A specific form of cPRC1 containing CBX4 is co-opted to mediate oncogenic gene repression in diffuse midline glioma

Summary

Diffuse midline glioma (DMG) is a fatal childhood brain tumour characterised primarily by mutant histone H3 (H3K27M). H3K27M causes a global reduction in Polycomb Repressive Complex 2 (PRC2)-mediated H3K27me3. Paradoxically, PRC2 is essential in DMG cells, although the downstream molecular mechanisms are poorly understood. Here, we’ve discovered a specific form of canonical PRC1 (cPRC1) containing CBX4 and PCGF4 drives oncogenic gene repression downstream of H3K27me3 in DMG cells. Via a novel functional region, CBX4 preferentially associates with PCGF4 containing cPRC1. The characteristic H3K27me3 landscape in DMG rewires the distribution of cPRC1 complexes, with CBX4/PCGF4-cPRC1 accumulating at H3K27me3 enriched CpG islands. Despite comprising <5% of cPRC1 in DMG cells, the unique repressive functions of CBX4/PCGF4-cPRC1 are essential for DMG growth. Our findings link the altered distribution of H3K27me3 to imbalanced cPRC1 function, which drives oncogenic gene repression in DMG, highlighting potential therapeutic opportunities for this incurable childhood brain cancer.

Graphical Absract

In brief

Lagan, Gannon et al. reveal that H3K27M-DMGs depend on a specific form of cPRC1 containing CBX4 and PCGF4. H3K27M alters H3K27me3 distribution, causing increased binding of CBX4-PCGF4-cPRC1 and oncogenic gene repression. CBX4’s ability to read H3K27me3 and to form a specific cPRC1 complex with PCGF4 make it essential in DMG.

Introduction

Diffuse Midline Gliomas (DMG) are uniformly lethal malignant brain tumours primarily affecting children and young adults^1,2, which typically arise in the pons, thalamus and spinal cord^3,4. The underlying genomic features of tumours at different locations and ages can differ, with tumours activating multiple growth factor signalling pathways^1,5. However, the unifying molecular feature across DMG, regardless of anatomical site or age of onset is a chromatin signature with low levels of H3K27 trimethylation (H3K27me3)^6,7. Remarkably, the H3K27me3 reduction in DMG can result from either of two independent mechanisms. About 85% of DMGs have a heterozygous point mutation in a histone H3 protein (H3K27M), affecting either canonical (H3.1 and H3.2) or non-canonical (H3.3) H3 variants^8–11. The remaining 15% have elevated expression of EZHIP (also known as CATACOMB)¹². The EZHIP protein contains a short, peptide sequence that mimics the mutant H3K27M histone^13–15. Both H3K27M and EZHIP inhibit PRC2, thereby driving the global reductions of H3K27me3^13,16,17.

The fact DMGs exploit H3K27M and EZHIP as independent means to inhibit PRC2 highlights the importance of altering H3K27 methylation for tumorigenesis. Initially, it was thought H3K27me3 reductions would lead to aberrant activation of PRC2 target genes⁶. However, we and others have shown that losses primarily occur in broad genomic domains with transiently deposited H3K27me3, while H3K27me3 remains high at regions with stable PRC2 binding^14,17,18. This residual PRC2 activity is critical in DMG, indicating these discrete H3K27me3 domains support disease biology^17,19,20. Indeed, hundreds of PRC2 target genes are aberrantly repressed in H3K27M mutant cells, which depends on PRC2 activity¹⁷. Therefore, retention of H3K27me3 at PRC2 bound sites directs oncogenic gene silencing. However, the molecular mechanisms driving this repression remain unexplored, limiting our understanding of DMG biology and our ability to design therapies to treat patients.

The main effectors downstream of H3K27me3 are canonical PRC1 (cPRC1) complexes^21,22. These complexes are defined by the presence of one of several mutually exclusive CBX subunits (CBX2/4/6/7/8), which recognise and bind to H3K27me3^23–25. This binding is thought to facilitate the recruitment of cPRC1 complexes, enabling chromatin compaction downstream of PRC2 activity^26,27. It’s unclear how the global changes of H3K27me3 in DMG impact cPRC1 binding and function. Interestingly, emerging evidence suggests cPRC1 is important in DMG, particularly the PCGF4 (BMI1) subunit^28–30. However, the biochemical diversity among distinct cPRC1 formations could provide important, disease relevant functional diversity. Therefore, investigating the mechanisms downstream of H3K27me3 in DMG remains critical, offering the potential to uncover new insights into disease biology.

Here, using custom and genome-wide CRISPR screens, proteomics, chromatin mapping, and functional studies, we show cPRC1 complexes containing CBX4 and PCGF4 are essential in H3K27M-mutant DMG. Despite representing less than 5% of cPRC1 in DMG, CBX4/PCGF4-cPRC1 is critical, while more abundant cPRC1 forms are dispensable. Remarkably, the presence of H3K27M leads to a redistribution of cPRC1 complexes and accumulation of CBX4/PCGF4-cPRC1 at H3K27me3 enriched CpG islands. Perturbation studies reveal that CBX4 and PCGF4 have non-redundant repressive activities downstream of PRC2 in DMG. This requirement is mediated by a novel region in CBX4 that preferentially forms cPRC1 with PCGF4. Our findings suggest the altered H3K27me3 landscape drives cPRC1 redistribution, leading to increased gene repression by CBX4/PCGF4-cPRC1. This highlights distinct, non-redundant roles of specific cPRC1 complexes in gene repression, with therapeutic implications for H3K27M-mutant DMG.

Results

Residual PRC2 activity maintains oncogenic gene repression in DMG

We and others have shown that PRC2 function is essential in DMG^17,19,20. However, at least two functionally distinct PRC2 formations – PRC2.1 and PRC2.2 - exist in cells (Figure 1A)^22,31–35. To understand if the PRC2 dependency is driven by a specific complex subclass, we characterised PRC2 composition using endogenous immunoprecipitation coupled with mass spectrometry (IP-MS) of EZH2 and SUZ12 (Figure 1B–C and S1A–C). Guided by PRC2 composition, we developed a saturating CRISPR/Cas9 tiling library, targeting all 11 PRC2 members identified in DMG cells (Figure 1D). This library totalled 3,599 sgRNAs and used all PAM sequences in the coding regions of PRC2 members^36–38. Strikingly, only sgRNAs targeting PRC2 enzymatic core members - EZH2, SUZ12 and EED - were robustly depleted in DMG cells (Figure 1E and S1D). In contrast, in Ewing sarcoma cells, which do not functionally rely on PRC2, no components were depleted (Figure S1D). Independent validation of this screen was performed using competitive growth assays (Figure S1E). This confirmed the pooled screen results, with sgRNAs targeting EZH2, SUZ12 and EED robustly depleting, while those targeting non-core PRC2 members showed limited depletion. Interestingly, sgRNAs targeting the additional PRC2 core member, EZH1 (EZH2 paralog) were not depleted (Figure 1E and S1D–E). Notably, EZH1-PRC2 is less enzymatically active than EZH2-PRC2 and EZH1 is largely dispensable for maintaining H3K27me3 in dividing cells^39–41. Therefore, only PRC2 components that contribute to enzymatic output are functionally required in DMG, indicating maintenance of residual H3K27me3 underpins the PRC2 dependency.

Figure 1: Residual PRC2 core activity maintains oncogenic gene repression in H3K27M-mutant Diffuse Midline Glioma.

A. Schematic representation of the PRC2 core complex (blue) with PRC2.1 (orange) and PRC2.2 (green) subclass defining members.

B. Volcano plots of endogenous immunoprecipitations coupled with mass spectrometry of EZH2 (left) and SUZ12 (right) in HSJD-DIPG-007 cells. PRC2 members are indicated and colour coded.

C. Table of peptide numbers identified for PRC2 members in control IgG, EZH2 or SUZ12 purifications.

D. Schematic of PRC2 scanning CRISPR library, indicated are the total number and proportion of sgRNAs targeting each complex member.

E. PRC2 tiling CRISPR dropout screen in HSJD-DIPG-021 cells. Each dot represents the log₂ fold-change (mean of n = 2) of individual sgRNAs targeting PRC2 members, and controls in pooled CRISPR dropout experiments. The median and interquartile range of all sgRNAs targeting a given gene are indicated.

F. Growth competition assays in an EZH2 SET-domain functional rescue. The relative RFP+ (sgRNA+) percentage is depicted at the indicated time points after transduction.

G. In vivo growth imaged using luciferase of EZH2 targeting and control sgRNA transduced DIPG-VI cells injected into the pons of NSG mice.

H. Growth competition assays in DIPGXIII-K27M (red) and KO (black) cells transduced with 2 independent EZH2 targeting shRNAs.

I. Quantitative qRT-PCRs of the indicated transcripts in DIPGXIII-K27M (red) or KO (black) cells transduced with 2 independent EZH2 targeting shRNAs (n = 3, data are represented as mean ± SD).

Functional rescue experiments using either wildtype (WT) or enzymatically inactive (F667I) EZH2 confirmed this finding, as only EZH2-WT was able to rescue the growth phenotype of EZH2-targeted cells (Figure 1F). To test the importance of EZH2 in vivo, we performed xenograft experiments by injecting DMG cells transduced with control (sgLUC) or two independent EZH2 targeting sgRNAs into the pons of recipient mice (Figure 1G). Disruption of EZH2 reduced overall tumour size and extended survival, demonstrating that EZH2/PRC2 function is essential for robust tumorigenicity in vivo (Figure 1G and S1F). Next, to understand if the PRC2 dependency relates specifically to the presence of H3K27M we performed growth competition assays using EZH2 targeting shRNAs in isogenic DIPGXIII-K27M (H3K27M-positive) or DIPGXIII-KO (H3K27M-negative) DMG cells (Figure 1H). Strikingly, shRNAs targeting EZH2 were strongly depleted in DIPGXIII-K27M cells but had no effect in DIPGXIII-KO cells. We also observed increased expression of PRC2 target genes, ZIC1, POU4F1, INK4A and TLE2 in DIPGXIII-K27M, but not DIPGXIII-KO cells, following EZH2 knockdown (Figure 1I and S1G). Collectively, these data demonstrate the enzymatic activity of PRC2 is required to maintain oncogenic repression of a cohort of Polycomb target genes in DMG cells^17,42.

