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Abstract

Clonal expansion and immunological memory are hallmark features of the mammalian adaptive immune response and essential for prolonged host control of pathogens. Recent work demonstrates that natural killer (NK) cells of the innate immune system also exhibit these adaptive traits during infection. Here we demonstrate that differentiating and ‘memory’ NK cells possess distinct chromatin accessibility states and that their epigenetic profiles reveal a ‘poised’ regulatory program at the memory stage. Furthermore, we elucidate how individual STAT transcription factors differentially control epigenetic and transcriptional states early during infection. Finally, concurrent chromatin profiling of the canonical CD8+ T cell response against the same infection demonstrated parallel and distinct epigenetic signatures defining NK cells and CD8+ T cells. Overall, our study reveals the dynamic nature of epigenetic modifications during the generation of innate and adaptive lymphocyte memory.

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Acknowledgements

We thank members of the Sun laboratory for comments, discussions, technical support, and experimental assistance. We thank S. Chhangawala, L. Fairchild, and C. Krishna for discussions and technical support. The Integrated Genomics Operation Core was supported by Cycle for Survival and the Marie-Josée and Henry R. Kravis Center for Molecular Oncology. C.M.L. was supported by a T32 award from the NIH (CA009149). N.M.A. was supported by a Medical Scientist Training Program grant from the National Institute of General Medical Sciences of the NIH under award number T32GM007739 to the Weill Cornell/Rockefeller/Sloan-Kettering Tri-Institutional MD-PhD Program and an F30 Predoctoral Fellowship from the NIH National Institute of Allergy and Infectious Diseases (F30 AI136239). M.R. was supported by a fellowship from the German Academic Exchange Service (DAAD; Germany). C.S.L. was supported by NIH grant U01 HG007893. J.C.S. was supported by the Ludwig Center for Cancer Immunotherapy, the Burroughs Wellcome Fund, the American Cancer Society, and grants from the NIH (AI100874, AI130043, and P30CA008748).

Author information

Affiliations

  1. Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    • Colleen M. Lau
    • , Nicholas M. Adams
    • , Clair D. Geary
    • , Orr-El Weizman
    • , Moritz Rapp
    •  & Joseph C. Sun
  2. Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    • Yuri Pritykin
    •  & Christina S. Leslie
  3. Department of Immunology and Microbial Pathogenesis, Weill Cornell Medical College, New York, NY, USA

    • Joseph C. Sun

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Contributions

C.M.L. and J.C.S. designed the study. N.M.A., C.D.G., O.-E.W., and M.R. performed the experiments. C.M.L. performed the bioinformatic analyses. C.S.L. and Y. P. consulted on the bioinformatic analyses. C.M.L. and J.C.S. wrote the manuscript.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Joseph C. Sun.

Integrated supplementary information

  1. Supplementary Figure 1 Differential chromatin accessibility in NK cells responding against MCMV infection.

    (A) Representative gating strategy of Ly49H+ NK cells from wild-type/adoptive transfer recipients for RNA-seq and ATAC-seq datasets. Numbers indicate relative frequencies within shown plots. Red boxes contain sorted populations. (B) Fragment length distribution of individual ATAC-seq samples of Ly49H+ NK cells from wild-type/adoptive transfer recipients. (C) Number of all differentially accessible (DA) regions that either gain (red) or lose (blue) accessibility at indicated transition time points, categorized by magnitude of log2 fold change (FC). (D) Dot-plot ranked by increasing mean normalized log2 counts of accessible regions. Color of dots represents peak type. (E) Peak-centered heatmaps of all high FC differentially accessible (DA) regions (FDR < 0.05; absolute log2FC > 1) during MCMV infection hierarchically clustered on mean-centered normalized log2 counts of peak regions, with stable and transient clusters as indicated. Percentages within boxes show proportion of peaks within each cluster that show a high FC difference between d0 and d35. Clusters that showed > 75% were designated as stable. (F) Heatmap of –log10 raw binomial P-values as calculated by GREAT. Shown are the top 10% most enriched pathways (FDR < 0.05, ranked by binomial P-value) within each cluster, after filtering for pathways that showed more than 10 gene hits.

  2. Supplementary Figure 2 Cell-sorting strategy and quality assessment of ATAC-seq libraries of STAT-deficient NK cells during MCMV infection.

    (A) Schematic of NK cell response to primary MCMV infection in Stat1–/– and Stat4–/– NK cells. (B) Representative gating strategy of Ly49H+ NK cells from infected STAT-deficient mixed bone marrow chimeras for RNA-seq and ATAC-seq datasets. Numbers indicate relative frequencies within shown plots. Red boxes contain sorted populations. (C) Fragment length distribution of individual ATAC-seq samples of Ly49H+ NK cells from STAT-deficient mixed bone marrow chimeras.

