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Immune imprinting in early life shapes cross-reactivity to influenza B virus haemagglutinin

Nature Microbiology volume 9, pages 2073–2083 (2024)Cite this article

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Abstract

Influenza exposures early in life are believed to shape future susceptibility to influenza infections by imprinting immunological biases that affect cross-reactivity to future influenza viruses. However, direct serological evidence linked to susceptibility is limited. Here we analysed haemagglutination-inhibition titres in 1,451 cross-sectional samples collected between 1992 and 2020, from individuals born between 1917 and 2008, against influenza B virus (IBV) isolates from 1940 to 2021. We included testing of ‘future’ isolates that circulated after sample collection. We show that immunological biases are conferred by early life IBV infection and result in lineage-specific cross-reactivity of a birth cohort towards future IBV isolates. This translates into differential estimates of susceptibility between birth cohorts towards the B/Yamagata and B/Victoria lineages, predicting lineage-specific birth-cohort distributions of observed medically attended IBV infections. Our data suggest that immunological measurements of imprinting could be important in modelling and predicting virus epidemiology.

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Fig. 1: Antibody reactivity against past isolates from different lineages varies by birth year.
Fig. 2: Early life exposures and antigenic seniority explain birth-cohort-specific reactivity to different lineages.
Fig. 3: HI cross-reactivity towards unencountered future isolates from different IBV lineages varies by birth year.
Fig. 4: Probability of infection for future isolates from different IBV lineages varies by birth year.

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Data availability

The raw HI datasets used for all analyses and figures are available via Zenodo at https://doi.org/10.5281/zenodo.10633085 (ref. 53). Source data are provided with this paper.

Code availability

The R codes used for all analyses and figures are available via Zenodo at https://doi.org/10.5281/zenodo.10633085 (ref. 53).

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Acknowledgements

We thank the participants for their generous involvement and provision of samples. We are grateful to the CSL investigators who originally initiated this study NCT00959049 trial. We are grateful to M. Vieira, K. Gostic and S. Cobey for discussions and advice on data analysis. This study has been generously supported by the Morningside Foundation and by Australian National Health and Medical Research Council Investigator grants (1195698 to M.K., 1173433 to A.K.W., 2009308 to J.A.J. and 1136322 to S.J.K.). The WHOCCRRI is supported by the Australian Government Department of Health. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper.

Author information

Authors and Affiliations

  1. Department of Infectious Diseases, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia

    Peta Edler & David J. Price

  2. Department of Microbiology and Immunology, University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Parkville, Victoria, Australia

    Lara S. U. Schwab, Jennifer A. Juno, Steve Rockman, Adam K. Wheatley, Stephen J. Kent, Ian G. Barr & Marios Koutsakos

  3. WHO Collaborating Centre for Reference and Research on Influenza, Royal Melbourne Hospital, at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia

    Malet Aban, Michelle Wille, Natalie Spirason, Yi-Mo Deng & Ian G. Barr

  4. Centre for Pathogen Genomics, University of Melbourne, Melbourne, Victoria, Australia

    Michelle Wille

  5. Center for Vaccines and Immunology, University of Georgia, Athens, GA, USA

    Michael A. Carlock & Ted M. Ross

  6. Department of Infectious Diseases, University of Georgia, Athens, GA, USA

    Michael A. Carlock & Ted M. Ross

  7. Florida Research and Innovation Centre, Cleveland Clinic, Port Saint Lucie, FL, USA

    Michael A. Carlock & Ted M. Ross

  8. Department of Infection Biology, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA

    Ted M. Ross

  9. Vaccine Product Development, CSL Seqirus Ltd, Parkville, Victoria, Australia

    Steve Rockman

  10. Melbourne Sexual Health Centre and Department of Infectious Diseases, Alfred Health, Central Clinical School, Monash University, Melbourne, Victoria, Australia

    Stephen J. Kent

  11. Centre for Epidemiology & Biostatistics, Melbourne School of Population & Global Health, The University of Melbourne, Melbourne, Victoria, Australia

    David J. Price

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Contributions

M.K. designed and supervised the study. M.K., L.S.U.S., M.A. and N.S. performed the experiments. M.K., P.E., L.S.U.S., M.W., N.S., Y-M.D. and D.J.P. analysed data. M.A.C., T.M.R., J.A.J., S.R., S.J.K., A.K.W. and I.G.B. provided samples, reagents and/or data critical for the study. M.K., J.A.J., A.K.W., P.E. and D.J.P. contributed to the drafting of the paper. All authors reviewed the final version of the paper.

Corresponding author

Correspondence to Marios Koutsakos.

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Competing interests

M.K. has acted as a consultant for Sanofi group of companies. S.R. is an employee of Seqirus, an influenza vaccine manufacturer. I.G.B. has shares in an influenza-vaccine-producing company. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Cohort and dataset details.

(a) Details of the cohort associated with the main dataset generated for this study. The virus passage is indicated for each isolate (e – egg; c – cell). (b) Schematic of the relationships between year of virus isolation, birth year and sampling year. (c) Details of the cohort associated with the dataset generated by Carlock et al. (d) Details of the cohort associated with the WHOCCRI dataset.

Extended Data Fig. 2 Overview of HI titres.

