SWI/SNF inactivation in the endometrial epithelium leads to loss of epithelial integrity

Abstract

Although ARID1A mutations are a hallmark feature, mutations in other SWI/SNF (SWItch/Sucrose Non-Fermentable) chromatin remodeling subunits are also observed in endometrial neoplasms. Here, we interrogated the roles of Brahma/SWI2-related gene 1 (BRG1, SMARCA4), the SWI/SNF catalytic subunit, in the endometrial epithelium. BRG1 loss affects more than one-third of all active genes and highly overlaps with the ARID1A gene regulatory network. Chromatin immunoprecipitation studies revealed widespread subunit-specific differences in transcriptional regulation, as BRG1 promoter interactions are associated with gene activation, while ARID1A binding is associated with gene repression. However, we identified a physiologically relevant subset of BRG1 and ARID1A co-regulated epithelial identity genes. Mice were genetically engineered to inactivate BRG1 specifically in the endometrial epithelium. Endometrial glands were observed embedded in uterine myometrium, indicating adenomyosis-like phenotypes. Molecular similarities were observed between BRG1 and ARID1A mutant endometrial cells in vivo, including loss of epithelial cell adhesion and junction genes. Collectively, these studies illustrate overlapping contributions of multiple SWI/SNF subunit mutations in the translocation of endometrium to distal sites, with loss of cell integrity being a common feature in SWI/SNF mutant endometrial epithelia.

Graphical Abstract

Introduction

Mutations in the multi-subunit SWI/SNF (SWItch/Sucrose Non-Fermentable, also known as BRG1-associated factors) chromatin remodeling complex are widespread in various cancers, benign diseases and developmental disorders. SWI/SNF-encoding gene mutations are detected across nearly all types of cancer (1). ARID1A (AT-rich interactive domain-containing protein 1A) is the most highly mutated SWI/SNF subunit, and ARID1A mutations are common in many gynecologic malignancies, including roughly half of endometriosis-associated ovarian carcinomas (OMIM #167000) (2,3) and uterine endometrial carcinomas (OMIM #608089) (4). More recently, ARID1A mutations and loss of ARID1A expression have been observed in the benign gynecologic diseases endometrial hyperplasia (5), ovarian (endometrioma) and deep infiltrating endometriosis (OMIM %131200) (6–8). Endometriosis and the related disease adenomyosis (OMIM #600458) are characterized by ectopic spread of endometrial epithelial glands. These genetic observations indicate that the uterine endometrial epithelium is susceptible to disease following ARID1A mutation, but the roles of other mammalian SWI/SNF subunits in this cell type have not yet been discerned.

BRG1 (Brahma/SWI2-related gene 1, SMARCA4) serves as a core catalytic subunit of the mammalian SWI/SNF complex, and the ARID1A subunit is often found within BRG1-containing SWI/SNF complexes (9,10). SMARCA4 mutations are observed in many cancer types and linked to some developmental disorders (11,12). Both oncogenic and tumor suppressive roles of BRG1 have been reported in various tumor contexts (13–16). Most SMARCA4 mutations in cancer affect the catalytic ATPase domain leading to defects in DNA translocase activity (17). In particular, SMARCA4 mutations are nearly ubiquitous in the hypercalcemic subtype of small cell ovarian carcinoma (18). A recent case study reported a patient harboring a deleterious germline SMARCA4 mutations with epithelial ovarian cancer and a past history of adenomyosis (19). BRG1 silencing has also been observed across many cancer types (20). Moreover, loss of BRG1 expression by immunohistochemistry was observed in one-third of undifferentiated endometrial carcinomas screened in a recent report (21), and BRG1 mutations have also been observed in this disease (22).

Here, we determined both shared and unique roles of the SWI/SNF complex subunits BRG1 and ARID1A in the endometrial epithelium. In immortalized human endometrial epithelial cells, BRG1 loss has a greater effect on gene transcription than ARID1A loss. Chromatin immunoprecipitation (ChIP) studies revealed that BRG1 and ARID1A co-regulate target gene promoters and distal regions. However, we also identified subunit-specific differences in transcriptional regulation, where BRG1 functions in gene activation and ARID1A functions in gene repression. We then developed a genetically engineered mouse model (GEMM) in which BRG1 is specifically deleted in the endometrial epithelium, and endometrial glands were observed in myometrium without obvious hyperplasia or oncogenic transformation. BRG1-null endometrial epithelia were isolated and profiled by RNA-seq, and comparative analyses support that both BRG1 and ARID1A mutations promote invasion programs by disrupting epithelial integrity. The experiments performed herein suggest that endometrial SWI/SNF mutations contribute to invasive pathogenesis through shared mechanisms, despite global differences in transcriptional regulation.

Results

BRG1 subunit loss in endometrial epithelial cells results in widespread transcriptional changes

Few reports have specifically focused on SMARCA4 mutations in the endometrial epithelium. We analyzed The Cancer Genome Atlas (TCGA) Uterine Corpus Endometrial Carcinoma (UCEC) data set and observed frameshift and nonsense SMARCA4 mutations predicted as ‘likely oncogenic’ by OncoKB annotation (Supplementary Material, Table S1) (4,23,24). Some of these tumors also had a relatively low background mutation rate (<1000 total mutations), suggesting deleterious SMARCA4 mutations may be important events in disease pathogenesis.

To explore the consequences of BRG1 loss in the endometrial epithelium, we utilized BRG1 knockdown approaches in the immortalized 12Z human endometrial epithelial cell line (25). BRG1 knockdown was achieved by siRNA (siBRG1) transfection and confirmed by western blot, compared to a non-targeting siRNA (control) (Fig. 1A and B). Knockdown of BRG1 did not result in a change in 12Z cell growth (Fig. 1C), suggesting that cellular health is not affected following depletion of BRG1. RNA-seq was performed following BRG1 knockdown (siBRG1). The data were further compared to our previously reported ARID1A knockdown (siARID1A) RNA-seq data set from 12Z cells (26). BRG1 loss resulted in the DE (differential expression; differentially expressed) of 7708 genes compared to controls [false discovery rate (FDR) < 0.0001], and a greater number of genes were affected by BRG1 loss compared to ARID1A loss (Fig. 1D and E). In addition to a greater number of genes, BRG1 loss resulted in a greater magnitude of gene expression change compared to ARID1A loss, with 11.5% of siBRG1 DE genes displaying >4-fold linear change in expression compared to <1% of siARID1A DE genes (Fig. 1F). Overall, siBRG1 DE genes had a significantly greater absolute fold change relative to control cells than siARID1A (two-tailed, unpaired Wilcoxon test, P < 10⁻¹⁵) (Fig. 1G). A total of 1492 overlapping DE genes were affected by both BRG1 and ARID1A loss (hypergeometric enrichment, P < 10⁻²⁸³) (Fig. 1H), indicating that ARID1A and BRG1 subunits regulate shared transcriptional programs.

We further examined intersecting gene regulatory networks mediated by both ARID1A and BRG1 in 12Z cells. DE genes identified in siBRG1-treated cells were more likely to be downregulated if they were also affected by ARID1A loss [two-tailed Fisher’s exact test, P < 10⁻⁵, odds ratio (OR) = 1.33] (Fig. 1I). In spite of differences, 60.9% of siBRG1 and siARID1A shared DE genes were affected in the same direction following BRG1 loss and ARID1A loss (Fig. 1J). Notably, among both activated and downregulated shared genes, BRG1 loss resulted in a greater magnitude of transcriptional change than ARID1A loss for these genes (Fig. 1K). We further compared the direction of gene expression change in both shared and unique DE gene classes (Fig. 1L). Enrichment for MSigDB Hallmark pathways (27) was then calculated among the varying classes of gene regulation orchestrated by ARID1A and BRG1, separated further by directionality (Fig. 1M). Certain processes exhibited distinct patterns of transcriptional regulation by SWI/SNF subunits: (1) pathways repressed by both BRG1 and ARID1A, such as interferon response and fatty acid metabolism, (2) repressed by ARID1A but activated by BRG1, such as unfolded protein response and (3) repressed by ARID1A but both repressive and activating regulation by BRG1, such as epithelial–mesenchymal transition (EMT) (Fig. 1M, Supplementary Material, Fig. S1).

