CBP/p300 Drives the Differentiation of Regulatory T Cells through Transcriptional and Non-Transcriptional Mechanisms

Regulatory T cells (Treg) are essential in maintaining immune homeostasis and active suppression of autoimmunity (1). FOXP3 is a key transcription factor (TF) required for the developmental differentiation and suppressive functions of Tregs (2). In cancer, accumulation of Tregs at tumor sites inhibits antitumor immune responses by establishing an immunosuppressive tumor microenvironment (3). A high proportion of FOXP3⁺ Tregs among tumor-infiltrating lymphocytes is associated with tumor progression and poor overall survival in various cancers (4). As such, Treg-mediated immunosuppression is thought to be a major obstacle in the use of cancer immunotherapy agents, which could be overcome by their reduction in tumors to reestablish effective antitumor immunity in patients with cancer.

Lysine acetylation on histones regulates chromatin structure and gene transcription (5). CBP and p300 histone acetyltransferases (HAT) act as transcriptional coactivators regulating gene expression (6) through targeted histone acetylation and recruitment of TFs to modulate gene expression (7) and through acetylation of nonhistone proteins including TFs (8). Dysregulation of CBP/p300 by mutation or altered expression has been implicated in the development and progression of various human cancers (9). Recurrent inactivating mutations in CBP/p300 HAT domain have been identified in 40% in follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL; ref. 10).

Recent studies have described the role of CBP/p300 in controlling Treg differentiation and led to the development of GNE-781, a highly potent and selective CBP/p300 inhibitor (11–13). Here we show that inhibition of CBP/p300 with GNE-781 impairs the differentiation of human CD4⁺ T cells into Tregs, providing a therapeutic approach to promoting a proinflammatory tumor microenvironment. As CBP/p300 inhibitors enter clinical testing (14), understanding the impact of CBP/p300 on immune cells can inform the selection of patients that may respond best to these agents.

Herein, we define the biological effects of CBP/p300 inhibition on histone acetylation and the downstream transcriptional programs that drive Treg differentiation. Using mass spectrometry, we identify a novel acetylation target of CBP/p300 and unveil a previously unrecognized pathway that alters cytokine production, which in turn regulates Treg differentiation. We also extend our mechanistic findings to B cells, where we demonstrate for the first time that CBP/p300 also regulates the production of cytokines in B cells, which potentiate Treg differentiation. We explore the role of CBP/p300 loss-of-function (LOF) mutations in FL and show their correlation with lower numbers of FOXP3⁺ Treg cells in FL patient tumors.

GNE-781 was used as described (13). The prostacyclin analogue Iloprost was obtained from Sigma-Aldrich (SML1651). PPARγ agonist ciglitazone was obtained from Cayman Chemical (71730).

Normal human peripheral blood CD4⁺/CD45RA⁺/CD25⁻ naïve T cells were procured from Allcells (PB009-5). Cells were isolated using Immunomagnetic CD4⁺ Isolation Kit, CD45RO microbeads to deplete CD45RO⁺ cells, and cultured using Dynabeads human T activator CD3/CD28 (Invitrogen; 11132D), 10 ng/mL rTGF-β1 and 10 ng/mL rIL2 (R&D Systems; 240-B-002 and 202-IL-010, respectively), in CTS OpTmizer T-Cell Expansion SFM plus T-cell expansion supplement complete with 5% CTS Immune Cell Serum Replacement (Gibco; A1048501 and A2596101, respectively), and 2 mmol/L glutamine and 10% penicillin/streptomycin solution.

Cells were stained using CD4-APC and CD25-PE (BD Biosciences; 555349 and 555432, respectively) or CD25-BUV395 (BD Biosciences; 564034). Cells were then fixed and permeabilized with the Foxp3/TF Staining Buffer Set (eBioscience; 00-5523-00), and labeled for intracellular Foxp3-AF488 (BD Biosciences; 560047) or Foxp3-BV421 (Biolegend; 320124). For proliferation assays, cells were labeled with CellTrace CFSE Proliferation Kit (Invitrogen; C34554). Samples were processed on BD LSR Fortessa using FACSDiva software (BD Biosciences) and FlowJo software (FlowJo, LLC).

Total RNA was isolated using miRNeasy Mini Kit (Qiagen; 217004), with an on-column DNase digestion, according to the manufacturer's protocol. Library preparation and sequencing were performed at Q² Solutions-EA Genomics.

After lane FASTQ demultiplexing, paired reads were (hard) clipped for low-quality bases and Illumina adapter sequence and filtered for homopolymers or if their clipped length falls below 25 bases. Reads are then aligned using the STAR aligner version 2.4 to the hg19 human reference. The resulting bam is fed into the quantification software RSEM version 1.2.14, where reads are quantified by gene isoform using the UCSC Known gene definition of the transcriptome + additional Ensembl-based lincRNAs excluding ribosomal RNAs. After counts are estimated by isoform, gene level estimates are created by combining known isoforms of the gene. Samples were normalized using upper quantile methods.

