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Deconvoluting global cytokine signaling networks in natural killer cells

Abstract

Cytokine signaling via signal transducer and activator of transcription (STAT) proteins is crucial for optimal antiviral responses of natural killer (NK) cells. However, the pleiotropic effects of both cytokine and STAT signaling preclude the ability to precisely attribute molecular changes to specific cytokine–STAT modules. Here, we employed a multi-omics approach to deconstruct and rebuild the complex interaction of multiple cytokine signaling pathways in NK cells. Proinflammatory cytokines and homeostatic cytokines formed a cooperative axis to commonly regulate global gene expression and to further repress expression induced by type I interferon signaling. These cytokines mediated distinct modes of epigenetic regulation via STAT proteins, and collective signaling best recapitulated global antiviral responses. The most dynamically responsive genes were conserved across humans and mice, which included a cytokine–STAT-induced cross-regulatory program. Thus, an intricate crosstalk exists between cytokine signaling pathways, which governs NK cell responses.

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Fig. 1: Cytokine levels and phosphorylation of STATs in NK cells in spleen and blood peak early during MCMV infection.
Fig. 2: Transcriptional cooperation and antagonism among proinflammatory and homeostatic cytokines.
Fig. 3: IL-12/IL-18 and IL-2/IL-15 are the predominant mediators of changes in chromatin accessibility.
Fig. 4: IFN-α promotes changes in H3K4me3 enrichment at promoter regions.
Fig. 5: Integration of cytokine-induced transcriptional and STAT-binding profiles reveal cross-regulatory interactions.
Fig. 6: Combination of proinflammatory and homeostatic cytokines best correlates with early MCMV infection in vivo.
Fig. 7: Highly modulated genes induced by cytokines are conserved between mice and humans.

Data availability

Data generated in this study have been deposited in the Gene Expression Omnibus (Super-Series accession numbers GSE140044 and GSE164116).

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Acknowledgements

We thank members of the Sun laboratory for comments, discussions, technical support and experimental assistance. We thank V. Pascual (Weill Cornell Medicine) for sharing reagents. We thank L. Lanier (University of California, San Francisco) for providing mice and critical feedback of our manuscript. We acknowledge the use of the Integrated Genomics Operation Core, funded by the National Cancer Institute Cancer Center Support Grant (P30CA08748); Cycle for Survival; and the Marie-Josée and Henry R. Kravis Center for Molecular Oncology. G.M.W. and S.G. were supported through research fellowships by the Deutsche Forschungsgemeinschaft (DFG Forschungsstipendium WI-4927/1-2 and GR 5503/1-1). N.M.A. was supported by a Medical Scientist Training Program grant from the National Institute of General Medical Sciences (T32GM007739 to the Weill Cornell–Rockefeller–Sloan Kettering Tri-Institutional MD–PhD Program) and by an F30 Predoctoral Fellowship from the National Institute of Allergy and Infectious Diseases (F30 AI136239). J.B.L. was supported by the François Wallace Monahan Fellowship. K.C.H. was supported by the National Institutes of Health (AI125651, HL129472 and AI069197) and the Leukemia Lymphoma Society Scholar Award. J.C.S. was supported by the Ludwig Center for Cancer Immunotherapy, the American Cancer Society, the Burroughs Wellcome Fund and the National Institutes of Health (AI100874, AI130043 and P30CA008748). C.M.L. was supported by the Cancer Research Institute as a Cancer Research Institute–Carson Family Fellow.

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G.M.W., C.M.L. and J.C.S. designed the study. G.M.W., C.M.L., E.K.S., S.G., S.S., J.B.L., N.M.A. and C.D. performed the experiments. C.M.L. performed the bioinformatic analyses. J.B.L. and K.C.H. consulted on the experimental procedures for the human data. C.M.L. and J.C.S. wrote the manuscript.

Corresponding authors

Correspondence to Joseph C. Sun or Colleen M. Lau.

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The authors declare no competing interests.

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Peer review information Nature Immunology thanks Lisa Forbes, Dagmar Gotthardt and Chiara Romagnani for their contribution to the peer review of this work. L. A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Cytokines peak early during MCMV infection.

Livers and spleen were harvested at days 0, 0.5, 1, 2, 3, 4, 5, 7 and 14 after MCMV infection. Organs were lysed for protein extracts, and then measured for cytokine levels. Plots show cytokine levels of IFN-α, IL-12p70, IL-18, IL-2, and IL-15 over time. For (a), n = 5, and for (b-c), n = 3-5. a, Plots show cytokine levels in the liver of IFN-α, IL-12p70, IL-18, IL-2, and IL-15 over time, with log10 scale on y-axis. Lines connect at medians of each time point. * p = 0.016-0.032 from two-sided Mann-Whitney-Wilcoxon test comparing to day 0. b, Graphs show mean ± SD of phosphorylated STAT MFI values (p-STAT1 [Ser], p-STAT4, [Tyr] and p-STAT5 [Tyr]) proteins from liver CD3ε TCRβ NK1.1+ NK cells. P-values are calculated by two-sided Welch’s t-test. c, Graphs show mean ± SD of p-STAT1 (Tyr) MFI values from spleen CD3ε TCRβ NK1.1+ NK cells. P-values are calculated by two-sided Welch’s t-test. d, Schematic of global profiling strategy. MFI = mean fluorescence intensity.

