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 | Hi-C | network and clustering analysis | interactions. epigenetics | 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 | PNAS | December 26, 2017 | vol. 114 | no. 52 www.pnas.org/cgi/doi/10.1073/pnas.1708028115 Downloaded by guest on October 2, 2021 Downloaded by guest on October 2, 2021 eyeae al. et Belyaeva 1. Fig. inter- in submatrices interact- average highly of large of determining identification consists (i) by method 1: domains Fig. Our ing in marks. outlined steps their epigenetic four reg- in and shared by similarities factors coregulated to be ulatory might due thus spatially and features whole- interact regulatory the at that regions scale chromosome Our of genome RNA-seq. clusters identify and to (DNase-seq), was aim hypersensitivity I ChIP- coregu- DNase TF experiments marks, seq, and Hi-C epigenetic namely, from colocalized information, information regulatory spatially and spatial both leveraged we are lated, that regions somal Domains. 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BIOPHYSICS AND COMPUTATIONAL BIOLOGY chromosomes such as 15–17 and 19–22 had a high number of based on the associated regulatory profiles (SI Appendix, Table intermingling 250-kb regions. In addition, as previously noted S2). We used eXtreme gradient boosting trees with 10-fold cross- (31), we found a striking difference between chromosomes 18 and validation to train our classifier. Using all features, the classi- 19—although these two chromosomes are approximately equal fier achieves an accuracy of 85% ± 5% and the corresponding in size, the gene-poor chromosome 18 has a low level of inter- receiver operating characteristic (ROC) curve in Fig. 2A has an mingling across most chromosomes, while the gene-rich chromo- area under the curve (AUC) of 0.77. some 19 tends to intermingle more with other chromosomes. To quantify the importance of each feature by itself and in con- junction with all other features, we computed its univariate and Integration of Functional Genomic Data and Network Analysis. We multivariate rank based on its depth in the decision trees of the obtained functional genomic data: TF ChIP-seq, histone modi- ensemble (Fig. 2B and SI Appendix, Fig. S2). The most impor- fications, DNase-seq, and RNA-seq data from ENCODE (32), tant features determined by this analysis are lamin B1 (LMNB1), Roadmap Epigenomics (33), and GEO databases (SI Appendix, H3K9me3, H3K56ac, and H2A.Z. The importance of both Table S2). We used these experimental data as a regulatory repressive (H3K9me3, LMNB1) and active (H3K56ac, H2A.Z) profile for all 250-kb regions that lay within the intermingling marks ties with the observation that intermingling regions con- domains. tain both active and repressed regions (35). Furthermore, pre- Considering each selected 250-kb region as a node, a whole- vious mapping of LMNB1 in the genome revealed the pres- genome network of chromosomal interactions was constructed ence of lamina-associated domains (LADs) that interact with as follows. Between chromosomes, the edges in the network the lamina on the nuclear envelope, spatially organize chromo- were placed between pairs of 250-kb regions that lay within the somes by anchoring them to the lamina, and display coordi- same submatrix as identified by the LAS algorithm. Within chro- nated gene repression (36–38). H3K9me3 is enriched in LADs mosomes, edges were placed between loci that fall within the and may facilitate gene silencing in LADs (37, 39). The context- same intrachromosomal domain, as determined in ref. 28. After dependent importance of this feature is in line with its low establishing the skeleton of the network, the edge weights were univariate, but high multivariate rank (SI Appendix, Fig. S2). calculated as follows. Since our goal was to determine spa- H3K56ac is a known mark of transcriptionally active chromatin tially coregulated regions, we weighted the edges by Spearman’s regions (40, 41). Finally, H2A.Z is enriched at transcription start correlation between the genomic profiles of adjacent 250-kb sites (42), indicating its involvement in transcription initiation, regions. This combined approach can mitigate some of the noise and it appears to be a defining feature of intermingling on its associated with using Hi-C contact frequencies alone. In addition, own (SI Appendix, Fig. S2). it allows us to identify chromosome intermingling regions with Performing stepwise feature elimination shows that ∼13 fea- coordinated activity, which might be controlled by the same set tures are sufficient for achieving high AUC (Fig. 2C) and the of TFs or epigenetic marks, as opposed to domains that interact corresponding features are