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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

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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.

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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.

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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.

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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

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  1. Robert C. Mettelman
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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

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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.

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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.

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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

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