Integration of transcriptomics and metabolomics data specifies the metabolic response of Chlamydomonas to rapamycin treatment

Flux phenotypes predicted by constraint‐based methods can be refined by the inclusion of heterogeneous data. While recent advances facilitate the integration of transcriptomics and proteomics data, purely stoichiometry‐based approaches for the prediction of flux phenotypes by considering metabolomic...

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Vydáno v:The Plant journal : for cell and molecular biology Ročník 81; číslo 5; s. 822 - 835
Hlavní autoři: Kleessen, Sabrina, Irgang, Susann, Klie, Sebastian, Giavalisco, Patrick, Nikoloski, Zoran
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Blackwell Scientific Publishers and BIOS Scientific Publishers in association with the Society for Experimental Biology 01.03.2015
Blackwell Publishing Ltd
Témata:
biochemical pathways
Botany
Chlamydomonas reinhardtii
Chlamydomonas reinhardtii - drug effects
Chlamydomonas reinhardtii - genetics
Chlamydomonas reinhardtii - metabolism
constraint‐based modeling
cysteine
data integration
Fluctuations
Genes
Growth conditions
Metabolic Networks and Pathways
Metabolism
Metabolites
Metabolomics
methionine
Models, Biological
phenotype
prediction
Proteomics
rapamycin
Scale models
Sirolimus - pharmacology
technical advance
transcriptomics
constraint-based modeling
metabolomics
data integration
Chlamydomonas reinhardtii
technical advance
rapamycin
ISSN:0960-7412, 1365-313X, 1365-313X
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Shrnutí:Flux phenotypes predicted by constraint‐based methods can be refined by the inclusion of heterogeneous data. While recent advances facilitate the integration of transcriptomics and proteomics data, purely stoichiometry‐based approaches for the prediction of flux phenotypes by considering metabolomics data are lacking. Here we propose a constraint‐based method, termed TREM‐Flux, for integrating time‐resolved metabolomics and transcriptomics data. We demonstrate the applicability of TREM‐Flux in the dissection of the metabolic response of Chlamydomonas reinhardtii to rapamycin treatment by integrating the expression levels of 982 genes and the content of 45 metabolites obtained from two growth conditions. The findings pinpoint cysteine and methionine metabolism to be most affected by the rapamycin treatment. Our study shows that the integration of time‐resolved unlabeled metabolomics data in addition to transcriptomics data can specify the metabolic pathways involved in the system's response to a studied treatment.
Bibliografie:http://dx.doi.org/10.1111/tpj.12763
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ISSN:0960-7412
1365-313X
1365-313X
DOI:10.1111/tpj.12763