Understanding prevalence of Listeria using 16S rRNA-based sequencing of culture enrichments and structural equation modeling within food processing environments.

Abstract

Surface microbiota in food processing environments are an important factor to consider when examining pathogen prevalence and potential food safety risks. This study examines microbial diversity and Listeria prevalence across 48 food production firms in 12 states, across four distinct processing environments: Seafood (n = 20), Dairy (n = 16), Produce (n = 8), and Ready-to-Eat food (RTE, n = 4). Using 16S rRNA amplicon sequencing of environmental swab culture enrichments, we analyzed 1,068 samples to characterize microbiota and Listeria prevalence. Microbial taxa varied by firm type and sampling location, with dominant genera including Enterococcus, Pseudomonas, Lactococcus, and Listeria. Bacterial communities that were positive for Listeria exhibited lower alpha diversity than non-positive communities. Structural equation modeling (SEM) was applied to investigate relationships between biocide use, Listeria prevalence, and microbial diversity. Industries, such as Seafood and Dairy, used biocides with higher pH scores, were associated with lower Listeria prevalence. Through statistical analysis of numerous variables, this study shows how 16S rRNA sequence data can inform food safety by using structural equation modeling (SEM) to understand Listeria prevalence and community responses to safety measures such as biocide use across diverse food production settings.