annotated by asterisks in Fig. 2B. in 3D by chance. A subnetwork containing six 250-kb regions from three distinct chromosomes is shown in Fig. 1C. The edge weights Intermingling Clusters Are Divided into Active and Inactive Clusters. in this subnetwork suggest the presence of two separate clusters. While it is interesting to evaluate intermingling regions alto- To retrieve intermingling regions that are coregulated, the gether, studying these on a cluster-by-cluster level may give weighted network of 250-kb regions was partitioned into clus- ters, using weighted correlation clustering (34) (see SI Appendix). This approach can for example identify regions that are brought together for transcription, since these would have high RNAPII A C and low repressive epigenetic marks. This approach indeed found two clusters in the subnetwork shown in Fig. 1C. The reg- ulatory profiles of the six regions, separated into two clusters, are illustrated in Fig. 1 D and E. As a consequence of using weighted correlation clustering, the genomic features within a cluster are more similar than across clusters. Interestingly, the particular cluster in Fig. 1D is enhanced for active genomic fea- tures (we analyzed H3K9ac, H3K36me3, H3K4me3, H3K4me2, H3K4me1, RNAPII, and RNA-seq) and depleted for repressive B features (we analyzed H3K27me3 and H3K9me3), while the clus- ter in Fig. 1E is depleted for active features. Using this method, 446 clusters (totaling 459.5 Mb; SI Appendix, Table S1) were identified (P value < 2.2 × 10−16 under a χ2 test) that consist of at least two nodes and span multiple chromosomes (SI Appendix, Table S3 and Dataset S1). On average, 2.5 chromosomes interact within one cluster (SI Appendix, Fig. S1). We analyzed the enrichment of regulatory marks in intermin- gling regions and found that these regions were most enriched for RNAPII, namely by a factor of 2.23 (Fig. 1F). We also found the active and repressive marks (e.g., H3K9ac, H3K4me3, and H3K9me3) to be enriched in intermingling clusters, which is con- sistent with a previous study (10).

Regulatory Features are Predictive of Intermingling. To character- ize intermingling regions as a whole and evaluate whether they Fig. 2. Performance and feature importance for classifying intermingling are distinct from nonintermingling regions on a regulatory level, regions. (A) ROC curve for eXtreme gradient boosting trees classifier that we built a classifier and determined the features that contribute was trained on genomic features of intermingling vs. nonintermingling the most to distinguishing between these two classes. These fea- regions. This results in AUC of 0.77. (B) Features ranked in order of impor- tures may represent a mechanism to spatially cluster genes for tance (relative depth of feature in the decision tree) for distinguishing inter- their coregulation. We annotated 250-kb regions as intermin- mingling domains. (C) AUC when recursively eliminating one feature at a gling or nonintermingling based on the results from our net- time based on 10-fold cross-validation. Near-optimal performance is reached work analysis and clustering. We then performed classification with 13 features, which are indicated by asterisks in B.

13716 | www.pnas.org/cgi/doi/10.1073/pnas.1708028115 Belyaeva et al. 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BIOPHYSICS AND COMPUTATIONAL BIOLOGY chromosome territories were identified in human fibroblast (BJ) Welch two-sample t test), showing that the chromosome pair 12 cells using DNA FISH and visualized using a laser scanning con- and 17 indeed contains an active mark at the site of intermingling. focal microscope (Fig. 5 A–F). To obtain a representative sample of the population, we imaged at least 200 cells for each chromo- Discussion some pair. We confirmed that chromosomes 12 and 17 consis- Understanding the spatial organization of the chromosomes tently intermingle in a population of cells (Fig. 5C; SI Appendix, within the cell nucleus has been a major question in cell biology. Fig. S9; and Movie S1), while the negative control chromosome A number of studies have suggested that the packing of DNA pair does not (Fig. 5F; SI Appendix, Fig. S10; and Movie S2). To plays a critical role in regulating genomic programs (3). Earlier quantify our results, the intermingling degree, i.e., the amount experiments took advantage of chromosome painting methods of overlap between the two pairs of chromosome territories, was and revealed that chromosomes are organized nonrandomly and calculated as explained in SI Appendix. We found that the chro- in a cell–type-specific manner (1, 8, 9). Analysis of gene position- mosome pair 12 and 17, which was predicted to interact, had a ing