ARID1A Mutations Promote P300-Dependent Endometrial Invasion through Super-Enhancer Hyperacetylation

Although ARID1A is mutated in several disorders of the endometrial epithelium, little is known about how ARID1A loss alters the epigenomic landscape in these cells. Here, in an unbiased approach, we examined chromatin features from both control and ARID1A-depleted cells and built a genome-wide segmentation model of unique chromatin states ( ChromHMM ). We profiled several post-translational histone modifications by chromatin immunoprecipitation sequencing (ChIP-seq), including H3K4me1, H3K4me3, H3K27me3, H3K27ac, and H3K18ac, following ARID1A-depletion in 12Z human endometrial epithelial cells ( Zeitvogel et al., 2001 ). These data were used in conjunction with assay for transposase-accessible chromatin sequencing (ATAC-seq) and total RNA-seq datasets from ARID1A-depleted 12Z cells ( Wilson et al., 2019 ), which allowed us to build a comprehensive model of chromatin state transitions accompanying ARID1A loss ( Figures 1A and 1B ). A series of genomic feature enrichment tests allowed us to annotate the predicted biological function of each of the 18 chromatin states, including 8 distinct classes of enhancer elements segregated by combinatorial chromatin features ( Figures 1C – 1F ). Using ARID1A ChIP-seq data ( Wilson et al., 2019 ), we observed that ARID1A binding is most strongly associated with highly active regulatory elements marked by H3K27ac, including SE chromatin states (S11–S13) and other highly active enhancer states (S14) ( Figure 1G – H ). SEs are enhancer clusters that control the transcription of genes involved in cellular identity and thus play key roles in developmental and disease processes ( Lovén et al., 2013 ; Whyte et al., 2013 ). Enhancers are characterized by abundant H3K27ac and accessible chromatin ( Calo and Wysocka, 2013 ). We used both H3K27ac ChIP-seq and ATAC-seq data to identify 18,050 putative active enhancers ( Figure S1A ). From this set of active enhancers, we used the Rank Ordering of Super-Enhancers (ROSE) algorithm to identify active SE and observed 413 unique SEs that contained 1,430 sites marked by H3K27ac and ATAC ( Figure S1A ). Active distal enhancer regions (located further than 3 kb from a transcription start site [TSS]) not categorized as SEs were designated as typical enhancers (TEs) (n = 16,620) ( Figure S1A ). We observed greater H3K27ac signal at SE peaks relative to TE peaks ( Figure S1B ). SEs comprised three H3K27ac peaks on average ( Figure S1C ). ARID1A associated with the majority of both SEs and TEs, but was bound to a higher proportion of SEs than TEs ( Figure 1I ), suggesting a role for ARID1A in the regulation of active SEs. To further understand the role of ARID1A in chromatin regulation, we analyzed the effects of ARID1A depletion on chromatin state classification and the abundance of histone modifications. ChromHMM modeling revealed that most chromatin states do not display substantial reprogramming following ARID1A loss ( Figure 2A ). SEs and other enhancer states bound by ARID1A typically did not change state, although some highly active enhancers (S14) gained further activation characteristics (S14 > S13, S14 > S12), while others lost active marks (S14 > S15) ( Figure 2B ). Among the histone modifications tested, H3K27ac displayed the greatest proportion of differentially regulated sites following ARID1A loss ( Figures 2C and S1D – S1H ). Interestingly, ARID1A loss did not affect H3K27me3 occupancy genome-wide ( Figure S1F ), even though SWI/SNF is known to antagonize polycomb chromatin silencing in other cellular contexts ( Bracken et al., 2019 ). Next, we examined the H3K27ac changes occurring in ARID1A-deficient cells. The majority of differential H3K27ac sites were found among distal elements and, among those sites, we observed decreased acetylation following ARID1A loss ( Figure 2D ). Furthermore, most H3K27ac changes occurred at SE and highly active enhancer chromatin states where ARID1A is bound ( Figures 2E – 2G ). Intriguingly, sites that gained H3K27ac following ARID1A loss tended to become SE states (S11–S13) ( Figure 2F ), while sites that lost H3K27ac tended to transition from SE to other enhancer states (S14–S18) ( Figure 2G ). Consistently, H3K27ac sites at promoters (within 3 kb of a TSS) were less likely to be affected by ARID1A loss than distal intergenic and intronic elements ( Figures 2H and S1I – S1L ), and SEs were marginally more likely to show changes in H3K27ac than TEs ( Figure 2I ). However, while most active TEs displayed decreased H3K27ac, most active SEs displayed increased H3K27ac following ARID1A loss ( Figure 2J ), suggesting a specific role for ARID1A in preventing H3K27ac hyperacetylation at SEs. Among the 413 active SEs, 74.1% displayed differential H3K27ac at one or more sites following ARID1A loss ( Figures 2K and 2L ). H3K27ac was increased at 360 peaks within active SEs following ARID1A loss ( Figure 2M ). Compared to TEs, SEs also displayed a greater proportion of sites with increased chromatin accessibility upon ARID1A loss ( Figure 2N ). Enhancer RNA (eRNA) transcription promotes enhancer activity through enhancer-promoter communication and chromatin looping, and eRNA is associated with SEs ( Ko et al., 2017 ). We explored the role of eRNA at ARID1A-regulated SEs as a marker of enhancer activity, and, among 3,668 intergenic enhancers with detectable eRNA expression, we observed 157 differentially expressed (DE) eRNAs upon ARID1A loss ( Figure 2O ). Among these, ARID1A binding was stronger at sites with upregulated eRNA following ARID1A loss ( Figure 2P ). Furthermore, upregulated eRNAs were associated with increased H3K27ac ( Figures 2Q and 2R ). Collectively, these data support a role for ARID1A in restricting SE activity, such that ARID1A loss results in H3K27 hyperacetylation, increased chromatin accessibility, and eRNA expression. Having observed a role for ARID1A in preventing H3K27ac at SE, we next asked whether ARID1A is associated with P300, a histone acetyltransferase (HAT) that acetylates H3K27 and H3K18 residues ( Jin et al., 2011 ; Schiltz et al., 1999 ) and has known roles at SEs ( Pott and Lieb, 2015 ). We used the Enrichr tool to screen ENCODE (Encyclopedia of DNA Elements) ChIP-seq datasets for factors with overlapping sets of target genes ( Chen et al., 2013 ; Kuleshov et al., 2016 ) and identified P300 as the top factor likely to co-regulate DE genes following endometrial ARID1A loss ( Figures S2A and S2B ). We performed P300 ChIP-seq in wild-type 12Z cells and identified 25,096 P300 binding sites throughout the genome, enriched within several chromatin states ( Figures 3A and 3B ). Intriguingly, P300 is more associated with active TSS (S10) than ARID1A ( Figure 3B , compared to Figure 1G ), and P300 binding was enriched and co-bound with ARID1A at promoters ( Figures S2C – S2M ). Known roles for P300 in enhancer regulation ( Long et al., 2016 ) led us to study ARID1A and P300 co-regulation at distal sites. We observed 2,609 distal sites with both ARID1A and P300 binding ( Figure 3C ). Chromatin accessibility marks the sites of regulatory activity ( Kornberg and Lorch, 1992 ), and ARID1A is associated with open chromatin states ( Kelso et al., 2017 ). Among P300-bound, accessible sites, ARID1A is associated more with the co-regulation of distal sites than promoters ( Figure 3D ). Chromatin remodeling enzymes regulate both the recruitment and catalytic activity of the histone modifying enzymes ( Clapier and Cairns, 2009 ; Swygert and Peterson, 2014 ). Given the changes in H3K27ac in ARID1A-deficient cells, we tested whether P300 localization