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.

Adjuvanted influenza-H1N1 vaccination reveals lymphoid signatures of age-dependent early responses and of clinical adverse events

A Corrigendum to this article was published on 19 May 2016

A Corrigendum to this article was published on 22 March 2016

This article has been updated

Abstract

Adjuvanted vaccines afford invaluable protection against disease, and the molecular and cellular changes they induce offer direct insight into human immunobiology. Here we show that within 24 h of receiving adjuvanted swine flu vaccine, healthy individuals made expansive, complex molecular and cellular responses that included overt lymphoid as well as myeloid contributions. Unexpectedly, this early response was subtly but significantly different in people older than 35 years. Wide-ranging adverse clinical events can seriously confound vaccine adoption, but whether there are immunological correlates of these is unknown. Here we identify a molecular signature of adverse events that was commonly associated with an existing B cell phenotype. Thus immunophenotypic variation among healthy humans may be manifest in complex pathophysiological responses.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: H1N1 vaccination leads to rapid and reversible early changes in the immune compartment, followed by a B cell–rich signature at day 7.
Figure 2: Early post-vaccine activation of myeloid and lymphoid cells is detectable across multiple assays.
Figure 3: Age factor in multivariate analysis of gene expression based on all genes.
Figure 4: A vaccine nonresponse signature is detectable by post-vaccination day 7.
Figure 5: High AE individuals show day 1 and prevaccine differences from asymptomatic study subjects.
Figure 6: Prevaccine transitional B cell frequencies and increased autoantibodies correlate with post-vaccination AE.

Accession codes

Primary accessions

ArrayExpress

Change history

  • 07 January 2016

    In the version of this article initially published online, the location of Momenta Pharmaceuticals was incorrectly stated as Boston. It should read "Cambridge." Also, in Table 1, the set of parentheses for HAI day 63 was incomplete. It should read "mean (s.d.)." The errors have been corrected for the print, PDF and HTML versions of this article.

  • 17 March 2016

    In the version of this article initially published, the affiliation for author Maria Zambon (Health Protection Agency, Porton Down, Salisbury, UK) was incorrect. The correct affiliation should be 'Public Health England, Colindale, London, UK'. The error has been corrected in the HTML and PDF versions of the article.

References

  1. Agnandji, S.T. et al. Phase 1 trials of rVSV Ebola vaccine in Africa and Europe—preliminary report. N. Engl. J. Med. doi:10.1056/NEJMoa1502924 (1 April 2015).

  2. Henao-Restrepo, A.M. et al. Efficacy and effectiveness of an rVSV-vectored vaccine expressing Ebola surface glycoprotein: interim results from the Guinea ring vaccination cluster-randomised trial. Lancet 386, 857–866 (2015).

    CAS  Article  Google Scholar 

  3. Gregor, M.F. & Hotamisligil, G.S. Inflammatory mechanisms in obesity. Annu. Rev. Immunol. 29, 415–445 (2011).

    CAS  Article  Google Scholar 

  4. Czirr, E. & Wyss-Coray, T. The immunology of neurodegeneration. J. Clin. Invest. 122, 1156–1163 (2012).

    CAS  Article  Google Scholar 

  5. Chen, D.S. & Mellman, I. Oncology meets immunology: the cancer-immunity cycle. Immunity 39, 1–10 (2013).

    Article  Google Scholar 

  6. Pulendran, B. Systems vaccinology: probing humanity's diverse immune systems with vaccines. Proc. Natl. Acad. Sci. USA 111, 12300–12306 (2014).

    CAS  Article  Google Scholar 

  7. Duffy, D. et al. Functional analysis via standardized whole-blood stimulation systems defines the boundaries of a healthy immune response to complex stimuli. Immunity 40, 436–450 (2014).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  9. Callahan, M.K., Wolchok, J.D. & Allison, J.P. Anti-CTLA-4 antibody therapy: immune monitoring during clinical development of a novel immunotherapy. Semin. Oncol. 37, 473–484 (2010).

