Poly(ADP-ribose) polymerase-1 (PARP-1) has gained considerable attention as a target for therapeutic inhibitors in breast cancers. Previously we showed that PARP-1 localizes to active gene promoters to regulate histone methylation and RNA polymerase II activity (Pol II), altering the expression of various tumor-related genes. Here we report a role for PARP-1 in estrogen-dependent transcription in estrogen receptor alpha (ERα)-positive (ER+) breast cancers. Global nuclear run-on and sequencing analyses functionally linked PARP-1 to the direct control of estrogen-regulated gene expression in ER+ MCF-7 breast cancer cells by promoting transcriptional elongation by Pol II. Furthermore, chromatin immunoprecipitation sequencing analyses revealed that PARP-1 regulates the estrogen-dependent binding of ERα and FoxA1 to a subset of genomic ERα binding sites, promoting active enhancer formation. Moreover, we found that the expression levels of the PARP-1– and estrogen-coregulated gene set are enriched in the luminal subtype of breast cancer, and high PARP-1 expression in ER+ cases correlates with poor survival. Finally, treatment with a PARP inhibitor or a transcriptional elongation inhibitor attenuated estrogen-dependent growth of multiple ER+ breast cancer cell lines. Taken together, our results show that PARP-1 regulates critical molecular pathways that control the estrogen-dependent gene expression program underlying the proliferation of ER+ breast cancer cells.

Implications:

PARP-1 regulates the estrogen-dependent genomic binding of ERα and FoxA1 to regulate critical gene expression programs by RNA Pol II that underlie the proliferation of ER+ breast cancers, providing a potential therapeutic opportunity for PARP inhibitors in estrogen-responsive breast cancers.

Estrogen signaling regulates molecular events that have profound effects on the normal functioning of critical biological processes in normal and disease states. Upon binding to 17β-estradiol (E2), estrogen receptor alpha (ERα) localizes to regulatory regions across the genome to promote the formation of transcriptional enhancers that drive the E2-dependent expression of target genes (1, 2). In many cases, the binding of pioneer factors, such as FoxA1, precedes E2-dependent binding of ERα, which is a critical step for E2-dependent transcription (3–5). Activation of ER-dependent transcription drives enhancer-promoter looping and transcription by RNA polymerase II (RNA Pol II) at target promoters (2, 6–9).

Aberrant estrogen signaling plays a critical role in several pathophysiologic conditions, including estrogen receptor alpha–positive (ER+) breast cancers (10). Around 70% of breast cancers express ERα at the time of diagnosis and, therefore, respond well to antiestrogen therapies; however, the majority of them relapse and become refractory (10). Understanding how additional signaling pathways, regulatory cofactors, and regulatory post-translational modifications (PTM) impact E2 signaling and ERα-dependent gene regulation may suggest new targets for treating ER+ breast cancers. Herein, we explore the role of one such cofactor, PARP-1, and the PTM it mediates, ADP-ribosylation (ADPRylation), in ERα-dependent gene regulation in ER+ breast cancers.

PARP-1, the founding and most abundant member of the PARP family of enzymes, is an ADP-ribosyl transferase with many hundreds of known substrates (11, 12). Yet, the precise nature of this modification, how it regulates its substrates' functions, and its global effects on the genome and proteome are not well understood. PARP-1 is upregulated in a number of cancer cell lines, as well as in malignant tissues (13, 14). In breast cancer cell lines, the basal activity of PARP-1 is highly variable and independent of DNA damage (15). In ovarian cancers, the levels and patterns of PARP-1–dependent ADPRylation have been shown to correlate well with clinical outcomes (16). Thus, accumulating evidence has brought PARP-1 to the forefront of clinical cancer research as an emerging therapeutic target in several cancers, including breast, ovarian, and prostate cancers (17).

In its historical role in DNA repair, inhibition of the catalytic activity of PARP-1 was shown to be efficient in treating BRCA1/2 mutant or homologous recombination (HR)-deficient cancers by inducing cell death through synthetic lethality (17). However, our recent findings indicate that the use of PARP inhibitors in the clinic could potentially be expanded to include a broader array of cancer types regardless of BRCA1/2 status, acting through alternate molecular pathways unrelated to DNA repair (16, 18). For example, PARP-1 has been shown to regulate gene expression by modulating chromatin structure and acting as a transcriptional coregulator (19). Our previous studies have shown that PARP-1 localizes to the promoters of more than 90% of expressed genes in MCF-7 ER+ breast cancer cells (20) and regulates transcriptional elongation by RNA Poll II (12). In this study, we interrogate the role of PARP-1 in E2-dependent transcription in ER+ breast cancer cells.

Additional details regarding the materials and methods are provided in the Supplementary Materials and Methods.

Antibodies

Details for the following antibodies used are provided in the Supplementary Materials and Methods: PARP-1, ERα, FoxA1, H3K27ac, and β-actin.

Cell culture and treatments

MCF-7 cells were kindly provided by Benita S. Katzenellenbogen (University of Illinois, Urbana-Champaign, Champaign, IL) and T47D cells were obtained from the ATCC and used for the genomic and cell-based assays described herein. Prior to all experiments, the MCF-7 and T47D cells were grown for 3 days in phenol red-free MEM Eagle or RPMI medium supplemented with 5% charcoal-dextran–treated calf serum (CDCS) or 10% charcoal-dextran–treated FBS (CDFBS), respectively. For experiments, cells were treated with 100 nmol/L 17β-estradiol (E2) or vehicle (ethanol) for 40 or 180 minutes. Fresh cell stocks were regularly replenished from the original stocks, verified for cell-type identity using the GenePrint 24 system (Promega, B1870), and confirmed as Mycoplasma-free every three months using a commercial testing kit.

