{"title":"鉴定代表轻度缺氮对玉米杂交种农艺性状影响的代谢和蛋白质标记。","authors":"Maria Urrutia, Mélisande Blein-Nicolas, Olivier Fernandez, Stéphane Bernillon, Mickaël Maucourt, Catherine Deborde, Thierry Balliau, Dominique Rabier, Camille Bénard, Sylvain Prigent, Isabelle Quilleré, Daniel Jacob, Yves Gibon, Michel Zivy, Catherine Giauffret, Bertrand Hirel, Annick Moing","doi":"10.1007/s11306-024-02186-z","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>A better understanding of the physiological response of silage maize to a mild reduction in nitrogen (N) fertilization and the identification of predictive biochemical markers of N utilization efficiency could contribute to limit the detrimental effect of the overuse of N inputs.</p><p><strong>Objectives: </strong>We integrated phenotypic and biochemical data to interpret the physiology of maize in response to a mild reduction in N fertilization under agronomic conditions and identify predictive leaf metabolic and proteic markers that could be used to pilot and rationalize N fertilization.</p><p><strong>Methods: </strong>Eco-physiological, developmental and yield-related traits were measured and complemented with metabolomic and proteomic approaches performed on young leaves of a core panel of 29 European genetically diverse dent hybrids cultivated in the field under non-limiting and reduced N fertilization conditions.</p><p><strong>Results: </strong>Metabolome and proteome data were analyzed either individually or in an integrated manner together with eco-physiological, developmental, phenotypic and yield-related traits. They allowed to identify (i) common N-responsive metabolites and proteins that could be used as predictive markers to monitor N fertilization, (ii) silage maize hybrids that exhibit improved agronomic performance when N fertilization is reduced.</p><p><strong>Conclusions: </strong>Among the N-responsive metabolites and proteins identified, a cytosolic NADP-dependent malic enzyme and four metabolite signatures stand out as promising markers that could be used for both breeding and agronomic purposes.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"20 6","pages":"128"},"PeriodicalIF":3.5000,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11550246/pdf/","citationCount":"0","resultStr":"{\"title\":\"Identification of metabolic and protein markers representative of the impact of mild nitrogen deficit on agronomic performance of maize hybrids.\",\"authors\":\"Maria Urrutia, Mélisande Blein-Nicolas, Olivier Fernandez, Stéphane Bernillon, Mickaël Maucourt, Catherine Deborde, Thierry Balliau, Dominique Rabier, Camille Bénard, Sylvain Prigent, Isabelle Quilleré, Daniel Jacob, Yves Gibon, Michel Zivy, Catherine Giauffret, Bertrand Hirel, Annick Moing\",\"doi\":\"10.1007/s11306-024-02186-z\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>A better understanding of the physiological response of silage maize to a mild reduction in nitrogen (N) fertilization and the identification of predictive biochemical markers of N utilization efficiency could contribute to limit the detrimental effect of the overuse of N inputs.</p><p><strong>Objectives: </strong>We integrated phenotypic and biochemical data to interpret the physiology of maize in response to a mild reduction in N fertilization under agronomic conditions and identify predictive leaf metabolic and proteic markers that could be used to pilot and rationalize N fertilization.</p><p><strong>Methods: </strong>Eco-physiological, developmental and yield-related traits were measured and complemented with metabolomic and proteomic approaches performed on young leaves of a core panel of 29 European genetically diverse dent hybrids cultivated in the field under non-limiting and reduced N fertilization conditions.</p><p><strong>Results: </strong>Metabolome and proteome data were analyzed either individually or in an integrated manner together with eco-physiological, developmental, phenotypic and yield-related traits. They allowed to identify (i) common N-responsive metabolites and proteins that could be used as predictive markers to monitor N fertilization, (ii) silage maize hybrids that exhibit improved agronomic performance when N fertilization is reduced.</p><p><strong>Conclusions: </strong>Among the N-responsive metabolites and proteins identified, a cytosolic NADP-dependent malic enzyme and four metabolite signatures stand out as promising markers that could be used for both breeding and agronomic purposes.</p>\",\"PeriodicalId\":18506,\"journal\":{\"name\":\"Metabolomics\",\"volume\":\"20 6\",\"pages\":\"128\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11550246/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Metabolomics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s11306-024-02186-z\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Metabolomics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11306-024-02186-z","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Identification of metabolic and protein markers representative of the impact of mild nitrogen deficit on agronomic performance of maize hybrids.
Introduction: A better understanding of the physiological response of silage maize to a mild reduction in nitrogen (N) fertilization and the identification of predictive biochemical markers of N utilization efficiency could contribute to limit the detrimental effect of the overuse of N inputs.
Objectives: We integrated phenotypic and biochemical data to interpret the physiology of maize in response to a mild reduction in N fertilization under agronomic conditions and identify predictive leaf metabolic and proteic markers that could be used to pilot and rationalize N fertilization.
Methods: Eco-physiological, developmental and yield-related traits were measured and complemented with metabolomic and proteomic approaches performed on young leaves of a core panel of 29 European genetically diverse dent hybrids cultivated in the field under non-limiting and reduced N fertilization conditions.
Results: Metabolome and proteome data were analyzed either individually or in an integrated manner together with eco-physiological, developmental, phenotypic and yield-related traits. They allowed to identify (i) common N-responsive metabolites and proteins that could be used as predictive markers to monitor N fertilization, (ii) silage maize hybrids that exhibit improved agronomic performance when N fertilization is reduced.
Conclusions: Among the N-responsive metabolites and proteins identified, a cytosolic NADP-dependent malic enzyme and four metabolite signatures stand out as promising markers that could be used for both breeding and agronomic purposes.
期刊介绍:
Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to:
metabolomic applications within man, including pre-clinical and clinical
pharmacometabolomics for precision medicine
metabolic profiling and fingerprinting
metabolite target analysis
metabolomic applications within animals, plants and microbes
transcriptomics and proteomics in systems biology
Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.