Lymphatic endothelial Tbx1 promotes an immunosuppressive microenvironment to facilitate post-myocardial infarction repair

Description

Title: Transcription factor Tbx1 binding site in post-myocardial infarction hearts. Data type: Genome binding/occupancy profiling by high throughput sequencing. Citation: Wang, W., Li, X., Ding, X., Xiong, S., Hu, Z., Lu, X., Zhang, K., Zhang, H., Hu, Q., Lai, K.S., Chen, Z., Yang, J., Song, H., Wang, Y., Wei, L., Xia, Z., Zhou, B., He, Y., Pu, J., Liu, X., Ke, R., Wu, T., Huang, C., Baldini, A., Zhang, M., Zhang, Z., 2023. Lymphatic endothelial transcription factor Tbx1 promotes an immunosuppressive microenvironment to facilitate post-myocardial infarction repair. Immunity 1–16. Summary: The heart is an autoimmune-prone organ. It is crucial for the heart to keep injury-induced autoimmunity in check to avoid autoimmune-mediated inflammatory disease. However, little is known about how injury-induced autoimmunity is constrained in hearts. Here we reveal an unknown intramyocardial immunosuppressive program driven by Tbx1, a DiGeorge syndrome disease gene that encodes a T-box transcription factor. We found induced profound lymphangiogenic and immunomodulatory gene expression changes in lymphatic endothelial cells (LECs) after myocardial infarction (MI). The activated LECs penetrated the infarcted area and functioned as intramyocardial immune hubs to increase the numbers of tolerogenic dendritic cells (tDCs) and regulatory T cells (Tregs) through the chemokineCcl21 and integrin Icam1, thereby inhibiting the expansion of autoreactive CD8+ T cells and promoting reparative macrophage expansion to facilitate post-MI repair. Mimicking its timing and implementation may be a novel approach to treating autoimmunity-mediated cardiac diseases. Experimental design: We generated a Tbx1FlagSBP allele to perform Tbx1 ChIP experiments to identify potential transcriptional targets of Tbx1. A total of 7,147 peaks were enriched compared to the input control, 26.7% of which contained the consensus T-box binding motif. To filter out injury-activated Tbx1 binding peaks, we compared our data with cardiac endothelial-specific ATAC-seq data from uninjured hearts (Yucel et al., 2020). The functional annotation of genes associated with Tbx1 peaks or Tbx1 ChIP-seq-only peaks showed a strong enrichment for genes involved in immune tolerance, autoimmune response, T cell activation, endothelial cell growth and migration, consistent with transcriptional analysis. These results suggested that the aforementioned lymphangiogenic and immunomodulatory genes were direct targets of Tbx1.

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Experimental procedure: We isolated cardiac CD31+ endothelial cells from five pooled hearts as one biological replicate and fixed the cells in 1% formaldehyde for 10 minutes. Then the cells were dissociated with a 1 mL dounce homogenizer and were sonicated for 10 cycles of 30 seconds on/off using a Bioruptor Pico instrument (Diagenode). Anti-FLAG M2 magnetic beads (MilliiporeSigma, cat# M8823) were added in PBS with 0.5% BSA and incubated with rotation for 4 C overnight. After wash and elusion, about 50ng of DNA was collected and submitted for sequencing on Illumina NovaSeq 6000 platform. Bioinformatics analysis: The sequencing reads were aligned to the mm10 genome using Bowtie2 (v2.3.4). The alignment option was set to “-q --very-fast”. The alignment step generated BAM files. File type: sorted BAM files, genome version mm10 Downstream analysis: For data analysis, we used the Homer package (Heinz et al., 2010) with a previously described pipeline (Morikawa et al., 2015). We used the following parameters for peak calling “findPeaks -style factor -o auto -i input -fdr 0.1 -localSize 50000 -L 3”. Two biological replicates were processed individually for measuring reproducibility and then combined for peak detection and visualization. For motif calling, we used the following command “annotatePeaks.pl peaks mm10 Tbx1motif” with 26.7% of peaks containing the Tbx1 motif. Next, the peaks were centralized around Tbx1 motifs to generate final peak coordinates using “annotatePeaks.pl -m Tbx1motif -mbed”. The functional analysis of cis-regulatory elements was performed using GREAT software (http://great.stanford.edu/) (McLean et al., 2010). The association rule was set up as “Two nearest genes within 200kb”. The cardiac endothelial ATAC-seq dataset was obtained from public available source (GSE144839), and aligned to the mm10 genome using Bowtie2 (v2.3.4). The alignment option was set to “-q --very-fast”. For peak calling, the same methods were used as ChIP-seq analysis.

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