Promoter chromatin interaction patterns underlie transcriptional contributions of SWI/SNF subunits

We next used ChIP followed by sequencing (ChIP-seq) to discern how ARID1A and BRG1 directly regulate transcription in endometrial epithelial cells. In wild-type 12Z cells, we profiled genome-wide BRG1 binding by ChIP-seq and identified 6343 significant binding signal peaks (Fig. 2A). Intergenic and intronic regions comprised the majority of BRG1 binding loci (collectively 91.1% of peaks), while gene promoter regions comprised the next largest binding annotation class (defined as within 3 kb of a gene TSS; 7.3% of peaks) (Fig. 2A). Previously, we identified a role for ARID1A in regulating gene expression via promoter interactions (26), so we first focused on this element of transcriptional regulation. We identified 270 expressed genes displaying significant BRG1 promoter binding, and Gene Ontology (GO) Biological Process (BP) gene set enrichment (28,29) showed that these genes were involved in relevant epithelial processes such as locomotion, cell motility and biological adhesion (Fig. 2B). Further, BRG1 promoter binding status was significantly associated with gene expression dysregulation following BRG1 loss (hypergeometric enrichment, P < 10⁻⁷⁴) (Fig. 2C), indicating that BRG1 promoter chromatin interactions regulate transcription, as has been previously demonstrated with the ARID1A subunit (26). Moreover, we leveraged our previously reported ARID1A ChIP-seq data from this cell line (26) to demonstrate that gene promoter chromatin regulation by BRG1 strongly overlaps with ARID1A (hypergeometric enrichment, P < 10⁻²¹⁸) (Fig. 2D).

Figure 1

BRG1 and ARID1A loss in endometrial epithelial cells lead to widespread differences in transcriptional regulation. (A) Western blot for BRG1 and β-actin following siRNA treatment with non-targeting control or siBRG1. (B) Relative densitometry quantification for BRG1 protein levels on western blots from independent experiments (n = 2) as in A. Statistic is two-tailed t-test, unpaired. (C) Cell growth assay results showing no significant difference in growth between 48 and 72 h following siRNA treatment in control and siBRG1 cells. (D) Transcriptomic visualization of DGE in BRG1 knockdown (siBRG1)-treated 12Z cells (n = 3 replicates; 23 148 expressed genes). x-axis displays regularized-logarithm (rlog) gene expression in control cells, and y-axis shows siBRG1-treated cells. Black dots represent stable (n.s.) genes, and red dots represent significant DE genes (FDR < 0.0001, n = 7708 genes). (E) Same as in D but for ARID1A knockdown (siARID1A)-treated cells (n = 2206 significant DE genes at same FDR threshold) based on previously reported data (26). (F) Histogram of expression log₂FC values in DE genes from siBRG1- versus siARID1A-treated compared to control 12Z cells. (G) Violin plot of DGE magnitude (represented by absolute log₂FC values) in siBRG1- versus siARID1A-treated 12Z cells. Statistic is two-tailed Wilcoxon test, unpaired. (H) Significant overlap of DE genes with siBRG1 and siARID1A treatment. Statistic is hypergeometric enrichment. (I) Frequency of gene upregulation versus downregulation in (top) siBRG1 DE genes, which are also affected by siARID1A versus not, compared to (bottom) siARID1A DE genes, which are also affected by siBRG1 versus not. Statistic is two-tailed Fisher’s exact test. (J) Heatmap of expression log₂ fold-change (log₂FC) values in overlapping DE genes by either BRG1 or ARID1A loss. Red values indicate gene upregulation and blue values indicate downregulation. 60.9% of shared DE genes display same direction between loss of the two subunits. (K) Violin plots displaying directional DGE magnitude for shared upregulated genes (left) or shared downregulated genes (right). Statistic is two-tailed Wilcoxon test, unpaired. (L) Classification of siBRG1/siARID1A shared DE genes into four groups based on directionality. y-axis of bar plots displays number of genes in each class. (M) Heatmap of MSigDB Hallmark pathway enrichment, represented as observed/expected ratios compared to the expressed gene universe, for different DGE classes based on shared versus unique SWI/SNF subunit regulation and directionality. Red to black values indicate pathway overrepresentation among the gene set of interest. White cells represent pathways with less than 2-fold overrepresentation. ^*P < 0.05, ^**P < 0.01, ^***P < 0.001.

Figure 2

BRG1 and ARID1A activate highly regulated gene promoters. (A) Genomic annotation of significant genome-wide BRG1 binding sites in 12Z cells as measured by ChIP-seq (6343 broad peaks overlapping in n = 2 IP). Gene promoter peaks are defined as located within 3 kb of a TSS. (B) Significant (FDR < 0.05) GO BP enrichment among 270 BRG1 promoter-bound and expressed genes. (C) Significant association between BRG1 promoter binding and DGE following BRG1 loss. Statistic is hypergeometric enrichment. (D) Significant overlap of BRG1 and ARID1A promoter binding at expressed genes in 12Z cells. Statistic is hypergeometric enrichment. (E) Violin plots displaying gene promoter ChIP fold enrichment (FE) by SWI/SNF subunits at respectively upregulated versus downregulated genes following subunit depletion. Left is BRG1 regulation, and right is ARID1A regulation. ChIP FE is quantified by IP/input chromatin signal, where FE = 1 indicates no enrichment, representative of the entire ±3 kb promoter region. Statistic is two-tailed Wilcoxon test, unpaired. (F) Measurement of (left) BRG1 binding, (center) ARID1A binding and (right) chromatin accessibility (ATAC) at gene promoters classified by SWI/SNF subunit depletion DGE. Promoter ATAC signal is quantified as fragments per kilobase per million mapped reads (FPKM). Statistic is two-tailed Wilcoxon test, unpaired. (G) Promoter chromatin SWI/SNF binding and accessibility as in F but instead at overlapping DE functional gene classes as described in Fig. 1. Statistic is two-tailed Wilcoxon test, unpaired. (H) DGE expression classes following (left) BRG1 loss or (right) ARID1A loss, further broken down into co-regulation status by the opposing subunit. Statistic is two-tailed Wilcoxon test, unpaired. ^*P < 0.05, ^**P < 0.01, ^***P < 0.001. UTR, untranslated region. TSS, transcription start site; TTS, transcription termination site.

