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Meta-analysis of cheese microbiomes highlights contributions to multiple aspects of quality

Abstract

A detailed understanding of the cheese microbiome is key to the optimization of flavour, appearance, quality and safety. Accordingly, we conducted a high-resolution meta-analysis of cheese microbiomes and corresponding volatilomes. Using 77 new samples from 55 artisanal cheeses from 27 Irish producers combined with 107 publicly available cheese metagenomes, we recovered 328 metagenome-assembled genomes, including 47 putative new species that could influence taste or colour through the secretion of volatiles or biosynthesis of pigments. Additionally, from a subset of samples, we found that differences in the abundances of strains corresponded with levels of volatiles. Genes encoding bacteriocins and other antimicrobials, such as pseudoalterin, were common, potentially contributing to the control of undesirable microorganisms. Although antibiotic-resistance genes were detected, evidence suggested they are not of major concern with respect to dissemination to other microbiomes. Phages, a potential cause of fermentation failure, were abundant and evidence for phage-mediated gene transfer was detected. The anti-phage defence mechanism CRISPR was widespread and analysis thereof, and of anti-CRISPR proteins, revealed a complex interaction between phages and bacteria. Overall, our results provide new and substantial technological and ecological insights into the cheese microbiome that can be applied to further improve cheese production.

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Fig. 1: The microbial composition of cheeses.
Fig. 2: The relationship between strain-level variation and the metabolome.
Fig. 3: Assembly and characterization of cheese MAGs.
Fig. 4: Analysis of ARGs on cheese MAGs and plasmids.
Fig. 5: LGTs in cheese microbiomes.
Fig. 6: Phages, CRISPRs and Acrs in cheese microbiomes.

Data availability

Raw reads have been deposited to the European Nucleotide Archive under the project accession number PRJEB32768, while MAGs are available at https://drive.google.com/file/d/1TCLYBX7kkxNUWn4jr4YGXNL_qV97lc70/view.

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Acknowledgements

We thank J. Mahony, D. Van Sinderen and members of the Vision 1 laboratory, particularly W. Barton, for helpful discussions and a critical review of the manuscript, as well as F. Crispie and L. Finnegan for their contributions to DNA sequencing. This research was conducted with the financial support of the Science Foundation Ireland (SFI) under grant numbers SFI/12/RC/2273P1 and SFI/12/RC/2273P2 (APC Microbiome Ireland). Research in the Cotter laboratory is also funded through MASTER, an Innovation Action funded by the European Commission under the Horizon 2020 Programme under grant number 818368, SFI and the Department of Agriculture, Food and Marine under grant 16/RC/3835 (VistaMilk) and the Enterprise Ireland Technology Centre, Food for Health Ireland.

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Authors and Affiliations

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Contributions

A.M.W. performed bioinformatic and statistical analysis and data visualization. G.M. collected samples and prepared DNA sequencing libraries. K.N.K. performed volatile analysis. A.M.W., G.M. and P.D.C. conceived the study. All authors contributed to the preparation of the manuscript.

Corresponding author

Correspondence to Paul D. Cotter.

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Supplementary information

Supplementary Information

Supplementary Methods, Results, Discussion and Figs. 1–5.

Supplementary Table 1

Taxa that were differentially abundant between the core versus the rind, as determined by LEfSe.

Supplementary Table 2

Taxa that were differentially abundant between cheeses of different maturity, as determined by LEfSe.

Supplementary Table 3

SUPER-FOCUS subsystems (level 2) that were differentially abundant between the core versus the rind, as determined by LEfSe.

Supplementary Table 4

SUPER-FOCUS subsystems that were differentially abundant between cheeses of different maturity, as determined by LEfSe.

Supplementary Table 5

SUPER-FOCUS subsystems that were differentially abundant between cheeses produced by different types of milk, as determined by LEfSe.

Supplementary Table 6

Volatile profile of CheeseSeq samples.

Supplementary Table 7

Volatile compounds detected in the CheeseSeq and Bertuzzi datasets.

Supplementary Table 8

The taxonomy of the 328 high-quality prokaryotic MAGs, as determined using CAT–BAT, PhyloPhlAn and FastANI.

Supplementary Table 9

Bacterial enzymes associated with the biosynthesis of indigo.

Supplementary Table 10

The genera to which MAGs containing bacteriocin genes, as determined by BAGEL3, were assigned by CAT–BAT.

Supplementary Table 11

Metadata describing the 77 newly sequenced cheese samples.

Supplementary Table 12

The accession numbers of each of the publicly available metagenomic samples that were included in the meta-analysis of the cheese microbiome.

Supplementary Table 13

The publicly available shotgun metagenomic datasets that were included in the meta-analysis of the cheese microbiome.

Supplementary Table 14

The components of the cheese agar medium (CAM) used to initialize genome-scale metabolic models with CarveMe.

Supplementary Table 15

a, The datasets from which non-cheese MAGs were downloaded. b, The ID of non-cheese MAGs included in this study.

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Walsh, A.M., Macori, G., Kilcawley, K.N. et al. Meta-analysis of cheese microbiomes highlights contributions to multiple aspects of quality. Nat Food 1, 500–510 (2020). https://doi.org/10.1038/s43016-020-0129-3

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