Enrichment of genes within TF family associated gene list was determined using Cistrome (15). Database filtering was performed by removing TF targets with relatively weak correlated expression, keeping target genes that show a minimum average correlation coefficient of at least 0.10 in either direction. Fisher exact test for enrichment with Benjamini–Hochberg correction for multiple testing was performed. Target analysis was performed by integration of transcriptome and chromatin immunoprecipitation sequencing (ChIP-seq) data with BETA (16) and filtered to exclude weak correlation (<0.5) between target gene and cancer.

Cells were fixed with 1% formaldehyde for 15 minutes and quenched with 0.125 M glycine. Chromatin was isolated by the addition of lysis buffer, followed by disruption with a Dounce homogenizer. Lysates were sonicated and the DNA sheared to an average length of 300 to 500 bp. Genomic DNA (Input) was prepared by treating aliquots of chromatin with RNase, proteinase K, and heat for de-crosslinking, followed by ethanol precipitation. Pellets were resuspended and the resulting DNA was quantified on a NanoDrop spectrophotometer. An aliquot of chromatin (30 μg) was precleared with protein A agarose beads (Invitrogen). Genomic DNA regions of interest were isolated using 3 μg of antibody against H3K27Ac (Active Motif, 39133). Complexes were washed, eluted from the beads with SDS buffer, and subjected to RNase and proteinase K treatment. Crosslinks were reversed by incubation overnight at 65°C, and ChIP DNA was purified by phenol–chloroform extraction and ethanol precipitation. qPCR reactions were carried out in triplicate on specific genomic regions using SYBR Green Supermix (Bio-Rad). The resulting signals were normalized for primer efficiency by carrying out qPCR for each primer pair using Input DNA.

lllumina sequencing libraries were prepared by the standard consecutive enzymatic steps of end-polishing, dA-addition, and adaptor ligation. After a final PCR amplification step, the resulting DNA libraries were quantified and sequenced on Illumina's NextSeq 500 (75 nt reads, single end). Reads were aligned to the human genome (hg19) using the BWA algorithm (default settings). Duplicate reads were removed and only uniquely mapped reads (mapping quality ≥25) were used for further analysis. Alignments were extended in silico at their 3′-ends to a length of 200 bp and assigned to 32-nt bins along the genome. The resulting histograms (genomic “signal maps”) were stored in bigWig files. Peak locations were determined using the MACS algorithm (2.1.0) with a cutoff of P-value = 1e−7. Peaks that were on the ENCODE blacklist of known false ChIP-seq peaks were removed. Signal maps and peak locations were used as input data to Active Motifs proprietary analysis program.

Cellular extracts were prepared in urea lysis buffer, sonicated, centrifuged, reduced with DTT, and alkylated with iodoacetamide. Samples were digested with trypsin and purified over C18 columns for enrichment with the Acetyl-Lysine Motif Antibody (Cell Signaling Technology; 13416). Enriched peptides were purified over C18 STAGE tips (Rappsilber) and subjected to secondary digest with trypsin and second STAGE tip prior to LC/MS-MS analysis. Peptides were eluted using a 90-minute linear gradient of acetonitrile in 0.125% formic acid delivered at 280 nL/min. Tandem mass spectra were collected with an Orbitrap QExactive or Fusion Lumos mass spectrometer running XCalibur 2.0.7 SP1 using a top-twenty MS-MS method. Real-time recalibration of mass error was performed using lock mass with a singly charged polysiloxane ion m/z = 371.101237. MS-MS spectra were evaluated using SEQUEST and the Core platform from Harvard University. Files were searched against the NCBI homo sapiens FASTA database updated on April 29, 2015. A mass accuracy of ±5 ppm was used for precursor ions and 1.0 Da for product ions. Enzyme specificity was limited to trypsin, with at least one tryptic (K- or R-containing) terminus required per peptide and up to 4 miscleavages allowed. Reverse decoy databases were included for all searches to estimate FDRs, and filtered using a 5% FDR in the linear discriminant module of core. Peptides were also manually filtered using a ±5 ppm mass error range and reagent-specific criteria. Results were filtered to include only peptides containing at least one acetyl-lysine residue. All quantitative results were generated using Progenesis V4.1 (Waters Corporation) and Skyline V3.7.0 (University of Washington) to extract the integrated peak area of the corresponding peptide assignments. Accuracy of quantitative data was ensured by manual review in Progenesis, Skyline, or in the ion chromatogram files.

