Recently, CRISPR–Cas9 knockout genetic screens have emerged as a powerful way in which to probe genetic interactions (Horlbeck et al., 2018; Przybyla and Gilbert, 2022; Bock et al., 2022), with the haploid human cell line HAP1 serving a popular model for these studies (Hundley et al., 2021; Stok et al., 2023; Aregger et al., 2020; Hundley and Toczyski, 2021; Olbrich et al., 2019; Llargués-Sistac et al., 2023). To identify novel genes involved in cell cycle regulation, we carried out genome-wide CRISPR–Cas9 chemogenomic screens in HAP1 cells treated with each of three well-characterized inhibitors of cell cycle progression: palbociclib (a CDK4/6 and G1 progression inhibitor), colchicine (a microtubule polymerization and mitosis inhibitor), and camptothecin (a Topoisomerase I and S/G2 inhibitor). We used a concentration for each inhibitor that reduced cellular proliferation by 30–50% (Figure 1—figure supplement 1A; see also Hundley et al., 2021) and confirmed their effects on cell cycle progression (Figure 1—figure supplement 1B). We then performed a CRISPR–Cas9 whole-genome screen for each of the three inhibitors (Figure 1A) by introducing the Toronto Knockout Library (Hart et al., 2017) via lentiviral transduction into an HAP1 cell line constitutively expressing Cas9. Following puromycin selection for 2 days, cells were propagated in either DMSO (Mock) or in the presence of drug (Treated) for 18 days. Following propagation, genomic DNA was extracted from the initial and final pools and subjected to deep sequencing, and gene–compound interactions were determined using the Orobas pipeline (Source Code File 1, Supplementary file 1, Supplementary file 2). A gene was considered as being significantly enriched or de-enriched in a given condition if both the Loess-adjusted differential gene effect between mean Treated and Mock control was ±0.5 and the and a false discovery rate (FDR) <0.4.

This approach resulted in the recovery of predicted compound–gene interactions demonstrating the robustness of both the screen and our analysis approach. For example, targeting of genes known to play roles in DNA damage repair (DDR) (Groelly et al., 2023; Su et al., 2020), including RAD54L, MUS81, and 16 proteins in the Fanconi Anemia pathway, strongly sensitized cells to camptothecin, which generates protein–DNA adducts (Figure 1B, C). The molecular target of camptothecin, TOP1 (Hsiang et al., 1985), is the most resistant gene in the camptothecin screen, as are proteins involved in p53 transcriptional regulation, such as TP53BP1 and STAGA members TAF2, TAF4, TAF5, TAF11, and TAF13 (Figure 1B). Similarly, colchicine sensitized cells to the mutation of genes encoding proteins involved in mitotic spindle assembly, nuclear division and cytoskeletal assembly, such as CLASP1, DLGAP5, and KNTC1 (Figure 1D, E). Interestingly, inactivation of genes involved in the adaptive immune system, such as BIRC6, UBA5, and USP14, also resulted in sensitivity to colchicine. This observation is intriguing, as colchicine is used clinically as an immunomodulator in the treatment of gout (Dalbeth et al., 2014). CCNE1, CDK6, CDK2, CCND2, and CCND1, all of which are integral to promoting the G1/S phase transition, ranked as the 2nd, 24th, 27th, 29th, and 46th most important genes for palbociclib sensitivity, respectively (Figure 1F, G). CCND1 and CCND2 bind either CDK4 or CDK6, the molecular targets of palbociclib, whereas CDK2 and CCNE1 form a related CDK kinase that promotes the G1/S transition. Similarly, cells with sgRNAs targeting RB1, whose phosphorylation by CDK4/6 is a critical step in G1 progression, displayed substantial resistance to palbociclib. The recovery of genes known to function in the relevant biological processes supports the strength of this dataset and bolstered our confidence to use the results obtained to identify novel chemical–genetic interactions.

To identify genes whose inactivation rendered cells sensitive or resistant to a specific cell cycle perturbation, we compared how the Orobas-calculated differential gene effect for a given targeted gene varied in each compound across our CRISPR–Cas9 screen. The majority of genes that conferred either sensitivity or resistance were specific to only one cell cycle inhibitor, with little overlap between the multiple conditions, suggesting that we identified genes that play roles in distinct biological processes (Figure 2A, Figure 2—figure supplement 1). For example, genes encoding DNA repair proteins, mitotic spindle components, and CDK2/4/6 holoenzyme components were only required for proliferation in camptothecin, colchicine, and palbociclib, respectively. We found that only 13 and 20 genes resulted in sensitivity or resistance, respectively, in all the compounds tested and were deemed non-specific to a given condition, and excluded from any further analysis (see Supplementary file 2).

We next turned our attention to unexpected and novel compound–gene interactions. To probe these interactions, we analyzed genes that significantly altered response to our three compounds using the gene annotation and analysis portal Metascape and the protein–protein interaction network analysis STRING. In addition to DDR genes, Metascape and STRING analysis of the results of our camptothecin treatment revealed de-enrichment for sgRNAs targeting genes encoding members of the KICSTOR complex (KPTN, SZT2, ITFG2, and KICS2), which negatively regulates the mTOR pathway. In contrast, sgRNAs targeting of genes involved in RNA metabolism and chromatin organization increased resistance to this drug (Figure 2B–D). It has been suggested that mTOR is involved in attenuating the DDR response through phosphorylation of RNF168, leading to its degradation (Xie et al., 2018), which could provide one explanation of the observed sensitivity. The loss of genes involved in chromatin structure and the metabolism of RNA conferred resistance to camptothecin is unexpected, given that both these processes have been implicated in DNA repair after damage (Bader et al., 2020; Stadler and Richly, 2017). Genes whose inactivation enhanced sensitivity to colchicine included those involved in the amino acid starvation response (DEPDC5 TSC1, SZT2, and NPRL2) and mRNA splicing (SNRPB2, SF3B2, PPIL1, RBM22, and DHX35), while mutation of genes that control vesicle trafficking (VPS16, VPS18, VPS29, VPS41, VPS51, and VPS52) or encode members of the Mediator complex (CCNC, CDK8, and MED26, MED1, MED7, MED12, MED18, and MED11) attenuated the cellular response to the drug. Unexpectedly, inactivation of genes encoding members of the TP53 signaling pathway (TFDP1 and HIPK2), SAGA H3 acetylation complex components (KAT2A, TRRAP, TADA3, TAF5L, TADA2B, SGF29, and TADA1), and the Fanconi Anemia complex (FANCA, FANCC, FANCE, FANCF, FANCG, FANCL, and FAAP100), all implicated in DDR, resulted in resistance to colchicine (Figure 2E–G). Sensitivity to palbociclib was enhanced in cells expressing sgRNAs targeting H4 acetylation, positive regulators of Pol II transcription and regulators of DDR (Figure 3A, B), although this sensitivity was much weaker than that seen with DNA damaging agents. This observation is consistent with long-term treatment with palbociclib inducing DNA damage, as has been suggested by a number of recent publications (Crozier et al., 2023; Crozier et al., 2022; Wilson et al., 2023). Unexpectedly, Metascape analysis of our palbociclib chemogenetic screen revealed that sensitivity to palbociclib was enhanced when genes involved in chromatin organization were targeted (Figure 3A). Inactivation of members of the SIN3 histone deacetylase (SIN3B, SINHCAF, and ARID4B), the NuA4 histone acetyltransferase (ING3, DMAP1, MORF4L2, YEATS4, and VPS73), the STAGA histone acetyltransferase (KAT2A, TADA1, TADA2B, TAF5L, and SUPT20H), and the Mediator (MED13, MED25, MED10, MED15, TAF7, TAG13, and CCNC) complexes all resulted in palbociclib sensitivity (Figure 3B, C). Each of these protein complexes promotes gene expression, suggesting that palbociclib sensitivity might be a result of a reduction in the transcription of genes important for the G1/S transition.

