Kenneth M. Dombek

Cyclic changes in metabolic state during the life of a yeast cell

Budding yeast undergo robust oscillations in oxygen consumption during continuous growth in a nutrient-limited environment. Using liquid chromatography-mass spectrometry and comprehensive 2D gas chromatography-mass spectrometry-based metabolite profiling methods, we have determined that the intracellular concentrations of many metabolites change periodically as a function of these metabolic cycles. These results reveal the logic of cellular metabolism during different phases of the life of a yeast cell. They may further indicate that oscillation in the abundance of key metabolites might help control the temporal regulation of cellular processes and the establishment of a cycle. Such oscillations in metabolic state might occur during the course of other biological cycles.

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.

Comprehensive analysis of yeast metabolite GC×GC–TOFMS data: combining discovery-mode and deconvolution chemometric software

The first extensive study of yeast metabolite GC x GC-TOFMS data from cells grown under fermenting, R, and respiring, DR, conditions is reported. In this study, recently developed chemometric software for use with three-dimensional instrumentation data was implemented, using a statistically-based Fisher ratio method. The Fisher ratio method is fully automated and will rapidly reduce the data to pinpoint two-dimensional chromatographic peaks differentiating sample types while utilizing all the mass channels. The effect of lowering the Fisher ratio threshold on peak identification was studied. At the lowest threshold (just above the noise level), 73 metabolite peaks were identified, nearly three-fold greater than the number of previously reported metabolite peaks identified (26). In addition to the 73 identified metabolites, 81 unknown metabolites were also located. A Parallel Factor Analysis graphical user interface (PARAFAC GUI) was applied to selected mass channels to obtain a concentration ratio, for each metabolite under the two growth conditions. Of the 73 known metabolites identified by the Fisher ratio method, 54 were statistically changing to the 95% confidence limit between the DR and R conditions according to the rigorous Students t-test. PARAFAC determined the concentration ratio and provided a fully-deconvoluted (i.e. mathematically resolved) mass spectrum for each of the metabolites. The combination of the Fisher ratio method with the PARAFAC GUI provides high-throughput software for discovery-based metabolomics research, and is novel for GC x GC-TOFMS data due to the use of the entire data set in the analysis (640 MB x 70 runs, double precision floating point).

Identification of potential target genes for Adr1p through characterization of essential nucleotides in UAS1.

Adr1p is a regulatory protein in the yeast Saccharomyces cerevisiae that binds to and activates transcription from two sites in a perfect 22-bp inverted repeat, UAS1, in the ADH2 promoter. Binding requires two C2H2 zinc fingers and a region amino terminal to the fingers. The importance for DNA binding of each position within UAS1 was deduced from two types of assays. Both methods led to an identical consensus sequence containing only four essential base pairs: GG(A/G)G. The preferred sequence, TTGG(A/G)GA, is found in both halves of the inverted repeat. The region of Adr1p amino terminal to the fingers is important for phosphate contacts in the central region of UAS1. However, no base-specific contacts in this portion of UAS1 are important for DNA binding or for ADR1-dependent transcription in vivo. When the central 6 bp were deleted, only a single monomer of Adr1p was able to bind in vitro and activation in vivo was severely reduced. On the basis of these results and previous knowledge about the DNA binding site requirements, including constraints on the spacing and orientation of sites that affect activation in vivo, a consensus binding site for Adr1p was derived. By using this consensus site, potential Adr1p binding sites were located in the promoters of genes known to show ADR1-dependent expression. In addition, this consensus allowed the identification of new potential target genes for Adr1p.

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.

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

ABSTRACT 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. ConstitutiveADH2 expression in reg1 mutant cells did require Snf1 protein kinase activity like constitutive SUC2expression 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 repressingADH2 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.

Time-Dependent Profiling of Metabolites from Snf1 Mutant and Wild Type Yeast Cells

