Use of Metagenomic Shotgun Sequencing Technology To Detect Foodborne Pathogens within the Microbiome of the Beef Production Chain

Abstract

Foodborne illnesses associated with pathogenic bacteria are a global public health and economic challenge. The diversity of microorganisms (pathogenic and nonpathogenic) that exists within the food and meat industries complicates efforts to understand pathogen ecology. Further, little is known about the interaction of pathogens within the microbiome throughout the meat production chain. Here, a metagenomic approach and shotgun sequencing technology were used as tools to detect pathogenic bacteria in environmental samples collected from the same groups of cattle at different longitudinal processing steps of the beef production chain: cattle entry to feedlot, exit from feedlot, cattle transport trucks, abattoir holding pens, and the end of the fabrication system. The log read counts classified as pathogens per million reads for Salmonella enterica,Listeria monocytogenes,Escherichia coli,Staphylococcus aureus, Clostridium spp. (C. botulinum and C. perfringens), and Campylobacter spp. (C. jejuni,C. coli, and C. fetus) decreased over subsequential processing steps. Furthermore, the normalized read counts for S. enterica,E. coli, and C. botulinumwere greater in the final product than at the feedlots, indicating that the proportion of these bacteria increased (the effect on absolute numbers was unknown) within the remaining microbiome. From an ecological perspective, data indicated that shotgun metagenomics can be used to evaluate not only the microbiome but also shifts in pathogen populations during beef production. Nonetheless, there were several challenges in this analysis approach, one of the main ones being the identification of the specific pathogen from which the sequence reads originated, which makes this approach impractical for use in pathogen identification for regulatory and confirmation purposes.

FIG 1

Least-square means with standard error of log read counts per million reads of each investigated pathogen/bacterium from samples collected at different sites/times (arrival, n = 24; exit, n = 24; truck, n = 8; holding pen, n = 15; and market-ready samples, n = 16).

FIG 2

(A to D) Pairwise comparison of log fold change (logFC) of normalized counts of investigated pathogens and nonpathogens between market-ready and arrival samples (A), exit and arrival samples (B), truck and exit samples (C), and holding pen and arrival samples (D). Red circles indicate a significant (adjusted P value [adj.P.Val] < 0.05) increase in normalized counts of bacterial species in samples collected at a former site/time, and green circles illustrate a significant (adjusted P < 0.05) decrease in normalized counts of bacterial species in samples collected at a former site/time within comparisons. Gray circles indicate that the change between samples collected at different sites was not significant. The size of the circles is proportional to average normalized counts of corresponding bacterial species across all samples.

FIG 3

Microbiome composition at the phylum level for samples collected at different sites/times. The top 5 phyla (accounts for >97% of matches at the phylum level) were reported for each site/time, and all other phyla were grouped into “Other” for each site/time.

FIG 4

Box plot of Shannon's diversity index for samples collected at different sites/times (arrival, n = 24; exit, n = 24; truck, n = 8; holding pen, n = 15; and market-ready samples, n = 16). Different letters (A and B) indicate that the least-squares means of Shannon's diversity index differed among samples collected at different sites/times (P < 0.05). The circle indicates an outlier.

FIG 5

Nonmetric multidimensional scaling (NMDS) for ordination plots of normalized counts at the species level. The results of the analysis of similarities (anosim) for them were R = 0.3888, P = 0.001, stress = 0.166, by site/time (A); R = 0.7217, P = 0.001, stress = 0.166, by matrix (B); R = 0.2128, P = 0.001, stress = 0.105, by site/time (for fecal samples only) (C); and R = 0.4143, P = 0.001, stress = 0.131, by site (for water samples only) (D).

FIG 6

Proportion of arrival (n = 24), exit (n = 24), and holding pen (n= 15) samples that contained at least one virulence factor from four superfamilies.