Harnessing Whole Genome Sequence Data for Facility-Specific Signatures for Listeria monocytogenes: A Case Study With Turkey Processing Plants in the United States

Listeria monocytogenes is a Gram-positive foodborne pathogen responsible for the severe disease listeriosis and notorious for its ability to persist in food processing plants, leading to contamination of processed, ready-to-eat foods. L. monocytogenes persistence in various food processing environments (FPEs) has been extensively investigated by various subtyping tools, with increasing use of whole genome sequencing (WGS). However, major knowledge gaps remain. There is a need for facility-specific molecular signatures not only for adequate attribution of L. monocytogenes to a specific FPE but also for improved understanding of the ecology and evolution of L. monocytogenes in the food processing ecosystem. Furthermore, multiple strains can be recovered from a single FPE sample, but their diversity can be underestimated with common molecular subtyping tools. In this study we investigated a panel of 54 L. monocytogenes strains from four turkey processing plants in the United States. A combination of WGS and phenotypic assays was employed to assess strain persistence as well as identify facility-specific molecular signatures. Comparative analysis of allelic variation across the whole genome revealed that allelic profiles have the potential to be specific to individual processing plants. Certain allelic profiles remained associated with individual plants even when closely-related strains from other sources were included in the analysis. Furthermore, for certain sequence types (STs) based on the seven-locus multilocus sequence typing scheme, presence and location of premature stop codons in inlA, inlB length, prophage sequences, and the sequence content of a genomic hotspot could serve as plant-specific signatures. Interestingly, the analysis of different isolates from the same environmental sample revealed major differences not only in serotype and ST, but even in the sequence content of strains of the same ST. This study highlights the potential for WGS data to be deployed for identification of facility-specific signatures, thus facilitating the tracking of strain movement through the food chain. Furthermore, deployment of WGS for intra-sample strain analysis allows for a more complete environmental surveillance of L. monocytogenes in food processing facilities, reducing the risk of failing to detect strains that may be clinically relevant and potentially novel.