The effect of sampling time in the context of growth conditions on a dynamic metabolic system was investigated in order to assess to what extent a single sampling time may be sufficient for general application, as well as to determine if useful kinetic information could be obtained. A wild type yeast strain (W) was compared to a snf1Delta mutant yeast strain (S) grown in high-glucose medium (R) and in low-glucose medium containing ethanol (DR). Under these growth conditions, different metabolic pathways for utilizing the different carbon sources are expected to be active. Thus, changes in metabolite levels relating to the carbon source in the growth medium were anticipated. Furthermore, the Snf1 protein kinase complex is required to adapt cellular metabolism from fermentative R conditions to oxidative DR conditions. So, differences in intracellular metabolite levels between the W and S yeast strains were also anticipated. Cell extracts were collected at four time points (0.5, 2, 4, 6 h) after shifting half of the cells from R to DR conditions, resulting in 16 sample classes (WR, WDR, SR, SDR) x (0.5, 2, 4, 6 h). The experimental design provided time course data, so temporal dependencies could be monitored in addition to carbon source and strain dependencies. Comprehensive two-dimensional (2D) gas chromatography coupled to time-of-flight mass spectrometry (GC x GC-TOFMS) was used with discovery-based data mining algorithms ( Anal. Chem. 2006, 78, 5068-5075 (ref 1); J. Chromatogr., A 2008, 1186, 401-411 (ref 2)) to locate regions within the 2D chromatograms (i.e., metabolites) that provided chemical selectivity between the 16 sample classes. These regions were mathematically resolved using parallel factor analysis to positively identify the metabolites and to acquire quantitative results. With these tools, 51 unique metabolites were identified and quantified. Various time course patterns emerged from these data, and principal component analysis (PCA) was utilized as a comparison tool to determine the sources of variance between these 51 metabolites. The effect of sampling time was investigated with separate PCA analyses using various subsets of the data. PCA utilizing all of the time course data, averaged time course data, and each individual time point data set independently were performed to discern the differences. For the yeast strains examined in the current study, data collection at either 4 or 6 h provided information comparable to averaged time course data, albeit with a few metabolites missing using a single sampling time point.

ADH2 expression is repressed by REG1 independently of mutations that alter the phosphorylation of the yeast transcription factor ADR1.

In Saccharomyces cerevisiae, expression of the ADH2 gene is undetectable during growth on glucose. The transcription factor ADR1 is required to fully activate expression when glucose becomes depleted. Partial activation during growth on glucose occurred in cells carrying a constitutive allele of ADR1 in which the phosphorylatable serine of a cyclic AMP (cAMP)-dependent protein kinase phosphorylation site had been changed to alanine. When glucose was removed from the growth medium, a substantial increase in the level of this constitutive expression was observed for both the ADH2 gene and a reporter construct containing the ADR1 binding site. This suggests that glucose can block ADR1-mediated activation independently of cAMP-dependent phosphorylation at serine 230. REG1/HEX2/SRN1 was identified as a potential serine 230-independent repressor of ADH2 expression. Yeast strains carrying a deletion of the REG1 gene, reg1-1966, showed a large increase in ADR1-dependent expression of ADH2 during growth on glucose. A smaller increase in ADR1-independent expression was also observed. Additionally, an increase in the level of ADR1 expression and posttranslational modification of the ADR1 protein were observed. When the reg1-1966 allele was combined with various ADR1 constitutive alleles, the level of ADH2 expression was synergistically elevated. This indicates that REG1 can act independently of phosphorylation at serine 230. Our results suggest that glucose repression in the presence of ADR1 constitutive alleles occurs primarily through a REG1-dependent pathway which appears to affect ADH2 transcription at multiple levels.

Cyclic AMP-dependent protein kinase inhibits ADH2 expression in part by decreasing expression of the transcription factor gene ADR1

In Saccharomyces cerevisiae, the unregulated cyclic AMP-dependent protein kinase (cAPK) activity of bcy1 mutant cells inhibits expression of the glucose-repressible ADH2 gene. The transcription factor Adr1p is thought to be the primary target of cAPK. Here we demonstrate that the decreased abundance of Adr1p in bcy1 mutant cells contributes to the inhibition of ADH2 expression. Activation of ADH2 transcription was blocked in bcy1 mutant cells, and UAS1, the Adr1p binding site in the ADH2 promoter, was sufficient to mediate this effect. Concurrent with this loss of transcriptional activation was an up to 30-fold reduction in the level of Adr1p. Mutating the strong cAPK phosphorylation site at serine 230 did not suppress this effect. Analysis of ADR1 mRNA levels and ADR1-lacZ expression suggested that decreased ADR1 transcription was responsible for the reduced protein level. In contrast to the ADH2 promoter, however, deletion analysis suggested that cAPK does not act through a discrete DNA element in the ADR1 promoter. The amount of Adr1p found in bcy1 mutant cells should have been sufficient to support 23% of the wild-type level of ADH2 expression. Since no ADH2 expression was detectable in bcy1 mutant cells, cAPK must also act by other mechanisms. Overexpression of Adr1p only partially restored ADH2 expression, indicating that some of these mechanisms may impinge upon events at or subsequent to the ADR1-dependent step in ADH2 transcriptional activation.

Integrating external biological knowledge in the construction of regulatory networks from time-series expression data

BackgroundInference 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.ResultsWe formulate network construction as a series of variable selection problems and use linear regression to model the data. Our method summarizes additional data sources with an informative prior probability distribution over candidate regression models. We extend the Bayesian model averaging (BMA) variable selection method to select regulators in the regression framework. We summarize the external biological knowledge by an informative prior probability distribution over the candidate regression models.ConclusionsWe demonstrate our method on simulated data and a set of time-series microarray experiments measuring the effect of a drug perturbation on gene expression levels, and show that it outperforms leading regression-based methods in the literature.