Stable short hairpin RNA–mediated knockdown in MCF-7 cells

Retroviruses were generated by transfection of pSUPER.retro vectors, each expressing a different short hairpin (shRNA) sequence directed against the cognate target (Luciferase or PARP-1; ref. 12). The resulting viruses were collected, filtered, and used to infect the parental MCF-7 cell line. Stably transduced cells were isolated under appropriate drug selection with 0.5 μg/mL puromycin or 800 μg/mL G418, expanded, and frozen in aliquots for future use.

Preparation of cell extracts and Western blotting

Preparation of whole-cell lysates and determination of protein concentrations

Cells were collected, washed with ice-cold PBS, resuspended in Whole Cell Lysis Buffer [50 mmol/L Tris-HCl pH 7.5, 0.5 M NaCl, 1 mmol/L EDTA, 1% NP-40, 10% glycerol, and 1× complete protease inhibitor cocktail (Roche, 11697498001)]. The cell extracts were collected, aliquoted, flash frozen in liquid N2, and stored at −80°C.

Western blotting

Aliquots of the cell extracts were run on polyacrylamide-SDS gels, transferred to nitrocellulose membranes and blotted as described previously (5, 7, 8). The signals were detected using an ECL detection reagent (Thermo Fisher Scientific, 34077, 34095).

Cell proliferation assays

Cell proliferation was assessed using a crystal violet staining assay as described previously (21). MCF-7 or T47D cells were grown in CDCS or CDFBS medium, respectively, for 3 days and treated with vehicle or E2 (100 nmol/L), with or without PARP-1 inhibitor (niraparib, olaparib, talazoparib, at 625 nmol/L, 1.25 μmol/L, and 50 nmol/L for MCF-7 cells, respectively, and 10 μmol/L, 20 μmol/L, and 250 nmol/L for T47D cells, respectively) and with or without flavopiridol (7.5 nmol/L or 15 nmol/L for MCF-7 and T47D cells, respectively) as indicated. At selected timepoints, the cells were fixed with 10% formaldehyde and stained with 0.1% crystal violet, which was extracted using 10% glacial acetic acid read at 595 nm.

Preparation of global run-on sequencing libraries

Nuclei from MCF-7 cells with shRNA-mediated knockdown (KD) of luciferase (Luc; as a control) or PARP-1 were isolated and subjected to global run-on sequencing (GRO-seq) as described previously (12, 22). Library quality was assessed using a 2200 TapeStation (Agilent Technologies).

Analysis of GRO-seq data

The GRO-seq data were analyzed using software described previously (5, 22) and the approaches described below. Software, scripts, and other information about the analyses can be obtained by contacting the corresponding author (W.L. Kraus).

Quality control

The GRO-seq data quality was assessed using the FastQC tool (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Adapter contamination and polyA tails were removed from GRO-seq reads using the default parameters of Cutadapt (v1.9.1) software (23). Reads >32 bp long were retained for alignment.

Read alignment and gene annotation

Reads were aligned to the human reference genome (hg19), including autosomes, the X chromosome, one complete copy of an rDNA repeat (GenBank ID: U13369.1) using the Burrows-Wheeler aligner (v 0.7.12; ref. 24). Overlaps and redundancies were removed from the combined gene lists to eliminate the possibility of double counting.

Determining gene regulation and generating heatmaps

The effects of E2, PARP-1 KD, and co-treatment (E2 + PARP-1 KD) on the expression of coding and noncoding genes were analyzed using edgeR (25). A FDR cutoff of 1% was used to identify significantly regulated genes. Genes in the heatmaps were ordered using hierarchical clustering. We used custom R scripts to convert counts to transcripts per million (TPM) for each replicate, as well as each pool of replicates.

Metagene analysis

The average read densities of sense and anti-sense reads were computed on adjacent lines for an 8 kb window surrounding regulated gene TSS (±4 kb) using the metagene function in the groHMM package (26).

Analysis of pausing indices

Pausing indices representing the base pair-normalized difference in read depth between the promoter proximal region (−100 to 300 bp) and the gene body (1–13 kb) were calculated using the pausing Index function in the groHMM package, as described previously (26, 27).

Preparation of chromatin immunoprecipitation sequencing libraries

Chromatin immunoprecipitation (ChIP) was performed as described previously (5, 8). Pre-cleared cross-linked chromatin-containing lysates were subjected to immunoprecipitation reactions with antibodies against ERα, FoxA1, or H3K27ac. Ten ng of ChIPed DNA for each condition was used to generate libraries for deep sequencing, as described previously (5, 28). After quality control analyses, the libraries were sequenced using a HiSeq 2000 sequencer (Illumina; Single-end reads, 50 bp for all samples).

Analysis of ChIP sequencing data

Quality control

Quality control for the ChIP sequencing (ChIP-seq) data was performed using the FastQC tool (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/).

Read alignment and peak calling

The raw reads were aligned to the human reference genome (GRCh37/hg19) using default parameters in Bowtie (ver. 1.0.0; ref. 29). The aligned reads were subsequently filtered for quality and uniquely mappable reads using Samtools (ver. 0.1.19; ref. 30) and Picard (ver. 1.127; http://broadinstitute.github.io/picard/). Library complexity was measured using BEDTools (v2.17.0; ref. 31) and met minimum ENCODE data quality standards (32). Relaxed peaks were called using MACS (v2.1.0; ref. 33) with a P = 1 × 10−2 for each replicate, pooled replicates' reads, and pseudoreplicates (Supplementary Table S1).

