Discordant bioinformatic predictions of antimicrobial resistance from whole-genome sequencing data of bacterial isolates: An inter-laboratory study

Abstract

Abstract

Background Antimicrobial resistance (AMR) poses a threat to public health. Clinical microbiology laboratories typically rely on culturing bacteria for antimicrobial susceptibility testing (AST). As the implementation costs and technical barriers fall, whole-genome sequencing (WGS) has emerged as a ‘one-stop’ test for epidemiological and predictive AST results. Few published comparisons exist for the myriad analytical pipelines used for predicting AMR. To address this, we performed an inter-laboratory study providing sets of participating researchers with identical short-read WGS data sequenced from clinical isolates, allowing us to assess the reproducibility of the bioinformatic prediction of AMR between participants and identify problem cases and factors that lead to discordant results.

Methods We produced ten WGS datasets of varying quality from cultured carbapenem-resistant organisms obtained from clinical samples sequenced on either an Illumina NextSeq or HiSeq instrument. Nine participating teams (‘participants’) were provided these sequence data without any other contextual information. Each participant used their own pipeline to determine the species, the presence of resistance-associated genes, and to predict susceptibility or resistance to amikacin, gentamicin, ciprofloxacin and cefotaxime.

Results Individual participants predicted different numbers of AMR-associated genes and different gene variants from the same clinical samples. The quality of the sequence data, choice of bioinformatic pipeline and interpretation of the results all contributed to discordance between participants. Although much of the inaccurate gene variant annotation did not affect genotypic resistance predictions, we observed low specificity when compared to phenotypic AST results but this improved in samples with higher read depths. Had the results been used to predict AST and guide treatment a different antibiotic would have been recommended for each isolate by at least one participant.

Conclusions We found that participants produced discordant predictions from identical WGS data. These challenges, at the final analytical stage of using WGS to predict AMR, suggest the need for refinements when using this technology in clinical settings. Comprehensive public resistance sequence databases and standardisation in the comparisons between genotype and resistance phenotypes will be fundamental before AST prediction using WGS can be successfully implemented in standard clinical microbiology laboratories.

Footnotes

* Additional text and declarations added to clarify that researchers participated in this study in an individual capacity. Only those named in the author list assisted in any analysis from their affiliated institution. Discussion was revised to reflect further on possible study limitations and two recent studies were also additionally referenced (41 and 42).

* https://www.ebi.ac.uk/ena/data/view/PRJEB34513

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Abbreviations

AMR

Antimicrobial resistance

ARG-ANNOT

Antibiotic resistance gene-annotation

ARIBA

Antimicrobial resistance identification by assembly

AST

Antimicrobial susceptibility testing

CARD

Comprehensive Antibiotic Resistance Database

EUCAST

The European Committee on Antimicrobial Susceptibility Testing

GOSH

Great Ormond Street Hospital

NCBI

The National Center for Biotechnology Information

SRST2

Short read sequence typing 2

UHG

University Hospital Galway

WGS

Whole-genome sequencing