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.

Publisher: American Society for Microbiology Country of Publication: United States NLM ID: 101680636 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2379-5077 (Electronic) Linking ISSN: 23795077 NLM ISO Abbreviation: mSystems Subsets: MEDLINE

Original Publication: Washington, DC : American Society for Microbiology, [2016]-

Whole Genome Sequencing*/standards ; European Union* ; Laboratory Proficiency Testing* ; Drug Resistance, Bacterial*/genetics ; Genome, Bacterial*; Humans ; Quality Control ; Bacteria/genetics ; Bacteria/drug effects ; Anti-Bacterial Agents/pharmacology ; Laboratories/standards ; Species Specificity

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.
Competing Interests: The authors declare no conflict of interest.

EFSA J. 2019 Dec 03;17(12):e05898. (PMID: 32626197)
Nat Rev Genet. 2024 Feb;25(2):142-157. (PMID: 37749210)
Comput Struct Biotechnol J. 2023 Mar 24;21:2352-2364. (PMID: 37025654)
Cell Syst. 2020 Jan 22;10(1):99-108.e5. (PMID: 31864964)
Clin Microbiol Infect. 2017 Jan;23(1):2-22. (PMID: 27890457)
Genome Res. 2017 May;27(5):737-746. (PMID: 28100585)
Bioinformatics. 2016 Jul 15;32(14):2103-10. (PMID: 27153593)
PLoS Comput Biol. 2017 Jun 8;13(6):e1005595. (PMID: 28594827)
Nat Rev Genet. 2016 May 17;17(6):333-51. (PMID: 27184599)
J Clin Microbiol. 2015 Aug;53(8):2410-26. (PMID: 25972421)
Front Microbiol. 2021 Dec 24;12:786146. (PMID: 35003019)
Front Microbiol. 2022 Jul 14;13:944770. (PMID: 35910628)
Appl Environ Microbiol. 2019 Mar 22;85(7):. (PMID: 30709819)
BMC Bioinformatics. 2009 Dec 15;10:421. (PMID: 20003500)
J Clin Microbiol. 2012 Apr;50(4):1355-61. (PMID: 22238442)
Genomics. 2016 Jan;107(1):1-8. (PMID: 26554401)
Biology (Basel). 2023 Jul 13;12(7):. (PMID: 37508427)
Microb Genom. 2023 Jan;9(1):. (PMID: 36748454)
One Health Outlook. 2020;2(1):20. (PMID: 33103064)
Cold Spring Harb Perspect Biol. 2015 Jun 22;7(8):a018119. (PMID: 26101080)
Genome Res. 2015 Jan;25(1):111-8. (PMID: 25491771)
J Mol Diagn. 2022 Apr;24(4):395-405. (PMID: 35085783)
Microb Genom. 2021 Feb;7(2):. (PMID: 33417534)
EFSA J. 2021 Jul 28;19(7):e06506. (PMID: 34335919)
Front Cell Infect Microbiol. 2020 Oct 19;10:527102. (PMID: 33194784)
BMJ Glob Health. 2020 Nov;5(11):. (PMID: 33239336)
Micromachines (Basel). 2023 Feb 16;14(2):. (PMID: 36838159)
Microb Genom. 2023 Aug;9(8):. (PMID: 37526643)
Antibiotics (Basel). 2021 Jan 12;10(1):. (PMID: 33445633)
Pathology. 2015 Apr;47(3):199-210. (PMID: 25730631)
PLoS One. 2014 Aug 11;9(8):e104984. (PMID: 25110940)
Front Microbiol. 2022 Aug 08;13:973605. (PMID: 36003946)
Genome Biol. 2019 Nov 28;20(1):257. (PMID: 31779668)
Front Public Health. 2019 Jun 27;7:172. (PMID: 31316960)

773830 EC | Horizon 2020 Framework Programme (H2020); UK AID Flemming Fund Regional Grant; European Union EURLs-EURCs 2021-2022

Keywords: antimicrobial resistance; genomic proficiency test; next-generation sequencing; quality control; short-read sequencing; statistical comparison; whole-genome sequencing

0 (Anti-Bacterial Agents)

Date Created: 20240806 Date Completed: 20240917 Latest Revision: 20240919

20240919

PMC11406893

10.1128/msystems.00160-24

39105591