Abstract
Systems vaccinology has defined molecular signatures and mechanisms of immunity to vaccination. However, comparative analysis of immunity to different vaccines is lacking. We integrated transcriptional data of over 3,000 samples, from 820 adults across 28 studies of 13 vaccines and analyzed vaccination-induced signatures of antibody responses. Most vaccines induced signatures of innate immunity and plasmablasts at days 1 and 7, respectively, after vaccination. However, the yellow fever vaccine induced an early transient signature of T and B cell activation at day 1, followed by delayed antiviral/interferon and plasmablast signatures that peaked at days 7 and 14–21, respectively. Thus, there was no evidence for a ‘universal signature’ that predicted antibody response to all vaccines. However, accounting for the asynchronous nature of responses, we defined a time-adjusted signature that predicted antibody responses across vaccines. These results provide a transcriptional atlas of immunity to vaccination and define a common, time-adjusted signature of antibody responses.
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Data availability
All data used in this study are available from ImmuneSpace (https://www.immunespace.org/is2.url).
Code availability
R code used to generate the figures is available from ImmuneSpace.
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Acknowledgements
This research was performed as a project of the HIPC and supported by the National Institute of Allergy and Infectious Diseases (NIAID). This work was supported in part by NIH grants U19AI118608, U19AI128949, U19AI090023, U19AI118626, U19AI089992, U19AI128914, U19AI128910, U19AI118610 and U19AI128913 and the Intramural Program of NIAID and NIH supporting the Trans-NIH Center for Human Immunology. O.L. is supported in part by the Department of Pediatrics at Boston Children’s Hospital. Work in the laboratory of B.P. is supported in part by the NIH (R37 DK057665, R01 AI048638, U19 AI057266 and U19 AI167903), Bill and Melinda Gates Foundation, Open Philanthropy and the Violetta L. Horton and Soffer Endowments to B.P.
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Conception or design of the work: B.P. and S.H.K. Analysis: T.H., B.G., L.E.T. and A.L. Acquisition: E.H., H.E.R.M., J.D.-A., P.D., the HIPC, O.L., R.G., M.M.S., J.S.T., M.S.-F., S.F., M.P.M., D.G.C. and D.R. Interpretation of the data and manuscript writing: T.H., B.P., S.H.K. and R.-P.S.
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O.L. is a named inventor on patents held by Boston Children’s Hospital regarding human in vitro systems modeling vaccine action and vaccine adjuvants. B.P. serves on the External Immunology Network of GSK, and on the scientific advisory board of Medicago, CircBio, Sanofi, EdJen and Boehringer-Ingelheim. S.H.K. receives consulting fees from Northrop Grumman and Peraton. T.H. owns stock in GSK and Pfizer. The remaining authors declare no competing interests.
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Nature Immunology thanks Galit Alter and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jamie D. K. Wilson, in collaboration with the Nature Immunology team.
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Extended data
Extended Data Fig. 1 Overlap in differentially expressed genes/modules and kinetics of common module clusters.
a, b) Histograms of overlap in DEGs (A) or differentially expressed modules (B) between vaccines. A gene/module is shared with another vaccine if it is significantly (FDR < 0.05) regulated in the same direction, irrespective of timepoint. Blue bars, number of genesets shared (y-axis) between the same number of vaccines (x-axis). Grey bars represent the null distribution generated by n = 10,000 permutations of gene/module labels within vaccine + timepoint groups. Data are presented as mean values + /− 95% confidence interval. c) Kinetics of the mean FC of cluster 2 BTMs across vaccines. d) Kinetics of the mean FC of cluster 4 modules across vaccines.
Extended Data Fig. 2 Gene-level correlations between vaccines and estimated cell frequencies.
a) Correlation matrix of pairwise Spearman correlations of Day 3 gene-level fold changes between vaccines. b) Correlation matrix of pairwise Spearman correlations of Day 7 gene-level fold changes between vaccines. c) Scatterplot of Day 1 gene FCs between HIV and Malaria vaccines. d) Scatterplot of Day 1 gene FCs between Yellow Fever and Pneumococcus vaccines. e) Boxplot of Day 1 FC in xCell31 estimated B cell frequencies across vaccines. Center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range. Ebola (RVV): n = 11, HIV (RVV): n = 10, Influenza (IN): n = 298, Malaria (RP): n = 42, Pneumococcus (PS): n = 12, Varicella Zoster (LA): n = 31, Yellow Fever (LA): n = 11. f-g) Kinetics of the mean FC of modules (F) M47.0 and (G) M75 across YF vaccine studies. In C-D, correlation coefficient and p value determined via Pearson correlation. In E, statistical differences were determined via two-sided paired Student’s t-tests within each study and integrating p values across studies within each vaccine using Stouffer’s method (see Methods for further details). *p < 0.05, **p < 0.01, ***p < 0.001, **** p < 0.0001.