Because mechanisms of clinical resistance to palbociclib are an area of active investigation, we turned our attention to focus on these pathways. Metascape analysis of genes whose loss conferred palbociclib resistance was highly enriched for splicing factors, oxidative phosphorylation and mitochondrial translation, in addition to chromatin modification (Figure 3A). STRING analysis of the high-confidence, physical interactions of proteins important for palbociclib sensitivity revealed multiple highly connected interaction networks (Figure 3C). Strikingly, almost 25% (170 out of the 689) of the genes whose mutation conferred unique resistance to palbociclib have terms associated with mitochondrial respiratory chain complex assembly, ATP synthesis, or mitochondrial gene expression. Specifically, we see many components implicated in assembly of Mitochondrial Respiratory Chain Complex I and IV, as well as core mitochondrial ribosome and mitochondrial translation initiation and termination (Figure 3A–C). To confirm this positive genetic interaction between mitochondrial homeostasis and resistance to palbociclib, and to dissect whether specific electron transport chain steps might be implicated in this resistance, we asked whether chemical inhibition of oxidative phosphorylation could rescue sensitivity to palbociclib. To target different stages of the oxidative phosphorylation, we employed rotenone, TTFA, and oligomycin, which inhibit Complex I, Complex II, and ATP synthase, respectively. Cells were grown in the presence of palbociclib alone or in combination with each drug for 48 hr and viability was determined by PrestoBlue assay. Cells exposed to rotenone, TTFA, and oligomycin displayed dose-dependent, positive proliferation interactions when treated in combination with palbociclib (Figure 3D, Figure 3—figure supplement 1A–C), indicating that combined inhibition of oxidative phosphorylation and CDK4/6 activity mutually rescue the proliferation defect imposed by agents targeting the other process. While alternative explanations could explain the observed novel chemical–genetic interactions we uncovered here, such as either changes in phenotypic lag rates due to alterations in protein stability or more general screen variability (Rahman et al., 2021), these results suggest a connection between mitochondrial gene function and CDK4/6 inhibitors.

Intriguingly, inactivation of EZH2, SUZ12, and EED, the three core members of the PRC2 complex, resulted in profound resistance to palbociclib, being the eighth, fourth, and third strongest resistance hits out of the 18,053 genes examined when ranked by the score of differential effect (Figure 4A, Supplementary file 1). Mutation of RBBP7, which associates with the core PRC2 complex (Schuettengruber et al., 2017) along with a number of histone deacetylases (De La Fuente, 2014), also de-sensitized cells to palbociclib, but to a more modest extent. Satisfyingly, inactivation of RING1, RNF2, and PCGF6, which are members of PRC1, also displayed resistance to palbociclib. The PRC1 complex contains a ubiquitin ligase that works in concert with PRC2 through H2AK119ub1 deposition, a histone mark that influences both PRC2 chromatin localization and catalytic activity (Barbour et al., 2020; Kalb et al., 2014). As expected, PRC1 and PRC2 components identified in our palbociclib chemogenetic screen formed a highly interconnected STRING physical interaction network (Figure 4B), indicating that loss of either H3K27me3 or H2AK119ub1 reduced sensitivity to this drug. In contrast, inactivation of genes encoding OGT, ASXL1, and HAT1, which are members of the H2AK119ub1 deubiquitinase complex that opposes PRC2-mediated gene repression (Chittock et al., 2017), resulted in sensitivity to palbociclib (Figure 4A). Importantly, no component of any PRC1 or PRC2 core complex displayed significant resistance or sensitivity to camptothecin and colchicine in our chemogenetic screens (Figure 4C), implicating PRC2 in the regulation of G1 specifically, and not to other phases of the cell cycle or the DNA damage response pathway. PR-DUB components ASXL1 and OGT did show resistance to camptothecin, consistent with their role in repressing the homologous recombination DNA repair pathway (Ismail et al., 2014). We sought to confirm the role of the core PRC2 complex in palbociclib resistance by treating cells with combinations of palbcociclib and the EZH2 inhibitor GSK126 using a quantitative Crystal Violet assay. After 9 days of drug combination treatments, we found that cells treated with increasing doses of GSK126 withstood palbociclib-induced proliferation suppression (Figure 4D), though even the highest dose utilized of 5 µM GSK126 had some synergistic effects with low doses of palbociclib. These results confirmed that inactivation of the PRC2 core complex, either through genetic inactivation or chemical inhibition, resulted in resistance to palbociclib.

The PRC2 core binds to auxiliary proteins to create biochemically distinct subcomplexes, termed PRC2.1 and PRC2.2 (Hauri et al., 2016; van Mierlo et al., 2019). These alternative complexes are thought to modify the chromatin localization and enzymatic activity of PRC2, reenforcing existing H3K27me3 in certain contexts (Healy et al., 2019; Zhu et al., 2022a; Youmans et al., 2021), while initiating H3K27me3 deposition at new loci in others (Glancy et al., 2023; Petracovici and Bonasio, 2021). Mutation of the PRC2.1 complex members PHF1, MTF2, PHF19, and EPOP/C17orf96 all display significant resistance to palbociclib, with MTF2 being the strongest of these (Figure 4A, C). Conversely, targeting the genes encoding the PRC2.2-specific accessory proteins AEBP2 or JARID2 resulted in enhanced or neutral palbociclib sensitivity, respectively. These data suggest that PRC2.1 plays a previously uncharacterized role in promoting G1 progression, while PRC2.2 antagonizes it. To confirm the results from our palbociclib chemogenetic screen, we generated polyclonal knockout mutant pools of the individual core and accessory proteins of PRC2. We generated these populations by independently infecting three distinct sgRNAs targeting genes for each PRC2 complex member, or positive and negative control genes, in GFP-positive and doxycycline-inducible Cas9 cells and induced DNA cleavage for 3 days (henceforth referred to as pooled knockouts). We performed western blots to confirm reduction in protein levels for the genes targeted by the sgRNAs used to generate our pooled knockouts (Figure 4—figure supplement 1B). After confirming reduction in the targeted proteins, we carried out a competitive proliferation assay using these pooled knockouts (schematic in Figure 4—figure supplement 1A or see Hundley et al., 2021). Briefly, GFP-positive pooled knockouts were mixed with GFP-negative wild-type cells and propagated in the presence or absence of palbociclib. The ratio of GFP-positive to GFP-negative cells was recorded every 3 days by flow cytometry for 18 days. Pools containing sgRNAs against EZH2, SUZ12, EED, and MTF2 all displayed resistance to palbociclib, similar to the level observed with sgRNAs targeting RB1, our positive control for palbociclib-induced proliferation defects (Figure 4E). Conversely, compared with the dramatic reduction seen in pools transduced with CCNE1 sgRNAs, our positive control for enhanced palbociclib sensitivity CCNE1, sgRNAs targeting PRC2.2 components showed a slight, but statistically signification reduction in proliferation in palbociclib over the 18-day assay (AEBP2: p-value = 0.002 and JARID2: p-value = 0.0148, unpaired two-tailed Student’s t-test). Thus, we confirmed the results of our chemogenetic screen that MTF2-containing PRC2.1 inhibits G1 progression, while PRC2.2 could promote it.