  3. Supplementary Figure 3 STAT4 and STAT1 differentially promote remodeling of chromatin in activated NK cells.

    (A) Frequency polygon of log2 fold changes from differentially accessible (DA) regions (FDR < 0.05) comparing STAT-deficient NK cells to WT cells two days post infection. Counts have a bin width of 0.2. (B) Heatmap of log2 fold changes of gene expression comparing with Stat4–/– or Stat1–/– to WT two days post infection. Shown are genes that are commonly differentially expressed (FDR < 0.05) in both conditions. (C) Bar plots of RNA-seq normalized counts of all STAT family genes in both uninfected and infected NK cells in Stat4–/–:WT (blue) or Stat1–/–:WT (green) two days post infection. Data represent mean ± s.d; *padj < 0.05 as calculated by DESeq2. (D) Pairwise overlap of ChIP-seq, RNA-seq, and ATAC-seq results. P-values were calculated by hypergeometric tests using all annotated genes for “common genes” tests and a union of accessible and occupied peaks for “common peaks” tests. (E) Differential expression (FDR < 0.05) of overlapping genes indicated in Fig. 3e from Stat1–/–:WT NK cells two days post infection. (F) Genomic tracks of aligned STAT1-ChIP-seq and Stat1–/–:WT ATAC-seq data. y-axis depicts normalized counts, while x-axis displays genomic axis with scale bar. Bar plots quantify number of reads within highlighted peak regions (dashed lines) shown as mean ± s.d.

  4. Supplementary Figure 4 ISRE, TCF–LEF, and NFκB regions are enriched within differentially accessible chromatin regions in memory NK cells.

    (A) MA plot shows normalized mean values on x-axis and log2 fold change on y-axis. Horizontal lines indicate log2 fold change of + /- 0.5 and + /- 1, with numbers on the right indicating the total number of differentially accessible (DA) regions (FDR < 0.05) within each section. (B) Boxplots of log2-transformed normalized reads overlapping the center of all instances of indicated motifs found in filtered differentially accessible (DA) regions. P-values were calculated using two-sided Wilcoxon signed-rank test. (C) Metacoverage of DA median normalized tag counts surrounding known motif regions listed, found among all filtered DA regions. (D) Venn diagrams of overlap between de novo and known motif instances among filtered DA regions. P-values are calculated by hypergeometric test, using all filtered DA regions described in Fig. 4a as background. (E) Gene expression in naïve NK cells from RNA-seq data of indicated genes plotted as log2 transcripts-per-million (TPM) plus pseudocount of 1 shown as mean ± s.d. Dashed line shows count of 1. (F) Gene expression from RNA-seq data of indicated genes plotted as normalized log2 counts. Line represents mean.

  5. Supplementary Figure 5 Cell-sorting strategy and quality assessment of ATAC-seq libraries of CD8+ T cell during MCMV infection.

    (A) Representative gating strategy of MCMV-specific (m45+) CD8+ T cells from infected wild-type hosts for RNA-seq and ATAC-seq datasets. Numbers indicate relative frequencies within shown plots. Red boxes contain sorted populations. (B) Fragment length distribution of individual ATAC-seq samples of naïve or m45+ CD8+ T cells from infected wild-type hosts.

  6. Supplementary Figure 6 Distinct and shared chromatin accessibility profiles of NK cells and CD8+ T cells during viral infection.

    (A) Heatmap of all atlas peaks in NK and CD8+ T cells, including peaks that are unique to NK cells, unique to CD8+ T cells, and common between the two. (B) Principal component analysis of ATAC-seq on all common atlas peaks between NK cells and CD8+ T-cells. Normalized log2 values were used for plotting. (C) Tabulation of differentially accessible (DA; FDR < 0.05; absolute log2 fold change > 0.5; left) common atlas peak regions and differentially expressed (DE; FDR < 0.05; absolute log2 fold change > 1; right) genes within categories defined by comparing memory and naïve cell states in both NK cells and CD8+ T cells. Common DA or DE regions are highlighted in gray. (D) Heatmap of DA atlas peaks in NK and CD8+ T cells, split by categories defined in (C). (E) Heatmap of DE genes in NK and CD8+ T cells, split by categories defined in (C). (F) Venn diagram of overlap between common DA gene loci (ATAC) and DE genes (RNA). P-values are calculated by hypergeometric test, using a union of all expressed genes and common atlas-assigned genes as background. (G) Gene expression from RNA-seq data of AP-1 transcription factor family genes plotted as log2 transcripts-per-million (TPM) plus pseudocount of 1 shown as mean ± s.d. Dashed line shows count of 1. Gapdh and Cd4 are plotted as positive and negative controls, respectively.

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https://doi.org/10.1038/s41590-018-0176-1