Shotgun-metagenomics reveals a highly diverse and communal microbial network present in the drains of three beef-processing plants

Abstract

BackgroundMulti-species biofilms pose a problem in various environments, especially food-processing environments. The diversity of microorganisms in these biofilms plays a critical role in their integrity and protection against external biotic and abiotic factors. Compared to single-species biofilms, mixed-species biofilms are more resistant to various stresses, including antimicrobials like sanitizers. Therefore, understanding the microbiome composition and diversity in biofilms and their metabolic potential is a priority when developing intervention techniques to combat foodborne pathogens in food processing environments. MethodsThis study aimed to describe and compare the microbiome profile of 75 drain biofilm samples obtained from five different locations (Hotscale, Hotbox, Cooler, Processing, & Grind room) of three beef-processing plants (Plant A, B & C) taken over two timepoints 2017-18 (T1) and 2021 (T2) by shotgun sequencingResultsCore microbiome analysis found Pseudomonas, Psychrobacter, and Acinetobacter to be the top three prevalent genera among the plants and locations. Alpha diversity analysis demonstrated a high diversity of microbiome present in all the plants and locations across the time points. Functional analysis showed the high metabolic potential of the microbial community with abundance of genes in metabolism, cell-adhesion, motility, and quorum sensing. Moreover, Quaternary Ammonium Compound (QAC) resistance genes were also observed, this is significant as QAC sanitizers are commonly used in many food processing facilities. Multi-functional genes such as transposases, polymerases, permeases, flagellar proteins, and Mobile Genetic Elements (MGEs) were found suggesting these are dynamic microbial communities that work together to protect themselves against environmental stresses through multiple defense mechanisms.ConclusionThis study provides a framework for understanding the collective microbial network spanning a beef processing system. The results can be used to develop intervention strategies to best control these highly communicative microbial networks.