Specific cPRC1 complex formations are functionally essential in DMG

PRC2 complexes work in concert with the variant (vPRC1) and canonical (cPRC1) forms of PRC1 to repress transcription (Figure 2A). To comprehensively map genetic dependencies within the PRC1/2 system, we performed genome-wide CRISPR/Cas9 screens in DMG cells. This again revealed that within PRC2, only core members scored as dependencies (Figure S2A). Strikingly, two specific cPRC1 members – CBX4 and PCGF4 – had comparable dependency scores to PRC2 core components, scoring in the top percentile of dependencies (Figure 2B–C). Both CBX4 and PCGF4 are part of larger paralog groups, which assemble into mutually exclusive cPRC1 formations (Figure 2B)²⁵. Of note, no other genes encoding cPRC1 members scored as dependencies (Figure 2B–C and S2B). Moreover, no genes encoding members of the biochemically distinct vPRC1 complexes, scored as dependencies (Figure 2A and S2A)²¹. Independent sgRNA depletion assays validated the screen, with sgRNAs targeting CBX4, but not its paralogs CBX2, CBX6, CBX7 or CBX8 depleting in multiple DMG cell lines (Figure 2D and S2C). Moreover, sgRNAs targeting PCGF4, but not its paralog PCGF2 (also known as MEL18), were depleted (Figure 2D and S2C). A separate custom CRISPR screen using a library of 2,303 sgRNAs targeting ~550 chromatin regulators, also showed CBX4 was the only CBX paralog exhibiting a functional dependency (Figure S2D). In an orthogonal shRNA depletion assay, we observed specific depletion of shRNAs targeting CBX4 and PCGF4, but not their paralogs (Figure S2E). Equivalent experiments in cell lines with wildtype histone H3 or a different oncohistone mutation (H3G34R), showed no depletion of shRNAs targeting CBX4 or PCGF4 (Figure S2E), suggesting the dependency is linked to the presence of H3K27M. Consistent with this, sgRNAs targeting CBX4 in multiple H3K27M-mutant DMG cell lines, either with histone H3.1 or H3.3 mutations, were depleted, whereas CBX4 appeared dispensable in glioma cell lines with wildtype H3 (Figure 2E). Notably, neither CBX4 nor PCGF4 represent general cancer or brain tumour dependencies (Figure S2F), as none of the 70 glioma cell lines in the DepMap dataset showed dependency on either gene (Figure S2G). Importantly, DMG cells transduced with CBX4 targeting sgRNAs had reduced tumour-forming capacity in mice (Figure 2F). Collectively, these data clearly demonstrate CBX4 and PCGF4 are essential for DMG viability.

Figure 2: CBX4 and PCGF4 containing cPRC1 complexes are functionally essential in DMG.

A. Schematic of the interplay between PRC1 and PRC2 complexes on chromatin. PRC2 mediated H3K27me3 recruits cPRC1 via direct binding of H3K27me3 by the chromodomain of the CBX subunit.

B. Heatmap representation of CRISPR dependency scores for all cPRC1 members from genome-wide screening experiments in HSJD-DIPG-021.

C. Table representing the percentile rank for the indicated gene from genome-wide screening experiments in HSJD-DIPG-021, based on CRISPR dependency score.

D. Growth competition assays in HSJD-DIPG-007-Cas9 cells expressing sgRNAs targeting each of the indicated CBX (left) and PCGF (right) genes (n = 3, data represents mean ± SD). P-values calculated using Student’s t-test comparing against negative control sgRNA targeting ROSA26, * = P ≤ 0.05, ** = P ≤ 0.01.

E. Growth competition assays in the indicated DMG and glioma cell lines expressing two independent CBX4 targeting or a control (sgLUC) sgRNA. The histone H3 genotype is indicated for each line.

F. Survival curves for mice injected with DIPGVI cells expressing control (sgLUC) or two independent CBX4 targeting sgRNAs. Represented is the survival difference between control mice and the average of the two independent CBX4 targeted conditions.

G. Growth competition assays in HSJD-DIPG-021-Cas9 cells expressing sgRNAs targeting endogenous CBX4 with or without a CRISPR-resistant CBX4 wild-type rescue construct (n = 3 data represents mean ± SD).

Previous reports indicate CBX4 can function as an E3-SUMO ligase, independent of cPRC1^43–46. To understand if CBX4 requires this activity in DMG, we performed rescue experiments with CBX4 lacking either one or both of the reported SUMO interacting motifs (SIMs). Rescue with wildtype CBX4 validated our screen results. However, CBX4 ΔSIM1, ΔSIM2 and ΔSIM1+2 also rescued CBX4 knockout (Figure 2G and S2H), indicating DMG cells are not reliant on the SUMO-ligase activity of CBX4.

CBX4 and PCGF4 containing complexes constitute a minor pool of cPRC1

To determine whether the dependencies on CBX4 and PCGF4 reflect them being the predominant members of cPRC1 in DMG, we performed endogenous IP-MS of RING1B in DMG cells (Figure 3A and S3A). As a core subunit of both vPRC1 and cPRC1, RING1B provides a comprehensive view of all PRC1 compositions^22,25,47. As expected, in addition to PCGF2/4, which are primarily found in cPRC1, vPRC1 specific PCGF1/3/5/6 also co-purified with RING1B (Figure 3A–B and S3A). Notably, cPRC1 components were more abundant than vPRC1 members (Figure 3A–C and S3A). Collectively, PCGF2/4 account for 77–85% of total PCGF protein signal in the two DMG lines, indicating most assembled complexes are cPRC1 (Figure S3B–D). Multiple mutually exclusive cPRC1 members co-purify with RING1B, including all three PHC proteins (PHC1–3), four out of five CBXs (CBX2/4/6/8) and PCGF2/4 (Figure 3B and S3A). Therefore, a heterogeneous mix of cPRC1 formations are present in DMG cells.

Figure 3: CBX4 and PCGF4 containing complexes account for a minor fraction of cPRC1 in DMG.

A. Schematic of RING1B IP-MS capturing PRC1 subclasses (left), with volcano plot of purifications in SU-DIPXIII-H3K27M. PRC1 members are colour-coded and indicated on the plot.

B. Table depicting peptide numbers for PRC1 members in RING1B IP-MS in SU-DIPGXIII-H3K27M.

C. Immunoblots of the indicated PRC1/2 proteins in RING1B IPs.

D. Pie chart showing percent of total CBX protein abundance accounted for by each individual CBX paralog in RING1B IP-MS.

E. Schematic of PCGF4 IP-MS approach (left), with a volcano plot of purifications in SU-DIPGXIII-H3K27M. cPRC1 members identified are indicated.

F. Table depicting peptide numbers identified for cPRC1 members in PCGF4 IP-MS in SU-DIPGXIII-H3K27M.

G. Pie chart showing percent of total CBX protein abundance accounted for by each individual CBX paralogs in PCGF4 IP-MS.

H. Heatmap depicting enrichment of the indicated proteins in RING1B and PCGF4 IP-MS in SU-DIPGXIII-H3K27M or H3K27M-KO cells.

To understand if CBX4 is the predominant CBX incorporated in cPRC1, we examined CBX paralog stoichiometry in RING1B purifications. Strikingly, this revealed that CBX4 accounted for only a very small portion (1.8–3.7%) of CBX protein signal (Figure 3D and S3E). Remarkably, despite being functionally dispensable, CBX8 alone accounted for the majority of CBX signal (79.4–83.6%), while CBX2 accounted for most of the remaining CBX abundance (13.7–16.1%) (Figure 3D and S3E). Collectively, CBX2 and CBX8 account for over 95% of CBX signal, with CBX6 comprising less than 1%. Therefore, despite CBX4 being the essential CBX paralog, it is present in less than 5% of cPRC1 complexes in DMG cells. Since PCGF4 is also a functional dependency, we wondered whether it preferentially forms complexes with CBX4 in DMG cells. To test this, we performed endogenous PCGF4 IP-MS (Figure 3E), and similar to RING1B, significant cPRC1 heterogeneity was evident (Figure 3E–F). PCGF4 co-purified both RING proteins (RING1A/B), all three PHCs (PHC1–3) and the same four CBX proteins (CBX2/4/6/8) present in RING1B-containing complexes (Figure 3E–F and S3F). Indeed, stoichiometric analysis of PCGF4 purifications demonstrated the CBX proteins exist in generally similar proportions to those seen in RING1B purifications (Figure 3D, G and S3E). However, interestingly, PCGF4 IPs enriched more CBX4 (~4-fold) compared to RING1B IPs (Figure 3D and 3G), suggesting PCGF4 has a slight preference for CBX4. Finally, to investigate H3K27M’s potential impact on PRC1 composition, we performed RING1B and PCGF4 IP-MS in isogenic DIPGXIII-K27M versus DIPGXIII-KO cells, revealing no major changes (Figure 3H). Collectively, while multiple cPRC1 complexes are present in DMG cells (Figure S3G), CBX4/PCGF4 containing forms constitute <5%, suggesting specialised roles despite its low abundance.

