Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Baseline innate and T cell populations are correlates of protection against symptomatic influenza virus infection independent of serology

Nature Immunology volume 24, pages 1511–1526 (2023)Cite this article

Subjects

Abstract

Evidence suggests that innate and adaptive cellular responses mediate resistance to the influenza virus and confer protection after vaccination. However, few studies have resolved the contribution of cellular responses within the context of preexisting antibody titers. Here, we measured the peripheral immune profiles of 206 vaccinated or unvaccinated adults to determine how baseline variations in the cellular and humoral immune compartments contribute independently or synergistically to the risk of developing symptomatic influenza. Protection correlated with diverse and polyfunctional CD4+ and CD8+ T, circulating T follicular helper, T helper type 17, myeloid dendritic and CD16+ natural killer (NK) cell subsets. Conversely, increased susceptibility was predominantly attributed to nonspecific inflammatory populations, including γδ T cells and activated CD16− NK cells, as well as TNFα+ single-cytokine-producing CD8+ T cells. Multivariate and predictive modeling indicated that cellular subsets (1) work synergistically with humoral immunity to confer protection, (2) improve model performance over demographic and serologic factors alone and (3) comprise the most important predictive covariates. Together, these results demonstrate that preinfection peripheral cell composition improves the prediction of symptomatic influenza susceptibility over vaccination, demographics or serology alone.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: SHIVERS-II study design, participant enrollment, sample collection and participant demographics.
Fig. 2: Individual serology measures correlate with protection from symptomatic influenza disease.
Fig. 3: Univariate effects of cell populations on symptomatic influenza by vaccination status.
Fig. 4: Cryptic infections are associated with unique cellular responses.
Fig. 5: Co-regulated CMI and innate immune cell modules.
Fig. 6: Decision tree model comparison.
Fig. 7: Baseline predictors of influenza virus infection susceptibility, accounting for demographic, vaccination, serology and cellular covariates.

Similar content being viewed by others

Data availability

The published article includes all datasets generated or analyzed as a part of this study. Individual source data are provided with associated figures (where appropriate) per the data sharing agreement stipulated under the Ruth L. Kirschstein National Research Service Award Individual Postdoctoral Fellowship (award no. F32AI157296; R.C.M.). Raw flow cytometry source files can be made available upon reasonable request. Source data are provided with this paper.

Code availability

A minimum dataset containing deidentified study participant information and biological assay results along with custom study-generated R code for analysis was uploaded to GitHub (https://github.com/kvegesan-stjude/SHIVERS2) per the data sharing agreement stipulated under the Ruth L. Kirschstein National Research Service Award Individual Postdoctoral Fellowship (award no. F32AI157296; R.C.M.). Additional basic R code can be made available upon reasonable request.

References

  1. Centers for Disease Control and Prevention. Estimated influenza illnesses, medical visits, hospitalizations, and deaths in the United States—2017–2018 influenza season. https://www.cdc.gov/flu/about/burden/2017-2018.htm#Table1 (2018).

  2. Iuliano, A. D. et al. Estimates of global seasonal influenza-associated respiratory mortality: a modelling study. Lancet 391, 1285–1300 (2018).

    Article  PubMed  Google Scholar 

  3. Flannery, B. et al. Interim estimates of 2016–17 seasonal influenza vaccine effectiveness—United States, February 2017. Morb. Mortal. Wkly. Rep. 66, 167–171 (2017).

    Article  Google Scholar 

  4. Flannery, B. et al. Interim estimates of 2017–18 seasonal influenza vaccine effectiveness—United States, February 2018. Morb. Mortal. Wkly. Rep. 67, 180–185 (2018).

    Article  Google Scholar 

  5. Jackson, M. L. et al. Influenza vaccine effectiveness in the United States during the 2015–2016 season. N. Engl. J. Med. 377, 534–543 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Centers for Disease Control and Prevention, National Center for Immunization and Respiratory Diseases. Estimated flu-related illnesses, medical visits, hospitalizations, and deaths in the United States—2019–2020 flu season. https://www.cdc.gov/flu/about/burden/2017-2018.htm (2021).