    CAS  Article  Google Scholar 

  10. Oswald, M. et al. Modular analysis of peripheral blood gene expression in rheumatoid arthritis captures reproducible gene expression changes in tumor necrosis factor responders. Arthritis Rheumatol. 67, 344–351 (2015).

    CAS  Article  Google Scholar 

  11. Chen, R.T. et al. The Vaccine Adverse Event Reporting System (VAERS). Vaccine 12, 542–550 (1994).

    CAS  Article  Google Scholar 

  12. Grohskopf, L.A. et al. Prevention and control of seasonal influenza with vaccines: recommendations of the Advisory Committee on Immunization Practices (ACIP)–United States, 2014–15 influenza season. MMWR 63, 691–697 (2014).

    PubMed  Google Scholar 

  13. Furman, D. et al. Apoptosis and other immune biomarkers predict influenza vaccine responsiveness. Mol. Syst. Biol. 9, 659 (2013).

    Article  Google Scholar 

  14. Zimmer, S.M. & Burke, D.S. Historical perspective—emergence of influenza A (H1N1) viruses. N. Engl. J. Med. 361, 279–285 (2009).

    CAS  Article  Google Scholar 

  15. Nakaya, H.I. et al. Systems biology of vaccination for seasonal influenza in humans. Nat. Immunol. 12, 786–795 (2011).

    CAS  Article  Google Scholar 

  16. Bucasas, K.L. et al. Early patterns of gene expression correlate with the humoral immune response to influenza vaccination in humans. J. Infect. Dis. 203, 921–929 (2011).

    CAS  Article  Google Scholar 

  17. Li, S. et al. Molecular signatures of antibody responses derived from a systems biology study of five human vaccines. Nat. Immunol. 15, 195–204 (2014).

    Article  Google Scholar 

  18. Roman, F. et al. Effect on cellular and humoral immune responses of the AS03 adjuvant system in an A/H1N1/2009 influenza virus vaccine administered to adults during two randomized controlled trials. Clin. Vaccine Immunol. 18, 835–843 (2011).

    CAS  Article  Google Scholar 

  19. Morel, S. et al. Adjuvant system AS03 containing α-tocopherol modulates innate immune response and leads to improved adaptive immunity. Vaccine 29, 2461–2473 (2011).

    CAS  Article  Google Scholar 

  20. Strid, J., Sobolev, O., Zafirova, B., Polic, B. & Hayday, A. The intraepithelial T cell response to NKG2D-ligands links lymphoid stress surveillance to atopy. Science 334, 1293–1297 (2011).

    CAS  Article  Google Scholar 

  21. Wencker, M. et al. Innate-like T cells straddle innate and adaptive immunity by altering antigen-receptor responsiveness. Nat. Immunol. 15, 80–87 (2014).

    CAS  Article  Google Scholar 

  22. Brandes, M. et al. Cross-presenting human γδ T cells induce robust CD8+ αβ T cell responses. Proc. Natl. Acad. Sci. USA 106, 2307–2312 (2009).

    CAS  Article  Google Scholar 

  23. Bolognese, J.A., Schnitzer, T.J. & Ehrich, E.W. Response relationship of VAS and Likert scales in osteoarthritis efficacy measurement. Osteoarthritis Cartilage 11, 499–507 (2003).

    CAS  Article  Google Scholar 

  24. Obermoser, G. et al. Systems scale interactive exploration reveals quantitative and qualitative differences in response to influenza and pneumococcal vaccines. Immunity 38, 831–844 (2013).

    CAS  Article  Google Scholar 

  25. Franco, L.M. et al. Integrative genomic analysis of the human immune response to influenza vaccination. eLife 2, e00299 (2013).

    Article  Google Scholar 

  26. Querec, T. et al. Yellow fever vaccine YF-17D activates multiple dendritic cell subsets via TLR2, 7, 8, and 9 to stimulate polyvalent immunity. J. Exp. Med. 203, 413–424 (2006).

