In this R01 proposal, we plan to use an integrative genomics (Omics) analysis to test two central hypotheses: 1) TF modules form different transcriptional chromatin hubs and co-regulate dynamical interactomes; and 2) an environmental or cellular stimulus such as hormones triggers different transcription modulators to facilitate dynamical chromatin conformation changes. The ultimate goal is to model and analyze TF modules from one- dimension (1D) to three-dimension (3D) scale and describe the relationship between chromatin organization and TF modules. Using a model system of ERa in breast cancer, our studies will examine 1) E2-mediated dynamic TF transcription modules and chromatin interactions; and 2) these hubs and interacting domains are altered in tamoxifen resistant breast cancer cells. The successful completion of our proposed studies will be of value to the genomics community and biologists in general, which may result in the better understanding of the principle of 3D transcriptional regulation and the regulatory role of E2/ERa in endocrine resistant breast cancer.

Public Health Relevance

Gene regulation is controlled not only by the interaction of transcription factors (TFs) bound next to each other in a linear fashion, but also via three-dimensional (3D) conformation of specific chromatin that brings different TFs into close spatial contact. We will use the estrogen receptor-a (ERa) upon estrogen (E2)-treatment in MCF7 and tamoxifen (Tam)-treatment in MCF7-T at five time points respectively, as a model system (named ERa-omics) to study three-dimension (3D) transcriptional regulation and the regulatory role of E2/ERa in endocrine resistant breast cancer. The successful completion of our proposed studies will be of value to the genomics community and biologists in general.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
3R01GM114142-02S1
Application #
9261154
Study Section
Program Officer
Sledjeski, Darren D
Project Start
2015-04-13
Project End
2019-01-31
Budget Start
2016-02-01
Budget End
2017-01-31
Support Year
2
Fiscal Year
2016
Total Cost
Indirect Cost
Name
University of Texas Health Science Center
Department
Other Basic Sciences
Type
Schools of Medicine
DUNS #
800772162
City
San Antonio
State
TX
Country
United States
Zip Code
78229
Li, Tianbao; Liu, Qi; Garza, Nick et al. (2018) Integrative analysis reveals functional and regulatory roles of H3K79me2 in mediating alternative splicing. Genome Med 10:30
Wan, Lixin; Xu, Kexin; Wei, Yongkun et al. (2018) Phosphorylation of EZH2 by AMPK Suppresses PRC2 Methyltransferase Activity and Oncogenic Function. Mol Cell 69:279-291.e5
Walter, Katherine R; Goodman, Merit L; Singhal, Hari et al. (2017) Interferon-Stimulated Genes Are Transcriptionally Repressed by PR in Breast Cancer. Mol Cancer Res 15:1331-1340
Liu, Qi; Bonneville, Russell; Li, Tianbao et al. (2017) Transcription factor-associated combinatorial epigenetic pattern reveals higher transcriptional activity of TCF7L2-regulated intragenic enhancers. BMC Genomics 18:375
Park, Jincheol; Lin, Shili (2017) A random effect model for reconstruction of spatial chromatin structure. Biometrics 73:52-62
Tang, Binhua; Cheng, Xiaolong; Xi, Yunlong et al. (2017) Advances in Genomic Profiling and Analysis of 3D Chromatin Structure and Interaction. Genes (Basel) 8:
Wang, Jianbo; Ye, Zhenqing; Huang, Tim H et al. (2017) Computational Methods and Correlation of Exon-skipping Events with Splicing, Transcription, and Epigenetic Factors. Methods Mol Biol 1513:163-170
Zhang, Xiaowen; Chiang, Huai-Chin; Wang, Yao et al. (2017) Attenuation of RNA polymerase II pausing mitigates BRCA1-associated R-loop accumulation and tumorigenesis. Nat Commun 8:15908
Tang, Binhua; Zhou, Yufan; Wang, Chiou-Miin et al. (2017) Integration of DNA methylation and gene transcription across nineteen cell types reveals cell type-specific and genomic region-dependent regulatory patterns. Sci Rep 7:3626
Tang, Binhua; Wang, Xihan; Jin, Victor X (2017) COPAR: A ChIP-Seq Optimal Peak Analyzer. Biomed Res Int 2017:5346793

Showing the most recent 10 out of 23 publications