PRC1 chromatin binding dynamics are altered in H3K27M-mutant cells

Next, we wanted to evaluate whether changes in cPRC1 occupancy on chromatin might explain the functional dependency on CBX4/PCGF4. We initially examined cPRC1 and PRC2 occupancy in DIPGXIII-K27M and KO cells using CUT&RUN-qPCR (Figure S4A). This showed that SUZ12, RING1B, CBX2, CBX4 and PCGF4 all increased at a PRC1/2 target gene promoter, whereas CBX8 and PCGF2 levels were slightly reduced. This motivated us to establish a quantitative CUT&RUN-Rx approach to accurately examine global PRC1/2 dynamics (Figure 4A). Importantly, to facilitate these global measurements, we identified PRC1/2 antibodies that recognise their cognate target in both human and mouse cells, and adapted the standard CUT&RUN protocol adding 10% mouse cells as an exogenous spike-in to human glioma cells^48,49. We could derive high-quality reads mapping to the spike-in genome for all antibodies (Table S7), allowing us to calculate quantitative spike-in normalisation factors (see Methods). We identified PRC1/2-bound genomic regions (4,475), based on overlapping RING1B and SUZ12 peaks, and confirmed that SUZ12 (PRC2) binding is increased at these regions in H3K27M mutant cells, as we and others previously showed (Figure S4B)^14,17. Consistent with our CUT&RUN-qPCRs, global analyses of RING1B, CBX2, CBX4 and PCGF4 demonstrated increased binding at PRC1/2-bound regions, whereas CBX8 and PCGF2 were reduced (Figure 4B–C and S4B). These dynamics are unlikely driven by shifts in protein abundance, since while CBX2 and PCGF4 were slightly elevated in DIPGXIII-K27M cells, all other proteins were unchanged (Figure S4C). The levels of the vPRC1 component RYBP and H2AK119ub were slightly increased at Polycomb bound regions, but less so than cPRC1 changes (Figure S4D). Notably, PRC1 binding shifts were primarily within the cPRC1 subclass, suggesting H3K27me3 redistribution as a potential driver. Consistent with this, cPRC1 gains (CBX2, CBX4, PCGF4) were strongest at sites with high H3K27me3 (Figure 4D). In contrast, slight increases in RYBP and H2AK119ub showed no correlation with H3K27me3 (Figure S4E).

Figure 4: cPRC1 chromatin occupancy is dramatically altered at Polycomb target sites in DMG cells.

A. Schematic of the quantitative CUT&RUN mapping of histone modifications and PRC1/2 members in SU-DIPGXIII-H3K27M and H3K27M-KO cells.

B. Tornado and average plots showing enrichments of cPRC1 members in biological triplicate CUT&RUN-Rx. Indicated are regions +/−10kb of PRC1/2 target sites. Scale bar denotes reference-adjusted counts per million mapped reads (r/CPM).

C. Genomic tracks showing CBX2, CBX4, CBX8 PCGF2, PCGF4 and H3K27me3 signal at the indicated genomic locus in SU-DIPGXIII-H3K27M and H3K27M-KO cells.

D. MA-plots showing changes in CBX2, CBX4, CBX8, PCGF2 and PCGF4 at target sites in SU-DIPGXIII-H3K27M and H3K27M-KO cells. Sites changing >2-fold are shown in red (increasing) or blue (decreasing) with the number of sites in these categories indicated.

E. Schematic representation of the TetO PRC2 recruitment system in mNSCs (left); CUT&RUN qPCR analysis for CBX2, CBX4, CBX8, and H3K27me3 occupancy across the TetO locus in mNSCs expressing H3.3 wild-type (black) or H3.3K27M (red).

F. Line plots showing average log₂ fold change in chromatin occupancy of CBX2, CBX4, CBX8, PCGF4 and PCGF2 at PRC1/2 target genes between SU-DIPGXIII-H3K27M and H3K27M-KO cells (left, middle); line plot showing average log₂ fold change in RNA abundance of PRC1/2 target genes between SU-DIPGXIII-H3K27M and H3K27M-KO cells (right); genes are separated into quartiles based on H3K27me3 levels in SU-DIPGXIII-H3K27M cells (n = 3 biological replicates).

To further examine the role of H3K27me3 in cPRC1 dynamics, in a context independent of the influences of an endogenous PRC1/2 recruitment site, we established an exogenous recruitment system in mouse neural stem cells (NSCs) using an integrated TetO array⁵⁰. By expressing an EED-TetR fusion protein, we induced PRC2 recruitment, establishing a H3K27me3 domain which led to co-recruitment of cPRC1 containing CBX2, CBX4 and CBX8 (Figure S4F). We then expressed wildtype or K27M-mutant H3.3 and observed the same dynamics observed in DMG cells, with increased CBX2, CBX4 and decreased CBX8 binding (Figure 4E). Importantly, these dynamics were also recapitulated at three endogenous PRC1/2 target sites in these NSCs (Figure S4G), consistent with altered cPRC1 binding being driven by changes in the H3K27me3 landscape.

Next, we examined gene expression of Polycomb target genes in DIPGXIII-K27M versus KO cells (Figure 4F). Previously, we found that Polycomb target genes have reduced expression in H3K27M-mutant human NSCs, linked to increased PRC2 binding at their promoters¹⁷. Here, we found that Polycomb target genes with the highest H3K27me3 have reduced expression in DIPGXIII-K27M cells (Figure 4F), confirming that many strong Polycomb target genes are aberrantly repressed in H3K27M-mutant cells. Notably, these genes also show the greatest increases in cPRC1 binding (Figure 4F), whereas changes in RYBP and H2AK119ub don’t correlate with H3K27me3 or gene repression (Figure S4H). This suggests that altered cPRC1 distribution drives aberrant gene repression in H3K27M-mutant DMG.

Displacement of cPRC1 complexes following PRC2 inhibition

Although complexes containing CBX2, CBX4 and PCGF4 accumulate at Polycomb bound sites in H3K27M cells, we hypothesized that CBX4 and PCGF4 accumulation contributes to increased repression of Polycomb target genes (Figure 4F). We previously demonstrated that PRC2 inhibition reverses this repression in H3K27M-mutant cells¹⁷. We therefore tested whether PRC2 inhibition could selectively displace CBX4 and PCGF4 by treating DIPGXIII-K27M cells with EZH2 inhibitor Tazemetostat (Figure S5A). Gene expression changes accumulated through 3, 6 and 9 days of treatment (Figure S5A). Consistent with previous observations that EZH2 inhibition requires 5–7 days to elicit a robust phenotypic impact^51,52, at day 3 only 23 genes were upregulated, with 78% being direct Polycomb targets (Figure S5A). By days 6 and 9, the number of differentially expressed genes increased to 465 and 2,082, respectively; with Gene ontology analyses showing an enrichment of neurodevelopmental terms among upregulated genes and DNA replication and cell cycle among downregulated genes (Figure S5B). This supports previous findings that PRC2 inhibition promotes differentiation and cell cycle arrest of DMG cells¹⁷. We mapped cPRC1 binding after 6 days of Tazemetostat treatment since many upregulated genes - 145 of 316 (46%) – were direct Polycomb targets (Figure S5A). In contrast, by day 9, most differentially expressed genes were not Polycomb targets, likely reflecting indirect effects (Figure S5A). Quantitative CUT&RUN-Rx mapping showed that binding of all cPRC1 members at Polycomb target genes were reduced and that this was associated with H3K27me3 loss (Figure 5A–B and S5C). PRC2 (SUZ12) was also partially displaced, consistent with a partial requirement of H3K27me3 for its optimal association with target sites³⁵ (Figure S5C–D). Interestingly, while all cPRC1 members were depleted from Polycomb-bound loci (Figure 5A–B), CBX2 levels were slightly increased at weaker (Q4) sites (Figure 5C), suggesting it may be less dependent on H3K27me3 at these regions. The greatest cPRC1 reductions occurred at strong (Q1) Polycomb-target sites, correlating with gene activation across quartiles (Figure 5C). Notably, the sites where CBX2, CBX4 and PCGF4 accumulated the most in H3K27M-mutant cells (Figure 4F) were the same as those losing cPRC1 members and activating gene expression to the greatest degree after Tazemetostat treatment (Figure 5C). While these data confirm H3K27me3 is central to aberrant PRC1/2 repression in DMG, further work was needed to understand the specific roles of CBX4 and PCGF4.

Figure 5: CBX4 and PCGF4 containing cPRC1 complexes specifically drive oncogenic gene repression downstream of H3K27me3 in DMG.

A. Tornado and average plots showing enrichments of cPRC1 members in CUT&RUN-Rx of SU-DIPGXIII-H3K27M cells treated with DMSO or EZH2 inhibitor for 6 days (n = 3 biological replicates). Indicated are regions +/−10kb of PRC1/2 target sites. Scale bar denotes reference-adjusted counts per million mapped reads (r/CPM).

B. Genomic tracks showing CBX2, CBX4, CBX8, PCGF2 and PCGF4 CUT&RUN-Rx signal at the indicated genomic locus in SU-DIPGXIII-H3K27M cell treated with control (DMSO) or EZH2 inhibitor for 6 days.

C. Line plots showing average log₂ fold change in chromatin occupancy of CBX2, CBX4, CBX8, PCGF4 and PCGF2 at PRC1/2 target genes between control and EZH2 inhibitor treated cells (left, middle); line plot showing average log₂ fold change in RNA abundance of PRC1/2 target genes between control and EZH2 inhibitor treated cells (right); genes are separated into quartiles based on H3K27me3 levels in SU-DIPGXIII-H3K27M cells (n = 3 biological replicates).

D. Volcano plots presenting differentially expressed genes in SU-DIPGXIII-H3K27M cells following CRISPR-Cas9 sgRNA mediated knock-out of PCGF4, CBX2, CBX4, or CBX8. The number of genes differentially expressed both up (red) and down (blue) are indicated on the plots (n = 2 biological replicates; p < 0.05; minimum 1.5-fold change).

E. Venn diagram overlap of differentially expressed PRC1/2 target genes following knock-out of EZH2, PCGF4, and CBX4 (n = 2 biological replicates).

