Quasimetagenomic source tracking of Listeria monocytogenes from naturally contaminated ice cream

Abstract

Abstract Background The more quickly bacterial pathogens responsible for foodborne illness outbreaks can be linked to a vehicle of transmission or a source, the more illnesses can be prevented. Whole genome sequencing (WGS) based approaches to source tracking have greatly increased the speed and resolution with which public health response can pinpoint the vehicle and source of outbreaks. Traditionally, WGS approaches have focused on the culture of an individual isolate before proceeding to DNA extraction and sequencing. For Listeria monocytogenes (Lm), generation of an individual isolate for sequencing typically takes about 6 days. Here we demonstrate that a hybrid, “quasimetagenomic” approach ie; direct sequencing of microbiological enrichments (first step in pathogen detection and recovery) can provide high resolution source tracking sequence data, 5 days earlier than response that focuses on culture and sequencing of an individual isolate. This expedited approach could save lives, prevent illnesses and potentially minimize unnecessary destruction of food. Methods Naturally contaminated ice cream (from a 2015 outbreak) was enriched to recover Listeria monocytogenes following protocols outlined in the Bacteriological Analytic Manual (BAM). DNA from enriching microbiota was extracted and sequenced at incremental time-points during the first 48 h of pre-enrichment using the Illumina MiSeq platform (2 by 250), to evaluate genomic coverage of target pathogen, Listeria monocytogenes. Results Quasimetagenomic sequence data acquired from hour 20 were sufficient to discern whether or not Lm strain/s were part of the ongoing outbreak or not. Genomic data from hours 24, 28, 32, 36, 40, 44, and 48 of pre-enrichments all provided identical phylogenetic source tracking utility to the WGS of individual isolates (which require an additional 5 days to culture). Conclusions The speed of this approach (more than twice as fast as current methods) has the potential to reduce the number of illnesses associated with any given outbreak by as many as 75% percent of total cases and potentially with continued optimization of the entire chain of response, contribute to minimized food waste.