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Microbial association networks give relevant insights into plant pathobiomes

hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorPAUVERT, Charlie
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorFORT, Tania
hal.structure.identifierSanté et agroécologie du vignoble [UMR SAVE]
dc.contributor.authorCALONNEC, Agnes
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorFAIVRE D’ARCIER, Julie
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorCHANCEREL, Emilie
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorMASSOT, Marie
hal.structure.identifierMathématiques et Informatique Appliquées [MIA Paris-Saclay]
dc.contributor.authorCHIQUET, Julien
hal.structure.identifierMathématiques et Informatique Appliquées [MIA Paris-Saclay]
dc.contributor.authorROBIN, Stéphane
hal.structure.identifierAgroécologie [Dijon]
dc.contributor.authorBOHAN, David
hal.structure.identifierSanté et agroécologie du vignoble [UMR SAVE]
dc.contributor.authorVALLANCE, Jessica
hal.structure.identifierBiodiversité, Gènes & Communautés [BioGeCo]
dc.contributor.authorVACHER, Corinne
dc.date.issued2020-02
dc.description.abstractEnInteractions between plant pathogens and other plant-associated microorganisms regulate disease. Deciphering the networks formed by these interactions, termed pathobiomes, is crucial to disease management. Our aim was to investigate whether microbial association networks inferred from metabarcoding data give relevant insights into pathobiomes, by testing whether inferred associations contain signals of ecological interactions. We used Poisson Lognormal Models to construct microbial association networks from metabarcoding data and then investigated whether some of these associations corresponded to interactions measurable in co-cultures or known in the literature, by using grapevine ( Vitis vinifera ) and the fungal pathogen causing powdery mildew ( Erysiphe necator ) as a model system. Our model suggested that the pathogen species was associated with 23 other fungal species, forming its putative pathobiome. These associations were not known as interactions in the literature, but one of them was confirmed by our co-culture experiments. The yeast Buckleyzyma aurantiaca impeded pathogen growth and reproduction, in line with the negative association found in the microbial network. Co-cultures also supported another association involving two yeast species. Together, these findings indicate that microbial networks can provide plausible hypotheses of ecological interactions that could be used to develop microbiome-based strategies for crop protection.
dc.language.isoen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/
dc.title.enMicrobial association networks give relevant insights into plant pathobiomes
dc.typeDocument de travail - Pré-publication
dc.typePrepublication/Preprint
dc.identifier.doi10.1101/2020.02.21.958033
dc.subject.halSciences de l'environnement
dc.subject.halSciences du Vivant [q-bio]
dc.subject.halMathématiques [math]
hal.identifierhal-03218256
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03218256v1
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