was affected by ARID1A loss using ChIP-seq. We observed no change in P300 binding following ARID1A loss at >99% of sites ( Figure 3E ), suggesting that ARID1A loss does not greatly affect P300 recruitment. We then explored the role of ARID1A and P300 co-localization at enhancers. Among the 18,050 putative active enhancers, the majority were bound predominantly by ARID1A without P300 ( Figures 3F and 3G ). However, ARID1A-P300 co-bound enhancers displayed greater H3K27ac peak signal and broader H3K27ac peak distribution ( Figures 3H and 3I ). Among enhancers that display differential H3K27ac, ARID1A was again bound without P300 at the majority of sites ( Figure 3J ), although the enrichment of ARID1A with or without P300 at enhancers with differential H3K27ac was not significantly different ( Figure 3K ). We next considered the role of P300 binding and ARID1A co-regulation at SEs, and found that P300 binding was observed at a greater proportion of distal SE peaks than TE peaks ( Figures 3L and 3M ). Among P300-bound sites, differential H3K27ac following ARID1A loss was more frequently observed at SE than TE ( Figure 3N ). Furthermore, among P300 bound sites with differential H3K27ac, a greater number of SEs than TEs displayed increased H3K27ac ( Figure 3O ). Lastly, we compared ARID1A, P300, and H3K27ac levels at SEs versus TEs that are either P300 bound or not bound. At enhancers where P300 is bound, P300 binding is strongest at TEs compared to SEs ( Figure 3P ). However, ARID1A binding signal is stronger at P300-bound SEs ( Figure 3Q ), where the H3K27ac signal is highest, compared to TEs ( Figure 3R ). These results collectively suggest that ARID1A differentially regulates SE through P300-dependent H3K27ac deposition in normal endometrium. ARID1A loss in the endometrial epithelium leads to collective invasion when combined with an activating PIK3CA mutation ( Wilson et al., 2019 ). To explore the functional relationship between ARID1A and P300, we used small interfering RNAs (siRNAs) targeting P300 (siP300), ARID1A (siARID1A), or non-targeting siRNAs (control). Knockdown of ARID1A and/or P300 in 12Z cells ( Figure 4A ) had no effect on cell growth or proliferation ( Figures S3A and S3B ). ARID1A loss increased cell invasion, and P300 loss alone had no effect, but co-knockdown of ARID1A and P300 completely rescued ARID1A mutant cell invasion ( Figure 4B ). Invasion was not observed in 12Z treated with broad-spectrum histone deacetylase inhibitors, suggesting that invasion does not depend solely on a global increase in histone acetylation ( Figures S3C and S3D ). These results demonstrate an essential role for P300 in driving invasive phenotypes in ARID1A mutant endometriotic cells. To determine whether P300 loss rescues the invasive phenotype in vivo , we crossed Ep300 conditional knockout mice ( Kasper et al., 2006 ) with our LtfCre 0/+ ; (Gt)R26Pik3ca * H1047R ; Arid1a fl/fl model, resulting in LtfCre 0/+ ; (Gt)R26Pik3ca * H1047R ; Arid1a fl/fl ; Ep300 fl/fl ( Figures S3E and S3F ). LtfCre 0/+ ; (Gt)R26Pik3ca * H1047R ; Arid1a fl/fl ; Ep300 fl/fl mice displayed an increased survival compared to LtfCre 0/+ ; (Gt)R26Pik3ca * H1047R ; Arid1a fl/fl mice ( Figure 4C ). LtfCre 0/+ ; Ep300 fl/fl mice displayed no phenotype ( Figures 4C and S3G ). P300 expression was lost in the endometrial epithelium of LtfCre 0/+ ; Ep300 fl/fl and LtfCre 0/+ ; (Gt)R26Pik3ca * H1047R ; Arid1a fl/fl ; Ep300 fl/fl mice by immunohistochemistry (IHC) ( Figure 4D ). Increased expression of the apoptotic marker cleaved caspase 3 was observed in the endometrial epithelium of LtfCre 0/+ ; (Gt)R26Pik3ca * H1047R ; Arid1a fl/fl ; Ep300 fl/fl mice, but not LtfCre 0/+ ; Ep300 fl/fl mice, indicating a specific effect of P300 loss on ARID1A and PIK3CA mutant endometrium ( Figure 4D ). The epithelial layer in LtfCre 0/+ ; (Gt)R26Pik3ca * H1047R ; Arid1a fl/fl ; Ep300 fl/fl mice appeared to desquamate from the endometrial stroma, and cleaved caspase 3 + , desquamated epithelial cells were observed throughout the lumen of the uterus ( Figure S3H ). P300 loss suppressed the proliferation occurring in LtfCre 0/+ ; (Gt)R26Pik3ca * H1047R ; Arid1a fl/fl epithelia ( Figure S3I ). While LtfCre 0/+ ; (Gt)R26Pik3ca * H1047R ; Arid1a fl/fl endometrial epithelium invade the myometrium, the presence of endometrial glands in the myometrium was not observed in LtfCre 0/+ ; (Gt) R26Pik3ca * H1047R ; Arid1a fl/fl ; Ep300 fl/fl mice. Similar to ARID1A-deficient 12Z cells, we observed the loss of H3K27ac, but not H3K18ac, in LtfCre 0/+ ; (Gt)R26Pik3ca * H1047R ; Arid1a fl/fl ; Ep300 fl/fl mice, suggesting that P300 loss leads to a specific reduction of H3K27ac in endometrial epithelial cells ( Figures 4D and 4E ). These results implicate P300 HAT activity in LtfCre 0/+ ; (Gt) R26Pik3ca * H1047R ; Arid1a fl/fl lesion development. We next explored the role of P300 HAT activity in promoting ARID1A mutant phenotypes. A-485 is a small-molecule P300/CREB-binding protein (CBP) HAT inhibitor ( Lasko et al., 2017 ; Weinert et al., 2018 ). We tested the efficacy of A-485 in 12Z cells and observed a dose-dependent reduction in H3K27ac with significant inhibition at 316 nM ( Figure 4F ). In both ARID1A-deficient and wild-type states, we observed a limited effect of A-485 on cell growth and viability ( Figures 4G and S4A – S4D ). These results suggest that A-485 treatment results in the inhibition of P300 HAT activity at low concentrations without an effect on cell health. Next, we tested the efficacy of A-485 in inhibiting P300-dependent, ARID1A mutant invasive phenotypes. We observed a significant reduction in ARID1A mutant invasion at concentrations that did not inhibit cell growth, with significant decreases in invasion at 10 nM A-485 and a complete rescue of the phenotype at 100 nM A-485 ( Figures 4H and S4E ), while the migration phenotype was inhibited at 31 nM and completely rescued at 316 nM ( Figure S4F ). Since apoptosis is induced in LtfCre 0/+ ; (Gt) R26Pik3ca * H1047R ; Arid1a fl/fl ; Ep300 fl/fl mice, we considered that P300 HAT inhibition may lead to anchorage-dependent cell death or anoikis ( Paoli et al., 2013 ). We tested whether A-485 induces anoikis under non-adherent conditions, and we observed increased caspase 3/7 activity in ARID1A-deficient cells following A-485 treatment, suggesting that A-485 induces anoikis ( Figure 4I ). Furthermore, we observed an increase in cell death in ARID1A-deficient cells embedded in Matrigel following A-485 treatment ( Figure 4J ). In mice, coexisting mutations in ARID1A and PIK3CA are required for lesion formation, so we wanted to determine whether the effect of A-485 in ARID1A-deficient cells was modulated by phosphatidylinositol 3-kinase (PI3K) activation. In cells with an overexpression of PIK3CA H1047R and ARID1A loss, we observed a similar inhibition of invasion and migration and an induction of anoikis ( Figure S5 ). These results suggest that the inhibition of P300 HAT activity via low-dose A-485 treatment blocks invasion and promotes anoikis of ARID1A-deficient endometriotic cells. To explore how ARID1A and P300 co-regulate H3K27ac, we used the targeted genome profiling approach cleavage under targets and release using nuclease (CUT&RUN) ( Skene et al., 2018 ). H3K27ac CUT&RUN showed significant overlap with H3K27ac ChIP-seq ( Figure 5A ). To determine the effects of P300 loss or HAT inhibition on H3K27ac in ARID1A-deficient cells, we next compared differential H3K27ac among 12Z cells treated with siARID1A versus control, and also compared cells co-treated with siARID1A + siP300 or 1 μM A-485 versus siARID1A alone ( Figure 5B ). Notably, the genome-wide effects of 1 μM