To further interrogate the role of PRC2.1 and PRC2.2 in the regulation of G1 progression, we generated SUZ12, MTF2, and JARID2-null monoclonal cell lines (which we will refer to as SUZ12∆, MTF2∆, and JARID2∆). We chose to mutate MTF2 to probe the function of PRC2.1, as it has been shown to be the most highly expressed and predominant PCL subunit associated with the PRC2 core complex in a variety of contexts (Oliviero et al., 2016; Smits et al., 2013). Furthermore, we selected SUZ12 for inactivation out of the core PRC2 complex members, and not the catalytic subunit EZH2, because the presence of the EZH2 paralog EZH1 might compensate for loss of EZH2 (Jadhav et al., 2020). Additionally, SUZ12 has a critical role in bridging accessory proteins with the catalytic core in all known PRC2 complexes (Petracovici and Bonasio, 2021). MTF2∆ cells displayed resistance to palbociclib when compared with wild-type cells in a 9-day quantitative Crystal Violet assay (MTF2∆ IC₅₀ = 1.033 µM, wild-type IC₅₀ = 0.3936 µM) while JARID2∆ cells were slightly more sensitive than wild-type (JARID2∆ IC₅₀ = 0.2216 µM) (Figure 4F). In addition to showing sensitivity to palbociclib, MTF2∆ cells also displayed resistance to, ribociclib and abemaciclib, two CDK4/6 inhibitors that are structurally related to palbociclib, in a competitive proliferation assay (Figure 4—figure supplement 2). These results confirmed our screen results that mutation of MTF2 leads in CDK4/6 inhibitor resistance with verified clonal mutants.

Palbociclib exerts its chemotherapeutic effects by inducing a G1 arrest and senescence in tumor cells with a functional RB-E2F pathway (Zhu et al., 2022b; Schoninger and Blain, 2020; Dean et al., 2010). However, a recent report demonstrates that palbociclib treatment induces both G1 arrest and apoptosis through the increase in DNA damage in cultured cells (Wang et al., 2021), introducing the possibility that PRC2.1 could be altering regulators of the DDR pathway, resulting in the observed palbociclib resistance. To determine if inactivation of PRC2.1 or PRC2.2 altered cell cycle progression, we examined how wild-type, SUZ12∆, MTF2∆, and JARID2∆ cells responded to palbociclib-induced G1 arrest. To assess this directly, we performed a BrdU incorporation assay by growing each mutant for 24 hr in palbociclib, pulsed the cells with BrdU for 1 hr prior to harvest and then measured BrdU incorporation by flow cytometry. Each of the four cell lines had similar levels of BrdU incorporation in the absence of drug (Figure 4G). However, MTF2∆ and SUZ12∆ mutants displayed significantly more cells entering S-phase in the presence of palbociclib, while palbociclib treatment resulted in significantly fewer JARID2∆ mutants cells in S phase (Figure 4G). To rule out the possibility that cellular viability was not compromised in our monoclonal knockout cell lines, we used western blotting to monitor changes in PARP cleavage or increased BCL2L11/BIM expression, which both serve as apoptosis indicators (Sionov et al., 2015). There was no detectible basal increase in markers of apoptosis in the monoclonal knockout mutant cell lines or when cells were treated with palbociclib for 48 hr (Figure 4—figure supplement 3), supporting the conclusion that the resistance to palbociclib observed in the MTF2∆ and SUZ12∆ cells was due to the repressive and promoting role the MTF2-containing PRC2.1 and PRC2.2 complexes play, respectively, in the canonical CDK4/6-RB1-E2F pathway.

To determine why the mutation of PRC2 subcomplex components altered the cellular response to palbociclib, we sought to see how H3K27me3 levels and gene expression changed in MTF2∆ and JARID2∆ cells. western blotting of total H3K27me3 levels in three independently generated clones indicated that there was no change in the bulk levels of H3K27me3 (Figure 5A), suggesting that any change of phenotype observed in the mutants was due to a change in the localization of this mark and not due to an overall reduction in its abundance. This is in contrast to SUZ12∆ cells, which displayed a significant reduction in the H3K27me3 mark (unpaired Student’s t-test, p-value = 0.0104). To probe the changes in transcription and H3K27me3 distribution genome-wide, we generated CUT&RUN libraries with an anti-H3K27me3 antibody and RNA-Seq libraries from total RNA isolated from our MTF2∆ and JARID2∆ cell lines, grown either in the presence or absence of palbociclib for 24 hr. Changes in H3K27me3 levels and mRNA expression were determined by comparing the enrichment of reads in the MTF2∆ and JARID2∆ libraries to the wild-type cell line (Supplementary file 3 and Supplementary file 4). Because cancer cells have been known to adapt to palbociclib treatment partially through changes to histone marks, chromatin structure and gene expression (Watt et al., 2021; Pancholi et al., 2020; Guarducci et al., 2018; Turner et al., 2019), we also investigated how both transcript levels and H3K27me3 distribution responded to treatment with palbociclib in our clonal knockout cell lines. Primary component analysis (PCA) of our called, reproducible H3K27me3 peaks and transcript abundance from our CUT&RUN and RNA-Seq data, respectively, showed a high percentage of variance between each of the genotypes tested, along with good clustering of repeats of the same genotype and treatments (Figure 5—figure supplement 1C), suggesting a shift in the epigenetic and transcriptional landscapes when either MTF2 or JARID2 are absent. PCA analysis of our RNA-Seq experiment revealed substantial shifts in variance between palbociclib- and Mock-treated samples for each genotype (Figure 5—figure supplement 1C, bottom), suggesting that exposure to palbociclib resulted in changes in gene expression, consistent with previous reports (Ferguson et al., 2023; Lanceta et al., 2021). However, the PCA of our CUT&RUN experiment did not reveal large differences in H3K27me3 distribution between palbociclib-treated and untreated samples (Figure 5—figure supplement 1C – top). In line with this observation, when we analyzed the change in distribution of H3K27me3 peaks between palbociclib- and Mock-treated cells using DESeq2, we found no significant changes in the location of H3K27me3 reproducible peaks in the presence or absence of palbociclib (data not shown). This suggests that MTF2∆ mutants are not resistant to palbociclib because MTF2 is required for a transcriptional adaptation to the drug, but instead because MTF2 alters expression of genes important for G1/S progression, even in unperturbed cells.

