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H3K27me3 spreading organizes canonical PRC1 chromatin architecture to regulate developmental programs

Nature Genetics (2026) Cite this article

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Abstract

Polycomb repressive complex 2 (PRC2)-mediated histone H3 K27 trimethylation (H3K27me3) recruits canonical PRC1 (cPRC1) to maintain heterochromatin. In early development, Polycomb-regulated genes can display long-range three-dimensional interactions, many of which resolve during lineage differentiation. Here we report that Polycomb-anchored looping is controlled by H3K27me3 spreading and regulates target gene silencing to influence cell fate specification. Using glioma-derived H3 Lys27-to-Met (H3K27M) mutations as tools to restrict H3K27me3 spreading, we show that H3K27me3 confinement concentrates the chromatin pool of cPRC1, resulting in heightened three-dimensional interactions that mirror the chromatin architecture of pluripotency. Conversely, H3K27me3 spread in pluripotent stem cells dilutes local cPRC1 chromatin concentration, weakening Polycomb loop contact frequencies. Disruption of cPRC1 binding or aggregation compromises stringent repression of Polycomb genes and induces differentiation and tumor regression of H3K27M-mutant glioma. These results identify the regulatory principles and disease implications of Polycomb looping and show that histone-modification-guided distribution of reader complexes is an important mechanism for nuclear compartment organization.

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Fig. 1: Restricted H3K27me3 deposition is associated with greater frequencies of chromatin interactions between Polycomb-marked CGIs.
The alternative text for this image may have been generated using AI.
Fig. 2: H3K27me3 confinement concentrates the chromatin pool of cPRC1.
The alternative text for this image may have been generated using AI.
Fig. 3: Concentration of cPRC1 drives pathological Polycomb interactions in H3K27M gliomas.
The alternative text for this image may have been generated using AI.
Fig. 4: H3K27me3 domain expansion weakens cPRC1 loop interactions in stem cells.
The alternative text for this image may have been generated using AI.
Fig. 5: Polycomb architecture defines H3K27M-silenced developmental programs that enforce a tumorigenic differentiation blockade.
The alternative text for this image may have been generated using AI.
Fig. 6: Disruption of cPRC1 recruitment or aggregation abrogates the H3K27M tumor phenotype.
The alternative text for this image may have been generated using AI.
Fig. 7: cPRC1 function is a requirement to maintain H3K27M tumorigenesis.
The alternative text for this image may have been generated using AI.

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Data availability

Sequencing files and bed files are available from the GEO repository: sequencing data, GSE205249 (code: ypwpeoyitrcrjsn) and tumor Hi-C, GSE186599 (code: sjunuwkevdyntsb). Sequencing depth and data quality are described in the Supplementary Information. Processed data matrices and genomic tracks are available for browsing at http://cprc1.com:8888, with password H3K27me3_looping. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with dataset identifier PXD044837. Data can be accessed with the following credentials: username: reviewer_pxd044837@ebi.ac.uk; password: VDSfUR33. Source data are provided with this paper.

Code availability

Scripts used for data processing and figure creation are available via Zenodo at https://doi.org/10.5281/zenodo.18837743 (ref. 121) and GitHub at https://github.com/bhu/prc1_loops.

