Abstract
Background
Interferon gamma (IFNγ) is a pro-inflammatory cytokine that directly activates the JAK/STAT pathway. However, the temporal dynamics of chromatin remodeling and transcriptional activation initiated by IFNγ have not been systematically profiled in an unbiased manner. Herein, we integrated transcriptomic and epigenomic profiling to characterize the acute epigenetic changes induced by IFNγ stimulation in a murine breast cancer model.
Results
We identified de novo activation of cis-regulatory elements bound by Irf1 that were characterized by increased chromatin accessibility, differential usage of pro-inflammatory enhancers, and downstream recruitment of BET proteins and RNA polymerase II. To functionally validate this hierarchical model of IFNγ-driven transcription, we applied selective antagonists of histone acetyltransferases P300/CBP or acetyl-lysine readers of the BET family. This highlighted that histone acetylation is an antecedent event in IFNγ-driven transcription, whereby targeting of P300/CBP acetyltransferase activity but not BET inhibition could curtail the epigenetic remodeling induced by IFNγ through suppression of Irf1 transactivation.
Conclusions
These data highlight the ability for epigenetic therapies to reprogram pro-inflammatory gene expression, which may have therapeutic implications for anti-tumor immunity and inflammatory diseases.
Graphical Abstract
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Background
Pro-inflammatory cytokines directly activate signaling cascades, however, epigenetic mechanisms are also critical for coordinated inflammatory gene expression. Interferon gamma (IFNγ) is a pleiotropic cytokine that is a key regulator of anti-tumor immunity [1, 2]. In the tumor microenvironment (TME), IFNγ induces the expression of genes essential for antigen processing and presentation, thereby promoting immune-surveillance [3]. Gene expression profiling in melanoma patients treated with nivolumab (anti-PD1) alone or combined with Ipilimumab (anti-CTLA4) revealed that an IFNγ-driven gene signature was the most predictive feature for clinical responses to these immunotherapies [4]. In contrast, genetic aberrations that suppress IFNγ signaling are detected in patients relapsing from immune checkpoint blockade demonstrating that tumor-intrinsic IFNγ signaling is a clinically relevant mechanism of immune evasion [33] and collapsed to unique values per gene symbol by selecting the most variable entrez ID among all samples for each gene symbol. Primary samples from the TCGA BRCA cohort were selected using the TCGAbiolinks (v2.14.0) R package [34] and were matched with progression-free interval end points from the TCGA Pan-Cancer Clinical Data Resource [35]. IFNg signature scores were calculated using the Singscore (v1.6) R package [36] from a set of genes found to be strongly interferon induced across multiple cell lines (Additional file 1: Fig. S1C). Samples were then stratified into 'High' (top 90th percentile) and 'Low' (bottom 10th percentile) signature score groups and log-rank p values were calculated using the Survival (v2.38) R package [37].
RNA-sequencing
5e6 AT3 cells were plated in technical triplicate and each pre-treated with A-241 (250 nM), A-485 (1 µM), or DMSO vehicle for 1 h prior to the addition of recombinant murine IFNy (1 ng/mL), or vehicle control, for an additional 2 h (3 h total incubation with small molecules). Following indicated treatments, cells were collected by centrifugation and washed once with ice-cold PBS prior to resuspension in TRIzol™ (ThermoFisher Scientific, 15596026). RNA was isolated using the Direct-zol RNA MiniPrep kit (Zymo Research, R2052) according to the manufacturers instructions and eluted in 50 µL nuclease-free H2O. Sequencing libraries were prepared by the Molecular Genomics Core Facility (Peter MacCallum Cancer Center) with 500 ng input RNA using the QuantSeq 3’-mRNA Seq Library Prep Kit for Illumina (Lexogen, Vienna, Austria). Libraries were then pooled and sequenced on the Illumina NextSeq 500 to obtain 75 b.p. single-end reads. Sequencing files were demultiplexed using Bcl2fastq (v2.17.1.14) to generate individual FASTQ files on which QC was performed using FASTQC (v0.11.5). Sequencing reads were trimmed using cutadapt (v1.7) and aligned to the mouse reference genome (GRCm38/Mm10) using HISAT2 (v2.1.0). Read counting across genomic features was performed using featureCounts and the following settings: -p -T 20 -O -F GTF -t exon. Differential gene expression analysis was performed on the resultant counts matrix in Rstudio (v3.5.1) using the Limma/Voom workflow [38, 39]. Gene set enrichment analyses were performed using Gene Set Enrichment Analysis (GSEA) software (v3.0; https://www.gsea-msigdb.org/gsea/index.jsp) using pre-ranked (ranked by t-statistic) and enrichment plots were re-plotted from GSEA output using replotGSEA function in Rstudio. All additional figure generation for RNA-sequencing datasets was performed in in Rstudio (v3.5.1).
