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J Clin Microbiol. 2019 Feb 27;57(3). pii: e01405-18. doi: 10.1128/JCM.01405-18. Print 2019 Mar.

Genome-Based Prediction of Bacterial Antibiotic Resistance.

Su M1,2,3, Satola SW1,3,4, Read TD5,3,4.

Author information

1
Department of Infectious Diseases, Emory University, Atlanta, Georgia, USA.
2
Antimicrobial Resistance and Therapeutic Discovery Training Program, Emory University, Atlanta, Georgia, USA.
3
Antibiotic Resistance Center, Emory University, Atlanta, Georgia, USA.
4
Emory Investigational Clinical Microbiology Laboratory, Emory University, Atlanta, Georgia, USA.
5
Department of Infectious Diseases, Emory University, Atlanta, Georgia, USA tread@emory.edu.

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.

KEYWORDS:

antibiotic resistance; genome-based prediction

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