F. Line plot showing average log₂ fold change in RNA abundance of PRC1/2 target genes following EZH2, PCGF4 or CBX4 knock-out in SU-DIPGXIII-H3K27M cells (n = 2 biological replicates). Genes are separated into quartiles based on H3K27me3 levels in SU-DIPGXIII-H3K27M cells.

G. CRISPR-resistant CBX4 wild-type or chromodomain mutant constructs (top) expressed in HSJD-DIPG-021-Cas9 cells for growth competition rescue assays with sgRNAs targeting endogenous CBX4 (bottom).

H. CUT&RUN qPCR analysis of HA-tagged CBX4 chromodomain-swaps expressed in mNSCs with H3.3 wild-type (black) or H3.3K27M (red). CD2 indicates CBX2 chromodomain, CD8 indicates CBX8 chromodomain. The relative enrichment of each protein is demonstrated at the indicated gene promoters.

CBX4 and PCGF4 specifically drive oncogenic repression of Polycomb targets

To evaluate the specific contributions of CBX4/PCGF4-cPRC1 to aberrant Polycomb-target gene repression, we knocked out cPRC1 components or EZH2 in DIPGXIII-K27M cells and performed RNA-seq (Figure 5D, S5E–F). Knockout of EZH2 caused a robust transcriptional response (Figure S5E), while knock out of PCGF4 had a more limited effect (Figure 5D and S5F). Strikingly, only knockout of CBX4 caused significant gene expression changes, whereas CBX2 or CBX8 did not (Figure 5D). These results were confirmed with independent sgRNAs targeting PCGF4, CBX2, CBX4, and CBX8 (Figure S5F). Additionally, an independent shRNA-mediated knockdown was used to deplete CBX2, CBX4, or CBX8 in DIPGXIII-K27M and KO cells (Figure S5G). Consistent with the knockout experiments, PRC1/2 target genes, including INK4A, ZIC1 and POU4F1, were only activated following knockdown of CBX4, but not CBX2 or CBX8 (Figure S5H). Furthermore, these effects were specific to H3K27M-mutant cells, as CBX4 knockdown in DIPGXIII-K27M KO cells did not activate these genes (Figure S5H). Collectively, this indicates CBX4-containing cPRC1 has non-redundant gene repressive activities in H3K27M-mutant cells.

As expected, many upregulated genes in knockout conditions were Polycomb targets. Of note, the Polycomb target gene CDKN2A was amongst the most significantly upregulated genes upon knockout of EZH2, PCGF4 or CBX4 (Figure 5D, S5E–F&H)⁵³. Importantly, CDKN2A upregulation is essential for Tazemetostat response in H3K27M-cells¹⁹. Overlapping differentially expressed Polycomb target genes in PCGF4, CBX4 and EZH2 knockouts, supports the idea PCGF4 and CBX4 act downstream of EZH2 (Figure 5E). Interestingly, loss of EZH2 causes a much larger transcriptional response compared to loss of PCGF4 or CBX4 (Figure 5D–E and S5E–F). This is likely because PRC2 impairment displaces all cPRC1 forms (Figure 5A–C), while CBX4 loss has a more specific effect. Consistent with this, using a dual sgRNA targeting system, we found targeting CBX2 and CBX8 in combination had a phenotypic impact in DMG cells (Figure S5I), unlike targeting either alone (Figure 2B–D and S2B–D). Intriguingly, this indicates that some level of functional redundancy/synergy exists across cPRC1 complexes, but that CBX4-cPRC1 has a specific non-redundant role in DMG cells.

Polycomb target genes with the highest H3K27me3 levels in DIPGXIII-K27M cells are most upregulated upon EZH2 inhibition or knockout, and also upon knockout of PCGF4 or CBX4 (Figure 5C and 5F). Given the correlation between H3K27me3 levels and the dependency on CBX4 for transcriptional repression, we hypothesized the CBX4 chromodomain is required. Mutations disrupting the ability of the CBX4 chromodomain (F11A/W35L) to bind to H3K27me3 abrogated its function in DMG cells (Figure 5G)²³. To explore whether the chromodomain of CBX4 is responsible for its specific accumulation on Polycomb target genes in DMG cells, we swapped it with those of CBX2 or CBX8 and expressed them in mouse NSCs with H3.3-WT or H3.3-K27M (Figure S5J). Interestingly, these CBX4 proteins accumulated similarly at Polycomb target genes in H3K27M-mutant cells, suggesting that while the ability of the CBX4 chromodomain to bind H3K27me3 is essential, other regions within CBX4 likely contribute to its specific role in DMG (Figure 5H).

A central region in CBX4 is functionally essential in DMG cells

To identify all region(s) of CBX4 and PCGF4 that contribute to their function in DMG, we performed a CRISPR scanning screen targeting CBX4 (385 sgRNAs) and PCGF4 (93 sgRNAs) in DMG cells (Figure 6A). The PCGF4 screen revealed depletion of sgRNAs targeting the RING and RAWUL domains, which mediate protein-protein interactions within cPRC1^21,54–56, indicating that PCGF4 assembly into cPRC1 is important for DMG biology (Figure S6A). No significant depletion was observed outside these domains. Interestingly, several regions within CBX4 were depleted (Figure 6A). Consistent with the rescue experiments showing the importance of its chromodomain (Figure 5G), sgRNAs targeting across the chromodomain were depleted (Figure 6A). Moreover, sgRNAs targeting the C-Box domain, which mediates interactions with RING1A/B were depleted (Figure 6A)⁵⁷. Similar to PCGF4, this suggests that assembly of CBX4 into cPRC1 is essential in DMG. Additionally, sgRNAs targeting a central region of CBX4 (amino acids 220–370) were depleted (Figure 6A). Interestingly, while the chromo- and C-Box domains have high sequence homology with the equivalent regions in CBX2 and CBX8 (Figure S6B), this central region of CBX4 is more distinct, potentially allowing for functional divergence. Consistent with this, a recent analysis of protein coding variation in 983,578 individuals⁵⁸ shows intolerance to missense mutations in the central region of CBX4 (Figure S6C), while all three CBX proteins show an intolerance to missense mutations in their respective chromo- and C-Box domains (Figure S6C). Significantly, functional rescue experiments demonstrated that, similar to a CBX4 ΔC-Box mutant, a CBX4 Δ220–370 mutant cannot rescue CBX4 knockout in DMG cells (Figure 6B). These results highlight the central region of CBX4 is essential in DMG cells.

Figure 6. The CBX4 disordered region is functionally essential in DMG and specifies cPRC1 complex composition.

A. CRISPR-Cas9 tiling screen of CBX4 in HSJD-DIPG-007-Cas9 (n = 2 biological replicates). sgRNAs are indicated by dots mapped to the target amino acid. Protein domains of interest are shaded in green. The average depletion of sgRNAs targeting a protein segment is indicated by the red line.

B. Growth competition rescues performed in HSJD-DIPG-021-Cas9 cells expressing CRISPR-resistant wild-type CBX4, C-box deletion CBX4, or amino acid 220–370 deletion CBX4 mutant constructs (n = 3 biological replicates).

C. IP-MS of wild-type wildtype CBX4, wildtype CBX2, C-box deletion CBX4, or amino acid 220–370 deletion CBX4 constructs expressed in HEK293T cells (n = 3 replicates). cPRC1 members are colour coded and indicated on the plots.

D. Principal component analysis of the IP-MS from (C).

E. Bar plot showing the log₁₀ fold change in abundance of the indicated cPRC1 protein subunits in the CBX4 220–370 deletion mutant IP-MS compared to the CBX4 wild-type IP-MS.

F. Immunoblots of the indicated proteins in PCGF2 immunoprecipitations in HEK293 cells overexpressing CBX4 wildtype, CBX4 C-box deletion, or CBX4 220–370 deletion constructs.

The central region of CBX4 promotes formation of a discrete cPRC1 complex

Next, we wanted to determine the function of the central region of CBX4. We hypothesised it impacts assembly of CBX4 into cPRC1, based on the essentiality of the protein-protein interaction domains within CBX4 and PCGF4 in our screen (Figure 6B and S6A). To test this, we expressed 3xHA tagged CBX4 WT, CBX4 ΔC-Box or CBX4 Δ220–370, and CBX2 WT, in HEK293 cells and performed anti-HA IP-MS (Figure 6C). This showed that CBX4 preferentially associated with PCGF4 and PHC2, whereas CBX2 binds all cPRC1 associated PCGF (PCGF2 and PCGF4) and PHC (PHC1, PHC2 and PHC3) subunits (Figure 6C). As expected, the deletion of the CBX4 C-Box completely prevented its association with cPRC1, and did not rescue CBX4 KO, supporting the idea that CBX4 function in DMG is dependent on its ability to incorporate into cPRC1 (Figure 6B–C). Remarkably, deletion of the CBX4 220–370 region altered its assembly, forming cPRC1 complexes more like CBX2, with PCGF2/4 and all three PHC proteins (Figure 6D–E and S6D). Consistent with this, endogenous CBX4 IP-MS in DMG cells also showed a preference for PHC2 over PHC1 and PHC3 (Figure S6E–F), and reciprocal PCGF2 purifications confirmed the preferential enrichment of CBX4 Δ220–370 (Figure 6F). Collectively, these data demonstrate the central region of CBX4 promotes preferential formation of a cPRC1 complex containing PCGF4 and PHC2.

Discussion

Altered chromatin regulation is a hallmark of childhood cancers^59,60, and its study is providing key insights into their biology^22,61. Here, we’ve shown the altered H3K27me3 landscape in DMG leads to a specific functional dependence on the CBX4/PCGF4 form of cPRC1. Despite representing less than 5% of cPRC1, CBX4/PCGF4-cPRC1 plays a non-redundant role in mediating aberrant gene repression characteristic of the H3K27M-DMG. These findings highlight the non-overlapping gene repressive activities of distinct cPRC1 complexes and provide new directions for mechanistically anchored therapies for this incurable disease.