ChIP-sequencing
Cells were pre-treated with JQ1 (1 µM), A-241 (250 nM), or A-485 (1 µM) for 1 h prior to the addition of recombinant murine IFNy, or vehicle control, for an additional 2 h (3 h total incubation with small molecules). Chromatin immunoprecipitation coupled with next-generation sequencing (ChIP-seq) was performed with reference exogenous genome (ChIP-Rx) using a modified protocol [27]. MM1.S cells (25e6/IP) were cultured in the presence or absence of A-485 or DMSO vehicle for indicated timepoints. At harvest, cells were washed once in ice-cold PBS prior to cross-linking. For cross-linking, 1/10th volume of fresh formaldehyde solution (11% formaldehyde, 0.05 mM EGTA, 1 mM EDTA, 100 mM NaCl, 50 mM Hepes–KOH pH 7.5) was added and incubated for 20 min at room temperature with rotation. Cross-linking was quenched by the addition of 1/20th volume of 2.5 M glycine and incubated for 5 min at room temperature with rotation. For isolation of nuclei, cell pellets were washed once in ice-cold PBS and then resuspended in ice-cold nuclear extraction buffer (0.5% NP-40, 2 mM EDTA, 10 mM NaCl, 20 mM Tris–HCl pH 8) and incubated for 5 min on ice. Following three sequential incubations in nuclear extraction buffer, cell nuclei were pelleted and resuspended in sonication buffer (0.3% SDS, 1% NP-40, 2 mM EDTA, 150 mM NaCl, 20 mM Tris–HCl pH 7.5) at a concentration equivalent to 50e6 cells per mL. Samples were sonicated in 12 × 24 mm Covaris tubes using the Covaris S2 instrument for 18 min using the following settings: 20% Duty Cycle, 1000 cycles/burst, and 10 Intensity. Prior to immunoprecipitation, sheared chromatin was diluted 1:1 in ChIP dilution buffer (1% Triton X-100, 2 mM EDTA, 150 mM NaCl, 20 mM Tris–HCl pH 8) and quantified using Qubit dsDNA HS assay kit. For ChIP-Rx, sheared Drosophila chromatin from S2 cells was spiked into immunoprecipitations at 1:40 ratio of Drosophila/human and processed as a single sample until ChIP-Rx normalization following DNA sequencing. Immunoprecipitations were performed overnight (12–16 h, 4 °C, with rotation) using Protein A and Protein G Dyna beads (Invitrogen) and the following antibodies: H3K27Ac (Abcam, ab4729) and IRF1 (Santa Cruz Biotechnology Inc., sc-497). Samples were washed once with ChIP dilution buffer, wash buffer 1 (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 500 mM NaCl, 20 mM Tris–HCl pH 8), wash buffer 2 (0.5% deoxycholate, 0.5% NP-40, 2 mM EDTA, 250 mM LiCl, 20 mM Tris–HCl pH 8), and TE buffer (1 mM EDTA, 10 mM Tris–HCl pH 7.5) prior to incubation in reverse cross-linking buffer (200 mM NaCl, 100 mM NaHCO3, 1% SDS, 300 μg/mL Proteinase-K) for 4 h at 55 °C with shaking. Finally, the supernatant was reverse-cross-linked overnight (12–16 h) at 65 °C prior to ChIP DNA isolation using Zymogen ChIP DNA Clean and Concentrator Kit (Zymo Research, D5205). For ChIP-Rx, libraries were generated using the NEBNext Ultra II DNA Library Prep Kit (NEB, E7645) and sequenced on an Illumina NextSeq 550 with 75 b.p. single-end reads. Library QC and quantification were performed using D1000 high-sensitivity screen tape with 4200 TapeStation Instrument (Agilent Technologies), and the size is selected between 200 and 500 bp using a Pippin Prep system (Sage Science).
