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. 2010 Apr;152(4):2120-9.
doi: 10.1104/pp.109.147306. Epub 2010 Feb 26.

Combined transcript and metabolite profiling of Arabidopsis grown under widely variant growth conditions facilitates the identification of novel metabolite-mediated regulation of gene expression

Affiliations

Combined transcript and metabolite profiling of Arabidopsis grown under widely variant growth conditions facilitates the identification of novel metabolite-mediated regulation of gene expression

Matthew A Hannah et al. Plant Physiol. 2010 Apr.

Abstract

Regulation of metabolism at the level of transcription and its corollary metabolite-mediated regulation of transcription are well-documented mechanisms by which plants adapt to circumstance. That said the function of only a minority of transcription factor networks are fully understood and it seems likely that we have only identified a subset of the metabolites that play a mediator function in the regulation of transcription. Here we describe an integrated genomics approach in which we perform combined transcript and metabolite profiling on Arabidopsis (Arabidopsis thaliana) plants challenged by various environmental extremes. We chose this approach to generate a large variance in the levels of all parameters recorded. The data was then statistically evaluated to identify metabolites whose level robustly correlated with those of a particularly large number of transcripts. Since correlation alone provides no proof of causality we subsequently attempted to validate these putative mediators of gene expression via a combination of statistical analysis of data available in publicly available databases and iterative experimental evaluation. Data presented here suggest that, on adoption of appropriate caution, the approach can be used for the identification of metabolite mediators of gene expression. As an exemplary case study we document that in plants, as in yeast (Saccharomyces cerevisiae) and mammals, leucine plays an important role as a regulator of gene expression and provide a leucine response gene regulatory network.

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Figures

Figure 1.
Figure 1.
Experimental approach.
Figure 2.
Figure 2.
Validation of sugar candidate metabolite mediators of gene expression. Summary tables showing the overlap between transcripts significantly correlated with a given metabolite (top) and those up- and down-regulated in response to its perturbation (left). See text for details. P values from Fisher exact tests are shown, with red and blue indicating over- and underrepresentation, respectively.
Figure 3.
Figure 3.
Validation of carotenoid candidate metabolite mediators of gene expression. Summary tables showing the overlap between transcripts significantly correlated with a given metabolite (top) and those up- and down-regulated in response to its perturbation by norflurazon (left). See text for details. P values from Fisher exact tests are shown, with red and blue indicating over- and underrepresentation, respectively.
Figure 4.
Figure 4.
Validation of Leu candidate metabolite mediators of gene expression. Summary tables showing the overlap between transcripts significantly correlated with a given metabolite (top) and those up- and down-regulated in response to its perturbation by transgenesis (left). See text for details. P values from Fisher exact tests are shown, with red and blue indicating over- and underrepresentation, respectively.
Figure 5.
Figure 5.
Meta network of coexpressed gene clusters and the Leu candidate network. Transcripts that were up-regulated in the Leu overexpression line and significantly correlated with Leu were visualized in the meta network of AraGenNet (Mutwil et al., 2009). Numbers represent clusters, and lines indicate connection between clusters. Gray circles highlighted clusters in which Leu candidates display coexpression pattern (false discovery rate < 0.05) and black lines (i.e. blue or red) indicate the connection between those clusters.
Figure 6.
Figure 6.
Expression of transcripts after Leu feeding. Arabidopsis suspension cell cultures derived from leaves (Pauly et al., 2001) were treated without (control) or with 50 μm Leu for 90 min. Transcript levels were normalized to UBQ10 as reference control (ΔΔCT). Fold change (−ΔΔCT) was calculated by subtracting ΔCT Leu treated from ΔCT control samples. Arrows indicate the transcripts that confirm our prediction. Values represented the mean of three independent biological replicates ± se. [See online article for color version of this figure.]

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