Background

Using phylogenomic analysis tools for tracking pathogens has become standard practice in academia, public health agencies, and large industries. Using the same raw read genomic data as input, there are several different approaches being used to infer phylogenetic tree. These include many different SNP pipelines, wgMLST approaches, k-mer algorithms, whole genome alignment and others; each of these has advantages and disadvantages, some have been extensively validated, some are faster, some have higher resolution. A few of these analysis approaches are well-integrated into the regulatory process of US Federal agencies (e.g. the FDA's SNP pipeline for tracking foodborne pathogens). However, despite extensive validation on benchmark datasets and comparison with other pipelines, we lack methods for fully exploring the effects of multiple parameter values in each pipeline that can potentially have an effect on whether the correct phylogenetic tree is recovered.

Results

To resolve this problem, we offer a program, TreeToReads, which can generate raw read data from mutated genomes simulated under a known phylogeny. This simulation pipeline allows direct comparisons of simulated and observed data in a controlled environment. At each step of these simulations, researchers can vary parameters of interest (e.g., input tree topology, amount of sequence divergence, rate of indels, read coverage, distance of reference genome, etc) to assess the effects of various parameter values on correctly calling SNPs and reconstructing an accurate tree.

Conclusions

Such critical assessments of the accuracy and robustness of analytical pipelines are essential to progress in both research and applied settings.

Salmonella enterica serotype Enteritidis is a foodborne pathogen of global concern, because it is frequently isolated from foods and patients. Draft genome sequences are reported here for 64 S Enteritidis strains isolated from the intestines and spleens of mice caught live on chicken farms in the U.S. Northeast. The availability of these genomes provides baseline information on the genomic diversity of S Enteritidis during the 1990s, when foodborne outbreaks traced to internal contamination of eggs were prevalent.

The ability to detect a specific organism from a complex environment is vitally important to many fields of public health, including food safety. For example, tomatoes have been implicated numerous times as vehicles of foodborne outbreaks due to strains of Salmonella but few studies have ever recovered Salmonella from a tomato phyllosphere environment. Precision of culturing techniques that target agents associated with outbreaks depend on numerous factors. One important factor to better understand is which species co-enrich during enrichment procedures and how microbial dynamics may impede or enhance detection of target pathogens. We used a shotgun sequence approach to describe taxa associated with samples pre-enrichment and throughout the enrichment steps of the Bacteriological Analytical Manual's (BAM) protocol for detection of Salmonella from environmental tomato samples. Recent work has shown that during efforts to enrich Salmonella (Proteobacteria) from tomato field samples, Firmicute genera are also co-enriched and at least one co-enriching Firmicute genus (Paenibacillus sp.) can inhibit and even kills strains of Salmonella. Here we provide a baseline description of microflora that co-culture during detection efforts and the utility of a bioinformatic approach to detect specific taxa from metagenomic sequence data. We observed that uncultured samples clustered together with distinct taxonomic profiles relative to the three cultured treatments (Universal Pre-enrichment broth (UPB), Tetrathionate (TT), and Rappaport-Vassiliadis (RV)). There was little consistency among samples exposed to the same culturing medias, suggesting significant microbial differences in starting matrices or stochasticity associated with enrichment processes. Interestingly, Paenibacillus sp. (Salmonella inhibitor) was significantly enriched from uncultured to cultured (UPB) samples. Also of interest was the sequence based identification of a number of sequences as Salmonella despite indication by all media, that samples were culture negative for Salmonella. Our results substantiate the nascent utility of metagenomic methods to improve both biological and bioinformatic pathogen detection methods.

Background

Research to understand and control microbiological risks associated with the consumption of fresh fruits and vegetables has examined many environments in the farm to fork continuum. An important data gap however, that remains poorly studied is the baseline description of microflora that may be associated with plant anatomy either endemically or in response to environmental pressures. Specific anatomical niches of plants may contribute to persistence of human pathogens in agricultural environments in ways we have yet to describe. Tomatoes have been implicated in outbreaks of Salmonella at least 17 times during the years spanning 1990 to 2010. Our research seeks to provide a baseline description of the tomato microbiome and possibly identify whether or not there is something distinctive about tomatoes or their growing ecology that contributes to persistence of Salmonella in this important food crop.

Results

DNA was recovered from washes of epiphytic surfaces of tomato anatomical organs; leaves, stems, roots, flowers and fruits of Solanum lycopersicum (BHN602), grown at a site in close proximity to commercial farms previously implicated in tomato-Salmonella outbreaks. DNA was amplified for targeted 16S and 18S rRNA genes and sheared for shotgun metagenomic sequencing. Amplicons and metagenomes were used to describe "native" bacterial microflora for diverse anatomical parts of Virginia-grown tomatoes.

Conclusions

Distinct groupings of microbial communities were associated with different tomato plant organs and a gradient of compositional similarity could be correlated to the distance of a given plant part from the soil. Unique bacterial phylotypes (at 95% identity) were associated with fruits and flowers of tomato plants. These include Microvirga, Pseudomonas, Sphingomonas, Brachybacterium, Rhizobiales, Paracocccus, Chryseomonas and Microbacterium. The most frequently observed bacterial taxa across aerial plant regions were Pseudomonas and Xanthomonas. Dominant fungal taxa that could be identified to genus with 18S amplicons included Hypocrea, Aureobasidium and Cryptococcus. No definitive presence of Salmonella could be confirmed in any of the plant samples, although 16S sequences suggested that closely related genera were present on leaves, fruits and roots.