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Epistatic relationships reveal the functional organization of yeast transcription factors.

Molecular systems biology | 2010

The regulation of gene expression is, in large part, mediated by interplay between the general transcription factors (GTFs) that function to bring about the expression of many genes and site-specific DNA-binding transcription factors (STFs). Here, quantitative genetic profiling using the epistatic miniarray profile (E-MAP) approach allowed us to measure 48 391 pairwise genetic interactions, both negative (aggravating) and positive (alleviating), between and among genes encoding STFs and GTFs in Saccharomyces cerevisiae. This allowed us to both reconstruct regulatory models for specific subsets of transcription factors and identify global epistatic patterns. Overall, there was a much stronger preference for negative relative to positive genetic interactions among STFs than there was among GTFs. Negative genetic interactions, which often identify factors working in non-essential, redundant pathways, were also enriched for pairs of STFs that co-regulate similar sets of genes. Microarray analysis demonstrated that pairs of STFs that display negative genetic interactions regulate gene expression in an independent rather than coordinated manner. Collectively, these data suggest that parallel/compensating relationships between regulators, rather than linear pathways, often characterize transcriptional circuits.

Pubmed ID: 20959818 RIS Download

Research resources used in this publication

None found

Antibodies used in this publication

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Associated grants

  • Agency: NIGMS NIH HHS, United States
    Id: R01 GM070808
  • Agency: NIGMS NIH HHS, United States
    Id: GM081879
  • Agency: NIGMS NIH HHS, United States
    Id: GM084279
  • Agency: NIGMS NIH HHS, United States
    Id: P50 GM081879
  • Agency: NIGMS NIH HHS, United States
    Id: GM084448
  • Agency: NIGMS NIH HHS, United States
    Id: R01 GM090293
  • Agency: NIGMS NIH HHS, United States
    Id: R01 GM084279
  • Agency: NIGMS NIH HHS, United States
    Id: GM90293
  • Agency: NIGMS NIH HHS, United States
    Id: R01 GM084448
  • Agency: NIGMS NIH HHS, United States
    Id: GM70808

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European Bioinformatics Institute (tool)

RRID:SCR_004727

Non-profit academic organization for research and services in bioinformatics. Provides freely available data from life science experiments, performs basic research in computational biology, and offers user training programme, manages databases of biological data including nucleic acid, protein sequences, and macromolecular structures. Part of EMBL.

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Krogan Lab Interactome Database (tool)

RRID:SCR_008121

This database currently holds E-MAP scores (individual interactions and correlation coefficients) for budding yeast genes involved in the early secretory pathway and chromosome function (including DNA damage and repair, transcriptional control, chromosome segregation and telomere regulation). E-MAPs (Epistatic Mini Array Profiles) are formed by creating and quantifying high-density genetic interaction maps. With this method, observed double mutant colony sizes are compared to those that would be expected from a distribution of typical double mutant colonies of each strain. Each interaction is assigned a score, which indicates the magnitude of the difference from the expected value and the certainty of the score. Negative (or aggravating) scores (< -2.5) correspond to synthetic sick/lethal interactions while positive (or alleviating) scores (> +2.5) corresponds to epistatic or suppressor interactions.

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ArrayExpress (tool)

RRID:SCR_002964

International functional genomics data collection generated from microarray or next-generation sequencing (NGS) platforms. Repository of functional genomics data supporting publications. Provides genes expression data for reuse to the research community where they can be queried and downloaded. Integrated with the Gene Expression Atlas and the sequence databases at the European Bioinformatics Institute. Contains a subset of curated and re-annotated Archive data which can be queried for individual gene expression under different biological conditions across experiments. Data collected to MIAME and MINSEQE standards. Data are submitted by users or are imported directly from the NCBI Gene Expression Omnibus.

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