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Metabolic intermediates selectively stimulate transcription factor interaction and modulate phosphate and purine pathways.

ABSTRACT: Cells use strategic metabolites to sense the metabolome and accordingly modulate gene expression. Here, we show that the purine and phosphate pathways are positively regulated by the metabolic intermediate AICAR (5'-phosphoribosyl-5-amino-4-imidazole carboxamide). The transcription factor Pho2p is required for up-regulation of all AICAR-responsive genes. Accordingly, the binding of Pho2p to purine and phosphate pathway gene promoters is enhanced upon AICAR accumulation. In vitro, AICAR binds both Pho2p and Pho4p transcription factors and stimulates the interaction between Pho2p and either Bas1p or Pho4p in vivo. In contrast, SAICAR (succinyl-AICAR) only affects Pho2p-Bas1p interaction and specifically up-regulates purine regulon genes. Together, our data show that Bas1p and Pho4p compete for Pho2p binding, hence leading to the concerted regulation of cellular nucleotide synthesis and phosphate consumption.

SUBMITTER: Pinson B

PROVIDER: S-EPMC2701576 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Metabolic intermediates selectively stimulate transcription factor interaction and modulate phosphate and purine pathways.

Pinson Benoît B Vaur Sabine S Sagot Isabelle I Coulpier Fanny F Lemoine Sophie S Daignan-Fornier Bertrand B

Genes & development 20090601 12

Cells use strategic metabolites to sense the metabolome and accordingly modulate gene expression. Here, we show that the purine and phosphate pathways are positively regulated by the metabolic intermediate AICAR (5'-phosphoribosyl-5-amino-4-imidazole carboxamide). The transcription factor Pho2p is required for up-regulation of all AICAR-responsive genes. Accordingly, the binding of Pho2p to purine and phosphate pathway gene promoters is enhanced upon AICAR accumulation. In vitro, AICAR binds bot ...[more]

PMID: 19528318

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Project description:BACKGROUND:Common metabolic diseases, including type 2 diabetes, coronary artery disease, and hypertension, arise from disruptions of the body's metabolic homeostasis, with relatively strong contributions from genetic risk factors and substantial comorbidity with obesity. Although genome-wide association studies have revealed many genomic loci robustly associated with these diseases, biological interpretation of such association is challenging because of the difficulty in mapping single-nucleotide polymorphisms (SNPs) onto the underlying causal genes and pathways. Furthermore, common diseases are typically highly polygenic, and conventional single variant-based association testing does not adequately capture potentially important large-scale interaction effects between multiple genetic factors. METHODS:We analyzed moderately sized case-control data sets for type 2 diabetes, coronary artery disease, and hypertension to characterize the genetic risk factors arising from non-additive, collective interaction effects, using a recently developed algorithm (discrete discriminant analysis). We tested associations of genes and pathways with the disease status while including the cumulative sum of interaction effects between all variants contained in each group. RESULTS:In contrast to non-interacting SNP mapping, which produced few genome-wide significant loci, our analysis revealed extensive arrays of pathways, many of which are involved in the pathogenesis of these metabolic diseases but have not been directly identified in genetic association studies. They comprised cell stress and apoptotic pathways for insulin-producing ?-cells in type 2 diabetes, processes covering different atherosclerotic stages in coronary artery disease, and elements of both type 2 diabetes and coronary artery disease risk factors (cell cycle, apoptosis, and hemostasis) associated with hypertension. CONCLUSIONS:Our results support the view that non-additive interaction effects significantly enhance the level of common metabolic disease associations and modify their genetic architectures and that many of the expected genetic factors behind metabolic disease risks reside in smaller genotyping samples in the form of interacting groups of SNPs.

Dataset Information

Metabolic intermediates selectively stimulate transcription factor interaction and modulate phosphate and purine pathways.

Publications

Metabolic intermediates selectively stimulate transcription factor interaction and modulate phosphate and purine pathways.

Similar Datasets

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