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Shared transcription factors contribute to distinct cell fates.

Ng FS, Calero-Nieto FJ, Göttgens B - Transcription (2015)

Bottom Line: However, not much is known about the relationship between cell-type-specific binding patterns and gene expression.A recent study demonstrated how the same TFs can have functional roles when binding to largely non-overlapping genomic regions in hematopoietic progenitor and mast cells.Cell-type specific binding profiles of shared TFs are therefore not merely the consequence of opportunistic and functionally irrelevant binding to accessible chromatin, but instead have the potential to make meaningful contributions to cell-type specific transcriptional programs.

View Article: PubMed Central - PubMed

Affiliation: a Department of Haematology; Wellcome Trust and MRC Cambridge Stem Cell Institute & Cambridge Institute for Medical Research ; Cambridge University ; Cambridge , UK.

ABSTRACT
Genome-wide transcription factor (TF) binding profiles differ dramatically between cell types. However, not much is known about the relationship between cell-type-specific binding patterns and gene expression. A recent study demonstrated how the same TFs can have functional roles when binding to largely non-overlapping genomic regions in hematopoietic progenitor and mast cells. Cell-type specific binding profiles of shared TFs are therefore not merely the consequence of opportunistic and functionally irrelevant binding to accessible chromatin, but instead have the potential to make meaningful contributions to cell-type specific transcriptional programs.

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Summary of computational and experimental approaches in the genome-wide comparison of HPC7 blood progenitor and mast cells. (A) Regression models using differential TF occupancy and differential gene expression. (B) Motif content analysis of cell-type-specific and common binding regions. (C) shRNA perturbation experiments compared to TF binding and gene expression data.
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f0001: Summary of computational and experimental approaches in the genome-wide comparison of HPC7 blood progenitor and mast cells. (A) Regression models using differential TF occupancy and differential gene expression. (B) Motif content analysis of cell-type-specific and common binding regions. (C) shRNA perturbation experiments compared to TF binding and gene expression data.

Mentions: We examined the observation of distinct binding patterns further by quantifying the differences in binding and their relationship to gene expression in a regression model (Fig. 1A). Regression models provide a useful and simple approach to quantify the relationships between multiple predictor variables (i.e., ‘shared’ TFs) to a response variable (i.e., gene expression). Furthermore, the availability of high resolution genome-wide data (i.e., ChIP-seq and RNA-seq) allowed the construction of accurate predictive models. By considering genes bound by at least one of these TFs, these models describe gene expression as a function of combinatorial effects of one or more relative TF binding strengths. In the past, other studies have also utilized regression statistics to build a variety of prediction models that include, for example, TF binding data, histone modification, and consensus binding motifs.27,28 Until recently, these studies have focused on predicting gene expression in one cell type, obtaining high levels of correlation with observed data. However, applying the model to another cell type often results in poor accuracy since static expression levels were used to construct the model. Our study, on the other hand, employed regression models for 2 cell types to predict changes in gene expression. Thus, when differential promoter and distal enhancer occupancy were encoded into the model, quantitative changes in TF binding were found to be predictive of quantitative changes in differential gene expression. Moreover, prediction accuracy improved when multiple binding events were taken into account.Figure 1.


Shared transcription factors contribute to distinct cell fates.

Ng FS, Calero-Nieto FJ, Göttgens B - Transcription (2015)

Summary of computational and experimental approaches in the genome-wide comparison of HPC7 blood progenitor and mast cells. (A) Regression models using differential TF occupancy and differential gene expression. (B) Motif content analysis of cell-type-specific and common binding regions. (C) shRNA perturbation experiments compared to TF binding and gene expression data.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4581352&req=5

f0001: Summary of computational and experimental approaches in the genome-wide comparison of HPC7 blood progenitor and mast cells. (A) Regression models using differential TF occupancy and differential gene expression. (B) Motif content analysis of cell-type-specific and common binding regions. (C) shRNA perturbation experiments compared to TF binding and gene expression data.
Mentions: We examined the observation of distinct binding patterns further by quantifying the differences in binding and their relationship to gene expression in a regression model (Fig. 1A). Regression models provide a useful and simple approach to quantify the relationships between multiple predictor variables (i.e., ‘shared’ TFs) to a response variable (i.e., gene expression). Furthermore, the availability of high resolution genome-wide data (i.e., ChIP-seq and RNA-seq) allowed the construction of accurate predictive models. By considering genes bound by at least one of these TFs, these models describe gene expression as a function of combinatorial effects of one or more relative TF binding strengths. In the past, other studies have also utilized regression statistics to build a variety of prediction models that include, for example, TF binding data, histone modification, and consensus binding motifs.27,28 Until recently, these studies have focused on predicting gene expression in one cell type, obtaining high levels of correlation with observed data. However, applying the model to another cell type often results in poor accuracy since static expression levels were used to construct the model. Our study, on the other hand, employed regression models for 2 cell types to predict changes in gene expression. Thus, when differential promoter and distal enhancer occupancy were encoded into the model, quantitative changes in TF binding were found to be predictive of quantitative changes in differential gene expression. Moreover, prediction accuracy improved when multiple binding events were taken into account.Figure 1.

Bottom Line: However, not much is known about the relationship between cell-type-specific binding patterns and gene expression.A recent study demonstrated how the same TFs can have functional roles when binding to largely non-overlapping genomic regions in hematopoietic progenitor and mast cells.Cell-type specific binding profiles of shared TFs are therefore not merely the consequence of opportunistic and functionally irrelevant binding to accessible chromatin, but instead have the potential to make meaningful contributions to cell-type specific transcriptional programs.

View Article: PubMed Central - PubMed

Affiliation: a Department of Haematology; Wellcome Trust and MRC Cambridge Stem Cell Institute & Cambridge Institute for Medical Research ; Cambridge University ; Cambridge , UK.

ABSTRACT
Genome-wide transcription factor (TF) binding profiles differ dramatically between cell types. However, not much is known about the relationship between cell-type-specific binding patterns and gene expression. A recent study demonstrated how the same TFs can have functional roles when binding to largely non-overlapping genomic regions in hematopoietic progenitor and mast cells. Cell-type specific binding profiles of shared TFs are therefore not merely the consequence of opportunistic and functionally irrelevant binding to accessible chromatin, but instead have the potential to make meaningful contributions to cell-type specific transcriptional programs.

Show MeSH
Related in: MedlinePlus