Enrichment dynamics of Listeria monocytogenes and the associated microbiome from naturally contaminated ice cream linked to a listeriosis outbreak

Abstract

BackgroundMicrobiota that co-enrich during efforts to recover pathogens from foodborne outbreaks interfere with efficient detection and recovery. Here, dynamics of co-enriching microbiota during recovery of Listeria monocytogenes from naturally contaminated ice cream samples linked to an outbreak are described for three different initial enrichment formulations used by the Food and Drug Administration (FDA), the International Organization of Standardization (ISO), and the United States Department of Agriculture (USDA). Enrichment cultures were analyzed using DNA extraction and sequencing from samples taken every 4 h throughout 48 h of enrichment. Resphera Insight and CosmosID analysis tools were employed for high-resolution profiling of 16S rRNA amplicons and whole genome shotgun data, respectively.ResultsDuring enrichment, other bacterial taxa were identified, including Anoxybacillus, Geobacillus, Serratia, Pseudomonas, Erwinia, and Streptococcus spp. Surprisingly, incidence of L. monocytogenes was proportionally greater at hour 0 than when tested 4, 8, and 12 h later with all three enrichment schemes. The corresponding increase in Anoxybacillus and Geobacillus spp.indicated these taxa co-enriched in competition with L. monocytogenes during early enrichment hours. L. monocytogenes became dominant after 24 h in all three enrichments. DNA sequences obtained from shotgun metagenomic data of Listeria monocytogenes at 48 h were assembled to produce a consensus draft genome which appeared to have a similar tracking utility to pure culture isolates of L. monocytogenes.ConclusionsAll three methods performed equally well for enrichment of Listeria monocytogenes. The observation of potential competitive exclusion of L. mono by Anoxybacillus and Geobacillus in early enrichment hours provided novel information that may be used to further optimize enrichment formulations. Application of Resphera Insight for high-resolution analysis of 16S amplicon sequences accurately identified L. monocytogenes. Both shotgun and 16S rRNA data supported the presence of three slightly variable genomes of L. monocytogenes. Moreover, the draft assembly of a consensus genome of L. monocytogenes from shotgun metagenomic data demonstrated the potential utility of this approach to expedite trace-back of outbreak-associated strains, although further validation will be needed to confirm this utility.Electronic supplementary materialThe online version of this article (doi:10.1186/s12866-016-0894-1) contains supplementary material, which is available to authorized users.

Whole genome sequencing analyses of Listeria monocytogenes that persisted in a milkshake machine for a year and caused illnesses in Washington State

“…However, L. monocytogenes in milkshakes prepared from the multistate outbreak-associated ice cream had a relatively long log phase (9 h) and a slow growth rate of 0.186 CFU/log/h at room temperature, which could be attributed to low level of initial amount of naturally occurring L. monocytogenes [36] and/or the variety and levels of competing microflora present in the ice cream samples [37]. The ice cream products associated with the Washington State outbreak were not available for such analysis.…”

SYN-004 (ribaxamase), an oral beta-lactamase, mitigates antibiotic-mediated dysbiosis in a porcine gut microbiome model

“…; Ottesen et al . ; Ponnusamy et al . ), to achieve bacterial identification to species, subspecies and/or strain level and quantification of micro‐organism relative abundance.…”

“…The samples were resequenced and all achieved well over 10 million reads per sample. Unassembled whole-genome shotgun metagenomic sequencing reads were directly analysed using the Cos-mosID, Inc. (Rockville, MD) bioinformatics software package, as described (Hasan et al 2014;Lax et al 2014;Ottesen et al 2016;Ponnusamy et al 2016), to achieve bacterial identification to species, subspecies and/or strain level and quantification of micro-organism relative abundance. The software utilizes curated genome databases (GenBook â ) and a high-performance data-mining algorithm that rapidly disambiguates hundreds of millions of short reads of a metagenomic sequence into discrete micro-organisms engendering the identified sequences, without the need for sequence assembly.…”

Metagenomic sequencing to replace semi-quantitative urine culture for detection of urinary tract infections: a proof of concept

“…Human sequences in the metagenomic datasets were removed with Deconseq version 0.4.3, human genome version 37(12). The remaining reads were analyzed for identification of microbial DNA at subspecies level and determination of the organism’s relative abundance using the CosmosID bioinformatics software package (CosmosID Inc., Rockville, MD)(13-16). A cloud version is accessible at https://app.cosmosid.com.…”