Modern epidemiology of foodborne bacterial pathogens in industrialized countries relies increasingly on whole genome sequencing (WGS) techniques. As opposed to profiling techniques such as pulsed-field gel electrophoresis, WGS requires a variety of computational methods. Since 2013, United States agencies responsible for food safety including the CDC, FDA, and USDA, have been performing whole-genome sequencing (WGS) on all Listeria monocytogenes found in clinical, food, and environmental samples. Each year, more genomes of other foodborne pathogens such as Escherichia coli, Campylobacter jejuni, and Salmonella enterica are being sequenced. Comparing thousands of genomes across an entire species requires a fast method with coarse resolution; however, capturing the fine details of highly related isolates requires a computationally heavy and sophisticated algorithm. Most L. monocytogenes investigations employing WGS depend on being able to identify an outbreak clade whose inter-genomic distances are less than an empirically determined threshold. When the difference between a few single nucleotide polymorphisms (SNPs) can help distinguish between genomes that are likely outbreak-associated and those that are less likely to be associated, we require a fine-resolution method. To achieve this level of resolution, we have developed Lyve-SET, a high-quality SNP pipeline. We evaluated Lyve-SET by retrospectively investigating 12 outbreak data sets along with four other SNP pipelines that have been used in outbreak investigation or similar scenarios. To compare these pipelines, several distance and phylogeny-based comparison methods were applied, which collectively showed that multiple pipelines were able to identify most outbreak clusters and strains. Currently in the US PulseNet system, whole genome multi-locus sequence typing (wgMLST) is the preferred primary method for foodborne WGS cluster detection and outbreak investigation due to its ability to name standardized genomic profiles, its central database, and its ability to be run in a graphical user interface. However, creating a functional wgMLST scheme requires extended up-front development and subject-matter expertise. When a scheme does not exist or when the highest resolution is needed, SNP analysis is used. Using three Listeria outbreak data sets, we demonstrated the concordance between Lyve-SET SNP typing and wgMLST.Availability: Lyve-SET can be found at https://github.com/lskatz/Lyve-SET.

The lone star tick (Amblyomma americanum) is an abundant and aggressive biter of humans, domestic animals, and wildlife in the southeastern-central USA and an important vector of several known and suspected zoonotic bacterial pathogens. However, the biological drivers of bacterial community variation in this tick are still poorly defined. Knowing the community context in which tick-borne bacterial pathogens exist and evolve is required to fully understand the ecology and immunobiology of the ticks and to design effective public health and veterinary interventions. We performed a metagenomic survey of the bacterial communities of questing A. americanum and tested 131 individuals (66 nymphs, 24 males, and 41 females) from five sites in three states. Pyrosequencing was performed with barcoded eubacterial primers targeting variable 16S rRNA gene regions 5–3. The bacterial communities were dominated by Rickettsia (likely R. amblyommii) and an obligate Coxiella symbiont, together accounting for 6.7–100% of sequences per tick. DNAs from Midichloria, Borrelia, Wolbachia, Ehrlichia, Pseudomonas, or unidentified Bacillales, Enterobacteriaceae, or Rhizobiales groups were also detected frequently. Wolbachia and Midichloria significantly co-occurred in Georgia (p<0.00001), but not in other states. The significance of the Midichloria-Wolbachia co-occurrence is unknown. Among ticks collected in Georgia, nymphs differed from adults in both the composition (p = 0.002) and structure (p = 0.002) of their bacterial communities. Adults differed only in their community structure (p = 0.002) with males containing more Rickettsia and females containing more Coxiella. Comparisons among adult ticks collected in New York and North Carolina supported the findings from the Georgia collection despite differences in geography, collection date, and sample handling, implying that the differences detected are consistent attributes. The data also suggest that some members of the bacterial community change during the tick life cycle and that some sex-specific attributes may be detectable in nymphs.

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