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Combination of metabolomics and machine learning to unravel environmental drivers of spatial heterogeneity of microbial metabolome assemblage in aquatic periphyton : The COMBO project

hal.structure.identifierEcosystèmes aquatiques et changements globaux [UR EABX]
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorMALHERBE, Amélie
hal.structure.identifierEcosystèmes aquatiques et changements globaux [UR EABX]
dc.contributor.authorEON, Mélissa
hal.structure.identifierEcosystèmes aquatiques et changements globaux [UR EABX]
dc.contributor.authorBONNINEAU, Chloé
hal.structure.identifierEcosystèmes aquatiques et changements globaux [UR EABX]
dc.contributor.authorBOUTRY, Sébastien
hal.structure.identifierBiologie du fruit et pathologie [BFP]
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorPRIGENT, Sylvain
hal.structure.identifierAgence de l'Eau Adour-Garonne
dc.contributor.authorLEBORGNE, Lomig
hal.structure.identifierEcosystèmes aquatiques et changements globaux [UR EABX]
dc.contributor.authorMORIN, Soizic
hal.structure.identifierEcosystèmes aquatiques et changements globaux [UR EABX]
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorMAZZELLA, Nicolas
hal.structure.identifierEcosystèmes aquatiques et changements globaux [UR EABX]
dc.contributor.authorMOREIRA, Aurélie
hal.structure.identifierEcosystèmes aquatiques et changements globaux [UR EABX]
dc.contributor.authorMILLAN-NAVARRO, Débora
hal.structure.identifierEcosystèmes aquatiques et changements globaux [UR EABX]
dc.contributor.authorVEDRENNE, Jacky
hal.structure.identifierEcosystèmes aquatiques et changements globaux [UR EABX]
dc.contributor.authorJAN, Gwilherm
hal.structure.identifierEcosystèmes aquatiques et changements globaux [UR EABX]
dc.contributor.authorMOREIRA, Sylvia
hal.structure.identifierPlateforme Bordeaux Metabolome
hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorPETRIACQ, Pierre
hal.structure.identifierPlateforme Bordeaux Metabolome
hal.structure.identifierEcosystèmes aquatiques et changements globaux [UR EABX]
dc.contributor.authorCREUSOT, Nicolas
dc.date.issued2025-04-10
dc.date.conference2025-03-17
dc.description.abstractEnFreshwater periphyton, playing a key role in ecosystem functions and services, are increasingly used in ecotoxicology to better understand the link between chemical exposure and ecosystem disturbance. To this end, one key challenge is to understand better how environmental conditions interact and modulate the community dynamics in situ. In this context, this project aims to gain understanding of the spatial heterogeneity of the natural periphyton metabolome assembly under various environmental conditions through the implementation of statistic and predictive meta-metabolomics approach. This attempts to characterize the assembling rules (stockastic vs deterministic) of metabolomes in periphyton and to identify the main environmental driving forces of meta-metabolome assemblages that contributes to its spatial heterogeneity. To do so, autochthonous periphytons were collected in 100 sites widely distributed in France encompassing various water physico-chemistry and chemical/ecological status (water agency data). The photosynthetic yield of the periphyton was characterized in situ in parallel of the measurement of the water physico-chemistry (nutrients, micropollutants) by the water agency. Physico-chemical results confirmed the heterogeneity between the sites. The global biomass parameters (e.g. proteins) will be soon characterized while untargeted metametabolomics based on LC-HRMS is ongoing. Those metametabolomics data will provide a comprehensive picture of the chemical landscape of the periphyton, potentially highlighting the existence of a core meta-metabolome. Then meta-community ecology metrics (α/β diversity) will be used to define assembling mechanisms. In parallel, statistics and machine-learning will be implemented in order to identify features or groups of features that can predict specific environmental conditions and water quality status. In addition, by linking metabolites to biomass or photosynthesis, COMBO would support the discovery of predictive effect biomarkers at the community level that may have impair ecosystem functions. Overall, this knowledge will support the translation of metametabolome responses of periphyton to chemical stress under controlled conditions into real applications for biomonitoring purposes.
dc.language.isoen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/
dc.subject.enPeriphytic biofilm
dc.subject.enMetabolome
dc.subject.enFreshwater
dc.subject.enIn situ sampling
dc.subject.enUntargeted Metabolomics
dc.subject.enUHPLC- HRMS
dc.title.enCombination of metabolomics and machine learning to unravel environmental drivers of spatial heterogeneity of microbial metabolome assemblage in aquatic periphyton : The COMBO project
dc.typeAutre communication scientifique (congrès sans actes - poster - séminaire...)
dc.subject.halSciences de l'environnement
bordeaux.conference.titleAquaecomics
bordeaux.countryFR
bordeaux.conference.cityThonon Les Bains
bordeaux.peerReviewedoui
hal.identifierhal-05029670
hal.version1
hal.invitednon
hal.proceedingsnon
hal.conference.organizerUMR Carrtel
hal.conference.end2025-03-20
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-05029670v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.date=2025-04-10&rft.au=MALHERBE,%20Am%C3%A9lie&EON,%20M%C3%A9lissa&BONNINEAU,%20Chlo%C3%A9&BOUTRY,%20S%C3%A9bastien&PRIGENT,%20Sylvain&rft.genre=conference


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