  7. Zimmerman, R. K. et al. 2014–2015 Influenza vaccine effectiveness in the United States by vaccine type. Clin. Infect. Dis. 63, 1564–1573 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Erbelding, E. J. et al. A universal influenza vaccine: the strategic plan for the National Institute of Allergy and Infectious Diseases. J. Infect. Dis. 218, 347–354 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Morens, D. M., Taubenberger, J. K. & Fauci, A. S. Rethinking next-generation vaccines for coronaviruses, influenzaviruses, and other respiratory viruses. Cell Host Microbe 31, 146–157 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Fourati, S. et al. Pan-vaccine analysis reveals innate immune endotypes predictive of antibody responses to vaccination. Nat. Immunol. https://doi.org/10.1038/s41590-022-01329-5 (2022).

  11. Hagan, T. et al. Transcriptional atlas of the human immune response to 13 vaccines reveals a common predictor of vaccine-induced antibody responses. Nat. Immunol. https://doi.org/10.1038/s41590-022-01328-6 (2022).

  12. Liston, A., Humblet-Baron, S., Duffy, D. & Goris, A. Human immune diversity: from evolution to modernity. Nat. Immunol. 22, 1479–1489 (2021).

    Article  CAS  PubMed  Google Scholar 

  13. Tsang, J. S. et al. Global analyses of human immune variation reveal baseline predictors of postvaccination responses. Cell 157, 499–513 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Tsang, J. S. et al. Improving vaccine-induced immunity: can baseline predict outcome? Trends Immunol. 41, 457–465 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Souquette, A. et al. Integrated drivers of basal and acute immunity in diverse human populations. Preprint at bioRxiv https://doi.org/10.1101/2023.03.25.534227 (2023).

  16. Gounder, A. P. & Boon, A. C. M. Influenza pathogenesis: the effect of host factors on severity of disease. J. Immunol. 202, 341–350 (2019).

    Article  CAS  PubMed  Google Scholar 

  17. Mettelman, R. C. & Thomas, P. G. Human susceptibility to influenza infection and severe disease. Cold Spring Harb. Perspect. Med. 11, a038711 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Brodin, P. et al. Variation in the human immune system is largely driven by non-heritable influences. Cell 160, 37–47 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Carr, E. J. et al. The cellular composition of the human immune system is shaped by age and cohabitation. Nat. Immunol. 17, 461–468 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lakshmikanth, T. et al. Human immune system variation during 1 year. Cell Rep. 32, 107923 (2020).

    Article  CAS  PubMed  Google Scholar 

  21. Lee, M. N. et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343, 1246980 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Mettelman, R. C., Allen, E. K. & Thomas, P. G. Mucosal immune responses to infection and vaccination in the respiratory tract. Immunity 55, 749–780 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. O’Neill, M. B. et al. Single-cell and bulk RNA-sequencing reveal differences in monocyte susceptibility to influenza A virus infection between Africans and Europeans. Front. Immunol. 12, 768189 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Orrù, V. et al. Genetic variants regulating immune cell levels in health and disease. Cell 155, 242–256 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Patin, E. et al. Natural variation in the parameters of innate immune cells is preferentially driven by genetic factors. Nat. Immunol. 19, 302–314 (2018).

    Article  CAS  PubMed  Google Scholar 

  26. Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Randolph, H. E. et al. Genetic ancestry effects on the response to viral infection are pervasive but cell type specific. Science 374, 1127–1133 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Roederer, M. et al. The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis. Cell 161, 387–403 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ye, C. J. et al. Intersection of population variation and autoimmunity genetics in human T cell activation. Science 345, 1254665 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Cox, R. J. Correlates of protection to influenza virus, where do we go from here? Hum. Vaccin. Immunother. 9, 405–408 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Guthmiller, J. J. et al. First exposure to the pandemic H1N1 virus induced broadly neutralizing antibodies targeting hemagglutinin head epitopes. Sci. Transl. Med. 13, eabg4535 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Memoli, M. J. et al. Evaluation of antihemagglutinin and antineuraminidase antibodies as correlates of protection in an influenza A/H1N1 virus healthy human challenge model. mBio 7, e00417 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Monto, A. S. et al. Antibody to influenza virus neuraminidase: an independent correlate of protection. J. Infect. Dis. 212, 1191–1199 (2015).

