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Characterization of the natural variation in Arabidopsis thaliana metabolome by the analysis of metabolic distance

Metabolite fingerprinting is widely used to unravel the chemical characteristics of biological samples. Multivariate data analysis and other statistical tools are subsequently used to analyze and visualize the plasticity of the metabolome and/or the relationship between those samples. However, there...

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Autores principales: Houshyani, Benyamin, Kabouw, Patrick, Muth, Dorota, de Vos, Ric C. H., Bino, Raoul J., Bouwmeester, Harro J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3337402/
https://www.ncbi.nlm.nih.gov/pubmed/22593725
http://dx.doi.org/10.1007/s11306-011-0375-3
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author Houshyani, Benyamin
Kabouw, Patrick
Muth, Dorota
de Vos, Ric C. H.
Bino, Raoul J.
Bouwmeester, Harro J.
author_facet Houshyani, Benyamin
Kabouw, Patrick
Muth, Dorota
de Vos, Ric C. H.
Bino, Raoul J.
Bouwmeester, Harro J.
author_sort Houshyani, Benyamin
collection PubMed
description Metabolite fingerprinting is widely used to unravel the chemical characteristics of biological samples. Multivariate data analysis and other statistical tools are subsequently used to analyze and visualize the plasticity of the metabolome and/or the relationship between those samples. However, there are limitations to these approaches for example because of the multi-dimensionality of the data that makes interpretation of the data obtained from untargeted analysis almost impossible for an average human being. These limitations make the biological information that is of prime importance in untargeted studies be partially exploited. Even in the case of full exploitation, current methods for relationship elucidation focus mainly on between groups variation and differences. Therefore, a measure that is capable of exploiting both between- and within-group biological variation would be of great value. Here, we examined the natural variation in the metabolome of nine Arabidopsis thaliana accessions grown under various environmental conditions and established a measure for the metabolic distance between accessions and across environments. This data analysis approach shows that there is just a minor correlation between genetic and metabolic diversity of the nine accessions. On the other hand, it delivers so far in Arabidopsis unexplored chemical information and is shown to be biologically relevant for resistance studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0375-3) contains supplementary material, which is available to authorized users.
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spelling pubmed-33374022012-05-14 Characterization of the natural variation in Arabidopsis thaliana metabolome by the analysis of metabolic distance Houshyani, Benyamin Kabouw, Patrick Muth, Dorota de Vos, Ric C. H. Bino, Raoul J. Bouwmeester, Harro J. Metabolomics Original Article Metabolite fingerprinting is widely used to unravel the chemical characteristics of biological samples. Multivariate data analysis and other statistical tools are subsequently used to analyze and visualize the plasticity of the metabolome and/or the relationship between those samples. However, there are limitations to these approaches for example because of the multi-dimensionality of the data that makes interpretation of the data obtained from untargeted analysis almost impossible for an average human being. These limitations make the biological information that is of prime importance in untargeted studies be partially exploited. Even in the case of full exploitation, current methods for relationship elucidation focus mainly on between groups variation and differences. Therefore, a measure that is capable of exploiting both between- and within-group biological variation would be of great value. Here, we examined the natural variation in the metabolome of nine Arabidopsis thaliana accessions grown under various environmental conditions and established a measure for the metabolic distance between accessions and across environments. This data analysis approach shows that there is just a minor correlation between genetic and metabolic diversity of the nine accessions. On the other hand, it delivers so far in Arabidopsis unexplored chemical information and is shown to be biologically relevant for resistance studies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0375-3) contains supplementary material, which is available to authorized users. Springer US 2011-10-19 2012 /pmc/articles/PMC3337402/ /pubmed/22593725 http://dx.doi.org/10.1007/s11306-011-0375-3 Text en © The Author(s) 2011 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
spellingShingle Original Article
Houshyani, Benyamin
Kabouw, Patrick
Muth, Dorota
de Vos, Ric C. H.
Bino, Raoul J.
Bouwmeester, Harro J.
Characterization of the natural variation in Arabidopsis thaliana metabolome by the analysis of metabolic distance
title Characterization of the natural variation in Arabidopsis thaliana metabolome by the analysis of metabolic distance
title_full Characterization of the natural variation in Arabidopsis thaliana metabolome by the analysis of metabolic distance
title_fullStr Characterization of the natural variation in Arabidopsis thaliana metabolome by the analysis of metabolic distance
title_full_unstemmed Characterization of the natural variation in Arabidopsis thaliana metabolome by the analysis of metabolic distance
title_short Characterization of the natural variation in Arabidopsis thaliana metabolome by the analysis of metabolic distance
title_sort characterization of the natural variation in arabidopsis thaliana metabolome by the analysis of metabolic distance
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3337402/
https://www.ncbi.nlm.nih.gov/pubmed/22593725
http://dx.doi.org/10.1007/s11306-011-0375-3
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