We next used our BRG1 and ARID1A ChIP-seq data in tandem to better understand how SWI/SNF promoter chromatin interactions influence subunit-specific transcriptional regulation. BRG1 and ARID1A binding were quantified across the entire 6 kb region flanking gene TSS (i.e. ±3 kb) as a standardized measurement of promoter regulation. Intriguingly, we noted that BRG1 promoter binding was significantly stronger at genes downregulated following BRG1 depletion as opposed to upregulated genes (two-tailed, unpaired Wilcoxon test, P < 10⁻¹⁵) (Fig. 2E). ARID1A displayed the opposite effect, as promoter binding was significantly stronger at genes upregulated following ARID1A depletion (two-tailed, unpaired Wilcoxon test, P < 10⁻¹⁵) (Fig. 2E). These results suggest that BRG1 overall plays direct roles in gene activation in the endometrial epithelium, while ARID1A overall functions in gene repression in this context. To further explore the relationship between ARID1A and BRG1, we used ARID1A and BRG1 ChIP-seq data in combination with our previously reported ATAC-seq (chromatin accessibility) data in 12Z cells (26) for comparisons with siARID1A and siBRG1 differential gene expression (DGE). ARID1A binding, BRG1 binding and chromatin accessibility were highest at promoters of genes displaying overlapping transcriptional regulation by both subunits (Fig. 2F). This suggests that overlapping ARID1A-BRG1 direct target genes are more likely to be associated with accessible chromatin and transcriptional regulation by SWI/SNF. We further investigated the promoter chromatin at specific SWI/SNF regulatory classes of shared DE genes (described in Fig. 1L) affected following loss of either subunit, further separated by directionality. Genes normally activated by BRG1 and ARID1A, corresponding to downregulated genes in siBRG1- and siARID1A-treated cells, displayed the strongest promoter SWI/SNF binding and chromatin accessibility (Fig. 2G). The RNA-seq data further indicate that these genes are more significantly downregulated with BRG1 loss compared to other siBRG1 DE genes (Fig. 2H). Overall, these analyses suggest that both BRG1 and ARID1A SWI/SNF subunits regulate gene expression through promoter chromatin interactions at many shared target genes, including a highly regulated class of genes normally activated by SWI/SNF.

BRG1 and ARID1A promote transcription at epithelial identity genes

The observation that ARID1A and BRG1 activate a similar set of highly regulated genes warranted further investigation into overlapping SWI/SNF complex roles in the endometrial epithelium. We narrowed down our findings to 35 genes that are directly activated by both BRG1 and ARID1A, which display ChIP promoter binding and DGE with subunit loss (Fig. 3A). MSigDB gene set enrichment analysis (GSEA) of all 35 genes identified 17 significant GO BP gene sets (FDR < 0.05) (Fig. 3B). Enriched gene sets included biological adhesion, cell projection organization, locomotion, taxis and cell–cell adhesion (Fig. 3B). Further, the top enriched Gene Transcription Regulation Database (GTRD) (30) transcription factor (TF) network among these genes was LIM homeobox 9 (LHX9) (Supplementary Material, Fig. S2). SWI/SNF target genes found in this GO BP were often highly expressed in 12Z cells (Fig. 3C). Representative genes showed overlapping BRG1 and ARID1A promoter chromatin binding patterns (Fig. 3D). We further investigated the TF motifs enriched specifically at BRG1 and ARID1A co-bound ChIP-seq peaks within SWI/SNF-regulated gene promoters. Among all 332 promoter peaks co-bound by BRG1 and ARID1A, the top motifs included AP-1, TEAD, RUNX and ETS (Fig. 3E). At 34 promoter peaks specifically within the direct activating SWI/SNF target genes, AP-1, ETS and TEAD were also significantly enriched (Fig. 3F). Altogether, these data suggest that SWI/SNF activated genes in endometrial epithelial cells are involved in cell–cell interactions and cell adhesion, which may be affected upon mutation or loss of expression.

Figure 3

SWI/SNF directly promotes gene transcription of epithelial identity genes. (A) Identification of 35 genes by RNA-seq and ChIP-seq experiments, which are directly promoter bound by BRG1 and ARID1A and downregulated following depletion of either subunit. (B) MSigDB GSEA results for GO BP, showing 17 significantly enriched (FDR < 0.05) gene sets among the 35 direct activating SWI/SNF target genes. Hash indicates the number of SWI/SNF target genes found within that GO BP gene set. Gene set constituent matrix shows the pairwise membership of each gene to the enriched GO BP gene sets, where black cells indicate a gene belongs to that gene set. Genes are arranged from left to right based on number of enriched gene sets. (C) Baseline expression ranking of all 23 148 expressed genes in 12Z cells with relevant SWI/SNF target genes of interest denoted in red. y-axis is gene expression, represented by rlog counts as reported by DESeq2. (D) Functional evidence of activating SWI/SNF target gene regulation by BRG1 and ARID1A subunits at ALCAM, MYPN, NTN4, RHOBTB3, ENAH, SHC3, NEGR1, ANXA2 and PAWR. Left, All listed genes are downregulated with both BRG1 loss and ARID1A loss. Statistic is FDR as reported by DESeq2 and IHW. Right, All listed genes display significant SWI/SNF overlapping promoter chromatin binding. Locus snapshot y-axis is log-likelihood ratio (LogLR) of ChIP signal, and the small bars under signal traces indicate the presence of significant binding peak. (E) Top significant results from known motif analysis of all BRG1 promoter ChIP-seq peaks co-bound by ARID1A (n = 332), comprising the 256 genes co-bound by BRG1 and ARID1A (described in A). (F) Motif analysis as in E but for the 34 BRG1 promoter peaks co-bound by ARID1A at direct activating SWI/SNF target genes defined in (A).

SWI/SNF functions at distal open chromatin regions to regulate epithelial identity genes

While these results suggest that BRG1 indeed regulates transcription through promoter chromatin interactions, the majority of BRG1 binding sites (92.7%) were located distally in our data. As such, we further investigated the roles of BRG1 and SWI/SNF at distal binding sites located at least 3 kb from a gene TSS. Motif analysis revealed a different set of TF enriched specifically at BRG1 distal binding sites, including STAT, GATA, ZF and SMAD (Fig. 4A). Genome-wide, >75% of BRG1 peaks and BRG1 bound genomic bases are co-occupied by ARID1A, a highly significant overlap (hypergeometric enrichment, P = 0) (Fig. 4B and C). A similar extent of overlap was also observed specifically at distal binding sites (Fig. 4D).

Figure 4

SWI/SNF activity at distal sites also regulates the expression of epithelial identity genes. (A) Top significant results from known motif analysis of BRG1 distal ChIP-seq peaks located at least 3 kb from a gene TSS (n = 5983). (B) Proportional Euler diagram displaying overlapping BRG1 and ARID1A genome-wide ChIP-seq peaks. Intersect number is an approximation. (C) Significant association of ARID1A binding at BRG1-occupied genomic bases. Statistic is hypergeometric enrichment. (D) Proportional Euler diagram as in B, but for only distal ChIP-seq peaks, located at least 3 kb from a gene TSS. (E) Proportional Euler diagram displaying distal ATAC-seq peaks overlapping with ARID1A and/or BRG1 binding or neither. Statistic is hypergeometric enrichment. (F) Violin plots quantifying ATAC peak width (bp), accessibility magnitude (ATAC FPKM), BRG1 binding (FE) and ARID1A binding (FE) across distal open chromatin sites segregated by SWI/SNF binding as defined in E. Statistic is two-tailed, unpaired Wilcoxon test. (G) Signal heatmap of ATAC, BRG1 binding and ARID1A binding across all distal open chromatin sites segregated by SWI/SNF binding as defined in E. ATAC is quantified as reads per million per bp (RPM/bp), while BRG1 and ARID1A binding are quantified by FE. (H) Comprehensive motif analysis and clustering of distal open chromatin sites. Heatmap displays FE for motif presence in regions of interest compared to background genomic regions, for 103 known motifs significant (HOMER -log(p) > 60) in at least one distal open chromatin peak set. Manually curated regulatory patterns of interest were then extracted and given an arbitrary color annotation. Representative motif logos are displayed. (I) Hallmark pathway and GO BP enrichment analysis for genes associated with distal open chromatin sites bound by BRG1 + ARID1A (n = 2972 genes), ARID1A only (n = 7622 genes), BRG1 only (n = 139 genes) and neither (n = 3819 genes). (J) Enrichment for siBRG1 (top) or siARID1A (bottom) DGE among genes associated with SWI/SNF-bound distal open chromatin sites via GeneHancer database, based on the number of associated sites: all genes (n = 23 148), genes with 0 associated sites (n = 14 727), genes with 1 (n = 3962), genes with 2 (n = 2130), genes with 3 (n = 1088), genes with 4 (n = 562), genes with 5 (n = 298) or genes with 6 or more (n = 381). Enrichment statistic is hypergeometric enrichment. Pairwise statistic is two-tailed Fisher’s exact test. (K) Baseline expression as quantified by rlog counts for genes as described in (J). Statistic is two-tailed, unpaired Wilcoxon test. (L) Distribution of SWI/SNF binding classification for associated sites of genes with exactly 1 (n = 3962 genes) versus 6+ (n = 381 genes) SWI/SNF-bound distal open chromatin sites. Statistic is two-tailed Fisher’s exact test. ^*P < 0.05, ^**P < 0.01, ^***P < 0.001.

