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Control of nutrient uptake by IRF4 orchestrates innate immune memory

Nature Immunology volume 24, pages 1685–1697 (2023)Cite this article

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Abstract

Natural killer (NK) cells are innate cytotoxic lymphocytes with adaptive immune features, including antigen specificity, clonal expansion and memory. As such, NK cells share many transcriptional and epigenetic programs with their adaptive CD8+ T cell siblings. Various signals ranging from antigen, co-stimulation and proinflammatory cytokines are required for optimal NK cell responses in mice and humans during virus infection; however, the integration of these signals remains unclear. In this study, we identified that the transcription factor IRF4 integrates signals to coordinate the NK cell response during mouse cytomegalovirus infection. Loss of IRF4 was detrimental to the expansion and differentiation of virus-specific NK cells. This defect was partially attributed to the inability of IRF4-deficient NK cells to uptake nutrients required for survival and memory generation. Altogether, these data suggest that IRF4 is a signal integrator that acts as a secondary metabolic checkpoint to orchestrate the adaptive response of NK cells during viral infection.

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Fig. 1: Dynamic regulation of IRF4 in NK cells in response to viral infection.
Fig. 2: IRF4 is required for NK cell-mediated protection against MCMV.
Fig. 3: IRF4 is required for adaptive NK cell differentiation during viral infection.
Fig. 4: IRF4 controls the transcriptional program of effector NK cells.
Fig. 5: IRF4 regulates nutrient uptake in antiviral NK cells.
Fig. 6: Disruption of iron homeostasis impairs the adaptive NK cell response to viral infection.
Fig. 7: IRF4 directly regulates adaptive features of NK cells.

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Data availability

All published data generated and supporting the findings of this study have been deposited to NCBI’s GEO or European Genome–Phenome Archive as described. scRNA-seq and ChIP-seq data generated in this study have been deposited at GEO under accession code GSE236556. Source data are provided with this paper.

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Acknowledgements

We thank members of the Sun laboratory, K. Lupo and X. Chen, as well as the Single-Cell Analytics Innovation Lab at the Sloan Kettering Institute for technical support and experimental assistance, especially R. Chaligné. We also thank L. Lanier for helpful discussions. K. Murphy and T. Murphy provided mice critical to this study. T. Holmes and Y. Bryceson shared sequencing data from their study. We acknowledge the use of the Integrated Genomics Operation Core, funded by the National Cancer Institute Cancer Center Support Grant (P30CA008748), Cycle for Survival and the Marie-Josée and Henry R. Kravis Center for Molecular Oncology. J.N.F. is a Cancer Research Institute Irvington Fellow supported by the Cancer Research Institute (award CRI4186). K.C.H. and J.-B.L.L were supported by funding from the National Institutes of Health (R01 AI150999, R01HL155741, U01AI069197). 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, AI155558 and P30CA008748).

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Authors and Affiliations

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

    Endi K. Santosa, Hyunu Kim, Jean-Benoît Le Luduec, Aamna J. Abbasi, Claire K. Wingert, Lila Peters, Joe N. Frost, Katharine C. Hsu & Joseph C. Sun

  2. Department of Immunology and Microbial Pathogenesis, Weill Cornell Medical College, New York, NY, USA

    Endi K. Santosa & Joseph C. Sun

  3. Innate Immunity, Deutsches Rheuma-Forschungszentrum Berlin (DRFZ), ein Leibniz Institut, Berlin, Germany

    Timo Rückert & Chiara Romagnani

  4. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Katharine C. Hsu

  5. Department of Medicine, Weill Cornell Medical College, New York, NY, USA

    Katharine C. Hsu

  6. Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Berlin, Germany

    Chiara Romagnani

  7. Leibniz-Science Campus Chronic Inflammation, Berlin, Germany

    Chiara Romagnani

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  1. Endi K. Santosa
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Contributions

E.K.S. and J.C.S. designed the study. E.K.S., H.K., J.-B.L.L., A.J.A., C.K.W., L.P. and J.N.F. performed experiments. E.K.S. and T.R. performed bioinformatics analyses. K.C.H., C.R. and J.C.S. provided critical resources and supervision for the study. E.K.S. and J.C.S. wrote the manuscript.

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Correspondence to Joseph C. Sun.

