Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of "unknown function"

Stephanie M. Quanbeck, Libuse Brachova, Alexis A. Campbell, Xin Guan, Ann Perera, Kun He, Seung Y. Rhee, Preeti Bais, Julie A. Dickerson, Philip Dixon, Gert Wohlgemuth, Oliver Fiehn, Lenore Barkan, Iris Lange, B. Markus Lange, In suk Lee, Diego Cortes, Carolina Salazar, Joel Shuman, Vladimir Shulaev & 7 others David V. Huhman, Lloyd W. Sumner, Mary R. Roth, Ruth Welti, Hilal Ilarslan, Eve S. Wurtele, Basil J. Nikolau

Research output: Contribution to journalArticle

53 Citations (Scopus)

Abstract

Metabolomics is the methodology that identifies and measures global pools of small molecules (of less than about 1,000 Da of a biological sample, which are collectively called the metabolome. Metabolomics can therefore reveal the metabolic outcome of a genetic or environmental perturbation of a metabolic regulatory network, and thus provide insights into the structure and regulation of that network. Because of the chemical complexity of the metabolome and limitations associated with individual analytical platforms for determining the metabolome, it is currently difficult to capture the complete metabolome of an organism or tissue, which is in contrast to genomics and transcriptomics. This paper describes the analysis of Arabidopsis metabolomics data sets acquired by a consortium that includes five analytical laboratories, bioinformaticists, and biostatisticians, which aims to develop and validate metabolomics as a hypothesis-generating functional genomics tool. The consortium is determining the metabolomes of Arabidopsis T-DNA mutant stocks, grown in standardized controlled environment optimized to minimize environmental impacts on the metabolomes. Metabolomics data were generated with seven analytical platforms, and the combined data is being provided to the research community to formulate initial hypotheses about genes of unknown function (GUFs. A public database (www.PlantMetabolomics.org has been developed to provide the scientific community with access to the data along with tools to allow for its interactive analysis. Exemplary datasets are discussed to validate the approach, which illustrate how initial hypotheses can be generated from the consortium-produced metabolomics data, integrated with prior knowledge to provide a testable hypothesis concerning the functionality of GUFs.

Original languageEnglish
Article number15
JournalFrontiers in Plant Science
Volume3
Issue numberFEB
DOIs
Publication statusPublished - 2012 Feb 10

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metabolome
metabolomics
Arabidopsis thaliana
genomics
genes
Matthiola
Arabidopsis
transcriptomics
environmental impact
mutants
organisms
DNA

All Science Journal Classification (ASJC) codes

  • Plant Science

Cite this

Quanbeck, Stephanie M. ; Brachova, Libuse ; Campbell, Alexis A. ; Guan, Xin ; Perera, Ann ; He, Kun ; Rhee, Seung Y. ; Bais, Preeti ; Dickerson, Julie A. ; Dixon, Philip ; Wohlgemuth, Gert ; Fiehn, Oliver ; Barkan, Lenore ; Lange, Iris ; Markus Lange, B. ; Lee, In suk ; Cortes, Diego ; Salazar, Carolina ; Shuman, Joel ; Shulaev, Vladimir ; Huhman, David V. ; Sumner, Lloyd W. ; Roth, Mary R. ; Welti, Ruth ; Ilarslan, Hilal ; Wurtele, Eve S. ; Nikolau, Basil J. / Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of "unknown function". In: Frontiers in Plant Science. 2012 ; Vol. 3, No. FEB.
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abstract = "Metabolomics is the methodology that identifies and measures global pools of small molecules (of less than about 1,000 Da of a biological sample, which are collectively called the metabolome. Metabolomics can therefore reveal the metabolic outcome of a genetic or environmental perturbation of a metabolic regulatory network, and thus provide insights into the structure and regulation of that network. Because of the chemical complexity of the metabolome and limitations associated with individual analytical platforms for determining the metabolome, it is currently difficult to capture the complete metabolome of an organism or tissue, which is in contrast to genomics and transcriptomics. This paper describes the analysis of Arabidopsis metabolomics data sets acquired by a consortium that includes five analytical laboratories, bioinformaticists, and biostatisticians, which aims to develop and validate metabolomics as a hypothesis-generating functional genomics tool. The consortium is determining the metabolomes of Arabidopsis T-DNA mutant stocks, grown in standardized controlled environment optimized to minimize environmental impacts on the metabolomes. Metabolomics data were generated with seven analytical platforms, and the combined data is being provided to the research community to formulate initial hypotheses about genes of unknown function (GUFs. A public database (www.PlantMetabolomics.org has been developed to provide the scientific community with access to the data along with tools to allow for its interactive analysis. Exemplary datasets are discussed to validate the approach, which illustrate how initial hypotheses can be generated from the consortium-produced metabolomics data, integrated with prior knowledge to provide a testable hypothesis concerning the functionality of GUFs.",
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Quanbeck, SM, Brachova, L, Campbell, AA, Guan, X, Perera, A, He, K, Rhee, SY, Bais, P, Dickerson, JA, Dixon, P, Wohlgemuth, G, Fiehn, O, Barkan, L, Lange, I, Markus Lange, B, Lee, IS, Cortes, D, Salazar, C, Shuman, J, Shulaev, V, Huhman, DV, Sumner, LW, Roth, MR, Welti, R, Ilarslan, H, Wurtele, ES & Nikolau, BJ 2012, 'Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of "unknown function"', Frontiers in Plant Science, vol. 3, no. FEB, 15. https://doi.org/10.3389/fpls.2012.00015

Metabolomics as a hypothesis-generating functional genomics tool for the annotation of Arabidopsis thaliana genes of "unknown function". / Quanbeck, Stephanie M.; Brachova, Libuse; Campbell, Alexis A.; Guan, Xin; Perera, Ann; He, Kun; Rhee, Seung Y.; Bais, Preeti; Dickerson, Julie A.; Dixon, Philip; Wohlgemuth, Gert; Fiehn, Oliver; Barkan, Lenore; Lange, Iris; Markus Lange, B.; Lee, In suk; Cortes, Diego; Salazar, Carolina; Shuman, Joel; Shulaev, Vladimir; Huhman, David V.; Sumner, Lloyd W.; Roth, Mary R.; Welti, Ruth; Ilarslan, Hilal; Wurtele, Eve S.; Nikolau, Basil J.

In: Frontiers in Plant Science, Vol. 3, No. FEB, 15, 10.02.2012.

Research output: Contribution to journalArticle

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AU - Quanbeck, Stephanie M.

AU - Brachova, Libuse

AU - Campbell, Alexis A.

AU - Guan, Xin

AU - Perera, Ann

AU - He, Kun

AU - Rhee, Seung Y.

AU - Bais, Preeti

AU - Dickerson, Julie A.

AU - Dixon, Philip

AU - Wohlgemuth, Gert

AU - Fiehn, Oliver

AU - Barkan, Lenore

AU - Lange, Iris

AU - Markus Lange, B.

AU - Lee, In suk

AU - Cortes, Diego

AU - Salazar, Carolina

AU - Shuman, Joel

AU - Shulaev, Vladimir

AU - Huhman, David V.

AU - Sumner, Lloyd W.

AU - Roth, Mary R.

AU - Welti, Ruth

AU - Ilarslan, Hilal

AU - Wurtele, Eve S.

AU - Nikolau, Basil J.

PY - 2012/2/10

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