We next used ATAC-seq peaks marking open chromatin as an indicator of potential regulatory activity at distal genomic regions bound by SWI/SNF, since chromatin accessibility is strongly associated with TF binding and other forms of chromatin regulation (31). Out of 27 206 distal ATAC peaks, we observed BRG1 and ARID1A co-bound at 3233 sites, ARID1A bound without significant BRG1 at 18 376 sites, BRG1 bound without ARID1A at 61 sites and neither SWI/SNF subunit bound at the remaining 5536 sites (Fig. 4E). Of these four classes of putative active regulatory elements, BRG1 + ARID1A co-bound distal open chromatin sites showed the greatest accessibility in terms of magnitude and peak width (Fig. 4F and G). Further, BRG1 and ARID1A binding were strongest at co-bound sites as opposed to sites bound by either individually (Fig. 4F and G). These results suggest that BRG1 + ARID1A co-bound distal open chromatin sites are likely highly active regulatory elements. Motif analysis was then performed on each of the four classes of distal open chromatin sites and a number of patterns emerged. The TF motif patterns included: enriched in BRG1 only sites (blue, e.g. STAT), enriched in all ARID1A-bound sites (green, e.g. TEAD), enriched in sites not bound by BRG1 (gray, e.g. RFX), enriched in sites bound by either subunit individually but not both (purple, e.g. CTCF) and universally enriched but most strongly in ARID1A-bound sites (black, e.g. AP-1) (Fig. 4H). In order to associate distal open chromatin sites with potential transcriptional regulatory functions, we used the GeneHancer database (32) to link distal ATAC sites to specific genes as supported by computational and experimental evidence (Fig. 4I). Hallmark pathway and GO BP enrichment analysis were then performed on genes associated with distal open chromatin sites segregated by SWI/SNF binding, and relevant cellular processes were highlighted in the BRG1 + ARID1A and ARID1A-only classes (Fig. 4I). Notably, this analysis revealed that BRG1 and ARID1A co-regulate distal elements associated with genes involved in extracellular structure organization, cell junction organization, cell junction assembly, cell substrate adhesion and cell matrix adhesion (Fig. 4I), corroborating promoter regulation results.

Further evidence that distal open chromatin sites bound by SWI/SNF are functional was achieved by segregating genes based on the number of associated SWI/SNF-bound distal open chromatin sites. We observed that genes with a greater number of associated SWI/SNF-bound distal open chromatin sites were more likely to be transcriptionally dysregulated following SWI/SNF subunit loss (Fig. 4J). For example, out of 3962 genes with exactly one associated SWI/SNF-bound distal open chromatin site, 43% were DE with siBRG1 treatment (Fig. 4J). In contrast, 55% of the 381 genes with six or more associated SWI/SNF-bound distal open chromatin sites were DE with siBRG1 treatment (Fig. 4J). We also noted that baseline expression levels were significantly higher at genes with 6+ associated SWI/SNF-bound distal open chromatin sites as opposed to those with exactly 1 (two-tailed, unpaired Wilcoxon test, P < 10⁻¹⁵) (Fig. 4K). Interestingly, associated distal open chromatin sites were more likely to be BRG1 + ARID1A co-bound at genes with 6+ SWI/SNF-bound distal open chromatin sites as opposed to those with exactly 1 (two-tailed Fisher’s exact test, OR = 6.16, P < 10⁻¹⁵) (Fig. 4L). Altogether, these results suggest that SWI/SNF regulation of distal elements contributes to gene expression at epithelial identity genes in the endometrial epithelium.

BRG1 loss in the mouse endometrial epithelium promotes gland translocation to the uterine myometrium

To discern the consequences of BRG1 loss in vivo, we developed a GEMM of BRG1 loss specifically in the endometrial epithelium. We utilized LtfCre (Tg(Ltf-iCre)14Mmul) (33) in crosses with the Brg1^fl allele (34) (Fig. 5A). LtfCre becomes active when mice reach sexual maturity (33). Inheritance of the Brg1^fl allele was confirmed by polymerase chain reaction (PCR; Fig. 5B). Heterozygous (LtfCre^0/+; Brg1^fl/+) or homozygous (LtfCre^0/+; Brg1^fl/fl) loss of BRG1 in the endometrial epithelium resulted in no macroscopic disease burden or deaths, and mice were aged out to a maximum of 48 weeks (n = 9 and 10, respectively). Gross inspection of LtfCre^0/+; Brg1^fl/+ or LtfCre^0/+; Brg1^fl/fl uterus revealed no obvious phenotypic consequences of BRG1 loss (Fig. 5C). Upon histological analysis, no abnormal features were observed in the uterus of LtfCre^0/+; Brg1^fl/+ mice (n = 3; Fig. 5D). However, endometrial glands were observed within the myometrium of all inspected LtfCre^0/+; Brg1^fl/fl mice (n = 4), indicating adenomyosis-like phenotypes (Fig. 5E, Supplementary Material, Fig. S3). Endometrial BRG1 expression localized to the glandular and luminal epithelium of LtfCre^0/+; Brg1^fl/fl mice varied, as some glands and epithelial cells retained BRG1 expression, while others displayed loss of BRG1 expression by immunohistochemistry (Fig. 5E, Supplementary Material, Fig. S3). Intriguingly, endometrial glands found within the myometrium displayed uniform loss of BRG1 expression (Fig. 5E, Supplementary Material, Fig. S3). The observation that exclusively BRG1-null glands were observed in the myometrium, but not the endometrium, suggests that BRG1 loss is a requirement for gland translocation of LtfCRE+ endometrial epithelial cells. Glands found both in the endometrium and myometrium expressed cytokeratin 8 (KRT8), a marker of endometrial epithelia, confirming cellular identity (Fig. 5E, Supplementary Material, Fig. S3). Since adenomyosis is further characterized as a progesterone-resistant condition (35) and often leads to PGR silencing (36), we additionally examined the expression of progesterone receptor (PGR). LtfCre^0/+; Brg1^fl/fl endometrial glands displayed PGR loss in myometrium-localized glands (Fig. 5E, Supplementary Material, Fig. S3). These results suggest that BRG1 normally prevents myometrial invasion of endometrial epithelia and genetic disruption promotes invasive pathology and adenomyosis-like phenotypes.

