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A Quantitative Epistasis Framework For Interrogating the Network of Enhancers

ABSTRACT: The mammalian genome possesses a network of non-coding regulatory elements with key genes regulated by many enhancers. It remains unknown why these genes require multiple enhancers for regulation and what is the functional role of each enhancer contributing to a coordinated enhancer network. Here we develop a novel framework, named SEER (Systematic Enhancer Epistasis Regulatory network analysis), which leverages a suite of perturbative, mapping, and imaging approaches, combined with machine learning and Genome-Wide Association Study (GWAS) analysis to systematically and quantitatively anatomize the enhancer epistasis network. Applying the framework to the MYC locus, we revealed a hierarchical two-layer epistasis model and defined a class of synergistic regulatory elements (SREs) which can maintain both high expression and robustness upon perturbation. Via machine learning, we identified and validated that two features, spatial contacts and BRD4 coactivator condensation, are major factors in maintaining the synergistic interactions of SREs. We used SEER to predict the synergistic functions of non-coding variants in SREs for their clinical risks in cancer and autoimmune disorders. The SEER framework provides a novel approach and theory for delineating roles of the massive enhancer network in gene regulation and interpreting non-coding variants for clinical risks in complex diseases.

ORGANISM(S): Homo sapiens

PROVIDER: GSE160768 | GEO | 2022/08/12

REPOSITORIES: GEO

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Project description:Contemporary high throughput technologies permit the rapid identification of transcription factor (TF) target genes on a genome-wide scale, yet the functional significance of TFs requires knowledge of target gene expression patterns, cooperating TFs and cis-regulatory element (CRE) structures. Here we investigated the myogenic regulatory network downstream of the Drosophila zinc finger TF Lame duck (Lmd) by combining both previously published and newly performed genomic data sets, including chromatin immunoprecipitation sequencing (ChIP-seq), genome-wide mRNA profiling, cell-specific expression patterns of putative transcriptional targets, analysis of histone mark signatures, studies of TF co-occupancy by additional mesodermal regulators, TF binding site determination using protein binding microarrays (PBMs), and machine learning of candidate CRE motif compositions. Our findings suggest that Lmd orchestrates an extensive myogenic regulatory network, a conclusion supported by the identification of Lmd-dependent genes, histone signatures of Lmd-bound genomic regions, and the relationship of these features to cell-specific gene expression patterns. The heterogeneous co-occupancy of Lmd-bound regions with additional mesodermal regulators revealed that different transcriptional inputs are used to mediate similar myogenic gene expression patterns. Machine learning further demonstrated diverse combinatorial motif patterns within tissue-specific Lmd-bound regions. PBM analysis established the complete spectrum of Lmd DNA binding specificities, and site-directed mutagenesis of Lmd and additional, newly discovered motifs in known enhancers demonstrated the critical role of these TF binding sites in supporting full enhancer activity. Collectively, these findings provide new insights into the transcriptional codes regulating muscle gene expression, and offer a generalizable approach for similar studies in other systems. Examination of Lmd occupancy to genomic DNA from sorted mesodermal cells

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A Quantitative Epistasis Framework For Interrogating the Network of Enhancers

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