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Enhancer Predictions and Genome-Wide Regulatory Circuits.

ABSTRACT: Spatiotemporal control of gene expression during development requires orchestrated activities of numerous enhancers, which are cis-regulatory DNA sequences that, when bound by transcription factors, support selective activation or repression of associated genes. Proper activation of enhancers is critical during embryonic development, adult tissue homeostasis, and regeneration, and inappropriate enhancer activity is often associated with pathological conditions such as cancer. Multiple consortia [e.g., the Encyclopedia of DNA Elements (ENCODE) Consortium and National Institutes of Health Roadmap Epigenomics Mapping Consortium] and independent investigators have mapped putative regulatory regions in a large number of cell types and tissues, but the sequence determinants of cell-specific enhancers are not yet fully understood. Machine learning approaches trained on large sets of these regulatory regions can identify core transcription factor binding sites and generate quantitative predictions of enhancer activity and the impact of sequence variants on activity. Here, we review these computational methods in the context of enhancer prediction and gene regulatory network models specifying cell fate.

SUBMITTER: Beer MA

PROVIDER: S-EPMC7644210 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Enhancer Predictions and Genome-Wide Regulatory Circuits.

Beer Michael A MA Shigaki Dustin D Huangfu Danwei D

Annual review of genomics and human genetics 20200522

Spatiotemporal control of gene expression during development requires orchestrated activities of numerous enhancers, which are <i>cis</i>-regulatory DNA sequences that, when bound by transcription factors, support selective activation or repression of associated genes. Proper activation of enhancers is critical during embryonic development, adult tissue homeostasis, and regeneration, and inappropriate enhancer activity is often associated with pathological conditions such as cancer. Multiple con ...[more]

PMID: 32443951

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Project description:Breast cancer is the leading cause of death in female cancers, and what's worse, tamoxifen resistance occurs in almost 30% breast cancer patients and has seriously attenuated the therapeutic effect. It is widely studied that epigenetic regulation has played important role in the development of tamoxifen resistance. FOXA1 is a pioneer transcription factor that can translate epigenetic signature into transcription regulation and also drive genome-wide enhancer reprogramming in breast cancer. However, the chromatin super enhancer landscape orchestrated by FOXA1 and the key downstream targets of the FOXA1 oncogenic network in tamoxifen resistance remain elusive. Through analyzing the FOXA1 ChIP-seq data in tamoxifen sensitive MCF7 and tamoxifen resistant MCF7/TamR cells, we show that the FOXA1 chromatin occupancy is enhanced in both the promoter and enhancer regions, and the recruitment events may be E2 dependent in both MCF7 and MCF7/TamR cells. By integratively analyzing the FOXA1 ChIP-seq data and RNA-seq data of MCF7 and MCF7/TamR cells, we find that the enhanced or reduced FOXA1 chromatin binding densities may synchronize the transcriptional activity in tamoxifen resistance. Besides, we identify 1003 super enhancer associated protein coding genes and five super enhancer associated lncRNAs (ATP1A1-AS1, CASC11, CASC15, KCTD21-AS1, LINC00885) in tamoxifen resistance. By KM survival analysis, we find that high expression level of ATP1A1-AS1 and its sense transcript ATP1A1 indicates favorable clinical outcome among the luminal endocrine treated breast cancer patients. Further coexpression analysis indicates that ATP1A1-AS1 is significantly correlated with ATP1A1, and RT-qPCR results show that they both are downregulated in MCF7/TamR cells. Our study shows that the FOXA1 transcriptional regulatory network may promote the development of tamoxifen resistance, and identifies one super enhancer associated lncRNA ATP1A1-AS1 that may work as promising biomarker or drug target in tamoxifen resistance.

Dataset Information

Enhancer Predictions and Genome-Wide Regulatory Circuits.

Publications

Enhancer Predictions and Genome-Wide Regulatory Circuits.

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