    Article  Google Scholar 

  27. Montecino-Rodriguez, E., Berent-Maoz, B. & Dorshkind, K. Causes, consequences, and reversal of immune system aging. J. Clin. Invest. 123, 958–965 (2013).

    CAS  Article  Google Scholar 

  28. Jiang, N. et al. Lineage structure of the human antibody repertoire in response to influenza vaccination. Sci. Transl. Med. 5, 171ra19 (2013).

    Article  Google Scholar 

  29. Whitney, A.R. et al. Individuality and variation in gene expression patterns in human blood. Proc. Natl. Acad. Sci. USA 100, 1896–1901 (2003).

    CAS  Article  Google Scholar 

  30. Nakaya, H.I. et al. Systems biology of vaccination for seasonal influenza in humans. Nat. Immunol. 12, 786–795 (2011).

    CAS  Article  Google Scholar 

  31. Querec, T.D. et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat. Immunol. 10, 116–125 (2008).

    Article  Google Scholar 

  32. Yang, M., Rui, K., Wang, S. & Lu, L. Regulatory B cells in autoimmune diseases. Cell. Mol. Immunol. 10, 122–132 (2013).

    Article  Google Scholar 

  33. Engel, P., Gómez-Puerta, J.A., Ramos-Casals, M., Lozano, F. & Bosch, X. Therapeutic targeting of B cells for rheumatic autoimmune diseases. Pharmacol. Rev. 63, 127–156 (2011).

    CAS  Article  Google Scholar 

  34. Jackson, L.A. et al. Effect of varying doses of a monovalent H7N9 influenza vaccine with and without AS03 and MF59 adjuvants on immune response: a randomized clinical trial. JAMA 314, 237–246 (2015).

    CAS  Article  Google Scholar 

  35. Neves, P.C.da C., Matos, D.C.de S., Marcovistz, R. & Galler, R. TLR expression and NK cell activation after human yellow fever vaccination. Vaccine 27, 5543–5549 (2009).

    CAS  Article  Google Scholar 

  36. Miller, J.D. et al. Human effector and memory CD8+ T cell responses to smallpox and yellow fever vaccines. Immunity 28, 710–722 (2008).

    CAS  Article  Google Scholar 

  37. 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  Google Scholar 

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

    CAS  Article  Google Scholar 

  39. Shin, M.S. et al. Maintenance of CMV-specific CD8+ T cell responses and the relationship of IL-27 to IFN-γ levels with aging. Cytokine 61, 485–490 (2013).

    CAS  Article  Google Scholar 

  40. Fülöp, T., Larbi, A. & Pawelec, G. Human T cell aging and the impact of persistent viral infections. Front. Immunol. 4, 271 (2013).

    Article  Google Scholar 

Download references

Acknowledgements

We thank S. Steiner, Z. Kozlakidis, and K. Thornberry for invaluable assistance in study setup and sample collection; D. Dunn-Walters, J. Spencer, J.-C. Weill (Institut Necker), A. Skowera, M. Shankar Hari and S. Sabbah for helpful advice and feedback; King's College London and Guy's Hospital Biobank, the Clinical Transplantation Lab, GSTS and King's Pathology and the Biomedical Research Centre Flow Cytometry and Bioinformatics facilities for outstanding clinical and technical support. Supported by Cancer Research UK and the UK Department of Health via the National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London and King's College Hospital NHS Foundation Trust. A.C.H. and M.H.M. are supported by Wellcome Trust Investigator awards.

Author information

Authors and Affiliations

Authors

Contributions

O.S. and E.B. coordinated experiments, designed and undertook phenotypic analyses of PBMC and sera and contributed to data analysis; S.O'F. designed and undertook molecular analyses of PBMC gene expression and provided data analyses; A.L., J.P., Y.H., J.D. and R.S. developed and applied statistical and bioinformatics tools for data analysis and presentation; J.C. supervised and undertook PBMC sample preparation and biobanking; M.Z. supervised and undertook HAI and MN assays of antibody titers; M.H.M. helped devise the study, oversaw biobanking and edited the manuscript; M.P. and A.C. oversaw and implemented T cell assays, clinical assessment measures and obtainment of ethical approval; I.C. and G.V.K. devised and implemented integrative data analysis strategies, prepared figures and edited the manuscript. A.C.H. designed and supervised the study, cowrote documents for ethical approval, reviewed all data sets and wrote and edited the manuscript with O.S. and S.O'F.

