Monitoring the microbiome for food safety and quality using deep shotgun sequencing

Kristen L Beck,Ed Seabolt,Matthew Davis,André Quintanar,Carl H Marlowe,James H Kaufman ,Sophie Pierre,Stefan Edlund,Peter J Markwell ,Ban Kawas,Robert J Prill,Nguyet Kong,Balasubramanian Ganesan,Géraud Dubois ,Niina Haiminen,R C Baker ,Laxmi Parida,Bihua C Huang ,D D Chambliss ,Mark Kunitomi,Harsha Krishnareddy,Bart C Weimer

doi:10.1038/s41538-020-00083-y

Abstract

In this work, we hypothesized that shifts in the food microbiome can be used as an indicator of unexpected contaminants or environmental changes. To test this hypothesis, we sequenced the total RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that improved microbe detection specificity to >99.96% during in silico validation. The pipeline identified 119 microbial genera per HPP sample on average with 65 genera present in all samples. The most abundant of these were Bacteroides, Clostridium, Lactococcus, Aeromonas, and Citrobacter. We also observed shifts in the microbial community corresponding to ingredient composition differences. When comparing culture-based results for Salmonella with total RNA sequencing, we found that Salmonella growth did not correlate with multiple sequence analyses. We conclude that microbiome sequencing is useful to characterize complex food microbial communities, while additional work is required for predicting specific species’ viability from total RNA sequencing.

Highlights

Sequencing the microbiome of food may reveal characteristics of the associated microbial content that culturing or targeted wholegenome sequencing (WGS) alone cannot
This work demonstrates that total RNA sequencing is a robust approach for monitoring the food microbiome for use in food safety and quality applications, while additional work is required for predicting pathogen viability
In total DNA or RNA sequencing of clinical or RESULTS Evaluation of microbial identification capability in total RNA and DNA sequencing Microbial identification in microbiomes often leverages shotgun animal or even plant microbiomes, eukaryotic content may often comprise >90% of the total sequencing reads. This presents an important bioinformatic challenge that we addressed by filtering matrix content using a custom-built reference database of 31 common food ingredient and DNA sequencing; total RNA sequencing can provide additional information about viable bacterial activity in a contaminant genomes (Supplementary Table 2) using the k-mer classification tool Kraken[37]

Summary

INTRODUCTION

Sequencing the microbiome of food may reveal characteristics of the associated microbial content that culturing or targeted wholegenome sequencing (WGS) alone cannot. Since WGS relies on culturing a microbial isolate prior to sequencing, there are inherent biases and limitations in its ability to describe the microorganisms and their interactions in a food sample Such information would be very valuable for food safety and quality applications. Microorganisms are sensitive to changes in temperature, salinity, study food microbiomes is novel, each step of the analysis pH, oxygen content, and many other physicochemical factors that workflow (Fig. 1) was carefully designed and scrutinized for alter their ability to grow, persist, and cause disease This work demonstrates that total RNA sequencing is a robust approach for monitoring the food microbiome for use in food safety and quality applications, while additional work is required for predicting pathogen viability.

RESULTS

DISCUSSION

CODE AVAILABILITY