Centre-specific bacterial pathogen typing affects infection-control decision making

Abstract

Whole-genome sequencing is becoming the de facto standard for bacterial outbreak surveillance and infection prevention. This is accompanied by a variety of bioinformatic tools and needs bioinformatics expertise for implementation. However, little is known about the concordance of reported outbreaks when using different bioinformatic workflows. In this multi-centre proficiency testing among 13 major Dutch healthcare-affiliated centres, bacterial whole-genome outbreak analysis was assessed. Centres who participated obtained two randomized bacterial datasets of Illumina sequences, a Klebsiella pneumoniae and a Vancomycin-resistant Enterococcus faecium, and were asked to apply their bioinformatic workflows. Centres reported back on antimicrobial resistance, multi-locus sequence typing (MLST), and outbreak clusters. The reported clusters were analysed using a method to compare landscapes of phylogenetic trees and calculating Kendall–Colijn distances. Furthermore, fasta files were analysed by state-of-the-art single nucleotide polymorphism (SNP) analysis to mitigate the differences introduced by each centre and determine standardized SNP cut-offs. Thirteen centres participated in this study. The reported outbreak clusters revealed discrepancies between centres, even when almost identical bioinformatic workflows were used. Due to stringent filtering, some centres failed to detect extended-spectrum beta-lactamase genes and MLST loci. Applying a standardized method to determine outbreak clusters on the reported de novo assemblies, did not result in uniformity of outbreak-cluster composition among centres.

Large-Scale Evaluation of a Rapid Fully Automated Analysis Platform to Detect and Refute Outbreaks Based on MRSA Genome Comparisons

“…The automated nature of the analysis system provides greater standardization and removes interoperator variability compared with research pipelines. This is much needed if genome sequencing it to be used in clinical practice based on the findings of a 13-center proficiency testing evaluation in the Netherlands ( 8 ). Centers received two data sets containing 40 genomes each of Klebsiella pneumoniae and vancomycin-resistant Enterococcus faecium , which were analyzed for antimicrobial resistance, sequence type, and outbreak clusters.…”

“…The reported outbreak clusters revealed discrepancies between centers even when almost identical bioinformatic workflows were used, and some centers failed to detect extended-spectrum beta-lactamase genes and MLST loci. Furthermore, applying a standardized method to determine outbreak clusters on the reported de novo assemblies did not result in uniformity of outbreak-cluster composition ( 8 ).…”

“…An automated analysis tool has the advantage that results are generated without the need for an operator with any bioinformatic training. It would also provide greater standardization of analysis and less interoperator variability compared with research pipelines ( 8 ). Here, we extend our evaluation to a 9-month evaluation of MRSA acquisition and outbreak detection in a cohort of 777 patients, which was completed prior to the onset of the COVID-19 pandemic.…”

ASGARD+: A New Modular Platform for Bacterial Antibiotic‐Resistant Analysis

“…These molecular mechanisms for tolerating antimicrobial drugs vary within and across bacterial species and spread among themselves rapidly, causing the development of resistance in many ways (Van Camp et al., 2020). The constant genetic variation in the different strains makes bacterial outbreak surveillance difficult, and although traditional techniques such as PCR‐based genotypic tests, multi‐locus sequence typing (MLST), and amplification fragment length polymorphism (AFLP) have provided robust results, they do not provide information on the mechanisms of antibiotic resistance acquisition and spreading among microbial communities (Carriço, Sabat, Friedrich, Ramirez, & ESCMID Study Group for Epidemiological Markers (ESGEM) 2013; Coolen et al., 2021). Nowadays, the use of genomic epidemiology through the application of whole‐genome sequencing (WGS) together with bioinformatics data analysis has facilitated the study of antimicrobial resistance on a large scale, allowing accurate, rapid, and cost‐effective genotyping of bacterial isolates and outbreak tracking (Shelenkov, 2021; Tümmler, 2020).…”

The Notable Achievements and the Prospects of Bacterial Pathogen Genomics

“…Again, some discordance was observed in predicting the antimicrobial susceptibility [ 105 ]. Along the same lines, a multi-centre study involving 13 major Dutch health care-affiliated centres revealed that, even when analysing the same raw sequencing data (Illumina), there were discrepancies in reporting the antimicrobial resistance, multi-locus sequence typing (MLST), and outbreak clusters [ 106 ]. Importantly, a clinical study of bile-duct cultures from pancreatic head resections compared the efficiency, cost, and time needed to obtain actionable results in terms of surgical site infection and antibiotic stewardship from the classical aerobic/anaerobic cultures and Oxford Nanopore sequencing [ 107 ].…”