H3K27me3 directing oncogenic gene repression

Disrupted chromatin regulation has been proposed to alter developmental processes to drive oncogenesis in childhood cancers. Consistent with this, DMG tumours are enriched in primitive oligodendrocyte precursors which appear less capable of undergoing differentiation^62,63. H3K27M is thought to limit the differentiation potential of DMG cells due to aberrant repression of PRC2 target genes^17,42, but the mechanisms were unclear. Here, we show that CBX4/PCGF4-containing cPRC1 complexes are essential for this process. Our data suggest the altered H3K27me3 landscape drives this functional dependency. In H3K27M and EZHIP expressing DMG cells, H3K27me3 distribution shifts to being more focused on PRC2 bound CpG-sites^14,17,18. These changes correlate with the redistribution of CBX4/PCGF4-cPRC1, indicating the H3K27me3 pattern directs these changes. Another childhood brain tumour, Posterior Fossa Type A (PFA) ependymoma, shares a similar H3K27me3 landscape with DMG⁴². PFA ependymoma has a shared aetiology with DMG, as most patents (95%) have elevated EZHIP expression, and the rest carry a H3K27M mutation^64,65. Strikingly, PFA tumour cells are also functionally reliant on CBX4 and PRC2 core components^66,67. The shared aetiology, H3K27me3 chromatin landscape and functional dependencies in DMG and PFA highlights the link between altered H3K27me3 distribution and functional dependency on CBX4. Molecular studies of cPRC1 in PFA ependymoma are lacking, making it important to examine CBX4 in this context, as mechanistic features may be shared between these diseases.

Therapeutic implications for DMG tumours

Inhibiting EZH2 offers clear therapeutic potential in DMG^17,19,20, supported by our in vivo patient-derived xenograft data. Disrupting PRC1/2 target gene repression reduces the differentiation blockade in DMG cells^17,42, and when combined with growth signalling inhibitors, could elicit a robust therapeutic response. However, the EZH2 inhibitor Tazemetostat has poor blood-brain penetrability⁶⁸, limiting its effectiveness for CNS tumours. However, efforts are being made to develop EZH2 inhibitors with better brain penetrability, making this a viable option⁶⁹. However, PRC2 is a tumour suppressor in several malignancies, including childhood acute lymphoblastic leukaemia^{22,61,70–72}. As such, systemic inhibition of PRC2 has the potential to cause secondary treatment-associated malignancies⁷³. Our work lays the mechanistic framework for an alternative approach, which would achieve the same goal of activating repressed Polycomb target genes - targeting CBX4/PCGF4-cPRC1. Targeting specific cPRC1 configurations could reduce side-effects like secondary malignancies compared to PRC2/EZH2 inhibition. Supporting this, CBX4 and PCGF4 are not tumour suppressors, unlike core PRC2 members^22,74. The CBX4 chromodomain could be therapeutically targeted, with inhibitors already being developed^75,76. Additionally, targeting protein-protein interactions within the complex could offer new therapeutic opportunities.

Functional diversity downstream of H3K27me3

Multiple H3K27me3 readers likely contribute to distinct downstream functional outcomes. However, compelling evidence for clear functional diversity between distinct cPRC1 formations in cells has been limited. Different CBXs may have distinct condensate formation abilities, in vitro and potentially in cells^21,77–79, implying complexes with different CBX proteins could have varying gene repressive activities. cPRC1 proteins have highly overlapping chromatin binding profiles, with sequential ChIP mapping of CBX, PHC and RING paralogs even suggesting distinct forms of cPRC1 co-bind chromatin in both space and time^80,81. Indeed, in DMG cells, we observe overlapping binding profiles for distinct cPRC1 complexes. However, our discovery that CBX4/PCGF4-cPRC1 has non-redundant gene repressive activities is a clear demonstration that within this milieu, distinct complexes do have differential roles. Moreover, our discovery of a functional region within CBX4 promoting preferential assembly with PCGF4, further highlights functional differences between different forms of cPRC1. Our data indicate the H3K27me3 landscape is instrumental in correctly balancing cPRC1 activities. H3K27me3 landscapes are frequently perturbed in cancers and developmental diseases^22,82–85. Therefore, it will be important to understand how altering H3K27me3 affects cPRC1 functions in other diseases. These insights will be essential to understanding the role of H3K27me3 and cPRC1 in normal biology and how disruptions contribute to disease development.

Limitations of this study

This study relied on established cancer cell lines, so extending these findings using additional in vivo mouse models and/or primary patient derived organoid models could provide greater insight into the clinical relevance of our findings. Related, we used genetic tools to highlight the therapeutic potential of targeting CBX4/PCGF4-cPRC1. Pharmacological targeting will be necessary to provide more translationally oriented insights into the clinical utility of CBX4/PCGF4 targeting. The bulk genomic measurements we’ve made show distinct chromatin dynamics among cPRC1 complexes. These bulk measurements, combined with the heterogeneity of complexes will need to be addressed using single-cell approaches to fully understand cPRC1 dynamics and the functional associations between cPRC1 complex subclasses.

RESOURCE AVAILABILITY

Lead contact

Further information and request for resources and reagents should be directed to and will be fulfilled by the lead contact – Dr. Gerard L. Brien (gbrien@ed.ac.uk)

Materials availability

Commercially available reagents are listed in the key resources table. All plasmids or cell lines generated in this study are available on request.

Key Resources Table:

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
Normal Rabbit IgG (CUT&RUN)	Cell Signaling	Cat#2729S; RRID: AB_1031062
Normal Rabbit IgG (IP)	Millipore	Cat#12-370; RRID: AB_145841
Normal Mouse IgG (IP)	Millipore	Cat#12-371; RRID: AB_145840
EZH2 (IP)	Bracken et al100	N/A
EZH2 (WB)	Cell Signaling	Cat#3147S; RRID: AB_10694383
SUZ12 (IP, CUT&RUN)	Cell Signaling	Cat#3737S; RRID: AB_2196850
RING1B (CUT&RUN, IP, WB)	Cell Signaling	Cat#5694S; RRID: AB_10705604
PCGF2 (CUT&RUN)	Prof. Diego Pasini Scelfo et al101	N/A
PCGF4 (CUT&RUN)	Cell Signaling	Cat#5856S; RRID: AB_10838137
PCGF4 (IP, WB)	Millipore	Cat#05-637-25UG; RRID: AB_309865
CBX2 (WB, CUT&RUN)	Cell Signaling	Cat#18687S; RRID: AB_2923356
CBX4 (CUT&RUN)	Cell Signaling	Cat#30559S; RRID: AB_2798991
CBX4 (IP, WB)	Abcam	Cat#ab242149; N/A
CBX8 (CUT&RUN, WB)	Cell Signaling	Cat#14696S; RRID: AB_2687589
H3K27me3 (CUT&RUN, WB)	Cell Signaling	Cat#9733T; RRID: AB_2616029
H2AK119ub (WB, CUT&RUN)	Cell Signaling	Cat#8240S; RRID: AB_10891618
RYBP (CUT&RUN)	Millipore	Cat#AB3637; RRID: AB_2285466
Histone H3 (WB)	Active Motif	Cat# 39763; RRID: AB_2650522
Goat anti-mouse (red) IRDye 680RD (WB)	Licor	Cat#926-68070; RRID: AB_10956588
Goat anti-rabbit (green) IRDye 800CW (WB)	Licor	Cat# 926-32211; RRID: AB_621843
Chemicals, peptides, and recombinant proteins
Tazemetostat (EPZ-6438)	Selleck Chemicals	Cat#S7128
Formaldehyde	Sigma-Aldrich	Cat#252549
Murine EGF	Peprotech	Cat#315-09
Human FGF	Peprotech	Cat#AF-100-18B
Accutase	Stemcell Technologies	Cat#7922
Laminin	Bio-Techne	Cat#3446-005-01
DMEM/F12	Sigma-Aldrich	Cat#D8437
Spermidine	Sigma-Aldrich	Cat#S2501-1G
Digitonin	Sigma-Aldrich	Cat#D141
CUTANA™ pAG-MNase	Stratech Scientific	Cat#15-1116
CUTANA™ Concanavalin A-Conjugated Paramagnetic Beads	Stratech Scientific	Cat#21-1411
Critical commercial assays
RNeasy Mini Kit	Qiagen	Cat#74106
RNase-Free DNase Set	Qiagen	Cat#79254
High Sensitivity D1000 Reagents	Agilent	Cat#5067-5585
High Sensitivity D1000 ScreenTape	Agilent	Cat#5067-5584
RNA ScreenTape	Agilent	Cat#5067-5576
RNA ScreenTape Sample Buffer	Agilent	Cat#5067-5577
Luna Universal qPCR Master Mix	New England Biolabs	Cat#M3003E
NEBNext Ultra II DNA Library Kit for Illumina	New England Biolabs	Cat#E7645L
NucleoSpin Blood L	Macherey-Nagel	Cat#12761021
OneStep PCR Inhibitor Removal Kit	Zymo Research	Cat#D6030
Monarch PCR & DNA Cleanup Kit	New England Biolabs	Cat#T1030L
High-Capacity cDNA Reverse Transcription Kit	ThermoFisher	Cat#4374966
QuantSeq 3’ mRNA-Seq Library Prep Kit (FWD)	Lexogen	Cat#015-96
PCR Add-on Kit for Illumina	Lexogen	Cat#020-96
Deposited data
CUT&RUN-Rx	This study	GEO: GSE246931
RNA-seq and Quant-seq	This study	GEO: GSE246931
PRC2 IP-MS	This study	PRIDE: PXD047401
PRC1 IP-MS	This study	PRIDE: PXD047451
Exogenous PRC1 IP-MS	This study	PRIDE: PXD058532
Original imaging data	This study	10.17632/ft63v4p2cp.2
Experimental models: Cell lines
HSJD-DIPG021	Dr Angel Montero Carcaboso	N/A
HSJD-DIPG007	Dr Angel Montero Carcaboso	N/A
HSJD-DIPG021-Cas9	This study	N/A
HSJD-DIPG007-Cas9	This study	N/A
SU-DIPGXIII	Dr Nada Jabado Harutyunyan et al18	N/A
SU-DIPGXIII H3K27M-KO	Dr Nada Jabado Harutyunyan et al18	N/A
Mouse Neural Stem Cells	This study	N/A
Experimental models: Organisms/strains
Mouse: NOD-scid IL2Rgammanull	Jackson	Strain: 005557
Oligonucleotides
See Table S1 for PRC2 CRISPR tiling screen sgRNAs	This study	N/A
See Table S2 for Brunello CRISPR screen PCR primers	This study	N/A
See Table S4 for shRNA sequences	This study	N/A
See Tables S5 and S6 for sgRNA sequences	This study	N/A
See Table S9 for PRC1 CRISPR tiling screen sgRNAs	This study	N/A
Recombinant DNA
LentiCas9-Blast	Sanjana et al102	Addgene #52962
LRG2.1	Tarumoto et al103	Addgene #108098
SGEP	Fellmann et al104	Addgene #111170
ipUSEPR Custom CBX4, PCGF4 and PRC2 CRISPR Knockout Pooled Tiling Libraries	This study	N/A
Human CRISPR Knockout Pooled Library (Brunello)	Doench et al91	Addgene #73178
Software and algorithms
Bowtie2 v2.3.4.3	Langmead and Salzberg105	N/A
macs2 v2.2.7.1	Feng et al106	N/A
Samtools v1.9	Li et al107	N/A
Bedtools v2.27.1	Quinlan and Hall108	N/A
DEseq2 v1.22.1	Love et al99	N/A
EAseq	Lerdrup et al109	N/A
MAGeCK	Wang et al97	N/A
PinAPL-Py	Spahn et al95	N/A
ProTiler	He et al96	N/A
Other
NextSeq 500/550 High Output Kit v2.5 (75 Cycles)	Illumina	Cat#20024906

