Large-Scale Functional Organization of Long-Range Chromatin Interaction Networks

Department of Biological Sciences, Indian Institute of Science Education and Research (IISER), Knowledge City, Sector 81, Mohali 140306, India. Electronic address: .
Cell Reports (Impact Factor: 8.36). 10/2012; 2(5). DOI: 10.1016/j.celrep.2012.09.022
Source: PubMed


Chromatin interactions play important roles in transcription regulation. To better understand the underlying evolutionary and functional constraints of these interactions, we implemented a systems approach to examine RNA polymerase-II-associated chromatin interactions in human cells. We found that 40% of the total genomic elements involved in chromatin interactions converged to a giant, scale-free-like, hierarchical network organized into chromatin communities. The communities were enriched in specific functions and were syntenic through evolution. Disease-associated SNPs from genome-wide association studies were enriched among the nodes with fewer interactions, implying their selection against deleterious interactions by limiting the total number of interactions, a model that we further reconciled using somatic and germline cancer mutation data. The hubs lacked disease-associated SNPs, constituted a nonrandomly interconnected core of key cellular functions, and exhibited lethality in mouse mutants, supporting an evolutionary selection that favored the nonrandom spatial clustering of the least-evolving key genomic domains against random genetic or transcriptional errors in the genome. Altogether, our analyses reveal a systems-level evolutionary framework that shapes functionally compartmentalized and error-tolerant transcriptional regulation of human genome in three dimensions.

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    • "With the availability of putative genome-wide enhancers along with their cell type-specific activity profile across numerous cell types, it is now possible to investigate an alternate layer of transcriptional regulation represented by a network of distal enhancers that exhibit correlated activities across cell types [76, 77] and jointly underlie correlated expression of functionally linked genes. Such analysis brings forth an alternative view of transcriptional regulation, where instead of a single gene regulated by one or more regulatory elements, one ought to consider the collective of enhancers and genes, co-localized in nuclear space to achieve coexpression of functionally linked genes [78, 79]. "
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    Full-text · Article · Mar 2014
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    • "Network topology analysis revealed that distributions of the community size (Supplementary Figure S6E) and node degree (Supplementary Figure S6F) exhibit a hallmark of scale-freeness. These findings are in line with a recent study (51) that used similar data sets but did not include MIRs within its analysis. "
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    ABSTRACT: Our knowledge of the role of higher-order chromatin structures in transcription of microRNA genes (MIRs) is evolving rapidly. Here we investigate the effect of 3D architecture of chromatin on the transcriptional regulation of MIRs. We demonstrate that MIRs have transcriptional features that are similar to protein-coding genes. RNA polymerase II–associated ChIA-PET data reveal that many groups of MIRs and protein-coding genes are organized into functionally compartmentalized chromatin communities and undergo coordinated expression when their genomic loci are spatially colocated. We observe that MIRs display widespread communication in those transcriptionally active communities. Moreover, miRNA–target interactions are significantly enriched among communities with functional homogeneity while depleted from the same community from which they originated, suggesting MIRs coordinating function-related pathways at posttranscriptional level. Further investigation demonstrates the existence of spatial MIR–MIR chromatin interacting networks. We show that groups of spatially coordinated MIRs are frequently from the same family and involved in the same disease category. The spatial interaction network possesses both common and cell-specific subnetwork modules that result from the spatial organization of chromatin within different cell types. Together, our study unveils an entirely unexplored layer of MIR regulation throughout the human genome that links the spatial coordination of MIRs to their co-expression and function.
    Full-text · Article · Dec 2013 · Nucleic Acids Research
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    • "Here we demonstrate how intuitive, biologically meaningful analyses of large genomic interaction datasets can be achieved purely through network abstractions. Although some groups have begun to employ networks for analyzing gene-gene and other types of interactions from Hi-C data [17], and transcription factor-biased ChIA-PET data [18], to our knowledge no network-based methods have been applied to unbiased maps of physical interactions throughout the genome. "
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    ABSTRACT: We propose a network-based approach for surmising the spatial organization of genomes from high-throughput interaction data. Our strategy is based on methods for inferring architectural features of networks. Specifically, we employ a community detection algorithm to partition networks of genomic interactions. These community partitions represent an intuitive interpretation of genomic organization from interaction data. Furthermore, they are able to recapitulate known aspects of the spatial organization of the Saccharomyces cerevisiae genome, such as the rosette conformation of the genome, the clustering of centromeres, as well as tRNAs, and telomeres. We also demonstrate that simple architectural features of genomic interaction networks, such as cliques, can give meaningful insight into the functional role of the spatial organization of the genome. We show that there is a correlation between inter-chromosomal clique size and replication timing, as well as cohesin enrichment. Together, our network-based approach represents an effective and intuitive framework for interpreting high-throughput genomic interaction data. Importantly, there is a great potential for this strategy, given the rich literature and extensive set of existing tools in the field of network analysis.
    Full-text · Article · Dec 2013 · PLoS ONE
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