Cell pellets were resuspended in 1× LDS sample buffer containing 1× reducing reagent (Invitrogen; NP0008 and NP0009, respectively) plus complete protease and phosphatase inhibitor mixture (Roche; 04693124001 and 04906837001, respectively),boiled, placed on ice, then sonicated using an ultrasonic cell disrupter (single 5 seconds, 50% pulse of sonication/sample). Rabbit antihuman H3K27Ac (Cell Signaling Technology; 8173); mouse antihuman Protaglandin I Synthase (Cayman Chemical; 10247) developed using Amersham ECL Select Western Blotting Detection Reagent (GE Healthcare Life Sciences; RPN2235).

Dynabeads human T activator CD3/CD28 beads were removed from T cell cultures, then supernatants spun for 1,500 RPM for 10 minutes and stored at −80°C. 6-Keto-prostaglandin F1α was measured according to the manufacturer's protocol (Cayman Chemical; 515211).

Cytokines were quantified using Protein Simple automated Simple Plex immunoassay platform. Multiplex assay kits containing IL10, IL2, IL1β, and TNFα analytes were used along with the associated controls, as per manufacturer's protocol.

TaqMan reactions (Applied Biosystems) were performed using RNA-to-Ct 1-Step Kit, FAM-labeled probes for FOXP3, CTLA4, LAG3, CCR4, TIM3, and RORα and VIC-labeled probe for 18s ribosomal RNA as a normalization control. Samples were run on ViiA7 Real-Time PCR Machine (Applied Biosystems) and data were analyzed and normalized by 2^−ΔC_t method.

FL patient tumor tissues were obtained from Avaden BioSciences. Total DNA from formalin-fixed, paraffin-embedded (FFPE) curls (10 μm) was isolated using QiaAMP DNA FFPE Kit (Qiagen; 564404) following manufacturer's instructions. DNA was used in targeted sequencing using the Comprehensive Cancer Panel assay performed at Q² Solutions-EA Genomics. Nanostring nSolver v3.0 software was used to assess the quality of raw data and to calculate the normalized expression data.

Immunofluorescence was performed on a Ventana Discovery ULTRA autostainer. Following antigen retrieval with Cell Conditioning 1 (CC1) solution (Ventana; 950-124), tumor samples were incubated with either anti-CD20 rabbit monoclonal SP32 (Spring Bioscience; M3320), anti-CD3 rabbit monoclonal SP162 (Spring Bioscience; M4620), anti-FOXP3 rabbit monoclonal SP97 (Spring Bioscience; M3970), and counterstained with DAPI (Thermo Fisher Scientific; D1306).

Whole stained slide images were acquired with a Nanozoomer XR automated slide scanning platform (Hamamatsu, Hamamatsu City, Japan) at ×200 final magnification. Scanned slides were analyzed in the Matlab software package (version R2017a by Mathworks). Tumor regions were defined as CD20-positive regions identified by a global intensity threshold and standard morphologic filters. Within tumor regions DAPI labeled nuclei were identified using an algorithm based on radial symmetry (17). Each nucleus was then scored as positive or negative for nuclear colocalization of FOXP3, as well as CD3 localization to a ∼2 μm ring immediately adjacent to the nucleus based on global intensity and percentage overlap thresholds.

Normal human peripheral blood CD19⁺/IGD⁺ naïve B cells were procured from Allcells (PB010-3F). Cells were negatively isolated using immunomagnetic isolation to deplete nonnaïve B cells. Cells were cultured with B-Cell Expansion Kit using human CD40 ligand multimer for B-cell activation and expansion (Miltenyi Biotec; 130-106-196).

Statistical tests were performed using Prism 7 software. Data are expressed as mean ± SD. P values were calculated using Welch t test or Paired t test for comparison of 2 samples and ordinary 1-way ANOVA was used in comparison of 3 or more samples.

A recent report showed that CBP/p300 inhibition in ex vivo differentiated human Tregs reduced the expression of FOXP3 and mediators of Treg suppressive functions (12). To define the transcriptional programs regulated by CBP/p300 during Treg differentiation, naïve human CD4⁺ T cells differentiated to Tregs with 85.8% conversion of CD4⁺ T cells to Tregs as measured by FOXP3⁺ cells (Fig. 1A) were used for pairwise analyses of the transcriptional profiles measured by global RNA sequencing (RNA-seq) and led to the identification of 2,153 genes differentially expressed in Tregs compared with naïve activated T cells, herein referred to as “Treg differentiation geneset” (Fig. 1B; Supplementary Table S1). Many Treg-associated genes including FOXP3, CTLA4, LAG3, and CCR4 (18, 19) were upregulated in Treg samples (Supplementary Fig. S1A; Supplementary Table S1).