A-485 on H3K27ac in siARID1A cells highly overlapped with siP300, validating that A-485 affects P300 targets. We identified 6,521 regions of H3K27ac that were affected by ARID1A loss and further affected by P300 loss or A-485 treatment ( Figure 5C ). Among these 6,521 intersecting regions, the majority of H3K27ac sites showed an additive increase or decrease in H3K27ac with combination treatments: decreased acetylation following ARID1A loss and further decreases with P300 loss or inhibition (n = 3,005) or increased acetylation following ARID1A loss and further increases with P300 loss or inhibition (n = 1,455) ( Figures 5D and 5E ). However, a subset of sites displayed increased H3K27ac following ARID1A loss, which was rescued by further P300 loss or A-485 treatment (“gain reversal,” n = 1,132) ( Figures 5D and 5E ). Interestingly, the gain reversal sites had the lowest levels of H3K27ac in control cells compared to other groups ( Figure 5F ), suggesting ARID1A normally limits acetylation at these sites. Furthermore, a large proportion of gain reversal regions are bound by ARID1A, while “acetylation gain” sites were infrequently bound by ARID1A ( Figure 5G ). This was further supported by genomic annotation showing that gain reversal sites were found at intergenic regions and introns, were enriched for SEs and other highly active enhancer chromatin states, and contained the highest proportion of active SE regions ( Figures 5H – 5K ), suggesting that gain reversal sites contain SE elements at which ARID1A antagonizes P300 HAT activity toward H3K27ac. To understand how increased P300 HAT activity affects transcriptional processes in ARID1A-deficient cells, we performed RNA-seq following knockdown of P300, ARID1A, or both in 12Z cells. We used the GeneHancer database ( Fishilevich et al., 2017 ) to associate regions of differential H3K27ac targets ( Figure 5L ). Genes linked to the gain reversal cluster were enriched for genes with differential expression (DE) following ARID1A knockdown versus control and for differential expression following ARID1A and P300 co-knockdown versus ARID1A knockdown alone ( Figure 5M ). Specifically, genes linked to gain reversal regions were more likely to be upregulated following ARID1A knockdown relative to control and downregulated following ARID1A and P300 co-knockdown relative to ARID1A knockdown alone ( Figures 5N and 5O ). We reasoned that upregulated genes driving ARID1A-deficient invasion would be rescued upon P300 loss or A-485 treatment. To narrow down a smaller subset of genes responsible for P300-dependent invasion in ARID1A-deficient cells, we performed additional RNA-seq using 100 nM A-485, a lower dose that has no effects on cell health or global H3K27ac reduction, but significantly inhibits invasion and migration ( Figures 4F – 4H , S3B , and S4F ). While P300 loss resulted in the differential expression of 2,657 genes (false discovery rate [FDR] < 0.0001), 100 nM A-485 treatment resulted in the differential expression of only 566 genes, suggesting a more specific effect ( Figure 6A ). Concordantly dysregulated genes between siP300 and A-485 overlapped, providing additional validation of this approach ( Figure 6A ). To determine gene regulation by SEs, we identified 3 groups of SE-regulated genes: active genes with a promoter directly within an SE ( Figure 6B ) ( Whyte et al., 2013 ), active genes with a promoter within 50 kb of an SE ( Figure 6C ) ( Sanyal et al., 2012 ), and active genes linked to SEs through the GeneHancer database ( Figure 6D ). In all cases, SE-regulated genes were enriched among DE genes with ARID1A loss and further P300 loss or HAT inhibition ( Figures 6B – 6D ). To identify genes implicated in ARID1A mutant invasion, we compared overlapping genes sets from siARID1A versus control, siARID1A + siP300 versus siARID1A, and siARID1A + 100 nM A-485 versus siARID1A comparisons and identified a set of 138 “triple intersect” genes ( Figure 6E ). These correspond to genes affected by ARID1A loss and further affected by P300 loss or inhibition of P300 HAT activity. This gene set was enriched for the hallmark epithelial-to-mesenchymal transition pathway and Gene Ontology (GO) gene sets related to invasive phenotypes ( Figures 6F and 6G ). Among the 138 triple intersect genes, we identified 50 genes that were upregulated by ARID1A loss and further suppressed by P300 loss or low-dose A-485-mediated HAT inhibition ( Figure 6H ). Of these, 16 genes were associated with H3K27ac gain reversal enhancers, and 3 gene loci have associated SE elements. Only SERPINE1 was identified as displaying gene expression reversal following ARID1A loss and further 100-nM A-485 treatment, association with H3K27ac gain reversal enhancer elements, and regulation by a SE. The serine protease inhibitor, SERPINE1 (also known as plasminogen activator inhibitor type 1 [PAI-1]), is a member of the urokinase plasminogen activator (uPA) system ( Smith and Marshall, 2010 ). This system regulates extracellular fibrin proteolysis and influences cell invasion, migration, and ECM remodeling ( Duffy, 2004 ). SERPINE1 is a biomarker for endometriosis, with high levels of expression observed in ovarian and deep infiltrating endometriosis ( Alotaibi et al., 2019 ; Gilabert-Estellés et al., 2003 ; Ramón et al., 2005 ; Ye et al., 2017 ). We examined a published RNA-seq dataset of human endometrial organoids and observed that SERPINE1 was upregulated in organoids derived from ectopic endometrial tissue compared to healthy endometrial tissue (log 2 fold change [FC] = 3.86, FDR = 0.051) ( Boretto et al., 2019 ). In 12Z cells, the SERPINE1 SE was ranked in the top 5% of active SEs ( Figure 7A ), and it displayed H3K27 hyperacetylation upon ARID1A loss, which was reversed by further P300 loss or A-485 treatment ( Figure 7B ). Notably, SERPINE1 was the most significant upregulated gene upon ARID1A loss ( Figure 7C ), and P300 co-knockdown or HAT inhibition rescued SERPINE1 expression ( Figures 7D and 7E ). SERPINE1 was also upregulated in the endometrial epithelium of LtfCre 0/+ ; (Gt)R26 Pik3ca*H1047R ; Arid1a fl/fl mice ( Figure 7F ). LtfCre 0/+ ; (Gt) R26 Pik3ca*H1047R ; Arid1a fl/fl eutopic endometrial epithelia and ectopic lesions showed increased SERPINE1 by IHC, which was not observed in LtfCre 0/+ ; (Gt)R26 Pik3ca*H1047R ; Arid1a fl/fl ; Ep300 fl/fl ( Figures 7G and 7H ). Among a cohort of deep infiltrating and ovarian endometriosis tissue samples ( Alotaibi et al., 2019 ), samples with a loss of ARID1A expression displayed the highest expression of SERPINE1 by IHC ( Figures S6A and S6B ). To determine whether SERPINE1 promotes the invasion of ARID1A-deficient cells, we inactivated SERPINE1 via siRNA transfection in 12Z ( Figure 7I ). While there was no change in invasion with SERPINE1 loss alone, SERPINE1 loss suppressed the invasive phenotype of ARID1A-deficient cells ( Figure 7J ). SERPINE1 loss had no effect on adherent cell growth ( Figures 7K and S3B ). In non-adherent conditions, ARID1A and SERPINE1 co-knockdown resulted in increased caspase 3/7 activity ( Figure 7L ) and increased death in cells suspended in Matrigel ( Figure 7M ), indicating that SERPINE1 is required for anoikis resistance in ARID1A mutant cells. These results suggest that ARID1A prevents hyperacetylation of the SERPINE1 SE in the wild-type state, while ARID1A loss results in P300-dependent hyperacetylation and increased activity of the SERPINE1 SE, increased SERPINE1 transcription, and the acquisition of invasive phenotypes. P300 inhibition in ARID1A-deficient cells suppresses H3K27 hyperacetylation of the SERPINE1 SE, resulting in decreased SERPINE1 expression and anoikis.