Due to the known role of PRC2 in repressing gene expression, we next asked how H3K27me3 distribution changed in promoters of genes. We defined promoters as 4 kb upstream and 1 kb downstream of all annotated transcription start sites, and calculated the total number of reads within each of these regions. Our parameters led to ~61,000 genomic regions being designated as promoters. In addition to annotated protein-coding genes, this included the promoters of non-coding transcribable units such as rRNA, miRNAs, lncRNAs, and pseudogenes. We observed a greater number of promoters with significantly decreased H3K27me3 (log₂ fold-change [LFC] ±1, adjusted p-value <0.1) in the MTF2∆ compared to JARID2∆ cell lines (5808 vs 1034 promoters, respectively). Of these, 5149 promoters displayed MTF2-dependent H3K27me3, 392 were JARID2-dependent, and 629 were co-dependent on MTF2 and JARID2 for wild-type levels of H3K27me3 (Figure 5B, D). Consistent with the greater change in H3K27me3 signal at promoters in MTF2∆ cells, 733 versus 114 transcripts were significantly upregulated upon MTF2 versus JARID2 inactivation, respectively, with 666 transcripts that were exclusively MTF2-dependent, 47 transcripts that were exclusively JARID2-dependent, and 67 transcripts displaying co-dependence on both MTF2 and JARID2. These results indicate that the MTF2-containing PRC2.1 complexes affect the deposition of H3K27me3 in the promoter regions of more genes than the JARID2-containing PRC2.2, and therefore, are more important for transcriptional repression in HAP1 cells.

Given the diverse regulatory roles of PRC2 in different biological contexts, and the limited information on PRC2.1 and PRC2.2 outside of stem cells, we were curious to see what classes of genes were being differentially regulated in the MTF2∆ and JARID2∆ cell lines. Only ~30–40% of the promoters with significantly changed levels of H3K27me3 were upstream of protein-coding genes (Figure 5—figure supplement 1A), while ~80–90% of the significantly differentially expressed transcripts encoded proteins (Figure 5—figure supplement 1B). Therefore, we focused a Metascape analysis on the promoters and mRNAs of protein-coding genes with differential H3K27me3 and transcript levels, respectively (Figure 5C). Analysis of the promoters of genes with decreased H3K27me3 and increased transcript levels in both MTF2∆ and JARID2∆ lines were terms associated with embryonic morphogenesis, cell-fate commitment, and developmental growth, all processes previously been shown to be regulated, at least in part, by PRC2 (German and Ellis, 2022). Intriguingly, terms for genes that specifically displayed decreased promoters H3K27me3 and upregulated mRNA in MTF2∆ cells included the pro-growth pathways cGMP and ERBB4 signaling. Conversely, terms for genes which displayed increased promoter H3K27me3 signal and decreased transcript levels in JARID2∆ cells contained pathways that could reduce cellular proliferation and viability, such as positive transcriptional regulation of RUNX1 and positive regulators of program cell death. We also saw terms that had opposite effects on H3K27me3 and transcript levels in MTF2∆ compared to JARID2∆ cells, such as secretion by the cell and regulation of cellular component biogenesis, which could potentially exacerbate palbociclib-induced proliferation defects (Uzhachenko et al., 2021; Franco et al., 2016). Together, these data support the notion that MTF2 antagonizes cell proliferation in normal cellular conditions, while JARID2 promotes it.

MTF2-containing PRC2.1 have been previously shown to localize to chromatin using a winged helix in its extended homology domain that has affinity for CG-rich sequences (Perino et al., 2018; Li et al., 2017), whereas PRC2.2 localization is dependent on chromatin context, specifically H2AK119ub1 deposited by PRC1 (Cooper et al., 2016; Kalb et al., 2014; Glancy et al., 2023). To determine whether CpG island targeting by PRC2.1 could help explain the palbociclib resistance we observed in the absence of MTF2, we identified and plotted 1877 peaks that overlapped with CpG islands in wild-type cells and had the greatest H3K27me3 signal in a 10-kb window surrounding the CpG islands. We then plotted the H3K27me3 signal observed in the MTF2∆ and JARID2∆ cells for these same loci. We observed a complete loss of H3K27me3 signal intensity at CpG islands in the MTF2∆ mutants, but only a partial loss at these loci in JARID2∆ cells (Figure 5E, Figure 5—figure supplement 2). When we expanded our findings genome-wide, we found a significant loss of H3K27me3 peaks at CpG islands in MTF2∆ cells (Fisher’s exact test, odds ratio = 20.4, p-value 2.2 × 10⁻³⁰⁸), compared with JARID2∆, where this loss was much more modest (Fisher’s exact test, odds ratio = 9.8, p-value = 6.5 × 10⁻⁷). This result is consistent with the interpretation that the MTF2-containing PRC2.1 is required for all H3K27me3 deposition at CpG islands, whereas JARID2-containing PRC2.2 is only required to achieve full wild-type H3K27me3 levels at these sites.

CpG islands are a very common feature of mammalian promoters, with 50–70% human promoters estimated to contain at least one CpG island (Zheng et al., 2021). Since promoters are highly associated with CpG islands, we examined 2000 promoters with the highest level of H3K27me3 signal intensity that overlapped with CpG islands in wild-type cells, then plotted the H3K27me3 signal intensity at those same loci in our mutant cell lines (Figure 5F). Consistent with the result seen at CpG islands genome-wide, we observed a complete loss of high signal intensity in the MTF2∆ cells, but only a slight loss in JARID2∆ cells. When we averaged the H3K27me3 signal intensity over all 25,124 promoters that contain CpG islands, we observed a pattern of MTF2∆ cells having greatly decreased H3K27me3 levels in these regions, particularly surrounding the transcription start site while JARID2∆ cells were widely similar to wild-type cells (Figure 5F) in line with what was seen at CpG islands genome-wide. Reactome and MSigDB analysis of the promoters of protein-coding genes that overlapped with CpG islands showed strong enrichment for terms associated with cell cycle and E2F target genes (Figure 5—figure supplement 3A) as well as enrichment binding E2F6 (Figure 5—figure supplement 3B), which both regulates transcription of G1 progression genes (Giangrande et al., 2004) and is a well-characterized component of Polycomb complexes (Hauri et al., 2016; Shirahama and Yamamoto, 2020). These results suggest that MTF2 is required for H3K27me3 deposition at promoters containing CpG islands involved in cell cycle regulation and can explain why MTF2∆ cells display a greater change in gene expression than do JARID2∆ cell lines.