Effect of PARP-1 KD on ERα and FoxA1 binding

To identify the ERα and FoxA1 binding sites affected by PARP-1 KD, we used BEDTools (v2.17.0; ref. 31) to find the closest peaks from PARP-1–affected target genes upon E2 treatment.

ChIP-qPCR

MCF-7 cells were treated with or without E2 (100 nmol/L; 40 min), and with or without PARP inhibitor (niraparib, 10 μmol/L, 1 hour pre-treatment). ChIP-qPCR assays were performed as described previously (5, 7, 8). Pre-cleared cross-linked chromatin-containing lysates were subjected to immunoprecipitation reactions with antibodies against ERα and FoxA1. The ChIPed DNA was extracted and analyzed by quantitative PCR using the primers listed in the Supplementary Materials and Methods.

Preparation of polyA+ RNA sequencing libraries

Total RNA isolation

Estrogen-withdrawn MCF-7 cells were treated with ethanol or 100 nmol/L E2 for 3 hours. Total RNA was isolated from MCF-7 cells using the RNeasy kit (Qiagen, 74136) according to the manufacturer's instructions.

Library preparation and sequencing

The total RNA samples were subjected to enrichment of polyA+ RNA and sequencing as described previously (34). After quality control analyses, the libraries were sequenced using a HiSeq 2000 sequencer (Illumina; single-end reads, 50 bp for all samples). At least two biological replicates were sequenced for each condition for a minimum of roughly 20 million raw reads.

Analysis of RNA sequencing data

Quality control

Quality control for the RNA sequencing (RNA-seq) data was performed using the FastQC tool (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/).

Read alignment

The reads were then mapped to the human reference genome (GRCh37/hg19) with previously described comprehensive gene annotation using the default parameters in Tophat (v2.0.12).

Differential gene expression

Differences in gene expression between RNA-seq datasets were calculated using the cufflinks suite with a statistical threshold of FDR, 0.05 (35).

Pathway analysis

Gene set enrichment analysis (GSEA) was performed by computing overlaps between pre-ranked genes with c2: curated gene sets (canonical pathways) obtained from the Broad Institute (http://software.broadinstitute.org/gsea/msigdb; ref. 36).

Kaplan–Meier and gene expression analyses in breast cancer tumor samples

Kaplan–Meier estimators (37, 38) were generated using the Gene Expression-Based Outcome for Breast Cancer Online (GOBO) tool (http://co.bmc.lu.se/gobo/; ref. 39). Gene expression levels in breast tumor samples were also obtained using the GOBO tool.

Genomic dataset availability

The new genomic datasets reported herein (GRO-seq, RNA-seq, and ChIP-seq from MCF-7 cells) are available from the NCBI's Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/) using the following accession numbers: GSE74142, GSE173976, and GSE166168 (see Supplementary Materials and Methods).

PARP-1 regulates estrogen-dependent transcription

To investigate the role of PARP-1 in estrogen-dependent gene regulation, we examined and quantified the direct transcriptional output in PARP-1–depleted ER+ MCF-7 cells treated with 17β-estradiol (E2) using global run-on coupled with deep sequencing (GRO-seq). MCF-7 cell lines were generated with stable shRNA-mediated KD of Luciferase (Luc) or PARP-1 (KD; LucKD control vs. PARP-1KD), which did not alter ERα or FoxA1 expression (Fig. 1A). The cells were treated with vehicle or E2 for 40 minutes and collected for GRO-seq analysis (Fig. 1B).

Figure 1.

PARP-1 modulates the estrogen-dependent transcriptional program in breast cancer cells. A, Western blot analysis showing the levels of PARP-1, ERα, and FoxA1 in MCF-7 whole-cell lysates after shRNA-mediated KD (LucKD control vs. PARP-1KD). B, Schematic representation showing a timeline of Luc or PARP-1 KD (LucKD control, PARP-1KD), E2 treatment, and cell collection for the GRO-seq experiment. C, Heatmap showing the transcription regulation of all protein-coding genes under each treatment condition, as revealed by GRO-seq. The gene expression values (TPM) shown in the heatmap were z-score normalized for each gene. D, Box plots showing changes in GRO-seq signals for E2-regulated protein-coding (E2-upregulated, top; E2-downregulated, bottom) genes that are highly (left) or moderately (right) dependent on PARP-1. Bars marked with different letters are significantly different from each other (Wilcoxon rank-sum test, P < 2.5 × 10−7).

Figure 1.

PARP-1 modulates the estrogen-dependent transcriptional program in breast cancer cells. A, Western blot analysis showing the levels of PARP-1, ERα, and FoxA1 in MCF-7 whole-cell lysates after shRNA-mediated KD (LucKD control vs. PARP-1KD). B, Schematic representation showing a timeline of Luc or PARP-1 KD (LucKD control, PARP-1KD), E2 treatment, and cell collection for the GRO-seq experiment. C, Heatmap showing the transcription regulation of all protein-coding genes under each treatment condition, as revealed by GRO-seq. The gene expression values (TPM) shown in the heatmap were z-score normalized for each gene. D, Box plots showing changes in GRO-seq signals for E2-regulated protein-coding (E2-upregulated, top; E2-downregulated, bottom) genes that are highly (left) or moderately (right) dependent on PARP-1. Bars marked with different letters are significantly different from each other (Wilcoxon rank-sum test, P < 2.5 × 10−7).