(a) Correlations between antibody titres against the 19 IBV isolates from the main dataset (n = 322 serum samples). Virus isolates have been ordered by hierarchical clustering. (b) Antigenic characterization using human monoclonal antibodies against the IBV HA. End point titres from an HI assay are shown starting at 50 μg/ml. (c) Log2 HI titres against time (years) between sample collection and virus isolation (across all three datasets). (d) Log2 HI titres against time (years) between sample collection and virus isolation (across all three datasets) with individuals pooled into sample collection groups. For (C) and (D) the lines represent estimates using generalized additive models (GAMs) with 95% CI, accounting for repeated measurements on each individual by specifying a random effect. (e) Comparison of HI titres against cell and egg isolates in paired serums (n = 61) samples tested against cell or egg grown B/Phuket/2013 and B/Washington/2019. The number in blue indicated the difference in titres on the log2 scale. The boxplots show the median (centre line), quartiles (box limits) and maximum/minimum within 1.5x IQR above Q3 and below Q1(whiskers). P-values were generated from a Wilcoxon matched-pairs signed rank test (n = 61 samples from 2020). (f) Correlation between log2 HI titres against cell and egg grown B/Phuket/2013 and B/Washington/2019 viruses as described in C. (g) Correlation between log2 HI titres and microneutralization titres in 17 individuals tested against 10 viruses. In (F) and (G) the line represent estimates from linear regression with with 95% CI.

Source data

Extended Data Fig. 3 HI titres to past isolates against year of birth.

(a) Log2 HI titres to different isolates against year of birth separated by IBV lineage and dataset. The lines represent estimated mean HI titres from generalized additive models (GAMs). (b) Estimated mean HI titres against past isolates from each lineage that circulated prior to sample collection, against the year of birth for each participant. (c) Comparison of the three datasets for HI against each lineage by year of birth. The data is the same as in (B) but overlaid for three datasets per lineage. For (B) and (C) The lines represent the estimated mean HI titre from generalized additive models (GAMs) with shading representing 95% CIs, accounting for repeated measurements on each individual by specifying a random effect.

Source data

Extended Data Fig. 4 HI reactivity against past isolates by birth cohort in the main dataset.

Box-plots of antibody titres to specific IBV isolates for each birth cohort for the main dataset. P-values were generated from a Kruskal-Wallis test with Dunn’s correction for multiple comparisons. The boxplots show the median (centre line), quartiles (box limits) and maximum/minimum within 1.5x IQR above Q3 and below Q1(whiskers). The sample size for each birth cohort and virus is available in the Supplementary Table 3.

Source data

Extended Data Fig. 5 HI reactivity against past isolates by birth cohort in the supplementary datasets.

Box-plots of antibody titres to specific IBV isolates for each birth cohort for (a) the Carlock et al dataset and (b) the WHOCCRI dataset. P-values were generated from a Kruskal-Wallis test with Dunn’s correction for multiple comparisons. Only p-values > 0.05 are shown. The boxplots show the median (centre line), quartiles (box limits) and maximum/minimum within 1.5x IQR above Q3 and below Q1(whiskers). The sample size for each birth cohort and virus is available in the Supplementary Tables 4 and 5.

Source data

Extended Data Fig. 6 Antigenic seniority in IBV HI titres.

(a) Distribution of the age of the participant at the time of isolation of the strain to which they had highest antibody titres, grouped by IBV lineage or birth cohort. (b) HI titres relative to the age of the participant at the time of virus isolation. (c) HI titres relative to the age of the participant at the time of sampling. For (B) and (C) the lines represent estimated mean HI titres from generalized additive models (GAMs) with shaded region representing 95% CIs, accounting for repeated measurements on each individual by specifying a random effect. Only titrations of viruses isolated prior to sample collection are included. Analysis shown for measurements from the main (A, B, C) or Carclock et al. (B).

Source data

Extended Data Fig. 7 HI titres to future isolates by birth year in supplementary datasets.

(a) Estimated mean HI titres against the future unencountered B/Colorado/02/2017 from the Carlock et al dataset. The lines represent estimated mean HI titres from generalized additive models (GAMs) with shaded region representing 95% CIs. (b) Box-plots of HI titres to future unencountered B/Colorado/02/2017 for each birth cohort from the Carlock et al dataset. The boxplots show the median (centre line), quartiles (box limits) and maximum/minimum within 1.5x IQR above Q3 and below Q1(whiskers). with n = 5 (1917–1939), n = 35 (1940–1960), n = 26 (1961–1980), n = 19 (1981–1998).

Source data

Extended Data Fig. 8 Estimated susceptibility to future isolates varies by birth year.

Sensitivity analysis of estimated HI (a, b) and probabilities of infection (c, d) by birth year for the effects of different virus isolates (A,C) or sampling year groups (B,D). The median estimated HI or probability is shown and the shaded areas represent the 25th and 75th percentiles. Data from the main dataset were used.

Source data

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Edler, P., Schwab, L.S.U., Aban, M. et al. Immune imprinting in early life shapes cross-reactivity to influenza B virus haemagglutinin. Nat Microbiol 9, 2073–2083 (2024). https://doi.org/10.1038/s41564-024-01732-8

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