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The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes
Understanding the molecular mechanisms underlying complex phenotypes requires systematic analyses of complicated metabolic networks and contributes to improvements in the breeding efficiency of staple cereal crops and diagnostic accuracy for human diseases. Here, we selected rice (Oryza sativa) hete...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8491067/ https://www.ncbi.nlm.nih.gov/pubmed/34608951 http://dx.doi.org/10.1093/plphys/kiab273 |
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author | Dan, Zhiwu Chen, Yunping Li, Hui Zeng, Yafei Xu, Wuwu Zhao, Weibo He, Ruifeng Huang, Wenchao |
author_facet | Dan, Zhiwu Chen, Yunping Li, Hui Zeng, Yafei Xu, Wuwu Zhao, Weibo He, Ruifeng Huang, Wenchao |
author_sort | Dan, Zhiwu |
collection | PubMed |
description | Understanding the molecular mechanisms underlying complex phenotypes requires systematic analyses of complicated metabolic networks and contributes to improvements in the breeding efficiency of staple cereal crops and diagnostic accuracy for human diseases. Here, we selected rice (Oryza sativa) heterosis as a complex phenotype and investigated the mechanisms of both vegetative and reproductive traits using an untargeted metabolomics strategy. Heterosis-associated analytes were identified, and the overlapping analytes were shown to underlie the association patterns for six agronomic traits. The heterosis-associated analytes of four yield components and plant height collectively contributed to yield heterosis, and the degree of contribution differed among the five traits. We performed dysregulated network analyses of the high- and low-better parent heterosis hybrids and found multiple types of metabolic pathways involved in heterosis. The metabolite levels of the significantly enriched pathways (especially those from amino acid and carbohydrate metabolism) were predictive of yield heterosis (area under the curve = 0.907 with 10 features), and the predictability of these pathway biomarkers was validated with hybrids across environments and populations. Our findings elucidate the metabolomic landscape of rice heterosis and highlight the potential application of pathway biomarkers in achieving accurate predictions of complex phenotypes. |
format | Online Article Text |
id | pubmed-8491067 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-84910672021-10-06 The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes Dan, Zhiwu Chen, Yunping Li, Hui Zeng, Yafei Xu, Wuwu Zhao, Weibo He, Ruifeng Huang, Wenchao Plant Physiol Regular Issue Understanding the molecular mechanisms underlying complex phenotypes requires systematic analyses of complicated metabolic networks and contributes to improvements in the breeding efficiency of staple cereal crops and diagnostic accuracy for human diseases. Here, we selected rice (Oryza sativa) heterosis as a complex phenotype and investigated the mechanisms of both vegetative and reproductive traits using an untargeted metabolomics strategy. Heterosis-associated analytes were identified, and the overlapping analytes were shown to underlie the association patterns for six agronomic traits. The heterosis-associated analytes of four yield components and plant height collectively contributed to yield heterosis, and the degree of contribution differed among the five traits. We performed dysregulated network analyses of the high- and low-better parent heterosis hybrids and found multiple types of metabolic pathways involved in heterosis. The metabolite levels of the significantly enriched pathways (especially those from amino acid and carbohydrate metabolism) were predictive of yield heterosis (area under the curve = 0.907 with 10 features), and the predictability of these pathway biomarkers was validated with hybrids across environments and populations. Our findings elucidate the metabolomic landscape of rice heterosis and highlight the potential application of pathway biomarkers in achieving accurate predictions of complex phenotypes. Oxford University Press 2021-06-14 /pmc/articles/PMC8491067/ /pubmed/34608951 http://dx.doi.org/10.1093/plphys/kiab273 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of American Society of Plant Biologists. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Regular Issue Dan, Zhiwu Chen, Yunping Li, Hui Zeng, Yafei Xu, Wuwu Zhao, Weibo He, Ruifeng Huang, Wenchao The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes |
title | The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes |
title_full | The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes |
title_fullStr | The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes |
title_full_unstemmed | The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes |
title_short | The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes |
title_sort | metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes |
topic | Regular Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8491067/ https://www.ncbi.nlm.nih.gov/pubmed/34608951 http://dx.doi.org/10.1093/plphys/kiab273 |
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