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dc.contributor.authorAlexa (Oniciuc), Elena A.
dc.contributor.authorWalsh, Calum J.
dc.contributor.authorCoughlan, Laura M.
dc.contributor.authorAwad, Amal
dc.contributor.authorSimon, Cezara A.
dc.contributor.authorRuiz, Lorena
dc.contributor.authorCrispie, Fiona
dc.contributor.authorCotter, Paul D.
dc.contributor.authorAlvarez-Ordóñez, Avelino
dc.date.accessioned2024-07-25T15:41:15Z
dc.date.available2024-07-25T15:41:15Z
dc.date.issued2020-02-18
dc.identifier.citationAlexa, Elena A., Calum J. Walsh, Laura M. Coughlan, Amal Awad, Cezara A. Simon, Lorena Ruiz, Fiona Crispie, Paul D. Cotter, and Avelino Alvarez-Ordóñez. Dairy products and dairy-processing environments as a reservoir of antibiotic resistance and quorum-quenching determinants as revealed through functional metagenomics. MSystems, 2020, 5 (1). DOI: https://doi.org/10.1128/msystems.00723-19en_US
dc.identifier.urihttp://hdl.handle.net/11019/3727
dc.descriptionPeer-revieweden_US
dc.description.abstractHere, the role of the dairy-processing chain as a reservoir of antimicrobial resistance (AR) determinants and a source of novel biocontrol quorum-sensing inhibitors is assessed through a functional metagenomics approach. A metagenomic library comprising ∼22,000 recombinant clones was built from DNA isolated from raw milk, raw milk cheeses, and cheese-processing environment swab samples. The high-throughput sequencing of 9,216 recombinant clones showed that lactic acid bacteria (LAB) dominated the microbial communities of raw milk cheese, while Gram-negative microorganisms of animal or soil origin dominated the microbiota of raw milk and cheese-processing environments. Although functional screening of the metagenomic library did not recover potential quorum-sensing inhibitors, in silico analysis using an in-house database built specifically for this study identified homologues to several genes encoding proteins with predicted quorum-quenching activity, among which, the QsdH hydrolase was the most abundant. In silico screening of the library identified LAB, and especially Lactococcus lactis, as a relevant reservoir of AR determinants in cheese. Functional screening of the library allowed the isolation of 13 recombinant clones showing an increased resistance toward ampicillin, which in all cases was accompanied by a reduced susceptibility to a wide range of β-lactam antibiotics. This study shows that the dairy-processing environment is a rich reservoir of AR determinants, which vary by sample source, and suggests that combining next-generation sequencing with functional metagenomics can be of use in overcoming the limitations of both approaches. IMPORTANCE The study shows the potential of functional metagenomics analyses to uncover the diversity of functions in microbial communities prevailing in dairy products and their processing environments, evidencing that lactic acid bacteria (LAB) dominate the cheese microbiota, whereas Gram-negative microorganisms of animal or soil origin dominate the microbiota of milk and cheese-processing environments. The functional and in silico screening of the library allowed the identification of LAB, and especially Lactococcus lactis, as a relevant reservoir of antimicrobial resistance (AR) determinants in cheese. Quorum-quenching (QQ) determinants were not recovered through the execution of wet-lab function-based screenings but were detected through in silico sequencing-based analyses.en_US
dc.description.sponsorshipScience Foundation Ireland
dc.language.isoenen_US
dc.publisherAmerican Society for Microbiologyen_US
dc.relation.ispartofseriesMSystems;Vol 5
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectAntibiotic resistanceen_US
dc.subjectFood safetyen_US
dc.subjectFunctional metagenomicsen_US
dc.subjectDairy productsen_US
dc.subjectQuorum quenchingen_US
dc.subjectβ-Lactam antibioticsen_US
dc.subjectNext-generation sequencingen_US
dc.titleDairy Products and Dairy-Processing Environments as a Reservoir of Antibiotic Resistance and Quorum-Quenching Determinants as Revealed through Functional Metagenomicsen_US
dc.typeArticleen_US
dc.identifier.doihttps://doi.org/10.1128/msystems.00723-19
dc.identifier.pii10.1128/mSystems.00723-19
dc.contributor.sponsorScience Foundation Ireland (SFI)en_US
dc.contributor.sponsorFundación BBVAen_US
dc.contributor.sponsorSpanish Ministry of Science, Innovation and Universitiesen_US
dc.contributor.sponsorGrantNumber13/SIRG/2157en_US
dc.contributor.sponsorGrantNumberAGL2016-78085-Pen_US
dc.contributor.sponsorGrantNumber12/RC/2273en_US
dc.contributor.sponsorGrantNumber16/SP/3827en_US
dc.source.volume5
dc.source.issue1
refterms.dateFOA2024-07-25T15:41:17Z
dc.source.journaltitlemSystems
dc.identifier.eissn2379-5077


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