ATAC-sequencing
Cells were pre-treated with JQ1 (1 µM) or A-241 (250 nM) for 1 h prior to the addition of recombinant murine IFNy (1 ng/mL), or vehicle control, for an additional 2 h (3 h total incubation with small molecules). Assay for Transposase-Accessible Chromatin using Sequencing (ATAC-seq) was performed using an improved protocol to reduce mitochondria from the transposition reaction [40]]. Briefly, 5e5 MM1.S cells were cultured in duplicate with JQ1, A-485, or DMSO vehicle as described above. Cells were washed once in ice-cold PBS and lysed in ATAC lysis buffer (0.1% Tween-20, 0.1% NP-40, 3 mM MgCl2, 10 mM NaCl, 10 mM Tris HCl pH 7.4). Tagmentation was then performed with Tn5 transposase and 2 × TD Buffer (Nextera DNA Library Prep Kit, Illumina) for 30 min at 37 °C (in a thermocycler). Tagmented DNA was immediately purified using a MinElute column (Qiagen, #28004) and then amplified for 12 cycles using 2 × KAPA HiFi HotStart ReadyMix (Kapa Biosystems, KK2602) and Illumina-compatible/barcoded primers. The amplified libraries were purified using MinElute columns (Qiagen) and sequenced on an Illumina NextSeq 500 with 75 b.p. single-end reads. Library QC and quantification were performed using D1000 high-sensitivity screen tape with 4200 TapeStation Instrument (Agilent Technologies), and the size was selected between 200 and 500 bp using a Pippin Prep system (Sage Science).
ATAC-seq and ChIP-seq analysis
Sequencing files were demultiplexed using Bcl2fastq (v2.17.1.14) to generate Fastq files on which QC was performed using FASTQC (v0.11.5). Sequencing reads were then aligned to custom reference genome consisting the mouse genome (Mm10) and the Drosophila melanogaster genome (Dm3) using Bowtie2 (v2.3.3). The resulting SAM files were converted to BAM files using Samtools (v1.4.1) using the view command, which were subsequently sorted, indexed, and potential PCR duplicates removed using the rmdup function. BAM files were converted into BigWig files using the bamCoverage function (Deeptools, v3.0.0) using the following settings (—normalizeUsing CPM—smoothLength 150—binSize 50—e 200 scaleFactor 1). For experiments with external normalization, the reads map** to either Mm10 or Dm3 genomes were quantified using FeatureCounts (Subread package, v1.5.0) and the percentage of mapped Dm3 reads as a total of total mapped Dm3 + Hg19 reads was calculated. A scale factor was then calculated as the ratio of Dm3 reads in the control treatment condition and the treatment sample, which was then manually applied as the scaleFactor in the bamCoverage function. BigWig files were imported into Integrative Genomics Viewer (IGV, v2.7.0) for visualization of specific loci. Using Deeptools (v3.0.0), heatmaps were generated by computing read average read density (from BigWig files) across defined genomic intervals using computeMatrix, which we subsequently plot using the plotHeatmap command. Average profile plots were created using matrices generated by computeMatrix using a custom script in Rstudio. Annotation of putative super-enhancer regions from H3K27ac ChIP-seq data was performed using Ranking Ordering of Super-Enhancer (ROSE) using a 12.5 k.b. stitching distance and a 2.5 k.b. TSS exclusion to reduce promoter bias. Peak calling was performed with MACS2 with default parameters. Annotation of ATAC-Seq/ChIP-Seq peaks to proximal genes was performed using annotatePeaks.pl (Homer, v4.8). Rstudio (v1.1.46) and R (v3.5.1) were used for all additional analyses and figure preparation using the following R packages: ggplots2, rcolorbrewer.
Super-enhancer and Coltron analysis
MACS2 (v2.2.1) was used for identification of (1) ATAC-seq peaks on BAM files from vehicle-treated and IFNγ-treated cells using the following parameters:—call-summits—nomodel—extsize 300, and (2) H3K27ac peaks on BAM files from vehicle-treated and IFNγ-treated cells using the following parameters:—cutoff-analysis. ATAC-seq and H3K27ac ChIP-seq peaks (.narrowPeak) map** to ENCODE’s recommended Blacklisted regions [41] for the Mm10 genome were then excluded using bedtools intersect. Rose2 (v1.0.5) was then used to identify super-enhancers using the following parameters: -g mm10 -s 12,500 -t 2000. Coltron (https://pypi.org/project/coltron/) was used to perform core regulatory circuit analysis using the following parameters: -g MM10 -l 300. Rose2 and Coltron steps were repeated for both vehicle-treated and IFNγ-treated samples. Rstudio (v1.1.46) was used for figure preparation.