In this study, we demonstrate that ARID1A prevention of SE hyperactivation plays an essential physiological role in maintaining endometrial tissue homeostasis and preventing cell invasion. ARID1A mutant cell invasion has been described in other diseases and malignancies, but the functional link between ARID1A loss, SE hyperactivation, and the subsequent acquisition of P300-dependent invasiveness is unique to the endometrium ( Lakshminarasimhan et al., 2017 ; Li et al., 2017 ; Sun et al., 2017 ; Yan et al., 2014 ). Retrograde menstruation is thought to play a role in the spread of abnormal endometrial tissue to ectopic sites. ARID1A mutations may predispose displaced endometrial cells to forming endometriotic lesions by promoting the acquisition of invasive phenotypes in a cell-autonomous manner ( Wilson et al., 2019 , 2020 ). Our findings suggest that epigenetic dysregulation of SEs promotes endometrial invasion and survival at ectopic sites. Alterations in SE activity may be an important feature of endometriotic epithelium. The SWI/SNF-mediated regulation of SEs may be cell type specific. SWI/SNF subunit SMARCB1 can antagonize chromatin accessibility at SEs in mouse embryonic stem cells ( Langer et al., 2019 ), while SMARCB1 loss in rhabdoid tumors impairs SWI/SNF binding to TEs, not SEs ( Wang et al., 2017 ). SWI/SNF can regulate Myc expression in acute myeloid leukemia through interactions with a lineage-specific SE ( Shi et al., 2013 ), as has been described among other enhancers ( Alver et al., 2017 ). In mouse embryonic fibroblasts, the deletion of SWI/SNF family members has been shown to reduce H3K27ac at enhancers ( Alver et al., 2017 ). SWI/SNF can promote chromatin accessibility at enhancers ( Kelso et al., 2017 ; Vierbuchen et al., 2017 ). In embryonic stem cells, mutations in SWI/SNF catalytic subunit Brahma result in enhancer reprogramming ( Gao et al., 2019 ). In breast cancer, ARID1A binds and represses enhancers containing estrogen receptor-binding elements through co-recruitment of HDAC1, and ARID1A loss results in H4 acetylation, BRD4 recruitment, and subsequent transcription ( Nagarajan et al., 2020 ). However, in that study, ARID1A knockout did not result in differential H3K27ac ( Nagarajan et al., 2020 ). Our data suggest a distinct role for ARID1A-P300 antagonism in the regulation of SE chromatin accessibly and H3K27ac deposition in the endometrial epithelium. We previously showed that ARID1A genome-wide binding is enriched at promoters, and promoter chromatin accessibility increases following ARID1A loss ( Wilson et al., 2019 ). Although ARID1A is enriched at promoters and we observed ARID1A-P300 co-binding at these sites, we demonstrate here that ARID1A-P300 antagonism uniquely occurs at SEs, which show hyperacetylated H3K27, increased chromatin accessibility, and eRNA transcription following ARID1A loss. Both SERPINE1 and SDC4 have large SEs spanning the promoter region, although we also show that SE regulation by ARID1A, P300 and A-485 affects the transcription of genes located up to 50 kb away. Interestingly, although ARID1A is bound at the SERPINE1 promoter and multiple sites throughout the 41-kb SERPINE1 SE, increased H3K27ac is observed throughout the majority of the SE interval in ARID1A-deficient cells, suggesting that ARID1A regulates chromatin over large chromatin domains. Recently, there has been interest in the therapeutic inhibition of SE activity in several diseases. Small-molecule inhibitors of SE factors, particularly the BET bromodomain inhibitor JQ1, have undergone clinical trials for multiple cancer types ( Shin, 2018 ). BRD4 interacts with H3K27ac-rich SE regions, and the disruption of BRD4 bromodomain-SE interactions using small molecules can decrease oncogene expression ( Sengupta et al., 2015 ). The inhibition of histone acetylation represents a growing area of interest in small-molecule therapeutics ( Simon et al., 2016 ). Targeted disruption of P300 HAT activity at SE may have therapeutic utility in endometrial diseases. Several studies have demonstrated a relationship between SERPINE1/PAI-1 expression and endometriosis ( Bruse et al., 1998 , 2004 ). SERPINE1 promoter polymorphisms linked to high levels of PAI-1 expression have been reported in endometriosis ( Bedaiwy et al., 2006 ; Ramón et al., 2005 ). Relative to other types of endometriosis, PAI-1 expression is increased in deep infiltrating ( Alotaibi et al., 2019 ) and ovarian endometriosis ( Gilabert-Estellés et al., 2003 ; Ramón et al., 2005 ). ARID1A mutations exist in both deep infiltrating and ovarian endometriosis ( Anglesio et al., 2017 ; Suda et al., 2018 ), and our clinical cohort suggests that ARID1A loss leads to PAI-1 overexpression in endometriosis. As a secreted factor, elevated plasma PAI-1 levels have been observed in women with recurrent pregnancy loss or preeclampsia, and the secretion of PAI-1 from endometriotic lesions may contribute to endometriosis-associated infertility and pain ( Ye et al., 2017 ). PAI-1 negatively regulates fibrinolysis and plays a role in endometrial hemostasis during menstruation ( Davies and Kadir, 2012 ; Mehta and Shapiro, 2008 ). In addition to its roles in cell adhesion and migration, it remains possible that increased PAI-1 affects menstrual clotting and promotes fibrosis or scar tissue formation in endometriosis. Lastly, PAI-1 may serve as a biomarker for invasive ARID1A mutant endometriosis.

The endometrium (the inner lining of the uterus) is composed of epithelia and stroma that continually proliferate, differentiate, and shed throughout the menstrual cycle in anticipation of pregnancy ( Gellersen and Brosens, 2014 ; Mihm et al., 2011 ). The multiple rounds of tissue regression and regeneration that occur throughout a woman’s reproductive years make the endometrium particularly prone to disease ( Gargett et al., 2012 ; Syed et al., 2020 ; Teixeira et al., 2008 ). As cyclical tissue breakdown, re-epithelialization, and stromal restoration occurs, the maintenance of proper cell identity is thought to be an important feature of a healthy endometrium ( Gellersen and Brosens, 2014 ). Alterations in normal endometrial function result in numerous conditions, including benign diseases, such as endometrial hyperplasia ( Montgomery et al., 2004 ), adenomyosis ( Maheshwari et al., 2012 ), and endometriosis ( Zondervan et al., 2018 , 2020 ), as well as endometrial cancer ( Morice et al., 2016 ) and ovarian cancer ( Kurman and Shih, 2016 ). Mutations in the SWI/SNF subunit ARID1A (BAF250A) were first identified in ovarian clear-cell carcinoma and ovarian endometrioid carcinoma, two epithelial ovarian cancer subtypes associated with endometriosis ( Jones et al., 2010 ; Wiegand et al., 2010 ). Inactivating ARID1A mutations have been identified in numerous other endometrial pathologies ( Mao and Shih, 2013 ; Wu et al., 2014 ). ARID1A levels are lower in eutopic endometrium, and it is required for embryo implantation in the uterus ( Kim et al., 2015 ). ARID1A mutations are observed in deep ovarian and deep infiltrating endometriosis ( Anglesio et al., 2017 ; Borrelli et al., 2016 ; Lac et al., 2019a , 2019b ; Samartzis et al., 2012 ; Suda et al., 2018 ). ARID1A mutations are also observed in atypical endometrial hyperplasia ( Mao et al., 2013 ; Werner et al., 2013 ) and endometrial cancer ( Guan et al., 2011 ; Wiegand et al., 2011 ). Endometriosis is characterized by the growth and spread of abnormal endometrial tissue at sites outside of the uterus ( Bulun, 2009 ; Giudice and Kao, 2004 ). The identification of high-frequency ARID1A somatic mutations in deep ovarian endometriosis supports epidemiological and experimental evidence linking endometriosis to endometriosis-associated ovarian cancer ( Pearce et al., 2012 ). ARID1A mutations may increase the risk of endometriosis and malignant transformation by providing a selective advantage to displaced endometrial cells undergoing retrograde menstruation ( Suda et al., 2018 ). Alterations in endometrial cell identity, such as the transdifferentiation of endometrial epithelium, promote the acquisition of invasive cell properties, a feature often observed in mesenchymal cells ( Bartley et al., 2014 ; Bilyk et al., 2017 ; Yang and Yang, 2017 ). Cellular invasion requires cells to migrate, degrade the extracellular matrix, and survive under anchorage-independent conditions ( Kalluri and Weinberg, 2009 ; Mareel and Leroy, 2003 ). These properties allow abnormal endometrial cells to spread locally or colonize distal sites. In this study, we identify a mechanism by which ARID1A represses invasive phenotypes by antagonizing P300 activity at super-enhancers (SEs).

Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Ronald Chandler ( [email protected] ). This study did not generate new unique reagents. The accession number for the sequencing data generated in this manuscript is GEO: GSE148474 . Previously published datasets analyzed herein are also available at GEO: GSE121198 . All mice were maintained on an outbred genetic background using CD-1 mice (Charles River). (Gt)R26Pik3ca * H1047R , LtfCre ( Tg(Ltf-iCre)14Mmul) and Ep300 fl alleles were purchased from The Jackson Laboratory and identified by PCR using published methods ( Adams et al., 2011 ; Daikoku et al., 2014 ; Kasper et al., 2006 ). Arid1a fl allele was distinguished by PCR as previously described ( Chandler et al., 2015 ). Genotyping primers are listed in Key Resources Table . Endpoints were vaginal bleeding, severe abdominal distension, and signs of severe illness including dehydration, hunching, jaundice, ruffled fur, signs of infection, or non-responsiveness. Sample sizes for each genotype were chosen based on the proportions of animals with vaginal bleeding between each experimental group and Kaplan-Meyer log rank test for survival differences. All mice analyzed in the study were between 6 and 32 weeks old. In cases where a mobility endpoint occurred, tissues were collected at the time of vaginal bleeding, including LtfCre 0/+ ; (Gt)R26Pik3ca * H1047R ; Arid1a fl/fl (μ 1/2 = 107 days) and LtfCre 0/+ ; (Gt)R26Pik3ca * H1047R ; Arid1a fl/fl ; Ep300 fl/fl (μ 1/2 = 143 days) mice. In cases where the animal did not reach a morbidity endpoint or show reduced survival, tissues were collected at comparable time points (between 90 and 150 days) from age-matched, littermate control mice from the mutant crosses. Uteri were collected at time of sacrifice and placed immediately into neutral-buffered formalin at 4°C. After 24 hr, tissues were washed with PBS and 50% EtOH, placed in 70% EtOH, and weight measurements were recorded. Mice were housed at the Michigan State University Grand Rapids Research Center in accordance with protocols approved by Michigan State University. Michigan State University is registered with the U.S. Department of Agriculture (USDA) and has an approved Animal Welfare Assurance from the NIH Office of Laboratory Animal Welfare (OLAW). MSU is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC). 12Z immortalized human endometrial epithelial cells ( Zeitvogel et al., 2001 ) were maintained in DMEM/F12 media supplemented with 10% fetal bovine serum (FBS), 1% L-glutamine and 1% penicillin/streptomycin (P/S). The 12Z cells were provided by the laboratory of Asgi Fazleabas, and cell line validation was performed by IDEXX BioResearch, finding the result that the 12Z cell line has a unique profile not found in the current public databases. A recent study found 12Z cells to be an authentic and pure endometriosis cell line based on marker analysis and short tandem repeat profiling ( Romano et al., 2020 ). Lenti-X™ 293T (Clontech) cells were maintained in DMEM +110 mg/L Sodium Pyruvate (GIBCO) supplemented with 10% FBS, 1% L-glutamine, 1% P/S. 12Z and Lenti-X 293T cells were regularly tested for mycoplasma using the Mycoplasma PCR Detection Kit (Applied Biological Materials). No commonly mis-identified cell lines were used in this study. For indirect immunohistochemistry (IHC), 10% neutral buffered formalin (NBF)-fixed paraffin sections were processed for heat-based antigen unmasking in 10 mM sodium citrate [pH 6.0]. Sections were incubated with antibodies at the following dilutions: 1:200 ARID1A (D2A8U) (12354, Cell Signaling); 1:1000 P300 (86377, Cell Signaling); 1:400 Phospho-S6 (4585, Cell Signaling); 1:100 KRT8 (TROMA1, DHSB); 1:200 Cleaved Caspase-3 (9579, Cell Signaling); 1:400 Ki67 (12202, Cell Signaling); 1:200 H3K27ac (39133, Active Motif); 1:200 H3K18ac (ab1191, Abcam); 1:1000 PAI-1 (SERPINE1) (ab66705, Abcam). TROMA-I antibody was deposited to the DSHB by Brulet, P./Kemler, R. (DSHB Hybridoma Product TROMA-I). Biotin-conjugated secondary antibodies were donkey anti-rabbit IgG (711-065-152, Jackson Immuno-research Lab) and donkey anti-rat IgG (#705-065-153, Jackson Immuno-research Lab). VECTASTAIN Elite ABC HRP Kit (Vector) was used for secondary antibody detection. Sections for IHC were lightly counter-stained with Hematoxylin QS or Methyl Green (Vector Labs). Routine Hematoxylin and Eosin (H&E) staining of sections was performed by the Van Andel Research Institute (VARI) Histology and Pathology Core. A VARI animal pathologist reviewed histological tumor assessments. To determine H-scores from mouse slides, one field of view (20X) on a Nikon Eclipse Ni-U upright microscope per mouse from a slide stained with antibody (SERPINE1, H3K27ac, H3K18ac) was used. Epithelial and stromal cells were assigned a value from 0 to 3 indicating intensity of staining (no staining = 0, low staining = 1, moderate staining = 2, and strong staining = 3) and the proportion of cells was determined for each staining intensity. For Ki67, a value of 1 (positive staining) or 0 (negative staining) was assigned to determine the number of Ki67+ cells. For human endometriosis tissue samples, SERPINE1 (PAI-1) IHC was carried out as published ( Alotaibi et al., 2019 ). Briefly, IHC using the EnVision+ Dual Link system (Dako) and 3,3-diaminobenzidine (DAB) was performed, using mouse monoclonal PAI-1 antibody C-9 (sc 5297, Santa Cruz). PAI-1 expression was evaluated in endometriotic epithelium and stroma using the Histoscore calculation. Areas of endometriosis epithelium and stroma were first scanned at low power (×10) and then analyzed at high power (×40) to evaluate the staining intensity and estimate the proportion of positive cells. ARID1A IHC was used as a surrogate of loss-of-function alterations ( Khalique et al., 2018 ; Trizzino et al., 2018 ) using a Dako Omnis automated immunostainer (Agilent Technologies) and the anti-ARID1A rabbit monoclonal D2A8U (Cell Signaling Technology). 12Z cells were seeded at a density of 30,000 cells/mL in DMEM/F12 media supplemented with 10% FBS and 1% L-glutamine. The following day, cells were transfected with 50 nM siRNA (Dharmacon, ON-TARGETplus Non-targeting Pool, human ARID1A #8289 SMARTpool, human P300 #3486 SMARTpool, human SERPINE1 #19376 SMARTpool) using the RNAiMax (ThermoFisher) lipofectamine reagent according to the manufacturer’s instructions at a ratio of 1:1 volume:volume in OptiMEM (GIBCO). After 24 hr, the media was replaced. For plasmid co-transfection experiments, cells were transfected the following day with 500ng pBabe vector containing PIK3CA H1047R (pPIK3CA H1047R ) or pBabe empty vector using the FuGene HD transfection reagent (Promega) according to the manufacturers’ instructions at a ratio of 2:1 volume:mass, and media was replaced after 4 hr. The pPIK3CA H1047R was a gift from Jean Zhao (Addgene plasmid 12524) ( Zhao et al., 2005 ). In A-485 co-treatment studies, A-485 was included in the media 24 hr-post transfection in 0.1% DMSO. 48 hr after transfection, media was replaced with DMEM/F-12 media supplemented with 0.5% FBS, 1% P/S and 1% L-glutamine. Cells were collected 72 hr-post siRNA transfection using the Quick-RNA Miniprep Kit (Zymo Research) for RNA, RIPA buffer (Cell Signaling) for whole cell lysate, or histone extraction. Lentiviral particles expressing shRNAs were produced in 293T cells according to the manufacturers’ instructions. Lenti-X™293T cells were transfected with lentiviral packaging mix composed of pNHP and pVSVG (generous gifts from Dr. Fredric Manfredsson) and MISSION pKLO.1 plasmid containing non-targeting shRNA (control) or pooled ARID1A shRNAs (shARID1A) (Sigma) using poly-ethylenimine (PEI) in DMEM + 4.5g/L D-Glucose, 110mg/L Sodium Pyruvate, 10% FBS, 1% L-glutamine. After 24 hr, media was replaced with DMEM/F12, 10% FBS, 1% L-glutamine, 1% P/S. Viral particles were collected after 48 and 96 hr, and viral titers were calculated using the qPCR Lentiviral Titration Kit (ABM). For lentiviral transduction of 12Z cells, cells were treated with a multiplicity of infection of 100 units per cell. After 24 hours, media was replaced. For plasmid co-transfection experiments, cells were transfected the following day with 500ng pBabe vector containing PIK3CA H1047R (pPIK3CA H1047R ) or pBabe empty vector using the FuGene HD transfection reagent (Promega) according to the manufacturers’ instructions at a ratio of 2:1 volume:mass, and media was replaced after 4 hr. In A-485 co-treatment studies, A-485 was included in the media 24 hr-post transfection in 0.1% DMSO. To generate stable expression cell lines, transduced cells were treated with 600 ng/mL puromycin (Sigma) for three weeks. Cells were washed with PBS and scraped in PBS containing 5 mM sodium butyrate. Cells were centrifuged and resuspended in TEB buffer (phosphate buffered saline supplemented with 0.5% Triton X-100, 5 mM sodium butyrate, 2 mM phenylmethylsulfonyl fluoride, 1 × protease inhibitor cocktail) and incubated on a 3D spindle nutator at 4°C for 10 min. Cells were centrifuged at 3,000 RPM for 10 min at 4°C. TEB wash step was repeated once. Following second wash, pellet was resuspended in 0.2 N HCl, and incubated