Our CUT&RUN results suggest that MTF2-containing PRC2.1 impacts gene expression, at least in part, through deposition of H3K27me3 at promoters with CpG islands. Therefore, we hypothesized this PRC2 complex must be antagonizing G1 progression through repression of cell cycle-promoting genes. When inspecting the results of our CUT&RUN and RNA-Seq experiments, we found that the promoters of both CCND1 and CCND2 had lost H3K27me3 signal and displayed strong transcriptional induction in MTF2∆ cells (Figure 6A, Figure 6—figure supplement 1A, B), suggesting that the increase in these transcripts was due directly to a change in H3K27me3 in their promoters. In fact, while CCND1 and CCND2 were both among the most upregulated statistically significant transcripts within the MTF2∆ cell line, their transcription and promoter H3K27 methylation were unaltered in JARID2∆ cells (Figure 6—figure supplement 1C–E). Given that increased CCND1 levels are sufficient to drive increased CDK4/6 kinase activity, upregulation of these D-type cyclins is likely to be a significant contributor to the palbociclib resistance in MTF2∆ cells. DESeq2 analysis of H3K27me3 density in MTF2∆ cells displayed a statistically significant 4.3 and 2.7 log₂ fold-decrease in H3K27me3 signal in the promoter region of CCND1 and CCND2, respectively, when compared to wild-type H3K27me3 levels (Figure 6—figure supplement 1A, Supplementary file 3), whereas changes in H3K27me3 levels in the CCND3 promoter were not statistically significant (Supplementary file 3). Given our observation that H3K27me3 signal is lost at CpG islands in MTF2∆ cells, we inspected the D-type cyclin promoters for CpG islands. Indeed, the regions upstream of all three D-type cyclins contained CpG islands, but CCND1 and CCND2 had regions of GC density about seven times larger (7460 and 6003 bp, respectively) than CCND3 (996 bp) (Figure 6B, Figure 6—figure supplement 1F). Furthermore, the promoter of CCND1 contained about twice as many CpG repeats than did CCND2 (575 vs 379) and about six times as many CpG repeats as CCND3 (575 vs 95). These results suggest that the levels of CCND1 and CCND2 mRNA transcripts, but not CCND3, were regulated by MTF2 in a CpG island-dependent manner.

We sought to confirm our observation that ablation of MTF2 resulted in increased levels of CCND1 and CCND2 protein. We generated pooled knockouts of MTF2, JARID2, and the core PRC2 components SUZ12, EZH2, and EED using three independent sgRNAs. In pooled knockouts of MTF2, EZH2, EED, and SUZ12, we observed an increase in both CCND1 and CCND2 protein levels by western blot, but not for CCND3 (Figure 6C, Figure 6—figure supplement 2A). Consistent with the results from our CUT&RUN and RNA-Seq datasets, we did not observe a significant change in either CCND1 or CCND2 levels in JARID2 pooled knockouts. We next examined mRNA and protein levels of the D-type cyclins in MTF2∆ and JARID2∆ clones by qRT-PCR and western blotting, respectively. Again clonal knockouts of SUZ12∆ and MTF2∆, but not JARID2∆ lines, had increased mRNA (Figure 6—figure supplement 2C) and protein levels (Figure 6—figure supplement 2B) for both CCND1 and CCND2, but not CCND3. To determine whether other genes involved in the canonical CDK4/6-RB-E2F pathway were also altered, we examined mRNA and protein levels of known cell cycle regulators in our knockout cell lines. In contrast to CCND1 and CCND2, none of the E-type cyclins, CIP/KIP CDK inhibitors, RB1 or E2F proteins displayed significantly altered mRNA transcript abundance in our RNA-Seq experiment in either MTF2∆ or JARID2∆ lines (Figure 6—figure supplement 3A). To confirm that protein stability of these factors was not altered in our knockout lines, we also examined protein levels of a panel of known G1 regulators by western blot (Figure 6—figure supplement 3B). Similarly, we did not observe an increase in levels of any of the tested proteins, confirming that CCND1 and CCND2 were the only upregulated canonical CDK4/6-RB-E2F pathway regulators in MTF2∆ cells.

Proper regulation of D-type cyclin expression is necessary to promote G1 progression, cellular fate specification, and organismal development, while dysregulation is seen in many cancers. Given the clear role of MTF2-dependent regulation of CCND1 and CCND2 in HAP1 cells, we sought to determine whether MTF2-containing PRC2.1 regulates the expression of D-type cyclins in other cell types in addition to HAP1. To probe this question, we determined levels of CCND1 by western blot in a panel of cell lines from a diversity of cell lineages treated with the EZH2 inhibitor GSK126 for 6 days. As expected, treatment of cells for 6 days with GSK126 resulted in a profound reduction in H3K27me3 levels in all cell lines tested and increased CCND1 expression in HAP1 cells (Figure 6D). Interestingly, in the non-adherent, haploid CML cell line KBM7, from which HAP1 was derived (Carette et al., 2011), we also observed a dramatic increase in CCND1 levels. This suggests that the regulation of D-type cyclin by PRC2 observed in HAP1 was not the results of artifacts introduced during the initial isolation of this cell line. Additionally, we observed that GSK126 treatment resulted in increased levels of CCND1 in the breast cancer line MDA-MB-231 and the SV40-immortalized monkey kidney fibroblast cell line COS7 (Figure 6D), suggesting that PRC2 activity can repress CCND1 expression in multiple mammalian cell lineages and species. In agreement with the context-dependent nature of PRC2 regulation of developmentally important loci, increased levels of CCND1 was not observed in the prostate adenocarcinoma cell line LNCaP and the immortalized mouse embryonic cell line NR-6, illustrating that regulation of CCND1 expression by PRC2 is not observed in all cellular lineages. To specifically probe the roles of PRC2.1 and PRC2.2 in the cell lines that displayed upregulation of CCND1 when all PRC2 activity was inhibited, we transduced COS7 and MDA-MB-231 cells with shRNAs targeting SUZ12, MTF2, and JARID2 and propagated cells for 2 weeks post selection. Consistent with results from propagation of these cell lines in GSK126, depletion of either SUZ12 and MTF2 in both COS7 and MDA-MB-231 resulted in significantly increased CCND1 expression (Figure 6E), suggesting that MTF2-containing PRC2.1 represses this loci in diverse cell types in addition to HAP1.

While D-type cyclins are necessary to promote the kinase activity of CDK4/6, they have also been shown to play roles outside of the RB1-E2F pathway (Jirawatnotai et al., 2012; Shimura et al., 2013; Aggarwal et al., 2007). Therefore, we sought to test if the increases in D-type cyclins seen in MTF2∆ cells lead to increased CDK4/6 activity, driving resistance to palbociclib treatment. First, we introduced alleles of CCND1 or CCND2 under the control of a doxycycline-inducible promoter into wild-type HAP1 cells. We then performed a competitive proliferation assay in the presence of palbociclib and monitored the advantage conferred by overexpression of D-type cyclins. Consistent with the interpretation that the increased levels of D-type cyclins resulted in palbociclib resistance observed in MTF2∆ cells, overexpression of either CCND1 or CCND2 was sufficient to induce resistance to palbociclib (Figure 6—figure supplement 4A). Critically, as elevated cellular levels of D-type cyclins alone would be insufficient to drive palbociclib resistance, we tested to see if the increased expression of D-type cyclins in PRC2.1 mutant cell lines drove CDK4/6 association. To test this, we introduced a FLAG epitope-tagged copy of CDK6 into our knockout mutant cell lines, immunoprecipitated CDK6 and blotted for CCND1. We observed increased association of CCND1 with FLAG-tagged CDK6 in both SUZ12∆ and MTF2∆, but not JARID2∆, knockout cells (Figure 6—figure supplement 4B), suggesting that elevated levels of D-type cyclins indeed drive the formation of active CDK4/6 complexes. Finally, if the increased cellular levels of CCND1 and CCND2 seen in the MTF2∆ cell lines enhanced CDK4/6 kinase activity. To probe this question, we determined the extent of RB1 phosphorylation at S807 and S811, two well-characterized CDK4/6 targeted residues that are common markers of proliferation and which are recognized by a single antibody. To do this, we titrated wild-type, MTF2∆, and JARID2∆ cells with increasing amounts of palbociclib and determined the levels of total RB1 and phosphorylated RB1 levels to calculate the ratio at each concentration. In each of our cell lines, higher concentrations of palbociclib resulted in decreased levels of phosphorylated RB1, as expected. However, compared to WT or JARID2∆ cells, MTF2∆ mutant cells maintained a higher ratio of phosphorylated to unphosphorylated RB1 at each concentration of palbociclib tested (Figure 6F). This result suggests that the increased levels of CCND1 and CCND2 in MTF2∆ cells increases CDK4/6 kinase activity, driving cells into S-phase (Figure 4G). In total, our results suggest that MTF2-contiaing PRC2.1 antagonizes G1 progression by repressing expression of the D-type cyclins CCND1 and CCND2 in certain cellular contexts.

