One of the fundamental questions in human biology is how one genome sequence can direct the cellular response to so many developmental and environmental cues. The answer lies, at least in part, in the intricate regulation of transcription such that different cues elicit different programs of gene expression. It is now clear that 3D genome organization is a key factor in the regulation of gene expression. In this component, we propose to generate a robust foundation of 3D interactome datasets that can support a paradigm shift in the study of the structure and function of our genome. The work proposed here will address major gaps in our knowledge of 3D genome organization by first mapping 3D interactions at high resolution in a diverse set of human cell types using improved methodology and data analysis strategies.
In Aim 1, we plan to focus on the differentiation of human embryonic stem cells to pancreatic progenitor cells, a multi-stage process that offers the opportunity for systematic assessment of the dynamics of chromatin organization and the functional relationship between chromatin architecture and lineage-specific gene expression. These high-resolution interactome maps will be analyzed together with complementary trasncriptome and epigenome datasets to illuminate the relationship between 3D genome organization and genome function.
In Aim 2, we will characterize chromatin organization at high resolution in a diverse panel of 30 primary human cell types, and leverage rich datasets of public transcriptome and epigenome data to illuminate the relationship between genome organization and lineage-specific gene expression. The proposed research, if completed, will help uncover functional interactions between cis-regulatory elements across a diverse panel of human cell types. Such datasets will be able to link distal regulatory regions to putative target genes, which will be of major value to the broader biomedical research community and will be useful in furthering our understanding of the mechanisms of distal disease associated regulatory variants in our genome.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54DK107977-04
Application #
9564891
Study Section
Special Emphasis Panel (ZRG1)
Project Start
Project End
Budget Start
2018-08-01
Budget End
2019-07-31
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Ludwig Institute for Cancer Research Ltd
Department
Type
DUNS #
627922248
City
La Jolla
State
CA
Country
United States
Zip Code
92093
Annunziatella, Carlo; Chiariello, Andrea M; Esposito, Andrea et al. (2018) Molecular Dynamics simulations of the Strings and Binders Switch model of chromatin. Methods 142:81-88
Wang, Yanli; Song, Fan; Zhang, Bo et al. (2018) The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions. Genome Biol 19:151
Esposito, Andrea; Annunziatella, Carlo; Bianco, Simona et al. (2018) Models of polymer physics for the architecture of the cell nucleus. Wiley Interdiscip Rev Syst Biol Med :e1444
Sun, Zhe; Wang, Ting; Deng, Ke et al. (2018) DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data. Bioinformatics 34:139-146
Babiuch, Amy S; Khan, Mehnaz; Hu, Ming et al. (2018) Comparison of OCT Angiography Review Strategies to Identify Vascular Abnormalities in the AVATAR Study. Ophthalmol Retina 2:606-612
Yan, Jian; Chen, Shi-An A; Local, Andrea et al. (2018) Histone H3 lysine 4 monomethylation modulates long-range chromatin interactions at enhancers. Cell Res 28:204-220
Zhu, Yina; Gong, Ke; Denholtz, Matthew et al. (2017) Comprehensive characterization of neutrophil genome topology. Genes Dev 31:141-153
Yu, Miao; Ren, Bing (2017) The Three-Dimensional Organization of Mammalian Genomes. Annu Rev Cell Dev Biol 33:265-289
Xiong, Xiong; Zhang, Yanxiao; Yan, Jian et al. (2017) A Scalable Epitope Tagging Approach for High Throughput ChIP-Seq Analysis. ACS Synth Biol 6:1034-1042
Hui, Daniel; Fang, Zhou; Lin, Jerome et al. (2017) LAIT: a local ancestry inference toolkit. BMC Genet 18:83

Showing the most recent 10 out of 23 publications