Next, the CBP/p300 inhibitor GNE-781 (13) was used to treat naïve CD4⁺ T cells during Treg polarizing culture, which reduced overall Treg differentiation as characterized by the significant decrease of FOXP3⁺ cells (Fig. 1C, Supplementary Fig. S1B). Pairwise analyses of transcriptional profiles identified 2,602 genes differentially expressed in Tregs treated with GNE-781 compared with DMSO (Supplementary Fig. S1C; Supplementary Table S2) and led to markedly decreased expression of Treg-associated markers (Supplementary Fig. S1D). Characterization of the growth effects of GNE-781 on T cells showed that CBP/p300 inhibition impaired the proliferation of both naïve activated T cells and induced Tregs (Supplementary Fig. S1E).

To identify genes specifically regulated by GNE-781 in Treg cells, the transcriptional changes induced by GNE-781 in naïve-activated CD4⁺ T cells (Supplementary Fig. S2A) were compared with those induced during differentiation of naïve CD4⁺ T cells to Tregs and identified the transcripts specifically regulated by CBP/p300 in Treg cells (Fig. 1D). Overall, we found that 25% (1,778 genes) of the CBP/p300-regulated genes overlapped between naïve activated CD4⁺ T cells and induced Tregs; 4,441 genes were specific to naïve-activated T cells whereas 824 genes were identified as CBP/p300-regulated genes specifically and solely in Treg cells, herein referred to as “Treg-specific CBP/p300 bromodomain-dependent geneset” (Fig. 1E; Supplementary Table S3).

To define the network of key TFs that control Treg differentiation and that are specifically regulated by CBP/p300, we performed TF enrichment analysis with the Treg differentiation geneset identified above (Fig. 1B). This analysis revealed 21 enriched TFs that cooperate to regulate the majority of transcripts in the Treg differentiation geneset (Supplementary Fig. S2B). To elucidate the higher order organization between these 21 TFs, we mapped their reciprocal regulation relationships using publicly available chromatin immunoprecipitation and parallel sequencing (ChIP-seq) datasets (Supplementary Fig. S2C; ref. 15). The first-order TFs were defined based on their ability to mostly regulate expression of other TFs more than they are reciprocally regulated; whereas the second order TFs are largely regulated by first-order TFs. Our analysis revealed FOXP3 at the top of the hierarchy among all first-order TFs, regulating the largest number of TFs with 12 of the 20 enriched TFs, whereas reciprocally regulated by only 5 of 20 enriched TFs (Supplementary Fig. S2C). Along with FOXP3, the other top first-order TFs identified included STAT4, ADNP, EBF1, and GATA3. Next, we used the Treg-specific CBP/p300-dependent geneset (Fig. 1D and E) as input in a TF enrichment analysis and identified 16 enriched TF families that regulate the majority of transcripts (Fig. 1E). Interestingly, 14 of the 16 TFs identified showed overlap with the TFs that control Treg differentiation (Supplementary Fig. S2B). Higher order organization and regulation relationship analysis once again identified FOXP3 at the top of the hierarchy regulating 12 of the 15 enriched TFs while being reciprocally regulated by only 6 of the 15 enriched TFs (Fig. 1F). Along with FOXP3, the other 3 first-order TFs identified included once again STAT4, ADNP, and GATA3, all of which were also identified as enriched TFs that control Treg differentiation (Supplementary Fig. S2B).

These findings suggest that CBP/p300 is a key transcriptional regulator of Treg differentiation, regulating transcripts that define Tregs and their biological functions and that the expression of these genes is largely regulated by the master TF FOXP3.

To investigate the mechanisms by which CBP/p300 regulates Treg differentiation at the chromatin level, we performed ChIP-seq studies in naïve T cells and Treg cells treated with GNE-781 to profile genome-wide H3K27 acetylation (H3K27Ac), a primary pharmacodynamic biomarker of CBP/p300 inhibition (Fig. 2A). ChIP-seq analysis identified peaks in “promoter regions” in 81% of unique genes identified, which is in line with the role of the H3K27Ac mark in active gene transcription (Supplementary Fig. S3A). Next, we compared GNE-781 Treg treated cells to Treg DMSO samples and required that the genes show differentials of >1.5-fold change in acetylation in all 3 biological replicates. We observe diminished H3K27 acetylation following CBP/p300 inhibition (Supplementary Fig. S3B) and identified a total of 2,794 transcripts regulated by H3K27 acetylation in Treg cells treated with GNE-781 versus DMSO (Fig. 2B, blue track). Next, we analyzed the effect of CBP/p300 inhibition on naïve activated T cells comparing GNE-781 to DMSO treatments and identified a total of 4,468 transcripts regulated by H3K27 acetylation (Fig. 2B, orange track). A comparison of these 2 genesets (GNE-781 vs. DMSO treatment of naïve T cells vs. Tregs) resulted in the identification of a ChIP-seq-based Treg-specific geneset of 959 transcripts regulated by CBP/p300-mediated acetylation on H3K27 (Fig. 2B, red track; Supplementary Table S4). To examine the molecular mechanisms underlying CBP/p300 regulation of transcription through histone acetylation, we performed TF enrichment analysis and identified 23 enriched TF families (Fig. 2B), with FOXP3 target genes being the most significantly enriched. Interestingly, we observed a strong correlation between ChIP-seq and RNA-seq TF enrichment analysis (Fig. 2C and B, TFs in blue text).

