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Cross-species chromatin interactions drive transcriptional rewiring in Epstein–Barr virus–positive gastric adenocarcinoma

Abstract

Epstein–Barr virus (EBV) is associated with several human malignancies including 8–10% of gastric cancers (GCs). Genome-wide analysis of 3D chromatin topologies across GC lines, primary tissue and normal gastric samples revealed chromatin domains specific to EBV-positive GC, exhibiting heterochromatin-to-euchromatin transitions and long-range human–viral interactions with non-integrated EBV episomes. EBV infection in vitro suffices to remodel chromatin topology and function at EBV-interacting host genomic loci, converting H3K9me3+ heterochromatin to H3K4me1+/H3K27ac+ bivalency and unleashing latent enhancers to engage and activate nearby GC-related genes (for example TGFBR2 and MZT1). Higher-order epigenotypes of EBV-positive GC thus signify a novel oncogenic paradigm whereby non-integrative viral genomes can directly alter host epigenetic landscapes (‘enhancer infestation’), facilitating proto-oncogene activation and tumorigenesis.

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Fig. 1: Hi-C analysis of EBV-positive gastric cancer reveals cross-species interactomes.
Fig. 2: Genomic and epigenomic features of EBV-interacting regions.
Fig. 3: EBV infection induces viral–host interactomes and epigenomic alterations.
Fig. 4: EBV infection induces EBV-interacting region-associated enhancer–promoter interactions.
Fig. 5: EBV-interacting region-associated genes are upregulated in primary EBV-positive gastric cancer.
Fig. 6: Interactions between EBV-interacting region-associated activated enhancers and neighboring genes.
Fig. 7

Data availability

Next generation sequencing data generated during this study have been deposited in Gene Expression Omnibus (GSE135176). Detailed information on deposited data is available in the Nature Research Reporting Summary. Previously deposited next generation sequencing data (GSE97837, GSE97838, GSM2253673 and GSM2253674) that are used in this study are also available at Gene Expression Omnibus. The authors declare that all other data are available within the article or associated supplementary information files, or are available from the author on request. Source data are provided with this paper.

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Acknowledgements

We thank A. Saraya, E. Ikeda and H. Maruyama for technical assistance. We thank S. Tsutsumi for support with analysis of the Hi-C data. This work was supported by P-CREATE 19cm0106510h0004 (A.K.) and Practical Research for Innovative Cancer Control 19ck0106263h0003 (A.K.) from the Japan Agency for Medical Research and Development (AMED), Grants-in-Aid for Scientific Research (KAKENHI) 19H03726 (A.K.) and 19K16101 (A.O.) from the Japan Society for the Promotion of Science, a Specific Research grant from Takeda Science Foundation (A.K.), Global and Prominent Research grant 2018-Y9 (A.K.) from Chiba University, Duke-NUS Medical School grant RL2016-080 (P.T.), National Medical Research Council grants NMRC/STaR/0026/2015 (P.T.) and OF-LCG18May-0023 (P.T.), and the Cancer Science Institute of Singapore, National University of Singapore, under the National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centres of Excellence initiative.

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A.O., P.T. and A.K. designed the study. A.O., K.K.H. and M.X. performed Hi-C experiments and analysis. X.O. performed Infinium experiments. A.O., B.R., M. Fukuyo and S.Y.R. performed ChIP–seq and 4C–seq experiments and analysis on EBV-positive GCs. A.O. performed EBV infection. A.O. and T.S. performed inhibitor treatment experiments. K.M., M.S., Y.M. and T.K. performed EBNA1 overexpression and episomal vector transfection experiments. G.U., K.M., T.U. and M. Fukayama performed immunohistochemistry. A.O. and T.H. performed enhancer deletion experiments. A.O., K.K.H. and M. Fukuyo performed statistical analysis. A.O. and A.K. analyzed and interpreted all the data under the supervision of P.T. and M. Fukayama. A.O. generated all the figures. A.O., P.T. and A.K. wrote the manuscript. All authors reviewed the manuscript.

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Correspondence to Patrick Tan or Atsushi Kaneda.

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Extended data

Extended Data Fig. 1 Integrative analysis of GC lines Hi-C data.

a, Hi-C contact maps at 25 kb resolution. Left and middle panels show Hi-C contact maps of Chromosome 4 for GES1 (left) and SNU719 (middle) at 25 kb resolution. TADs are drawn by black lines. Right panels show Hi-C interaction ratios of SNU719 to GES1 (right). TADs of SNU719 are drawn by black lines in the left lower side, and TADs of GES1 are in the upper right side. Interactions observed in GES1 were decreased in SNU719 (blue dots pointed by brown and green arrows), along with TAD alteration. b, To calculate p-value of clustering analysis, we used pvclust (ref. 44) to compute Approximately Unbiased (AU) p-value (red) and Bootstrap Probability (BP) value (green) for the cluster dendrogram. Cluster of three EBV(+) GC cells using all differential compartments showed high AU (100) and BP values (99-100), indicating high robustness of the cluster (p=0.007). These values remained high when clustered by B-to-A transitions only. c, Compartments undergoing active to inactive (A-to-B) transition in EBV(+) GC cells but not in EBV(-) GC cells (left, n=56), and compartments which remain active in all GC cells (A-to-A) (right, n=1,098), were extracted. d, DNA methylation levels around TSSs of genes (within 2.5 kb from TSS) in regions showing A-to-B transition in EBV(+) GC and A-to-A transition in all GC. DNA methylation levels were expressed as β values detected by Infinium HumanMethylation450 BeadChips (Illumina). Increased DNA methylation is observed in EBV(+) GCs at regions showing A-to-B transition in EBV(+) GC.

