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System vaccinology analysis of predictors and mechanisms of antibody response durability to multiple vaccines in humans

Nature Immunology volume 26, pages 116–130 (2025) Cite this article

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Abstract

We performed a systems vaccinology analysis to investigate immune responses in humans to an H5N1 influenza vaccine, with and without the AS03 adjuvant, to identify factors influencing antibody response magnitude and durability. Our findings revealed a platelet and adhesion-related blood transcriptional signature on day 7 that predicted the longevity of the antibody response, suggesting a potential role for platelets in modulating antibody response durability. As platelets originate from megakaryocytes, we explored the effect of thrombopoietin (TPO)-mediated megakaryocyte activation on antibody response longevity. We found that TPO administration enhanced the durability of vaccine-induced antibody responses. TPO-activated megakaryocytes also promoted survival of human bone-marrow plasma cells through integrin β1/β2-mediated cell–cell interactions, along with survival factors APRIL and the MIF–CD74 axis. Using machine learning, we developed a classifier based on this platelet-associated signature, which predicted antibody response longevity across six vaccines from seven independent trials, highlighting a conserved mechanism for vaccine durability.

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Fig. 1: AS03 induces potent early transcriptional signatures which are enhanced after a booster vaccination.
The alternative text for this image may have been generated using AI.
Fig. 2: Adjuvanting with AS03 enhances the magnitude, affinity and breadth of antibody responses to H5N1 vaccination.
The alternative text for this image may have been generated using AI.
Fig. 3: Durability of antibody responses to influenza vaccination is associated with a transcriptional signature of cellular migration.
The alternative text for this image may have been generated using AI.
Fig. 4: CITE-seq analysis reveals a platelet origin for transcriptional signature of antibody persistence.
The alternative text for this image may have been generated using AI.
Fig. 5: Antibody response durability is modulated by thrombopoietin receptor signaling.
The alternative text for this image may have been generated using AI.
Fig. 6: Thrombopoietin receptor signaling in human megakaryocytes promotes plasma cell survival and antibody production through APRIL, MIF–CD74 axis and integrin-dependent contact.
The alternative text for this image may have been generated using AI.
Fig. 7: Summary of proposed mechanism for involvement of megakaryocytes in promotion of durability of antibody responses to vaccination.
The alternative text for this image may have been generated using AI.
Fig. 8: Platelet signature association with antibody response durability is conserved across multiple vaccines.
The alternative text for this image may have been generated using AI.

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Data availability

The accession codes for datasets used in this study are included in Extended Data Table 1. Respective RNA-seq data were mapped to the human (GRCh38) and macaque (Mmul_1) genomes.

Code availability

Code used for data analysis in this study is available at https://github.com/HaganLab/ab_durability.

References

  1. Goldblatt, D., Alter, G., Crotty, S. & Plotkin, S. A. Correlates of protection against SARS-CoV-2 infection and COVID-19 disease. Immunol. Rev. 310, 6–26 (2022).

    CAS  PubMed  Google Scholar 

  2. Rappuoli, R., Alter, G. & Pulendran, B. Transforming vaccinology. Cell 187, 5171–5194 (2024).

    CAS  PubMed  Google Scholar 

  3. Bhattacharya, D. Instructing durable humoral immunity for COVID-19 and other vaccinable diseases. Immunity 55, 945–964 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Amanna, I. J., Carlson, N. E. & Slifka, M. K. Duration of humoral immunity to common viral and vaccine antigens. N. Engl. J. Med. 357, 1903–1915 (2007).

    CAS  PubMed  Google Scholar 

  5. Gu, X. X. et al. Waning immunity and microbial vaccines—workshop of the national institute of allergy and infectious diseases. Clin. Vaccine Immunol. 24, e00034–17 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Rerks-Ngarm, S. et al. Vaccination with ALVAC and AIDSVAX to prevent HIV-1 infection in Thailand. N. Engl. J. Med. 361, 2209–2220 (2009).

    CAS  PubMed  Google Scholar 

  7. Pulendran, B., P, S. A. & O’Hagan, D. T. Emerging concepts in the science of vaccine adjuvants. Nat. Rev. Drug Discov. 20, 454–475 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Kasturi, S. P. et al. 3M-052, a synthetic TLR-7/8 agonist, induces durable HIV-1 envelope-specific plasma cells and humoral immunity in nonhuman primates. Sci. Immunol. 5, eabb1025 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Dunkle, L. M. et al. Efficacy and Safety of NVX-CoV2373 in adults in the United States and Mexico. N. Engl. J. Med. 386, 531–543 (2022).