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Nature Immunology thanks Dagmar Gotthardt and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Evidence of IRF4-mediated transcriptional control in NK cells.

a. Heatmap of IRF family members expression in NK cells throughout the course of MCMV infection represented as z-score of log2 normalized counts based on RNA-seq. b. Representative histogram of IRF4 expression in WT and Irf4−/− NK cells on day 2 PI (n = 4 biological replicates). c. Tracks (top) show chromatin accessibility dynamics of the Irf4 locus in Ly49H+ NK cells at days 0, 2, 4, and 7 PI as assessed by ATAC-seq. Graphs (bottom) show normalized counts for each peak indicated. d. Tables of enriched known motifs of highly accessible regions (log2FC > 1 & padj < 0.05) on day 4 PI versus day 2 PI from ATAC-seq data of Ly49H+ NK cells after MCMV infection. e. Heatmap of motif score from de novo motif analysis on highly accessible regions (log2FC > 1 & padj < 0.05) during day 0, 2, 4, and 7 transition based on ATAC-seq data of Ly49H+ NK cells after MCMV infection. f. IRF4 expression of sorted naïve splenic NK cells after an overnight stimulation with the indicated stimuli. IRF4 induction is displayed as fold change of IRF4 MFI over unstimulated condition (n = 6 biological replicates per condition). Two-way ANOVA test adjusted for multiple comparisons was used for statistical analysis. g. Representative histogram of IRF4 expression upon indicated stimulation gated on human CD56bright NK cells. Data are presented as paired fold change of IRF4 MFI compared to unstimulated condition (n = 8 donors per condition). Two-way ANOVA test adjusted for multiple comparisons was used for statistical analysis. h. UMAP embedding of scATAC-seq data from in vitro stimulated human NK cells (sorted on CD3−CD14−CD19−CD7+NKG2C+) from HCMV− donors. i. Coverage plot of the IRF4 locus from in vitro stimulated human NK cells as in (H).

Source data

Extended Data Fig. 2 IRF4 is dispensable for NK cell development and homeostasis.

a–c. Number of NK cells in blood (A), liver (B), and spleen (C) of WT (n = 8), Irf4+/− (n = 4), or Irf4−/− (n = 6) mice at 8 weeks old. d–f. Maturation status based on the expression of CD27 and CD11b of WT (n = 8), Irf4+/− (n = 4), or Irf4−/− (n = 5 for blood, and n = 6 for liver and spleen) mice in the blood (D), liver (E), and spleen (F) at 8 weeks old. g. Percentage of Ly49H+ NK cells within NK cells (Lineage- NK1.1+ CD49b+) of WT (n = 8), Irf4+/− (n = 4), or Irf4−/− (n = 6) mice at 8 weeks old. h. Experimental schematic of WT:Irf4−/− mixed bone-marrow chimera (mBMC) generation. i–k. Percentage of NK cells of WT or Irf4−/− mice from WT:Irf4−/− mBMC in the blood (n = 60 biological replicates) (I) and spleen (n = 21 biological replicates) (J) after 8 weeks (I-J), 16 weeks (n = 3 biological replicates) or 6 months (n = 5 biological replicates) post-transplant (K). l. Representative of flow plots of CD62L versus CD27 of either WT or Irf4−/− NK cells from WT:Irf4−/− mBMC in the blood and spleen 8 weeks post-transplant. Data are represented as percentage of each subset within total NK cells of each genotype (n = 22 biological replicates in blood, and n = 13 biological replicates in spleen). m. Representative of flow plots of CD11b versus CD27 of either WT or Irf4−/− NK cells from WT:Irf4−/− mBMC in the blood and spleen 8 weeks post-transplant. Data are represented as percentage of each subset within total NK cells of each genotype (n = 22 biological replicates in blood, and n = 16 biological replicates in spleen). n. Histogram and percentage quantification of Ly49H+ NK cells within splenic NK cells of either WT or Irf4−/− NK cells from WT:Irf4−/− mBMC (n = 24 biological replicates). Data are represented as mean ± SEM and are representative of or pooled from at least two independent experiments. Unpaired (A-G) and paired (I-N) two-tailed t-tests were performed.

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Extended Data Fig. 3 IRF4 is not required for lymphopenia-driven proliferation and differentiation.

a. Experimental schematic of adoptive transfer of WT and Irf4−/− NK cells into Rag2−/− Il2rg−/− mice. b. Fold change chimerism between WT and Irf4−/− NK cells on day 7 over day 0 (pre-transfer) in blood, liver, and spleen (n = 3 biological replicates in blood, and n = 6 biological replicates in liver and spleen). c. Representative histogram of IRF4 expression between transferred WT and Irf4−/− NK cells on day 7 post-transfer into Rag2−/− Il2rg−/−. d. Representative flow plots of CD27 and CD11b expression between WT and Irf4−/− NK cells on day 7 post-transfer. Data are represented as percentage of each subset within WT or Irf4−/− NK cells in indicated tissues (n = 3 biological replicates). e. Histogram of CD122 and quantification of CD122 MFI between WT and Irf4−/− NK cells on day 7 post-transfer in the spleen (n = 9 biological replicates). f. Histogram of CD132 and quantification of CD132 MFI between WT and Irf4−/− NK cells on day 7 post-transfer in the spleen (n = 9 biological replicates). g. Histogram of pSTAT5 and quantification of pSTAT5 MFI between WT and Irf4−/− NK cells on day 7 post-transfer in the spleen (n = 9 biological replicates). H-J. h–j. Quantification of Ki-67 staining in the blood and spleen between WT and Irf4−/− NK cells (H, n = 5 biological replicates per group), BIM and BCL2 MFI from splenic NK cells (I-J, n = 9 biological replicates). Data are represented as mean ± SEM and are representative of or pooled from at least two independent experiments. Paired two-tailed t-test was performed unless stated otherwise.

