Combinatorial regulation of gene expression by multiple transcription factors (TFs) is a fundamental mechanism for regulating gene expression. Although this phenomenon has been studied for several decades, we still lack a systematic strategy to accurately identify combinatorial TF interactions at enhancers and to model their regulatory output on target gene(s), especially for mammalian species. If successful, the proposed research will remove a bottleneck in the field and represent the first step in a continuum of research that is expected to further our understanding of gene regulation. Research proposed in this application is innovative, in our opinion, because it represents a new and substantive departure from the status quo. We will address the following three challenges facing researchers in the field: 1) lack of strategies to identify combinatorial interactions among TFs at enhancers;2) lack of strategies to associate enhancers with their target genes;and 3) limited ability to translate enhancer sequence information to its regulatory output. At the completion of this project, we expect to have developed a set of computational methods that enable genome-scale identification of combinatorial interactions at enhancers and construction of predictive models of combinatorial regulation in mammals. In addition, by applying our methods to large public datasets, we expect to obtain new insights into the evolutionary, spatial, and temporal dynamics of enhancers. The computational methods developed will be implemented as open-source software and made publicly available to the research community.

Public Health Relevance

Many human diseases are caused by dysregulated gene expression. Combinatorial action by multiple transcription factors is critical to gene regulation. better understanding of this regulatory mechanism is essential for understanding and treatment of human diseases.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM108716-01
Application #
8612481
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Brazhnik, Paul
Project Start
2014-06-15
Project End
2018-03-31
Budget Start
2014-06-15
Budget End
2015-03-31
Support Year
1
Fiscal Year
2014
Total Cost
$298,057
Indirect Cost
$96,051
Name
University of Iowa
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
062761671
City
Iowa City
State
IA
Country
United States
Zip Code
52242
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Gao, Tianshun; He, Bing; Liu, Sheng et al. (2016) EnhancerAtlas: a resource for enhancer annotation and analysis in 105 human cell/tissue types. Bioinformatics 32:3543-3551
He, Bing; Tan, Kai (2016) Understanding transcriptional regulatory networks using computational models. Curr Opin Genet Dev 37:101-8
Teng, Li; He, Bing; Wang, Jiahui et al. (2015) 4DGenome: a comprehensive database of chromatin interactions. Bioinformatics 31:2560-4
Das, Jishnu; Gayvert, Kaitlyn M; Bunea, Florentina et al. (2015) ENCAPP: elastic-net-based prognosis prediction and biomarker discovery for human cancers. BMC Genomics 16:263
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Cai, Xiongwei; Gao, Long; Teng, Li et al. (2015) Runx1 Deficiency Decreases Ribosome Biogenesis and Confers Stress Resistance to Hematopoietic Stem and Progenitor Cells. Cell Stem Cell 17:165-77
Cao, Zhenning; Chen, Changya; He, Bing et al. (2015) A microfluidic device for epigenomic profiling using 100 cells. Nat Methods 12:959-62
Pu, Mintie; Ni, Zhuoyu; Wang, Minghui et al. (2015) Trimethylation of Lys36 on H3 restricts gene expression change during aging and impacts life span. Genes Dev 29:718-31
Teng, Li; He, Bing; Gao, Peng et al. (2014) Discover context-specific combinatorial transcription factor interactions by integrating diverse ChIP-Seq data sets. Nucleic Acids Res 42:e24

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