Corresponding author

Correspondence to Adrian C Hayday.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 HIRD study design.

Data that were collected for all study subjects (n=178) are indicated with an asterisk; where only a subset of the samples were analyzed, n is included next to the asterisk.

Supplementary Figure 2 Microarray data and quantitative RT-PCR validation.

(a) Overall gene expression change patterns between study time points. (b) Forward and reverse primers used for qRT-PCR validation of the microarray findings. (c) The expression of these 28 genes was validated in PBMC RNA for 18-20 individuals, normalized to Cyclophilin A and GAPDH, and plotted as log fold change to average pre-vaccine day -7 and 0 expression for the given gene and individual in comparison with the microarray findings for these same individuals and time points. (d) Plot of individual PBMC counts for all HIRD study subjects (n=169-186, black lines indicate median and interquartile range, p-values two-tailed paired t-test; **p=0.0002, ***p=0.0001). (e) Fold change in FACS monocyte counts at day +1 (n=46; black lines indicate mean), plotted alongside average microarray fold change in some key monocyte genes in the same subjects at day +1.

Supplementary Figure 3 Other lymphoid, myeloid and cytokine changes after vaccination.

(a) FACS analysis of monocyte and T cell contribution to overall PBMC composition at all study time points (n=53-65 subjects; plot of individual subject values, black lines indicating median and interquartile range; p-values two-tailed paired t-test, *=p<0.05, **=p<0.01, ***=p<0.0001, NS=not significant, p>0.05). (b) Luminex analysis of serum TNFα and IL-6 concentrations (n=132-135 subjects for TNFα, 124-127 for IL-6; plots and p-values as in panel a). (c, d) Luminex analysis of serum IL-10 displays a bi-modal distribution (c), serum GM-CSF and IL-15 levels increase at day +7, while CCL5 (RANTES) shows a significant increase at post-vaccination day +1 (d) (n=24 subjects; plots and p-values as in panel a).

Supplementary Figure 4 Age-analysis barrier method.

Summary of the “barrier” analysis method used to test the effect of subject age on gene expression before and after vaccination, in 40 HIRD study subjects with complete gene expression data available at all time points.

Supplementary Figure 5 Immunological differences correlated with age and sex.

(a) Luminex analysis of serum IFNγ and CXLC10 plotted individually for subjects <35 (n=107-110) and > 35 (n=26-28); black lines indicating median and interquartile range; p-values two-tailed Mann-Whitney test, *=p<0.05, **=p<0.01, ***=p<0.0001, NS (not significant) p>0.05. (b) Flow cytometry analysis of CXCR3+ IFNγ-producing CD8+ T cells at vaccination day +1 plotted individually for subjects <35 (n=24=26) and >35 (n=9-11), respectively; black lines indicating median and interquartile range; p-values two-tailed paired t-test, asterisks as in panel a. (c) Flow cytometry analysis of CD45RO+ CCR7+ central memory CD4 and CD8 T cells by age (analysis and plots as in panel b). Average of day -7 and day 0 pre-vaccination percentages of CD38+ CD8+ T cells shows a significant negative correlation with age (linear regression trendline and Pearson correlation r and p-value are shown). (d) Post-vaccination fold-increases in HAI and MN titers do not significantly correlate with subject age (grey circles, responders (R) in at least one assay; blue circles, those non-responders (NR) with negligible pre-vaccine H1N1 reactivity; red circles, vaccine NR whose starting titers were high: note some circles directly super-impose upon others; linear regression trendline and Pearson correlation r and p-value are shown. (e) Observed values of a multivariate statistic t, quantifying global PBMC gene expression dissimilarity between males and females (dots) at all study time points, are compared to values expected under 105 random reassignments of gender (boxes; median and quartiles), with the corresponding permutation p-values indicated above each box-whisker plot, ****=p<1x10; and similar analysis when for each subject and each probe, the log intensity values at day -7 are subtracted from the other study time points.