To define which genes previously identified by RNA-seq as the “Treg- specific CBP/p300 bromodomain-dependent geneset” are regulated by CBP/p300 through H3K27 acetylation, we analyzed its overlap with the ChIP-seq-based Treg-specific geneset of 959 transcripts (Fig. 2D, orange track) and identified a 57 gene signature whose expression is regulated by CBP/p300 through H3K27 acetylation (Fig. 2D, red track; Supplementary Table S5). The reduced H3K27 acetylation on active regions of the top 5 most modulated genes in this signature is evident in the visualization of ChIP-seq tracks (Fig. 2E). Additionally, TF enrichment analysis of the 57 gene signature identifies once again FOXP3 as well as ETS1, IKZF1, and FLI1 as the TFs regulating this gene signature (Fig. 2D), all of which were previously identified in the higher order TF regulation cascade as first- and second-order TFs following RNA-seq analysis (Fig. 1E and F).

Collectively, our transcriptional and epigenetic profiling shows that CBP/p300 controls Treg cell fate through the regulation of FOXP3 expression and transcriptional functions. Our findings point to FOXP3 as the master TF-regulating Treg differentiation in human cells through the modulation of a cascade of TFs regulated by CBP/p300.

In addition to transcriptional regulation, we also investigated the non-transcriptional mechanisms regulated by CBP/p300 including nonhistone protein acetylation. To identify direct protein acetylation targets of CBP/p300, we performed quantitative profiling of global lysine acetylation by immune-affinity enrichment and LC/MS-MS. As expected, acetylation of histones 3, 2A, and 2B were the most heavily downregulated by CBP/p300 inhibition (Supplementary Fig. S4A; Fig. 2A). To identify novel nonhistone CBP/p300 acetylation targets, we focused our analysis on targets whose acetylation levels are both regulated during Treg differentiation as well as following CBP/p300 inhibition. Our analysis identifies 3 groups; groups 1 and 2 include proteins with at least 2-fold increase in acetylation during Treg differentiation and either no change (group 2) or at least 2-fold decrease following CBP/p300 inhibition (group 1) whereas group 3 includes proteins with no change in acetylation during differentiation and at least 2-fold decrease following CBP/p300 inhibition. We focused our analysis on proteins in group 1 as these are acetylated by CBP/p300 during differentiation to Tregs and are deacetylated following CBP/p300 inhibition. Analysis of group 1 identified INO80D, prostacyclin synthase (PTGIS), and lamin B1 as direct acetylation targets of CBP/p300 (Fig. 3A). Although a direct link of INO80D and lamin B1 to T-cell biology has not been shown, PTGIS on the other hand has been linked to T-cell biology through its regulation of prostacyclin production leading to downstream regulation of PPARγ and IP receptor pathways (Fig. 3B; refs. 20–22). PTGIS converts prostaglandin H2 into prostacyclin, which has been shown to suppress both Th1 and Th2 differentiation and function while enhancing Th17 immune responses (23). Similarly, CBP/p300 has recently been linked to IL17A production and Th17 immune responses (24) and recent work suggests that IL9 production is also regulated by CBP/p300 (25). However, the effect of prostacyclin on Treg differentiation is largely unknown.

Considering the established link between PTGIS and T-cell biology, we first confirmed PTGIS protein expression in both naïve-activated T cells and Tregs (Fig. 3C). Next, we examined prostacyclin levels in naïve T cells and Tregs and observed increases of prostacyclin concentrations in the supernatant of Treg cells compared with naïve T cells, which decreased following treatment with GNE-781, corresponding to reduced Treg differentiation (Fig. 3D; Supplementary Fig. S4B). Because the biological effects of prostacyclin are mediated via IP receptor and PPARγ signaling pathways (22), we next examined whether CBP/p300 regulates Treg differentiation upstream of prostacyclin by performing rescue experiments using Iloprost, an IP receptor agonist, to stimulate IP receptor signaling and Ciglitazone, a PPARγ agonist, to stimulate the PPARγ pathway. As expected, we observed an increase in FOXP3⁺ Treg cells that were downregulated following treatment with GNE-781 (Fig. 3E, panels 1–6). Treatment with either Iloprost or Ciglitazone, which are expected to further stimulate IP receptor and PPARγ pathways independently of endogenous prostacyclin levels, failed to alter Treg differentiation alone (Fig. 3E, panels 7,9,11,13) and did not rescue the inhibitory effects of GNE-781 on Treg differentiation (Fig. 3E, panels 8,10,12,14). These data demonstrate that CBP/p300 functions upstream of IP receptor and PPARγ pathways and that the contribution of PTGIS to Treg differentiation is mediated via prostacyclin in a CBP/p300-dependent manner. Together, these data suggest that the ability of CBP/p300 to drive Treg differentiation is mediated in part via direct acetylation of PTGIS and regulation of its downstream pathways.

Prostacyclin has been shown to regulate the production of cytokines, primarily IL10, and chemokines by CD4⁺ T cells through IP receptor-cAMP signaling (26) whereas PPARγ has been shown to influence cytokine production (27). We evaluated the effect of CBP/p300 inhibition on the expression of IL10, a proinflammatory cytokine able to convert naïve T cells into Tregs in the periphery as well as mediate Treg suppressor activity (28). IL10 generally inhibits inflammatory pathologies; however, IL10 induction often occurs together with proinflammatory cytokines and its stimulatory effects on various immune cells have also been described (29–31). Our findings show IL10 expression increase during Treg differentiation whereas CBP/p300 inhibition strongly suppressed its production (Fig. 3F). Similarly for TNFα, whose expression is suppressed by prostacyclin analogs (32), we observed an increase in TNFα during Treg differentiation, which decreased following CBP/p300 inhibition (Fig. 3G). The observed modulation of TNFα by CBP/p300 is in line with its ability to act in concert with IL2 to selectively activate Tregs, resulting in upregulation of FOXP3 expression and increase in Treg suppressive functions (33). We also measured IL2 levels, however, because IL2 was exogenously added to stimulate Treg differentiation, we cannot ascertain the effects of CBP/p300 inhibition on IL2 production. Interestingly, our analysis of the transcriptional regulation of Treg differentiation by CBP/p300 showed that expression of LAPTM5 and ENG, 2 regulators of TGFβ, a key regulator of Treg differentiation (34, 35), are both part of the 57 Treg-specific gene signature regulated by CBP/p300 through histone acetylation (Fig. 2D; Supplementary Table S5).

Herein, we showed that CBP/p300 regulates Treg differentiation by altering the production of proinflammatory cytokines including IL10 and TNFα via acetylation of PTGIS at lysine 121 and regulation of prostacyclin levels and its downstream pathways.

Inactivating mutations of CBP/p300 are found at high frequencies in FL and diffuse large B-cell lymphoma (DLBCL), suggesting a role of CBP/p300 in malignant transformation of B cells (10, 36). Studies have shown that a high percentage of FOXP3-positive Tregs in FL is predictive of better clinical outcome (37), suggesting that reduction of Treg cells may be a tumor evasion mechanism in FL. To determine if CBP/p300 LOF mutations in tumor B cells results in the reduction of Treg differentiation, we used a cohort of 18 FL patient tissue samples for targeted sequencing of CBP and p300 and categorized samples into HAT domain mutant or wild type (WT) with an overall mutation frequency of 33% for CBP and 17% for p300 (Supplementary Fig. S5A). We used the computational prediction software Mutation Assessor to predict the functional consequence of each mutation identified and it proved to highly correlate with experimental findings from the literature on the functional impact of each mutation (Supplementary Fig. S5B; ref. 10). In total, we classified 44% of our FL cohort as CBP/p300 LOF mutants and 56% as WT (Supplementary Fig. S5A).

Multiplex immunofluorescence for FOXP3, CD3, and CD20 was used along with digital image analysis to create a CD20⁺ tumor area and quantify the density of intratumoral FOXP3⁺ cells. We observed a lower number of FOXP3⁺ cells in CBP/p300 LOF mutant tumors compared with WT samples (P value 0.05; Fig. 4A–C; Supplementary Fig. S5C).

n addition, the density of CD3⁺ intratumoral T cells excluding Treg cells showed no difference between CBP/p300 WT and LOF mutant samples (Fig. 4D; Supplementary Fig. S5D). Examination of the activation status of T cells showed significantly lower expression of CD25 and CD44, 2 T-cell activation markers, in LOF mutant samples compared with WT samples (Supplementary Fig. S5E), suggesting less activated T cells present in patients with FL with CBP/p300 LOF mutations. We also evaluated the presence and activation of B cells (Supplementary Fig. S6A–S6B) and observed no difference in CD20⁺ B-cell numbers or activation as measured by CD80 and CD86 between CBP/p300 WT and LOF mutant samples (Supplementary Fig. S6C). Our findings suggest that not all immune cells are regulated by CBP/p300 and further support that Treg (CD3⁺/FOXP3⁺) cell differentiation is specifically regulated by CBP/p300 HAT domain activity.