    CAS  PubMed  Google Scholar 

  10. Datoo, M. S. et al. Efficacy and immunogenicity of R21/Matrix-M vaccine against clinical malaria after 2 years’ follow-up in children in Burkina Faso: a phase 1/2b randomised controlled trial. Lancet Infect. Dis. 22, 1728–1736 (2022).

    CAS  PubMed  Google Scholar 

  11. Querec, T. D. et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat. Immunol. 10, 116–125 (2009).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Nakaya, H. I. et al. Systems analysis of immunity to influenza vaccination across multiple years and in diverse populations reveals shared molecular signatures. Immunity 43, 1186–1198 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Fourati, S. et al. Pre-vaccination inflammation and B-cell signalling predict age-related hyporesponse to hepatitis B vaccination. Nat. Commun. 7, 10369 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Oh, J. Z. et al. TLR5-mediated sensing of gut microbiota is necessary for antibody responses to seasonal influenza vaccination. Immunity 41, 478–492 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Ravindran, R. et al. Vaccine activation of the nutrient sensor GCN2 in dendritic cells enhances antigen presentation. Science 343, 313–317 (2014).

    CAS  PubMed  Google Scholar 

  19. Luo, W. et al. SREBP signaling is essential for effective B cell responses. Nat. Immunol. 24, 337–348 (2023).

    CAS  PubMed  Google Scholar 

  20. Hagan, T. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Garcon, N., Vaughn, D. W. & Didierlaurent, A. M. Development and evaluation of AS03, an adjuvant system containing α-tocopherol and squalene in an oil-in-water emulsion. Expert Rev. Vaccines 11, 349–366 (2012).

    CAS  PubMed  Google Scholar 

  22. Chu, D. W. et al. Immunogenicity and tolerability of an AS03(A)-adjuvanted prepandemic influenza vaccine: a phase III study in a large population of Asian adults. Vaccine 27, 7428–7435 (2009).

    CAS  PubMed  Google Scholar 

  23. Langley, J. M. et al. Safety and cross-reactive immunogenicity of candidate AS03-adjuvanted prepandemic H5N1 influenza vaccines: a randomized controlled phase 1/2 trial in adults. J. Infect. Dis. 201, 1644–1653 (2010).

    CAS  PubMed  Google Scholar 

  24. Couch, R. B. et al. Superior antigen-specific CD4+ T-cell response with AS03-adjuvantation of a trivalent influenza vaccine in a randomised trial of adults aged 65 and older. BMC Infect. Dis. 14, 425 (2014).

    PubMed  PubMed Central  Google Scholar 

  25. Givord, C. et al. Activation of the endoplasmic reticulum stress sensor IRE1α by the vaccine adjuvant AS03 contributes to its immunostimulatory properties. NPJ Vaccines 3, 20 (2018).

    PubMed  PubMed Central  Google Scholar 

  26. Vono, M. et al. The adjuvant MF59 induces ATP release from muscle that potentiates response to vaccination. Proc. Natl Acad. Sci. USA 110, 21095–21100 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Seubert, A. et al. Adjuvanticity of the oil-in-water emulsion MF59 is independent of Nlrp3 inflammasome but requires the adaptor protein MyD88. Proc. Natl Acad. Sci. USA 108, 11169–11174 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Kaushansky, K. Lineage-specific hematopoietic growth factors. N. Engl. J. Med. 354, 2034–2045 (2006).

    CAS  PubMed  Google Scholar 

  29. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 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).

    PubMed  Google Scholar 

  31. Arunachalam, P. S. et al. Systems vaccinology of the BNT162b2 mRNA vaccine in humans. Nature 596, 410–416 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Li, C. et al. Mechanisms of innate and adaptive immunity to the Pfizer-BioNTech BNT162b2 vaccine. Nat. Immunol. 23, 543–555 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Kazmin, D. et al. Memory-like innate response to booster vaccination with MF-59 adjuvanted influenza vaccine in children. NPJ Vaccines 8, 100 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Leroux-Roels, I. et al. Priming with AS03 A-adjuvanted H5N1 influenza vaccine improves the kinetics, magnitude and durability of the immune response after a heterologous booster vaccination: an open non-randomised extension of a double-blind randomised primary study. Vaccine 28, 849–857 (2010).

