Abstract
Diagnostic testing for foodborne pathogens relies on culture-based techniques that are not rapid enough for real-time disease surveillance and do not give a quantitative picture of pathogen abundance or the response of the natural microbiome. Powerful sequence-based culture-independent approaches, such as shotgun metagenomics, could sidestep these limitations and potentially reveal a pathogen-specific signature on the microbiome that would have implications not only for diagnostics but also for better understanding disease progression and pathogen ecology. However, metagenomics have not yet been validated for foodborne pathogen detection. Toward closing these gaps, we applied shotgun metagenomics to stool samples collected from two geographically isolated (Alabama and Colorado) foodborne outbreaks, where the etiologic agents were identified by culture-dependent methods as distinct strains of Salmonella enterica subsp. enterica serovar Heidelberg. Metagenomic investigations were consistent with the culture-based findings and revealed, in addition, the in situ abundance and level of intrapopulation diversity of the pathogen, the possibility of coinfections with Staphylococcus aureus, overgrowth of commensal Escherichia coli, and significant shifts in the gut microbiome during infection relative to reference healthy samples. Additionally, we designed our bioinformatics pipeline to deal with several challenges associated with the analysis of clinical samples, such as the high frequency of coeluting human DNA sequences and assessment of the virulence potential of pathogens. Comparisons of these results to those of other studies revealed that in several, but not all, cases of diarrheal outbreaks, the disease and healthy states of the gut microbial community might be distinguishable, opening new possibilities for diagnostics. IMPORTANCE: Diagnostic testing for enteric pathogens has relied for decades on culture-based techniques, but a total of 38.4 million cases of foodborne illness per year cannot be attributed to specific causes. This study describes new culture-independent metagenomic approaches and the associated bioinformatics pipeline to detect and type the causative agents of microbial disease with unprecedented accuracy, opening new possibilities for the future development of health technologies and diagnostics. Our tools and approaches should be applicable to other microbial diseases in addition to foodborne diarrhea.