Data availability
RNA-sequencing data of B16-F10 cells stimulated with IFNγ or vehicle control were downloaded from NIH’s Gene Expression Omnibus (GEO) under the accession number GSE134264. RNA-sequencing of MC38 cells stimulated with IFNγ or control was downloaded from GEO (GSE112252). RNA-sequencing of AT3 cells stimulated with IFNγ ± JQ1, or vehicle control, from our previous study was downloaded from GEO (GSE94057). ChIP-sequencing for RNA polymerase II, BRD4, IRF1, and H3K27ac in AT3 cells stimulated with IFNγ ± JQ1, or vehicle control, from our previous study was downloaded from GEO (GSE94130). Next-generation sequencing data generated in this study have been deposited in the NIH’s Gene Expression Omnibus (GEO) under the accession number GSE201883.
Availability of data and materials
Next-generation sequencing data generated in this study have been deposited in the NIH’s Gene Expression Omnibus (GEO) under the accession number GSE201883.
Abbreviations
- ATAC-seq:
-
Assay for Transposase-Accessible Chromatin with high-throughput sequencing
- BET:
-
Bromodomain and extra-terminal domain
- CBP:
-
CREB binding protein
- ChIP-seq:
-
Chromatin immunoprecipitation and sequencing
- CRC:
-
Core regulatory circuit
- GSEA:
-
Gene set enrichment analysis
- IFNγ:
-
Interferon gamma
- IRF:
-
Interferon-regulatory factors
- ISRE:
-
Interferon-sensitive response element
- KAT:
-
Lysine acetyltransferase
- PROTAC:
-
Proteolysis-targeting chimera
- P-TEFb:
-
Positive transcription elongation factor b
- RNAPII:
-
RNA Polymerase II
- RNA-seq:
-
RNA sequencing
- SE:
-
Super-enhancer
- TF:
-
Transcription factor
- TME:
-
Tumor microenvironment
References
Ribas A. Adaptive immune resistance: how cancer protects from immune attack. Cancer Discov. 2015;5(9):915–9.
Castro F, Cardoso AP, Gonçalves RM, Serre K, Oliveira MJ. Interferon-gamma at the crossroads of tumor immune surveillance or evasion. Front Immunol. 2018;9:847.
Sucker A, Zhao F, Pieper N, Heeke C, Maltaner R, Stadtler N, et al. Acquired IFNγ resistance impairs anti-tumor immunity and gives rise to T-cell-resistant melanoma lesions. Nat Commun. 2017;8(1):1–15.
Grasso CS, Tsoi J, Onyshchenko M, Abril-Rodriguez G, Ross-Macdonald P, Wind-Rotolo M, et al. Conserved interferon-γ signaling drives clinical response to immune checkpoint blockade therapy in melanoma. Cancer Cell. 2020;38(4):500-15.e3.
Gao J, Shi LZ, Zhao H, Chen J, **ong L, He Q, et al. Loss of IFN-γ pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy. Cell. 2016;167(2):397-404.e9.
Kearney CJ, Vervoort SJ, Hogg SJ, Ramsbottom KM, Freeman AJ, Lalaoui N, et al. Tumor immune evasion arises through loss of TNF sensitivity. Sci Immunol. 2018;3(23):eaar3451.
Manguso RT, Pope HW, Zimmer MD, Brown FD, Yates KB, Miller BC, et al. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature. 2017;547(7664):413–8.
Arenas EJ, Martínez-Sabadell A, Rius Ruiz I, Román Alonso M, Escorihuela M, Luque A, et al. Acquired cancer cell resistance to T cell bispecific antibodies and CAR T targeting HER2 through JAK2 down-modulation. Nat Commun. 2021;12(1):1237.