using FISH showed that coregulated genes were coclustered significantly higher intermingling degree than the negative con- (11, 12). Such clusters of genes were also found to be colocalized trol pair 3 and 20 (Fig. 5G, P value = 0.005 under a Welch two- with transcription-related machinery such as active RNAPII and sample t test). The percentage of nuclei that were intermingling TFs (11, 12). Recent developments in chromosome capture tech- (intermingling degree >0) was higher in the predicted pair of nologies further revealed that genome-wide chromosome con- interacting chromosomes, 12 and 17, than in the negative con- tact maps are correlated with epigenetic marks (6, 7). The major- trol, 3 and 20 (SI Appendix, Fig. S11). In addition, we also cal- ity of studies using chromosome conformation capture focused culated the enrichment of active RNAPII in the intermingling on linking chromatin contacts with epigenetic modifications at regions for the aforementioned pairs (SI Appendix). We found the resolution of genes in intrachromosomal regions (6, 7). How- that the predicted chromosome pair, 12 and 17, which belongs to ever, the coupling between the global organization of chromo- an active cluster, had significantly higher enrichment for active somes with genome-wide epigenetic marks and the intermingling RNAPII in the intermingling regions compared with the nega- regions as an additional layer of transcriptional regulation has tive control pair, 3 and 20 (Fig. 5H, P value = 7.125e-05 under a not been well studied. In this paper, we developed a network analysis approach to reveal the principles of transcription-dependent chromosome intermingling by taking advantage of 3D contact maps obtained A G using Hi-C and 1D epigenetic marks, TF ChIP-seq, DNA acces- sibility, and RNA-seq. Our computational approach focuses on interchromosomal domains, since their organizational princi- ples have been largely unknown. The proposed quantitative framework enables the prediction of chromosome intermingling regions at a genome-wide scale, thereby complementing exper- B C imental methods such as FISH that can be used to study spe- cific clusters of interchromosomal interactions. The novelty of our method lies in leveraging 1D genomic features in combina- tion with 3D interactions from Hi-C data. This allows us to study functionally colocalized regions: Since interactions can occur by chance in 3D, some intermingling regions may not be of bio- D H logical relevance. By leveraging epigenetic marks and data from TF binding and DNA accessibility, as well as gene expression, we can determine interchromosomal regions that are colocalized and coregulated. Our predictions reveal intriguing patterns of chromosome organization and have been validated by FISH experiments. Our EF findings recapitulate known principles of chromosome interac- tions, such as the tendency of gene-dense chromosomes to inter- mingle more frequently (2, 30) and the enrichment of RNAPII in intermingling regions (10), suggesting that RNAPII may play a crucial role in establishing and maintaining chromosome interac- tions. We observe that the clusters of interchromosomal regions Fig. 5. Experimental validation. (A) Representative images of the fall broadly into two categories, active and inactive, where active maximum-intensity Z projections of the nucleus, active RNAPII, and chro- clusters are enriched for active epigenetic marks and RNAPII mosomes 17 and 12, from Left to Right, respectively. (B) Raw image result- and inactive clusters are enriched for H3K9me3. Interestingly, ing from merging the nuclear (blue) and the two chromosome chan- we found that active clusters are hotspots for TF binding sites, nels depicting the overlap between chromosomes 17 (purple) and 12 with several TFs being shared among multiple chromosomes (cyan). (C) Image in B after segmentation with nucleus (white), chromo- within a cluster. These clusters contain genes with biologically some 17 (red), and chromosome 12 (green). Yellow regions are the over- relevant GO terms. We established the predictive power of lapping or intermingling regions. (C, Right) Enlargement of the region our model through experimental validation. Using FISH experi- in the dotted white boxes in C, Left.(D) Representative images of the ments we showed that the predicted intermingling chromosomes maximum-intensity Z projections of the nucleus, active RNAPII, and chro- interact consistently across a population of cells and that such mosomes 20 and 3, from Left to Right, respectively. (E) Raw image result- intermingling regions are enriched for active RNAPII. Our quan- ing from merging the nuclear (blue) and the two chromosome channels titative analysis provides evidence that TF hotspots in active clus- depicting the overlap between chromosomes 20 (purple) and 3 (cyan). ters are colocalized with active epigenetic modifications and with (F) Image in E after segmentation with nucleus (white), chromosome 20 (red), and chromosome 3 (green). (F, Right) Enlargement of the region in the RNAPII and have a significantly higher gene expression than dotted white boxes in F, Left.