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Can biochemical traits bridge the gap between genomics and plant performance? A study in rice under drought

hal.structure.identifierWageningen University and Research [Wageningen] [WUR]
hal.structure.identifierCornell University [New York]
hal.structure.identifierBiologie du fruit et pathologie [BFP]
dc.contributor.authorMELANDRI, Giovanni
hal.structure.identifierCornell University [New York]
hal.structure.identifierUniversidad de la República [Montevideo] [UDELAR]
dc.contributor.authorMONTEVERDE, Eliana
hal.structure.identifierJulius Kühn-Institut [JKI]
hal.structure.identifierLeibniz Institute of Plant Genetics and Crop Plant Research [Gatersleben] [IPK-Gatersleben]
dc.contributor.authorRIEWE, David
hal.structure.identifierUniversity of Antwerp [UA]
hal.structure.identifierBeni-Suef University
dc.contributor.authorABDELGAWAD, Hamada
hal.structure.identifierCornell University [New York]
dc.contributor.authorMCCOUCH, Susan
hal.structure.identifierWageningen University and Research [Wageningen] [WUR]
hal.structure.identifierUniversity of Amsterdam [Amsterdam] = Universiteit van Amsterdam [UvA]
dc.contributor.authorBOUWMEESTER, Harro
dc.date.issued2022-06-01
dc.identifier.issn0032-0889
dc.description.abstractEnAbstract The possibility of introducing metabolic/biochemical phenotyping to complement genomics-based predictions in breeding pipelines has been considered for years. Here we examine to what extent and under what environmental conditions metabolic/biochemical traits can effectively contribute to understanding and predicting plant performance. In this study, multivariable statistical models based on flag leaf central metabolism and oxidative stress status were used to predict grain yield (GY) performance for 271 indica rice (Oryza sativa) accessions grown in the field under well-watered and reproductive stage drought conditions. The resulting models displayed significantly higher predictability than multivariable models based on genomic data for the prediction of GY under drought (Q2 = 0.54–0.56 versus 0.35) and for stress-induced GY loss (Q2 = 0.59–0.64 versus 0.03–0.06). Models based on the combined datasets showed predictabilities similar to metabolic/biochemical-based models alone. In contrast to genetic markers, models with enzyme activities and metabolite values also quantitatively integrated the effect of physiological differences such as plant height on GY. The models highlighted antioxidant enzymes of the ascorbate–glutathione cycle and a lipid oxidation stress marker as important predictors of rice GY stability under drought at the reproductive stage, and these stress-related variables were more predictive than leaf central metabolites. These findings provide evidence that metabolic/biochemical traits can integrate dynamic cellular and physiological responses to the environment and can help bridge the gap between the genome and the phenome of crops as predictors of GY performance under drought.
dc.language.isoen
dc.publisherOxford University Press ; American Society of Plant Biologists
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/
dc.title.enCan biochemical traits bridge the gap between genomics and plant performance? A study in rice under drought
dc.typeArticle de revue
dc.identifier.doi10.1093/plphys/kiac053
dc.subject.halSciences du Vivant [q-bio]
dc.subject.halSciences du Vivant [q-bio]/Biologie végétale
bordeaux.journalPlant Physiology
bordeaux.page1139-1152
bordeaux.volume189
bordeaux.issue2
bordeaux.peerReviewedoui
hal.identifierhal-03764886
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03764886v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Plant%20Physiology&rft.date=2022-06-01&rft.volume=189&rft.issue=2&rft.spage=1139-1152&rft.epage=1139-1152&rft.eissn=0032-0889&rft.issn=0032-0889&rft.au=MELANDRI,%20Giovanni&MONTEVERDE,%20Eliana&RIEWE,%20David&ABDELGAWAD,%20Hamada&MCCOUCH,%20Susan&rft.genre=article


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