Because these findings implicate a role of CBP/p300 in the regulation of tumor B cells, we then sought to determine if the T-cell mechanism of regulation we identified also occurs in B cells. Reports have revealed a role of B cells in the expansion and differentiation of Treg cells (38, 39); therefore, we hypothesized that Treg cell numbers in FL tissues may be influenced by cytokines secreted by B cells in the tumor microenvironment. To test this hypothesis, we treated naïve primary CD40-activated B cells with GNE-781 and evaluated the effects on IP receptor/PPARγ pathway and cytokine production. We confirmed protein expression of PTGIS in B cells in the absence and presence of GNE-781 (Fig. 4E) and examined prostacyclin levels in cell culture supernatants of naïve B cells treated with GNE-781 versus DMSO and observed a reduction in prostacyclin production following GNE-781 treatment (Fig. 4F). We next measured the effects on cytokine production and observed reduced secretion of both IL10 and IL2 following GNE-781 treatment (Figs. 4G and H), whereas modulation of TNFα was subject to donor variability (Supplementary Fig. S6D). To prove that Treg differentiation can be regulated by cytokines secreted from B cells in a CBP/p300-dependent fashion, we next evaluated the effects of B-cell culture supernatant on T-cell differentiation after the B cells were cultured in presence or absence of GNE-781 (Fig. 4I). First, we observed a reduction of FOXP3 mRNA in Tregs cultured in B-cell supernatants generated in presence of GNE-781 compared with DMSO control and that indeed the longer naïve T cells are cultured in B-cell supernatant, the higher is FOXP3 expression and Treg differentiation (Fig. 4I). The ability of these donor naïve T cells to differentiate into Tregs was also confirmed and showed the expected upregulation of FOXP3 mRNA and Treg differentiation (Supplementary Fig. S6E). These data suggest that B cells are able to regulate Treg cell differentiation via cytokine secretion through a CBP/p300-dependent process mediated through regulation of prostacyclin.

Using transcriptional profiling, chromatin immunoprecipitation, and mass spectrometry, we demonstrate for the first time an association between CBP/p300 LOF mutations and Treg cell presence in tumors and their microenvironment. We show that CBP/p300 is a key transcriptional regulator of Treg differentiation through gene expression modulation of the master TF FOXP3. Although FOXP3 expression is known to be essential for the developmental differentiation and suppressor function of Tregs (40), CBP/p300 has previously been shown to be indispensable for FOXP3⁺ Treg lineage through various potential mechanisms (41). Using the CBP/p300 inhibitor GNE-781 and quantitative profiling of global lysine acetylation by mass spectrometry, we identified a novel substrate and pathway regulated by CBP/p300 through direct acetylation of PTGIS on lysine 121, which in turn controls prostacyclin and proinflammatory cytokine production upstream of IP receptor and PPARγ pathways controlling Treg differentiation. Interestingly, despite previous suggestions that CBP/p300 may modulate FOXP3 expression either through its direct acetylation (12) or acetylation of its regulators (41), we did not identify FOXP3 or any of its regulators in our mass spectrometry analysis. In addition, we show for the first time that the expression levels of proinflammatory cytokines, which are required for induced Treg differentiation and suppressive functions (33, 42), are regulated by CBP/p300 transcriptionally and downstream of PTGIS acetylation and prostacyclin production.

Using FL patient tissues and in vitro B-cell assessments, we show for the first time that CBP/p300-mediated regulation of PTGIS and proinflammatory cytokine production and secretion into the tumor microenvironment extends to B cells where we observe lower presence of FOXP3⁺ Tregs in FL patient tissues carrying CBP/p300 LOF mutations. These findings suggest that patients with FL harboring CBP/p300 mutations may have less immunosuppressive microenvironments and could benefit from cancer immunotherapy agents. In fact, tissues from these CBP/p300 mutant patients did not show any difference in overall CD3⁺ T-cell numbers compared with their WT counterparts, suggesting that CBP/p300 specifically regulates Treg cells and not all immune cells. Because accumulation of Tregs at tumor sites was shown to inhibit antitumor immune responses (43), our novel findings suggest that although CBP/p300 does not alter effector T-cell presence within the tumor, it promotes a Treg-rich and immunosuppressive microenvironment. Studies have shown that Treg infiltration has a positive prognostic significance in FL with some reporting a 5-year overall survival of 80% in patients classified as having a median Treg cell percentage above 10% (37, 44). Conversely, mechanistic studies have suggested that tumor infiltration by CD8⁺ T cells often triggers recruitment of other immune cells including Tregs (45), suggesting that Treg infiltration in FL may represent a proxy for effector T-cell presence or a measure of immune presence in general. However, in our study, the presence of CD3⁺ T and Treg cells in FL tissues did not correlate, suggesting that CBP/p300 may promote the differentiation of CD3⁺ T cells within FL tissues to Treg cells, which could explain the reduced Treg cell presence overall in CBP/p300 mutant tissues.

