A quasi-metagenomics approach for subspecies level differentiation of Listeria monocytogenes in foods based on assembly alignment discrepancies.

Listeriosis is a serious bacterial illness most often caused by consuming food contaminated with Listeria monocytogenes. Rapid response to listeriosis outbreaks, including the identification and tracking of the contamination source, is crucial for safeguarding public health. Despite its utility in foodborne outbreak investigations, high-resolution whole-genome sequencing analysis suffers from inherent delays, as it remains contingent upon recovery of pure isolates via culture-dependent methods. Here, we hypothesize that a quasi-metagenomics approach leveraging bacterial enrichment sequencing can provide preliminary genomic information sufficient to distinguish L. monocytogenes strains. Simulation and sequencing of mock bacterial community samples exposed detectable average nucleotide identity gap and primary metagenome assembly dissimilarity between non-identical L. monocytogenes strains, provided that assembly alignment achieved ≥ 10% proportional coverage of the reference genome. By combining simple Listeria enrichment procedures, a mutanolysin-based bacterial DNA extraction protocol, and multiple displacement amplification technique, an isolate-free sequencing workflow was developed. Using fresh, ready-to-eat microgreens as model food matrices with a challenging microbial background, we present a proof-of-principle demonstration of an end-to-end workflow that utilizes a best-match strategy against a scenario-specific and curated genome reference panel to detect a suspected L. monocytogenes strain in fabricated contamination events. By enabling earlier provision of the genomic fingerprint information pertaining to the implicated L. monocytogenes strain, this quasi-metagenomic sequencing approach may prove useful for supporting time-sensitive outbreak and traceback investigations, with subsequent confirmation achievable through whole-genome sequencing.