合泛基因组和泛转录组分析，我们鉴定出了以前未被识别的基因，这些基因在具有高多药适应性的疫情菌株中赋予多药耐药性方面发挥着关键作用。从这些基因中，我们鉴定出了能够准确预测高度多药适应的CGSC菌株的生物标志物，使用机器学习算法分别实现了0.79和0.86的最大准确率和曲线下面积（AUC）。总体而言，我们开发了一种泛组学方法来鉴定隐球菌的多药耐药决定因素，并预测可能引起重大临床关注的高度多药适应的CGSC菌株。