Close modal

Sequencing of newly synthesized transcripts identified a subset of the E2-regulated protein-coding genes that are dependent on PARP-1 for efficient expression (Fig. 1C; Supplementary Fig. S1A). Within this subset, E2-upregulated protein-coding genes that were highly or moderately dependent on PARP-1 were suppressed upon PARP-1 KD (Fig. 1D, top, and Supplementary Fig. S1B, top). In contrast, E2-downregulated protein-coding genes dependent on PARP-1 were de-repressed upon PARP-1 KD (Fig. 1D, bottom, and Supplementary Fig. S1B, bottom). Importantly, PARP-1 KD had no significant effect on all the total set of expressed genes in MCF-7 cells, indicating that PARP-1 selectively regulates E2- regulated genes (Supplementary Fig. S1C). A large fraction of the E2-regulated genes controlled by PARP-1 at the transcriptional level (as assessed by GRO-seq at 40 minutes of E2 treatment) were similarly affected at the steady-state RNA level (as assessed by RNA-seq at 3 hours of E2; Supplementary Fig. S1D). We also identified a third category comprising a small number of synergistically upregulated or downregulated genes in response to E2 and PARP-1 KD (Supplementary Fig. S2A and S2B). Similar regulation patterns were also observed for E2-regulated lncRNA genes (Supplementary Fig. S2C and S2D).

PARP-1 regulates estrogen-dependent transcription by promoting release of Pol II into active elongation

Our previous studies have shown that the estrogen-dependent transcriptional response in breast cancer cells is rapid and dynamic, and involves a regulatory step the releases promoter-proximal paused Pol II into active elongation (22, 27). Furthermore, our studies have shown that PARP-1 regulates transcriptional elongation by ADPRylating the RNA Pol II–associated complex negative elongation factor (NELF; ref. 12), which is also a target of inhibition by the P-TEFb kinase complex (40). Therefore, we explored the potential role of PARP-1 in regulating estrogen-dependent Pol II activity, including loading, pausing, and elongation genome wide. For this, we analyzed GRO-seq data across the PARP-1– and E2-coregulated gene sets (Fig. 2). The GRO-seq read density at transcription start sites (TSS) or in gene bodies is an indicator of Pol II activity (40). The change in read density indicates the effect of PARP-1 depletion on E2-dependent transcription.

Figure 2.

PARP-1 positively regulates estrogen-dependent Pol II loading and elongation at E2-upregulated genes. A and D, Metagenes of GRO-seq read density at gene promoter regions within −4 to +4 kb of TSS (left) and cumulative gene distribution plots of the pausing indices (right) of E2-upregulated (A) and E2-downregulated (D) protein-coding genes that are highly or moderately dependent on PARP-1. The data are from E2-treated MCF-7 cells subjected to shRNA-mediated KD (LucKD control vs. PARP-1KD). B and E, Box plots showing changes in GRO-seq signal (TPM) for PARP-1–dependent E2-upregulated (B) and E2-downregulated (E) genes around the promoter (100 bp to + 300 bp relative to the TSS; left) and gene body (+300 to 13 kb relative to the TSS; right). C and F, Box plots showing changes in the pausing index (PI) for PARP-1–dependent E2-upregulated (C) and E2-downregulated (F) genes. In B, C, E, and F, bars marked with different letters are significantly different from each other (Wilcoxon rank-sum test, P < 2.5 × 10−7 or 0.02557).

Figure 2.

PARP-1 positively regulates estrogen-dependent Pol II loading and elongation at E2-upregulated genes. A and D, Metagenes of GRO-seq read density at gene promoter regions within −4 to +4 kb of TSS (left) and cumulative gene distribution plots of the pausing indices (right) of E2-upregulated (A) and E2-downregulated (D) protein-coding genes that are highly or moderately dependent on PARP-1. The data are from E2-treated MCF-7 cells subjected to shRNA-mediated KD (LucKD control vs. PARP-1KD). B and E, Box plots showing changes in GRO-seq signal (TPM) for PARP-1–dependent E2-upregulated (B) and E2-downregulated (E) genes around the promoter (100 bp to + 300 bp relative to the TSS; left) and gene body (+300 to 13 kb relative to the TSS; right). C and F, Box plots showing changes in the pausing index (PI) for PARP-1–dependent E2-upregulated (C) and E2-downregulated (F) genes. In B, C, E, and F, bars marked with different letters are significantly different from each other (Wilcoxon rank-sum test, P < 2.5 × 10−7 or 0.02557).

Close modal

We observed that PARP-1 depletion reduced the density of elongating Pol II in the gene bodies of E2-upregulated genes, but only modestly affected the density of Pol II in the proximal promoter region (Fig. 2A, left). As a consequence, there was a modest increase in the pausing indices (i.e., GRO-seq reads in the paused peak/reads in the gene body) upon PARP-1 KD (Fig. 2A, right; rightward shift of the curve). In contrast, PARP-1 depletion increased the density of both gene body and promoter-proximal Pol II for the E2-downregulated genes (Fig. 2D, left), resulting in no observable change in the pausing indices (Fig. 2D, right). A more quantitative assessment of these analyses are shown in the boxplots in Fig. 2B, C, E, and F, which highlight these observations. The effects of PARP-1 depletion on the pausing index was much greater for a selected set of E2-regulated genes that are highly dependent on PARP-1 (Supplementary Fig. S3A), as well as a selected set of E2-upregulated genes in various PARP-1–regulated pathways (Supplementary Fig. S3B and S3C). Together, these results demonstrate that PARP-1 regulates the estrogen-dependent gene expression program at the transcriptional level, in part by acting to promote release of paused Pol II into productive elongation in response to estrogen signaling.