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Acknowledgements

This work was supported by a Large-Scale Applied Research Project grant from Genome Quebec, Genome Canada, the Government of Canada and Ministère de l’Économie, de la Science et de l’Innovation du Québec, with the support of the Ontario Research Fund through funding provided by the Government of Ontario to N.J., J.M. and C.L.K.; the Canadian Institutes for Health Research (CIHR; grants PJT-156086 to C.L.K., PJT-183939 to J.M., and MOP-286756 and FDN-154307 to N.J.); and the National Sciences and Engineering Research Council (NSERC; grant RGPIN-2016-04911 to C.L.K.). J.M. and N.J. are supported by funding from the United States National Institutes of Health (NIH) grant P01-CA196539; C.L. is supported by funding from NIH grant R35GM138181 and the Pew-Stewart Scholars for Cancer Research Award; B.A.G. is supported by NIH grants CA196539 and AI118891, and the St. Jude Children’s Hospital Chromatin Consortium; R.K.S is supported by NIH NCI grant R50 CA293857; B.K. is supported by a CIHR Postdoctoral Fellowship and a Fellowship grant from the ChadTough Defeat DIPG Foundation; B.H. is supported by the CIHR Banting and Best Graduate Scholarship; and C.L.K. is supported by a salary award from Fonds de recherche du Québec (FRQS). N.J. is a member of the Penny Cole Laboratory and holds a Canada Research Chair Tier 1 in Pediatric Oncology from CIHR. This work was performed within the context of the International Childhood Astrocytoma Integrated Genomic and Epigenomic (ICHANGE) consortium. N.A.D. was supported by NIH grant K08NS121592 and a pilot grant through the University of Colorado Cancer Center. Data analyses were enabled by computing and storage resources provided by the Digital Research Alliance of Canada and Calcul Québec. We are especially grateful for philanthropic donations from the Charles Bruneau Foundation, the WeLoveYouConnie Foundation and the Cedars/Sarah Cook funds. This study used the Confocal and Specialized Microscopy Shared Resource of the Herbert Irving Comprehensive Cancer Center at Columbia University, funded in part through the NIH/NCI Cancer Center Support Grant P30CA013696. We thank W. Bickmore and S. Boyle for assistance with DNA fluorescence in situ hybridization; and the McGill University Health Centre Immunophenotyping Core Facility, Molecular Imaging Services and Histopathology Core for assistance.

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Author notes

  1. Benjamin A. Garcia

    Present address: Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA

  2. These authors contributed equally: Brian Krug, Bo Hu.

  3. These authors jointly supervised this work: Jacek Majewski, Nada Jabado, Chao Lu.

Authors and Affiliations

  1. Department of Human Genetics, McGill University, Montreal, Quebec, Canada

    Brian Krug, Bo Hu, Haifen Chen, Claudia Negrón-Lomas, Ahmed El Mouatani, Adam Ptack, Nisha Kabir, Augusto Faria Andrade, Ashot S. Harutyunyan, Robert Taylor, Maud Hulswit, Claudia L. Kleinman, Jacek Majewski & Nada Jabado

  2. Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY, USA

    Brian Krug, Xiao Chen, Kristjan H. Gretarsson, Xinjing Xu, Jiahan Yang & Chao Lu

  3. McGill University Genome Centre, Montreal, Quebec, Canada

    Bo Hu, Haifen Chen, Rola Dali & Jacek Majewski

  4. Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto, Japan

    Bo Hu

  5. Marine College, Shandong University, Weihai, China

    Xiao Chen

  6. Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, Quebec, Canada

    Shriya Deshmukh, Wajih Jawhar, Elias Jabbour, Xinrui Wang & Nada Jabado

  7. The Lady Davis Institute, Jewish General Hospital, Montreal, Quebec, Canada

    Nisha Kabir, Audrey Baguette & Claudia L. Kleinman

  8. Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Augusto Faria Andrade, Marco Gallo & Michael D. Taylor

  9. Department of Molecular Biology and Biochemistry, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Augusto Faria Andrade, Marco Gallo & Michael D. Taylor

  10. The Arthur and Sonia Labatt Brain Tumor Research Center, The Hospital for Sick Children, Toronto, Ontario, Canada

    John J. Y. Lee & Michael D. Taylor

  11. Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada

    John J. Y. Lee & Nada Jabado

  12. Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada

    John J. Y. Lee

  13. Department of Pediatrics, McGill University, The Research Institute of the McGill University Health Center, Montreal, Quebec, Canada

    Damien Faury & Caterina Russo

  14. Quantitative Life Sciences, McGill University, Montreal, Quebec, Canada

    Audrey Baguette

  15. Morgan Adams Foundation Pediatric Brain Tumor Research Program, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA

    Nathan A. Dahl

  16. Pediatric Neuro-Oncology, Center for Cancer and Blood Disorders, Children’s Hospital Colorado, Aurora, CO, USA

    Nathan A. Dahl

  17. Department of Pediatric Neurosurgery, Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montreal, Quebec, Canada

    Alexander G. Weil

  18. Department of Pathology, Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montreal, Quebec, Canada

    Benjamin Ellezam

  19. School of Computer Science, McGill University, Montreal, Quebec, Canada

    Mathieu Blanchette

  20. Department of Biochemistry and Biophysics and Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Khadija Wilson & Benjamin A. Garcia

  21. Proteomics and Macromolecular Crystallography Shared Resource, Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA

    Rajesh Kumar Soni

  22. Alberta Children’s Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

    Marco Gallo

  23. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

    Michael D. Taylor

  24. Division of Neurosurgery, The Hospital for Sick Children, Toronto, Ontario, Canada

    Michael D. Taylor

  25. The Research Institute of the McGill University Health Center, Montreal, Quebec, Canada

    Nada Jabado

  26. Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA

    Chao Lu

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Contributions

B.K. and B.H. generated data and contributed to study design, data interpretation and manuscript preparation. H.C., X.C., N.K., A.B., R.D. and M.B. contributed to analysis and interpretation of sequencing data. A.P., S.D., K.H.G., A.F.A., E.J., A.H., J.J.Y.L., M.H., D.F., C.R., X.X., C.N.-L., W.J., A.E.M., R.T., X.W. and J.Y. contributed to data generation, analysis and interpretation. N.A.D., A.G.W. and B.E. assisted with collection of patient samples, study design and data interpretation. R.K.S. led the interactome and global quantitative proteomics design, analysis and interpretation. K.W. and B.A.G. led the histone proteomics data generation and analysis. M.G., M. D.T., C.L.K., J.M., N.J. and C.L. contributed to study design, data interpretation and manuscript preparation.

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Correspondence to Nada Jabado.

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Extended data

Extended Data Fig. 1 Summary of investigated global chromatin architecture features and technical strategy for confinement analysis.

A. Hi-C data generated from brain tumors and normal brain tissues are analyzed at genome-wide scales. UMAP embedding based on genome-wide comparison across Hi-C contact matrices at three different scales: compartmentalization (first principal component / compartment score), topologically associating domain organization (RobusTAD boundary score), and matrix similarity (HiCRep coefficient). H3K27M pHGGs do not separate from H3 WT pHGGs by any of the three modalities examined. B. From Hi-C datasets in A, silhouette width based on inter-sample similarity in terms of three different modalities, with more positive values indicating that a sample is closer to other samples belonging to the same class whereas more negative samples indicating lack of cohesion (that is, class label is not reflected by high inter-sample similarity for those belonging to the same class). H3K27M pHGGs emerge as the only tumor subtype demonstrating lack of distinct signatures across all three scales considered, generally showing negative silhouette scores (that is, less similar to other H3K27M pHGGs than to tumors of another type). H3K27M thus does not impose a specific signature on large-scale genome organization. C. Euler diagram of CTCF peaks identified in isogenic H3K27M pHGG cell lines and their KO counterparts, demonstrating a substantial overlap. D. Pile-up of pairwise Hi-C interactions among the union CTCF peak set across all H3K27M and KO samples; only pairs of sites with convergent motif orientations were considered. This reveals a lack of global differences in CTCF interaction strength between isogenic H3K27M and H3K27M-KO pHGG cells. E. Correlation of compartment/insulation score differences (H3K27M versus KO/WT) between isogenic comparisons wherein H3K27M is knocked out of BT245, DIPGXIII and HSJ019 glioma cell lines and H3.3K27M is overexpressed in the G477 histone-WT glioma line. The weak correlation coefficients demonstrate lack of consistent changes in compartment/domain structures upon the removal or overexpression of H3K27M. F. Representative tracks of experimental and simulated ChIP–seq datasets, demonstrating the distinction between confined versus diffuse profiles of H3K27me3 or CTCF. G. Genome-wide fragment cluster score computed at either 1 kb shift distance and simulated H3K27me3 at varying shift distances. Our choice for measuring ‘confinement’ can quantitatively distinguish confined versus diffuse experimental ChIP–seq profiles. H. Metaplots showing aggregate depth-normalized H3K27me3 signals from simulated datasets with varying degrees of confinement, with hypothetically no difference in true modification levels at the very center. This reinforces that depth-normalization (for example, CPM) of a more diffuse profile will yield the impression of a lower peak as compared to confined profile, despite no difference in the absolute value at the center (that is, a by-product of ChIP–seq depth-normalization). This phenomenon can be important to consider when assessing normalized metaplots. I. Confinement scores of H3K27me3 (fragment cluster score at 10 kb, see methods) for published ChIP–seq data from the developing mouse brain, ranging from embryonic day 10.5 (E10.5) to birth (P0), in Gorkin et al. (2020). Diminishing scores indicate the spread of H3K27me3 accompanies early brain development.