    Article  CAS  PubMed  Google Scholar 

  34. Ng, S. et al. Novel correlates of protection against pandemic H1N1 influenza A virus infection. Nat. Med. https://doi.org/10.1038/s41591-019-0463-x (2019).

  35. Steel, J. et al. Influenza virus vaccine based on the conserved hemagglutinin stalk domain. mBio 1, e00018 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Bandaranayake, D. et al. Risk factors and immunity in a nationally representative population following the 2009 influenza A(H1N1) pandemic. PLoS ONE 5, e13211 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Huang, Q. S. et al. Risk factors and attack rates of seasonal influenza infection: results of the Southern Hemisphere Influenza and Vaccine Effectiveness Research and Surveillance (SHIVERS) seroepidemiologic cohort study. J. Infect. Dis. 219, 347–357 (2019).

    Article  PubMed  Google Scholar 

  38. Institute of Environmental Science and Research. 2018 Annual influenza summary. https://www.esr.cri.nz/assets/Intelligence-Hub-2023/Surveillance-reports-and-dashboards/Influenza/InfluenzaAnn2018.pdf (2018).

  39. Honce, R. & Schultz-Cherry, S. Impact of obesity on influenza A virus pathogenesis, immune response, and evolution. Front. Immunol. 10, 1071 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ursin, R. L. & Klein, S. L. Sex differences in respiratory viral pathogenesis and treatments. Annu. Rev. Virol. 8, 393–414 (2021).

    Article  PubMed  Google Scholar 

  41. Wu, Y., Goplen, N. P. & Sun, J. Aging and respiratory viral infection: from acute morbidity to chronic sequelae. Cell Biosci. 11, 112 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Institute of Environmental Science and Research. 2017 Annual influenza summary. https://www.esr.cri.nz/assets/Intelligence-Hub-2023/Surveillance-reports-and-dashboards/Influenza/2017-Influenza-Annual-report.pdf (2017).

  43. Dormann, C. F. et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).

    Article  Google Scholar 

  44. Hamada, H. et al. Tc17, a unique subset of CD8 T cells that can protect against lethal influenza challenge. J. Immunol. 182, 3469–3481 (2009).

    Article  CAS  PubMed  Google Scholar 

  45. Annunziato, F. et al. Phenotypic and functional features of human Th17 cells. J. Exp. Med. 204, 1849–1861 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Takagi, R. et al. B cell chemoattractant CXCL13 is preferentially expressed by human Th17 cell clones. J. Immunol. 181, 186–189 (2008).

    Article  CAS  PubMed  Google Scholar 

  47. Wang, X. et al. A critical role of IL-17 in modulating the B-cell response during H5N1 influenza virus infection. Cell. Mol. Immunol. 8, 462–468 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Bao, J. et al. Decreased frequencies of Th17 and Tc17 cells in patients infected with avian influenza A (H7N9) virus. J. Immunol. Res. 2019, 1418251 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Schultz-Cherry, S. Role of NK cells in influenza infection. Curr. Top. Microbiol. Immunol. 386, 109–120 (2015).

    CAS  PubMed  Google Scholar 

  50. Riese, P. et al. Responsiveness to influenza vaccination correlates with NKG2C-expression on NK cells. Vaccines 8, 281 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Jost, S. et al. Changes in cytokine levels and NK cell activation associated with influenza. PLoS ONE 6, e25060 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Björkström, N. K., Ljunggren, H.-G. & Sandberg, J. K. CD56 negative NK cells: origin, function, and role in chronic viral disease. Trends Immunol. 31, 401–406 (2010).

