Dataset Information

A chemical genomics study identifies Snf1 as a repressor of GCN4 translation.

ABSTRACT: The Saccharomyces cerevisiae Snf1 kinase plays a critical role in recalibrating cellular metabolism in response to glucose depletion. Hundreds of genes show changes in expression levels when the SNF1 gene is deleted. However, cells can adapt to the absence of a specific gene when grown in long term culture. Here we apply a chemical genetic method to rapidly and selectively inactivate a modified Snf1 kinase using a pyrazolopyrimidine inhibitor. By allowing cells to adjust to a change in carbon source prior to inhibition of the Snf1 kinase activity, we identified a set of genes whose expression increased when Snf1 was inhibited. Prominent in this set are genes that are activated by Gcn4, a transcriptional activator of amino acid biosynthetic genes. Deletion of Snf1 increased Gcn4 protein levels without affecting its mRNA levels. The increased Gcn4 protein levels required the Gcn2 kinase and Gcn20, regulators of GCN4 translation. These data indicate that Snf1 functions upstream of Gcn20 to regulate control of GCN4 translation in S. cerevisiae.

SUBMITTER: Shirra MK

PROVIDER: S-EPMC2602918 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A chemical genomics study identifies Snf1 as a repressor of GCN4 translation.

Shirra Margaret K MK McCartney Rhonda R RR Zhang Chao C Shokat Kevan M KM Schmidt Martin C MC Arndt Karen M KM

The Journal of biological chemistry 20081027 51

The Saccharomyces cerevisiae Snf1 kinase plays a critical role in recalibrating cellular metabolism in response to glucose depletion. Hundreds of genes show changes in expression levels when the SNF1 gene is deleted. However, cells can adapt to the absence of a specific gene when grown in long term culture. Here we apply a chemical genetic method to rapidly and selectively inactivate a modified Snf1 kinase using a pyrazolopyrimidine inhibitor. By allowing cells to adjust to a change in carbon so ...[more]

PMID: 18955495

Similar Datasets

Project description:A whole genome screen was used to assay every gene of Escherichia coli strain BW25113 to identify genes involved in susceptibility to the monobactam (beta-lactam) antibiotic aztreonam. The methodology has been called TraDIS-Xpress, and is a version of TraDIS or Tn-seq. A transposon mutant library consisting of several hundred thousand mutants was constructed using a Tn5-derived transposon incorporating an inducible outward transcribing promoter. All the mutants were grown in LB broth cultures supplemented with aztreonam at 2 x, 1 x, 0.5 x and 0.25 x MIC with induction of the transposon promoter using 0.2 mM IPTG or 1 mM IPTG or without induction. Following growth, mutants with increased susceptibility show reduced numbers and those with reduced susceptibility show increased numbers. Each condition was performed in duplicate. The methodology enable genes to be assayed by insertional inactivation or by changes in expression. Expression changes result from altered transcription from upstream transposon insertions transcribing into the gene, or downstream insertions transcribing into the gene in the reverse direction leading to RNA interference through the generation of reverse and complementary RNA. Thus, essential genes into which transposon insertions are not tolerated may be assayed also by changes in numbers of upstream or downstream insertion mutants. Changes to high throughput sequencing protocols permit the generation of nucleotide sequence reads from the known transposon sequences into the surrounding insertion site for all the mutants in the mixture simultaneously. Matching the sequence of the reads to the genome nucleotide sequence of E. coli BW25113 then allows the precise locations of all the transposon insertion sites of all the mutants to be mapped simultaneously. The relative changes in mutants between control (without) and selective condition (with aztreonam) then indicates which genes are involved in susceptibility. The numbers of sequence reads that match is reflected by the number of mutants, and so the degree of susceptibility can also be estimated.

Project description:BackgroundThe yeast Saccharomyces cerevisiae responds to amino acid starvation by inducing the transcription factor Gcn4. This is mainly mediated via a translational control mechanism dependent upon the translation initiation eIF2·GTP·Met-tRNAiMet ternary complex, and the four short upstream open reading frames (uORFs) in its 5' mRNA leader. These uORFs act to attenuate GCN4 mRNA translation under normal conditions. During amino acid starvation, levels of ternary complex are reduced. This overcomes the GCN4 translation attenuation effect via a scanning/reinitiation control mechanism dependent upon uORF spacing.ResultsUsing published experimental data, we have developed and validated a probabilistic formulation of GCN4 translation using the Chemical Master Equation (Model 1). Model 1 explains GCN4 translation's nonlinear dependency upon uORF placements, and predicts that an as yet unidentified factor, which was proposed to regulate GCN4 translation under some conditions, only has pronounced effects upon GCN4 translation when intercistronic distances are unnaturally short. A simpler Model 2 that does not include this unidentified factor could well represent the regulation of a natural GCN4 mRNA. Using parameter values optimised for this algebraic Model 2, we performed stochastic simulations by Gillespie algorithm to investigate the distribution of ribosomes in different sections of GCN4 mRNA under distinct conditions. Our simulations demonstrated that ribosomal loading in the 5'-untranslated region is mainly determined by the ratio between the rates of 5'-initiation and ribosome scanning, but was not significantly affected by rate of ternary complex binding. Importantly, the translation rate for codons starved of cognate tRNAs is predicted to be the most significant contributor to the changes in ribosomal loading in the coding region under repressing and derepressing conditions.ConclusionsOur integrated probabilistic Models 1 and 2 explained GCN4 translation and helped to elucidate the role of a yet unidentified factor. The ensuing stochastic simulations evaluated different factors that may impact on the translation of GCN4 mRNA, and integrated translation status with ribosomal density.

Project description:Recent genomic approaches have suggested the existence of multiple distinct subtypes of medulloblastoma. We studied a large cohort of medulloblastomas to determine how many subgroups of the disease exist, how they differ, and the extent of overlap between subgroups. We determined gene expression profiles and DNA copy number aberrations for 103 primary medulloblastomas. Bioinformatic tools were used for class discovery of medulloblastoma subgroups based on the most informative genes in the dataset. Immunohistochemistry for subgroup-specific ‘signature’ genes was used to determine subgroup affiliation for 294 non-overlapping medulloblastomas on two independent tissue microarrays (TMAs). Multiple unsupervised analyses of transcriptional profiles identified four distinct, non-overlapping molecular variants: WNT, SHH, Group C, and Group D. Supervised analysis of these four subgroups revealed significant subgroup-specific demographics, histology, metastatic status, and DNA copy number aberrations. Immunohistochemistry for DKK1 (WNT), SFRP1 (SHH), NPR3 (Group C), and KCNA1 (Group D) could reliably and uniquely classify formalin fixed medulloblastomas in ~98% of cases. Group C patients (NPR3 +ve tumors) exhibited a significantly diminished progression free and overall survival irrespective of their metastatic status. Our integrative genomics approach to a large cohort of medulloblastomas has identified four disparate subgroups with distinct demographics, clinical presentation, transcriptional profiles, genetic abnormalities, and clinical outcome. Medulloblastomas can be reliably assigned to subgroups through immunohistochemistry, thereby making medulloblastoma sub-classification widely available. Future research on medulloblastoma and the development of clinical trials should take into consideration these four distinct types of medulloblastoma. A total of 103 primary medulloblastoma specimens were profiled by Affymetrix exon array and gene-level analysis was performed.

Dataset Information

A chemical genomics study identifies Snf1 as a repressor of GCN4 translation.

Publications

A chemical genomics study identifies Snf1 as a repressor of GCN4 translation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets