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The transcription factor Fli1 restricts the formation of memory precursor NK cells during viral infection

Nature Immunology volume 23, pages 556–567 (2022)Cite this article

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Abstract

Natural killer (NK) cells are innate lymphocytes that possess traits of adaptive immunity, such as memory formation. However, the molecular mechanisms by which NK cells persist to form memory cells are not well understood. Using single-cell RNA sequencing, we identified two distinct effector NK cell (NKeff) populations following mouse cytomegalovirus infection. Ly6C– memory precursor (MP) NK cells showed enhanced survival during the contraction phase in a Bcl2-dependent manner, and differentiated into Ly6C+ memory NK cells. MP NK cells exhibited distinct transcriptional and epigenetic signatures compared with Ly6C+ NKeff cells, with a core epigenetic signature shared with MP CD8+ T cells enriched in ETS1 and Fli1 DNA-binding motifs. Fli1 was induced by STAT5 signaling ex vivo, and increased levels of the pro-apoptotic factor Bim in early effector NK cells following viral infection. These results suggest that a NK cell-intrinsic checkpoint controlled by the transcription factor Fli1 limits MP NK formation by regulating early effector NK cell fitness during viral infection.

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Fig. 1: scRNA-seq reveals two clusters of NKeff cells following MCMV infection.
Fig. 2: Ly6C– NKeff cells preferentially persist following MCMV infection.
Fig. 3: Ly6C– NKeff cells differentiate into Ly6C+ memory NK cells.
Fig. 4: MP NK cells are transcriptionally distinct and require Bcl2 for optimal survival during the contraction phase of the response to MCMV.
Fig. 5: MP NK cells share a core epigenetic signature with MP CD8+ T cells.
Fig. 6: Fli1 is induced by IL-15-mediated STAT5 signaling in mature NK cells.
Fig. 7: Fli1 restricts the formation of MP NK cells during viral infection.

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Data availability

Sequencing datasets are accessible from GEO with accession number GSE176208. Original western blot scans can be found in source data. Source data are provided with this paper.

References

  1. Henning, A. N., Roychoudhuri, R. & Restifo, N. P. Epigenetic control of CD8(+) T cell differentiation. Nat. Rev. Immunol. 18, 340–356 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Youngblood, B., Hale, J. S. & Ahmed, R. T-cell memory differentiation: insights from transcriptional signatures and epigenetics. Immunology 139, 277–284 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Bantug, G. R., Galluzzi, L., Kroemer, G. & Hess, C. The spectrum of T cell metabolism in health and disease. Nat. Rev. Immunol. 18, 19–34 (2018).

    Article  CAS  PubMed  Google Scholar 

  4. Chang, J. T., Wherry, E. J. & Goldrath, A. W. Molecular regulation of effector and memory T cell differentiation. Nat. Immunol. 15, 1104–1115 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Kaech, S. M. & Cui, W. Transcriptional control of effector and memory CD8+ T cell differentiation. Nat. Rev. Immunol. 12, 749–761 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Kaech, S. M. et al. Selective expression of the interleukin 7 receptor identifies effector CD8 T cells that give rise to long-lived memory cells. Nat. Immunol. 4, 1191–1198 (2003).

    Article  CAS  PubMed  Google Scholar 

  7. Joshi, N. S. et al. Inflammation directs memory precursor and short-lived effector CD8(+) T cell fates via the graded expression of T-bet transcription factor. Immunity 27, 281–295 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Sarkar, S. et al. Functional and genomic profiling of effector CD8 T cell subsets with distinct memory fates. J. Exp. Med. 205, 625–640 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Biron, C. A., Byron, K. S. & Sullivan, J. L. Severe herpesvirus infections in an adolescent without natural killer cells. N. Engl. J. Med. 320, 1731–1735 (1989).

    Article  CAS  PubMed  Google Scholar 

  10. Bukowski, J. F., Warner, J. F., Dennert, G. & Welsh, R. M. Adoptive transfer studies demonstrating the antiviral effect of natural killer cells in vivo. J. Exp. Med. 161, 40–52 (1985).

    Article  CAS  PubMed  Google Scholar 

  11. Weizman, O. E. et al. ILC1 confer early host protection at initial sites of viral infection. Cell 171, 795–808 e712 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Riggan, L., Freud, A. G. & O’Sullivan, T. E. True detective: unraveling group 1 innate lymphocyte heterogeneity. Trends Immunol. 40, 909–921 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Weizman, O. E. et al. Mouse cytomegalovirus-experienced ILC1s acquire a memory response dependent on the viral glycoprotein m12. Nat. Immunol. 20, 1004–1011 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Sun, J. C., Beilke, J. N. & Lanier, L. L. Adaptive immune features of natural killer cells. Nature 457, 557–561 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. O’Sullivan, T. E., Sun, J. C. & Lanier, L. L. Natural killer cell memory. Immunity 43, 634–645 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Grassmann, S. et al. Distinct surface expression of activating receptor Ly49H drives differential expansion of NK cell clones upon murine cytomegalovirus infection. Immunity 50, 1391–1400 e1394 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Arase, H., Mocarski, E. S., Campbell, A. E., Hill, A. B. & Lanier, L. L. Direct recognition of cytomegalovirus by activating and inhibitory NK cell receptors. Science 296, 1323–1326 (2002).

    Article  CAS  PubMed  Google Scholar 

  18. Brown, M. G. et al. Vital involvement of a natural killer cell activation receptor in resistance to viral infection. Science 292, 934–937 (2001).

    Article  CAS  PubMed  Google Scholar 

  19. Crinier, A. et al. High-dimensional single-cell analysis identifies organ-specific signatures and conserved NK cell subsets in humans and mice. Immunity 49, 971–986 e975 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Horowitz, A. et al. Genetic and environmental determinants of human NK cell diversity revealed by mass cytometry. Sci. Transl. Med. 5, 208ra145 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Karo, J. M., Schatz, D. G. & Sun, J. C. The RAG recombinase dictates functional heterogeneity and cellular fitness in natural killer cells. Cell 159, 94–107 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Rahim, M. M. et al. Expansion and protection by a virus-specific NK cell subset lacking expression of the inhibitory NKR-P1B receptor during murine cytomegalovirus infection. J. Immunol. 197, 2325–2337 (2016).

    Article  CAS  PubMed  Google Scholar 

  23. Kamimura, Y. & Lanier, L. L. Homeostatic control of memory cell progenitors in the natural killer cell lineage. Cell Rep. 10, 280–291 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Adams, N. M., Diaz-Salazar, C., Dang, C., Lanier, L. L. & Sun, J. C. Cutting edge: heterogeneity in cell age contributes to functional diversity of NK cells. J. Immunol. 206, 465–470 (2021).

    Article  CAS  PubMed  Google Scholar 

  25. Min-Oo, G., Bezman, N. A., Madera, S., Sun, J. C. & Lanier, L. L. Proapoptotic Bim regulates antigen-specific NK cell contraction and the generation of the memory NK cell pool after cytomegalovirus infection. J. Exp. Med. 211, 1289–1296 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  27. van Helden, M. J. et al. Terminal NK cell maturation is controlled by concerted actions of T-bet and Zeb2 and is essential for melanoma rejection. J. Exp. Med. 212, 2015–2025 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Arguello, R. J. et al. SCENITH: a flow cytometry-based method to functionally profile energy metabolism with single-cell resolution. Cell Metab. 32, 1063–1075 e1067 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. O’Sullivan, T. E., Johnson, L. R., Kang, H. H. & Sun, J. C. BNIP3- and BNIP3L-mediated mitophagy promotes the generation of natural killer cell memory. Immunity 43, 331–342 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Bezman, N. A. et al. Molecular definition of the identity and activation of natural killer cells. Nat. Immunol. 13, 1000–1009 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Min-Oo, G. & Lanier, L. L. Cytomegalovirus generates long-lived antigen-specific NK cells with diminished bystander activation to heterologous infection. J. Exp. Med. 211, 2669–2680 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Johnnidis, J. B. et al. Inhibitory signaling sustains a distinct early memory CD8(+) T cell precursor that is resistant to DNA damage. Sci. Immunol. 6, eabe3702 (2021).