    Article  PubMed  Google Scholar 

  53. Fox, A. et al. Severe pandemic H1N1 2009 infection is associated with transient NK and T deficiency and aberrant CD8 responses. PLoS ONE 7, e31535 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Heltzer, M. L. et al. Immune dysregulation in severe influenza. J. Leukoc. Biol. 85, 1036–1043 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Bongen, E., Vallania, F., Utz, P. J. & Khatri, P. KLRD1-expressing natural killer cells predict influenza susceptibility. Genome Med. 10, 45 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Dou, Y. et al. Influenza vaccine induces intracellular immune memory of human NK cells. PLoS ONE 10, e0121258 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Kay, A. W. et al. Enhanced natural killer-cell and T-cell responses to influenza A virus during pregnancy. Proc. Natl Acad. Sci. USA 111, 14506–14511 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Giurgea, L. T., Morens, D. M., Taubenberger, J. K. & Memoli, M. J. Influenza neuraminidase: a neglected protein and its potential for a better influenza vaccine. Vaccines 8, 409 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Rajendran, M., Krammer, F. & McMahon, M. The human antibody response to the influenza virus neuraminidase following infection or vaccination. Vaccines 9, 846 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Wohlbold, T. J. et al. Vaccination with adjuvanted recombinant neuraminidase induces broad heterologous, but not heterosubtypic, cross-protection against influenza virus infection in mice. mBio 6, e02556 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Turner, J. S. et al. Human germinal centres engage memory and naive B cells after influenza vaccination. Nature 586, 127–132 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Bentebibel, S. E. et al. Induction of ICOS+CXCR3+CXCR5+ TH cells correlates with antibody responses to influenza vaccination. Sci. Transl. Med. 5, 176ra32 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Bentebibel, S. E. et al. ICOS+PD-1+CXCR3+ T follicular helper cells contribute to the generation of high-avidity antibodies following influenza vaccination. Sci. Rep. 6, 26494 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Lindgren, G. et al. Induction of robust B cell responses after influenza mRNA vaccination is accompanied by circulating hemagglutinin-specific ICOS+PD-1+CXCR3+ T follicular helper cells. Front. Immunol. 8, 1539 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Currenti, J. et al. Tracking of activated cTfh cells following sequential influenza vaccinations reveals transcriptional profile of clonotypes driving a vaccine-induced immune response. Front. Immunol. 14, 1133781 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Pizzolla, A. et al. Influenza-specific lung-resident memory T cells are proliferative and polyfunctional and maintain diverse TCR profiles. J. Clin. Invest. 128, 721–733 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Lorenzo-Herrero, S., Sordo-Bahamonde, C., Gonzalez, S. & López-Soto, A. CD107a degranulation assay to evaluate immune cell antitumor activity. in Cancer Immunosurveillance: Methods and Protocols (eds. López-Soto, A. & Folgueras, A. R.) 119–130 (Springer, 2019); https://doi.org/10.1007/978-1-4939-8885-3_7

  68. Wilkinson, T. M. et al. Preexisting influenza-specific CD4+ T cells correlate with disease protection against influenza challenge in humans. Nat. Med. 18, 274–280 (2012).

    Article  CAS  PubMed  Google Scholar 

  69. Krammer, F. The human antibody response to influenza A virus infection and vaccination. Nat. Rev. Immunol. 19, 383–397 (2019).

    Article  CAS  PubMed  Google Scholar 

  70. Guo, X. J. et al. Lung γδ T cells mediate protective responses during neonatal influenza infection that are associated with type 2 immunity. Immunity 49, 531–544 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Clark, B. L. & Thomas, P. G. A cell for the ages: human γδ T cells across the lifespan. Int. J. Mol. Sci. 21, 8903 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Sant, S. et al. Human γδ T‐cell receptor repertoire is shaped by influenza viruses, age and tissue compartmentalisation. Clin. Transl. Immunol. 8, e1079 (2019).

    Article  CAS  Google Scholar 

  73. Savic, M. et al. Distinct T and NK cell populations may serve as immune correlates of protection against symptomatic pandemic influenza A(H1N1) virus infection during pregnancy. PLoS ONE 12, e0188055 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  74. McElhaney, J. E. et al. T cell responses are better correlates of vaccine protection in the elderly. J. Immunol. 176, 6333–6339 (2006).