Requests for further information and reagents should be directed to and will be fulfilled by the Lead Contact, David Toczyski (dpt4darwin@gmail.com).

Cas9-expressing HAP1 and KBM7 cells were cultured in IMDM (Gibco) supplemented with 4 mM glutamine (Gibco), 10% Tetracycline-free FBS (Sigma-Aldrich) and either 1× Antibiotic, Antimycotic (Invitrogen) or 1% penicillin–streptomycin (Sigma-Aldrich). HAP1 cells stably expressed Cas9 were employed for the whole-genome screen, while for subsequent experiments, an HAP1 line harboring a doxycycline-inducible Cas9 was utilized. COS7, MDA-MB-231, NR-6, and HEK293T cells used for the production of virus were cultured in DMEM (Gibco) supplemented with 2mM glutamine (Gibco), 10% Tetracycline-free FBS (Sigma-Aldrich) in 1× Antibiotic, Antimycotic (Invitrogen). LNCaP M-1 #2166 cells were cultured in RPMI-1640 (Gibco) supplemented with 2 mM glutamine (Gibco), 10% Tetracycline-free FBS (Sigma-Aldrich) in 1× Antibiotic, Antimycotic (Invitrogen). Cells were detached from tissue culture dishes using 0.25% Trypsin (Gibco) and maintained at 37°C, 5% CO₂. Our laboratory conducts regular mycoplasma testing of cultured cells with the MycoAlert Mycoplasma Detection kit (Lonza), and no mycoplasma contamination of any cell line was detected during this study.

The lentiviral TKOv3 sgRNA library (Addgene #90294) was used to perform pooled genome-wide CRISPR knockout screens. The library contains 70,948 guides, targeting 18,053 protein-coding genes (4 guides/gene). Ninety million HAP1 cells stably expressing Cas9 were seeded into 15 cm dishes and infected with TKOv3 lentivirus at a multiplicity of infection of roughly 0.3, such that every sgRNA is represented in approximately 200–300 cells after selection (>200-fold coverage). After 24 hr of infection, cells with successful viral integration were selected in 25 ml IMDM medium containing 1 µg/ml puromycin (Sigma-Aldrich). Selection took place for 48 hr. Following selection, cells were harvested, pooled, and split into three replicates of 15 million cells each to maintain >200-fold coverage of the sgRNA library (day 0). At day 3, each replicate was split such that every drug screen had a at least 15 million cells per replicate to maintain >200-fold coverage. The drug concentrations (IC₃₀–IC₅₀ determined as described below) used in the genome-wide chemical screens were as follows: palbociclib – 0.7 µM, colchicine – 9.2 nM, and camptothecin – 1 nM. An increase in potency was observed for most drugs when used in the pooled screens, thus screening concentrations were adjusted to preserve IC₃₀–IC₅₀ throughout each passage. Cells were subject to treatment with drug in 0.1% DMSO, or 0.1% DMSO alone. Drug-containing media was refreshed every 3 days, along with the passaging of cells and the collection of cell pellets. To preserve >200-fold coverage, 20 million cells were pelleted with every passage, from day 0 to 18.

Genomic DNA extraction and sequencing library preparation were performed as described previously (Aregger et al., 2020). Briefly, genomic DNA from cell pellets were extracted using the Wizard Genomics DNA Purification Kit (Promega) and quantified using the Qubit dsDNA Broad Range Assay kit (Invitrogen). Sequencing libraries were prepared as described previously (Michael et al., 2019). Briefly, two PCR amplification steps were performed to first enrich for the sgRNA regions in the genome and second, attach Illumina sequencing indices to the amplified regions. Sequencing libraries were prepared from 50 µg of genomic DNA (200-fold library coverage) using the NEBNext Ultra II Q5 Polymerase (NEB). Primers used included Illumina TruSeq adapters with i5 and i7 indices. Barcoded libraries were gel-purified using the PureLink Quick Gel Extract kit (Thermo Fisher) and sequenced on an Illumina HiSeq2500.

Drug dosing experiments were performed to determine screening concentrations. HAP1 cells stably expressing Cas9 were seeded at a density of 2.5 million cells per 15 cm dish. Cells were treated with 0.1% DMSO, or drug in 0.1% DMSO, 2 hr after seeding. Viable adherent cells were counted 2 days post-treatment on a Coulter counter, and inhibitory concentrations were determined. The following are ranges of drug concentrations used in the dosing experiments: palbociclib: 1.5–10 µM, colchicine: 1.5–150 nM, and camptothecin: 1–5 nM.

The Orobas pipeline (version 0.5.0) was used to score chemical genetic interactions from the genome-wide CRISPR/Cas9 screen data. The process is summarized here, and the complete R code is provided as a source code file. sgRNAs were normalized to sequencing depth for each sample and the LFC in sgRNA abundance was calculated for each condition relative to the corresponding T0 sample. Guides with fewer than 30 read counts in the T0 sample were filtered out from further analysis, and genes with fewer than threeremaining guides post-filtering were also filtered out from scoring. Residual effects were computed for each gene by calculating the residual LFC between sgRNAs in treated versus DMSO samples after averaging technical replicate LFCs. Residual effects were then M-A transformed and Loess-normalized to account for potential skew and non-linearity present in the data, and per-gene effect sizes and FDRs were computed by applying the moderated t-test to normalized residual effects. Hits were called as genes with FDRs less than 0.4 and per-gene effect sizes greater than 0.5 or less than −0.5 (a complete list of effect sizes and FDRs is included as Supplementary file 1).

STRING networks were set to only display physical interactions scores that were returned with high confidence (0.7) and taken from text-mining, experiments, and databases.

