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Dynamic BRG1 recruitment during T helper differentiation and activation reveals distal regulatory elements.

ABSTRACT: T helper cell differentiation and activation require specific transcriptional programs accompanied by changes in chromatin structure. However, little is known about the chromatin remodeling enzymes responsible. We performed genome-wide analysis to determine the general principles of BRG1 binding, followed by analysis of specific genes to determine whether these general rules were typical of key T cell genes. We found that binding of the remodeling protein BRG1 was programmed by both lineage and activation signals. BRG1 binding positively correlated with gene activity at protein-coding and microRNA (miRNA) genes. BRG1 binding was found at promoters and distal regions, including both novel and previously validated distal regulatory elements. Distal BRG1 binding correlated with expression, and novel distal sites in the Gata3 locus possessed enhancer-like activity, suggesting a general role for BRG1 in long-distance gene regulation. BRG1 recruitment to distal sites in Gata3 was impaired in cells lacking STAT6, a transcription factor that regulates lineage-specific genes. Together, these findings suggest that BRG1 interprets both differentiation and activation signals and plays a causal role in gene regulation, chromatin structure, and cell fate. Our findings suggest that BRG1 binding is a useful marker for identifying active cis-regulatory regions in protein-coding and miRNA genes.

SUBMITTER: De S

PROVIDER: S-EPMC3135292 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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Dynamic BRG1 recruitment during T helper differentiation and activation reveals distal regulatory elements.

De Supriyo S Wurster Andrea L AL Precht Patricia P Wood William H WH Becker Kevin G KG Pazin Michael J MJ

Molecular and cellular biology 20110124 7

T helper cell differentiation and activation require specific transcriptional programs accompanied by changes in chromatin structure. However, little is known about the chromatin remodeling enzymes responsible. We performed genome-wide analysis to determine the general principles of BRG1 binding, followed by analysis of specific genes to determine whether these general rules were typical of key T cell genes. We found that binding of the remodeling protein BRG1 was programmed by both lineage and ...[more]

PMID: 21262765

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Project description:BackgroundThe data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the bounds of the hardware constraints and the model's complexity. In our system AIKYATAN, we annotate distal epigenomic regulatory sites, e.g., enhancers. Specifically, we develop a binary classifier that classifies genome sequences as distal regulatory regions or not, given their histone modifications' combinatorial signatures. This problem is challenging because the regulatory regions are distal to the genes, with diverse signatures across classes (e.g., enhancers and insulators) and even within each class (e.g., different enhancer sub-classes).ResultsWe develop a suite of ML models, under the banner AIKYATAN, including SVM models, random forest variants, and deep learning architectures, for distal regulatory element (DRE) detection. We demonstrate, with strong empirical evidence, deep learning approaches have a computational advantage. Plus, convolutional neural networks (CNN) provide the best-in-class accuracy, superior to the vanilla variant. With the human embryonic cell line H1, CNN achieves an accuracy of 97.9% and an order of magnitude lower runtime than the kernel SVM. Running on a GPU, the training time is sped up 21x and 30x (over CPU) for DNN and CNN, respectively. Finally, our CNN model enjoys superior prediction performance vis-'a-vis the competition. Specifically, AIKYATAN-CNN achieved 40% higher validation rate versus CSIANN and the same accuracy as RFECS.ConclusionsOur exhaustive experiments using an array of ML tools validate the need for a model that is not only expressive but can scale with increasing data volumes and diversity. In addition, a subset of these datasets have image-like properties and benefit from spatial pooling of features. Our AIKYATAN suite leverages diverse epigenomic datasets that can then be modeled using CNNs with optimized activation and pooling functions. The goal is to capture the salient features of the integrated epigenomic datasets for deciphering the distal (non-coding) regulatory elements, which have been found to be associated with functional variants. Our source code will be made publicly available at: https://bitbucket.org/cellsandmachines/aikyatan.

Dataset Information

Dynamic BRG1 recruitment during T helper differentiation and activation reveals distal regulatory elements.

Publications

Dynamic BRG1 recruitment during T helper differentiation and activation reveals distal regulatory elements.

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