    Article  CAS  PubMed  Google Scholar 

  75. McElhaney, J. E. et al. Granzyme B: correlates with protection and enhanced CTL response to influenza vaccination in older adults. Vaccine 27, 2418–2425 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Mersha, T. B. & Abebe, T. Self-reported race/ethnicity in the age of genomic research: its potential impact on understanding health disparities. Hum. Genomics 9, 1 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  77. World Health Organization. Global Epidemiological Surveillance Standards for Influenza. https://www.who.int/publications/i/item/9789241506601 (World Health Organization, 2013).

  78. Brunner, E., Bathke, A. C. & Konietschke, F. Rank and Pseudo-Rank Procedures for Independent Observations in Factorial Designs (Springer, 2018); https://doi.org/10.1007/978-3-030-02914-2

  79. Bürkner, P. C., Doebler, P. & Holling, H. Optimal design of the Wilcoxon–Mann–Whitney-test. Biom. J. 59, 25–40 (2017).

    Article  PubMed  Google Scholar 

  80. Happ, M., Bathke, A. C. & Brunner, E. Optimal sample size planning for the Wilcoxon–Mann–Whitney test. Stat. Med. 38, 363–375 (2019).

    Article  PubMed  Google Scholar 

  81. Huang, Q. S. et al. Implementing hospital-based surveillance for severe acute respiratory infections caused by influenza and other respiratory pathogens in New Zealand. Western Pac. Surveill. Response J. 5, 23–30 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Kim, C. et al. Comparison of nasopharyngeal and oropharyngeal swabs for the diagnosis of eight respiratory viruses by real-time reverse transcription-PCR assays. PLoS ONE 6, e21610 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Kodani, M. et al. Application of TaqMan low-density arrays for simultaneous detection of multiple respiratory pathogens. J. Clin. Microbiol. 49, 2175–2182 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Olsen, S. J. et al. Incidence of respiratory pathogens in persons hospitalized with pneumonia in two provinces in Thailand. Epidemiol. Infect. 138, 1811–1822 (2010).

    Article  CAS  PubMed  Google Scholar 

  85. Shu, B. et al. Design and performance of the CDC real-time reverse transcriptase PCR swine flu panel for detection of 2009 A (H1N1) pandemic influenza virus. J. Clin. Microbiol. 49, 2614–2619 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Matrosovich, M., Matrosovich, T., Carr, J., Roberts, N. A. & Klenk, H.-D. Overexpression of the α-2,6-sialyltransferase in MDCK cells increases influenza virus sensitivity to neuraminidase inhibitors. J. Virol. 77, 8418–8425 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Sandbulte, M. R. et al. Cross-reactive neuraminidase antibodies afford partial protection against H5N1 in mice and are present in unexposed humans. PLoS Med. 4, e59 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Wong, S. S. et al. H5N1 influenza vaccine induces a less robust neutralizing antibody response than seasonal trivalent and H7N9 influenza vaccines. NPJ Vaccines 2, 16 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Couzens, L. et al. An optimized enzyme-linked lectin assay to measure influenza A virus neuraminidase inhibition antibody titers in human sera. J. Virol. Methods 210, 7–14 (2014).

    Article  CAS  PubMed  Google Scholar 

  90. Ferrer‐Font, L. et al. Panel design and optimization for high‐dimensional immunophenotyping assays using spectral flow cytometry. Curr. Protoc. Cytom. 92, e70 (2020).

    PubMed  Google Scholar 

  91. Liechti, T. et al. An updated guide for the perplexed: cytometry in the high-dimensional era. Nat. Immunol. https://doi.org/10.1038/s41590-021-01006-z (2021).

  92. R Core Team. R: A Language and Environment for Statistical Computing (R Project for Statistical Computing, 2018).

  93. Vegesana, K. sjTabone. Github https://github.com/kvegesan-stjude/sjTabone#readme (2023).

  94. Raftery, A., Hoeting, J., Volinsky, C., Painter, I. & Yeung, K. Y. BMA: Bayesian model averaging. https://cran.r-project.org/web/packages/BMA/BMA.pdf (2022).