Oligos for sgRNA targets were designed to contain the 5′ overhang CACCG- for the sense oligo 5′ and for that antisense 3′ over hang AAAC- and -C, respectively. 10 µM each of sense and antisense oligos (Integrated DNA Technologies) were mixed in 1× T4 DNA Ligase buffer and water to a total volume of 10 µl. This mixture was heated to 95°C for 5 min, then oligos were annealed by decreasing the temperature at a rate of −0.1°C/s till the mix reached 25°C. Annealing reactions were diluted 1:10 with water and then 1 µl was used to ligate into 100 ng of BsmBI digested pLentiGuidePuro vector (Addgene #52963) in 1× T4 DNA Ligase Buffer. 600 units of T4 DNA Ligase (NEB) and water to a total volume of 25 µl. After incubating for 1hr at 37°C, 2 µl of the ligation reaction was transformed into β-ME pre-treated XL10-Gold cells (Agilent) per the manufacturer’s instructions and plated on LB + 100 µg/ml carbenicillin plates for selection. Plasmids recovered from single colonies were confirmed by Sanger sequencing.

Cas9-expressing HAP1 cells were transduced with pLentiGuidePuro vectors (Addgene #52963) expressing a single sgRNA (see Supplementary file 5 for sgRNA sequence). Lentiviral transduction was conducted at low MOI (~30%) following standard protocols. Integration of the sgRNA was selected with 1 µg/ml puromycin for up to 2 days, followed by combined puromycin selection and Cas9 induction for 3 days with 1 µg/ml doxycycline. This polyclonal pool of pLentiGuidePuro transduced cells was then used for ‘pooled’ knockout experiments or used to generate monoclonal cell lines. Trypsinized, single cells were then sorted into individual wells in a 96-well plate using the Sony SH800 sorter (UCSF, LCA). Isolated single-cell-derived colonies were screened for mutation by PCR, followed by Sanger sequencing of the purified PCR product and ICE analysis (Synthego) of the resulting chromatographs. Candidate clonal knockouts were then confirmed by western blot. Only monoclonal lines that clearly displayed knockout alleles and had no protein product by western blot were utilized further.

For pooled knockout competitive growth assays, HAP1 cells harboring an inducible Cas9 and expressing GFP and HAP1 cells expressing BFP (Hundley et al.) were mixed at a ratio of 1:4 GFP:BFP HAP1 cells into a single well, with three GFP/BFP cell mixtures for each gene targeted for inactivation. Mixtures were transduced at a low MOI with a pLentiGuidePuro vector expressing one sgRNA (three biological replicates per gene, sgRNAs in Supplementary file 5). After 24 hr of lentiviral transduction, pools of cells were selected with 1 µg/ml puromycin for 1 day, followed by 1 µg/ml puromycin and doxycycline for 3 days to select for sgRNA integration and to induce Cas9 expression. After 3 days of Cas9 induction, pools were split into media with or without palbociclib every 3 days, for 18 days. The GFP/BFP ratio was monitored on the Attune NxT (Invitrogen) flow cytometer every 3 days. FlowJo v10 was used to determine the GFP/BFP ratio at each time point. The ratio of GFP to BFP was normalized to the day 0 ratio (prior to splitting into palbociclib), and subsequently to the matched untreated ratio at each time point.

For HAP1 GFP⁺/GFP⁻ competitive growth assays, wild-type, doxycycline-inducible CCND1, doxycycline-inducible CCND2, clonal MTF2∆, or JARID2∆ GFP⁻ cells were mixed with HAP1 wild-type mGFP⁺ clones at 1:5 GFP⁻:GFP⁺ ratio, split into media with or without drug, and analyzed by flow cytometry as described above.

Harvested cell pellets were lysed in 1× RIPA buffer supplemented with 1× EDTA-free cOmplete protease inhibitor (Roche) and 1× PhosphoSTOP phosphatase inhibitor (Roche) for 30 min on ice with two rounds of 15-s vortexing. Lysates were cleared at 21,000 × g for 10 min at 4°C. Protein concentration was determined by BCA assay and BSA standard curve (Pierce), and samples were adjusted to 1 µg/µl total protein with 1× RIPA and SDS–PAGE sample loading buffer was added (62.5 mM Tris-HCl (pH 6.8), 2.5% SDS, 0.1% bromophenol blue, 10% glycerol, 5% β-mercaptoethanol (vol/vol)). 10 µg of total protein was loaded per lane onto a 4–20% Criterion Tris-HCl Protein gel (Bio-Rad) and separated by electrophoresis at 150 V for 1 hr. Proteins were transferred and immobilized onto a nitrocellulose membrane (GE Healthcare) by electrophoresis for 1 hr at 100 V in standard transfer buffer containing 20% methanol. Membranes were blocked for an hour at room temperature and then then probed overnight in a 1:1000 dilution of 1° antibody (unless otherwise indicated) at 4°C and in a 1:10,000 2º antibody at room temperature for 1 hr at in the appropriate blocking buffer. Chemiluminescent and fluorescent signals were visualized with an Odyssey FC imager (LICOR).

200,000 cells/well were plated in 6-well dishes at, as to be 10–20% confluent at the time of treatment. Cells were treated with inhibitors 24 hr after plating, then harvested 48 hr later by trypsinization, washed twice with cold 1× PBS, fixed by dropwise addition of ice-cold 70% ethanol, and incubated at 4°C overnight. Fixed samples were washed twice with 1× PBS + 1% BSA prior to resuspension in a solution of 1× PBS, 1 mg/ml RNase A and 50 µg/ml propidium iodide for 1 hr at 37°C. DNA content of at least 20,000 single cells was determined by Attune NxT flow cytometer (Invitrogen), and data were analyzed using FlowJo v10.

250,000 cells/well were plated in 6-well dishes and grown for 24 hr prior to treatment. Cells were then treated with either DMSO (mock) or 1.5 µM palbociclib for a total of 24 hr, with 10 µM BrdU being added to the culture medium 1 hr prior to harvesting. Cells were counted using the Countess automatic hemocytometer (Invitrogen) to ensure that only 1 million cells were stained. Cells were prepared for analysis using BD Pharmagen BrdU Flow Kits (BD Biosciences) according to the manufacturer’s instructions. BrdU incorporation was determined for at least 20,000 single cells by Attune NxT flow cytometer (Invitrogen), and data were analyzed using FlowJo v10.

1 ml of a 1000 cells/ml suspension were seeded into a per well in a 6-well plate containing 1 ml IMDM supplemented with double the indicated concentration of palbociclib and GSK126 in technical triplicate. Cells were allowed to proliferate for 9 days, with the media supplemented with the drug at the concentration indicated replaced every 3 days. After 9 days, cells were washed once with 1× PBS, followed by staining and fixation in a 0.25% Crystal Violet, 20% methanol solution for 10 min at room temperature. Following staining, cells were washed six times with 1× PBS and lysed in a 100 mM sodium citrate and 50% ethanol solution for 30 min at room temperature on an orbital shaker. Lysates were recovered and absorbance at 590 nM was detected using a Synergy Neo2 Microplate Reader (BioTek). Proliferation at each concentration was determined relative to untreated wells.

45 µl of a 50,000 cells/ml cell suspension was seeded into a 96-well plate containing 45 µl of IMDM supplemented with the indicated concentration of palbociclib, antimycin A, TTFA or oligomycin in triplicate. After proliferation for 48 hr, 10 µl of PrestoBlue (Invitrogen) was added to each well and incubated for 30 min at 37°C. Conversion of PrestoBlue was determined by recording the fluorescence excitation at 560 nM and emission at 590 nM using a Synergy Neo2 Microplate Reader (BioTek). Proliferation at each concentration was determined relative to untreated wells.