on 3D spindle nutator at 4°C overnight. The following day, samples were neutralized with 1:10 volume 1M Tris-HCl pH 8.3. Sample was centrifuged at 3,000 RPM for 10 min at 4°C, and supernatant containing histone proteins was collected. Protein whole cell lysates and histone extracts were quantified using the Micro BCA Protein Assay Kit (ThermoFisher) and a FlexSystem3 plate reader. Protein lysates were run on a 4%–15% gradient SDS-PAGE gel (BioRad) and transferred to PVDF membrane using the TransBlot Turbo system (BioRad). Primary antibodies dilutions were 1:1,000 ARID1A (D2A8U) (12354, Cell Signaling); 1:100 P300 (NM11) (sc-32244, Santa Cruz); 1:1,000 β-Actin (8457, Cell Signaling); 1:100 PAI-1 (sc-5297, Santa Cruz); 1:1,000 Akt (4691, Cell Signaling); 1:2,000 Phospho-Akt (Ser473) (4060, Cell Signaling). Horseradish peroxidase (HRP) conjugated secondary antibodies (Cell Signaling) were used at a dilution of 1:2,000. Clarity Western ECL Substrate (BioRad) was used for protein band visualization, and western blot exposures were captured using the ChemiDoc XRS+ imaging system (BioRad). For histone extracts, samples were run on a 15% SDS-PAGE gel and transferred to nitrocellulose membrane in 20 mM sodium phosphate pH 6.7 at 400 mA for 90 min. Primary antibody dilutions were 1:2,000 Histone H3 (4499, Cell Signaling); 1:1,000 H3K27ac (Active Motif, 39133). Donkey anti-rabbit IgG, IRDye 800CW conjugated secondary antibody (LI-COR Biosciences) was used at a dilution of 1:10,000 and fluorescence imaging was performed using the LI-COR Odyssey CLx imaging system (LI-COR Biosciences). Uncropped western blot images are collected in Figure S7 . 12Z cells were seeded in 6-well dishes at a density of 50,000 cells per well. After 24 hr, cells were transfected with siRNA as described above. For drug treatment experiments, cells were treated drug 24 hr after transfection. At 48 hr post-transfection, cells were trypsinized, and 100 μL of cell mixture containing 30,000 cells and 0.3 mg/mL Matrigel was seeded into transwell plates (8 μm pore polycarbonate membrane, Corning) pre-coated with 100 μL of 0.3 mg/mL Matrigel. After 1 hr, serum-free DMEM/F12 1% P/S, 1% L-glutamine media was added to the top chamber and DMEM/F12, 5% FBS, 1% P/S, 1% L-glutamine was added to the bottom chamber. For drug studies, drug was included in both top and bottom chamber media. After 16 hr, transwell units were transferred to plates containing 2 μg/mL calcein-AM in DMEM/F12. After 1 hr, media was aspirated from the top chamber and unmigrated cells were removed with a cotton swab. Images were collected using a Nikon Eclipse T i microscope in five non-overlapping fields per well. ImageJ software (National Institutes of Health) was used to quantify cells based on size and intensity. 12Z cells were seeded in 6-well dishes at a density of 50,000 cells per well. After 24 hr, cells were transfected with siRNA as described above. For drug treatment experiments, cells were treated drug 24 hr after transfection. At 48 hr post-transfection, cells were trypsinized, and 50 μL of cell mixture containing 10,000 cells and 0.3 mg/mL Matrigel was seeded into 96-well plates pre-coated with 100 μL of 0.3 mg/mL Matrigel. After 1 hr, 50 μL of serum-free DMEM/F12 1% P/S, 1% L-glutamine media was added. For drug studies, 100 nM A-485 or vehicle was included in the media. After 16 or 24 hr, 2 μg/mL calcein-AM and 4 μg/mL ethidium homodimer III were added. Wells were imaged using a Nikon Eclipse T i microscope, and ImageJ software (National Institutes of Health) was used to quantify cells based on size and intensity. 12Z cells were seeded into 35mm dishes containing 4-well culture inserts at a density of 4,000 cells per well. Cells were treated with lentiviral particles expressing non-targeting shRNA (control) or shARID1A at a multiplicity of infection of 100, 24 hr after seeding. Media was replaced with serum-free DMEM/F12 containing 1% L-glutamine and 1% P/S including drug or vehicle after 24 hr. Culture inserts were removed and serum-free media containing vehicle or drug was replenished after 16 hr. At 0 and 24 hr of migration, images were taken using a Nikon Eclipse T i microscope. Distances between migration fronts were measured using NIS Elements Advanced Research software at 16 different points 100 μm apart. Migration distance was calculated by subtracting the average distance across migration fronts at 24 hr from the average distance at 0 hr. Cells were counted within a window surrounding the 1050 mm 2 migration area. Cells were seeded in 6-well plates at a density of 100 cells/well. After 24h, cells were treated with A-485 at concentrations from 10 nM to 100 μM. After 6 days, cells were stained with crystal violet and counted. Cells were seeded at a density of 4,000 cells per well in a 96-well plate. After 24 hr, cells were transfected as described above. After 24 hr, cells were treated with drugs for 48–72 hr. Cells were incubated with 2 μg/mL calcein-AM for 1 hr and fluorescence was measured using a SpectraMax i3× (Molecular Devices). The Caspase-Glo 3/7 Assay (Promega) was used according to the manufacturer’s instructions. Following transfection (48 hr) and drug treatment (24 hr) cells were seeded at 10,000 cells per well in a 96-well Cellstar Cell-Repellent plate (Greiner Bio-one) in serum-free DMEM/F12, 1% L-glutamine, 1% P/S containing A-485 or vehicle. After 24 hr, cells were treated with Caspase-Glo at a ratio of 1:1 and incubated at 37°C for 1 hr. Cells were then transferred to a white 96-well plate (costar) and luminescence was measured using a SpectraMax i3x (Molecular Devices). Expression of Annexin V was measured by flow cytometry using the Annexin V-FITC Kit (Miltenyi Biotec) according to the manufacturer’s instructions. Flow cytometry was performed using a BD Accuri C6 flow cytometer (BD Biosciences) and analyzed using FlowJo v10 software (BD Biosciences). The Click-iT Plus EdU Flow cytometry Assay Kit (Invitrogen) was used for cell cycle assays. Cells were treated with 10 μM of EdU for 2 hours in culture media. Cells were harvested by trypsinization and washed in 1% BSA in PBS. Cells were resuspended in 100 μL of ice cold PBS, and 900 μL of ice cold 70% ethanol was added dropwise while vortexing. Cells were incubated on ice for two hours. Cells were washed with 1% BSA in PBS and then treated with the Click-iT Plus reaction cocktail including Alexa Fluor 488 picolyl azide according to the manufacturer’s instructions for 30 min. Cells were washed with 1X Click-iT permeabilization buffer and wash reagent, and then treated with 5 μM of Vybrant Dye Cycle Ruby Stain (ThermoFisher) diluted in 1% BSA in PBS for 30 min at 37°C. Flow cytometry was performed using a BD Accuri C6 flow cytometer (BD Biosciences) and analyzed using FlowJo v10 software (BD Biosciences). RNA samples were collected 72 hr following siRNA transfection using the Quick-RNA Miniprep Kit (Zymo Research). Libraries were prepared by the VARI Genomics Core from 500 ng of total RNA using the KAPA mRNA HyperPrep kit (v4.17) (Kapa Biosystems). RNA was sheared to 300–400 bp. Prior to PCR amplification, cDNA fragments were ligated to IDT for Illumina unique dual adapters (IDT DNA Inc). Quality and quantity of the finished libraries were assessed using a combination of Agilent DNA High Sensitivity chip (Agilent Technologies), QuantiFluor® dsDNA System (Promega), and Kapa Illumina Library Quantification qPCR assays (Kapa Biosystems). Individually indexed libraries were pooled and 100 bp, single end sequencing was performed on an Illumina NovaSeq6000 sequencer using an SP, 100 cycle sequencing kit (Illumina) and each library was sequenced to an average raw depth of 35M reads. Base calling was done by Illumina RTA3 and output of NCS was demultiplexed and converted to FastQ format with Illumina Bcl2fastq v1.9.0. 