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

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.

SUBMITTER: Dai C

PROVIDER: S-EPMC4895025 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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

Dai Chao C Li Wenyuan W Tjong Harianto H Hao Shengli S Zhou Yonggang Y Li Qingjiao Q Chen Lin L Zhu Bing B Alber Frank F Jasmine Zhou Xianghong X

Nature communications 20160531

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 populati ...[more]

PMID: 27240697

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Project description:Decoding post-transcriptional regulatory programs underlying gene expression is a crucial step toward a predictive dynamical understanding of cellular state transitions. Despite recent systematic efforts, the sequence determinants of such mechanisms remain largely uncharacterized. An important obstacle in revealing these elements stems from the contribution of local secondary structures in defining interaction partners in a variety of regulatory contexts, including but not limited to transcript stability, alternative splicing and localization. There are many documented instances where the presence of a structural regulatory element dictates alternative splicing patterns (e.g. human cardiac troponin T) or affects other aspects of RNA biology. Thus, a full characterization of post-transcriptional regulatory programs requires capturing information provided by both local secondary structures and the underlying sequence. We have developed a computational framework based on context-free grammars and mutual information that systematically explores the immense space of structural elements and reveals motifs that are significantly informative of genome-wide measurements of RNA behavior. The application of this framework to genome-wide mammalian mRNA stability data revealed eight highly significant elements with substantial structural information, for the strongest of which we showed a major role in global mRNA regulation. Through biochemistry, mass-spectrometry, and in vivo binding studies, we identified HNRPA2B1 as the key regulator that binds this element and stabilizes a large number of its target genes. Ultimately, we created a global post-transcriptional regulatory map based on the identity of the discovered linear and structural cis-regulatory elements, their regulatory interactions and their target pathways. This approach can also be employed to reveal the structural elements that modulate other aspects of RNA behavior. This SuperSeries is composed of the following subset Series: GSE35749: sRSM1 synthetic decoy vs. scrambled transfections in MDA-MB-231 cells GSE35753: HNRPA2B1 RIP-chip GSE35756: Whole-genome decay rate measurements in MDA-MB-231 cells transfected with HNRPA2B1 siRNAs versus controls GSE35757: siRNA-mediated HNRPA2B1 knock-down in MDA-MB-231 cells GSE35799: HNRPA2B1 HITS-CLIP Refer to individual Series

Dataset Information

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

Publications

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

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