  95. Mandrekar, J. N. Receiver operating characteristic curve in diagnostic test assessment. J. Thorac. Oncol. 5, 1315–1316 (2010).

    Article  PubMed  Google Scholar 

  96. Greiner, M., Pfeiffer, D. & Smith, R. D. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev. Vet. Med. 45, 23–41 (2000).

    Article  CAS  PubMed  Google Scholar 

  97. Kuhn, M. et al. caret: classification and regression training. https://cran.r-project.org/web/packages/caret/caret.pdf (2016).

  98. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009); https://doi.org/10.1007/978-0-387-98141-3

  99. Kassambara, A. ggpubr: ‘ggplot2’ based publication ready plots. https://rpkgs.datanovia.com/ggpubr/index.html (2022).

Download references

Acknowledgements

We thank A. DeCleene, K. MacGregor, M. Gawith and M. Mitchell for their work as study nurses from Regional Public Health and all unnamed members of the SHIVERS-II team. We thank T. Hertz (Ben-Gurion University of the Negev) for insightful discussions and expert feedback on modeling methodology. We thank all consented enrollees and their families for their participation and commitment to the SHIVERS-II study. This publication was supported by the American Lebanese Syrian Associated Charities at St. Jude Children’s Research Hospital (SJCRH), the SJCRH Center of Excellence for Influenza Research and Surveillance (P.G.T., R.J.W., Q.S.H.) contract HHSN272201400006C, US Department of Health and Human Services (HHS) contract 75N93021C00016 for the St. Jude Centers of Excellence for Influenza Research and Response, HHS contract 75N93019C00052 for the Center for Influenza Vaccine Research for High Risk Populations of the Collaborative Influenza Vaccine Innovation Centers, National Institute of Allergy and Infectious Diseases award 3U01AI144616-02S1 (P.G.T.), U01AI150747 (P.G.T.), R01AI154470 (P.G.T.), and Ruth L. Kirschstein National Research Service Award Individual Postdoctoral Fellowship award F32AI157296 (R.C.M.). The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author information

Author notes

  1. These authors contributed equally: Robert C. Mettelman, Aisha Souquette.

  2. These authors jointly supervised this work: Richard J. Webby, Q. Sue Huang, Paul G. Thomas.

Authors and Affiliations

  1. Department of Immunology, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Robert C. Mettelman, Aisha Souquette, Lee-Ann Van de Velde, Kasi Vegesana, E. Kaitlynn Allen, Taylor L. Wilson, Deryn G. St. James, Smrithi S. Menon & Paul G. Thomas

  2. Department of Host–Microbe Interactions, St. Jude Children’s Research Hospital, Memphis, TN, USA

    Christina M. Kackos, Sanja Trifkovic, Jennifer DeBeauchamp & Richard J. Webby

  3. Department of Microbiology, Immunology and Biochemistry, College of Graduate Health Sciences, University of Tennessee Health Science Center, Memphis, TN, USA

    Taylor L. Wilson & Deryn G. St. James

  4. Institute of Environmental Science and Research Limited (ESR), Wallaceville Science Centre, Upper Hutt, New Zealand

    Timothy Wood, Lauren Jelley, Q. Sue Huang, Judy Bocacao, Jacqui Ralston, Wendy Gunn, Nayyereh Aminisani, Ben Waite & R. Pamela Kawakami

  5. Institute of Environmental Science and Research Limited (ESR), Mt Albert Science Centre, Auckland, New Zealand

    Jessica Danielewicz

  6. Te Whatu Ora Health New Zealand (Capital, Coast and Hutt Valley), Wellington, New Zealand

    Annette Nesdale & Michelle Balm

  7. Department of General Practice and Primary Health Care, School of Population Health, University of Auckland, Auckland, New Zealand

