Microbiome profiling through 16S rRNA gene sequence analysis has proven to be a useful research tool in the study of C. difficile infection (CDI); however, CDI microbiome studies typically report results at the genus level or higher, thus precluding identification of this pathogen relative to other members of the gut microbiota. Accurate identification of C. difficile relative to the overall gut microbiome may be useful in assessments of colonization in research studies or as a prognostic indicator for patients with CDI. To investigate the burden of C. difficile at the species level relative to the overall gut microbiome, we applied a high-resolution method for 16S rRNA sequence assignment to previously published gut microbiome studies of CDI and other patient populations. We identified C. difficile in 131 of 156 index cases of CDI (average abundance 1.78%), and 18 of 211 healthy controls (average abundance 0.008%). We further detected substantial levels of C. difficile in a subset of infants that persisted over the first two to 12 months of life. Correlation analysis of C. difficile burden compared to other detected species demonstrated consistent negative associations with C. scindens and multiple Blautia species. These analyses contribute insight into the relative burden of C. difficile in the gut microbiome for multiple patient populations, and indicate that high-resolution 16S rRNA gene sequence analysis may prove useful in the development and evaluation of new therapies for CDI.

Culture based methods are commonly employed to detect pathogens in food and environmental samples. These methods are time consuming and complex, requiring multiple non-selective and selective enrichment broths, and usually take at least 1 week to recover and identify pathogens. Improving pathogen detection in foods is a primary goal for regulatory agencies and industry. Salmonella detection in food relies on a series of culture steps in broth formulations optimized to resuscitate Salmonella and reduce the abundance of competitive bacteria. Examples of non-selective pre-enrichment broths used to isolate Salmonella from food include Lactose, Universal Pre-enrichment, BPW, and Trypticase Soy broths. Tetrathionate (TT) and Rappaport–Vassiliadis (RV) broths are employed after a 24-h non-selective enrichment to select for Salmonella and hamper the growth of competitive bacteria. In this study, we tested a new formulation of TT broth that lacks brilliant green dye and has lower levels of TT . We employed this TT broth formulation in conjunction with a 6-h non-selective pre-enrichment period and determined that Salmonella recovery was possible one day earlier than standard food culture methods. We tested the shortened culture method in different non-selective enrichment broths, enumerated Salmonella in the non-selective enrichments, and used 16S rRNA gene sequencing to determine the proportional abundances of Salmonella in the TT and RV selective enrichments. Together these data revealed that a 6-h non-selective pre-enrichment reduces the levels of competitive bacteria inoculated into the selective TT and RV broths, enabling the recovery of Salmonella 1 day earlier than standard culture enrichment methods.

Food microbiome composition impacts food safety and quality. The resident microbiota of many food products is influenced throughout the farm to fork continuum by farming practices, environmental factors, and food manufacturing and processing procedures. Currently, most food microbiology studies rely on culture-dependent methods to identify bacteria. However, advances in high-throughput DNA sequencing technologies have enabled the use of targeted 16S rRNA gene sequencing to profile complex microbial communities including non-culturable members. In this study we used 16S rRNA gene sequencing to assess the microbiome profiles of plant and animal derived foods collected at two points in the manufacturing process; post-harvest/pre-retail (cilantro) and retail (cilantro, masala spice mixes, cucumbers, mung bean sprouts, and smoked salmon). Our findings revealed microbiome profiles, unique to each food, that were influenced by the moisture content (dry spices, fresh produce), packaging methods, such as modified atmospheric packaging (mung bean sprouts and smoked salmon), and manufacturing stage (cilantro prior to retail and at retail). The masala spice mixes and cucumbers were comprised mainly of Proteobacteria, Firmicutes, and Actinobacteria. Cilantro microbiome profiles consisted mainly of Proteobacteria, followed by Bacteroidetes, and low levels of Firmicutes and Actinobacteria. The two brands of mung bean sprouts and the three smoked salmon samples differed from one another in their microbiome composition, each predominated by either by Firmicutes or Proteobacteria. These data demonstrate diverse and highly variable resident microbial communities across food products, which is informative in the context of food safety, and spoilage where indigenous bacteria could hamper pathogen detection, and limit shelf life.

16S rRNA community profiling continues to be a useful tool to study microbiome composition and dynamics, in part due to advances in next generation sequencing technology that translate into reductions in cost. Reliable taxonomic identification to the species-level, however, remains difficult, especially for short-read sequencing platforms, due to incomplete coverage of the 16S rRNA gene. This is especially true for Salmonella enterica, which is often found as a low abundant member of the microbial community, and is often found in combination with several other closely related enteric species. Here, we report on the evaluation and application of Resphera Insight, an ultra-high resolution taxonomic assignment algorithm for 16S rRNA sequences to the species level. The analytical pipeline achieved 99.7% sensitivity to correctly identify S. enterica from WGS datasets extracted from the FDA GenomeTrakr Bioproject, while demonstrating 99.9% specificity over other Enterobacteriaceae members. From low-diversity and low-complexity samples, namely ice cream, the algorithm achieved 100% specificity and sensitivity for Salmonella detection. As demonstrated using cilantro and chili powder, for highly complex and diverse samples, especially those that contain closely related species, the detection threshold will likely have to be adjusted higher to account for misidentifications. We also demonstrate the utility of this approach to detect Salmonella in the clinical setting, in this case, bloodborne infections.

The iQ-Check Salmonella II Real-Time PCR test kit utilizes Salmonella-specific oligonucleotide probes and primers for the rapid and specific detection of Salmonella species in select food types. The alternative method was evaluated by using 375 g test portions in an unpaired study design for two matrices, milk chocolate and dry dog food. Each matrix was compared with the U.S. Food and Drug Administration Chapter 5 Salmonella reference method. Fourteen technicians from 12 laboratories, including academia and industry, located within the United States and Canada participated in the collaborative study. Three levels of contamination were evaluated for each matrix: an uninoculated control level (0 CFU/test portion), a low inoculum level (0.2-2 CFU/test portion), and a high inoculum level (2-5 CFU/test portion). The statistical analysis was conducted according to the Probability of Detection (POD) statistical model. The results obtained for the low inoculum level test portions produced a difference in the candidate presumptive and confirmatory results (dLPOD) value with a 95% confidence interval of -0.05, (-0.15, 0.06) for the milk chocolate and 0.10, (-0.01, 0.21) for the dry dog food. The dLPOD results indicate an equivalence between the candidate method and reference method for the matrices evaluated, and the method demonstrated acceptable interlaboratory reproducibility as determined in the collaborative evaluation. False positive and false negative rates were determined for each matrix and produce values of <2%. Based on the data generated, the method demonstrated acceptable interlaboratory reproducibility data and statistical analysis.