Hogg SJ, Beavis PA, Dawson MA, Johnstone RW. Targeting the epigenetic regulation of antitumour immunity. Nat Rev Drug Discov. 2020;19(11):776–800.
Hogg SJ, Vervoort SJ, Deswal S, Ott CJ, Li J, Cluse LA, et al. BET-Bromodomain inhibitors engage the host immune system and regulate expression of the immune checkpoint ligand PD-L1. Cell Rep. 2017;18(9):2162–74.
Zhu H, Bengsch F, Svoronos N, Rutkowski Melanie R, Bitler Benjamin G, Allegrezza Michael J, et al. BET bromodomain inhibition promotes anti-tumor immunity by suppressing PD-L1 expression. Cell Rep. 2016;16(11):2829–37.
Green MR, Monti S, Rodig SJ, Juszczynski P, Currie T, O’Donnell E, et al. Integrative analysis reveals selective 9p24.1 amplification, increased PD-1 ligand expression, and further induction via JAK2 in nodular sclerosing Hodgkin lymphoma and primary mediastinal large B-cell lymphoma. Blood. 2010;116(17):3268–77.
Vervoort SJ, Welsh SA, Devlin JR, Barbieri E, Knight DA, Offley S, et al. The PP2A-Integrator-CDK9 axis fine-tunes transcription and can be targeted therapeutically in cancer. Cell. 2021;184(12):3143-62.e32.
Ortega E, Rengachari S, Ibrahim Z, Hoghoughi N, Gaucher J, Holehouse AS, et al. Transcription factor dimerization activates the p300 acetyltransferase. Nature. 2018;562(7728):538–44.
Lin CY, Erkek S, Tong Y, Yin L, Federation AJ, Zapatka M, et al. Active medulloblastoma enhancers reveal subgroup-specific cellular origins. Nature. 2016;530(7588):57–62.
Ma Y, Shepherd J, Zhao D, Bollu LR, Tahaney WM, Hill J, et al. SOX9 is essential for triple-negative breast cancer cell survival and metastasis. Mol Cancer Res. 2020;18(12):1825–38.
van Bragt MP, Hu X, **e Y, Li Z. RUNX1, a transcription factor mutated in breast cancer, controls the fate of ER-positive mammary luminal cells. Elife. 2014;3:e03881.
Takaku M, Grimm SA, Wade PA. GATA3 in breast cancer: tumor suppressor or oncogene? Gene Expr. 2015;16(4):163.
Yang Z, Yik JH, Chen R, He N, Jang MK, Ozato K, et al. Recruitment of P-TEFb for stimulation of transcriptional elongation by the bromodomain protein Brd4. Mol Cell. 2005;19(4):535–45.
Jang MK, Mochizuki K, Zhou M, Jeong H-S, Brady JN, Ozato K. The bromodomain protein Brd4 is a positive regulatory component of P-TEFb and stimulates RNA polymerase II-dependent transcription. Mol Cell. 2005;19(4):523–34.
Winter GE, Mayer A, Buckley DL, Erb MA, Roderick JE, Vittori S, et al. BET bromodomain proteins function as master transcription elongation factors independent of CDK9 recruitment. Mol Cell. 2017;67(1):5-18.e9.
Muhar M, Ebert A, Neumann T, Umkehrer C, Jude J, Wieshofer C, et al. SLAM-seq defines direct gene-regulatory functions of the BRD4-MYC axis. Science. 2018;360(6390):800–5.
Crump NT, Ballabio E, Godfrey L, Thorne R, Repapi E, Kerry J, et al. BET inhibition disrupts transcription but retains enhancer-promoter contact. Nat Commun. 2021;12(1):1–15.
Lasko LM, Jakob CG, Edalji RP, Qiu W, Montgomery D, Digiammarino EL, et al. Discovery of a selective catalytic p300/CBP inhibitor that targets lineage-specific tumours. Nature. 2017;550(7674):128–32.
Ji Z, Clark RF, Bhat V, Hansen TM, Lasko LM, Bromberg KD, et al. Discovery of spirohydantoins as selective, orally bioavailable inhibitors of p300/CBP histone acetyltransferases. Bioorg Med Chem Lett. 2021;39:127854.
Hogg SJ, Motorna O, Cluse LA, Johanson TM, Coughlan HD, Raviram R, et al. Targeting histone acetylation dynamics and oncogenic transcription by catalytic P300/CBP inhibition. Mol Cell. 2021;81(10):2183-200.e13.