Extended Data Fig. 2 Analysis of cPRC1 chromatin occupancy, expression and genome-wide distributions.

A. Mass spectrometry-based measurement of protein abundance (iBAQ) for all subunits of PRC1 and PRC2 complexes, showing most subunits are comparably present in both nucleoplasm (soluble) and chromatin-bound protein fractions of H3K27M and KO cells for the pHGG line DIPGXIII. H3K27M mutations do not therefore dramatically alter the composition or abundance of PRC1/2 (Significance derived from two-sided Student’s t test for n = 3 biological replicates). B. Expression of cPRC1 subunit genes (CBX2, CBX4, CBX6, CBX7, CBX8) in pHGG H3K27M cell lines based on bulk RNA-seq. None of these genes achieve significance (adjuted P value < 0.05) based on DESeq2 test of differential expression. C. Metaplot of CBX2 and RING1B aggregate ChIP–seq signals around H3K27me3-enriched CpG islands (union set of top 1000 most enriched in both conditions per cell line, as defined previously), normalized by read depth. CBX2 and RING1B occupancy at H3K27me3 sites are consistently diluted by KO of H3K27M. D. RING1B/CBX2 ChIP–seq signal confinement scores (fragment cluster score at 10 kb, see Methods) in 3 distinct cell lines (BT245, DIPGXIII, HSJ019). RING1B/CBX2 are less confined (ie. more diluted) upon KO of H3K27M mutations. E. Heatmap of correlation coefficients for H3K27me3, RING1B, CBX2 and H2AK119ub enrichment at CGIs of either DIPGXIII or HSJ019 cells, demonstrating the weak correlation between H2AK119ub changes and the changes of H3K27me3, RING1B and CBX2. F. Density plots showing differential CGI enrichment of H3K27me3 (x-axis), RING1B (y-axis), and CBX2 (color code) between H3K27M and H3K27M-KO DIPGXIII (top) and HSJ019 (bottom) cells. Each dot represents a CGI and the differential enrichment is plotted as log2 ratio of K27M/KO. Retainment of H3K27me3 enrichment at CGIs associates with several fold greater enrichment for RING1B and CBX2 ChIP–seq signals, indicating the correlation between H3K27me3 confinement and enhanced cPRC1 recruitment. G. Western blot showing similar levels of H2AK119ub between H3K27M and KO lines of BT245 and DIPGXIII (representative blot shown from n = 3 independent replications). H. ChIP–seq/CUT&RUN-seq tracks for H3K27me3 and all cPRC1 subunits profiled, showing that broad domain spreading of H3K27me3 correlates with enrichment of RING1B, CBX2, CBX8 and PHC2 subunits (less so for CBX4) at Mb scale, when comparing data of H3K27M lines (red) and isogenic KO lines (blue). A single representative track of BT245 datasets is shown.

Extended Data Fig. 3 Chromatin state classification and profiling across H3K27M models.

