Genomics for antimicrobial resistance-progress and future directions

Abstract

Antimicrobial resistance (AMR) is a critical global public health threat, with bacterial pathogens of primary concern. Pathogen genomics has revolutionized the study of bacterial pathogens and provided deep insights into the mechanisms and dissemination of AMR, with the precision of whole-genome sequencing informing better control strategies. However, generating actionable data from genomic surveillance and diagnostic efforts requires integration at the public health and clinical interface that goes beyond academic efforts to identify resistance mechanisms, undertake post hoc analyses of outbreaks, and share data after research publications. In addition to timely genomics data, consideration also needs to be given to epidemiological sampling frames, analysis, and reporting mechanisms that meet International Organization for Standardization (ISO) standards and generation of reports that are interpretable and actionable for public health and clinical "end-users." Importantly, ensuring all countries have equitable access to data and technology is critical, through timely data sharing following the FAIR principles (findable, accessible, interoperable, and re-usable). In this review, we describe (i) advances in genomic approaches for AMR research and surveillance to understand emergence, evolution, and transmission of AMR and the key requirements to enable this work and (ii) discuss emerging and future applications of genomics at the clinical and public health interface, including barriers to implementation. Harnessing advances in genomics-enhanced AMR research and embedding robust and reproducible workflows within clinical and public health practice promises to maximize the impact of pathogen genomics for AMR globally in the coming decade.

Keywords: antimicrobial resistance; genomics.

Fig 1

Summary of the inputs and outputs of genomics research for AMR. Highlighted are (A) the relevant data inputs, possible research outputs, and sectors; (B) scale of the investigation; and (C) enablers of high-quality research outputs. MGE, mobile genetic elements; GWAS, genome-wide association studies; ML, machine learning; AI, artificial intelligence.

Fig 2

Implementation models for genomics for AMR. The implementation of genomics for AMR into practice can be categorized into different models (public health surveillance, infection prevention and control, clinical diagnostics), each with their own data inputs, analyses that can be undertaken by the relevant responsible team members, analyses that can be undertaken and who is responsible, and the ultimate actions that can flow from these analyses. There are many areas of implementation that require further research/validation or have been identified as key actions requiring further development. These are highlighted by #. QC, quality control; MGEs, mobile genetic elements; IPC, infection control and prevention.