12Z cells were crosslinked 72 hr post-transduction with lentiviral particles containing control shRNAs or ARID1A-targeting shRNAs (differential P300, H3K18ac, H3K27ac, H3K27me3, H3K4me3 and H3K4me1 ChIP-seq) or untreated cells were used (wild-type P300 ChIP-seq). For crosslinking, cells were treated with 1% formaldehyde in cell culture media for 15 min at room temp. Formaldehyde was quenched by the addition of 0.125 M Glycine, and cells were washed with PBS. 4×10 6 crosslinked cells were used per IP for H3K4me3 and H3K27me3, and 1×10 7 crosslinked cells were used per IP for all other antibodies. Chromatin from crosslinked cells was fractionated by digestion with micrococcal nuclease using the SimpleChIP Enzymatic Chromatin IP Kit (Cell Signaling) per the manufacturers’ instructions. IPs were performed in duplicate per antibody and condition by adapting established methods ( Boyd and Farnham, 1997 ). For P300 IPs, nuclei were resuspended in nuclear lysis buffer (50 mM Tris-HCl [pH 8.0], 10 mM EDTA [pH 8.0], 1% SDS) and sonicated for 30 s. Protein G magnetic beads (Cell Signaling) were preconjugated with antibody overnight at 4°C in wash buffer (1X PBS, 0.5% BSA, 0.02% Tween-20). Antibody used was 5 μg P300 (sc-32244, Santa Cruz). Fractionated chromatin was diluted into IP buffer (0.01%SDS, 1.1% TrionX-100, 1.2 mM EDTA [pH 8.0], 16.7 mM Tris-HCl [pH 8.0], 167 mM NaCl) and incubated with preconjugated antibody/Dynabeads overnight at 4°C. Samples were washed at 4°C with high-salt buffer (0.1% SDS, 1% Triton X-100, 2 mM EDTA [pH 8.0], 20 mM Tris-HCl [pH 8.0], 0.5 M NaCl), low-salt buffer (0.1% SDS, 1% Triton X-100, 2 mM EDTA [pH 8.0], 20 mM Tris-HCl [pH 8.0], 150 mM NaCl), dialysis buffer (0.2% Sarcosyl, 2 mM EDTA [pH 8.0], 50mM Tris-HCl [pH 8.0]), IP wash buffer (0.25M LiCl, 1% NP-40, 1% Deoxycholate, 1 mM EDTA [pH 8.0], 10 mM Tris-HCl [pH 8.0]) and TE (10mM Tris-HCL [pH 8.0], 1mM EDTA [pH 8.0]). IP chromatin was eluted for 30 min at 37°C with elution buffer (1% SDS, 0.1 M NaHCO3). Crosslinks were reversed with 0.4 mg/mL Proteinase K (ThermoFisher) and 0.2 M NaCl at 65°C for 2 hr. DNA was purified using the ChIP DNA Clean & Concentrator Kit (Zymo). For H3K27ac, H3K18ac, H3K27me3, H3K4me3 and H3K4me1 IPs were performed using the SimpleChIP Enzymatic Chromatin IP Kit per the manufacturers’ instructions. For H3K27ac and H3K18ac the addition of 5 mM sodium butyrate included in Buffer A and ChIP Buffer. Antibodies used were 10 μg H3K27ac (Active Motif, 39133), 5 μg H3K18ac (ab1191, Abcam), 10 μL H3K27me3 (Cell Signaling, 9733), 10 μL H3K4me3 (Cell Signaling, 9751), or 4 μg H3K4me1 (Abcam, ab8895) per IP. DNA was purified as described above. Libraries for Input and IP samples were prepared by the VARI Genomics Core from 10 ng of input and IP material when available, and all material when less than 10 ng available, using the KAPA Hyper Prep Kit (v5.16) (Kapa Biosystems). Prior to PCR amplification, end repaired and A-tailed DNA fragments were ligated to Bioo Scientific NEXTflex Adapters (Bioo Scientific). Quality and quantity of the finished libraries were assessed using a combination of Agilent DNA High Sensitivity chip (Agilent Technologies), QuantiFluor® dsDNA System (Promega), and Kapa Illumina Library Quantification qPCR assays (Kapa Biosystems). Individually indexed libraries were pooled. For P300 ChIP in wild-type cells, 100 bp, single end sequencing was performed on an Illumina NovaSeq6000 sequencer using an SP, 100 cycle sequencing kit (Illumina) and each library was sequenced to minimum read depth of 100M reads per input library, 50M reads per IP library. Base calling was done by Illumina NextSeq Control Software (NCS) v2.0. For differential P300 ChIP, 75 bp, paired end sequencing was performed on an Illumina NextSeq 500 sequencer using 150 cycle HO and MO sequencing kits (v2) (Illumina), with all libraries run across 2 flowcells to return a minimum read depth of 80M reads per input library and 40M read per IP library. Base calling was done by NCS v2.0. For differential H3K27ac, H3K18ac and H3K4me1 ChIP-seq IPs, 50 bp, paired end sequencing was performed on an Illumina NovaSeq6000 sequencer using an S1, 100 cycle sequencing kit and each library was sequenced to minimum read depth of 50M reads per IP library. Input samples were sequenced using 100 bp, single end sequencing to a minimum read depth of 100M reads. Base calling was done by Illumina RTA3. For H3K27me3 and H3K4me3 ChIP, 75 bp, single end sequencing was performed on an Illumina NextSeq 500 sequencer using 75 cycle HO sequencing kits (v2), with all libraries run across two flow cells to return a minimum read depth of 80 M reads per input library and 40 M read per IP library. Base calling was done by Illumina NextSeq Control Software (NCS) v2.0. For all experiments, output data was demultiplexed and converted to FastQ format with Illumina Bcl2fastq v1.9.0. The CUT&RUN protocol was adapted from established protocols ( Skene et al., 2018 ). BioMag Plus Concanavalin A–coated magnetic beads (Bangs Laboratories) were washed in Binding Buffer (20 mM HEPES-KOH pH 7.9, 10 mM KCl, 1 mM CaCl2, 1 mM MnCl2). 72 hr following siRNA transfection, 500,000 12Z cells were harvested and resuspended in Wash Buffer (20 mM HEPES-NaOH pH 7.5, 150 mM NaCl, 0.5 mM Spermidine, 1X protease inhibitor cocktail) and washed twice by centrifuge at 600 × g for 3 min, and then added to the concanavalin A bead suspension and mixed on a tube rotator for 10 min at room temp. Cell/bead conjugates were resuspended in 500 μL of Antibody Buffer (Wash Buffer with 0.05% Digitonin and 2 mM EDTA) containing 5 μg of H3K27ac antibody (Active Motif, cat# 39133) or Rabbit IgG (Cell Signaling, cat#2729) and incubated in a tube nutator overnight at 4°C. The following day, cells were washed in Digitonin Buffer (Wash Buffer with 0.05% Digitonin) three times, resuspended in 250 μL of Digitonin Buffer and 12.5 μL of CUTANA pAG-MNase (EpiCypher, cat# 15–1016) was added. Cells were mixed on a nutator at room temp for 1 hr, followed by two washes in Digitonin Buffer and one wash with Low-Salt Rinse Buffer (20 mM HEPES-NaOH pH 7.5, 0.5 mM Spermidine, 1X protease inhibitor cocktail). Tubes were chilled on ice, 1 mL of Calcium Incubation Buffer (3.5 mM HEPES-NaOH pH 7.5, 10 mM CaCl2, 0.05% Digitonin) was added, and tubes were nutated at 4°C. After 2.5 min, beads were bound to magnet, supernatant was removed and 250 μL of EGTA-STOP Buffer (170 mM NaCl, 20 mM EGTA, 0.05% Digitonin, 20 μg/mL RNase A, 20 μg/mL Glycogen, 0.8 pg/ml S. cerevisiae fragmented nucleosomal DNA) was added. Beads were nutated at 37°C for 30 min, followed by centrifuge at 16,000 × g for 5 min at 4°C. DNA was purified using the NucleoSpin Gel and PCR Clean-up Kit (Takara, cat# 740609.50). Libraries for CUT&RUN samples were prepared by the Van Andel Genomics Core from 0.5–1 ng of IP material, using the KAPA Hyper Prep Kit (v5.16) (Kapa Biosystems). Prior to PCR amplification, end-repaired and A-tailed DNA fragments were ligated to Bioo Scientific NEXTflex Adapters (Bioo Scientific) at a concentration of 500 nM. Quality and quantity of the finished libraries were assessed using a combination of Agilent DNA High Sensitivity chip (Agilent Technologies, Inc.), QuantiFluor® dsDNA System (Promega Corp.), and Kapa Illumina Library Quantification qPCR assays (Kapa Biosystems). Individually indexed libraries were pooled and 50 bp, paired end sequencing was performed on an Illumina NovaSeq6000 sequencer using an S1, 100 cycle sequencing kit (Illumina Inc.) Each library was sequenced to an average depth of 75M reads. Base calling was done by Illumina RTA3 and output was demultiplexed and converted to FastQ format with Illumina Bcl2fastq v1.9.0. For standard mRNA gene-level expression analysis, single-end raw reads were trimmed with cutadapt ( Martin, 2011 ) and Trim Galore! ( http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ ) followed by quality control analysis via FastQC ( Andrews, 2010 ) and MultiQC ( Ewels et al., 2016 ). Trimmed reads were aligned to GRCh38.p12 genome assembly and indexed to GENCODE ( Frankish et al., 2019 ) v28 GFF3 annotation via STAR ( Dobin et al., 2013 ) aligner with flag ‘–quantMode GeneCounts’ for feature counting. Reverse-stranded, gene-level counts were extracted from the STAR output files and constructed into an experimental read count matrix in R. Low count genes were filtered (1 count per sample on average) prior to DESeq2 ( Love et al., 2014 ) count normalization and differential expression analysis. Modeling design matrices were constructed with a single “condition” variable and included an intercept. Calculated differential expression probabilities were corrected for multiple testing by independent hypothesis weighting ( IHW ) ( Ignatiadis et al., 2016 ) for downstream analysis. Threshold for differential expression significance was set at FDR < 0.0001. Relative expression heatmaps were produced using relative regularized-logarithm (rlog) ( Love et al., 2014 ) counts by subtracting mean rlog counts of the control group. Relative linear expression bar plots were produced from DESeq2 normalized counts table. Previously published GEMM expression data ( Wilson et al., 2019 ) were extracted from (GEO: GSE129784 ). Intergenic eRNA and associated differential expression analysis were also analyzed similarly. Briefly, previously published, paired end total RNA sequencing data from 12Z cells treated with siARID1A or non-targeting siRNA control ( Wilson et al., 2019 ) were extracted from (GEO: GSE129782 ). Reads were trimmed and aligned as described above. Aligned BAMs were inputted to HOMER ( Heinz et al., 2010 ) in order to count integer RNA-seq reads at each of the 18,050 distal, putatively active enhancer elements described in this study. Counted regions were then excluded which overlapped with any genic regions, including introns, using the genes() function of TxDb.Hsapiens.UCSC.hg38.knownGene (Bioconductor Core Team and Bioconductor Package Maintainer, 2016) R package. Expression status of each eRNA locus was then determined by observation of at least 1 count per sample on average, resulting in 3,668 expressed intergenic eRNAs. The filtered eRNA counts table was then normalized and modeled for differential expression analysis by DESeq2 ( Love et al., 2014 ) as described above. Wild-type P300 and differential H3K27ac, H3K18ac, H3K27me3, and H3K4me3 ChIP-seq experiments were analyzed as single-end libraries, while differential P300 and H3K4me1 ChIP-seq were analyzed as paired-end. Raw