Orlando DA, Chen MW, Brown VE, Solanki S, Choi YJ, Olson ER, et al. Quantitative ChIP-Seq normalization reveals global modulation of the epigenome. Cell Rep. 2014;9(3):1163–70.
Pelham-Webb B, Polyzos A, Wojenski L, Kloetgen A, Li J, Di Giammartino DC, et al. H3K27ac bookmarking promotes rapid post-mitotic activation of the pluripotent stem cell program without impacting 3D chromatin reorganization. Mol Cell. 2021;81:1732.
Narita T, Ito S, Higashijima Y, Chu WK, Neumann K, Walter J, et al. Enhancers are activated by p300/CBP activity-dependent PIC assembly, RNAPII recruitment, and pause release. Mol Cell. 2021;81(10):2166-82. e6.
Dornan D, Eckert M, Wallace M, Shimizu H, Ramsay E, Hupp TR, et al. Interferon regulatory factor 1 binding to p300 stimulates DNA-dependent acetylation of p53. Mol Cell Biol. 2004;24(22):10083–98.
Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 2011;12(1):1–16.
Head T, Carcinoma NSC. Broad Institute TCGA Genome Data Analysis Center. Firehose Stddata__2016_01_28 run. 2016.
Durinck S, Spellman PT, Birney E, Huber W. Map** identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc. 2009;4(8):1184–91.
Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2016;44(8):e71-e.
Liu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD, et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell. 2018;173(2):400-16. e11.
Foroutan M, Bhuva DD, Lyu R, Horan K, Cursons J, Davis MJ. Single sample scoring of molecular phenotypes. BMC Bioinform. 2018;19(1):1–10.
Therneau T. A package for survival analysis in S. version 2.38. 2015.
Law CW, Chen Y, Shi W, Smyth GK. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):1–17.
Smyth GK. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3(1):1–25.
Corces MR, Trevino AE, Hamilton EG, Greenside PG, Sinnott-Armstrong NA, Vesuna S, et al. An improved ATAC-seq protocol reduces background and enables interrogation of frozen tissues. Nat Methods. 2017;14(10):959.
Amemiya HM, Kundaje A, Boyle AP. The ENCODE blacklist: identification of problematic regions of the genome. Sci Rep-UK. 2019;9(1):1–5.
Vannam R, Sayilgan J, Ojeda S, Karakyriakou B, Hu E, Kreuzer J, Morris R, et al. Targeted degradation of the enhancer lysine acetyltransferases CBP and p300. Cell Chem Biol. 2021;28(4):503–14.
Acknowledgements
The authors would like to thank the Peter MacCallum Foundation and Australian Cancer Research Foundation (ACRF) who provide generous support for equipment and core facilities at the Peter MacCallum Cancer Center (Peter Mac) and the Molecular Genomics Core and the Flow Cytometry Core Facilities at the Peter MacCallum Cancer Center.
Funding
S.J.H. was supported by the Cancer Council of Victoria (CCV), a National Health and Medical Research Council of Australia (NHMRC) Investigator Grant, and a Haematology Society of Australia & New Zealand (HSANZ) Educational Grant. R.W.J. was supported by the CCV, the NHMRC, and The Kids’ Cancer Project. S.J.V. was supported by a Rubicon fellowship, an NHMRC Investigator Grant, and The Kids’ Cancer Project. J.S. is supported by a Medical Research Future Fund Clinician Researcher Fellowship.
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Simon J. Hogg and Olga Motorna contributed equally
Ricky W. Johnstone and Stephin J. Vervoort contributed equally
Contributions
OM and SJH designed, performed, and analyzed experiments. CJK, EBD and IGH performed in vitro experiments. IT and MJK assisted with NGS analysis. MZ performed TCGA analysis. KDB and AL provided critical reagents. JS and PAB contributed to data interpretation and study supervision. RWJ and SJV contributed to data interpretation, manuscript writing, and study supervision. All authors read and approved the manuscript.
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Competing interests
The Johnstone laboratory receives funding support from Roche, BMS, AstraZeneca and MecRx. R.W.J. is a shareholder and consultant for MecRx. S.J.H, A.L. and K.D.B. are employees of AbbVie and hold stocks in the company. S.J.H. and R.W.J. are inventors on a patent (WO2017059319A3) related to combining bromodomain inhibitors and immune-modulating therapies. All other authors declare no relevant competing interests.