Data and code availability

• All high-throughput next generation sequencing CUT&RUN-Rx, RNA-seq and Quant-seq datasets can be accessed from the Gene Expression Omnibus (GEO) database via accession number GSE246931. All proteomics data can be accessed from the ProteomeXchange Consortium via the PRIDE partner repository identifiers PXD047401 (PRC2 IP-MS), PXD047451 (PRC1 IP-MS) and PXD058532 (exogenous PRC1 IP-MS).

• This paper does not report original code.

• Any additional information required to reanalyse the data reported in this paper is available from the lead contact upon request.

STAR Methods:

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Cell Culture and Lentiviral Production

All cell cultures were maintained in a humidified, normoxic incubator at 37°C, 5% CO₂. Patient-derived HSJD-DIPG-007, HSJD-DIPG-021, DIPGXIII-K27M and DIPGXIII-KO cells were grown as monolayers. SU-DIPGVI, SU-DIPG-XIII, SU-DIPG19, SU-DIPG50, GBM667, GBM9120 and GBM10372 were grown as neurospheres. Monolayer cultures were grown in Neural Stem Cell (NSC) media composed of DMEM/F-12 media (Sigma) supplemented with 8mM Glucose (Sigma), 100mM MEM Non-Essential Amino Acids (Gibco), 100U/ml Penicillin/100mg/ml Streptomycin (Gibco), 0.12g/l BSA (Gibco), 100mM β-mercaptoethanol (Gibco), 1% B27 (Gibco), 0.5% N2 (Gibco), 10ng/ml mouse EGF (Peprotech), 10ng/ml human FGF (Peprotech) and 2–4μg/ml Laminin (Cultrex), or in Tumour Stem Media (TSM) media composed of 50:50 DMEM/F12 and Neurobasal-A media supplemented with 1% HEPES 1 M (Gibco), 100mM MEM Non-Essential Amino Acids (Gibco), 1 mM Sodium Pyruvate (Gibco), 1% GlutaMAX (Gibco), 2% B27 without vitamin A (Gibco), 20ng/ml human EGF (Peprotech), 20ng/ml human FGF (Peprotech), 10ng/ml human PDGF-AA (Peprotech), 10ng/ml human PDGF-BB (Peprotech), 5 IU/ml Heparin (Sigma) and 2–4μg/ml Laminin (Cultrex). For neurosphere culture Laminin was omitted from NSC media supplemented with 20ng/ml EGF, 20ng/ml FGF, 10ng/ml PDGF-AA and 10ng/ml PDGF-BB. Cells were passaged every 3–5 days using Accutase.

Lentiviral packaging HEK293T and A673 (Ewing sarcoma) cells were cultured in DMEM media (Sigma) supplemented with 10% foetal bovine serum and 100 U/ml Penicillin/100 μg/ml Streptomycin (Gibco). Lentiviral particles were generated by co-transfection of HEK293T cells with a lentiviral expression vector (sgRNA/shRNA/cDNA) or pooled sgRNA library with a viral packaging (PAX2) and envelope (VSV-G) vectors using PEI or X-tremegene (Roche) in accordance with standard protocols. Viral supernatants were collected between 24–72hrs post-transfection and either used directly for infection of target cells after filtering through a 0.45mm syringe filter and addition of 8.5 mg/ml Polybrene; or concentrated by ultracentrifugation (20,000 rpm, 2hrs) before being resuspended in 0.5–1ml of neural stem cell culture media and used for infection of target cells.

Method Details

Endogenous and exogenous immunoprecipitations

Harvested cells were washed twice with cold PBS and washed pellets resuspended with Buffer A (25mM HEPES pH 7.6, 5mM MgCl₂, 25mM KCl, 0.05mM EDTA, 10% (v/v) glycerol, 0.1% (v/v) NP40, 1mM DTT and 1mM PMSF) and kept on ice for 10 minutes. Samples were centrifuged at 1,500rpm for 10 minutes at 4°C and the supernatant was discarded. To lyse nuclei, pellets were resuspended in Buffer C (10mM HEPES pH 7.6, 3mM MgCl₂, 100mM KCl, 0.5mM EDTA, 10% (v/v) glycerol, 1mM DTT and 1mM PMSF). Next, 10% (v/v) 3M (NH₄)₂SO₄ prepared in Buffer C was added to the samples, which were rotated at 4°C for 20 minutes. Samples were ultracentrifuged at 350,000 rcf for 15 minutes at 4°C in a Beckman Coulter Optima L-100XP using a SW 55Ti rotor. The supernatant was collected and 300mg of (NH₄)₂SO₄ was added for each 1mL of sample, to precipitate nuclear extracts. Samples were vortexed and kept on ice for 15 minutes before being ultracentrifuged using the same parameters as above. Pellets were resuspended in IP buffer (300mM NaCl, 50mM Tris-HCl pH 7.5, 1mM EDTA, 1% (v/v) Triton-X-100, 1mM DTT and 1mM PMSF) and Bradford assays were performed to determine protein concentration. Benzonase Nuclease (Sigma) and 1μg of the respective antibody (for endogenous proteins), anti-HA (Thermo; 88836) or anti-V5 (MBL; M215–11) magnetic beads were added to each IP and samples were rotated at 4°C overnight. For endogenous purifications the following day, Protein A Sepharose 4B, Fast Flow beads (Sigma) (for PRC2 experiments) or Protein G Dynabeads (Thermo Fisher) (for PRC1 experiments) were added to each sample, followed by rotation at 4°C for 2.5 hours. Beads were washed 5x with IP buffer, before being processed for immunoblotting or mass spectrometry sample preparation.

PRC2 mass spectrometry

Beads were resuspended in elution buffer (2M Urea, 100 mM Tris pH 8, 10 mM DTT) and incubated 20 min on a shaker (1300 rpm) at RT. After incubation, iodoacetamide was added to a final concentration of 50 mM, followed by 10 min shaking in the dark at RT. Partial digestion and elution from the beads was initiated by adding 0.25 mg Trypsin (Promega; V5113) for 2 hr. The supernatant containing the IP samples was collected and the beads were resuspended in 50 μL elution buffer followed by a 5 min incubation shaking at RT. Both supernatants were combined, and 0.1 mg Trypsin was added followed by overnight incubation at RT. The digestion was stopped by adding TFA (final concentration 0.5%). The resulting digested samples were desalted and purified using StageTips⁸⁶. The peptides were eluted from StageTips with buffer B (80% acetonitrile, 0.1% formic acid), concentrated to 5 μL by SpeedVac centrifugation at room temperature, and filled up to 12 μL using buffer A (0.1% formic acid). Pull-down samples were measured using a gradient from 9%–32% Buffer B for 114 min followed by washes at 50% then 95% Buffer B resulting in total 140 min data collection time. Mass spectra were recorded on an LTQ-Orbitrap Fusion Tribrid mass spectrometer (Thermo Fisher Scientific). Scans were collected in data-dependent top speed mode with dynamic exclusion set at 60 s.

PRC1 mass spectrometry

In-solution tryptic digestions were performed as described previously⁸⁷. Samples were run on a Bruker timsTof Pro mass spectrometer connected to an Evosep One liquid chromatography system. Tryptic peptides were resuspended in 0.1% formic acid and each sample was loaded on to an Evosep tip and separated on a Evosep EV1137 Performance Column – 15 cm × 150 um, 1.5 um. The Evosep tips were placed in position on the Evosep One, in a 96-tip box. The autosampler is configured to pick up each tip, elute and separate the peptides using a set chromatography method (15 samples a day)⁸⁸. The mass spectrometer was operated in positive ion mode with a capillary voltage of 1650 V, dry gas flow of 3 l/min and a dry temperature of 180 °C. All data was acquired with the instrument operating in trapped ion mobility spectrometry (TIMS) mode. Trapped ions were selected for ms/ms using parallel accumulation serial fragmentation (PASEF). A scan range of (100–1700 m/z) was performed at a rate of 5 PASEF MS/MS frames to 1 MS scan with a cycle time of 1.03s⁸⁹.

