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Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae.

ABSTRACT: Microarray studies are capable of providing data for temporal gene expression patterns of thousands of genes simultaneously, comprising rich but cryptic information about transcriptional control. However available methods are still not adequate in extraction of useful information about transcriptional regulation from these data. This study presents a dynamic model of gene expression which allows for identification of transcriptional regulators using time series of gene expression. The algorithm was applied for identification of transcriptional regulators controlling 40 cell cycle regulated genes of Saccharomyces cerevisiae. The presented algorithm uses a dynamic model of time continuous gene expression with the assumption that the target gene expression profile results from the action of the upstream regulator. The goal is to apply the model to putative regulators to estimate the transcription pattern of a target gene using a least squares minimization procedure. The procedure iteratively tests all possible transcription factors and selects those that best approximate the target gene expression profile. Results were compared with independently published data and good agreement between the published and identified transcriptional regulators was found.

SUBMITTER: Vu TT

PROVIDER: S-EPMC1802551 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae.

Vu Tra Thi TT Vohradsky Jiri J

Nucleic acids research 20061214 1

Microarray studies are capable of providing data for temporal gene expression patterns of thousands of genes simultaneously, comprising rich but cryptic information about transcriptional control. However available methods are still not adequate in extraction of useful information about transcriptional regulation from these data. This study presents a dynamic model of gene expression which allows for identification of transcriptional regulators using time series of gene expression. The algorithm ...[more]

PMID: 17170011

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Project description:Saccharomyces cerevisiae myosin type II-deficient (myo1?) strains remain viable and divide, despite the absence of a cytokinetic ring, by activation of the PKC1-dependent cell wall integrity pathway (CWIP). Since the myo1? transcriptional fingerprint is a subset of the CWIP fingerprint, the myo1? strain may provide a simplified paradigm for cell wall stress survival.To explore the post-transcriptional regulation of the myo1? stress response, 1,301 differentially regulated ribosome-bound mRNAs were identified by microarray analysis of which 204 were co-regulated by transcription and translation. Four categories of mRNA were significantly affected - protein biosynthesis, metabolism, carbohydrate metabolism, and unknown functions. Nine genes of the 20 CWIP fingerprint genes were post-transcriptionally regulated. Down and up regulation of selected ribosomal protein and cell wall biosynthesis mRNAs was validated by their distribution in polysomes from wild type and myo1? strains. Western blot analysis revealed accumulation of the phosphorylated form of eukaryotic translation initiation factor 2 (eIF2?-P) and a reduction in the steady state levels of the translation initiation factor eIF4Gp in myo1? strains. Deletion of GCN2 in myo1? abolished eIF2?p phosphorylation, and showed a severe growth defect. The presence of P-bodies in myo1? strains suggests that the process of mRNA sequestration is active, however, the three representative down regulated RP mRNAs, RPS8A, RPL3 and RPL7B were present at equivalent levels in Dcp2p-mCh-positive immunoprecipitated fractions from myo1? and wild type cells. These same RP mRNAs were also selectively co-precipitated with eIF2?-P in myo1? strains.Quantitative analysis of ribosome-associated mRNAs and their polyribosome distributions suggests selective regulation of mRNA translation efficiency in myo1? strains. Inhibition of translation initiation factor eIF2? (eIF2?-P) in these strains was by Gcn2p-dependent phosphorylation. The increase in the levels of eIF2?-P; the genetic interaction between GCN2 and MYO1; and the reduced levels of eIF4Gp suggest that other signaling pathways, in addition to the CWIP, may be important for myo1? strain survival. Selective co-immunoprecipitation of RP mRNAs with eIF2?-P in myo1? strains suggests a novel mode of translational regulation. These results indicate that post-transcriptional control is important in the myo1? stress response and possibly other stresses in yeast.

Project description:BackgroundControl effective flux (CEF) of a reaction is the weighted sum of all fluxes through that reaction, derived from elementary flux modes (EFM) of a metabolic network. Change in CEFs under different environmental conditions has earlier been proven to be correlated with the corresponding changes in the transcriptome. Here we use this to investigate the degree of transcriptional regulation of fluxes in the metabolism of Saccharomyces cerevisiae. We do this by quantifying correlations between changes in CEFs and changes in transcript levels for shifts in carbon source, i.e. between the fermentative carbon source glucose and nonfermentative carbon sources like ethanol, acetate, and lactate. The CEF analysis is based on a simple stoichiometric model that includes reactions of the central carbon metabolism and the amino acid metabolism.ResultsThe effect of the carbon shift on the metabolic fluxes was investigated for both batch and chemostat cultures. For growth on glucose in batch (respiro-fermentative) cultures, EFMs with no by-product formation were removed from the analysis of the CEFs, whereas those including any by-products (ethanol, glycerol, acetate, succinate) were omitted in the analysis of growth on glucose in chemostat (respiratory) cultures. This resulted in improved correlations between CEF changes and transcript levels. A regression correlation coefficient of 0.60 was obtained between CEF changes and gene expression changes in the central carbon metabolism for the analysis of 5 different perturbations. Out of 45 data points there were no more than 6 data points deviating from the correlation. Additionally, up- or down-regulation of at least 75% of the genes were in qualitative agreement with the CEF changes for all perturbations studied.ConclusionThe analysis indicates that changes in carbon source are associated with a high degree of hierarchical regulation of metabolic fluxes in the central carbon metabolism as the change in fluxes are correlating directly with the change in transcript levels of genes encoding their corresponding enzymes. For amino acid biosynthesis there was, however, not found to exist a similar correlation, and this may point to either post-transcriptional and/or metabolic regulation, or be due to the absence of a direct perturbation on the amino acid pathways in these experiments.

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Dataset Information

Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae.

Publications

Nonlinear differential equation model for quantification of transcriptional regulation applied to microarray data of Saccharomyces cerevisiae.

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