reads for IPs and inputs were trimmed with cutadapt ( Martin, 2011 ) and Trim Galore! followed by quality control analysis via FastQC ( Andrews, 2010 ) and MultiQC ( Ewels et al., 2016 ). Trimmed reads were aligned to GRCh38.p12 reference genome via Bowtie2 ( Langmead and Salzberg, 2012 ) with flag ‘–very-sensitive’. Aligned reads were sorted and indexed with samtools ( Li et al., 2009 ). For paired-end analyses, only properly-paired read fragments were retained by samtools view with flag ‘-f 3’ followed by sorting and indexing. Specifically for libraries with differential comparisons, molecular complexity was then estimated from duplicate rates by ATACseqQC ( Ou et al., 2018 ) and preseqR ( Daley and Smith, 2013 ), and libraries were subsampled to equivalent molecular complexity within an experimental design based on these estimates with samtools . Picard MarkDuplicates ( http://broadinstitute.github.io/picard/ ) was used to remove PCR duplicates, followed by sorting and indexing. MACS2 ( Zhang et al., 2008 ) was used to call peaks on each ChIP replicate against the respective input control. For P300 IPs, MACS2 called broadPeaks with FDR < 0.05 threshold and otherwise default settings. For H3K4me3, H3K4me1, H3K27ac and H3K18ac IPs, MACS2 called narrowPeaks with FDR < 0.05 threshold and flags ‘–nomodel–extsize 146’ to bypass model building. For H3K27me3, MACS2 called broadPeaks with FDR < 0.05 threshold and flags ‘–nomodel–extsize 146’ to bypass model building. The resulting peaks were repeat-masked by ENCODE blacklist filtering and filtered for non-standard contigs ( Amemiya et al., 2019 ). A naive overlapping peak set, as defined by ENCODE, was constructed by calling peaks on pooled replicates followed by bedtools intersect ( Quinlan and Hall, 2010 ) to select for peaks of at least 50% overlap with each biological replicate. ChIP-seq differential binding or abundance analysis was performed with csaw ( Lun and Smyth, 2016 ). Briefly, a consensus peak set was constructed for each differential experiment from the union of replicate-intersecting, filtered MACS2 peaks called in each condition. The replicate intersection criteria used here are less stringent than the naive overlap; any partial intersect between ChIP replicates was accepted as a query region tested for differential binding/abundance. ChIP reads were counted in these query regions by csaw , then filtered for low abundance peaks with average logCPM > −3. When comparing ChIP libraries, any global differences in IP efficiency observed between the two conditions were considered a result of technical bias to ensure a highly conservative analysis. As such, we employed a non-linear loess-based normalization to the peak count matrix, as is implemented in csaw ( Lun and Smyth, 2016 ), to assume a symmetrical MA distribution. A design matrix was then constructed from one “condition” variable, without an intercept. The count matrix and loess offsets were then supplied to edgeR ( Robinson et al., 2010 ) for estimating dispersions and fitting the quasi-likelihood generalized linear model for hypothesis testing. Nearby query regions were then merged up to 500 bp apart for a maximum merged region width of 5 kb, and the most significant probability was used to represent the merged region. An FDR < 0.05 threshold was used to define significant differentially bound/abundant regions. Analysis of CUT&RUN data followed a highly similar procedure as paired-end ChIP-seq. Briefly, raw paired-end reads for H3K27ac or IgG CUT&RUN were trimmed and aligned, filtered for only properly-paired reads, then molecular complexity was estimated, and libraries were subsampled to equalize based on complexity estimates. PCR duplicates were removed, and MACS2 was used to call narrowPeaks against the IgG negative control as input, with FDR < 0.05 threshold and flags ‘–nomodel–extsize 146’ to bypass model building. Peaks were then blacklist-filtered, and a naive overlapping peak set was constructed as described above. Differential H3K27ac CUT&RUN analysis was computed with csaw . In order to promote similarity between the differential H3K27ac CUT&RUN and ChIP-seq experiments, we used the same consensus peak set as defined by our differential H3K27ac ChIP-seq experiment for the CUT&RUN analysis here. Briefly, H3K27ac CUT&RUN reads were counted in these query regions by csaw , then filtered for low abundance peaks with average logCPM > −3. When comparing CUT&RUN libraries, any global differences in CUT&RUN reaction efficiency observed between two conditions were considered a result of technical bias to ensure a highly conservative analysis. As such, we employed a non-linear loess-based normalization to the peak count matrix, as is implemented in csaw, to assume a symmetrical MA distribution. A design matrix was then constructed from one “condition” variable, without an intercept. The count matrix and loess offsets were then supplied to edgeR for estimating dispersions and fitting the quasi-likelihood generalized linear model for hypothesis testing. Nearby query regions were then merged up to 500 bp apart for a maximum merged region width of 5 kilobases, and the most significant probability was used to represent the merged region. ChromHMM ( Ernst and Kellis, 2017 ) was used to segment the hg38 genome based on combinatorial chromatin features in control and ARID1A-depleted 12Z cell conditions. Briefly, all seven chromatin features (total RNA, ATAC, H3K4me1, H3K4me3, H3K27me3, H3K27ac, and H3K18ac) were binarized from aligned BAM files, and chromatin features were modeled in both conditions simultaneously through the “concatenated” option. The concatenated option was selected because it creates a unified model for direct comparison between control and ARID1A-depleted conditions used to identify chromatin state changes. Chromatin state models were built from 5 to 25 states, and each model was manually curated based on inferred biological function to select one with balance between unique and overlapping combinatorial features. We selected 18 states as our final model for downstream analysis. State emissions were then user-reordered to group based on inferred biological function. BED files containing coordinates for each chromatin state in each condition were constructed into non-overlapping GenomicRanges objects in R for downstream enrichment analyses, differential chromatin state analysis, and plotting. For RNA-seq experiments, three biological replicates were analyzed (n = 3). For ChIP-seq and CUT&RUN experiments two independent IPs were used (n = 2) and were compared against a condition-respective input chromatin sample or IgG negative control, respectively. For in vivo experiments, n represents number of mice. For cell-based assays, n represents biological replicates or independent experiments as indicated in the figure legend. Multiple hypothesis tests corrections via FDR were employed when appropriate to reduce type I errors. Presented probability ( p ) values are representative of the associated statistical tests as indicated in the figure legends. All boxplots presented for genomic analyses are in the style of Tukey without outliers. The ROSE algorithm ( Lovén et al., 2013 ; Whyte et al., 2013 ) was used to define active super-enhancers from H3K27ac peaks which overlapped with accessibility (ATAC) in control 12Z cells. GeneHancer ( Fishilevich et al., 2017 ) database was used to associate enhancers to genes with a GeneHancer score > 1 threshold. Various HOMER ( Heinz et al., 2010 ) functions were applied to annotate genomic regions of interest, quantify signal and count reads at sites of interest for tag density heatmaps and meta peak plots. Chromatin analyses involving ChIP signal quantification at regions of interest used pooled reads from both IP replicates, per feature. TxDb.Hsapiens.UCSC.hg38.-knownGene ( Bioconductor Core Team and Bioconductor Package Maintainer, 2016 ) was used to define gene promoters for all standard hg38 genes as 3 kilobase regions surrounding the primary TSS. MACS2 ( Zhang et al., 2008 ) was used to produce genome-wide signal log-likelihood ratio (logLR) tracks for IGV ( Robinson et al., 2011 ) visualization. ClusterProfiler ( Yu et al., 2012 ) was used to compute and visualize pathway enrichment from a list of gene symbols with respective gene universes. Hallmark pathways and GO Biological Process gene sets were retrieved from MSigDB ( Liberzon et al., 2015 ). ComplexHeatmap ( Gu et al., 2016 ) was used for hierarchical clustering by Euclidean distance and general heatmap visualization. GenomicRanges ( Lawrence et al., 2013 ) functions were frequently used to intersect and manipulate genomic coordinates e.g., for genome-wide association tests. eulerr ( Larsson, 2020 ) was used to produce proportional Euler diagrams. biomaRt ( Durinck et al., 2005 ; Durinck et al., 2009 ) was used for all gene nomenclature and ortholog conversions. ggplot2 ( Wickham, 2016 ) was used for certain plotting applications. The statistical computing language R ( R Core Team, 2018 ) was used for many applications throughout this manuscript. Mantel-Cox tests and t tests were performed using GraphPad Prism 8 software.