150,000 cells were seeded into 6-well plates and allowed to grow overnight. Cells were treated with DMSO (Mock) or 1.5 µM palbociclib for 24 hr prior to harvesting directly in TRIzol reagent (Invitrogen). After chloroform extraction, the aqueous phase was transferred to a fresh tube and 1 volume of 100% ethanol was added and mixed thoroughly before binding to an RNA Clean & Concentrator (Zymo). RNA was DNase I digested on-column (Zymo), purified according to the manufacturer’s instructions and eluted in nuclease-free water. To prepare RNA-Seq libraries, 2 µg of total RNA was polyA, followed by Illimina adaptor ligation and paired-end sequencing on an Illumina HiSeq at a depth of at least 22 million reads per sample by Azenta.

2 µg of total RNA was first heat denatured in the presence of dNTPs and oligo-dT at 65°C for 5 min. RNase inhibitor and Tetro reverse transcriptase (Bioline) was then added to heat denatured total RNA and cDNA was synthesized at 45°C for 1 hr, followed by heat inactivation at 85°C for 5 min. cDNA synthesis reactions were then diluted 1:5 and 2 µl was added into qRT-PCR reaction mix, utilizing SensiFast Lo-ROX qRT-PCR Mastermix (Bioline) in both biological and technical triplicate. Reactions were carried out and analyzed using a QuantStudio5 machine (Applied Biosystems). See Supplementary file 5 for qRT-PCR primer sequences.

2xFLAG-2xStrep-CDK6 expressing PRC2 component monoclonal knockout mutant HAP1 cells were harvested and lysed on ice in a 10 mM HEPES, 150 mM NaCl, 1 mM MgCl₂, 1 mM EDTA, 1% NP-40 lysis buffer supplemented with cOmplete Protease Inhibitor (Roche), and PhosphoSTOP (Roche) tablets by passage through an 21-gauge syringe and rotation at 4°C for 30 min. Lysates were clarified by centrifugation at 21,000 rcf for 15 min at 4°C and the protein content was quantified and the input was normalized to 5 µg/immunoprecipitation. 150 µl of FLAG-conjugated Dynabeads slurry (Invitrogen) was added to each normalized lysate and immunoprecipitated overnight at 4°C under constant rotation. The following immunoprecipitation, Dynabeads were washed five times with a 1× PBS, 0.1% NP-40 buffer and bound protein was eluted from the beads in 1× PBS, 0.1% NP-40 buffer supplemented with 5 µg of 3x-FLAG peptide (Sigma). Resulting eluates were then analyzed by SDS–PAGE and western blots.

CUT&RUN libraries were generated by first lysing 300,000–500,000 cells in 500 µl of Nuclei Extraction Buffer (20 mM HEPES–KOH pH 7.9, 10 mM KCl, 1 mM MgCl₂, 0.1% Triton X-100, 20% glycerol, and 1× protease inhibitor) for 10 min on ice. Next, samples were spun down and washed twice with Nuclei Extraction Buffer before being resuspended in 500 µl nuclei extraction buffer. 10 µl of Concanavalin A-coated beads (EpiCypher) previously washed in Wash Buffer (20 mM HEPES–KOH pH 7.5, 150 mM NaCl, 2 mM EDTA, 0.5 mM spermidine, and 1× protease inhibitor) and resuspended in Binding Buffer (20 mM HEPES–KOH pH 7.5, 1 mM CaCl₂, and 1 mM MnCl₂) were then added to the samples and incubated with rotation for 15 min at 4°C. Next, samples were washed once with Binding Buffer before being resuspended in 50 µl of Buffer 2 containing 0.1% BSA, 2 µM EDTA, and 0.5 µl H3K27me3 1° antibody, followed by overnight incubation with rotation at 4°C. Following the incubation, samples were washed twice with Buffer 2 before being incubated in 50 µl of Buffer 2 containing ~700 ng/ml Protein A-MNase fusion protein (Batch #6 from the Henikoff Lab) for 1 hr with rotation at 4°C. Samples were washed two more times and resuspended in 100 µl of Buffer 2 before starting the MNase digestion by adding CaCl₂ to a concentration of 2 mM on ice for 30 min, after which the reaction was quenched with the addition of 100 µl 2× Stop Buffer (200 mM NaCl, 20 mM EDTA, 4 mM EGTA, 50 µg/ml RNase A, 40 µg/ml GlycoBlue (Ambion), and 2 pg/ml spike-in DNA) to inactivate the MNase. Samples were incubated for 30 min at 37°C and spun down for 5 min at 4°C to release DNA fragments. DNA was phenol:chloroform extracted and 200 µl of the recovered aqueous phase was ethanol precipitated with 500 µl ethanol, 20 µl 3 M NaOAc, 2 µl GlycoBlue at −80°C. Libraries were prepared using 2S Plus DNA Library Kit adapters (Swift Biosciences) and size-selected using SPRIselect beads (Beckman Coulter) before being amplified and sent for paired-end sequencing on the NovaSeq 6000 (150 bp reads).

CUT&RUN paired reads were aligned to a reference human genome (hg38) by the bwa-mem algorithm. PCR duplicate reads were removed by Picardand peaks were called using macs2 with the broad flag and an FDR of 0.05. Bedtools intersect was used to identify reproducible peaks between biological replicates of each condition, and reproducible peaks from each condition were compiled into a list. Bedtools multicov was used to build a matrix with the number of reads from each dataset falling in each region in this list. This matrix was used for all ‘genome-wide’ analyses. Bedtools multicov was also used to build a matrix with the number of reads from each dataset in a 5000-bp window around the transcription start site (4 kb upstream, 1 kb downstream) of all hg38 genes defined by gencode v41. The gencode v41annotation for CCND2 was originally incorrectly assigned to chr12:4,265,771–4,270,771 and reassigned using the Refseq coordinates chr12:4,269,762–4,274,762. Count matrices were analyzed with DESeq2 to compare changes in H3K27me3 deposition globally, and changes in H3K27me3 deposition in promoters. For heatmaps, deduplicated BAM files were converted to bigwigs and BED files and normalized reads per kilobase per million mapped read using deepTools bamCoverage. For genome-wide analyses, H3K27me3 CUT&RUN signal in normalized bigwigs was measured using deepTools computeMatrix in 10 kb regions centered around WT peaks overlapping with CpG islands. For promoter analyses, H3K27me3 CUT&RUN signal in normalized bigwigs was measured using deepTools computeMatrix in 5 kb regions (4 kb upstream, 1 kb downstream) around transcription start sites for promoters overlapping with CpG islands. Promoters with the highest average H3K27me3 signal intensity in wild-type replicate 1 and sorted in descending order.

RNA-Seq paired reads were quantified using Salmon. Transcript-level abundance estimates from Salmon and gene-level count matrices were created using Tximport and analyzed using DESeq2. Paired reads were aligned using STAR to generate BAM files. BAM files were converted to BED files using bamCoverage and normalized using RPKMs and to effective genome size of hg38 (2,913,022,398) with a bin size of 10.