Automated antimicrobial susceptibility testing and antimicrobial resistance genotyping using Illumina and Oxford Nanopore Technologies sequencing data among Enterobacteriaceae

Whole-genome sequencing for antimicrobial surveillance: species-specific quality thresholds and data evaluation from the network of the European Union Reference Laboratory for Antimicrobial Resistance genomic proficiency tests of 2021 and 2022

As antimicrobial resistance (AMR) surveillance shifts to genomics, ensuring the quality of whole-genome sequencing (WGS) data produced across laboratories is critical. Participation in genomic proficiency tests (GPTs) not only increases individual laboratories' WGS capacity but also provides a unique opportunity to improve species-specific thresholds for WGS quality control (QC) by repeated resequencing of distinct isolates. Here, we present the results of the EU Reference Laboratory for Antimicrobial Resistance (EURL-AR) network GPTs of 2021 and 2022, which included 25 EU national reference laboratories (NLRs). A total of 392 genomes from 12 AMR-bacteria were evaluated based on WGS QC metrics. Two percent (n = 9) of the data were excluded, due to contamination, and 11% (n = 41) of the remaining genomes were identified as outliers in at least one QC metric and excluded from computation of the adjusted QC thresholds (AQT). Two QC metric correlation groups were identified through linear regression. Eight percent (n = 28) of the submitted genomes, from 11 laboratories, failed one or more of the AQTs. However, only three laboratories (12%) were identified as underperformers, failing across AQTs for uncorrelated QC metrics in at least two genomes. Finally, new species-specific thresholds for "N50" and "number of contigs > 200 bp" are presented for guidance in routine laboratory QC. The continued participation of NRLs in GPTs will reveal WGS workflow flaws and improve AMR surveillance data. GPT data will continue to contribute to the development of reliable species-specific thresholds for routine WGS QC, standardizing sequencing data QC and ensure inter- and intranational laboratory comparability.IMPORTANCEIllumina next-generation sequencing is an integral part of antimicrobial resistance (AMR) surveillance and the most widely used whole-genome sequencing (WGS) platform. The high-throughput, relative low-cost, high discriminatory power, and rapid turnaround time of WGS compared to classical biochemical methods means the technology will likely remain a fundamental tool in AMR surveillance and public health. In this study, we present the current level of WGS capacity among national reference laboratories in the EU Reference Laboratory for AMR network, summarizing applied methodology and statistically evaluating the quality of the obtained sequence data. These findings provide the basis for setting new and revised thresholds for quality metrics used in routine WGS, which have previously been arbitrarily defined. In addition, underperforming participants are identified and encouraged to evaluate their workflows to produce reliable results.

Is Carbapenem Therapy Necessary for the Treatment of Non-CTX-M Extended-Spectrum β-Lactamase-Producing Enterobacterales Bloodstream Infections?

BACKGROUND

Investigations into antibiotics for extended-spectrum β-lactamase-producing Enterobacterales (ESBL-E) bloodstream infections (BSIs) have focused on blaCTX-M genes. Patient outcomes from non-CTX-M-producing ESBL-E BSIs and optimal treatment are unknown.

METHODS

A multicenter observational study investigating 500 consecutive patients with ceftriaxone-resistant Enterobacterales BSIs during 2018-2022 was conducted. Broth microdilution and whole-genome sequencing confirmed antibiotic susceptibilities and ESBL gene presence, respectively. Inverse probability weighting (IPW) using propensity scores ensured patients with non-CTX-M and CTX-M ESBL-E BSIs were similar before outcome evaluation.

RESULTS

396 patients (79.2%) were confirmed to have an ESBL-E BSI. ESBL gene family prevalence was as follows: blaCTX-M (n = 370), blaSHV (n = 16), blaOXY (n = 12), and blaVEB (n = 5). ESBL gene identification was not limited to Escherichia coli and Klebsiella species. In the IPW cohort, there was no difference in 30-day mortality or ESBL-E infection recurrence between the non-CTX-M and CTX-M groups (odds ratio [OR], 0.99; 95% confidence interval [CI], .87-1.11; P = .83 and OR, 1.10; 95% CI, .85-1.42; P = .47, respectively). In an exploratory analysis limited to the non-CTX-M group, 86% of the 21 patients who received meropenem were alive on day 30; none of the 5 patients who received piperacillin-tazobactam were alive on day 30.

CONCLUSIONS

Our findings suggest that non-CTX-M and CTX-M ESBL-E BSIs are equally concerning and associated with similar clinical outcomes. Meropenem may be associated with improved survival in patients with non-CTX-M ESBL-E BSIs, underscoring the potential benefit of comprehensive molecular diagnostics to enable early antibiotic optimization for ESBL-E BSIs beyond just blaCTX-M genes.

Metagenomic Antimicrobial Susceptibility Testing from Simulated Native Patient Samples

Genomic antimicrobial susceptibility testing (AST) has been shown to be accurate for many pathogens and antimicrobials. However, these methods have not been systematically evaluated for clinical metagenomic data. We investigate the performance of in-silico AST from clinical metagenomes (MG-AST). Using isolate sequencing data from a multi-center study on antimicrobial resistance (AMR) as well as shotgun-sequenced septic urine samples, we simulate over 2000 complicated urinary tract infection (cUTI) metagenomes with known resistance phenotype to 5 antimicrobials. Applying rule-based and machine learning-based genomic AST classifiers, we explore the impact of sequencing depth and technology, metagenome complexity, and bioinformatics processing approaches on AST accuracy. By using an optimized metagenomics assembly and binning workflow, MG-AST achieved balanced accuracy within 5.1% of isolate-derived genomic AST. For poly-microbial infections, taxonomic sample complexity and relatedness of taxa in the sample is a key factor influencing metagenomic binning and downstream MG-AST accuracy. We show that the reassignment of putative plasmid contigs by their predicted host range and investigation of whole resistome capabilities improved MG-AST performance on poly-microbial samples. We further demonstrate that machine learning-based methods enable MG-AST with superior accuracy compared to rule-based approaches on simulated native patient samples.