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An eco-evo-devo genetic network model of stress response

The capacity of plants to resist abiotic stresses is of great importance to agricultural, ecological and environmental sustainability, but little is known about its genetic underpinnings. Existing genetic tools can identify individual genetic variants mediating biochemical, physiological, and cellul...

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Detalles Bibliográficos
Autores principales: Feng, Li, Dong, Tianyu, Jiang, Peng, Yang, Zhenyu, Dong, Ang, Xie, Shang-Qian, Griffin, Christopher H, Wu, Rongling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433980/
https://www.ncbi.nlm.nih.gov/pubmed/36061617
http://dx.doi.org/10.1093/hr/uhac135
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author Feng, Li
Dong, Tianyu
Jiang, Peng
Yang, Zhenyu
Dong, Ang
Xie, Shang-Qian
Griffin, Christopher H
Wu, Rongling
author_facet Feng, Li
Dong, Tianyu
Jiang, Peng
Yang, Zhenyu
Dong, Ang
Xie, Shang-Qian
Griffin, Christopher H
Wu, Rongling
author_sort Feng, Li
collection PubMed
description The capacity of plants to resist abiotic stresses is of great importance to agricultural, ecological and environmental sustainability, but little is known about its genetic underpinnings. Existing genetic tools can identify individual genetic variants mediating biochemical, physiological, and cellular defenses, but fail to chart an overall genetic atlas behind stress resistance. We view stress response as an eco-evo-devo process by which plants adaptively respond to stress through complex interactions of developmental canalization, phenotypic plasticity, and phenotypic integration. As such, we define and quantify stress response as the developmental change of adaptive traits from stress-free to stress-exposed environments. We integrate composite functional mapping and evolutionary game theory to reconstruct omnigenic, information-flow interaction networks for stress response. Using desert-adapted Euphrates poplar as an example, we infer salt resistance-related genome-wide interactome networks and trace the roadmap of how each SNP acts and interacts with any other possible SNPs to mediate salt resistance. We characterize the previously unknown regulatory mechanisms driving trait variation; i.e. the significance of a SNP may be due to the promotion of positive regulators, whereas the insignificance of a SNP may result from the inhibition of negative regulators. The regulator-regulatee interactions detected are not only experimentally validated by two complementary experiments, but also biologically interpreted by their encoded protein–protein interactions. Our eco-evo-devo model of genetic interactome networks provides an approach to interrogate the genetic architecture of stress response and informs precise gene editing for improving plants’ capacity to live in stress environments.
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spelling pubmed-94339802022-09-01 An eco-evo-devo genetic network model of stress response Feng, Li Dong, Tianyu Jiang, Peng Yang, Zhenyu Dong, Ang Xie, Shang-Qian Griffin, Christopher H Wu, Rongling Hortic Res Method The capacity of plants to resist abiotic stresses is of great importance to agricultural, ecological and environmental sustainability, but little is known about its genetic underpinnings. Existing genetic tools can identify individual genetic variants mediating biochemical, physiological, and cellular defenses, but fail to chart an overall genetic atlas behind stress resistance. We view stress response as an eco-evo-devo process by which plants adaptively respond to stress through complex interactions of developmental canalization, phenotypic plasticity, and phenotypic integration. As such, we define and quantify stress response as the developmental change of adaptive traits from stress-free to stress-exposed environments. We integrate composite functional mapping and evolutionary game theory to reconstruct omnigenic, information-flow interaction networks for stress response. Using desert-adapted Euphrates poplar as an example, we infer salt resistance-related genome-wide interactome networks and trace the roadmap of how each SNP acts and interacts with any other possible SNPs to mediate salt resistance. We characterize the previously unknown regulatory mechanisms driving trait variation; i.e. the significance of a SNP may be due to the promotion of positive regulators, whereas the insignificance of a SNP may result from the inhibition of negative regulators. The regulator-regulatee interactions detected are not only experimentally validated by two complementary experiments, but also biologically interpreted by their encoded protein–protein interactions. Our eco-evo-devo model of genetic interactome networks provides an approach to interrogate the genetic architecture of stress response and informs precise gene editing for improving plants’ capacity to live in stress environments. Oxford University Press 2022-06-07 /pmc/articles/PMC9433980/ /pubmed/36061617 http://dx.doi.org/10.1093/hr/uhac135 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nanjing Agricultural University https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method
Feng, Li
Dong, Tianyu
Jiang, Peng
Yang, Zhenyu
Dong, Ang
Xie, Shang-Qian
Griffin, Christopher H
Wu, Rongling
An eco-evo-devo genetic network model of stress response
title An eco-evo-devo genetic network model of stress response
title_full An eco-evo-devo genetic network model of stress response
title_fullStr An eco-evo-devo genetic network model of stress response
title_full_unstemmed An eco-evo-devo genetic network model of stress response
title_short An eco-evo-devo genetic network model of stress response
title_sort eco-evo-devo genetic network model of stress response
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9433980/
https://www.ncbi.nlm.nih.gov/pubmed/36061617
http://dx.doi.org/10.1093/hr/uhac135
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