Antimicrobial susceptibility prediction from genomes: a dream come true?

Guido Werner; Hege Vangstein Aamot; Natacha Couto

doi:10.1016/j.tim.2024.02.012

Antimicrobial susceptibility prediction from genomes: a dream come true?

Trends Microbiol. 2024 Apr;32(4):317-318. doi: 10.1016/j.tim.2024.02.012. Epub 2024 Mar 3.

Authors

Guido Werner¹, Hege Vangstein Aamot², Natacha Couto³

Affiliations

¹ Department of Infectious Diseases, Robert Koch Institute, Wernigerode Branch, Wernigerode, Germany; ESCMID Study Group for Epidemiological Markers - ESGEM; ESCMID Study Group for Genomic and Molecular Diagnostics - ESGMD. Electronic address: wenerg@rki.de.
² Department of Microbiology and Infection Control, Akershus University Hospital, Lørenskog, Norway; ESCMID Study Group for Epidemiological Markers - ESGEM; ESCMID Study Group for Genomic and Molecular Diagnostics - ESGMD.
³ Centre for Genomic Pathogen Surveillance, Pandemic Sciences Institute, University of Oxford, Oxford, UK; ESCMID Study Group for Epidemiological Markers - ESGEM; ESCMID Study Group for Genomic and Molecular Diagnostics - ESGMD.

PMID: 38433028
DOI: 10.1016/j.tim.2024.02.012

Abstract

Genome-based diagnostics provides relevant information to guide patient treatment and support pathogen and resistance surveillance. Recently, Coll et al. introduced a curated database for predicting antimicrobial resistance (AMR) from Enterococcus faecium genomics data, offering excellent predictive values for susceptibility to important antimicrobials. Challenges to predict resistance to last-resort antimicrobials remain.

Keywords: AMR genotypes; AMR prediction; antibacterial; antimicrobial therapy.

MeSH terms

Anti-Bacterial Agents / pharmacology
Anti-Infective Agents* / pharmacology
Drug Resistance, Bacterial / genetics
Enterococcus faecium* / genetics
Genomics
Humans

Substances

Anti-Infective Agents
Anti-Bacterial Agents