Stitching together Multiple Data Dimensions Reveals Interacting Metabolomic and Transcriptomic Networks That Modulate Cell Regulation

DNA variation can be used as a systematic source of perturbation in segregating populations as a way to infer regulatory networks via the integration of large-scale, high-dimensional molecular profiling data

Functional Analysis of the Yeast Glc7-Binding Protein Reg1 Identifies a Protein Phosphatase Type 1-Binding Motif as Essential for Repression of ADH2 Expression

In Saccharomyces cerevisiae, the protein phosphatase type 1 (PP1)-binding protein Reg1 is required to maintain complete repression of ADH2 expression during growth on glucose. Surprisingly, however, mutant forms of the yeast PP1 homologue Glc7, which are unable to repress expression of another glucose-regulated gene, SUC2, fully repressed ADH2. Constitutive ADH2 expression in reg1 mutant cells did require Snf1 protein kinase activity like constitutive SUC2 expression and was inhibited by unregulated cyclic AMP-dependent protein kinase activity like ADH2 expression in derepressed cells. To further elucidate the functional role of Reg1 in repressing ADH2 expression, deletions scanning the entire length of the protein were analyzed. Only the central region of the protein containing the putative PP1-binding sequence RHIHF was found to be indispensable for repression. Introduction of the I466M F468A substitutions into this sequence rendered Reg1 almost nonfunctional. Deletion of the central region or the double substitution prevented Reg1 from significantly interacting with Glc7 in two-hybrid analyses. Previous experimental evidence had indicated that Reg1 might target Glc7 to nuclear substrates such as the Snf1 kinase complex. Subcellular localization of a fully functional Reg1-green fluorescent protein fusion, however, indicated that Reg1 is cytoplasmic and excluded from the nucleus independently of the carbon source. When the level of Adr1 was modestly elevated, ADH2 expression was no longer fully repressed in glc7 mutant cells, providing the first direct evidence that Glc7 can repress ADH2 expression. These results suggest that the Reg1-Glc7 phosphatase is a cytoplasmic component of the machinery responsible for returning Snf1 kinase activity to its basal level and reestablishing glucose repression. This implies that the activated form of the Snf1 kinase complex must cycle between the nucleus and the cytoplasm

Integrating External Biological Knowledge in the Construction of Regulatory Networks From Time-Series Expression Data

Inference about regulatory networks from high-throughput genomics data is of great interest in systems biology. We present a Bayesian approach to infer gene regulatory networks from time series expression data by integrating various types of biological knowledge

Artificial Recruitment of Mediator by the DNA-Binding Domain of Adr1 Overcomes Glucose Repression of ADH2 Expression▿

The transcription factor Adr1 activates numerous genes in nonfermentable carbon source metabolism. An unknown mechanism prevents Adr1 from stably binding to the promoters of these genes in glucose-grown cells. Glucose depletion leads to Snf1-dependent binding. Chromatin immunoprecipitation showed that the Adr1 DNA-binding domain could not be detected at the ADH2 promoter under conditions in which the binding of the full-length protein occurred. This suggested that an activation domain is required for stable binding, and coactivators may stabilize the interaction with the promoter. Artificial recruitment of Mediator tail subunits by fusion to the Adr1 DNA-binding domain overcame both the inhibition of promoter binding and glucose repression of ADH2 expression. In contrast, an Adr1 DNA-binding domain-Tbp fusion did not overcome glucose repression, although it was an efficient activator of ADH2 expression under derepressing conditions. When Mediator was artificially recruited, ADH2 expression was independent of SNF1, SAGA, and Swi/Snf, whereas ADH2 expression was dependent on these factors with wild-type Adr1. These results suggest that in the presence of glucose, the ADH2 promoter is accessible to Adr1 but that other interactions that occur when glucose is depleted do not take place. Artificial recruitment of Mediator appears to overcome this requirement and to allow stable binding and transcription under normally inhibitory conditions

Construction of regulatory networks using expression time-series data of a genotyped population

The inference of regulatory and biochemical networks from large-scale genomics data is a basic problem in molecular biology. The goal is to generate testable hypotheses of gene-to-gene influences and subsequently to design bench experiments to confirm these network predictions. Coexpression of genes in large-scale gene-expression data implies coregulation and potential gene–gene interactions, but provide little information about the direction of influences. Here, we use both time-series data and genetics data to infer directionality of edges in regulatory networks: time-series data contain information about the chronological order of regulatory events and genetics data allow us to map DNA variations to variations at the RNA level. We generate microarray data measuring time-dependent gene-expression levels in 95 genotyped yeast segregants subjected to a drug perturbation. We develop a Bayesian model averaging regression algorithm that incorporates external information from diverse data types to infer regulatory networks from the time-series and genetics data. Our algorithm is capable of generating feedback loops. We show that our inferred network recovers existing and novel regulatory relationships. Following network construction, we generate independent microarray data on selected deletion mutants to prospectively test network predictions. We demonstrate the potential of our network to discover de novo transcription-factor binding sites. Applying our construction method to previously published data demonstrates that our method is competitive with leading network construction algorithms in the literature