PARP-1 regulates estrogen-dependent binding of ERα and FoxA1 to their target sites

Distally located ERα-bound regions in the genome can function as enhancers to control target gene expression by establishing contact with neighboring promoters through chromatin looping (Fig. 3A). E2-dependent ERα binding in breast cancer cells is directly regulated by the “pioneer” transcription factor FoxA1 (3, 4), and ERα binding sites are marked by acetylated histone H3 lysine 27 (H3K27ac, an indicator of active enhancers; ref. 8). To assess whether PARP-1 regulates E2-dependent binding of ERα or FoxA1 to cognate sites located near estrogen target genes, we performed ChIP-seq analyses for ERα or FoxA1 upon PARP-1 KD with or without E2 treatment (Fig. 3A). We observed a global decrease in ERα and FoxA1 binding with an increase in the distance between E2-upregulated target gene promoters and the nearest significant ERα or FoxA1 peak (Fig. 3B, rightward shift of the curve), and a significant increase in the distance to the closest ERα or FoxA1 peaks from the promoters of E2-upregulated protein-coding genes that are highly or moderately dependent on PARP-1 (Fig. 3C).

Figure 3.

Role of PARP-1 in estrogen-dependent binding of ERα and FoxA1 near estrogen upregulated genes. A, Schematic overview of the pipeline for integrating ChIP-seq and GRO-seq data to link ERα and FoxA1 binding to PARP-1 affected target genes in MCF-7 cells upon E2 treatment. B, Cumulative distribution plots of distance to closest ERα or FoxA1 peaks from E2-upregulated protein-coding genes that are highly or moderately dependent on PARP-1. C, Box plots showing the distance to the closest ERα or FoxA1 peaks from the promoters of E2-upregulated protein-coding genes that are highly or moderately dependent on PARP-1. Bars marked with different letters are significantly different from each other (Wilcoxon rank-sum test, P < 2.5 × 10−7). D, Metagene plots and boxplots showing a positive correlation between PARP-1 KD-mediated suppression at ERα binding sites within −1 and +1 kb with known co-binding markers. Left, Metagene plots of ChIP-seq read counts in E2-treated MCF-7 cells subjected to shRNA-mediated KD for luciferase (black) or PARP-1 (yellow). Right, Box plot representations of the corresponding ChIP-seq data at ERα binding sites. Bars marked with different letters are significantly different from each other (Wilcoxon rank-sum test, P < 2.92 × 10−8).

Figure 3.

Role of PARP-1 in estrogen-dependent binding of ERα and FoxA1 near estrogen upregulated genes. A, Schematic overview of the pipeline for integrating ChIP-seq and GRO-seq data to link ERα and FoxA1 binding to PARP-1 affected target genes in MCF-7 cells upon E2 treatment. B, Cumulative distribution plots of distance to closest ERα or FoxA1 peaks from E2-upregulated protein-coding genes that are highly or moderately dependent on PARP-1. C, Box plots showing the distance to the closest ERα or FoxA1 peaks from the promoters of E2-upregulated protein-coding genes that are highly or moderately dependent on PARP-1. Bars marked with different letters are significantly different from each other (Wilcoxon rank-sum test, P < 2.5 × 10−7). D, Metagene plots and boxplots showing a positive correlation between PARP-1 KD-mediated suppression at ERα binding sites within −1 and +1 kb with known co-binding markers. Left, Metagene plots of ChIP-seq read counts in E2-treated MCF-7 cells subjected to shRNA-mediated KD for luciferase (black) or PARP-1 (yellow). Right, Box plot representations of the corresponding ChIP-seq data at ERα binding sites. Bars marked with different letters are significantly different from each other (Wilcoxon rank-sum test, P < 2.92 × 10−8).

Close modal

From related metagene analyses and box plot quantifications, we observed that PARP-l KD significantly reduced E2-dependent ERα and FoxA1 binding to target sites neighboring E2-upregulated genes (Fig. 3D). We did not, however, observe a significant effect of PARP-1 depletion on H3K27ac enrichment (Fig. 3D, bottom), likely because the sites retained significant, albeit reduced, ERα and FoxA1 binding. The effects of PARP-1 depletion on ERα binding occurred regardless of whether there was co-binding with FoxA1 at the enhancer (Supplementary Fig. S4), suggesting that PARP-1 may act through multiple mechanisms, one targeting FoxA1 (which would also affect ERα) and one targeting ERα directly. The key features of these observations were evident in browser tracks of genomic data covering key E2 target genes (e.g., GREB1 and NRIP1; Fig. 4A; Supplementary Fig. S5, respectively). The effects of PARP-1 KD on ERα and FoxA1 binding were recapitulated using the PARP-1 inhibitor Niraparib, pointing to a role for PARP-1 catalytic activity (Fig. 4B and C).

Figure 4.

