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Network Analysis Identifies Chromosome Intermingling Regions Network analysis identifies chromosome intermingling regions as regulatory hotspots for transcription Anastasiya Belyaevaa,b, Saradha Venkatachalapathyc, Mallika Nagarajanc, G. V. Shivashankarc,d, and Caroline Uhlera,b,1 aLaboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA 02139; bInstitute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139; cMechanobiology Institute, National University of Singapore, Singapore 117411; and dInstitute of Molecular Oncology, Italian Foundation for Cancer Research, Milan 20139, Italy Edited by David A. Weitz, Harvard University, Cambridge, MA, and approved November 13, 2017 (received for review May 15, 2017) The 3D structure of the genome plays a key role in regulatory con- transcriptional machinery, and regulatory factors to coordinate trol of the cell. Experimental methods such as high-throughput expression, also known as transcription factories, has been pro- chromosome conformation capture (Hi-C) have been developed posed as a model for gene regulation (14–16). Collectively, to probe the 3D structure of the genome. However, it remains these studies suggest that interchromosomal regions could har- a challenge to deduce from these data chromosome regions bor coregulated gene clusters. However, missing in this picture is that are colocalized and coregulated. Here, we present an inte- a systematic analysis linking 1D epigenetic marks and 3D inter- grative approach that leverages 1D functional genomic features mingling regions and their roles in transcription control. (e.g., epigenetic marks) with 3D interactions from Hi-C data to Various methods have been developed to infer the spatial con- identify functional interchromosomal interactions. We construct nectivity of the whole genome from Hi-C data. Restraint-based a weighted network with 250-kb genomic regions as nodes and approaches transform Hi-C contact matrices into distances to Hi-C interactions as edges, where the edge weights are given by deduce one consensus structure (17–21). However, it remains a the correlation between 1D genomic features. Individual interact- challenge to map contact frequencies to spatial distances due to ing clusters are determined using weighted correlation clustering biases in Hi-C matrices (22). A different approach is to produce on the network. We show that intermingling regions generally an ensemble of structures that could explain the experimental fall into either active or inactive clusters based on the enrich- data (23, 24). Computational methods have largely focused on ment for RNA polymerase II (RNAPII) and H3K9me3, respectively. inferring the 3D genome structure based on Hi-C data alone We show that active clusters are hotspots for transcription fac- without leveraging functional genomic data for studying its archi- tor binding sites. We also validate our predictions experimentally tecture. A recent study has explored this idea by superimposing by 3D fluorescence in situ hybridization (FISH) experiments and ChIP-seq data of three transcription factors (TFs) on the 3D show that active RNAPII is enriched in predicted active clusters. genome architecture inferred from Hi-C and determined func- Our method provides a general quantitative framework that cou- tional hotspots in Saccharomyces cerevisiae (25). Another study ples 1D genomic features with 3D interactions from Hi-C to probe used 1D epigenomic tracks to predict 3D interactions (26). But the guiding principles that link the spatial organization of the there remains a lack of a general quantitative framework that genome with regulatory control. integrates 1D functional genomic features with 3D intermingling regions to determine a regulatory code for interchromosomal chromosome intermingling j Hi-C j network and clustering analysis j interactions. epigenetics j 3D FISH In this paper, we take a unique approach by integrating Hi-C and functional genomic data to predict regions that are he 3D structure of the genome plays a key role in regu- Tlatory control of the cell. Historically, the spatial organiza- Significance tion of the genetic material has been probed with fluorescence in situ hybridization (FISH), and it was shown that chromo- We develop a network analysis approach for identifying clus- some organization is nonrandom. Each chromosome occupies ters of interactions between chromosomes, which we vali- its own territory with gene-dense chromosomes more likely to date experimentally. Our method integrates 1D features of be in the nuclear interior (1). As an addition to FISH, chromo- the genome, such as epigenetic marks, with 3D interactions, some conformation capture methods (3C, 4C, 5C, and Hi-C) have allowing us to study spatially colocalized regions