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Supplementary Information
Additional file 1: Fig S1
. Conserved activation of IRF1 in response to IFNγ. (A) RNA-seq of B16-F10 melanoma cells stimulated with IFNγ. (B) RNA-seq of MC38 colon adenocarcinoma cells stimulated with IFNγ. (C) Venn diagram overlap of genes induced by IFNγ stimulation (Log2 fold-change > 1, P-value < 0.01) in AT3, B16-F10, and MC38 cells. (D) Genomic localization of IRF1-bound cis-regulatory elements identified following IFNγ stimulation. (E) De novo motif analysis in ATAC-seq peaks specifically found in IFNγ stimulated AT3 cells using peaks found in vehicle-treated AT3 cells as the background. (F) Clique fraction for highly connected core transcription factors in vehicle-treated cells. (G) Clique fraction for highly connected core transcription factors in IFNγ-treated cells.
Additional file 2: Fig S2
. BET inhibition with JQ1 depleted BRD4 binding genome-wide. (A) BRD4 ChIP-seq signal (normalized log2 counts per million) in presence of JQ1 (1 µM) or DMSO for 3 h at cis-regulatory elements active under baseline conditions (in the absence of IFNγ) as identified by ATAC-seq. (B) ATAC-seq signal (normalized log2 counts per million) at cis-regulatory elements active under baseline conditions (in the absence of IFNγ) as identified by ATAC-seq. (C) Log2 fold-change in BRD4 signal (by ChIP-seq) at active cis-regulatory elements across 10 quantiles ranked by loss of BRD4. (D) Log2 fold-change in ATAC-seq signal at active cis-regulatory elements across 10 quantiles ranked by loss of BRD4. ****p < 0.0001, Mann–Whitney U test.
Additional file 3: Fig S3
. Catalytic P300/CBP inhibitors perturb cellular transcription. (A) RNA-seq AT3 cells treated for 3 h with A-485 (1 µM) or A-241 (250 nM) relative to DMSO. (B) Venn diagram overlap of differentially expressed genes (P-value < 0.01) following treatment of AT3 cells with A-485 or A-241 relative to DMSO, respectively. (B) Row-normalized heatmap of gene expression values from RNA-seq of AT3 cells treated with A-485, A-241, or DMSO vehicle for genes responsive to A-241 treatment (P-value < 0.01). (D) H3K27ac ChIP-Rx signal (normalized log2 counts per million) in presence of A-241 or DMSO for 3 h at cis-regulatory elements active under baseline conditions (in the absence of IFNγ) as identified by ATAC-seq. (E) Log2 fold-change in H3K27ac ChIP-Rx at active cis-regulatory elements across 10 quantiles ranked by loss of H3K27ac. (F) Log2 fold-change in ATAC-seq signal at active cis-regulatory elements across 10 quantiles ranked by loss of H3K27ac. ****p < 0.0001, Mann–Whitney U test.
Additional file 4: Fig S4
. P300/CBP and BET inhibition have disparate effects on steady-state and IFNγ-inducible transcription. (A) Venn diagram overlap of genes down-regulated following 3 h treatment with JQ1 and A-241 (P-value < 0.05, log2 fold-change < -1 relative to DMSO) in AT3 cells. (B) Row-scaled heatmap of normalized gene expression values for (log2 counts per million) of IFNγ-induced genes following treatment of AT3 cells with IFNγ ± A-241 or A-485, respectively. (C) Normalized counts (log2 counts per million) of Stat1 and Tap1 following IFNγ ± A-241. (D) Normalized counts (log2 counts per million) of IFNγ-induced genes following treatment of AT3 cells with IFNγ ± JQ1 or IFNγ ± A-241. *p < 0.05, ****p < 0.0001 t test.
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Hogg, S.J., Motorna, O., Kearney, C.J. et al. Distinct modulation of IFNγ-induced transcription by BET bromodomain and catalytic P300/CBP inhibition in breast cancer. Clin Epigenet 14, 96 (2022). https://doi.org/10.1186/s13148-022-01316-5
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DOI: https://doi.org/10.1186/s13148-022-01316-5