Extended Data Fig. 2 IL-2/IL-15 synergizes with IL-12/18.

RNA-seq was performed on sorted NK cells (CD3ε TCRβ CD19 F4/80 NK1.1+) cultured for 3 h with indicated cytokine conditions. n = 3. a, Flow plots show gating strategy for sorted murine NK cells before stimulation. b, Heatmap shows row-scaled log-transformed expression values from RNA-seq data of the all DE genes that show an FDR-adjusted p-value < 0.05, |log2 FC | > 1, and TPM > 5, as grouped in Fig. 2c. c, Heatmap shows row-scaled log-transformed expression values from top 100 synergistic genes, defined as DE (FDR-adjusted p-value < 0.05; TPM > 5) in IL-12/IL-18 plus IL-2/IL-15 versus unstimulated conditions, but not DE (FDR-adjusted p-value > 0.05; TPM > 5) in either IL-12/IL-18 or IL-2/IL-15 compared to unstimulated conditions. Genes were ranked by p-value from IL-12/IL-18 plus IL-2/IL-15 versus unstimulated comparison. d, Bar plot depicts number of cooperative IL-12/18:IL-2/IL-15 genes that show the same or different directional log2FC (versus unstimulated conditions) among indicated DE (FDR-adjusted p-value < 0.05) genes compared to combined stimulation by IL-12/IL-18. Genes that are designated as “different” display at least one condition where the FC does not modulate in the same direction as combined IL-12/18 conditions. DE = differentially expressed; FDR = false discovery rate; FC = fold change.

Extended Data Fig. 3 IL-12/18 antagonizes IFN-α signaling.

RNA-seq or flow cytometry was performed on sorted NK cells (CD3ε TCRβ CD19 F4/80 NK1.1+) cultured for 3 h with indicated cytokine conditions. n = 3. a, Bar plots show mean of normalized counts of Stat1 transcript across various cytokine conditions assayed by RNA-seq. b, Graphs depict percent of NK cells positive for intracellular IFN-γ staining (left), mean fluorescence intensity (MFI) of IFN-γ+ cells (middle), and MFI of p-STAT4 (Tyr; right). Bar indicates mean ± SD. P-values are calculated by unpaired two-sided Student’s t-test comparing WT to Stat1–/–. c, Bar plot depicts number of antagonistic IL-12/18:IFN-α genes, as shown in Extended Data Fig. 2d. d, Left panel shows heatmap of row-scaled log-transformed expression values of the 86 common differentially expressed genes between IFN-α, IL-12/IL-18 and IL-2/IL-15, hierarchically clustered into 4 groups. Box plots of z-scores from each cluster are depicted in the right panel. Centre, median; box limits, first and third percentiles; whiskers, 1.5 × interquartile range. Dotted lines show median z-score of IFN-α plus IL-12/IL-18 plus IL-2/15 condition. Tiles to the left of gene names indicate whether the genes are bound by STAT1 (green), STAT4 (blue), or STAT5 (orange) by ChIP-seq.

Extended Data Fig. 4 Epigenetic contribution of IL-12 versus IL-18.

ATAC-seq was performed on sorted NK cells (CD3ε TCRβ CD19 F4/80 NK1.1+) cultured for 3 h with IL-12, IL-18, or IL-12 + L-18. n = 3. a, Tables show results for de novo motif analysis by HOMER software on top 25% DA regions (see Methods). From left to right, columns show discovered motif sequence (top) with the best matched known motif (bottom), best matched known transcription factor family, p-value, similarity score, source of data used to derive known motif, and DA group. For all groups, top 3 ranked on p-value are shown. b, Bar plot depicts number of DA regions (FDR-adjusted p-value < 0.05) that overlap with STAT4-bound ChIP-seq regions, broken down by DA group. Colors indicate FC direction of DA regions.FDR = false discovery rate; DA = differentially accessible.

Extended Data Fig. 5 Presence of all cytokine conditions best correlates with early in vivo MCMV infection.