  33. Viant, C. et al. Cell cycle progression dictates the requirement for BCL2 in natural killer cell survival. J. Exp. Med. 214, 491–510 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Riggan, L. et al. CRISPR-Cas9 ribonucleoprotein-mediated genomic editing in mature primary innate immune cells. Cell Rep. 31, 107651 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Wiedemann, G. M. et al. Deconvoluting global cytokine signaling networks in natural killer cells. Nat. Immunol. 22, 627–638 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Kurtulus, S. et al. Bcl-2 allows effector and memory CD8+ T cells to tolerate higher expression of Bim. J. Immunol. 186, 5729–5737 (2011).

    Article  CAS  PubMed  Google Scholar 

  37. Dunkle, A., Dzhagalov, I., Gordy, C. & He, Y. W. Transfer of CD8+ T cell memory using Bcl-2 as a marker. J. Immunol. 190, 940–947 (2013).

    Article  CAS  PubMed  Google Scholar 

  38. Sathe, P. et al. Innate immunodeficiency following genetic ablation of Mcl1 in natural killer cells. Nat. Commun. 5, 4539 (2014).

    Article  CAS  PubMed  Google Scholar 

  39. Mujal, A. M., Delconte, R. B. & Sun, J. C. Natural killer cells: from innate to adaptive features. Annu Rev. Immunol. 39, 417–447 (2021).

    Article  CAS  PubMed  Google Scholar 

  40. Omilusik, K. D. et al. Transcriptional repressor ZEB2 promotes terminal differentiation of CD8+ effector and memory T cell populations during infection. J. Exp. Med. 212, 2027–2039 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Dominguez, C. X. et al. The transcription factors ZEB2 and T-bet cooperate to program cytotoxic T cell terminal differentiation in response to LCMV viral infection. J. Exp. Med. 212, 2041–2056 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Omilusik, K. D. et al. Sustained Id2 regulation of E proteins is required for terminal differentiation of effector CD8(+) T cells. J. Exp. Med. 215, 773–783 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Delconte, R. B. et al. The helix-loop-helix protein ID2 governs NK cell fate by tuning their sensitivity to interleukin-15. Immunity 44, 103–115 (2016).

    Article  CAS  PubMed  Google Scholar 

  44. Ikawa, T., Fujimoto, S., Kawamoto, H., Katsura, Y. & Yokota, Y. Commitment to natural killer cells requires the helix-loop-helix inhibitor Id2. Proc. Natl Acad. Sci. USA 98, 5164–5169 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zook, E. C. et al. Transcription factor ID2 prevents E proteins from enforcing a naive T lymphocyte gene program during NK cell development. Sci. Immunol. 3, eaao2139 (2018).

  46. Madera, S. et al. Cutting edge: divergent requirement of T-box transcription factors in effector and memory NK cells. J. Immunol. 200, 1977–1981 (2018).

    Article  CAS  PubMed  Google Scholar 

  47. Rapp, M. et al. Core-binding factor beta and Runx transcription factors promote adaptive natural killer cell responses. Sci. Immunol. 2, eaan3796 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Grund, E. M., Spyropoulos, D. D., Watson, D. K. & Muise-Helmericks, R. C. Interleukins 2 and 15 regulate Ets1 expression via ERK1/2 and MNK1 in human natural killer cells. J. Biol. Chem. 280, 4772–4778 (2005).