Extended Data Fig. 2 Enrichment of Hi-C interchromosomal reads.

a, Proportion of intra- and inter-chromosomal interaction reads at B-to-A regions in EBV(+) GCs (25 kb window size) in 2 normal and 14 GC samples. Green, normal samples; red, EBV(+) GC; black, EBV(-) GC. These proportions were generally similar across samples. b, Distribution of inter-chromosomal interaction reads. For B-to-A regions in EBV(+) GCs, the number of the interacting reads associated with each chromosome was calculated, and normalized by chromosome size. See Fig. 1f for SNU719. c, Copy number of EBV genome in three EBV(+) GC cells. Copy numbers are calculated using whole genome sequencing data.

Extended Data Figure 3 Putative integration sites in EBV(+) GC cells.

a, Predicted integration sites in SNU719. No integration site is detected. b, Predicted integration sites in YCC10. Integration of 518-bp and 75-bp viral DNA was predicted with low confidence. c, Genomic sequences at putative integration sites. Direct PCR-based sequencing showed no integration of EBV genomic sequences, while we did not obtain any PCR product containing the integrated viral DNA. d, Predicted integration sites in NCC24. Integration of 805-bp and 1,042-bp viral DNA was predicted at frequency of 2.0% - 5.5% with low confidence. e, Genomic sequences at putative integration sites. Direct PCR primers could not be designed in the region due to highly repetitive sequence. Collectively, these data (a–e) indicated that EBV genome primarily exists as episomal DNA in the three EBV(+) GC cell lines.

Extended Data Fig. 4 Genomic and epigenomic features of EBV interacting regions (EBVIRs) in YCC10 and NCC24.

a, 4 C primers at the OriP region of EBV. To detect chromatin interactions between the EBV genome and the host genome, primers were designed at Bait 1 and Bait 2 around FR repeats that are known as EBNA1 binding sites. b, Overlap of EBVIRs with Bait 1-interacting regions (purple) or Bait 2-interacting regions (blue) in 4C-seq analysis of the three EBV(+) GC cells. Among 108 Mb, 91 Mb, and 37 Mb detected by Bait 2 (SNU719, YCC10, and NCC24, respectively), 82 Mb (76%), 64 Mb (70%), and 23 Mb (62%) were overlapped with the 133 Mb, 87 Mb, and 42 Mb EBVIRs (all p<1 × 10−15). c, AT-content, gene-density, and association with nuclear lamina. EBVIRs were compared to random background controls, A-to-A, A-to-B, B-to-B, B-to-A (GES1 -> EBV(+) GC cells) regions. For AT contet and gene density, p-values were calculated by Welch’s t-test (*p<1 × 10−15). For laminin overlap, p-values were calculated by Fisher’s exact test (*p<1 × 10−2). d, Enrichment of EBVIRs at each chromatin state. Genomic regions were divided into 15 different chromatin states using the histone modification patterns in GES1, MKN7, YCC10 or NCC24 by ChromHMM (left each). A heat map (right each) represents enrichment of EBVIRs against random expectation and shown on a log2 scale. e, H3K27ac levels at a representative EBVIRs. The EBVIRs show high H3K27ac signals in SNU719, YCC10, NCC24, and hESC, but not in adult normal stomach tissue samples and EBV(-) GC cell lines OCUM1 and NCC59. Source data

Extended Data Fig. 5 Overlap of compartment shift.

a, Reproduction of A-to-B regions by EBV in vitro infection model. P-values were calculated by χ2-test to show significant overlap. 12,079 regions showed A-to-B conversion from MKN7 to MKN7_EBVi, of which 54.8% (6,619 regions, p<1×10-15), 54.6% (6,597 regions, p<1×10-15), and 53.2% (6,428 regions, p<1×10-15) were the reproduction of the alterations observed in SNU719, YCC10, and NCC24. b, Reproduction of B-to-A regions by EBV in vitro infection model. P-values were calculated by χ2-test to show significant overlap. 11,336 regions showed B-to-A conversion from MKN7 to MKN7_EBVi, of which 39.8% (4,514 regions, p<1×10−15), 39.9% (4,524 regions, p<1×10−15), and 39.5% (4,477 regions, p<1×10−15) were the reproduction of the alterations observed in SNU719, YCC10, and NCC24. c, Representative regions of A-to-B or B-to-A conversions reproduced by in vitro EBV infection.