For in vitro studies, RNA-seq and ATAC-seq was performed as described in Figs. 2 and 3, respectively. H3K4me3 ChIP-seq was performed on sorted NK cells (CD3ε TCRβ CD19 F4/80 NK1.1+) cultured for 3 h with IFN-α, IL-12/IL-18, IL-2/IL-15, or a combination of all three. For in vivo studies, RNA-seq and ATAC-seq was performed on sorted Ly49H+ NK cells (TCRβCD19CD3εF4/80NK1.1+Ly49H+) at indicated timepoints after MCMV infection. H3K4me3 ChIP-seq was performed on sorted Ly49H+ NK cells (TCRβCD19CD3εF4/80NK1.1+Ly49H+) at indicated time points after MCMV infection from either WT mice or NKp46-CreERT2 Rosa26-tdTomato reporter mice (gated additionally on TdTom+). Heatmaps show matrices of Spearman correlation coefficients using log2 FC comparing cytokine-stimulated versus unstimulated conditions or d2 versus d0 post-infection using (a) RNA-seq data, (b) ATAC-seq, or (c) H3K4me3 ChIP-seq data. Circle sizes are proportional to coefficients. X-ed out values indicate p > 0.05. For in vitro studies, n = 2-8, and for in vivo studies, n = 2-4. (d) Scatter plots of ChIP-seq log2 FC comparing cytokine-stimulated versus unstimulated (x-axis) conditions to d2 versus d0 post-infection (y-axis). (e) Center dots indicate Spearman correlations calculated from ChIP-seq values depicted in (c). Ranges show 95% confidence interval calculated by Fisher’s z-transformation.

Extended Data Fig. 6 Combination of various cytokine conditions supports optimal NK cell effector responses in vitro.

a, Bar plots show intracellular IFN-γ after 3 h of indicated cytokine stimulation on sorted NK cells (CD3ε TCRβ CD19 F4/80 NK1.1+). Calculated p-values for all pairwise conditions are represented as a heatmap. n = 6. b, Bar plots show calculated percent lysis of Calcein AM-labeled YAC-1 cells incubated with sorted NK cells (CD3ε TCRβ CD19 F4/80 NK1.1+) in the indicated cytokine conditions. Calculated p-values for all pairwise conditions are represented as a heatmap. n = 3. c, Representative histograms of Cell Trace Violet staining on sorted NK cells from 3-day in vitro cultures under indicated conditions. Representative of 3 independent experiments. Dotted lines connect biological replicates, which are colored. * p < 0.05, ** p < 0.01, *** p < 0.001 by two-tailed paired Student’s t-test.

Extended Data Fig. 7 Transcriptional profiles of cytokine signaling in human NK cells.

RNA-seq was performed on sorted NK cells (CD3ε CD14 CD56+) derived from PBMCs of healthy donors and cultured for 3 h with IFN-α, IL-12/IL-18, or IL-2/IL-15. n = 6. a, Sorting strategy of human NK cells and NK cell percentages for each donor. b, Representative flow cytometric plot (left) and summarized proportions (right) of different NK cell subsets defined by cell surface phenotype. Bar plots show mean, and numbers indicate donor identifier. c, Principal component analysis of RNA-seq donor-corrected, log-transformed counts of all detectable genes in humans. d, Venn diagram of all commonly expressed, mappable DE (FDR-adjusted p-value < 0.05, |log2 FC|>1) human orthologs when comparing cytokine-stimulated versus unstimulated conditions. e, Heatmap matrix of Spearman correlation coefficients using log2 FC of each condition compared to unstimulated from all commonly expressed, mappable human DE genes. Circle sizes are proportional to coefficients. f, Volcano plots highlight top 15 DE genes (FDR-adjusted p-value < 0.05) ranked on p-value. x-axis depicts log2 FC and y-axis depicts –log10 (p-value) (Wald test). Shown are all expressed genes (TPM>5) comparing indicated cytokine stimulation versus unstimulated. Horizontal dashed line delineates p = 0.05, vertical dashed lines mark log2 FC of -1 and 1. Colored dots show DE genes. Triangles indicate capped p-values. DE = differentially expressed; FDR = false discovery rate; FC = fold change; TPM = transcripts-per-million.

Extended Data Fig. 8 Highly modulated genes induced by cytokines are conserved between mouse and human.

For human data, RNA-seq was performed on sorted NK cells (CD3ε CD14 CD56+) derived from PBMCs of healthy donors and cultured for 3 h with IFN-α, IL-12/IL-18, or IL-2/IL-15. Mouse data was performed as described in Fig. 2. For mouse, n = 3, and for human, n = 6. a, Venn diagram of all genes in the UCSC Known Gene reference annotation (white), all mappable orthologs annotated by NCBI and retrieved through HGNC (blue), and counted genes that showed an average TPM >5 across all conditions. Numbers show absolute numbers. b, Venn diagram of mappable and expressed orthologs from mouse and human. Numbers show absolute numbers. Intersection depicts commonly expressed mappable orthologs used for downstream DE analysis. c, Principal component analysis of RNA-seq uncorrected log-transformed counts on (left) and those corrected for replicate/donor variation (right) on commonly expressed mappable orthologs. d, Scatter plots of RNA-seq log2 FC comparing cytokine-stimulated versus unstimulated from human datasets (x-axis) and mouse datasets (y-axis). Shown are genes commonly DE in all three cytokine conditions across human and mouse. Plots highlight genes that show |log2 FC|> 1 in all three conditions (top) or two of the three conditions (bottom). DE = differentially expressed; FC = fold change; TPM = transcripts-per-million.

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Wiedemann, G.M., Santosa, E.K., Grassmann, S. et al. Deconvoluting global cytokine signaling networks in natural killer cells. Nat Immunol 22, 627–638 (2021). https://doi.org/10.1038/s41590-021-00909-1

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