Figure 5

Genetically engineered mice harboring BRG1 loss in the endometrial epithelium develop adenomyosis-like phenotypes. (A) Diagram of Brg1^fl allele. (B) PCR verification of Brg1^fl allele presence in wild-type, heterozygous and homozygous mice. (C) Gross images of LtfCre^0/+; Brg1^fl/+ and LtfCre^0/+; Brg1^fl/fl mice. White arrowheads denote the uterus without visible, gross abnormalities. (D, E) Representative hematoxylin and eosin (H&E) staining and IHC for BRG1, KRT8 and PGR (only in homozygotes) in the uterus of (D) LtfCre^0/+; Brg1^fl/+ or (E) LtfCre^0/+; Brg1^fl/fl mice at 5× (scale bar = 500 μm), 10× (scale bar = 300 μm) or 20× (scale bar = 200 μm) magnification. Endometrial and myometrial images are representative magnifications of sections identified within whole uterus surrounded by black box. BRG1 expression is lost in the endometrial epithelium of LtfCre^0/+; Brg1^fl/fl mice. KRT8 is a marker of endometrial epithelium. Arrowheads indicate endometrial epithelium, and high magnification images of these glands have been provided in Supplementary Material, Fig. S3.

Transcriptomic analysis of BRG1-null endometrial epithelium reveals actin cytoskeletal and anchoring junction defects

In order to characterize molecular programs driving the BRG1 mutant endometrial epithelial phenotype, we performed RNA-seq on sorted endometrial epithelial cells from 120-day-old (60 days post-CRE induction) LtfCre^0/+; Brg1^fl/fl mice. The magnetic sorting method we developed utilizes an antibody against epithelial cell adhesion molecule (EPCAM) and magnetic beads to positively enrich endometrial epithelia from enzyme-digested uterine tissue (26). EPCAM-positive endometrial epithelial cells were enriched from LtfCre^0/+; Brg1^fl/fl mice at 82% purity on average (Supplementary Material, Fig. S4), which is not significantly different from our previously reported control mice (26). Compared to endometrial epithelial cells from control mice, cells from LtfCre^0/+; Brg1^fl/fl mice displayed widespread transcriptomic dysregulation, with 3145 genes significantly DE (Fig. 6A). As validation, BRG1 expression (Smarca4) was uniformly depleted (FDR < 10⁻¹⁰) among sorted mutant cells (Fig. 6B). In mutant cells, equal numbers of DE genes with increasing (51.6%) and decreasing (48.4%) expression were observed (Fig. 6C). Gene expression changes in LtfCre^0/+; Brg1^fl/fl significantly overlapped with those from siBRG1 12Z cells (hypergeometric enrichment, P < 10⁻⁸⁹) (Fig. 6D). Pathway analyses revealed that upregulated genes were enriched for Hallmark pathways, including G2M checkpoint and E2F targets (Fig. 6E), while downregulated genes were enriched for TNFα signaling, p53 pathway and hypoxia response (Fig. 6F). Among GO BP gene sets, many downregulated pathways were related to cellular invasion, including anchoring junction, actin filament-based process, cell substrate junction and actin cytoskeleton (Fig. 6F). These results suggest that BRG1 normally maintains non-invasive, cell-adherent phenotypes in the endometrial epithelium in vivo by promoting transcription of cell junction and actin cytoskeletal processes, similarly as observed in 12Z cell studies.

Figure 6

LtfCre^0/+; Brg1^fl/fl endometrial epithelial cells display loss of actin cytoskeletal and cellular junction programs. (A) Volcano plot of RNA-seq differential gene expression in sorted EPCAM-positive endometrial epithelial cells purified from LtfCre^0/+; Brg1^fl/fl (n = 4) versus control (n = 3) mice. x-axis is log₂ fold-change (log₂FC) mutant versus control, and y-axis is significance as −log₁₀(FDR). 3145 genes were identified as significant by FDR < 0.05 threshold. (B) Relative Smarca4 (BRG1) linear expression in control and LtfCre^0/+; Brg1^fl/fl cells. Significance is FDR value reported by DESeq2 and IHW. (C) Relative expression heatmap of 3145 significant DE genes. Heatmap displays expression as scaled (Z-score) rlog counts. 51.6% of DE genes are upregulated in LtfCre^0/+; Brg1^fl/fl cells, and 48.4% of DE genes are downregulated. (D) Significant overlap between DE genes in LtfCre^0/+; Brg1^fl/fl versus control sorted endometrial epithelial cells and siBRG1-treated versus control 12Z cells. Statistic is hypergeometric enrichment. (E, F), Significant MSigDB Hallmark (top) and GO BP (bottom) gene set enrichment results from human orthologs of (E) upregulated and (F) downregulated genes in LtfCre^0/+; Brg1^fl/fl cells.

SWI/SNF subunits commonly regulate epithelial cell adhesion and junction programs

We previously reported two GEMM of endometrial epithelial ARID1A loss, with or without PI3K activation via oncogenic PIK3CA^H1047R expression. While mice with ARID1A loss alone (LtfCre^0/+; Arid1a^fl/fl) display endometrial cell death, ARID1A/PIK3CA co-mutant mice (LtfCre^0/+; (Gt)R26^{PIK3CA*H1047R}; Arid1a^fl/fl) display myometrial and peritoneal invasion (26,37). In order to identify consistent features among SWI/SNF-mutant, invasive endometrial epithelium, we next compared gene expression changes observed in sorted endometrial epithelial cells from LtfCre^0/+; (Gt)R26^{PIK3CA*H1047R}; Arid1a^fl/fl to LtfCre^0/+; Brg1^fl/fl. We initially observed 578 overlapping DE genes affected in both GEMM, a significant overlap (hypergeometric enrichment, P < 10⁻⁶¹) (Fig. 7A). Moreover, 88% of these overlapping DE genes displayed the same directionality change between the two models, with half of these genes downregulated in both models (Fig. 7B), indicating overlapping functions of BRG1 and ARID1A in vivo. Broad GSEA was performed on endometrial epithelial transcriptomes of each GEMM versus control cells for MSigDB Hallmark pathways and GO BP gene sets, and normalized enrichment score (NES) results were compared between the two GEMM. Relatively few gene sets were activated in BRG1-null endometrial epithelia, but many critical Hallmark pathways were downregulated in both GEMM, including Hallmark estrogen and androgen hormone responses, hypoxia, and the unfolded protein response (Fig. 7C). Notably, EMT was downregulated in BRG1-null mice (NES = −1.46, P = 0.07, FDR = 0.15) (Fig. 7C). We have previously demonstrated that EMT activation is a feature of ARID1A mutant cells (26), suggesting that this process may not occur following BRG1 loss. Moreover, among GO BP gene sets (Fig. 7D and E), other gene sets were significantly downregulated in BRG1-null mice and activated in ARID1A/PIK3CA mutant mice, including positive regulation of actin cytoskeleton reorganization (Fig. 7F). However, gene sets downregulated in both GEMM include epithelial cell–cell adhesion and cell junction assembly (Fig. 7G and H). We also observed three related cellular junction GO BP gene sets that were significantly enriched in genes downregulated in 12Z following subunit depletion (Fig. 7I). These data suggest that, while subunit-specific roles are frequently observed, loss of epithelial cell adhesion and cell junction regulation are shared phenotypes among endometrial SWI/SNF subunit mutations.

Figure 7

Endometrial SWI/SNF mutant mouse models converge on loss of cellular adhesion and junction. (A) Proportional Euler diagram displaying significant overlap of DE genes in sorted epithelial cells from both endometrial epithelial-specific SWI/SNF mutant GEMM versus control mice. Statistic is hypergeometric enrichment. (B) Directional classification of 578 overlapping DE genes between both GEMM. 88% of overlapping DE genes are affected in the same direction, with the dominant class being shared downregulated genes in both GEMM. (C, D) Overview of Broad GSEA results for (C) MSigDB Hallmark pathways (n = 50) and (D) GO BP gene sets (n = 3879) in both GEMM. Enrichment is presented as GSEA NES, where positive values indicate pathway upregulation and negative values indicate downregulation. x-axis displays enrichment in LtfCre^0/+; Brg1^fl/fl mice, and y-axis displays enrichment in LtfCre^0/+; (Gt)R26^{PIK3CA*H1047R}; Arid1a^fl/fl mice. (E) Zoom into enriched (|NES| > 1) pathways based on directionality between the two models. Representative, highly enriched pathway titles are overlaid on each plot. Red and bolded pathways are representatively predicted as important to GEMM phenotype and further display associated waterfall plots and statistical results in F–H. (F) Positive regulation of actin cytoskeleton reorganization is downregulated in LtfCre^0/+; Brg1^fl/fl cells but upregulated in LtfCre^0/+; (Gt)R26^{PIK3CA*H1047R}; Arid1a^fl/fl cells. (G, H) Epithelial cell–cell adhesion and cell junction assembly are mutually downregulated between both GEMM. (I) GO BP enrichment results for three significant (FDR < 0.10) cellular junction gene sets in 12Z cells treated with (left) siBRG1 or (right) siARID1A versus control. Hash indicates the number of DE genes in each condition belonging to that gene set.