Extended Data Fig. 6 Histone modification changes under epigenetic inhibitor treatments.

a, b, Histone modification alterations after treatment with histone acetyl transferase (HAT) and lysine demethylase (KDM) inhibitors. ChIP-seq signals of H3K27ac at EBVIRs are significantly reduced after HATi treatment, while H3K9me3 signals are significantly increased after KDMi treatment. P-values were calculated by Welch’s t-test. Source data

Extended Data Fig. 7 Histone modification changes under EBVNA1 overexpression or episomal vector transfection.

a, EBNA1 overexpression and episomal vector transfection in MKN7 cells. Histone modifications at a representative EBVIR in EBNA1 overexpressing MKN7 cells (WT_EBNA1), mock vector transfected MKN7 cells (WT_Mock), episomal vector transfected cells (WT_episomal vectos). Alterations of H3K9me3 and H3K27ac that were observed in MKN7_EBVi, did not occur after EBNA1 overexpression or episomal vector transfection. b, Histone modification alterations at EBVIRs. The number of overlapping H3K27ac peaks (upper) and ChIP signals of H3K9me3 (lower) are shown as box-plots or violin-plot. There are no significant differences between WT, WT_Mock, WT_EBNA1 cells, or WT_episomal vector. c, Representative regions showing episomal vector-host interactions in WT_episomal vector. 106 Mb regions (n=70) were detected as episomal vector interacting regions (VecIRs). d, Overlap of VecIRs with Bait 3-interacting regions (gray) or Bait 2-interacting regions (orange) in 4C-seq analysis. Among 167 Mb detected by Bait 4, 99 Mb (59%) were overlapped with the 106 Mbp VecIRs (p<1×10−15). e, Episomal vector interacting regions hardly overlapped with EBVIRs (3 among 103 Mb, p=1), and no histone modification alterations were detected at these regions. f,g,h, In contrast to EBVIRs, VecIRs show lower AT content (j), higher gene density (k), and lower association with nuclear lamina (l). For AT contet and gene density, p-values were calculated by Welch’s t-test (*p<1 × 10−15). For laminin overlap, p-values were calculated by Fisher’s exact test. (*p<1 × 10−2). i, VecIR-associated chromatin states. Genomic regions were divided into 15 chromatin states by ChromHMM in MKN7 (left). The heat map (right) represents VecIR enrichment against random expectation (log2 scale) at each chromatin states of MKN7. While EBVIRs are enriched in H3K9me3(+) heterochromatin regions, VecIRs are enriched in different regions with other chromatin states. Source data

Extended Data Fig. 8 Histone modification changes under EBV elimination.

a, Elimination of EBV genome by induction of dominant negative EBNA1 (mtEBNA1). EBV copy numbers were analyzed by qPCR. b, The numbers of overlapping H3K27ac peaks (left) and ChIP-seq signals of H3K9me3 (right) at MKN7_EBVi EBVIRs, shown as box-plots and violin plots. H3K27ac and H3K9me3 levels are not altered after EBV genome elimination. Source data

Extended Data Fig. 9 Epigenomic and chromatin structural alterations in GES1 normal gastric epithelial cells after EBV infection.

a, EBVIRs (red) detected by 4C-seq using EBV genomic Baits 1 (purple) and 2 (blue) in GES1_EBVi. 75 Mbp were detected as MKN7_EBVi EBVIRs (n=113) and used for further analysis. b, Association of GES1_EBVi EBVIRs with four compartment change states. EBVIRs are preferentially associated with those states harboring a parental B compartment (that is B-to-B or B-to-A). c, Histone modification alterations induced at GES1_EBVi EBVIRs. ChIP-seq signals of H3K9me3 and H3K27ac are shown as in violin-plots. P-values were calculated by paired two-sided t-test to show significant differences. Marked decrease of H3K9me3 with increase of H3K27ac are observed. d, Proportions of TAD alterations. While the majority of TADs are classified as ‘unaltered’ (left), EBVIR-associated TADs frequently exhibit a narrower (‘shrink’) pattern (right) (χ2-test, p=0.02). e, Histone modification ChIP-seq signals around a representative GES1_EBVi EBVIRs (red bars) located at the PGRMC2 gene locus. After EBV infection, H3K9me3 signals are lost while H3K27ac signals are increased. Lower panels show long-range interactions between activated enhancers and the PGRMC2 promoter, detected by two 4C-seq analyses. f, Expression of PGRMC2 before and after EBV infection in GES1. P-values were calculated by Welch’s t-test. RNA-seq data shows transcriptional upregulation of PGRMC2 in GES1_EBVi cells (left), which is validated by RT-qPCR (p=0.0006). Source data

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Okabe, A., Huang, K.K., Matsusaka, K. et al. Cross-species chromatin interactions drive transcriptional rewiring in Epstein–Barr virus–positive gastric adenocarcinoma. Nat Genet 52, 919–930 (2020). https://doi.org/10.1038/s41588-020-0665-7

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