Xin Fan, Lei Chen, Min Chen, Na Zhang, Hong Chang, Mingjie He, Zhenghao Shen, Lanyue Zhang, Hao Ding, Yuyan Xie, Yemei Huang, Weixin Ke, Meng Xiao, Xuelei Zang, Heping Xu, Wenxia Fang, Shaojie Li, Cunwei Cao, Yingchun Xu, Shiguang Shan, Wenjuan Wu, Changbin Chen, Xinying Xue, Linqi Wang
{"title":"基于泛组学的疫情真菌物种复合体高度多药适应菌株的特征描述与预测","authors":"Xin Fan, Lei Chen, Min Chen, Na Zhang, Hong Chang, Mingjie He, Zhenghao Shen, Lanyue Zhang, Hao Ding, Yuyan Xie, Yemei Huang, Weixin Ke, Meng Xiao, Xuelei Zang, Heping Xu, Wenxia Fang, Shaojie Li, Cunwei Cao, Yingchun Xu, Shiguang Shan, Wenjuan Wu, Changbin Chen, Xinying Xue, Linqi Wang","doi":"10.1016/j.xinn.2024.100681","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":36121,"journal":{"name":"The Innovation","volume":null,"pages":null},"PeriodicalIF":33.2000,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Pan-omics-based characterization and prediction of highly multidrug-adapted strains from an outbreak fungal species complex\",\"authors\":\"Xin Fan, Lei Chen, Min Chen, Na Zhang, Hong Chang, Mingjie He, Zhenghao Shen, Lanyue Zhang, Hao Ding, Yuyan Xie, Yemei Huang, Weixin Ke, Meng Xiao, Xuelei Zang, Heping Xu, Wenxia Fang, Shaojie Li, Cunwei Cao, Yingchun Xu, Shiguang Shan, Wenjuan Wu, Changbin Chen, Xinying Xue, Linqi Wang\",\"doi\":\"10.1016/j.xinn.2024.100681\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":36121,\"journal\":{\"name\":\"The Innovation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":33.2000,\"publicationDate\":\"2024-07-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Innovation\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://doi.org/10.1016/j.xinn.2024.100681\",\"RegionNum\":1,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Innovation","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1016/j.xinn.2024.100681","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Pan-omics-based characterization and prediction of highly multidrug-adapted strains from an outbreak fungal species complex
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.
期刊介绍:
The Innovation is an interdisciplinary journal that aims to promote scientific application. It publishes cutting-edge research and high-quality reviews in various scientific disciplines, including physics, chemistry, materials, nanotechnology, biology, translational medicine, geoscience, and engineering. The journal adheres to the peer review and publishing standards of Cell Press journals.
The Innovation is committed to serving scientists and the public. It aims to publish significant advances promptly and provides a transparent exchange platform. The journal also strives to efficiently promote the translation from scientific discovery to technological achievements and rapidly disseminate scientific findings worldwide.
Indexed in the following databases, The Innovation has visibility in Scopus, Directory of Open Access Journals (DOAJ), Web of Science, Emerging Sources Citation Index (ESCI), PubMed Central, Compendex (previously Ei index), INSPEC, and CABI A&I.