    Article  CAS  PubMed  Google Scholar 

  49. Ramirez, K. et al. Gene deregulation and chronic activation in natural killer cells deficient in the transcription factor ETS1. Immunity 36, 921–932 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Chen, Z. et al. In vivo CD8(+) T cell CRISPR screening reveals control by Fli1 in infection and cancer. Cell 184, 1262–1280 e1222 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Wang, T. et al. Gene essentiality profiling reveals gene networks and synthetic lethal interactions with oncogenic Ras. Cell 168, 890–903 e815 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Shen, M. W. et al. Predictable and precise template-free CRISPR editing of pathogenic variants. Nature 563, 646–651 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Hildreth, A. D., Riggan, L. & O’Sullivan, T. E. CRISPR-Cas9 ribonucleoprotein-mediated genomic editing in primary innate immune cells. STAR Protoc. 1, 100113 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Deng, J. et al. BH3 profiling identifies three distinct classes of apoptotic blocks to predict response to ABT-737 and conventional chemotherapeutic agents. Cancer Cell 12, 171–185 (2007).

    Article  CAS  PubMed  Google Scholar 

  55. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 e1821 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).

    Article  CAS  PubMed  Google Scholar 

  57. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  59. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Yu, B. et al. Epigenetic landscapes reveal transcription factors that regulate CD8(+) T cell differentiation. Nat. Immunol. 18, 573–582 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Kechin, A., Boyarskikh, U., Kel, A. & Filipenko, M. cutPrimers: a new tool for accurate cutting of primers from reads of targeted next generation sequencing. J. Comput Biol. 24, 1138–1143 (2017).

    Article  CAS  PubMed  Google Scholar 

  62. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  65. Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank members of the O’Sullivan and Sun labs for helpful discussion. The Regents of the University of California have filed a provisional patent application with the United States Patent and Trademark Office towards methods for adoptive cell immunotherapy targeting Fli1 expression in NK cells. L.R., J.H.L. and T.E.O’S. are listed as inventors on this patent application. T.E.O’S. was supported by the NIH (AI145997) and UC CRCC (CRN-20-637105). J.H.L. was supported by the NIH NIGMS (T32GM008042).

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Authors and Affiliations

  1. Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Luke Riggan, Joey H. Li & Timothy E. O’Sullivan

  2. Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA, USA

    Luke Riggan, Joey H. Li & Timothy E. O’Sullivan

  3. Department of Molecular, Cell, and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA

    Feiyang Ma & Matteo Pellegrini

  4. Institute for Genomics and Proteomics, University of California, Los Angeles, Los Angeles, CA, USA

    Feiyang Ma & Matteo Pellegrini

  5. Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Elizabeth Fernandez & David A. Nathanson

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Contributions

L.R. and T.E.O’S. designed the study; L.R. J.H.L. and E.F. performed the experiments; F.M. and M.P. performed bioinformatics analysis; D.A.N. provided reagents; T.E.O’S. and L.R. wrote the manuscript.

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Extended data

Extended Data Fig. 1 Phenotypic and single-cell sequencing analysis of naive, D7 PI and D14 PI Ly49H+ NK cells.

(a) Gating strategy to sort adoptively transferred TCRβ–CD3ε–NK1.1+KLRG1+Ly49H+ NK cells from the spleen of recipient Ly49H–/– mice on D7 and D14 following MCMV infection. (b-e) Ly49H+ NK cells were adoptively transferred into Ly49H–/– mice and infected with MCMV i.p. 16 hours later. TCRβ–CD3ε–NK1.1+Ly49H+KLRG1+ NK cells were sorted on D7 and D14 PI. Cells were immediately processed for single-cell sequencing using 10x Genomics Chromium droplet single-cell RNA sequencing. (b) Heatmap showing the top differentially expressed genes between the two clusters of D7 PI NK cells (padj < 0.05). (c) Representative flow plots of cell-surface expression of CD27, CD11b, NK1.1 and KLRG1 on naive (left) and D7 PI (right) TCRβ–CD3ε–NK1.1+Ly49H+ NKeff cells. (d) Heatmap showing the top differentially expressed genes between the six clusters from NK cells at D7 and D14 PI (padj < 0.05). (e) GO enrichment analysis of marker genes for each cluster from (d). Terms were considered statistically significantly enriched if -log10(padj)<0.05. For (c-e), differentially expressed genes were identified using Wilcoxon Rank Sum test in Seurat v3.1.2.

