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Hierarchical regulation of the resting and activated T cell epigenome by major transcription factor families

Nature Immunology volume 23, pages 122–134 (2022)Cite this article

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Abstract

T cell activation, a key early event in the adaptive immune response, is subject to elaborate transcriptional control. In the present study, we examined how the activities of eight major transcription factor (TF) families are integrated to shape the epigenome of naive and activated CD4 and CD8 T cells. By leveraging extensive polymorphisms in evolutionarily divergent mice, we identified the ‘heavy lifters’ positively influencing chromatin accessibility. Members of Ets, Runx and TCF/Lef TF families occupied the vast majority of accessible chromatin regions, acting as ‘housekeepers’, ‘universal amplifiers’ and ‘placeholders’, respectively, at sites that maintained or gained accessibility upon T cell activation. In addition, a small subset of strongly induced immune response genes displayed a noncanonical TF recruitment pattern. Our study provides a key resource and foundation for the understanding of transcriptional and epigenetic regulation in T cells and offers a new perspective on the hierarchical interactions between critical TFs.

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Fig. 1: Effect of TF-binding motif polymorphisms on allele-specific chromatin accessibility in naive and activated T cells.
Fig. 2: Runx1, TCF1 and Ets1 occupy most accessible chromatin regions.
Fig. 3: Pre-accessible Ets1-bound chromatin regions are not responsive to activation-induced TFs.
Fig. 4: A small subset of immune response-related elements recruits Ets1 upon T cell activation.
Fig. 5: Activation-dependent TF partners influence Runx1 localization.
Fig. 6: Context-dependent functions of TCF1 in naive and activated CD4 and CD8 T cells.
Fig. 7: Drivers of chromatin accessibility across T cell subpopulations.

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Data availability

All next-generation sequencing data generated in this paper were deposited in the Gene Expression Omnibus under SuperSeries accession nos, GSE166718 and GSE184006. Processed data are included with the manuscript as supplementary tables. The B6 reference genome (GRCm38) was downloaded from the National Center for Biotechnology Information. Files CAST_EiJ.mgp.v5.snps.dbSNP142.vcf and CAST_EiJ.mgp.v5.indels.dbSNP142.normed.vcf with SNP and indel genetic variants between B6 and Cast genomes were obtained from the Mouse Genome Project53. Gene annotations were downloaded from the ENSEMBL database at ftp.ensembl.org/ensembl/pub/release-83/gtf or obtained from GENCODE v.M5 (ref. 60). Experimentally determined Mus musculus motifs identified by CHiP-seq, PBM, SELEX or HocoMoco were retrieved from the CIS-BP database57.

Code availability

The customized Shell and Python code used in the present study is publicly available at the MSKCC website: http://cbio.mskcc.org/public/Leslie/zhongy/downloads/index8.html

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Acknowledgements

We thank all members of the Rudensky lab for discussions and technical assistance. We thank the NIH tetramer core for providing critical reagents. The present study was supported by NIH grants (nos. R01AI034206, U54 CA209975) and NIH/National Cancer Institute (NCI, grant no. P30 CA008748), AACR-Bristol-Myers Squibb Immuno-oncology Research Fellowship (no. 19-40-15-PRIT), National Natural Science Foundation of China (grant no. 32170883 to Y.Z.), the Ludwig Center at MSKCC and the Parker Institute for Cancer Immunotherapy. A.Y.R. is an investigator with the Howard Hughes Medical Institute. We acknowledge the use of the MSKCC Single Cell Research Initiative and the Integrated Genomics Operation Core, funded by the NIH/NCI Cancer Center Support (grant no. P30 CA008748), Cycle for Survival, and the Marie-Josée and Henry R. Kravis Center for Molecular Oncology.

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Author notes

  1. These authors jointly supervised this work: Alexander Y. Rudensky, Joris van der Veeken.

Authors and Affiliations

  1. Howard Hughes Medical Institute and Immunology Program, Sloan Kettering Institute and Ludwig Center at Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Yi Zhong, Alexander Y. Rudensky & Joris van der Veeken

  2. Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Yi Zhong & Christina S. Leslie

  3. Shanghai Immune Therapy Institute, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Yi Zhong

  4. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    Sarah K. Walker & Yuri Pritykin

  5. Quantitative and Computational Biology Graduate Program, Princeton University, Princeton, NJ, USA

    Sarah K. Walker

  6. Department of Computer Science, Princeton University, Princeton, NJ, USA

    Yuri Pritykin

  7. Research Institute of Molecular Pathology, Vienna Biocenter, Vienna, Austria

    Joris van der Veeken

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Contributions

J.v.d.V. and A.Y.R. designed the study. J.v.d.V. performed the experiments and analyzed the data. Y.Z. and C.S.L. designed the allele-specific analysis pipeline. Y.Z. performed the computational analysis of sequencing data. S.K.W. and Y.P. analyzed the scATAC-seq data. J.v.d.V. and A.Y.R. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Alexander Y. Rudensky or Joris van der Veeken.

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A.Y.R. is an SAB member, has equity in Sonoma Biotherapeutics and Vedanta Biosciences, and is a co-inventor or has IP licensed to Takeda that is unrelated to the content of the present study. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 RNA-seq and ATAC-seq of naive and activated CD4 and CD8 T cells.

a: Example of gating strategy for cell isolation from the spleen. Live lymphocytes were identified based on FSC-A/SSC-A. Doublets were gated out and CD4 and CD8 T cells were identified as TCRβ+CD4+ and TCRβ+CD8+, respectively. Naive CD4 and CD8 T cells used as input for RNA-seq, ATAC-seq, and CUT&RUN experiments were isolated from uninfected mice as CD44+CD62L− cells. Bulk activated CD4 and CD8 T cells used as input for CUT&RUN experiments were isolated as CD44+ cells from LCMV Armstrong infected mice on day 7-8 post-infection (p.i.). LCMV-specific activated and memory CD4 and CD8 T cells used as input for ATAC-seq and RNA-seq experiments were isolated based on tetramer staining on day 7 or day 60 p.i. b: RNA-seq and ATAC-seq reads at the Cd4, Cd8a, Sell and Cd44 loci in naive and activated CD4 and CD8 T cells. c-e: Effect of TF binding motif variants on chromatin accessibility measured as ΔMean in naive and activated CD4 T cells (A), naive CD4 and CD8 T cells (B), activated CD4 and CD8 T cells (C). Data points are colored by TF family and scaled according to the –log10 p-value of a two-sided t-test comparing the mean allelic ratios between peaks with stronger matches on the B6 vs Cast allele. The most significant p-value obtained across the two cell types (naive and activated CD8) was used for scaling. Motifs with <50 variant-containing motif occurrences were excluded from the plot. f: Mean RNA-seq counts for Tbx21, Eomes, Tcf7 and Lef1 in naive and activated CD4 and CD8 T cells.

Extended Data Fig. 2 Association between gene expression and chromatin accessibility changes.

a: Correlation between changes in chromatin accessibility and gene expression in activated vs naive CD8 T cells. Showing individual ATAC-seq peaks linked to the nearest gene (left) or the sum of counts across all ATAC-seq peaks linked to the same gene (right). b: Fold change in gene expression in activated vs naive CD8 T cells (x-axis) plotted against the fold change in gene expression in memory vs naive CD8 T cells (y-axis). A subset of genes whose expression is transiently induced upon T cell activation is highlighted in red (left). Aggregated chromatin accessibility and gene expression changes for this gene set (right). c: Gene ontology (GO) term enrichment analysis on genes highlighted in panel B.

Extended Data Fig. 3 RNA-seq and ATAC-seq changes at Runx1, TCF1, and Ets1-bound genes.

a-b: Gene tracks of RNA-seq, ATAC-seq and CUT&RUN coverage at the Bcl2 and Arhgap30 loci in naive and activated CD8 T cells. c: Track examples showing the effect of selected Ets1 motif variants on allelic bias in chromatin accessibility and TF binding in activated CD8 T cells from (B6/Cast) F1 mice. Vertical bars mark the position of genetic variants, with colors indicating the fraction of variant-containing reads coming from either the B6 (black) or Cast (red) allele. A genetic variant in the Camk2b locus results in a stronger Ets1 motif match on the B6 allele and a weaker match on the Cast allele and is associated with B6-specific chromatin accessibility and TF binding. The opposite pattern is observed at the Dnah8 locus. d: Gene expression changes at genes (>100 reads in at least one cell type) nearest to Ets1-bound and -unbound ATAC-seq peaks. e: Mean expression of genes nearest to Ets1-bound and -unbound ATAC-seq peaks.

Extended Data Fig. 4 TF and cofactor binding at Ets1-bound and –unbound chromatin regions.

a: Gene tracks of RNA-seq, ATAC-seq and CUT&RUN coverage at the Gzmb and Tbx21 loci in naive and activated CD8 T cells. b: Fraction of peaks bound by Brg1 at Ets1-unbound ATAC-seq peaks occupied by distinct combinations of activation-induced TFs. Data points are scaled according to the number of peaks in each set. c: Effect of TF binding motif variants on Brg1 occupancy at Ets1-bound (Ets1+) and Ets1-unbound (Ets1−) sites measured as ΔMean in activated CD8 T cells. Data points are colored by TF family and scaled according to the –log10 p-value of a t-test comparing the allelic ratios between peaks with stronger motif matches on the B6 vs Cast allele. The most significant p-value obtained across the two cell types (naive and activated CD8) was used for scaling.

Extended Data Fig. 5 TF binding at TCF1-bound peak sets in naive and activated CD4 and CD8 T cells.

a-c: Gene tracks of RNA-seq, ATAC-seq and CUT&RUN read coverage at the Il7r, Pde3b and Klrg1 loci in naive and activated CD4 and CD8 T cells. d: Fraction of TCF1-bound sites and TCF1up sites containing specific TF-binding motifs. e: Median motif match p-value at sites bound at TCF1-bound sites and TCF1up sites. f: Percentage of TCF1+ Ets1− peaks and TCF1up peaks bound by each activation-induced TF.

Extended Data Fig. 6 Single cell ATAC-seq analysis of T cell subsets during acute LCMV infection.

a: Pseudo-bulk scATAC-seq tracks showing chromatin accessibility at CD4 and CD8 loci across splenic cell populations in LCMV infected (B6/Cast) F1 mice. b: Effect of genetic variation in TF binding motifs on allele-specific ATAC-seq counts from bulk cell or single cell experiments. Data points are colored by TF family and scaled according to the -log10 p-value of a two-sided t-test comparing the allelic ratios between peaks with stronger matches on B6 vs Cast Allele. c: Violin plots and box plots showing accessibility of indicated peak groups across T cell subsets. The center line in the box plots represents the median, the box limits represent the 25th and 75th percentiles, and the whiskers are the minimum/maximum values within 1.5 times the interquartile range. n = the number of cells in each cell annotation (420 Naive CD4, 344 Naive CD8, 234 Mem CD4, 231 Mem CD8, 198 Treg, 1321 Tfh, 652 Th1, 2583 Effector CD8).

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Zhong, Y., Walker, S.K., Pritykin, Y. et al. Hierarchical regulation of the resting and activated T cell epigenome by major transcription factor families. Nat Immunol 23, 122–134 (2022). https://doi.org/10.1038/s41590-021-01086-x

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