    Nikki Turner

  8. Immunisation Advisory Centre, University of Auckland, Auckland, New Zealand

    Nikki Turner

  9. Department of Primary Health Care and General Practice, University of Otago, Wellington, New Zealand

    Tony Dowell

Authors
  1. Robert C. Mettelman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aisha Souquette
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lee-Ann Van de Velde
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kasi Vegesana
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. E. Kaitlynn Allen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Christina M. Kackos
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sanja Trifkovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jennifer DeBeauchamp
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Taylor L. Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Deryn G. St. James
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Smrithi S. Menon
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Timothy Wood
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Lauren Jelley
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Richard J. Webby
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Q. Sue Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Paul G. Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

SHIVERS-II Investigation Team

  • Judy Bocacao
  • , Jacqui Ralston
  • , Jessica Danielewicz
  • , Wendy Gunn
  • , Nayyereh Aminisani
  • , Ben Waite
  • , R. Pamela Kawakami
  • , Annette Nesdale
  • , Michelle Balm
  • , Nikki Turner
  •  & Tony Dowell

Contributions

R.C.M. and A.S. contributed equally as co-first authors. L.-A.V.d.V. and K.V. contributed equally as co-second authors. Conceptualization: R.C.M., A.S., R.J.W., Q.S.H. and P.G.T. Formal analysis: R.C.M., A.S., L.-A.V.d.V. and K.V. Investigation: R.C.M., A.S., L.-A.V.d.V., K.V., E.K.A., C.M.K., S.T., J.D.B., T.L.W., D.G.S.J. and S.S.M. Methods development: R.C.M., A.S., L.-A.V.d.V., K.V., E.K.A., T.L.W., C.M.K., J.D.B. and S.T. Resources: T.W., L.J. and Q.S.H. Data and sample curation: T.W., L.J., Q.S.H. and the SHIVERS-II Investigation Team. Writing—original draft: R.C.M. and A.S. Writing—review and editing: R.C.M., A.S., L.-A.V.d.V., K.V., E.K.A., R.J.W., Q.S.H. and P.G.T. Visualization: R.C.M. and K.V. Supervision: R.J.W., Q.S.H. and P.G.T. Funding acquisition: R.C.M., R.J.W., Q.S.H. and P.G.T.

Corresponding authors

Correspondence to Richard J. Webby, Q. Sue Huang or Paul G. Thomas.

Ethics declarations

Competing interests

P.G.T. has consulted or received honoraria and/or travel support from Illumina, J&J, Pfizer and 10x Genomics. P.G.T. serves on the scientific advisory board of ImmunoScape and CytoAgents. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks Peter Openshaw, Sophie Valkenburg and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: L.A. Dempsey, in collaboration with the Nature Immunology team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Data analysis pipeline for predictive and statistical modeling.

The analysis pipeline was designed to integrate participant-level demographic, serology, vaccine histories, and cellular flow cytometry data into statistical and predictive models. Univariate analyses, including Fisher’s exact test, Kruskal-Wallis, logistic regression, and ROC thresholds were performed first on single, independent variables. These analyses help determine if an individual immune measure is statistically different between influenza virus infection and vaccination comparator groups (Fisher’s exact test; Kruskal-Wallis), the risk of symptomatic influenza associated with an individual measure (logistic regression), and the threshold at which an individual immune measure can accurately describe 50% of symptomatic cases (ROC threshold). As univariate comparisons do not account for confounding factors, multivariate analyses were performed on combined variables including decision tree analysis (random forest) and logistic regression. The random forest allows comparison of performance (that is categorization accuracy) across models (ROC; Sensitivity & Specificity) as well as the relative importance of individual covariates within a model (VIP analyses). While random forest considers which models or individual covariates best categorize cases (symptomatic influenza) and controls (uninfected/cryptic), they do not provide information on association or risk. Multivariate generalized linear modeling (GLM) was used to determine the risk of symptomatic influenza associated with individual immune measures while accounting for the effects of others. The GLM was built on a select set of variables determined following reduction of dimensionality (correlation-based clustering) and multicollinearity (VIF) using stepwise regression (Akaike Information Criteria; AIC) and evaluated using Bayesian Model Averaging (BMA).