Extended Data Fig. 2 Time-resolved putative differentiation pathways of NKeff and MP NK cells during MCMV infection.

(a-d) Ly49H+ NK cells were adoptively transferred into Ly49H–/– mice and infected with MCMV i.p. 16 hours later. TCRβ–CD3ε–NK1.1+Ly49H+KLRG1+ NK cells were sorted on D7 and D14 PI. Cells were immediately processed for single-cell sequencing using 10x Genomics Chromium droplet single-cell RNA sequencing. (a) RNA velocity analysis of D7 and D14 NKeff cell clusters with velocity field arrows projected onto the UMAP plot. (b) Arrows show the local average velocity evaluated on a regular grid and indicate the extrapolated future states of cells. (c) Monocle pseudotime analysis of NK cell clusters, indicating cluster identities (left) and pseudotime (right). (d) Scatter plot displaying relative expression (y-axis) of selected genes along pseudotime (x-axis).

Extended Data Fig. 3 NKeff subsets do not demonstrate differential trafficking or proliferation following MCMV infection.

WT splenic CD45.1 and CD45.2 Ly49H+ NK cells were transferred into Ly49H–/– mice, and infected with MCMV i.p. 16 hours later. 7 days after MCMV infection, TCRβ–CD3ε–NK1.1+Ly49H+KLRG1+CD45.1+CX3CR1+ and TCRβ–CD3ε–NK1.1+Ly49H+KLRG1+CD45.2+CX3CR1– NKeff were sorted and then transferred into naive Ly49H–/– hosts at a 1:1 ratio. Recipient spleens were harvested 12 days post transfer and analyzed by flow cytometry. (a) Quantification of the change in frequency of CX3CR1+ (CD45.1) and CX3CR1– (CD45.2) Ly49H+ NK cells subsets from recipient mice on D12 (post transfer). (b-d) Splenic WT Ly49H+ NK cells were transferred into Ly49H–/– mice and infected with MCMV i.p. 16 hours later. (b) Quantification of Ly6C+ and Ly6C– Ly49H+ NK cells from the indicated peripheral organs at various timepoints PI and (c) D10 PI. (d) Quantification of Ki-67+ Ly49H+ splenic NK cells at D10 PI. (a) Data represent 2 independent experiments with n = 4 mice per group. (b-d) Data represent 2 independent experiments with n = 6 mice per group. Samples were compared using two-tailed Student’s t-test with Welch’s correction, assuming unequal SD, and data points are presented as individual mice with the mean ± SEM (ns = not significant).

Extended Data Fig. 4 Ly6C+ and Ly6C– NKeff cells do not display differences in mitophagy, cell-intrinsic metabolism and memory functionality.