Moreover, our novel findings that CBP/p300 regulates cytokine production and secretion by B cells, which influences T-cell differentiation to Tregs, are in line with recent studies proposing a role for regulatory B cells in negatively altering T-cell immune responses through expression of the inhibitory cytokine IL10 (46). Although the ability of regulatory B cells to negatively regulate inflammation has been linked to IL10, the mechanisms controlling its production remain unknown. Our data points to PTGIS and downstream prostacyclin as the direct mechanisms used by CBP/p300 to regulate proinflammatory cytokine production and secretion in B cells. Although expression of other cytokines is regulated by CBP/p300 in B cells, our findings suggest that IL10 is the primary target. As such, we propose that one mechanism by which B cells (and possibly regulatory B cells) regulate IL10 production and selectively inhibit T-cell function by promoting their differentiation to Tregs is through CBP/p300 direct acetylation of PTGIS, regulation of prostacyclin production, and activation of downstream pathways controlling cytokine expression and secretion.

CBP/p300 LOF mutations lead to reduced acetyltransferase activity and have been shown to arise as an early event in FL disease (47). Studies in primary human FL tumors have shown that CBP/p300 mutations are associated with reduced MHC class II expression, reduced T-cell numbers, and function with no significant difference in Treg presence (48). This is in contrast to our findings suggesting lower Treg presence and no difference in T-cell numbers in CBP/p300 LOF mutant FL samples compared with WT samples. The differences in Treg findings between our studies could be attributed to the markers used to define the Treg cell population (CD3⁺CD4⁺CD25⁺), which are T-cell activation markers whereas our markers (CD3⁺CD4⁺FOXP3⁺) may be more specific to Treg cells.

Finally, CBP/p300 alterations have been associated with poor clinical outcome in FL (49) but the clinical response to cancer immunotherapy agents of this biomarker subset has not been specifically evaluated. Taking into consideration the immunosuppressive functions of Tregs and our data suggesting a role of CBP/p300 in driving Treg differentiation, we envision that CBP/p300 LOF mutant patients may show better response to immunotherapy agents, specifically to checkpoint blockade with anti-PD1, PDL-1, or CTLA-4. Collectively, our findings provide new insights into the regulation of T- and B-cell biology orchestrated by CBP/p300 and its role in the dynamic regulation of Treg differentiation with potential clinical implications in the alteration of the immune landscape.

K.E. Hutchinson has ownership interest (including stock, patents, etc.) in Roche/Genentech, Inc. S.-M.A. Huang has ownership interest (including stock, patents, etc.) in Roche. J.M. Giltnane is a Research Pathologist at Genentech; and has ownership interest (including stock, patents, etc.) in Genentech. M.R. Lackner has ownership interest (including stock, patents, etc.) in Roche. No potential conflicts of interest were disclosed by the other authors.

Conception and design: J. Castillo, S. Magnuson, V. Plaks, M.R. Lackner, Z. Mounir

Development of methodology: J. Castillo, S. Jayakar, M.P. Stokes, F.A. Romero, V. Plaks

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): J. Castillo, C. Lowe, S. Jayakar, B. Liu, K.E. Hutchinson, M.P. Stokes, S.S. Tarighat, A. Glibicky, J.M. Giltnane

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): J. Castillo, E. Wu, C. Lowe, R. McCord, J. Eastham-Anderson, K.E. Hutchinson, W. Jones, M.P. Stokes, S.S. Tarighat, A. Glibicky, V. Plaks, J.M. Giltnane, Z. Mounir

Writing, review, and/or revision of the manuscript: J. Castillo, E. Wu, S. Srinivasan, R. McCord, M.-C. Wagle, S. Jayakar, K.E. Hutchinson, T. Holcomb, S. Magnuson, S.-M.A. Huang, J.M. Giltnane, M.R. Lackner, Z. Mounir

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): J. Castillo, S. Srinivasan, K.E. Hutchinson, W. Jones, Z. Mounir

Study supervision: Z. Mounir

Other (providing images): M.G. Edick

We thank An Do for RT-qPCR consultation, Denise de Almeida Nagata and Jane Grogan for helpful discussions.

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.