Abstract
Cheese microbiota and metabolites and their inter-relationships that underpin specific cheese quality attributes remain poorly understood. Here we report that multi-omics and integrative data analysis (multiple co-inertia analysis, MCIA) can be used to gain deeper insights into these relationships and identify microbiota and metabolite fingerprints that could be used to monitor product quality and authenticity. Our study into different brands of artisanal and industrial cheddar cheeses showed that Streptococcus, Lactococcus and Lactobacillus were the dominant taxa with overall microbial community structures differing not only between industrial and artisanal cheeses but also among different cheese brands. Metabolome analysis also revealed qualitative and semi-quantitative differences in metabolites between different cheeses. This also included the presence of two compounds (3-hydroxy propanoic acid and O-methoxycatechol-O-sulphate) in artisanal cheese that have not been previously reported in any type of cheese. Integrative analysis of multi-omics datasets revealed that highly similar cheeses, identical in age and appearance, could be distinctively clustered according to cheese type and brand. Furthermore, the analysis detected strong relationships, some previously unknown, which existed between the cheese microbiota and metabolome, and uncovered specific taxa and metabolites that contributed to these relationships. These results highlight the potential of this approach for identifying product specific microbe/metabolite signatures that could be used to monitor and control cheese quality and product authenticity.
Highlights
Cheese microbiota and metabolites and their inter-relationships that underpin specific cheese quality attributes remain poorly understood
Microbial communities vary depending on the cheese type, in hard cheeses such as cheddar that undergo ripening, after the starter bacteria die off lactobacilli are dominant, and may be found in combination with a number of other genera not belonging to the lactic acid bacteria group – for example coagulase-negative staphylococci and Actinobacteria[5,6,7]
Sequencing of PCR-amplified 16S rRNA gene amplicons targeting the V4-region from total DNA revealed 159 operational taxonomic units (OTUs) binned at 97% identity and a threshold of detection of an OTU being present in more than one sample
Summary
Cheese microbiota and metabolites and their inter-relationships that underpin specific cheese quality attributes remain poorly understood. A more complete understanding of cheese ripening can be gained through improvements in analytical techniques such as mass spectrometry combined with untargeted chemical analysis and systems to handle these very large datasets, with some studies indicating the presence of hundreds or thousands of compounds including many not previously reported in cheese[11] Despite these advances, the interrelationships between cheese microbiota and their metabolites remain largely unstudied. This type of information could be useful if translated into cheesemaking practice; for example, if ripening conditions or application of adjuncts were adjusted to favour the growth of specific microorganisms associated with production of desirable flavour compounds To better understand these interactions, integrative analysis of large multiple-omics datasets can be applied, facilitated by the application of new computational algorithms and high-end computing systems, as we have previously advocated[12]. Tools that facilitate the integration of multiple omics data sets are becoming increasingly available[13]
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