Microbiome-based environmental monitoring of a dairy processing facility highlights the challenges associated with low microbial-load samples

Aoife J Mchugh,Paul D Cotter,Colin Hill,Fiona Crispie,Min Yap,Conor Feehily

doi:10.1038/s41538-021-00087-2

Abstract

Efficient and accurate identification of microorganisms throughout the food chain can potentially allow the identification of sources of contamination and the timely implementation of control measures. High throughput DNA sequencing represents a potential means through which microbial monitoring can be enhanced. While Illumina sequencing platforms are most typically used, newer portable platforms, such as the Oxford Nanopore Technologies (ONT) MinION, offer the potential for rapid analysis of food chain microbiomes. Initial assessment of the ability of rapid MinION-based sequencing to identify microbes within a simple mock metagenomic mixture is performed. Subsequently, we compare the performance of both ONT and Illumina sequencing for environmental monitoring of an active food processing facility. Overall, ONT MinION sequencing provides accurate classification to species level, comparable to Illumina-derived outputs. However, while the MinION-based approach provides a means of easy library preparations and portability, the high concentrations of DNA needed is a limiting factor.

Highlights

Dairy processing environments harbour microorganisms that have the potential to contaminate food before and during processing[1,2,3,4,5]
Full LAST alignment against the nr database followed by MEGAN long length amplicon 16S rRNA gene-based sequencing of the simple read (LR) lowest common ancestor (LCA) analysis resulted in mock metagenomic DNA using the Oxford Nanopore Technologies (ONT) 16S barcoding kit SQK- 74.76% bases being classified to some taxonomic level
These reads contained a total of 1,454,835,092 bases group level and 8.15% classified to genus level, accounting for with an average read length of 1460 bp and a median read length 97.06% of classified reads. 64.37% of bases classified to genus of 1561 bp. 16S rRNA reads aligned by BLASTn to the Silva 16S database with MEGAN 6 classification resulted in level only were attributed to Geobacillus, with the remaining 35.63% classified as Bacillus (Fig. 1a)

Summary

Introduction

Dairy processing environments harbour microorganisms that have the potential to contaminate food before and during processing[1,2,3,4,5]. It uncovers information relating to the functional potential of species and strains present, including virulence and spoilage properties Despite these benefits, high throughput metagenomic sequencing approaches typically require expensive reagents and platforms as well as personnel skilled in molecular biology, data generation and interpretation. The MinION’s portability and workflows are designed to facilitate their use by less experienced personnel and could allow easier detection and identification of the causative agents of microbial contamination Such approaches have recently been tested in a clinical setting to identify causative agents of disease from metagenomic samples[15], including studies where the results were compared with those generated through Illumina sequencing[16,17] or culture-based analysis[18]. This approach has yet to be applied to food processing settings for environmental monitoring

Methods

Results

Conclusion