1. Simultaneous detection of genotype and phenotype enables rapid and accurate antibiotic susceptibility determination
- Author
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Jonathan Livny, Lorrie L He, Kimberly A. Musser, Jamin Liu, Sophie S. Son, Jill Taylor, Peijun Ma, Milesh M. Patel, Roby P. Bhattacharyya, Lidan Wu, Rui Yang, Nirmalya Bandyopadhyay, Gustavo C. Cerqueira, Alejandro Pironti, Deborah T. Hung, Joseph M. Beechem, Lisa A. Cosimi, Jennifer Skerry, Rich Boykin, Robert F. Rudy, Rustem Khafizov, Ashlee M. Earl, Noam Shoresh, Virginia M. Pierce, and Elizabeth Nazarian
- Subjects
0301 basic medicine ,Genotype ,medicine.drug_class ,Antibiotics ,Computational biology ,Drug resistance ,Microbial Sensitivity Tests ,Biology ,General Biochemistry, Genetics and Molecular Biology ,Article ,Bacterial genetics ,Machine Learning ,03 medical and health sciences ,0302 clinical medicine ,Antibiotic resistance ,Drug Resistance, Bacterial ,medicine ,Humans ,Pathogen ,Molecular epidemiology ,General Medicine ,Anti-Bacterial Agents ,Multiple drug resistance ,RNA, Bacterial ,030104 developmental biology ,Phenotype ,030220 oncology & carcinogenesis - Abstract
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
- Published
- 2019