(G) Boxplot depicting intermingling degree inactive clusters, suggesting that the relative positioning of the between chromosomes 12 and 17 and chromosomes 3 and 20 (P value = chromosomes in the cell nucleus is optimized to facilitate the 0.005 under a Welch two-sample t test). (H) Boxplot depicting the enrich- clustering of coregulated genes, TFs, epigenetic modifications, ment of active RNAPII between chromosomes 12 and 17 and chromosomes and transcriptional machinery. 3 and 20 (P value = 7.125e-05 under a Welch two-sample t test). (All scale Collectively, these findings suggest that the spatial organiza- bars, 5 µm.) tion of the genomic material in the cell nucleus is optimized for

13718 | www.pnas.org/cgi/doi/10.1073/pnas.1708028115 Belyaeva et al. Downloaded by guest on October 2, 2021 Downloaded by guest on October 2, 2021 6 he ,Siahna V(07 hoooeitrigig ehnclhtpt for hotspots Mechanical intermingling: Chromosome (2017) GV Shivashankar C, Uhler 16. distant between contacts regulatory Polycomb-dependent (2011) al. et F, Bantignies 13. TNFα (2012) genes al. co-regulated et A, between Papantonis associations Preferential 12. (2010) al. chromo- et of S, basis Schoenfelder physical intermingling-the 11. Chromosome (2016) al. et S, Maharana 10. 8 en ,Rps ,RgrP ora ,Mzioac 21)3 eoereconstruc- genome 3D (2014) J Mozziconacci A, Cournac P, Roger semi-definite J, with Riposo modeling A, chromosome Lesne 3D (2013) 18. WK Sung KC, Toh G, Li Z, Zhang 17. nucleome. 4D human the of organization Functional (2015) al. et H, Chen gene and 15. organization Genome factories: Transcription (2013) PR Cook A, Papantonis 14. 5 aus ,BntsnH ea R(06 icvrn ososi ucinlgenomic functional in hotspots Discovering (2016) MR Segal H, driving Bengtsson reveals D, analysis Capurso structure genome 25. 3D Population-based (2016) al. et H, Tjong 24. pretty Beyond chromosomes: Modeling (2015) LA and Mirny genomes G, of Fudenberg MV, modeling Imakaev three-dimensional 22. Restraint-based (2015) al. two- et a F, via Serra architecture genome 21. 3D the of Reconstruction inferring (2015) for HL approach Bengtsson statistical MR, A Segal (2014) 20. JP Vert WS, Noble F, Ay N, Varoquaux 19. 3 agS uJ egJ(05 neeta oeigo Dcrmtnstructure. chromatin 3D of modeling Inferential (2015) J Zeng J, Xu S, Wang 23. 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(W911NF-16-1-0551), NSF C.U. Agency funding. the for Projects by Program Grant supported Tier-3 and tially Education Singapore, G.V.S. of of (NSF) and University Ministry M.N., National Foundation the S.V., Institute, Science 1122374. Mechanobiology the Grant National thank under the Fellowship Research and Graduate T32GM87232 Grant Training ACKNOWLEDGMENTS. protein-levels Chromsome-intermingling-region-indentifcation-and-characterisation-of- at available is analysis image the at available is clusters anastasiyabel/functional of analysis and identification the in provided are analysis used image settings and and imaging methods cell confocal the the for and features, protocols, FISH genomic chromosome of and enrichment culture fold of computation the domains, h oefritrhoooa ewr osrcinvaLSadfor and LAS via construction network interchromosomal for code The rnilso hoai looping. chromatin of principles Biol spect mun i flregn it sn AI iifraisresources. bioinformatics DAVID using lists gene 57. large of sis lists. gene large of analysis 37:1–13. functional comprehensive the toward atlas. expression mammalian genome. human the in regions HOT of (4C). capture-on-chip conformation 38:1348–1354. chromosome by uncovered domains genome. 56. lysine on H3 Cycle mapping. imaging high-throughput using 923. factors positioning gene cells. human in genes apn ula aiainteractions. lamina nuclear mapping function. and structure anisms. 2017. https://dl.acm.org/citation.cfm?id=1611638.1611641. 4, December at Accessed Available PA). 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Nature M Genomics BMC AscainfrCmuainlLinguistics, Computational for (Association h oefrperforming for code The interactions. 453:948–951. 3:985–1012. | o.114 vol. 17:733. https://github.com/ odSrn abPer- Harb Spring Cold IAppendix SI | o 52 no. a Protoc Nat uli cd Res Acids Nucleic Cell a Genet Nat a Com- Nat | 162:911– . 13719 4:44– Cell

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