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Extended Data Fig. 4 IRF4-deficient NK cells have intact effector functions.

a, b Representative histograms and quantification of IFN-γ production (A) and LAMP-1 (also known as CD107a) (B) from either naive WT (gray) or Irf4−/− (orange) NK cells upon in vitro stimulation with nothing, IL-12 + IL-18, PMA + Ionomycin, or PMA + Ionomycin + IL-12 for 3 hours. c–f. Representative histograms (upper panels) and quantifications (bottom panels) of CD25, Gzmb, CD69, and IFN-γ expression gated on Ly49H+ NK cells from MCMV-infected WT:Irf4−/− mBMC on day 2 PI. Data are represented as mean ± SEM and are representative of or pooled from at least two independent experiments. n = 3 biological replicates per group. Paired two-tailed t-test was performed unless stated otherwise.

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Extended Data Fig. 5 scRNA-seq Analysis of wild-type versus IRF4-deficient NK cells.

a. UMAP embedding of WT (left) or Irf4−/− (right) of Ly49H+ NK cells colored by genotypes and days post-infection. b. Proportion of each genotype from each time points per Louvain clusters. c. Volcano plot of scRNA-seq as determined by MAST between WT and Irf4−/− Ly49H+ NK cells on day 0, 2, 4 and 7 PI. Blue and yellow points represent significant genes (FDR < 0.05) that are upregulated in WT or Irf4−/− Ly49H+ NK cells, respectively. d. Number of differentially expressed genes between WT or Irf4−/− Ly49H+ NK cells from each time point as identified by MAST. e. Violin plot quantification of normalized reads for MYC transcript by scRNA-seq between WT and Irf4−/− Ly49H+ NK cells. f. Representative histogram of Myc protein by flow cytometry between WT (gray) and Irf4−/− (orange) Ly49H+ NK cells on day 5 PI. g. Violin plot quantification of MAGIC-imputed reads for CD71 (Tfrc), IRP1 (Aco1), and IRP2 (Ireb2) on day 7 PI between WT (blue) and Irf4−/− (yellow) Ly49H+ NK cells by scRNA-seq.

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Extended Data Fig. 6 Nutrient requirements for adaptive NK cell response.

a. Experimental schematic of PBS or BCH treatment upon MCMV infection in WT mice. b. Quantification of Ly49H+ NK cell numbers of MCMV-infected mice treated with PBS or BCH on day 5 PI from blood (n = 3 biological replicates per group). c. Percent of CD62L versus CD27 subsets on day 5 PI of PBS- (black) or BCH-treated (red) mice (n = 3 biological replicates per group). d. Representative histogram and gMFI of CD71 expression on Ly49H+ NK cells on day 5 PI in PBS or Deferiprone-treated mice. Upregulation of CD71 was used as a readout for successful iron chelation (n = 5 biological replicates per group). a–g. Percent of KLRG1+ Ly49H+ NK cells (E), and gMFIs of IL18Ra (F) and CD44 (G) on Ly49H+ NK cells on day 5 PI from PBS or Deferiprone-treated mice (n = 5 biological replicates per group). Data are represented as mean ± SEM and are representative of at least two independent experiments. Two-way ANOVA adjusted for multiple comparisons was performed unless stated otherwise.

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Extended Data Fig. 7 Gating strategies to identify NK cells.

a. Gating strategy for sorting WT or Irf4−/− Ly49H+ NK cells. b. Gating strategy for phenotypic analysis of Ly49H+ NK cells after lymphocytes, singlets and live cells cleanup (as depicted in Extended Data Fig. 7a). c. Gating strategy for human NK cells.

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Supplementary information

Reporting Summary

Supplementary Table 1

A list of antibodies used for flow cytometric analysis and scRNA-seq HTO-tag.

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Statistical source data.

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Santosa, E.K., Kim, H., Rückert, T. et al. Control of nutrient uptake by IRF4 orchestrates innate immune memory. Nat Immunol 24, 1685–1697 (2023). https://doi.org/10.1038/s41590-023-01620-z

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