Supplementary Figure 6 Other correlates of vaccine nonresponse.

(a) Genes significantly changed between vaccine responders (R) and non-responders (NR) at day +7 post vaccination; genes listed correspond to the heat map in Figure 4c. Genes that have been implicated in vaccine non-response in other publications are additionally highlighted in red type and indicated with an arrowhead. (b, c, d) While there is no difference in overall % γδ T cells between R and NR subjects at any time point analyzed (b), NR have a lower pre-existing percentage of CD45RO- CCR7+, and a higher percentage of CD45RO+ CCR7- γδ T cells (c). There is also a higher expression of CD69 on γδ T cells and NK cells of NR subjects at all time points (d) (n=26-33 R, 19-24 NR; each point represents percentage of indicated cell type as determined by FACS for an individual study subject, while bars represent mean and SEM for each time point; p-value in two-tailed Mann-Whitney test comparison of R vs NR at each time point is indicated where significant; for panel c, *=p<0.05). (e) Luminex analysis of serum IL-17 content in R, “baseline” NR with day -7 MN < 800, and “glass ceiling” NR with day -7 MN > 800 (n=102-103 R, 18-19 “baseline” NR, 14-15 “glass ceiling” NR; each point represents an individual IL-17 measurement, while bars represent mean and SEM for each time point; p-value in two-tailed Mann-Whitney test comparison of R vs “baseline” NR, and R vs “glass ceiling” NR, at each time point; *=p<0.05, **=p<0.01, ***=p<0.0001, NS (not significant) p>0.05).

Supplementary Figure 7 Prevaccine memory T cell compartments overall show no correlation with adverse events after vaccination.

Analysis of pre-vaccination percentages of CD4 (a), CD8 (b), and γδ (c) CD45RO+ CCR7+ and CD45RO+ CCR7- T cell compartments (plotted as average FACS percentages of days -7 and 0 for each individual study subject, n=34-36), as compared to the combined day +1 and +7 AE score for the same individuals, shows no significant correlation between AE and any of the pre-existing memory T cell subsets (linear regression trendline and Pearson correlation r values are shown for each plot).

Supplementary Figure 8 B cell contribution to the AE signature but no apparent correlation of B cell subsets with vaccine nonresponse.

(a) Gene list corresponding to the heat map in Figure 6a of genes expressed differentially in low AE (clinical score 0-8) vs medium/high AE (clinical score of 9-24) study subjects, in order of descending z-score, with genes upregulated in medium/high AE subjects in red font and downregulated genes in blue font (q-value <0.05, centered and scaled by gene across all subjects). (b) Analysis of pre-vaccination percentage of transitional, naive, and memory B cells (plotted as average of days -7 and 0 for each individual study subject, n=37), as compared to the MN titre day +63/day -7 ratio for the same individuals, shows no correlation between these variables. 8 vaccine non-responders with day -7 HAI of 8-32 and MN < 200 (non-responders with the lowest pre-existing H1N1 reactivity) are highlighted in red. (c) FACS analysis of transitional, naive, and memory B cells at all time points in serological vaccine responders (n=22-23), vaccine non-responders with the lowest pre-vaccination H1N1 reactivity (n=7-8), and vaccine non-responders with higher pre-vaccination H1N1 reactivity (n=7) (each point represents B cell percentage as determined by FACS in an individual subject, while bars represent mean and SEM for each time point; p-values in two-tailed Mann-Whitney test comparisons show no significant differences between any of the groups at any of the time points).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Tables 1–3 (PDF 2706 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sobolev, O., Binda, E., O'Farrell, S. et al. Adjuvanted influenza-H1N1 vaccination reveals lymphoid signatures of age-dependent early responses and of clinical adverse events. Nat Immunol 17, 204–213 (2016). https://doi.org/10.1038/ni.3328

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ni.3328

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

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