Discussion

These studies provide evidence that subunit-specific mutations within the SWI/SNF chromatin remodeling complex contribute to endometrial pathogenesis by inducing invasion-like phenotypes. In vitro studies in immortalized human endometrial epithelial cells revealed that loss of either ARID1A or BRG1 subunits results in dysregulation of physiologically relevant gene regulatory networks. Despite widespread differences in subunit-specific target gene regulation, overlapping downregulated genes following loss of either BRG1 or ARID1A, corresponding to genes requiring normal SWI/SNF function for proper transcriptional activation, play roles in cell adhesion and cell junction. BRG1 and ARID1A were also shown to directly co-regulate distal open chromatin sites associated with genes involved in epithelial identity processes. In vivo, endometrial glands without obvious oncogenic transformation or hyperplasia were observed in the myometrium following BRG1 deletion in the endometrial epithelium in our newly characterized GEMM. Abnormal endometrial glands in the uterine myometrium are a pathological hallmark of adenomyosis. Co-existent ARID1A/PIK3CA mutations in the endometrial epithelium also promote myometrial invasion alongside hyperplasia, although ARID1A mutation alone is not sufficient for this process to occur (26). These results suggest that SWI/SNF subunits have overlapping roles in preventing endometrial invasion, while also displaying subunit-specific functions.

The loss of cellular adhesion and cellular junction programs may be a common mechanism by which invasive phenotypes manifest in SWI/SNF mutant endometrial pathologies. Cell–cell adhesion and cell–matrix interactions are characteristic properties of epithelial cells required for various cellular processes (38). Extracellular matrix and basement membrane provide anchoring support for epithelial cell spatial stability and functional membrane polarization, and degradation of these features permits tissue dissociation and stromal invasion (38). In the endometrium, matrix degradation and disruption of adhesion normally occur with endometrial sloughing during menstruation (39,40). However, malignant cells also disrupt these processes to metastasize from the primary tissue site, through the acquisition of mesenchymal characteristics following EMT (41). ARID1A mutant endometrial epithelia undergo EMT and are capable of collective myometrial invasion in the presence of PIK3CA^H1047R (26). However, in this study, we have shown that BRG1 mutant endometrial epithelia do not display canonical EMT transcriptional features but can still invade nearby myometrium. These findings suggest that disruption of other epithelial integrity processes related to cell–cell adhesion and cell junctions likely contributes to invasive phenotypes, which are shared features of BRG1 and ARID1A mutant endometrial epithelia. Previous studies have demonstrated that SWI/SNF alterations modulate extracellular matrix and cellular adhesion (42–44). Other features of disease pathogenesis, such as abnormal steroid hormone signaling, may promote endometrial invasion through similar mechanisms (45).

In vivo, ARID1A loss causes endometrial epithelial cells to undergo EMT, which triggers apoptosis probably through anchorage-dependent cell death, i.e. anoikis (26,46). The addition of the PIK3CA mutation promotes a partial EMT state, resulting in collective invasion, and likely allows ARID1A mutant cells to bypass anoikis. Our previous findings suggested that among ARID1A/PIK3CA mutant endometrium, ARID1A loss is the major driver of EMT and invasion, while PIK3CA mutations temper EMT and allow cells to bypass apoptosis (26). Recent evidence supports that clonal PIK3CA mutations are observed in non-malignant, eutopic endometrium, but ARID1A mutations are not (7). Based on these data, endometrial PIK3CA mutations likely occur before deleterious ARID1A mutations during endometriosis development. Since PIK3CA mutations are not required for BRG1 mutant cell invasion, additional cellular properties are likely provided by BRG1 null states that do not occur following ARID1A disruption alone. This is evidenced by transcriptomic studies in 12Z cells described here, which demonstrate that BRG1 loss elicits a greater effect on gene expression than ARID1A loss. This may be expected, as BRG1 functions as a core catalytic subunit of additional SWI/SNF complexes containing other ARID subunits (47). We speculate that BRG1 loss allows endometrial epithelium to undergo matrix detachment or loss of cell adhesion and bypass anoikis in the absence of PIK3CA alteration, and PIK3CA mutations may not be required for myometrial translocation in the context of SMARCA4 mutations. Notably, adenomyosis is a reported risk factor for endometrial cancer development (48), so it is possible that some BRG1 mutant endometrial cancers may originate from adenomyotic lesions. A whole-exome sequencing study in adenomyotic lesions detected possible ARID1A mutations, but they could not be verified by targeted sequencing potentially due to insufficient sequencing read depth required to detect rare variant alleles (36).

Mechanistically, ARID1A and BRG1 globally regulate common cellular processes as measured by direct transcriptional regulation, both at proximal gene promoters and distal elements. However, widespread subunit-specific regulation was also observed. At the level of chromatin interactions, ~95% of gene promoters significantly bound by BRG1 were also bound by ARID1A, suggesting that the strongest promoter targets of BRG1-containing SWI/SNF complexes likely also contain ARID1A. These SWI/SNF gene targets were involved in processes such as cell motility and adhesion, corroborating invasive phenotypes in vivo. At directly regulated gene promoters and distal elements, BRG1 and SWI/SNF were identified to interact with chromatin specifically at AP-1, ETS and TEAD motifs. Previous studies have shown that SWI/SNF complexes are recruited to regulatory elements containing these motifs, where they can interact with associated TFs to promote target gene expression (49–52). Across the entire promoter regions, LHX9 was also predicted as the top TF regulating direct activating SWI/SNF target genes in 12Z cells, and this TF has been previously demonstrated as essential for gonadogenesis as well as implicated in cancer and in vitro migration and invasion (53,54). ANXA2 is a SWI/SNF target gene involved in cellular adhesion, with a predicted regulatory LHX9 binding site, that has previously been linked to proper embryo implantation in the endometrial epithelium (55). Critical subunit-specific distinctions were observed, though, as BRG1 ChIP enrichment was significantly higher at genes normally activated by BRG1, whereas ARID1A binding was higher at genes normally repressed by ARID1A. This result suggests that these SWI/SNF subunits serve primarily activating versus repressive roles in the endometrial epithelium, respectively, which may contribute to phenotypic disparities.

Opposing changes in gene expression following ARID1A or BRG1 knockdown may be attributed to differences in SWI/SNF combinatorial subunit composition (56). For example, BRG1 displays mutual exclusivity with the alternate SWI/SNF catalytic subunit, BRM (Brahma; SMARCA2), while the ARID1B and ARID2 subunits are mutually exclusive with ARID1A (56–58). We previously identified that ARID1A interacts with both BRG1 and BRM by co-immunoprecipitation in the 12Z cell model (26). Here, we demonstrated that while ARID1A binding occurs preferentially at genes that are repressed by ARID1A (and upregulated upon ARID1A loss), ARID1A/BRG1 co-binding is strongest at genes that are normally activated by ARID1A/BRG1 complexes. ARID1A/BRM complexes may play an important role in repressing gene transcription at co-bound genes, and future studies will interrogate the role of BRM in the regulation of ARID1A-dependent phenotypes.