Exogenous PRC1 spectrometry

Cleared lysate was incubated with 5 μl of anti-HA Magnetic Beads (Thermo; 88836) for 4 h at 4 °C under mixing. Following incubation, the beads were washed 2x with 0.3 ml of wash buffer (50 mM Tris-HCl 150 mM NaCl, 1% NP40) and 3x with wash buffer without NP40. The beads were transferred into 100 μl of 2 M Urea, 50 mM TrisCl pH 8, 0.5 μg porcine Trypsin (Promega) and Trypsin and processed as previously described⁹⁰. Mass spectrometry was done using a Lumos Fusion (Thermo) mass spectrometer coupled to an RLSC-nano uHPLC (Thermo). Peptides were separated by a 40-min linear gradient from 5 to 30% Acetonitrile, 0.05% Acetic acid separated on an Aurora column (Ionoptiks, Australia). The mass spectrometer was operated using the following setting: MS was set 120 k resolution in the Orbitrap, ions selected for fragmentation were isolated with a window of 1.1 Th, fragmented in the HCD trap with a normalized collision energy of 30 and read out in the ion trap using rapid scanning. Proteins were identified and quantified using Fragpipe 20 software suite using the preset LFQ-MBR workflow with following alterations: Mass accuracy 0.5 Da & no deisotoping and searching against the human Uniprot database plus common contaminants.

Design and synthesis of human PRC1 and PRC2 sgRNA libraries

For the PRC2 complex member gene tiling CRISPR library, a total of 3,599 sgRNA sequences targeting the coding exons of the PRC2 complex genes (AEBP2, EED, EPOP, EZH1, EZH2, JARID2, PALI1, MTF2, PHF1, PHF19 and SUZ12) were designed using the Genetic Perturbation Platform (Broad Institute)⁹¹. For the PRC1 complex member gene tiling CRISPR library, 385 sgRNAs targeting CBX4 and 94 sgRNAs targeting PCFG4 were designed using the Genetic Perturbation Platform (Broad Institute)⁹¹. Briefly, guide RNA oligos were synthesized by microarray (CustomArray) and cloned into the ipUSEPR lentiviral sgRNA vector (hU6-driven sgRNA co-expressed with EF-1a-driven red fluorescent protein [RFP] and puromycin-resistance gene) using the BsmBI (NEB) restriction sites. For a complete list of PRC1 and PRC2 scanning library sgRNA sequences see Table S1. Individual sgRNAs selected for validation experiments are listed in Tables S4 and S5. CRISPR lentivirus was produced in HEK293 cells as described above.

Tiled PRC1 and PRC2 CRISPR screens

Cas9-expressing HSJD-DIPG-007, HSJD-DIPG-021 were transduced with the PRC1 or PRC2 sgRNA lentiviral library at a multiplicity of infection (MOI) between 0.1–0.3 at 1000x library coverage. A673 cells were also transduced in the same way with the PRC2 sgRNA lentiviral library. Approximately 24 hours following transduction, puromycin was added to cell cultures to select for sgRNA expressing cells. Cell pellets were harvested for genomic DNA extraction at Day 3 and Day 18–21 post-transduction for the PRC2 screen, or Day 3 and Day 28 for the PRC1 screen. Genomic DNA was extracted using the Qiagen QIAmp DNA Blood Mini Kit, following the manufacturer’s protocol. PCR using ipUSEPR specific primers (DCF01 5’-CTTGTGGAAAGGACGAAACACCG-3’ and DCR03 5’-CCTAGGAACAGCGGTTTAAAAAAGC-3’) was performed: 50μL DreamTaq Hot Start Green PCR Master Mix (Thermo Scientific), 0.5μL P5 primer mix (100μM), 0.5μL P7 index primer (100μM) using the following cycling conditions: 95°C for 60s, 28 cycles of 95°C for 30s, 53°C for 30s, 72°C for 30s; 72°C for 10min. Multiple PCRs on 2.5–5 μg input gDNA were performed for each gDNA pool. PCR products were subject to agarose gel electrophoresis, gel purified, and isopropanol precipitated. Individually indexed libraries were pooled and sequenced using an Illumina NextSeq500. Sequences of sgRNAs used in PRC2 and PRC1 member tiling screens are available in Table S1 (PRC2) and Table S9 (PRC1).

Genome-wide Brunello CRISPR screen

Cas9-expressing HSJD-DIPG-007 and HSJD-DIPG-021 cells were transduced with the Brunello lentiviral library at a target MOI ~0.3 at 350–500x library coverage. Approximately 24 hours following transduction, puromycin was added to cell cultures to select for sgRNA expressing cells. Cell pellets were harvested for genomic DNA extraction at Day 21 or Day 28 post-transduction. Genomic DNA was extracted using the NucleoSpin Blood L (Macherey-Nagel), following the manufacturer’s protocol. Prior to PCR, the OneStep PCR Inhibitor Removal Kit (Zymo Research) was used, following the manufacturer’s protocol. PCR using lentiGuide-Puro specific primers (from Broad GPP) and PCR purification was performed as per tiled pooled PRC2 screen above. Individually indexed libraries were pooled and sequenced using an Illumina NextSeq500.

Chromatin focused CRISPR screen

Cas9-expressing SU-DIPG-VI cells were transduced with a custom sgRNA library containing sgRNAs targeting ~550 genes cloned into the ipUSEPR vector backbone as previously described^92,93. An MOI of ~0.2 at 1000x library coverage was used. 5 days following transduction, puromycin was added to cell cultures to select for sgRNA expressing cells. Cell pellets were harvested for genomic DNA extraction at approximately 15 cumulative population doublings post-transduction. Genomic DNA was extracted using the Plasmid Plus Maxi Kit (Qiagen). We used a modified two-step PCR version of the protocol published by Doench and colleagues⁹¹. Briefly, we performed an initial “enrichment” PCR, whereby the integrated sgRNA cassettes were amplified from gDNA, followed by a second PCR to append Illumina sequencing adapters on the 5′- and 3′-ends of the amplicon, as well as a random nucleotide stagger and unique demultiplexing barcode on the 5′-end. Each “PCR1” reaction contains 25 μL of Q5 High-Fidelity 2X Master Mix (NEB), 2.5 μL of Nuc PCR#1 Fwd Primer (10 μm), 2.5 μL of Nuc PCR#1 Rev Primer (10 μmol/L), and 5 μg of gDNA in 20 μL of water (for a total volume of 50 μL per reaction). The number of PCR1 reactions is scaled-accordingly; therefore, we performed six PCR1 reactions per technical replicate, per time point (T₀ or T_F). PCR1 amplicons were purified using the QIAquick PCR Purification Kit (Qiagen) and used as a template for “PCR2” reactions. Each PCR2 reaction contains 25 μL of Q5 High-Fidelity 2X Master Mix (NEB), 2.5 μL of a unique Nuc PCR#2 Fwd Primer (10 μmol/L), 2.5 μL of Nuc PCR#2 Rev Primer (10 μmol/L), and 300 ng of PCR1 product in 20 μL of water (for a total volume of 50 μL per reaction). We performed two PCR2 reactions per PCR1 product. Library amplicons were size-selected on a 2.5% agarose gel, purified using the QIAquick Gel Extraction Kit (Qiagen), and sequenced on an Illumina NextSeq 500 instrument (100-nt, single-end reads).

Negative selection assays

Cas9-expressing DMG cell lines were transduced with sgRNA-GFP or RFP expressing lentivirus at a low MOI and passaged without selection. The percentage of GFP or RFP-positive cells was measured at each passage using the Guava easyCyte Flow Cytometer (Cytek) or LSRII (BD). The relative proportion of GFP/RFP positive cells was monitored throughout this serial cell culture. For shRNA negative selection experiments DMG cells were transduced with shRNA-GFP expressing lentivirus and selected with puromycin. Selected cultures were mixed 50:50 with non-transduced (GFP negative) cells and the percentage of GFP positive cells was monitored at each passage using the Guava easyCyte Flow Cytometer (Cytek). Sequences of sgRNAs and shRNAs used in these experiments can be found in Tables S4–S6.

CUT&RUN-Rx

Cells were harvested using Accutase (STEMCELL Technologies) and counted. Following counting, 100,000 mouse NSCs were spiked-in to 900,000 human DIPGXIII cell suspensions (10% spike-in). Cells were fixed in culture media containing 0.1% formaldehyde at room temperature for 1 min. Formaldehyde was quenched with glycine at 0.125 M, followed by a 5-min incubation at room temperature. Fixed cells were washed with PBS. Fixed cells were incubated on ice for 10 min in nuclear extraction buffer (20mM HEPES pH 7.5, 10mM KCL, 0.1% Triton X-100, 20% Glycerol,1X protease inhibitor cocktail and 0.5mM spermidine). Extracted nuclei were collected by centrifugation (4°C, 600g) and resuspended in cold nuclei extraction buffer (100μL per sample). CUT&RUN was performed following Epicypher CUT&RUN Protocol v1.5.1. CUT&RUN DNA was purified using the Monarch PCR & DNA Cleanup Kit (New England Biolabs) in accordance with the manufacturer’s instructions.

CUT&RUN-Rx Library Preparation

Purified DNA was quantified using a Qubit fluorimeter (Invitrogen). 1–10 ng of DNA was used to generate CUT&RUN-Rx libraries with the NEBNext Ultra II DNA Library Prep Kit for Illumina (New England Biolabs) as per the manufacturer’s instructions. Library DNA was quantified using the Qubit, and size distributions were ascertained on a TapeStation (Agilent) using the D1000 ScreenTape assay reagents (Agilent; 5067–5583). This information was used to calculate pooling ratios for multiplex library sequencing. Pooled libraries were diluted and processed for 38-bp paired-end sequencing on an Illumina NextSeq500 instrument using the NextSeq 500 High Output v2 kit (Illumina; FC-404–2005) in accordance with the manufacturer’s instructions.

