Abstract
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.
Highlights
Enrichment protocols to detect pathogens of interest vary from pathogen to pathogen and depending on matrix of origin
It is important to note that the de novo assembly was done on reads that had not been merged, the Meta-Velvetg reads are, on average, shorter than the FLASHed reads for some samples (Table 1)
We examined the microflora of samples pre and post enrichment to describe non-target microbial species that co-culture during enrichment steps associated with the Bacterial Analytical Manual (BAM) for isolation of Salmonella from tomato phyllosphere samples
Summary
Enrichment protocols to detect pathogens of interest vary from pathogen to pathogen and depending on matrix of origin. The level of specialization required to isolate the same species from different matrices illustrates the complexities associated with culturing methods for detecting pathogens. The substantial number of enrichment procedures suggests that no single method is superior to others in all cases Despite these levels of specialization, it is often even difficult to detect pathogens from samples that have been intentionally spiked. Work by Gorski [2] demonstrated that for currently unknown reasons, some serotypes of Salmonella appear to be more fit in certain media, which could clearly bias certain investigations by favoring recovery of serovars with less relevance to outbreaks These studies and others [3] suggest that serotype recovery is likely biased in many situations and that we may fail to recover pathogens of importance more often than we realize. Explanations for some biases are likely correlated with the currently un-described microbial dynamics associated with the complex assemblages of microflora that coculture during efforts to culture target pathogens
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