The STAT3-SETDB2 Axis Dictates NF?B-mediated Inflammation in Macrophages during Wound Repair
Macrophage transition from an inflammatory to reparative phenotype after tissue injury is controlled by epigenetic enzymes that regulate inflammatory gene expression. We have previously identified the histone methyltransferase, SETDB2, in macrophages drives tissue repair by repressing NF?B-mediated inflammation. Complementary ATAC and RNA sequencing of wound macrophages isolated from mice deficient in SETDB2 in myeloid cells revealed that SETDB2 suppresses the inflammatory gene program by inhibiting chromatin accessibility at NFkB-dependent gene promoters. We found that STAT3 was required for SETDB2 expression in macrophages yet, paradoxically, it also functioned as a binding partner of SETDB2 where it repressed SETDB2 activity by inhibiting its interaction with the NFKB component, RELA, leading to increased RELA/NFKB-mediated inflammatory gene expression. Further, RNA sequencing in wound macrophages from STAT3-deficient mice corroborated this and revealed STAT3 and SETDB2 transcriptionally co-regulate overlapping genes. Finally, in diabetic wound macrophages STAT3 expression and STAT3-SETDB2 binding were increased. As such, we identify what we believe to be a novel SETDB2-STAT3 axis that modulates macrophage phenotype during tissue repair and may be an important therapeutic target for nonhealing diabetic wounds. Overall design: For ATAC-seq, wound macrophages were harvested from mice at day 5 as described above using MACS and nuclei were isolated as described previously (45). After counting, 50K nuclei per sample were resuspended in the appropriate volume, spun down, and collected for use in downstream transposition reaction per modified ENCODE protocol (46). Data was obtained and processed according to the ENCODE pipeline and then converted to readable format as similarly described. Briefly, minimum read length prior to trimming was 45 base pairs. Paired-end sequencing was performed using NovaSeq (Illumina) and sequences were mapped to the genome. For RNA-seq, day 5 wounds were harvested from Setdb2flox/floxLyz2Cre, Stat3flox/floxLyz2Cre, and cre-negative littermate control mice, digested, and underwent negative selection for CD3, CD19, and Ly6G lineage markers. Remaining cells were surface stained and sorted by FACs into CD11b+Ly6CHi and CD11b+Ly6CLo populations. RNA isolation was performed using a RNeasy Kit (Qiagen) with DNase digestion. Library construction and analysis of reads was performed as described previously (41). Briefly, reads were trimmed using Trimmomatic and mapped using HiSAT2 (47, 48). Read counts were performed using the feature-counts option from the subRead package followed by the elimination of low reads, normalization and differential gene expression using edgeR (49, 50). Differential expression was performed on mapped reads using the taqwise dispersion algorithm in edgeR.