PARP-1 catalytic activity supports estrogen-dependent binding of ERα and FoxA1 to enhancers. A, Genome browser tracks of GRO-seq, RNA-seq, and ChIP-seq data at the PARP-1-dependent E2-upregulated GREB1 gene in LucKD versus PARP-1KD MCF-7 cells upon E2 treatment. B and C, ChIP-qPCR analysis showing ERα and FoxA1 binding in the presence of E2 and/or niraparib (PARP inhibitor; PARPi) to the distal ERα enhancer of the GREB1 gene (region highlighted in A; B) and the FMN1 gene (C). Asterisks indicate significant differences assessed by unpaired t tests (P: *, <0.05; **, <0.005; ***, <0.0005).

Figure 4.

PARP-1 catalytic activity supports estrogen-dependent binding of ERα and FoxA1 to enhancers. A, Genome browser tracks of GRO-seq, RNA-seq, and ChIP-seq data at the PARP-1-dependent E2-upregulated GREB1 gene in LucKD versus PARP-1KD MCF-7 cells upon E2 treatment. B and C, ChIP-qPCR analysis showing ERα and FoxA1 binding in the presence of E2 and/or niraparib (PARP inhibitor; PARPi) to the distal ERα enhancer of the GREB1 gene (region highlighted in A; B) and the FMN1 gene (C). Asterisks indicate significant differences assessed by unpaired t tests (P: *, <0.05; **, <0.005; ***, <0.0005).

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Figure 5.

PARP-1 regulates estrogen signaling, and predicts ER+ luminal subtypes and poorer overall survival of patients with ER+ breast cancer. A, GSEA enriched pathways for PARP-1–dependent E2-regulated protein-coding genes. The number of genes represented in each term and the P (−log10) are shown. B, Box plots of expression values for PARP-1–dependent E2-upregulated protein-coding genes suppressed by PARP-1 KD in patient breast tumor samples stratified by subtype (PAM50; left), confirming the differential expression of these genes in patients with ER+ breast cancer (ER status; right). Observed differences are significant as determined by an ANOVA comparison of the means (P < 0.00001). C, Kaplan–Meier survival analyses for breast cancer based on PARP1 mRNA expression. High expression of PARP1 mRNA is predictive of poor outcome. D, Kaplan–Meier survival analyses for breast cancer based on expression of the PARP-1 signature gene set (PARP-1–dependent, E2-upregulated protein-coding genes).

Figure 5.

PARP-1 regulates estrogen signaling, and predicts ER+ luminal subtypes and poorer overall survival of patients with ER+ breast cancer. A, GSEA enriched pathways for PARP-1–dependent E2-regulated protein-coding genes. The number of genes represented in each term and the P (−log10) are shown. B, Box plots of expression values for PARP-1–dependent E2-upregulated protein-coding genes suppressed by PARP-1 KD in patient breast tumor samples stratified by subtype (PAM50; left), confirming the differential expression of these genes in patients with ER+ breast cancer (ER status; right). Observed differences are significant as determined by an ANOVA comparison of the means (P < 0.00001). C, Kaplan–Meier survival analyses for breast cancer based on PARP1 mRNA expression. High expression of PARP1 mRNA is predictive of poor outcome. D, Kaplan–Meier survival analyses for breast cancer based on expression of the PARP-1 signature gene set (PARP-1–dependent, E2-upregulated protein-coding genes).

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Similar results were observed for FoxA1 with E2-downregulated genes (Supplementary Fig. S6A; ERα binding is negligible for these genes, so this assessment cannot be made). PARP-l KD also significantly reduced E2-dependent FoxA1 binding to target sites neighboring E2-downregulated genes (Supplementary Fig. S6B; again, ERα binding is negligible for these genes, so this assessment cannot be made). Together, these results indicate that PARP-1 regulates E2-dependent transcription, in part, by controlling the binding of ERα and FoxA1 to regulatory sites in the genome.

PARP-1 expression predicts clinical outcomes in ER+ breast cancers

To further examine the biological significance of PARP-1–mediated regulation of E2-dependent gene expression, we performed GSEA to identify enriched functional pathways. GSEA identified relevant terms associated with breast biology and estrogen-dependent gene regulation, including “nuclear estrogen receptor alpha” and “FoxA1 transcription factor” networks (Fig. 5A), as well as the term “pathways in cancer” (Fig. 5A), suggesting that these genes are essential players in the development of breast cancer. We defined a “PARP-1 signature gene set” (PSGS), which contains the PARP-1-dependent E2-upregulated protein-coding genes from Fig. 1. As expected, expression of PARP1 mRNA is directly correlated with expression of the PSGS across ER+ breast cancers (Supplementary Fig. S7). We examined the expression of the PSGS in patient breast tumor samples stratified by molecular subtype (PAM50; Fig. 5B, left). Expression of the genes in this set was significantly elevated in the luminal subtype of breast cancer, which is characterized by ERα expression. Indeed, the stratification of patient samples by ERα status confirms the differential expression of these genes in ER versus ER+ breast cancers (Fig. 5B, right). A similar analysis of the PARP-1–independent E2-upregulated protein-coding genes revealed that although the magnitude of the expression is comparable with PARP-1-dependent E2-upregulated genes, their expression does not exhibit the same stratification across breast cancer types (Fig. 5B vs. Supplementary Fig. S8A).

