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Integration of large-scale data for extraction of integrated Arabidopsis root cell-type specific models
Plant organs consist of multiple cell types that do not operate in isolation, but communicate with each other to maintain proper functions. Here, we extract models specific to three developmental stages of eight root cell types or tissue layers in Arabidopsis thaliana based on a state-of-the-art con...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5962614/ https://www.ncbi.nlm.nih.gov/pubmed/29784955 http://dx.doi.org/10.1038/s41598-018-26232-8 |
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author | Scheunemann, Michael Brady, Siobhan M. Nikoloski, Zoran |
author_facet | Scheunemann, Michael Brady, Siobhan M. Nikoloski, Zoran |
author_sort | Scheunemann, Michael |
collection | PubMed |
description | Plant organs consist of multiple cell types that do not operate in isolation, but communicate with each other to maintain proper functions. Here, we extract models specific to three developmental stages of eight root cell types or tissue layers in Arabidopsis thaliana based on a state-of-the-art constraint-based modeling approach with all publicly available transcriptomics and metabolomics data from this system to date. We integrate these models into a multi-cell root model which we investigate with respect to network structure, distribution of fluxes, and concordance to transcriptomics and proteomics data. From a methodological point, we show that the coupling of tissue-specific models in a multi-tissue model yields a higher specificity of the interconnected models with respect to network structure and flux distributions. We use the extracted models to predict and investigate the flux of the growth hormone indole-3-actetate and its antagonist, trans-Zeatin, through the root. While some of predictions are in line with experimental evidence, constraints other than those coming from the metabolic level may be necessary to replicate the flow of indole-3-actetate from other simulation studies. Therefore, our work provides the means for data-driven multi-tissue metabolic model extraction of other Arabidopsis organs in the constraint-based modeling framework. |
format | Online Article Text |
id | pubmed-5962614 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-59626142018-05-24 Integration of large-scale data for extraction of integrated Arabidopsis root cell-type specific models Scheunemann, Michael Brady, Siobhan M. Nikoloski, Zoran Sci Rep Article Plant organs consist of multiple cell types that do not operate in isolation, but communicate with each other to maintain proper functions. Here, we extract models specific to three developmental stages of eight root cell types or tissue layers in Arabidopsis thaliana based on a state-of-the-art constraint-based modeling approach with all publicly available transcriptomics and metabolomics data from this system to date. We integrate these models into a multi-cell root model which we investigate with respect to network structure, distribution of fluxes, and concordance to transcriptomics and proteomics data. From a methodological point, we show that the coupling of tissue-specific models in a multi-tissue model yields a higher specificity of the interconnected models with respect to network structure and flux distributions. We use the extracted models to predict and investigate the flux of the growth hormone indole-3-actetate and its antagonist, trans-Zeatin, through the root. While some of predictions are in line with experimental evidence, constraints other than those coming from the metabolic level may be necessary to replicate the flow of indole-3-actetate from other simulation studies. Therefore, our work provides the means for data-driven multi-tissue metabolic model extraction of other Arabidopsis organs in the constraint-based modeling framework. Nature Publishing Group UK 2018-05-21 /pmc/articles/PMC5962614/ /pubmed/29784955 http://dx.doi.org/10.1038/s41598-018-26232-8 Text en © The Author(s) 2018 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Scheunemann, Michael Brady, Siobhan M. Nikoloski, Zoran Integration of large-scale data for extraction of integrated Arabidopsis root cell-type specific models |
title | Integration of large-scale data for extraction of integrated Arabidopsis root cell-type specific models |
title_full | Integration of large-scale data for extraction of integrated Arabidopsis root cell-type specific models |
title_fullStr | Integration of large-scale data for extraction of integrated Arabidopsis root cell-type specific models |
title_full_unstemmed | Integration of large-scale data for extraction of integrated Arabidopsis root cell-type specific models |
title_short | Integration of large-scale data for extraction of integrated Arabidopsis root cell-type specific models |
title_sort | integration of large-scale data for extraction of integrated arabidopsis root cell-type specific models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5962614/ https://www.ncbi.nlm.nih.gov/pubmed/29784955 http://dx.doi.org/10.1038/s41598-018-26232-8 |
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