Mining 3D genome structure populations identifies major factors governing the stability of regulatory communities

Chao Dai, Wenyuan Li, Harianto Tjong, Shengli Hao, Yonggang Zhou, Qingjiao Li, Lin Chen, Bing Zhu, Frank Alber () and Xianghong Jasmine Zhou ()
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Chao Dai: Molecular and Computational Biology, University of Southern California
Wenyuan Li: Molecular and Computational Biology, University of Southern California
Harianto Tjong: Molecular and Computational Biology, University of Southern California
Shengli Hao: Molecular and Computational Biology, University of Southern California
Yonggang Zhou: Molecular and Computational Biology, University of Southern California
Qingjiao Li: Molecular and Computational Biology, University of Southern California
Lin Chen: Molecular and Computational Biology, University of Southern California
Bing Zhu: National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences
Frank Alber: Molecular and Computational Biology, University of Southern California
Xianghong Jasmine Zhou: Molecular and Computational Biology, University of Southern California

Nature Communications, 2016, vol. 7, issue 1, 1-11

Abstract: Abstract Three-dimensional (3D) genome structures vary from cell to cell even in an isogenic sample. Unlike protein structures, genome structures are highly plastic, posing a significant challenge for structure-function mapping. Here we report an approach to comprehensively identify 3D chromatin clusters that each occurs frequently across a population of genome structures, either deconvoluted from ensemble-averaged Hi-C data or from a collection of single-cell Hi-C data. Applying our method to a population of genome structures (at the macrodomain resolution) of lymphoblastoid cells, we identify an atlas of stable inter-chromosomal chromatin clusters. A large number of these clusters are enriched in binding of specific regulatory factors and are therefore defined as ‘Regulatory Communities.’ We reveal two major factors, centromere clustering and transcription factor binding, which significantly stabilize such communities. Finally, we show that the regulatory communities differ substantially from cell to cell, indicating that expression variability could be impacted by genome structures.

Date: 2016
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DOI: 10.1038/ncomms11549

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