Evidence map›Paper›PMID 32081987›Full record

ArticleScientific reports2020

New insights into cheddar cheese microbiota-metabolome relationships revealed by integrative analysis of multi-omics data.

Roya Afshari, Christopher J Pillidge, Elizabeth Read, Simone Rochfort, Daniel A Dias, A Mark Osborn, Harsharn Gill

Erratum issuedAbstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Roya AfshariSchool of Science, RMIT University, Bundoora, PO Box 71, Bundoora, VIC, 3083, Australia.
Christopher J PillidgeSchool of Science, RMIT University, Bundoora, PO Box 71, Bundoora, VIC, 3083, Australia.
Elizabeth ReadBiosciences Research Division, Department of Environment and Primary Industries, AgriBiosciences, 5 Ring Road, Bundoora, Victoria VIC, 3083, Australia.
Simone RochfortBiosciences Research Division, Department of Environment and Primary Industries, AgriBiosciences, 5 Ring Road, Bundoora, Victoria VIC, 3083, Australia.ORCID http://orcid.org/0000-0001-8442-6081
Daniel A DiasSchool of Health and Biomedical Sciences, RMIT University, Bundoora, PO Box 71, Bundoora, VIC, 3083, Australia.
A Mark OsbornSchool of Science, RMIT University, Bundoora, PO Box 71, Bundoora, VIC, 3083, Australia.
Harsharn GillSchool of Science, RMIT University, Bundoora, PO Box 71, Bundoora, VIC, 3083, Australia. harsharn.gill@rmit.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Food AnalysisFood MicrobiologyMetabolomeMicrobiotaBiodiversityCheeseDNA, BacterialLactobacillusLactococcusMetabolomicsMetagenomicsPrincipal Component AnalysisRNA, Ribosomal, 16SStreptococcusDNA, BacterialRNA, Ribosomal, 16S

Identifiers

PMID32081987
PMCPMC7035325

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.