To determine the potential clinical utility of PARP-1 as a target in ER+ breast cancers, we stratified patient samples based on expression of PARP1 mRNA or expression of the PSGS. Kaplan–Meier survival analysis indicated that high expression of PARP1 mRNA (Fig. 5C) or high expression of the PSGS (Fig. 5D) is predictive of poor outcomes. Importantly, a similar correlation was not observed with a PARP-1–independent E2-upregulated gene set (Supplementary Fig. S8B), confirming the specific connection to PARP-1. Poor outcomes related to high expression of the PSGS was also observed in data from The Cancer Genome Atlas (Supplementary Fig. S8C). Similar analyses of the PARP-1–dependent E2-downregulated gene set identified cancer-relevant terms (by GSEA; Supplementary Fig. S9A) and stratification of their expression across breast cancer types (Supplementary Fig. S9B). Together, these results indicate that PARP-1–dependent E2-regulated genes are associated with luminal breast cancer and that expression of PARP1 mRNA or the PSGS predicts clinical outcomes in patients with ER+ cancer.

PARP-1 regulates estrogen-dependent growth in ER+ breast cancer cells

Having defined some mechanisms for PARP-1–mediated regulation of E2-dependent transcription, as well as the potential of PARP-1–regulated genes as predictive markers in ER+ breast cancers, we sought to connect the mechanisms to the biology. Our previous results identified a role for PARP-1 in regulating transcriptional elongation by Pol II through the direct ADPRylation and inhibition of NELF (ref. 12; Fig. 6A). NELF is also targeted for inhibition by the P-TEFb kinase complex (ref. 40; Fig. 6A). Interestingly, flavopiridol, a chemical inhibitor of P-TEFb, also inhibits PARP-1–mediated ADPRylation of NELF (12). Thus, both PARP-1 and P-TEFb inhibitors work to enhance NELF activity and reduce transcriptional elongation, similar to the effects of PARP-1 depletion shown in Fig. 2A and C. Thus, we surmised that flavopiridol would show similar growth inhibitory effects as PARP inhibitor. To test this, we performed cell proliferation assays in the presence of vehicle or E2 treatment, with or without FDA-approved PARP inhibitors (niraparib, olaparib, or talazoparib) with or without flavopiridol in MCF-7 and T47D ER+ breast cancer cells (Fig. 6B). We observed that both flavopiridol and the PARP inhibitors inhibited E2-mediated cell proliferation. These results suggest that transcriptional regulatory mechanisms involving the regulation of transcriptional elongation by Pol II play important roles in E2-mediated cell proliferation.

Figure 6.

PARP-1 and P-TEFb inhibition attenuate estrogen-stimulated cell growth. A, Model depicting the roles of PARP-1 and P-TEFb in estrogen-dependent transcription and effects of their cognate inhibitors. B, Crystal violet proliferation assays after 6 days of growth in the presence of vehicle, E2, PARP inhibitor (niraparib, Nir; olaparib, Olap; or talazoparib, Tal), and/or flavopiridol (FP) as indicated in MCF-7 (top) and T47D (bottom) cells. Relative cell density based on OD595 was normalized to cells growing without E2, PARP inhibitor, or FP at day 6. Asterisks indicate observed differences are significant as assessed by unpaired t tests (**, P < 0.05; ***, P <0.005).

Figure 6.

PARP-1 and P-TEFb inhibition attenuate estrogen-stimulated cell growth. A, Model depicting the roles of PARP-1 and P-TEFb in estrogen-dependent transcription and effects of their cognate inhibitors. B, Crystal violet proliferation assays after 6 days of growth in the presence of vehicle, E2, PARP inhibitor (niraparib, Nir; olaparib, Olap; or talazoparib, Tal), and/or flavopiridol (FP) as indicated in MCF-7 (top) and T47D (bottom) cells. Relative cell density based on OD595 was normalized to cells growing without E2, PARP inhibitor, or FP at day 6. Asterisks indicate observed differences are significant as assessed by unpaired t tests (**, P < 0.05; ***, P <0.005).

Close modal

In this study, we explored a role for PARP-1 in regulating estrogen-dependent transcription, as well as downstream biological processes that impact cell growth and potentially clinical outcomes in patients with breast cancer. Our study reveals PARP-1′s role in hormone-dependent gene expression and expands previous findings showing that PARP-1 is required for chromatin remodeling, transcription factor binding, and transcriptional regulation in breast cancer cells (12, 20, 41, 42).

PARP-1 plays a critical role in regulating estrogen-dependent gene expression through ERα in breast cancer cells

In this study, we sought to understand the role of PARP-1 in estrogen-dependent transcription. GRO-seq in PARP-1–depeleted cells demonstrated a key role for PARP-1 in regulating E2-dependent gene expression (Fig. 1; Supplementary Figs. S1 and S2), including effects on productive elongation by Pol II (Fig. 2; Supplementary Fig. S3) and by modulating the binding of ERα and FoxA1 (Fig. 3; Supplementary Fig. S4). However, H3K27ac enrichment was unaffected by PARP-1 depletion despite the decreases in ERα or FoxA1 recruitment (Fig. 3D) likely because the ERα binding sites retain significant, albeit reduced, ERα and FoxA1 binding. Furthermore, we have shown previously that the levels of H3K27ac are only modestly dependent on the presence of liganded ERα bound at an enhancer (8). Moreover, other chromatin modulators, such as RING1B, alter ERα recruitment without affecting H3K27ac enrichment at E2-regulated de novo super enhancers (43). Thus, we would not necessarily expect a change in H3K27ac enrichment with reduced ERα or FoxA1.

