Research ArticleHuman Immunology

Human NK cell repertoire diversity reflects immune experience and correlates with viral susceptibility

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Science Translational Medicine  22 Jul 2015:
Vol. 7, Issue 297, pp. 297ra115
DOI: 10.1126/scitranslmed.aac5722
  • Fig. 1. Human NK cell repertoire and function are stable for 6 months in an individual, yet can be rapidly mobilized by exogenous factors.

    (A) NKp30 expression over 6 months in donor HIP11. Time points T1 to T5 occurred weekly within a 6-week period; T6 occurred 6 months later. (B) Stability of the human NK repertoire based on single receptor expression patterns at T1 to T6. Columns represent six blood draws for a single donor; circle size represents frequency of receptor expression. n = 12. (C) Mean SDs of each receptor’s expression over the 6-month period given by Bayesian inference hierarchical model (see Methods for details). Black bars, 95% CrI for mean SD of each receptor for T1 to T6; Healthy Immune Profiling (HIP) donors. Colored dots, mean SD for each donor after in vitro IL-15 (n = 10) or IL-2 (n = 12) stimulation for 72 hours; Stanford Blood Bank (SBB) donors. (D) Up-regulation of NKp30 after 72-hour treatment with IL-15 or IL-2 in donor SBB10. (E) Stability of NK cell function over 6 months. The diagram shows the 95% CrI for mean SD of each function (CD107a, IFN-γ, and TNF) after 721.221 stimulation at T1 to T6. n = 3.

  • Fig. 2. NK cell diversity is stable and donor-specific.

    T1 to T6 box plot [box = median + interquartile range (IQR); whiskers = 1.5 × IQR] distributions of NK diversity (inverse Simpson Index) in HIP donors (n = 12).

  • Fig. 3. Diverse NK repertoires contain differentiated NK cells unique to an individual.

    (A and B) CD57 and (A) NKG2A (B) frequency on NK cells versus NK cell repertoire diversity for HIP donors. Circles, individual time points. Triangles, mean of all time points in each individual. Linear regression, black; 95% CI, gray. Generalized estimating equation, AR-1 correlation structure: CD57, P = 7.8 × 10−3; NKG2A, P = 7.5 × 10−5. n = 12. (C to E) CD57 frequency (C), NKG2A frequency (D), and NK diversity (inverse Simpson index) (E) on umbilical cord bloods (n = 20) versus HIP donors (n = 12). Mann-Whitney U tests: CD57, P = 8.9 × 10−9; NKG2A, P = 3.7 × 10−5; diversity, P = 1.4 × 10−4. (F) Convergence analysis of NK phenotypes. Total proportion of phenotypes expressing CD57 (magenta) or NKG2A (blue) shared at baseline. Linear regression: CD57, P = 5.6 × 10−5; NKG2A, P = 0.031. n = 12, SBB donors.

  • Fig. 4. NK cell repertoire diversity correlates with acquisition of HIV-1 in Kenyan women.

    (A) Distributions of NK cell repertoire diversity (inverse Simpson index) in cases (n = 13) and matched controls (n = 23) (logistic regression odds ratio per 100-point diversity increase, 2.5; 95% CI, 1.2 to 6.2). (B) Box plots of NK receptor diversity on CD4+ T, CD8+ T, and NK cells in cases and controls (logistic regression of diversity versus HIV-1 acquisition for CD4+ and CD8+ T cells is not significant; NK, P = 0.029). (C) Box plots show distributions of expression frequency of each receptor on CD4+ T, CD8+ T, and NK cells in cases versus controls. All case-control receptor comparisons are not significant by logistic regression.

  • Fig. 5. Differentiated NK repertoires are skewed toward cytokine production in response to virus-infected cells.

    (A to D) Frequency of cells producing IFN-γ (A), TNF (B), CD107a (C), and IdU (D) in CD57 or CD57+ NK cells after stimulation with IL-2 and HIV-1–infected target cells. Wilcoxon signed-rank tests: IFN-γ, P = 3.4 × 10−3; TNF, P = 0.021; CD107a, P = 0.027; IdU, P = 0.077. SBB donors, n = 11.

  • Fig. 6. Short-term exposure to virus-infected cells augments NK diversity.

    (A to F) Correspondence analysis of Boolean phenotypes for HIV-1 (A to C, n = 12) or WNV (D to F, n = 33). (A and D) Components 1 and 2 of analysis of all phenotypes after culture with IL-2 alone (HIV-1) or unstimulated (WNV). For HIV-1, 11,523 total phenotypes were detected, and for WNV, 8397 phenotypes. (B and E) Components 1 and 2 and total change in component 3 (colored arrows) after culture with IL-2 + HIV-1–infected CD4+ T cells (B) or after PBMC infection with WNV (E). (C and F) Sum of squared distances to centroid of point cloud in NK cells cultured with IL-2 alone (HIV-1) or IL-2 + HIV-1–infected CD4+ T cells (HIV-1+) (C) or PBMCs uninfected with WNV (WNV) or with WNV infection (WNV+) (F). Permutation test (10,000×) on sum of squared distances from centroid: HIV-1, P = 0.038; WNV, P = 1.0 × 10−4. (G and H) Diversity of NK cells cultured with IL-2 alone (HIV-1) or IL-2 + HIV-1–infected CD4+ T cells (HIV-1+) (G, n = 12) or uninfected with WNV (WNV) or with WNV infection (WNV+) (H, n = 33); Wilcoxon signed-rank tests: HIV-1, P = 0.034; WNV, P = 0.0033. (I) Model for NK cell diversity and differentiation. A naïve NK repertoire is an effective fence for infection prevention. As the NK repertoire encounters novel pathogens over the course of a lifetime, it diversifies with each response mounted. Its increasingly branched, diffuse nature increases the chance that a newly encountered virus will penetrate the barrier.