Immunoblotting

Whole cell protein samples were prepared in RIPA buffer (25 mM Tris-HCl. pH7.6, 150 mM NaCl, 1% NP-40, 1% Sodium Deoxycholate, 0.1% SDS) containing a 1X protease inhibitor cocktail. Protein lysates were separated on pre-cast Bolt 4–12% Bis-Tris Plus Gels (Invitrogen, NW04127BOX) and transferred to nitrocellulose membranes. Membranes were probed using the relevant primary and secondary antibodies and relative protein levels were determined using the Odyssey CLx or Fc Imager (LI-COR).

Quant-seq sample prep and sequencing

Total RNA was isolated from untreated DIPGXIII-K27M/KO cells, or DIPGXIII-K27M following 3, 6 or 9 days of Tazemetostat (2μM) treatment. The quality of extracted RNA was confirmed using the TapeStation (Agilent) with the RNA ScreenTape assay reagents (Agilent; 5067–5576). Total RNA (500 ng) was used as input material from each sample for library preparation. Libraries were generated using the QuantSeq 3′ mRNA-Seq Library Prep Kit FWD for Illumina (Lexogen; 015.24) in accordance with the manufacturer’s instructions. Library DNA was quantified using the Qubit, and size distributions were ascertained on a TapeStation (Agilent) using the D1000 ScreenTape assay reagents (Agilent; 5067–5583). This information was used to calculate pooling ratios for multiplex library sequencing. Pooled libraries were diluted and processed for 75-bp single-end sequencing on an Illumina NextSeq instrument using the NextSeq 500 High Output v2 kit (75 cycles) (Illumina; FC-404–2005) in accordance with the manufacturer’s instructions.

RNA-seq

DIPGXIII cells expressing Cas9 were transduced with pUSEPR constructs containing sgRNAs specific for individual Polycomb genes. Transduced cells were selected for 5 days using 5 ug/uL of puromycin and protein knockdown confirmed by western blot. RNA was extracted from harvested cells (n=2 replicates) using Quick-RNA microprep kit (Zymocat#R1051). Poly(A) mRNA enrichment and library preparation were performed (Genewiz) and sequencing was performed on a NovaSeq Xplus instrument on a 25B lane (150bp, paired-end reads).

Quantitative RT-PCR

Total RNA was extracted from cells using the RNeasy kit (Qiagen) according to the manufacturer’s protocol. RNA was used to generate cDNA by reverse transcriptase PCR using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). Relative mRNA expression levels were determined using SYBR Green I detection chemistry (Applied Biosystems) on the QuantStudio instrument.

Xenograft Experiments

All mouse in vivo experiments were approved by the Institutional Animal Care and Use Committee (IACUC) at the University of Pennsylvania and the Memorial Sloan Kettering Cancer Center. Intracranial xenografts were established by stereotactic injection (Stoelting) of sgRNA/Cas9 expressing SU-DIPG-VI cells into the brainstem of NSG recipient mice at the age of 6–8 weeks as previously described⁹⁴. Tumor engraftment/burden was determined via either MRI or in vivo luminescence imaging (IVIS Spectrum, PerkinElmer), and total flux was calculated by included software (Living Image, Perkin Elmer) as the radiance through standard circular ROIs centered on the animal’s head. Experiment endpoint was triggered by morbidity criteria for euthanasia. The investigators were blinded to animals group at time of outcome assessment. Kaplan-Meier survival curves of mice and the Log-rank test were preformed using the Prism 6 software.

QUANTIFICATION AND STATISTICAL ANALYSIS

CRISPR screen data analysis

For the PRC1 and PRC2 tiling screens, sgRNA read counts were generated using PinAPL-py⁹⁵. For scatter plots presenting log₂ fold-change of sgRNA abundance, any sgRNAs with Off-target Tier I and II Bin 1 scores > 0 were removed and for a given cell line, any sgRNAs with less than 50 reads in any Day 3 replicate in the PRC2 screen, or less than 100 reads in any Day 4 replicate in the PRC1 screen, were also removed. For the PRC2 screen, log₂ fold-changes for each sgRNA were calculated by comparing the number of reads mapping to individual sgRNAs at Day 3 and Day 18/21 normalised to library sequencing depth. Plots were generated using Prism. For the PRC1 (CBX4 and PCGF4) screen, the log₂ fold changes for each sgRNA were analysed using the pipeline ProTiler⁹⁶ to identify inefficient sgRNAs, generate plots, and segment the protein into regions of similar sgRNA dropout using the TGUH method. For the genome-wide CRISPR screens, the MAGeCK algorithm was used to calculate CRISPR dependency beta scores comparing sgRNA abundance in the original plasmid pool with gDNA extracted at the end of a screen, using the count and mle functions⁹⁷. In the genome-wide CRISPR dependency score plots, the frequency of CRISPR dependency scores was calculated by rounding beta scores for each gene to two decimal places and counting the number of times each of the rounded scores was present. Common essential genes, as defined by depmap.org (Achilles, 19Q3), were removed and plots were generated using Prism. See Table S3 for a list of beta scores calculated from genome-wide screening experiments.

Mass spectrometry data analysis

PRC2 IP-MS analysis

Mass spectrometry analysis Thermo RAW files were searched against the Homo sapiens subset of the Uniprot Swissprot database (reviewed) using the search engine Maxquant. Additional parameters that were enabled were match-between-runs, label-free quantification (LFQ) and IBAQ. After filtering for proteins that were present at least in all replicates of one condition, LFQ values were log2 transformed and missing values were imputed in Perseus using default parameters (width = 0.3, shift = 1.8). Statistical outliers for the pull-downs were determined using a two-tailed t test. Multiple testing correction was performed using a permutation-based false discovery rate (FDR) method in Perseus. For these analyses PALI1/2 were manually added to the UniProt database.

PRC1 IP-MS analysis

All mass spectrometry data was processed using MaxQuant software using the human UniProt database. The raw data was searched against the Homo sapiens subset of the Uniprot Swissprot database (reviewed) using the search engine Maxquant (Version 2.0.3.0) using specific parameters for trapped ion mobility spectra data dependent acquisition (TIMS DDA). Each peptide used for protein identification met specific Maxquant parameters, i.e., only peptide scores that corresponded to a false discovery rate (FDR) of 0.01 were accepted from the Maxquant database search. The normalised protein intensity of each identified protein was used for label free quantitation (LFQ)⁹⁸.

CUT&RUN-Rx data analysis

Firstly, the prefix mm10\_ was added to the chromosome names of the mm10 genome build. Next, a human/mouse hybrid genome was generated by appending the human hg38 genome build to the mm10 genome build prior to indexing with bowtie2. Bowtie2 was used to align reads to the hybrid genome in the --very-sensitive mode. SAMtools was employed to retain reads with a mapping quality >2, separate the human and mouse reads, remove duplicate reads and convert SAM files to BAM files. The spike-in normalisation factor for each target was calculated using the formula for normalised reference-adjusted reads per million (RRPM) (1 per million spike-in reads). Alignment numbers to the reference and exogenous genomes can be found in Tables S7 and S8. The bamCoverage module from the deepTools suite was used to generate RRPM normalised bigwig files for genome browser visualisation with the following parameters where SCALEFACTOR is equal to the respective RRPM normalization factor. Peaks were called using macs2, BroadPeak files were converted to bed format using standard bash commands. The bedtools intersect module was used to exclude regions that overlapped with a custom hg38 blacklist, and to overlap peaks to generate consensus peak sets from replicate experiments. Peaks were annotated with HOMER.

Quant-seq data analysis

The BBDuk module from the BBTools package was used to perform adapter trimming and quality filtering as recommended by the manufacturer. The STAR aligner was used to align filtered reads to the hg38 human genome build. The htseq-count tool was used to calculate the number of reads that align to each gene. Differentially expressed genes were detected using a >1.5-fold change and a <0.05 Benjamini-Hochberg adjusted P value using the DESeq2 R package.

RNA-seq analysis

Paired-end reads from sequencing were aligned to the GRCh38 genome build (GCA_000001405.15 assembly) using the HISAT2 aligner. Read extraction was performed with the featureCounts program (doi:10.1093/bioinformatics/btt656) using the corresponding GTF reference annotation and keeping only primary alignments. Read counts table and sample metadata were imported into R, and Differentially expressed genes were determined using DESeq2⁹⁹.

Supplementary Material

2

Supplementary Table 1 Human PRC2 sgRNA library, sgRNAs targeting PRC2 components, Related to Figure 1

3

Supplementary Table 2 Brunello CRISPR screen PCR primers, Related to Figure 2

4

Supplementary Table 3 Brunello CRISPR screen MaGECK ranks, Related to Figure 2

5

Supplementary Table 4 shRNA target sequences, Related to Figures 1 and S2

6

Supplementary Table 5 PRC2 sgRNA target sequences, Related to Figure 1 and S1

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Supplementary Table 6 PRC1 sgRNA target sequences, Related to Figure 2 and S2

8

Supplementary Table 7 CUT&RUN-Rx reference and spike-in genome alignment numbers in DIPGXIII-Parental versus KO cells, Related to Figure 4 and Methods

9

Supplementary Table 8 CUT&RUN-Rx reference and spike-in genome alignment numbers in DIPGXIII cells treated with Tazemetostat, Related to Figure 5 and Methods

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Supplementary Table 9 CBX4 and PCGF4 scanning library sequences, Related to Figure 6

Highlights.

• cPRC1 complexes with CBX4 and PCGF4 are essential in H3K27M-mutant DMG

• The altered H3K27me3 landscape in DMG redistributes cPRC1 complexes on chromatin

• CBX4-PCGF4-cPRC1 acts non-redundantly to repress PRC1/2 target genes in DMG

• CBX4’s central region promotes preferential formation of cPRC1 with PCGF4