Targeting Setdb1 in T Cells Induces Transplant Tolerance without Compromising Antitumor Immunity
Suppressing immune responses promotes allograft survival but also favors tumor progression and recurrence. How to selectively suppress allograft rejection while maintaining or even enhancing antitumor immunity is challenging. Here, we found mice deficient in Setdb1, an H3K9 methyltransferase, retained allograft-related rejection but exhibited retentive antitumor immunity. RNA-Seq showed that Setdb1 deficiency did not affect T-cell activation or cytokine production, but it induced an increase in regulatory T (Treg) cell-associated gene expression. Treg cell depletion impaired graft acceptance in Setdb1-deficient mice, indicating that the Treg cells promoted allograft survival. Surprisingly, Treg cell-specific Setdb1 deficiency did not prolong allograft survival, suggesting that Setdb1 may function prior to Foxp3 induction. Using single-cell RNA sequencing, we found that Setdb1 deficiency induced a new Treg population in the thymus. This subset of Treg cells expressed less IL-1R2 and IL-18R1. Mechanistically, during Treg cell induction, Setdb1 was recruited by transcription factor ATF and modified histone methylation. Our data define Setdb1 in T cells as a target for suppressing allograft rejection while maintaining antitumor immunity. Overall design: CUT&Tag was performed using the Hyperactive Universal CUT&Tag Assay Kit. The sequencing process was outsourced to Novogene Co., Ltd. Briefly, after harvesting, CD25+ enriched T cells were counted and divided into groups of 200,000 cells each. The cells were centrifuged at 600 g for 5 minutes at room temperature. Each sample was resuspended in 100 µl of pre-cooled NE Buffer, mixed gently, and incubated on ice. Following incubation, the cells were centrifuged again at 600 g for 5 minutes at room temperature, and the pellets were resuspended in 100 µl of wash buffer. Next, 10 µl of Concanavalin A-coated magnetic beads, pre-activated at room temperature, were added to each sample and incubated for 10 minutes. The unbound supernatant was removed, followed by an overnight reaction between the H3K9me3 group sample and anti-H3K9me3 antibodies (1:50, Proteintech), as well as between the SETDB1 group sample and anti-SETDB1 antibodies (2 µg, Proteintech) in Antibody Buffer at 4°C. After washing, the bound antibodies were reacted with secondary antibodies and then incubated with pA/G-Tnp Pro (0.04 µM). Next, the samples were fragmented with TTBL, followed by the addition of 10% SDS and DNA spike-in to each sample. To extract the genomic DNA, DNA Extract Beads Pro were activated and added to each sample. The extracted DNA fragments were amplified by PCR with indexing primers (Vazyme, TD202), and the library was purified using VAHTS DNA Clean Beads (Vazyme, N411). The specific gene sequences were analyzed by DNA sequencing on an Illumina PE150 platform (Illumina) by Vazyme. Adapter sequences and low-quality reads in the raw CUT&Tag data were filtered using BBDuk (version 38.44). The filtered reads were then aligned to the mouse mm10 reference genome using Bowtie2 (version 2.4.2) with the parameters “--local --very-sensitive --no-mixed --no-discordant --phred33 -I 10 -X 700.” Duplicate reads were removed with the MarkDuplicates function in GATK (version 4.2.0). Coverage (bigWig) files were generated from the resulting BAM files using the bamCoverage tool in deepTools. Peak calling was performed using MACS2 (version 2.2.7.1) with the parameters “--keep-dup all --broad --broad-cutoff 0.05.” Finally, quantitative differences in binding and corresponding p-values were determined using MAnorm (version 1.3.0).