(a-e) Splenic Ly49H+ NK cells were transferred into Ly49H–/– mice i.v. and infected i.p with MCMV 16 hours after adoptive transfer. (a) Single cell metabolic analysis of naive (top) or D7 PI (bottom) NKeff using SCENITH. (b) MFI for tetramethylrhodamine ethyl ester (TMRE) staining (top) or MitoTracker Green staining (bottom) in adoptively transferred Ly49H+ NK cells from recipient spleens at the indicated time points PI. (c) Percentage of IFN-γ+ naive or adoptively transferred D14 PI Ly6C+ or Ly6C– Ly49H+ NK cells following no stimulation or 4 hr stimulation ex vivo with anti-NK1.1 monoclonal plate-bound antibody. Ly6C+ p = 0.000083, Ly6C- p = 0.000101. (d) 2.5 × 103 D7 PI sorted TCRβ–CD3ε–NK1.1+KLRG1+CD45.1+Ly49H+Ly6C+ or TCRβ–CD3ε–NK1.1+KLRG1+CD45.1+Ly49H+Ly6C– NK cells were adoptively transferred i.v. into naive Ly49H–/– mice and infected with MCMV 7 days later. (d) Kaplan-Meier survival curves of Ly49H–/– mice that received PBS or indicated sorted NK cell populations i.v. p = 0.0174. (e) The number of Ly6C+ and Ly6C– Ly49H+ NK cells was quantified in various organs at D28 PI. Lung p = 0.001997, liver p = 0.000495, spleen p = 0.0003. (a-c,e) Data represent 2-3 independent experiments with n = 3 mice per group. Samples were compared using two-tailed Student’s t-test with Welch’s correction, assuming unequal SD, and data are presented as individual points with the mean ± SEM. (d) Data are pooled from 2 independent experiments with n = 4–5 mice per group per experiment. Data points are presented as individual mice. Conditions were compared using the Log-rank (Mantel–Cox) test with correction for testing multiple hypotheses. (**p < 0.01, ***p < 0.001).

Extended Data Fig. 5 MP NK cells are transcriptionally distinct from MP CD8 T cells.

Splenic Ly49H+ NK cells were transferred into Ly49H–/– mice i.v. and infected i.p with MCMV 16 hours after adoptive transfer. Splenic TCRβ–CD3ε–NK1.1+KLRG1+Ly49H+Ly6C+ and TCRβ–CD3ε–NK1.1+KLRG1+Ly49H+Ly6C– NKeff were sorted from recipient mice on D7 PI. Sorted NK cells were immediately processed for mRNA extraction, library preparation and sequencing. (a) Differentially expressed genes common between MP Ly6C– NK and Ly6C+ NKeff cells compared to MP and TE CD8+ T cells (padj < 0.05). Differentially expressed genes were determined using the Wald test in DESeq2. (b) Normalized read counts of selected genes in indicated cell types. Data are representative of n = 3 mice per group, presented as mean ± SEM.

Extended Data Fig. 6 MP NK cells and MP CD8+ T cells display similar chromatin accessibility at specific gene loci.

ATAC sequencing reads from Fig. 5 in the indicated cell subsets mapping to the (a) Bcl2 locus and (b) Cxcr3 locus. Highlighted peaks represent differentially accessible peaks (padj < 0.15). For (a-b), differentially accessible peaks were identified using the Wald test in DESeq2. (c) GO term analysis from differentially accessible peaks shared between MP NK cells and MP CD8+ T cells. Terms were considered statistically significantly enriched if -log10(padj)<0.05. Data are representative of three independent replicates for MP and TE CD8+ T cells, D7 PI Ly6C– NKeff and two independent replicates for D7 PI Ly6C+ NKeff. GO term analysis was calculated using the cumulative hypergeometric distribution in HOMER.

Extended Data Fig. 7 Proposed model of MP NK cell formation and mechanism of Fli1 induction.

(a) IL-15 stimulated NK cells signal through STAT5 to induce Fli1 expression. During infection, Fli1 increases Bim levels to limit the number of early effector NK cells contributing to the D7 effector pool. In the absence of Fli1, Bim levels are reduced while BCL2 is unaffected, allowing more early effector NK cells to persist and form MP NK cells. (b) In the contraction phase post D7, MP NK cells continually give rise to Ly6C+ memory NK cells. The majority of D7 PI Ly6C+ NK cells undergo cell death, while a small number can become Ly6C+ memory cells. Lacking a stem-like transcriptional program, the pool of MP cells is depleted over time.

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Source Data Fig. 7

Unprocessed western blots, annotated in the file.

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Riggan, L., Ma, F., Li, J.H. et al. The transcription factor Fli1 restricts the formation of memory precursor NK cells during viral infection. Nat Immunol 23, 556–567 (2022). https://doi.org/10.1038/s41590-022-01150-0

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