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Interspecies predictions of growth traits from quantitative transcriptome data acquired during fruit development

hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorBEAUMONT, Chloé
hal.structure.identifierBiologie du fruit et pathologie [BFP]
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorPRIGENT, Sylvain
hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorMORI, Kentaro
hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorBALDET, Pierre
hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorJORLY, Joana
hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorRANDRIAFANOMEZANTSOA-RADOHERY, Georges
hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorBEAUVOIT, Bertrand
hal.structure.identifierInstitut des Sciences des Plantes de Paris-Saclay [IPS2 (UMR_9213 / UMR_1403)]
dc.contributor.authorPATEYRON, Stéphanie
hal.structure.identifierInstitut des Sciences des Plantes de Paris-Saclay [IPS2 (UMR_9213 / UMR_1403)]
dc.contributor.authorDELANNOY, Etienne
hal.structure.identifierBiologie du fruit et pathologie [BFP]
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorPÉTRIACQ, Pierre
hal.structure.identifierBiologie du fruit et pathologie [BFP]
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorGIBON, Yves
hal.structure.identifierBiologie du fruit et pathologie [BFP]
hal.structure.identifierPlateforme Bordeaux Metabolome
dc.contributor.authorCOLOMBIÉ, Sophie
dc.date.issued2025-03-18
dc.identifier.issn0022-0957
dc.description.abstractEnLinking genotype and phenotype is a fundamental challenge in biology. In this respect, machine learning is playing a pivotal role in systems biology. As a central phenotypic trait, fruit development and its relative growth rate (RGR) result from interactions between gene regulation, metabolism and environment. In the present study, we carried out a multispecies transcriptomic analysis of nine different fruits. To illustrate fruit transcriptomes, transcripts were first compared using multivariate methods, revealing main similar profiles. They were then used as variables to predict four growth traits, i.e. RGR, developmental progress, fruit weight and protein content, using generalised linear models (GLMs) to decipher the mechanisms involving gene expression in development. The predictions were very satisfactory despite disparities when the model did not include the entire panel of fruit species. Based on orthogroups derived from BLAST and annotated consensus sequences from gene ontology (GO) terminology, variables annotated for metabolic processes, especially those involving cell wall carbohydrates and proteins, were found to be the most effective in predicting growth. In addition, predictions were improved for RGR when introducing a seven-day lag between transcript contents and growth traits, suggesting the necessity of considering the proteins produced to enhance phenotypic trait predictions. These original results showed that growth traits can be predicted very well with GLMs based on orthogroups from multi-species transcriptomes.
dc.description.sponsorshipModélisation intégrative du fruit pour un système de sélection unifié
dc.description.sponsorshipDéveloppement d'une infrastructure française distribuée pour la métabolomique dédiée à l'innovation - ANR-11-INBS-0010
dc.description.sponsorshipCentre français de phénomique végétale
dc.language.isoen
dc.publisherOxford University Press (OUP)
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/
dc.subject.enFruit development
dc.subject.enML predictions
dc.subject.enmultispecies
dc.subject.enorthology
dc.subject.entime series
dc.subject.entranscriptome
dc.title.enInterspecies predictions of growth traits from quantitative transcriptome data acquired during fruit development
dc.typeArticle de revue
dc.identifier.doi10.1093/jxb/eraf122
dc.subject.halSciences de l'environnement
dc.description.sponsorshipEuropeEuropean Commission’s Horizon 2020 Research and Innovation program via the GLOMICAVE project under grant agreement no. 952908
bordeaux.journalJournal of Experimental Botany
bordeaux.pageeraf122
bordeaux.peerReviewedoui
hal.identifierhal-05037526
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-05037526v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal%20of%20Experimental%20Botany&rft.date=2025-03-18&rft.spage=eraf122&rft.epage=eraf122&rft.eissn=0022-0957&rft.issn=0022-0957&rft.au=BEAUMONT,%20Chlo%C3%A9&PRIGENT,%20Sylvain&MORI,%20Kentaro&BALDET,%20Pierre&JORLY,%20Joana&rft.genre=article


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