Extended Data Fig. 2 Participant demographic and serologic correlations.

a) Spearman Rank correlations between serology measures. Significant values (FDR-adjusted; q ≤ 0.05) depicted with correlation coefficients and within correlation groups (black rectangles). Insignificant values blank. b) Frequency of unvaccinated or vaccinated study participants with baseline anti-HA and anti-NA antibody titers at elevated (≥1:40) or reduced (<1:40) levels for each influenza strain. c) Spearman Rank correlation (R; coefficient) between participant age (years) and BMI (kg m−2) by sex. d-k) Spearman Rank Correlation (R; coefficient) between participant BMI (kg m−2) and age (years) by baseline serologic measures stratified by sex. Reciprocal inhibiting antibody titer against (d,f) HA or (e,g) NA. Inhibiting titer calculated from HAI or NAI assays using A(H1N1), A(H3N2), B/Victoria (lineage), and B/Yamagata (lineage) viruses. Total (h,j) anti-HA or (i,k) anti-NA binding antibody titers. Total binding antibody titers reported as AUC values calculated from ELISA assay against purified, full-length HA or NA proteins derived from influenza A(H1N1), A(H3N2), B/Victoria lineage, and B/Yamagata lineage viruses. Regression analysis using locally estimated scatterplot smoothing (LOESS) method depicted with LOESS fit line (center line; smoothed local regression using least squares) and 95% CI (grey). Significant associations defined at p ≤ 0.05.

Source data

Extended Data Fig. 3 Myeloid panel gating strategy.

Flow cytometry gating strategy to resolve cell populations within the myeloid compartment. All gates applied to leukocyte-sized, single, live cells. Gates depict frequency as % of parent gate.

Extended Data Fig. 4 Lymphoid and ICS panel gating strategy.

Flow cytometry gating strategy to resolve cell populations within the lymphoid/functional compartment by ICS. All gates applied to lymphocyte-sized, single, live cells. Gates depict frequency as % of parent gate.

Extended Data Fig. 5 Co-regulated immune cell clusters by vaccine status.

a-d, Co-regulated cell modules (‘clusters’) from Vaccinated participants’ myeloid (a) and lymphoid/functional (c) cell populations, or Unvaccinated participants’ myeloid (b) and lymphoid/functional (d) cell populations determined by average frequency (% parent) of individual cell populations with significant positive Pearson’s bivariate correlation. Lymphoid/functional panel cell frequencies represent the average frequency (% parent) across virus (MOI = 4A/Michigan/45/2015 H1N1pdm09 or A/Singapore/INFIMH-16-019/2016 H3N2) and peptide (1–5 μM /peptide pools containing M1, NP, PB1) stimulation groups. P values were adjusted using false discovery rate (FDR; q) correction for multiple comparisons with significance q ≤0.05 denoted with color; not significant (blank).

Source data

Extended Data Fig. 6 Decision tree model comparison from cellular covariates.

Comparison of the Base (demographic factors + serology + vaccination status), Myeloid Only (myeloid panel cell populations), Lymphoid Only (lymphoid/functional panel cell populations), and Lymphoid+Myeloid (cell populations from the lymphoid/functional and myeloid panel) random forest models built to categorize symptomatic and uninfected/cryptic influenza cases. Participants were split 80:20 into a training set (symptomatic cases n = 31, uninfected/cryptic controls n = 128) and testing set (symptomatic cases n = 8, uninfected/cryptic controls n = 33) ensuring equal proportions of cases and controls. Models were trained, tested, and cross-validated using 10× CV-10. Sensitivity, Specificity and AUROC (area under the receiver-operating characteristic curve) provided. An out-of-sample evaluation of the models (bottom) shows a comparison of the AUC accuracy. Boxes represent the median and 25th to 75th percentiles; whiskers indicate the minimum and maximum values no further than 1.5 times the interquartile (IQR).

Source data

Supplementary information

Source data

Source Data

Statistical source data for Figs. 1–7 and Extended Data Figs. 2, 5 and 6.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mettelman, R.C., Souquette, A., Van de Velde, LA. et al. Baseline innate and T cell populations are correlates of protection against symptomatic influenza virus infection independent of serology. Nat Immunol 24, 1511–1526 (2023). https://doi.org/10.1038/s41590-023-01590-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-023-01590-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology