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Development of a Chlamydomonas reinhardtii metabolic network dynamic model to describe distinct phenotypes occurring at different CO(2) levels

The increase in atmospheric CO(2) due to anthropogenic activities is generating climate change, which has resulted in a subsequent rise in global temperatures with severe environmental impacts. Biological mitigation has been considered as an alternative for environmental remediation and reduction of...

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Autores principales: Mora Salguero, Daniela Alejandra, Fernández-Niño, Miguel, Serrano-Bermúdez, Luis Miguel, Páez Melo, David O., Winck, Flavia V., Caldana, Camila, González Barrios, Andrés Fernando
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6126472/
https://www.ncbi.nlm.nih.gov/pubmed/30202653
http://dx.doi.org/10.7717/peerj.5528
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author Mora Salguero, Daniela Alejandra
Fernández-Niño, Miguel
Serrano-Bermúdez, Luis Miguel
Páez Melo, David O.
Winck, Flavia V.
Caldana, Camila
González Barrios, Andrés Fernando
author_facet Mora Salguero, Daniela Alejandra
Fernández-Niño, Miguel
Serrano-Bermúdez, Luis Miguel
Páez Melo, David O.
Winck, Flavia V.
Caldana, Camila
González Barrios, Andrés Fernando
author_sort Mora Salguero, Daniela Alejandra
collection PubMed
description The increase in atmospheric CO(2) due to anthropogenic activities is generating climate change, which has resulted in a subsequent rise in global temperatures with severe environmental impacts. Biological mitigation has been considered as an alternative for environmental remediation and reduction of greenhouse gases in the atmosphere. In fact, the use of easily adapted photosynthetic organisms able to fix CO(2) with low-cost operation is revealing its high potential for industry. Among those organism, the algae Chlamydomonas reinhardtii have gain special attention as a model organism for studying CO(2) fixation, biomass accumulation and bioenergy production upon exposure to several environmental conditions. In the present study, we studied the Chlamydomonas response to different CO(2) levels by comparing metabolomics and transcriptomics data with the predicted results from our new-improved genomic-scale metabolic model. For this, we used in silico methods at steady dynamic state varying the levels of CO(2). Our main goal was to improve our capacity for predicting metabolic routes involved in biomass accumulation. The improved genomic-scale metabolic model presented in this study was shown to be phenotypically accurate, predictive, and a significant improvement over previously reported models. Our model consists of 3726 reactions and 2436 metabolites, and lacks any thermodynamically infeasible cycles. It was shown to be highly sensitive to environmental changes under both steady-state and dynamic conditions. As additional constraints, our dynamic model involved kinetic parameters associated with substrate consumption at different growth conditions (i.e., low CO(2)-heterotrophic and high CO(2)-mixotrophic). Our results suggest that cells growing at high CO(2) (i.e., photoautotrophic and mixotrophic conditions) have an increased capability for biomass production. In addition, we have observed that ATP production also seems to be an important limiting factor for growth under the conditions tested. Our experimental data (metabolomics and transcriptomics) and the results predicted by our model clearly suggest a differential behavior between low CO(2)-heterotrophic and high CO(2)-mixotrophic growth conditions. The data presented in the current study contributes to better dissect the biological response of C. reinhardtii, as a dynamic entity, to environmental and genetic changes. These findings are of great interest given the biotechnological potential of this microalga for CO(2) fixation, biomass accumulation, and bioenergy production.
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spelling pubmed-61264722018-09-10 Development of a Chlamydomonas reinhardtii metabolic network dynamic model to describe distinct phenotypes occurring at different CO(2) levels Mora Salguero, Daniela Alejandra Fernández-Niño, Miguel Serrano-Bermúdez, Luis Miguel Páez Melo, David O. Winck, Flavia V. Caldana, Camila González Barrios, Andrés Fernando PeerJ Computational Biology The increase in atmospheric CO(2) due to anthropogenic activities is generating climate change, which has resulted in a subsequent rise in global temperatures with severe environmental impacts. Biological mitigation has been considered as an alternative for environmental remediation and reduction of greenhouse gases in the atmosphere. In fact, the use of easily adapted photosynthetic organisms able to fix CO(2) with low-cost operation is revealing its high potential for industry. Among those organism, the algae Chlamydomonas reinhardtii have gain special attention as a model organism for studying CO(2) fixation, biomass accumulation and bioenergy production upon exposure to several environmental conditions. In the present study, we studied the Chlamydomonas response to different CO(2) levels by comparing metabolomics and transcriptomics data with the predicted results from our new-improved genomic-scale metabolic model. For this, we used in silico methods at steady dynamic state varying the levels of CO(2). Our main goal was to improve our capacity for predicting metabolic routes involved in biomass accumulation. The improved genomic-scale metabolic model presented in this study was shown to be phenotypically accurate, predictive, and a significant improvement over previously reported models. Our model consists of 3726 reactions and 2436 metabolites, and lacks any thermodynamically infeasible cycles. It was shown to be highly sensitive to environmental changes under both steady-state and dynamic conditions. As additional constraints, our dynamic model involved kinetic parameters associated with substrate consumption at different growth conditions (i.e., low CO(2)-heterotrophic and high CO(2)-mixotrophic). Our results suggest that cells growing at high CO(2) (i.e., photoautotrophic and mixotrophic conditions) have an increased capability for biomass production. In addition, we have observed that ATP production also seems to be an important limiting factor for growth under the conditions tested. Our experimental data (metabolomics and transcriptomics) and the results predicted by our model clearly suggest a differential behavior between low CO(2)-heterotrophic and high CO(2)-mixotrophic growth conditions. The data presented in the current study contributes to better dissect the biological response of C. reinhardtii, as a dynamic entity, to environmental and genetic changes. These findings are of great interest given the biotechnological potential of this microalga for CO(2) fixation, biomass accumulation, and bioenergy production. PeerJ Inc. 2018-09-03 /pmc/articles/PMC6126472/ /pubmed/30202653 http://dx.doi.org/10.7717/peerj.5528 Text en ©2018 Mora Salguero et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Computational Biology
Mora Salguero, Daniela Alejandra
Fernández-Niño, Miguel
Serrano-Bermúdez, Luis Miguel
Páez Melo, David O.
Winck, Flavia V.
Caldana, Camila
González Barrios, Andrés Fernando
Development of a Chlamydomonas reinhardtii metabolic network dynamic model to describe distinct phenotypes occurring at different CO(2) levels
title Development of a Chlamydomonas reinhardtii metabolic network dynamic model to describe distinct phenotypes occurring at different CO(2) levels
title_full Development of a Chlamydomonas reinhardtii metabolic network dynamic model to describe distinct phenotypes occurring at different CO(2) levels
title_fullStr Development of a Chlamydomonas reinhardtii metabolic network dynamic model to describe distinct phenotypes occurring at different CO(2) levels
title_full_unstemmed Development of a Chlamydomonas reinhardtii metabolic network dynamic model to describe distinct phenotypes occurring at different CO(2) levels
title_short Development of a Chlamydomonas reinhardtii metabolic network dynamic model to describe distinct phenotypes occurring at different CO(2) levels
title_sort development of a chlamydomonas reinhardtii metabolic network dynamic model to describe distinct phenotypes occurring at different co(2) levels
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6126472/
https://www.ncbi.nlm.nih.gov/pubmed/30202653
http://dx.doi.org/10.7717/peerj.5528
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