Our observations are consistent with a previous study examining the role of PARP-1 in androgen-dependent gene regulation through androgen receptor (AR), which showed a role for PARP-1 in AR binding to chromatin, AR-dependent transcription, and androgen-dependent proliferation in prostate cancer cells (44). However, another recent study showed that PARP-2, but not PARP-1, interacts with FoxA1 to facilitate AR recruitment to enhancer regions in prostate cancer cells (45). The reasons for the difference with our results on PARP-1 and FoxA1 are unclear, but could be due to intrinsic differences in the nuclear receptors, the cell types, or the genomic localization or activities of the PARP proteins.

Regarding the mechanism of PARP-1 effects on ERα and FoxA1 binding, we and others have previously shown that PARP-1 can affect some, but not all, transcription factors through (1) chromatin structural effects independent of its catalytic activity (46) and (2) direct ADPRylation of the transcription factors (47). Regarding the latter, previous studies have shown that both ERα (48) and FoxA1 (49) are ADPRylated proteins. The extent to which ADPRylation affects the activity of ERα and FoxA1 in breast cancer has yet to be determined, but it represents a potential regulatory mechanism. PARP-1 activity also supports transcriptional elongation by Pol II on estrogen-regulated genes. In this regard, we found that both PARP-1 and P-TEFb inhibitors work to enhance NELF activity and reduce transcriptional elongation (Fig. 6), similar to the effects of PARP-1 depletion. Collectively, these studies are a step forward toward establishing PARP-1 as a key player in signal-dependent transcription in hormone-dependent cancers.

Clinical utility of PARP-1 in ER+ breast cancers

PARP-1 has been shown to be an effective target for therapeutic intervention in BRCA1/2- or HR-defective cancers, including breast, ovarian, and prostate (17). Blocking DNA damage repair by PARP inhibitors renders these cancers sensitive to the endogenous BRCA1/2 or HR defects, resulting in cell death via synthetic lethality. FDA-approved PARP-1 inhibitors, such as olaparib, rucaparib, niraparib, talazoparib, and veliparib are currently being used clinically to treat BRCA1/2-mutant cancers (17). Although approximately 70% of breast cancers are ER+ at the time of diagnosis (10), analysis of TCGA suggests that only a small fraction of these breast cancers are BRCA1/2 mutant (∼5%). The evidence presented here, along with previous studies from breast and ovarian cancers (18, 50–53), supports the rationale for expanding the use of PARP inhibitors to ER+ breast cancers with wild-type BRCA1/2.

Our results demonstrate that PARP-1 controls cancer-related pathways, including those relevant to ER+ breast cancers (Fig. 5A), by regulating the gene expression program in breast cancer cells. Furthermore, PARP1 mRNA expression directly correlates with clinical outcomes (i.e., overall survival) in patients with ER+ breast cancer (Fig. 5C and D). Interestingly, AR inhibitors can promote “BRCAness” (i.e., reduced expression of HR genes, including BRCA1, RAD54L, and RMI2) in castration-resistant prostate cancers, which is synthetically lethal with PARP inhibitors (54). Whether such a mechanism might also be functional in hormone-resistant breast cancer is unknown. Taken together, our results suggest a potential use of PARP inhibitors in the treatment of luminal breast cancers irrespective of BRCA1/2 status. Perhaps the most significant clinical utility of PARP-1 inhibitors in the treatment of ER+ breast cancers would be if they exhibit efficacy in tamoxifen-resistant cancers. Interestingly, many therapy-induced Tamoxifen-resistant breast cancers arise as a result of activating mutations in ERα that turn the receptor into a constitutive activator even in the presence of tamoxifen (55). We posit that PARP inhibitors could be effective in these cases, perhaps by mechanisms similar to those that are used to attenuate the activity of E2-activated ERα: effects on ERα binding, effects on FoxA1 binding, and effects on transcriptional elongation of ERα-regulated genes.

W.L. Kraus reports personal fees from Ribon Therapeutics, Inc. and ARase Therapeutics, Inc. outside the submitted work; in addition, W.L. Kraus has a patent for ADP-ribose detection reagents (U.S. Patent 9,599,606) issued, licensed, and with royalties paid from Sigma Millipore; and is a founder, consultant, and SAB member for Ribon Therapeutics, Inc. and ARase Therapeutics, Inc. No disclosures were reported by the other authors.

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the article; or in the decision to publish the results.

S.S. Gadad: Conceptualization, formal analysis, supervision, funding acquisition, investigation, visualization, methodology, writing–original draft, project administration, writing–review and editing. C.V. Camacho: Conceptualization, formal analysis, investigation, visualization, writing–original draft, writing–review and editing. V. Malladi: Formal analysis, investigation, visualization, methodology, writing–original draft, writing–review and editing. C.R. Hutti: Investigation. A. Nagari: Formal analysis. W.L. Kraus: Conceptualization, formal analysis, supervision, funding acquisition, visualization, methodology, project administration, writing–review and editing.

This work was supported by grants from the Cancer Prevention and Research Institute of Texas (RP190236) and the NIH/NIDDK (R01 DK069710) to W.L. Kraus, funds from the Cecil H. and an Ida Green Center for Reproductive Biology Sciences Endowment to W.L. Kraus, and a postdoctoral fellowship from the Susan G. Komen Foundation (PDF12230441) to S.S. Gadad.

The authors would like to thank members of the Kraus lab for their careful review and helpful suggestions on this work; the UT Southwestern Next Generation Sequencing Core, under the direction of Ralf Kittler; and Rosemary Plagens and Kristine Hussey for assistance with the genomic assays. S.S. Gadad is a CPRIT scholar in cancer research and is supported by a First-time Faculty Recruitment Award from the Cancer Prevention and Research Institute of Texas (CPRIT; RR170020).

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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