Translatability of WGS typing results can simplify data exchange for surveillance and control of Listeria monocytogenes. Issue 1 (4th January 2021)

• Record Type: Journal Article
• Authors: Lüth, Stefanie; Deneke, Carlus; Kleta, Sylvia; Al Dahouk, Sascha
• Is Part Of: Microbial genomics. Volume 7:Issue 1(2021)
• Publication Date: 2021-01-04
• Subjects: core genome MLST -- genomic epidemiology -- outbreak -- single nucleotide polymorphism -- standardisation -- whole genome sequencing; Microbial genomics -- Periodicals; 572.8629
• DOI: 10.1099/mgen.0.000491
• Languages: English
• ISSNs: 2057-5858

Abstract

Abstract : Where classical epidemiology has proven to be inadequate for surveillance and control of foodborne pathogens, molecular epidemiology, using genomic typing methods, can add value. However, the analysis of whole genome sequencing (WGS) data varies widely and is not yet fully harmonised. We used genomic data on 494 Listeria monocytogenes isolates from ready-to-eat food products and food processing environments deposited in the strain collection of the German National Reference Laboratory to compare various procedures for WGS data analysis and to evaluate compatibility of results. Two different core genome multilocus sequence typing (cgMLST) schemes, different reference genomes in single nucleotide polymorphism (SNP) analysis and commercial as well as open-source software were compared. Correlation of allele distances from the different cgMLST approaches was high, ranging from 0.97 to 1, and unified thresholds yielded higher clustering concordance than scheme-specific thresholds. The number of detected SNP differences could be increased up to a factor of 3.9 using a specific reference genome compared with a general one. Additionally, specific reference genomes improved comparability of SNP analysis results obtained using different software tools. The use of a closed or a draft specific reference genome did not make a difference. The harmonisation of WGS data analysis will finally guarantee seamless data exchange, but, in the meantime, knowledge on threshold values that lead to comparable clustering of isolates by different methods may improve communication between laboratories. We therefore established a translation code between commonly applied cgMLST and SNP methods based on optimised clustering concordances. This code can work as a first filter to identify WGS-based typing matches resulting from different methods, which opens up a new perspective for data exchange and thereby accelerates time-critical analyses, such as in outbreak investigations.