Genome-Based Prediction of Bacterial Antibiotic Resistance

• Type of Material: Article
• Authors: Michelle Su, Emory University; Sarah Satola, Emory University; Timothy D Read, Emory University
• Language: English
• Date: 2019-03-01
• Publisher: American Society for Microbiology
• Publication Version: Final Published Version
• Copyright Statement: Copyright © 2019 Su et al.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1128/JCM.01405-18
• Title of Journal or Parent Work: Journal of Clinical Microbiology
• ISSN: 0095-1137
• Volume: 57
• Issue: 3
• Grant/Funding Information: M.S. was supported in part by the Antimicrobial Resistance and Therapeutic Discovery Training Program funded by NIAID T32 award AI106699-05.; T.D.R. was supported by National Institute of Allergy and Infectious Diseases (NIAID) award AI121860.
• Keywords: Microbiology; Science & Technology; KLEBSIELLA-PNEUMONIAE; PUBLIC-HEALTH; SUSCEPTIBILITY; MYCOBACTERIUM-TUBERCULOSIS; DRUG-RESISTANCE; genome-based prediction; REAL-TIME; GENES; Life Sciences & Biomedicine; PSEUDOMONAS-AERUGINOSA; antibiotic resistance; ANTIMICROBIAL RESISTANCE; ESCHERICHIA-COLI
• Research Categories: Health Sciences, Public Health; Biology, Microbiology; Health Sciences, Medicine and Surgery

Abstract

Clinical microbiology has long relied on growing bacteria in culture to determine antimicrobial susceptibility profiles, but the use of whole-genome sequencing for antibiotic susceptibility testing (WGS-AST) is now a powerful alternative. This review discusses the technologies that made this possible and presents results from recent studies to predict resistance based on genome sequences. We examine differences between calling antibiotic resistance profiles by the simple presence or absence of previously known genes and single-nucleotide polymorphisms (SNPs) against approaches that deploy machine learning and statistical models. Often, the limitations to genome-based prediction arise from limitations of accuracy of culture-based AST in addition to an incomplete knowledge of the genetic basis of resistance. However, we need to maintain phenotypic testing even as genome-based prediction becomes more widespread to ensure that the results do not diverge over time. We argue that standardization of WGS-AST by challenge with consistently phenotyped strain sets of defined genetic diversity is necessary to compare the efficacy of methods of prediction of antibiotic resistance based on genome sequences.