A. Expression of genes (transcripts per million, TPM) associated with the promoters from the four clusters derived in Fig. 3, demonstrating the lowest expression levels in the cPRC1 cluster in H3K27M-mutant cell lines. Boxplots’ hinges correspond to the 25th and 75th percentiles, with whiskers extending to the most extreme value within 1.5 × interquartile range from the hinges, whereas the central band mark the median value. Significance measures derive from p-values of Wilcoxon rank-sum tests of TPM values. B. Euler diagram of sites identified for the four clusters showing concordance of ‘Active’ and ‘Other’ cluster sites among the three H3K27M pHGG cell lines. A substantial fraction of cPRC1 cluster sites also overlap, termed the consensus cPRC1 sites, whereas the PRC2-only cluster sites show less concordance. C. Enrichment of CTCF ChIP–seq signal among the UMAP projection and cluster classification. CTCF is not strongly enriched in the cPRC1 cluster, compared to Active and PRC2-only clusters. D. Enrichr pathway enrichment analysis of consensus cPRC1 targets among three H3K27M pHGG cell lines, demonstrating the enrichment in genes annotated as relating to development and neuron differentiation.

Extended Data Fig. 4 Analysis of models displaying altered H3K27me3 confinement in stem cells and cancers.

A. Western blot (left) and mass spectrometry (right) measurements of H3K36me2 and H3K27me3 abundance in WT and Nsd1-KO mESCs, revealing quantitative gain of H3K27me3 upon H3K36me2 loss. A representative western blot of n = 3 replicates is shown, and mass spectrometry data is from n = 2 biological replicates. B. Confinement scores reveal H3K27me3 focal distribution of WT mESCs is lost in Nsd1-KO lines. C. Density plot showing differential CGI enrichment of H3K27me3 (x-axis), RING1B (y-axis), and CBX2 (color code) between WT and Nsd1-KO mESCs. Each dot represents a CGI and the differential enrichment is plotted as log2 ratio of Nsd1 KO/WT. Loss of CBX2 binding correlates with decreases in H3K27me3 and Ring1b at CGIs upon Nsd1-KO. D. Average signals of transcription and chromatin features for CGIs & promoters in each of the four clusters in mESCs (see Fig. 4d), demonstrating the characteristic chromatin state of each cluster. Symbols indicate data sources: * = Chen 2022, \ = Kundu 2017, ^ = Healy 2019, ‘ = Mas 2018, ° = ENCODE, no symbol = this study. E. Confinement scores of Vector or EZH2-Y641N expressing mESCs for H3K27me3 CUT&RUN profiles. F. Genomic distribution of H3K27me3 (ChIP–seq coverage tracks in units of counts-per-million-alignments) at representative loci in germinal center B cells (Histone H1 double knockout vs WT) and acute lymphoblastic leukemia cells (NSD2-E1099K vs WT-resembling NSD2 loss by shRNA knockdown), demonstrating distinctive profiles of confined versus diffuse H3K27me3. CpG islands are denoted with green dashed bars. G. Measure of H3K27me3 ChIP–seq signal confinement (fragment cluster score at 1 kb separation, computed using the tool ‘ssp’, see methods), comparing confined (H1-KO, NSD2 mutant) versus diffuse profiles. Individual data points correspond to a replicate, with connected points indicating replicates from the same batch; connections not linking points indicate that multiple replicates were sequenced in a batch, and so the links are drawn between the average value per condition. H. Pile-up of Hi-C interactions among H3K27me3-enriched CpG islands, as defined above, portraying average pairwise contact strength between such regions (in units of enrichment, that is, observed / expected). Punctate enrichment signal in the center indicates elevated long-range interaction anchored at H3K27me3-enriched CGIs in cells with confined H3K27me3.

Source data

Extended Data Fig. 5 Additional profiling of orthotopic xenograft states in H3K27M models.

