Strengths and caveats of identifying resistance genes from whole genome sequencing data

ABSTRACT

Introduction

Antimicrobial resistance (AMR) continues to present major challenges to modern healthcare. Recent advances in whole-genome sequencing (WGS) have made the rapid molecular characterization of AMR a realistic possibility for diagnostic laboratories; yet major barriers to clinical implementation exist.

Areas covered

We describe and compare short- and long-read sequencing platforms, typical components of bioinformatics pipelines, tools for AMR gene detection and the relative merits of read- or assembly-based approaches. The challenges of characterizing mobile genetic elements from genomic data are outlined, as well as the complexities inherent to the prediction of phenotypic resistance from WGS. Practical obstacles to implementation in diagnostic laboratories, the critical role of quality control and external quality assurance, as well as standardized reporting standards are also discussed. Future directions, such as the application of machine-learning and artificial intelligence algorithms, linked to clinically meaningful outcomes, may offer a new paradigm for the clinical application of AMR prediction.

Expert opinion

AMR prediction from WGS data presents an exciting opportunity to advance our capacity to comprehensively characterize infectious pathogens in a rapid manner, ultimately aiming to improve patient outcomes. Collaborative efforts between clinicians, scientists, regulatory bodies and healthcare administrators will be critical to achieve the full promise of this approach.

KEYWORDS:

• Genotype-phenotype
• bioinformatics
• antimicrobial resistance
• genes
• whole genome sequencing

Article highlights

• Whole-genome sequencing (WGS) is able to capture the entire complement of genes associated with antimicrobial resistance (AMR) within an organism

• Numerous methodologies for genomic data analysis and AMR databases are available, serve variable purposes and have specific advantages/disadvantages

• Detection of AMR genes correlate most closely with wild-type/non-wild-type categorization, whereas prediction of clinical susceptibility or resistance is more complex

• Mobile genetic elements, which may contribute to the resistance phenotype and are a key mechanism of AMR dissemination, can be a challenge to resolve using short-read data alone

• Application of microbial WGS in clinical laboratories will need robust quality management systems across the full range of analysis and reporting steps

• Machine-learning and artificial intelligence algorithms hold promise to define new paradigms in the application of AMR prediction

• Communication of results that are applicable to clinical practice, training of health personnel to understand genomic data, quality assurance and the establishment of international standards for reporting are pressing challenges for the field

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

PNA Harris is supported by a National Health and Medical Research Council (NHMRC) Early Career Fellowship (GNT1157530).