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Modeling photosynthetic resource allocation connects physiology with evolutionary environments

The regulation of resource allocation in biological systems observed today is the cumulative result of natural selection in ancestral and recent environments. To what extent are observed resource allocation patterns in different photosynthetic types optimally adapted to current conditions, and to wh...

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Autores principales: Sundermann, Esther M., Lercher, Martin J., Heckmann, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8342476/
https://www.ncbi.nlm.nih.gov/pubmed/34354112
http://dx.doi.org/10.1038/s41598-021-94903-0
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author Sundermann, Esther M.
Lercher, Martin J.
Heckmann, David
author_facet Sundermann, Esther M.
Lercher, Martin J.
Heckmann, David
author_sort Sundermann, Esther M.
collection PubMed
description The regulation of resource allocation in biological systems observed today is the cumulative result of natural selection in ancestral and recent environments. To what extent are observed resource allocation patterns in different photosynthetic types optimally adapted to current conditions, and to what extent do they reflect ancestral environments? Here, we explore these questions for C(3), C(4), and C(3)–C(4) intermediate plants of the model genus Flaveria. We developed a detailed mathematical model of carbon fixation, which accounts for various environmental parameters and for energy and nitrogen partitioning across photosynthetic components. This allows us to assess environment-dependent plant physiology and performance as a function of resource allocation patterns. Models of C(4) plants optimized for conditions experienced by evolutionary ancestors perform better than models accounting for experimental growth conditions, indicating low phenotypic plasticity. Supporting this interpretation, the model predicts that C(4) species need to re-allocate more nitrogen between photosynthetic components than C(3) species to adapt to new environments. We thus hypothesize that observed resource distribution patterns in C(4) plants still reflect optimality in ancestral environments, allowing the quantitative inference of these environments from today’s plants. Our work allows us to quantify environmental effects on photosynthetic resource allocation and performance in the light of evolutionary history.
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spelling pubmed-83424762021-08-06 Modeling photosynthetic resource allocation connects physiology with evolutionary environments Sundermann, Esther M. Lercher, Martin J. Heckmann, David Sci Rep Article The regulation of resource allocation in biological systems observed today is the cumulative result of natural selection in ancestral and recent environments. To what extent are observed resource allocation patterns in different photosynthetic types optimally adapted to current conditions, and to what extent do they reflect ancestral environments? Here, we explore these questions for C(3), C(4), and C(3)–C(4) intermediate plants of the model genus Flaveria. We developed a detailed mathematical model of carbon fixation, which accounts for various environmental parameters and for energy and nitrogen partitioning across photosynthetic components. This allows us to assess environment-dependent plant physiology and performance as a function of resource allocation patterns. Models of C(4) plants optimized for conditions experienced by evolutionary ancestors perform better than models accounting for experimental growth conditions, indicating low phenotypic plasticity. Supporting this interpretation, the model predicts that C(4) species need to re-allocate more nitrogen between photosynthetic components than C(3) species to adapt to new environments. We thus hypothesize that observed resource distribution patterns in C(4) plants still reflect optimality in ancestral environments, allowing the quantitative inference of these environments from today’s plants. Our work allows us to quantify environmental effects on photosynthetic resource allocation and performance in the light of evolutionary history. Nature Publishing Group UK 2021-08-05 /pmc/articles/PMC8342476/ /pubmed/34354112 http://dx.doi.org/10.1038/s41598-021-94903-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Sundermann, Esther M.
Lercher, Martin J.
Heckmann, David
Modeling photosynthetic resource allocation connects physiology with evolutionary environments
title Modeling photosynthetic resource allocation connects physiology with evolutionary environments
title_full Modeling photosynthetic resource allocation connects physiology with evolutionary environments
title_fullStr Modeling photosynthetic resource allocation connects physiology with evolutionary environments
title_full_unstemmed Modeling photosynthetic resource allocation connects physiology with evolutionary environments
title_short Modeling photosynthetic resource allocation connects physiology with evolutionary environments
title_sort modeling photosynthetic resource allocation connects physiology with evolutionary environments
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8342476/
https://www.ncbi.nlm.nih.gov/pubmed/34354112
http://dx.doi.org/10.1038/s41598-021-94903-0
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