The phenotypes observed following SWI/SNF disruption in the endometrial epithelium are likely reflective of the functions of this complex in normal endometrial physiology. Throughout the menstrual cycle, constant tissue remodeling is stimulated by steroid hormone cycling to permit blastocyst implantation (59). Proper uterine receptivity during the window of implantation requires extracellular matrix remodeling and loss of cell–cell interactions to facilitate embryo invasion (60). If implantation does not occur, progesterone withdrawal during menstruation promotes endometrial tissue desquamation and sloughing into the lumen for further regeneration in the next cycle (59), and this process also requires extracellular matrix remodeling and loss of adhesion (39,40). The loss of cellular junctions and adhesion observed following genetic SWI/SNF loss in this context might suggest that SWI/SNF is instrumental to governing these processes in normal endometrial physiology. It is possible that SWI/SNF activity may be altered during certain periods of the menstrual cycle to carefully promote such behaviors appropriately under normal physiological conditions.

Materials and Methods

Data availability and analyzed data sets

RNA-seq and ChIP-seq data generated in this study were deposited into Gene Expression Omnibus (GEO) at accession series GSE152663. A list of all data sets used or generated in this study is described in Supplementary Material, Table S2. In summary, previously reported wild-type 12Z cell ARID1A ChIP-seq data (n = 2), non-targeting siRNA control-treated 12Z cell ATAC-seq data (n = 2), siARID1A and non-targeting siRNA control-treated 12Z cell RNA-seq data (n = 3), and LtfCre^0/+; (Gt)R26^{PIK3CA*H1047R}; Arid1a^fl/fl (n = 4) and control (n = 3) GEMM sorted EPCAM-positive endometrial epithelial cell RNA-seq data were all retrieved and reanalyzed from GEO accession series GSE121198 (26). SMARCA4 somatic mutation data from the TCGA-UCEC PanCancer cohort were retrieved from cBioportal (4,23,61).

Mice

All mice were maintained on an outbred genetic background using CD-1 mice (Charles River, Wilmington, MA). LtfCre (Tg(Ltf-iCre)14Mmul) allele was purchased from The Jackson Laboratory (Bar Harbor, ME) and verified by PCR using published methods (33). Inheritance of the Brg1^fl allele was confirmed using published genotyping methods (34). Mice were monitored for signs of distress including vaginal bleeding and severe abdominal distension, as well as signs of severe illness including dehydration, hunching, jaundice, ruffled fur, signs of infection or non-responsiveness. 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 and has an approved Animal Welfare Assurance from the NIH Office of Laboratory Animal Welfare. Michigan State University is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care.

Cell lines

12Z immortalized human endometrial epithelial cells (25) were maintained in DMEM/F12 media supplemented with 10% fetal bovine serum (FBS), 1% L-glutamine and 1% penicillin/streptomycin (P/S).

Transfection of 12Z cells with siRNA

12Z cells were seeded at a density of 40 000 cells/ml in DMEM/F12 media supplemented with 10% FBS and 1% l-glutamine. After 24 h, cells were transfected with 50 pmol/ml of siRNA (Dharmacon, ON-TARGETplus Non-targeting Pool and human SMARCA4 #6597 SMARTpool) using the RNAiMax (ThermoFisher, Waltham, MA) lipofectamine reagent at a ratio of 1:1 volume:volume in OptiMEM (Gibco, ThermoFisher), and the media were replaced after 24 h. Media were replaced again 48 h post-transfection with DMEM/F-12 media supplemented with 0.5% FBS, 1% P/S and 1% l-glutamine. Transfected 12Z cells were harvested 72 h post-transfection using the Quick-RNA Miniprep Kit (Zymo Research, Irvine, CA) for RNA or RIPA buffer (Cell Signaling, Danvers, MA) for protein.

Cell growth assay

For cell growth assay, cells were initially seeded at a density of 4000 cells per well in a 96-well plate (n = 4 wells per condition). At 48 and 72 h post-transfection, cells were incubated with 2 μg/ml calcein-AM for 1 h, and fluorescence was measured using a SpectraMax i3x (Molecular Devices, San Jose, CA).

Histology and immunohistochemistry

Mice were euthanized by carbon dioxide inhalation, and uteri were collected. For indirect immunohistochemistry (IHC), 10% neutral buffered formalin-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 BRG1 (ab110641, Abcam, Cambridge, United Kingdom); 1:400 PGR (SAB5500165, Sigma, St. Louis, MO); 1:100 KRT8 (Cytokeratin 8) (TROMA-I, DHSB, Iowa City, IA). TROMA-I antibody was deposited to the Developmental Studies Hybridoma Bank (DSHB) by Brulet, P./Kemler, R. (DSHB Hybridoma Product TROMA-I). The following biotin-conjugated secondary antibodies were used: donkey anti-rabbit IgG (711-065-152, Jackson Immuno Research Labs, West Grove, PA) and donkey anti-rat IgG (#705-065-153, Jackson Immuno Research Labs). Secondary antibodies were detected using VECTASTAIN Elite ABC HRP Kit (Vector, Burlingame, CA). Sections for IHC were lightly counterstained with Hematoxylin QS or Methyl Green (Vector Labs). Routine hematoxylin and eosin staining of sections was performed by the Van Andel Research Institute, Grand Rapids, MI (VARI) Histology and Pathology Core. A VARI animal pathologist reviewed histological sections.

Cell sorting

EPCAM-positive endometrial epithelial cells were purified as previously described (26). Briefly, mouse uteri were surgically removed and minced using scissors, followed by digestion with the MACS Multi Tissue Dissociation Kit II (Miltenyi Biotec, Gladbach, Germany) for 80 min at 37°C. Digested tissues were strained through a 40 μm nylon mesh (ThermoFisher), red blood cells were removed using the red cell lysis buffer (Miltenyi Biotec) and dead cells were removed by the MACS Dead Cell Removal Kit (Miltenyi Biotec). EPCAM-positive cells were purified using a PE-conjugated EPCAM antibody and anti-PE MicroBeads (Miltenyi Biotec). Purity of cell populations was confirmed using a BD Accuri C6 flow cytometer (BD Biosciences) and analysis was performed using FlowJo v10 software (BD Biosciences, San Jose, CA).

RNA isolation

The Arcturus PicoPure RNA Isolation Kit (ThemoFisher) was used to purify RNA from in vivo EPCAM-sorted endometrial epithelial cells from 120-day-old LtfCre^0/+; Brg1^fl/fl mice (n = 4 biological replicates), and DNA was digested on-column using the RNAse-free DNAse set (Qiagen). RNA samples were collected from 12Z cells 72 h post siRNA transfection using the Quick-RNA Miniprep Kit (Zymo Research).

Construction and sequencing of directional mRNA-seq libraries

Libraries were prepared by the Van Andel Genomics Core from 500 ng of total RNA using the KAPA mRNA HyperPrep kit (v4.17) (Kapa Biosystems), Wilmington, MA. 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, Coralville, IA). Quality and quantity of the finished libraries were assessed using a combination of Agilent DNA High Sensitivity chip (Agilent Technologies, Santa Clara, CA), QuantiFluor® dsDNA System (Promega, Madison, WI) 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, San Diego, CA). Each library was sequenced to an average raw depth of 20 M reads. Base calling was performed by Illumina RTA3 and NextSeq Control Software (NCS) output was demultiplexed and converted to FastQ format with Illumina Bcl2fastq v1.9.0.

