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

Nature Immunology volume 17, pages 204–213 (2016)Cite this article

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A Corrigendum to this article was published on 19 May 2016

A Corrigendum to this article was published on 22 March 2016

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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.

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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.

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  • 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.

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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

Author notes

  1. Olga Sobolev and Elisa Binda: These authors contributed equally to this work.

Authors and Affiliations

  1. Peter Gorer Department of Immunobiology, King's College London, London, UK

    Olga Sobolev, Elisa Binda, Sean O'Farrell, Mark Peakman & Adrian C Hayday

  2. ImmunoSurveillance Laboratory, Francis Crick Institute, Lincoln's Inn Laboratories, London, UK

    Olga Sobolev, Sean O'Farrell & Adrian C Hayday

  3. Biomedical Research Centre at Guy's and St Thomas' Hospital and King's College London, London, UK

    Olga Sobolev, Elisa Binda, Sean O'Farrell, Anna Lorenc, Reiner Schulz, Michael H Malim, Mark Peakman, Andrew Cope & Adrian C Hayday

  4. Division of Rheumatology, Rheumatology and Translational Immunology Research Laboratories (LaRIT), IRCCS Policlinico San Matteo/University of Pavia, Pavia, Italy

    Elisa Binda

  5. Momenta Pharmaceuticals, Cambridge, Massachusetts, USA

    Joel Pradines, Yongqing Huang, Jay Duffner, Ishan Capila & Ganesh V Kaundinya

  6. Department of Genetics, King's College London, London, UK

    Reiner Schulz

  7. Department of Infectious Diseases, King's College London, London, UK

    John Cason & Michael H Malim

  8. Public Health England, Colindale, London, UK

    Maria Zambon

  9. Academic Department of Rheumatology, Centre for Molecular and Cell Biology of Inflammation, King's College London, UK

    Andrew Cope

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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.

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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).

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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

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