between been designed to probe the 3D organization of the genome by chromosomes that are functionally relevant. We observe that measuring the genome-wide contact frequencies over a popula- tion of cells (2–5). Computational and experimental efforts have clusters of interchromosomal regions fall into active and inac- largely focused on investigating intrachromosomal contacts. Stud- tive categories. We find that active clusters share transcrip- ies where these interactions have been analyzed together with epi- tion factors and are enriched for transcriptional machinery, genetic modifications as measured by chromatin immunoprecip- suggesting that chromosome intermingling regions play a key itation sequencing (ChIP-seq) showed that epigenetic marks are role in genome regulation. Our method provides a unique tightly linked to shaping the architecture of the genome (6, 7). quantitative framework that can be broadly applied to study Few studies have considered interchromosomal interactions. It the principles of genome organization and regulation during was shown that regions on neighboring chromosome territories processes such as cell differentiation and reprogramming. may loop out and intermingle with each other in a transcription- dependent manner (8, 9). In addition, a recent study has revealed Author contributions: A.B., S.V., G.V.S., and C.U. designed research; A.B., S.V., M.N., that intermingling regions are enriched in both active and repres- G.V.S., and C.U. performed research; A.B., S.V., G.V.S., and C.U. contributed new reagents/analytic tools; A.B., S.V., G.V.S., and C.U. analyzed data; and A.B., S.V., G.V.S., sive epigenetic marks, as well as the active form of RNA poly- and C.U. wrote the paper. merase II (RNAPII) and transcription factors (10). Further- more, it was identified that genes are spatially colocalized and The authors declare no conflict of interest. coregulated by sharing common transcription factors (11, 12) This article is a PNAS Direct Submission. and epigenetic machinery like the polycomb proteins (13). For This open access article is distributed under Creative Commons Attribution- example, TNFα-responsive genes (on the same and different NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND). chromosomes) have been shown to colocalize upon their stim- 1To whom correspondence should be addressed. Email: [email protected]. ulation. Their spatial clustering was found to be correlated with This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10. their temporal expression patterns (12). The clustering of genes, 1073/pnas.1708028115/-/DCSupplemental. 13714–13719 j PNAS j December 26, 2017 j vol. 114 j no. 52 www.pnas.org/cgi/doi/10.1073/pnas.1708028115 Downloaded by guest on October 2, 2021 colocalized and coregulated in 3D. The model of gene regula- chromosomal Hi-C maps, (ii) superimposing regulatory marks tion that is captured by our analysis is the spatial clustering of on the interacting domains, (iii) construction of a network of genomic regions for their coregulation (27). This mode of gene interacting regions with edges weighted by the correlation of the regulation may enable the cell to coordinate gene expression and superimposed marks as a measure of coregulation, and (iv) net- activate or repress pathways that are important for cell function work clustering to obtain spatially colocalized and coregulated in a coordinated manner. We focus on interchromosomal inter- domains. actions to study chromosome intermingling regions. Using a net- We analyzed Hi-C data from human lung fibroblast (IMR- work analysis approach, we construct a network of chromosomal 90) cells at 250-kb resolution, obtained from ref. 28. After bias interactions weighted by correlations in their genomic features correction, filtering, and transforming the data (SI Appendix), at a 250-kb resolution. We find that intermingling regions can we identified a stringent set of highly interacting interchromo- be divided into active and inactive clusters, where active clusters somal regions by solving the following submatrix finding prob- are hotspots for TF binding. We validate our predictions using lem in Hi-C maps. We sought a contiguous submatrix U (k × l) FISH by comparing a predicted active cluster vs. a predicted neg- that has a high average τ, within the real-valued data matrix ative control and also confirm that active RNAPII is significantly X (m × n), where each entry is an interchromosomal contact fre- enriched in the predicted active cluster. quency between two 250-kb regions. We used the iterative large average submatrix (LAS) algorithm (29) that balances matrix Results size and average value, as outlined in SI Appendix to discover Identification of Intermingling Domains. To identify interchromo-
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