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A quantitative model of transcription factor-activated gene expression.

ABSTRACT: A challenge facing biology is to develop quantitative, predictive models of gene regulation. Eukaryotic promoters contain transcription factor binding sites of differing affinity and accessibility, but we understand little about how these variables combine to generate a fine-tuned, quantitative transcriptional response. Here we used the PHO5 promoter in budding yeast to quantify the relationship between transcription factor input and gene expression output, termed the gene-regulation function (GRF). A model that captures variable interactions between transcription factors, nucleosomes and the promoter faithfully reproduced the observed quantitative changes in the GRF that occur upon altering the affinity of transcription factor binding sites, and implicates nucleosome-modulated accessibility of transcription factor binding sites in increasing the diversity of gene expression profiles. This work establishes a quantitative framework that can be applied to predict GRFs of other eukaryotic genes.

SUBMITTER: Kim HD

PROVIDER: S-EPMC2696132 | biostudies-literature | 2008 Nov

REPOSITORIES: biostudies-literature

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A quantitative model of transcription factor-activated gene expression.

Kim Harold D HD O'Shea Erin K EK

Nature structural & molecular biology 20081012 11

A challenge facing biology is to develop quantitative, predictive models of gene regulation. Eukaryotic promoters contain transcription factor binding sites of differing affinity and accessibility, but we understand little about how these variables combine to generate a fine-tuned, quantitative transcriptional response. Here we used the PHO5 promoter in budding yeast to quantify the relationship between transcription factor input and gene expression output, termed the gene-regulation function (G ...[more]

PMID: 18849996

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Project description:Precise patterns of spatial and temporal gene expression are central to metazoan complexity and act as a driving force for embryonic development. While there has been substantial progress in dissecting and predicting cis-regulatory activity, our understanding of how information from multiple enhancer elements converge to regulate a gene's expression remains elusive. This is in large part due to the number of different biological processes involved in mediating regulation as well as limited availability of experimental measurements for many of them. Here, we used a Bayesian approach to model diverse experimental regulatory data, leading to accurate predictions of both spatial and temporal aspects of gene expression. We integrated whole-embryo information on transcription factor recruitment to multiple cis-regulatory modules, insulator binding and histone modification status in the vicinity of individual gene loci, at a genome-wide scale during Drosophila development. The model uses Bayesian networks to represent the relation between transcription factor occupancy and enhancer activity in specific tissues and stages. All parameters are optimized in an Expectation Maximization procedure providing a model capable of predicting tissue- and stage-specific activity of new, previously unassayed genes. Performing the optimization with subsets of input data demonstrated that neither enhancer occupancy nor chromatin state alone can explain all gene expression patterns, but taken together allow for accurate predictions of spatio-temporal activity. Model predictions were validated using the expression patterns of more than 600 genes recently made available by the BDGP consortium, demonstrating an average 15-fold enrichment of genes expressed in the predicted tissue over a naïve model. We further validated the model by experimentally testing the expression of 20 predicted target genes of unknown expression, resulting in an accuracy of 95% for temporal predictions and 50% for spatial. While this is, to our knowledge, the first genome-wide approach to predict tissue-specific gene expression in metazoan development, our results suggest that integrative models of this type will become more prevalent in the future.

Dataset Information

A quantitative model of transcription factor-activated gene expression.

Publications

A quantitative model of transcription factor-activated gene expression.

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