Extended Data Fig. 3 Impact of pre-existing antibody levels on transcriptional responses to influenza vaccination.
a) Differentially expressed modules at Day 1 (FDR < 0.05, t-test between mean fold changes) between participants with high and low baseline antibody titers (negative score indicates increased expression in the low baseline antibody group). Differentially expressed modules at Day 7 between high and low baseline antibody groups. c, d) Boxplots of (C) IFN signature module M75 expression at Day 1 and (D) plasma cell module M156.1 expression at Day 7 between high and low baseline antibody groups at Day 1. Center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range. Day 1: SDY1276_D: High – n = 35, Low – n = 31; SDY1276_V: High – n = 31, Low – n = 31; SDY180: High – n = 4, Low – n = 4; SDY56: High – n = 4, Low – n = 7; SDY80: High – n = 14, Low – n = 14. Day 7: SDY1119: High – n = 6, Low – n = 6; SDY180: High – n = 4, Low – n = 4; SDY270: High – n = 10, Low – n = 9; SDY56: High – n = 4, Low – n = 7; SDY61: High – n = 3, Low – n = 3; SDY63: High – n = 3, Low – n = 4; SDY640: High – n = 6, Low – n = 4; SDY80: High – n = 13, Low – n = 13. e, f) Line graphs of (E) M75 and (F) M156.1 expression across time in high and low baseline antibody groups. Error bars represent standard error of the mean. Day 1: High – n = 88, Low – n = 87; Day 3: High – n = 83, Low – n = 88; Day 7: High – n = 49, Low – n = 50; Day 14: High – n = 64, Low – n = 66; In C-F, statistical differences were determined via two-sided unpaired Student’s t-tests. *p < 0.05, **p < 0.01, ***p < 0.001.
Extended Data Fig. 4 Antibody response prediction across vaccines.
a) Boxplots of Day 30 antibody responses to vaccination across vaccines. Center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range. Hepatitis A/B (IN/RP): n = 25, Influenza (IN): n = 412, Influenza (LA): n = 28, Meningococcus (CJ): n = 17, Meningococcus (PS): n = 13, Pneumococcus (PS): n = 6, Smallpox (LA): n = 8, Tuberculosis (RVV): n = 12, Varicella Zoster (LA): n = 16, Yellow Fever (LA): n = 35. b) Barplot of feature importance for the GLM classifier trained on inactivated influenza datasets only. c) AUC barplot of antibody response prediction performance across vaccines for the GLM classifier trained on inactivated influenza datasets only. Data are presented as mean values + /- 95% confidence interval. n = 2000 bootstrap replicates. d) AUC barplot of antibody response prediction performance of the leave-one-vaccine-out GLM classifier. Data are presented as mean values + /- 95% confidence interval. n = 2000 bootstrap replicates. e) AUC barplot of antibody response prediction performance of the 10-fold cross-validation GLM classifier. Data are presented as mean values + /- 95% confidence interval. n = 2000 bootstrap replicates.
Extended Data Fig. 5 Comparison of common transcriptional responses between age groups.
a) Scatterplots of module activity scores in each vaccine among young (x-axis) and older participants (y-axis) of the most commonly expressed modules (Fig. 2a) on days 1–7. Correlation coefficient and p value determined via Pearson correlation.
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Hagan, T., Gerritsen, B., Tomalin, L.E. et al. Transcriptional atlas of the human immune response to 13 vaccines reveals a common predictor of vaccine-induced antibody responses. Nat Immunol 23, 1788–1798 (2022). https://doi.org/10.1038/s41590-022-01328-6
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DOI: https://doi.org/10.1038/s41590-022-01328-6
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