抗生素耐药性
抗生素
基因型
生物
计算生物学
病菌
多重耐药
表型
微生物学
遗传学
基因
作者
Roby P. Bhattacharyya,Nirmalya Bandyopadhyay,Peijun Ma,Sophie S. Son,Jamin Liu,Lorrie L. He,Lidan Wu,Rustem Khafizov,Rich Boykin,Gustavo C. Cerqueira,Alejandro Pironti,Robert F. Rudy,Milesh M. Patel,Rui Yang,Jennifer Skerry,Elizabeth Nazarian,Kimberlee A. Musser,Jill Taylor,Virginia Pierce,Ashlee M. Earl
出处
期刊:Nature Medicine
[Nature Portfolio]
日期:2019-11-25
卷期号:25 (12): 1858-1864
被引量:107
标识
DOI:10.1038/s41591-019-0650-9
摘要
Multidrug resistant organisms are a serious threat to human health1,2. Fast, accurate antibiotic susceptibility testing (AST) is a critical need in addressing escalating antibiotic resistance, since delays in identifying multidrug resistant organisms increase mortality3,4 and use of broad-spectrum antibiotics, further selecting for resistant organisms. Yet current growth-based AST assays, such as broth microdilution5, require several days before informing key clinical decisions. Rapid AST would transform the care of patients with infection while ensuring that our antibiotic arsenal is deployed as efficiently as possible. Growth-based assays are fundamentally constrained in speed by doubling time of the pathogen, and genotypic assays are limited by the ever-growing diversity and complexity of bacterial antibiotic resistance mechanisms. Here we describe a rapid assay for combined genotypic and phenotypic AST through RNA detection, GoPhAST-R, that classifies strains with 94-99% accuracy by coupling machine learning analysis of early antibiotic-induced transcriptional changes with simultaneous detection of key genetic resistance determinants to increase accuracy of resistance detection, facilitate molecular epidemiology and enable early detection of emerging resistance mechanisms. This two-pronged approach provides phenotypic AST 24-36 h faster than standard workflows, with <4 h assay time on a pilot instrument for hybridization-based multiplexed RNA detection implemented directly from positive blood cultures.
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