A. Kaplan-Meier survival analysis (with 95% confidence interval shaded) for xenograft-bearing mice using two other H3K27M cell lines, displaying loss of tumor formation by H3K27M-KO cells in DIPGXIII (n = 17 animals H3K27M group, 15 KO), and substantially greater latency and decreased penetrance of tumor formation by H3K27M-KO cells in HSJ019 (n = 11 H3K27M group, 16 KO group). B. Bubble plots representing fractions of tumor cell populations annotated by cell type classification (bubble size) based on ssGSEA mapping to reference cell types (see Methods). Each bubble is also colored based on the level of cPRC1 looping target gene expression (bubble color), revealing consistent depletion across classified cell types in H3K27M samples, and lower proportions of differentiated cell types. C. In scRNA-seq datasets, the expression levels cPRC1 looping genes are measured on a cell-by-cell level using single sample Gene Set Enrichment Analysis Score (see Methods for more detail). Distributions of scores across population of cells are plotted, with annotations of cell type population on the x axis (see Fig. 5 and Methods for more detail). These scores reveal that cPRC1 looping genes are repressed in a homogenous manner across all cell types in H3K27M pHGG, whereas in WT pHGG, various cell types more highly express these genes.

Extended Data Fig. 6 Analysis of cPRC1 perturbations in H3K27M models.

A. Heatmap and aggregate signal plots of CBX2 and RING1B ChIP–seq signals around CpG islands of cPRC1 loop targets, for BT245 H3K27M lines treated with DMSO control or CBX-AM compound. CBX-AM treatment attenuates the enrichment of RING1B and CBX2 at cPRC1 target sites. P-values for tests of changes in profiles derive from Wilcoxon rank-sum test on the average depth-normalized coverage in the 5 kb window around CpG islands, using n = 714 cPRC1 loop CGIs. B. Western blot validation of CBX2 and CBX4 homozygous knockout resulting in complete loss of protein. A representative western blot of n = 3 independent replications is shown. C. Hi-C pile-up interaction frequencies of cPRC1 loop genes in WT control, CBX2 and CBX4 KO lines. D. Western blot validation of BT245 H3K27M lines expressing PHC2-WT/L307R HaloTag fusion proteins (70 kD) at levels comparable to endogenously occurring PHC2 (37 kD). These constructs did not appreciably alter the abundance of H3K27me3 in these lines, which is consistently lowered by the H3K27M mutation. A representative western blot of n = 3 independent replications is shown. E. Confocal nuclear microscopy imaging of cells expressing PHC2-WT/L307R HaloTag fusion proteins labelled with fluorophores to reveal punctate distribution patterns of PHC2-WT constructs and diffuse patterns of PHC2-L307R mutants. A representative micrograph of n = 3 independent replications is shown. F. Multicolor immunofluorescence staining and imaging of endogenous PHC2 protein in H3K27M cells of the BT245 line, alongside H3K27me3. The punctate pattern of PHC2 distribution is consistent with patterns of labelled PHC2-WT overexpressed in D. A representative micrograph of n = 3 independent replications is shown. G. Heatmap and aggregate signal plots of ChIC-seq profiles for H3K27me3, RING1B, CBX2, and PHC2 in BT245 cells overexpressing either PHC2-WT or L307R constructs. PHC2-L307R expressing lines exhibit minor decrease in H3K27me3 signal enrichment, whereas RING1B and CBX2 enrichment is not appreciable affected, and total PHC2 enrichment is significantly depleted. P-values for tests of changes in profiles derive from Wilcoxon rank-sum test on the average depth-normalized coverage in the 5 kb window around cPRC1 site, using n = 714 cPRC1 loop CGIs.

Source data

Extended Data Fig. 7 Survival analysis of CBX2-KO and CBX4-KO tumor xenografts.

A. Survival analysis of mice bearing orthotopic xenografts of WT control, CBX2-KO or CBX4-KO cells (BT245); solid step curves depict the Kaplan-Meier estimate of survival with shaded bands indicating 95% confidence intervals centered on the Kaplan–Meier estimate, and the P value derived from two-sided log-rank test using n = 5 animals per group.

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Krug, B., Hu, B., Chen, H. et al. H3K27me3 spreading organizes canonical PRC1 chromatin architecture to regulate developmental programs. Nat Genet (2026). https://doi.org/10.1038/s41588-026-02586-y

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