Supplementary Materials

  • www.sciencetranslationalmedicine.org/cgi/content/full/7/297/297ra115/DC1

    Fig. S1. Serial negative gating strategy used to define NK, CD4+ T, and CD8+ T cells from PBMCs.

    Fig. S2. Representative gates to evaluate marker expression on NK cells.

    Fig. S3. Stability analysis of CD56bright versus CD56dim NK cells.

    Fig. S4. Human NK cell repertoire and function are stable for 6 months in an individual.

    Fig. S5. Subpopulation analysis for the 10 most frequently detected NK subpopulations.

    Fig. S6. Donor age does not correlate with NK cell diversity.

    Fig. S7. CMV serostatus does not correlate with NK cell diversity or the frequency of NKG2A or CD57.

    Fig. S8. NK diversity does not correlate with viral suppression in vitro.

    Fig. S9. No correlative features distinguish NK cells responding to HIV-1–infected CD4+ T cells.

    Fig. S10. Cytokine-producing NK cells are more diverse than non–cytokine-producing NK cells.

    Fig. S11. Proportion of variance explained in correspondence analysis.

    Table S1. Mass cytometry antibody panels used in each experiment.

    Table S2. HIP and SBB cohort information.

    Table S3. Mama Salama Study cohort information.

    Table S4. Frequencies of NK markers used in repertoire stability analysis.

    Table S5. Frequencies of NK markers after 72-hour culture with IL-15 or IL-2, or without stimulation, for the SBB cohort.

    Table S6. Frequencies of functional markers used in stability analysis.

    Table S7. CD57 and NKG2A frequencies and NK diversity scores for HIP and cord blood cohorts.

    Table S8. Proportion of phenotypes expressing CD57 and NKG2A.

    Table S9. Functional frequencies of CD57+ and CD57 NK cells.

    Table S10. Diversity of NK cells in the presence or absence of HIV-1–infected CD4+ T cells.

    Table S11. Diversity of NK cells in the presence or absence of WNV-infected cells.

    Reference (48)

  • Supplementary Material for:

    Human NK cell repertoire diversity reflects immune experience and correlates with viral susceptibility

    Dara M. Strauss-Albee, Julia Fukuyama, Emily C. Liang, Yi Yao, Justin A. Jarrell, Alison L. Drake, John Kinuthia, Ruth R. Montgomery, Grace John-Stewart, Susan Holmes, Catherine A. Blish*

    *Corresponding author. E-mail: cblish{at}stanford.edu

    Published 22 July 2015, Sci. Transl. Med. 7, 297ra115 (2015)
    DOI: 10.1126/scitranslmed.aac5722

    This PDF file includes:

    • Fig. S1. Serial negative gating strategy used to define NK, CD4+ T, and CD8+ T cells from PBMCs.
    • Fig. S2. Representative gates to evaluate marker expression on NK cells.
    • Fig. S3. Stability analysis of CD56bright versus CD56dim NK cells.
    • Fig. S4. Human NK cell repertoire and function are stable for 6 months in an individual.
    • Fig. S5. Subpopulation analysis for the 10 most frequently detected NK subpopulations.
    • Fig. S6. Donor age does not correlate with NK cell diversity.
    • Fig. S7. CMV serostatus does not correlate with NK cell diversity or the frequency of NKG2A or CD57.
    • Fig. S8. NK diversity does not correlate with viral suppression in vitro.
    • Fig. S9. No correlative features distinguish NK cells responding to HIV-1–infected CD4+ T cells.
    • Fig. S10. Cytokine-producing NK cells are more diverse than non–cytokine-producing NK cells.
    • Fig. S11. Proportion of variance explained in correspondence analysis.
    • Table S1. Mass cytometry antibody panels used in each experiment.
    • Table S2. HIP and SBB cohort information.
    • Table S3. Mama Salama Study cohort information.
    • Reference (48)

    [Download PDF]

    Other Supplementary Material for this manuscript includes the following:

    • Table S4 (Microsoft Excel format). Frequencies of NK markers used in repertoire stability analysis.
    • Table S5 (Microsoft Excel format). Frequencies of NK markers after 72-hour culture with IL-15 or IL-2, or without stimulation, for the SBB cohort.
    • Table S6 (Microsoft Excel format). Frequencies of functional markers used in stability analysis.
    • Table S7 (Microsoft Excel format). CD57 and NKG2A frequencies and NK diversity scores for HIP and cord blood cohorts.
    • Table S8 (Microsoft Excel format). Proportion of phenotypes expressing CD57 and NKG2A.
    • Table S9 (Microsoft Excel format). Functional frequencies of CD57+ and CD57NK cells.
    • Table S10 (Microsoft Excel format). Diversity of NK cells in the presence or absence of HIV-1–infected CD4+ T cells.
    • Table S11 (Microsoft Excel format). Diversity of NK cells in the presence or absence of WNV-infected cells.

    [Download Tables S4 to S11]

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