RNA-seq analysis

Raw paired-end reads were trimmed with cutadapt (62) and Trim Galore! (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/), and quality was analyzed using FastQC (63) and MultiQC (64). Trimmed mouse reads were aligned to mm10 genome assembly and indexed to GENCODE vM16 GFF3 annotation via STAR (65) aligner with flag ‘—quantMode GeneCounts’ for feature counting. Trimmed human reads were aligned to GRCh38.p12 and indexed to GENCODE v28. Output gene count files were constructed into an experimental read count matrix in R, where they were combined with respective model system raw counts from previously reported control and experimental RNA-seq data, retrieved from GEO accession series GSE121198. Low count genes were filtered (1 count per sample on average) prior to normalization factor generation, dispersion estimation, negative binomial generalized linear model fitting and hypothesis testing in DESeq2 (66) for DGE analysis. For linear modeling, the design matrix was constructed from a single ‘condition’ variable with the inclusion of an intercept, in the form: |$\sim condition$|⁠. Calculated probabilities were corrected for multiple testing by independent hypothesis weighting (IHW) (67) for downstream analysis. Significance threshold for differential gene expression was set at FDR < 0.0001 for 12Z cell studies and FDR < 0.05 for sorted mouse cell studies. RNA-seq expression heatmaps were generated using scaled regularized-logarithm counts for visualization.

Chromatin immunoprecipitation

Wild-type 12Z cells were treated with 1% formaldehyde in fresh DMEM/F12 media without supplements for 10 min at room temperature, and crosslinking was quenched with 0.125 M Glycine and incubation for 5 min at room temperature, followed by wash with PBS. 1 × 10⁷ crosslinked cells were used per IP. Chromatin was fractionated by digestion of crosslinked cells with micrococcal nuclease using the SimpleChIP Enzymatic Chromatin IP Kit (Cell Signaling) per the manufacturers’ instructions, followed by 30 s of sonication using a Bioruptor Pico sonicator (Diagenode, Liège, Belgium). IP were performed in duplicate using the SimpleChIP Enzymatic Chromatin IP Kit per the manufacturers’ instructions with 1:100 anti-BRG1 antibody (ab110641, Abcam). Crosslinks were reversed with 0.4 mg/ml Proteinase K (ThermoFisher) and 0.2 M NaCl at 65°C for 2 h. DNA was purified using the ChIP DNA Clean & Concentrator Kit (Zymo).

Construction and sequencing of ChIP-seq libraries

Libraries for input and IP samples were prepared by the Van Andel Genomics Core from 10 ng of 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 IDT for Illumina UDI 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 S1 sequencing kit (Illumina). Each library was sequenced to minimum read depth of 80 million reads per input library and 40 million reads per IP library. Base calling was performed by Illumina NCS v2.0, and NCS output was demultiplexed and converted to FastQ format with Illumina Bcl2fastq v1.9.0.

ChIP-seq analysis

Raw single-end reads for IP and inputs were trimmed with cutadapt (62) and Trim Galore! (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) followed by quality control analysis via FastQC (63) and MultiQC (64). Trimmed reads were aligned to GRCh38.p12 reference genome via Bowtie2 (68) with flag ‘—very-sensitive’. Aligned reads were sorted and indexed with samtools (69). Picard MarkDuplicates (http://broadinstitute.github.io/picard/) was used to remove PCR duplicates, followed by sorting and indexing. MACS2 (70) was used to call broad peaks on each ChIP replicate against the input control with FDR < 0.05 threshold and otherwise default settings. The resulting peaks were repeat-masked by the ENCODE blacklist (71) and filtered for non-standard contigs. A naïve overlapping peak set, as defined by ENCODE (72), was constructed by calling MACS2 peaks on pooled replicates followed by bedtools intersect (73) to select for pooled peaks of at least 50% bp overlap with each biological replicate.

Western blotting

Crude protein lysates were quantified using the Micro BCA Protein Assay Kit (ThermoFisher) and a FlexSystem3 plate reader (Molecular Devices). Protein lysates were run on a 4–15% gradient SDS-PAGE gel (Bio-Rad, Hercules, CA) and transferred to PVDF membrane using the TransBlot Turbo system (BioRad). Primary antibodies were used at the following dilutions: 1:1000 BRG1 (G-7) (sc-17 796, Santa Cruz, Dallas, TX) and 1:1000 β-Actin (8457, Cell Signaling). Secondary antibodies conjugated to horseradish peroxidase (Cell Signaling) were used at a dilution of 1:2000. Protein band visualization was performed using Clarity Western ECL Substrate (BioRad) and a ChemiDoc XRS+ imaging system (BioRad). Densitometry calculations were performed using ImageJ software (National Institutes of Health, Bethesda, MD).

Bioinformatics and statistics

Various HOMER (74) functions were applied to count reads at loci of interest, perform motif analysis on peak coordinates and annotate genomic regions, with a modification to gene promoter classification as within 3 kb of a TSS. Motif logos generated by HOMER are scaled by information content. TxDb.Hsapiens.UCSC.hg38.knownGene (75) was used to define gene promoters for all standard hg38 genes as 3 kb regions surrounding the primary TSS. ATAC-seq data were analyzed as previously described (26,76). MACS2 (70) was used to produce genome-wide signal log-likelihood ratio tracks for IGV (77) visualization. Broad GSEA (78) was performed via GenePattern (79) on DESeq2 normalized counts, which were converted to human orthologs in the case of mouse data. clusterProfiler (80) was used to compute and visualize pathway and gene set enrichment from a list of gene symbols compared to the respective gene universe. Hallmark pathways, GO BPs and GTRD TF target gene sets were retrieved from MSigDB (v7.1) (27–30), and MSigDB GSEA ‘Compute Overlaps’ function was used for (Fig. 3, Supplementary Material, Fig. S2) enrichment analyses and visualization. GeneHancer database (32) was used to link distal open chromatin regions to regulation of specific genes, and only GeneHancer associations above an arbitrary score threshold of 1 were considered. ComplexHeatmap (81) was used for hierarchical clustering by Euclidean distance and general heatmap visualization. GenomicRanges (82) functions were used to intersect and manipulate genomic coordinates. eulerr (83) was used to produce proportional Euler diagrams. biomaRt (84,85) was used for all gene nomenclature and ortholog conversions. ggplot2 (86) was used for certain plotting applications. The cumulative hypergeometric distribution was used for manual enrichment tests. The statistical computing language R (87) was used for many applications throughout this manuscript.

Acknowledgements

We thank Dr John Risinger for helpful discussions. The Brg1^fl allele was a generous gift from Terry Magnuson. We thank the Van Andel Research Institute Genomics Core for providing library construction and sequencing facilities and services. We thank the Van Andel Research Institute Histology and Pathology Core for histology services and Dr Galen Hostetter for his assistance with pathology. Graphical abstract was produced using vectors from BioRender.com. Conflict of Interest Statement. The authors declare no competing interests.

Funding

American Cancer Society (PF-17-163-02-DDC to M.R.W.); National Institute of Child Health and Human Development (HD099383-01 to R.L.C.).

Author Contributions

R.L.C. conceived the study. J.J.R., M.R.W., J.H. and R.L.C. designed the presented experiments. J.H. and R.L.C. performed animal husbandry. J.H. and R.L.C. performed immunohistochemistry. M.R.W. performed endometrial epithelial cell isolation and sorting. M.R.W. performed cell culture experiments and prepared RNA samples. M.R.W. generated protein lysates and performed immunoblotting. J.J.R. prepared chromatin and generated immunoprecipitation samples. M.W. and M.A. constructed and sequenced libraries. J.J.R. performed formal data analysis and curation. J.J.R. and M.R.W. prepared the initial manuscript draft with edits by R.L.C. All authors participated in manuscript revisions.

The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.