    CAS  PubMed  Google Scholar 

  35. Leroux-Roels, I. et al. Antigen sparing and cross-reactive immunity with an adjuvanted rH5N1 prototype pandemic influenza vaccine: a randomised controlled trial. Lancet 370, 580–589 (2007).

    CAS  PubMed  Google Scholar 

  36. Khurana, S. et al. AS03-adjuvanted H5N1 vaccine promotes antibody diversity and affinity maturation, NAI titers, cross-clade H5N1 neutralization, but not H1N1 cross-subtype neutralization. NPJ Vaccines 3, 40 (2018).

    PubMed  PubMed Central  Google Scholar 

  37. Ellebedy, A. H. et al. Adjuvanted H5N1 influenza vaccine enhances both cross-reactive memory B cell and strain-specific naive B cell responses in humans. Proc. Natl Acad. Sci. USA 117, 17957–17964 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Crotty, S. Follicular helper CD4 T cells (TFH). Annu. Rev. Immunol. 29, 621–663 (2011).

    CAS  PubMed  Google Scholar 

  39. Chevalier, N. et al. CXCR5 expressing human central memory CD4 T cells and their relevance for humoral immune responses. J. Immunol. 186, 5556–5568 (2011).

    CAS  PubMed  Google Scholar 

  40. He, J. et al. Circulating precursor CCR7(lo)PD-1(hi) CXCR5(+) CD4(+) T cells indicate Tfh cell activity and promote antibody responses upon antigen reexposure. Immunity 39, 770–781 (2013).

    CAS  PubMed  Google Scholar 

  41. Locci, M. et al. Human circulating PD-1+CXCR3-CXCR5+ memory Tfh cells are highly functional and correlate with broadly neutralizing HIV antibody responses. Immunity 39, 758–769 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Morita, R. et al. Human blood CXCR5(+)CD4(+) T cells are counterparts of T follicular cells and contain specific subsets that differentially support antibody secretion. Immunity 34, 108–121 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Bentebibel, S. E. et al. Induction of ICOS+CXCR3+CXCR5+ TH cells correlates with antibody responses to influenza vaccination. Sci. Transl. Med. 5, 176ra132 (2013).

    Google Scholar 

  44. Marazzi, S. et al. Characterization of human fibroleukin, a fibrinogen-like protein secreted by T lymphocytes. J. Immunol. 161, 138–147 (1998).

    CAS  PubMed  Google Scholar 

  45. Ogawa, K. et al. A novel serum protein that is selectively produced by cytotoxic lymphocytes. J. Immunol. 166, 6404–6412 (2001).

    CAS  PubMed  Google Scholar 

  46. Zhao, F. et al. S100A9 a new marker for monocytic human myeloid-derived suppressor cells. Immunology 136, 176–183 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Zhang, Z., Han, N. & Shen, Y. S100A12 promotes inflammation and cell apoptosis in sepsis-induced ARDS via activation of NLRP3 inflammasome signaling. Mol. Immunol. 122, 38–48 (2020).

    CAS  PubMed  Google Scholar 

  48. Arunachalam, P. S. et al. Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans. Science 369, 1210–1220 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Burel, J. G. et al. Circulating T cell-monocyte complexes are markers of immune perturbations. eLife 8, e46045 (2019).

    PubMed  PubMed Central  Google Scholar 

  51. Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Raab, M., Strebhardt, K. & Rudd, C. E. Immune adaptor SKAP1 acts a scaffold for Polo-like kinase 1 (PLK1) for the optimal cell cycling of T-cells. Sci. Rep. 9, 10462 (2019).

    PubMed  PubMed Central  Google Scholar 

  53. Zhou, D. et al. Inhibition of Polo-like kinase 1 (PLK1) facilitates the elimination of HIV-1 viral reservoirs in CD4(+) T cells ex vivo. Sci. Adv. 6, eaba1941 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 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  PubMed  PubMed Central  Google Scholar 

  55. Arunachalam, P. S. et al. Adjuvanting a subunit COVID-19 vaccine to induce protective immunity. Nature 594, 253–258 (2021).

    CAS  PubMed  Google Scholar 

  56. Apps, R. et al. Acute and persistent responses after H5N1 vaccination in humans. Cell Rep. 43, 114706 (2024).

    CAS  PubMed  Google Scholar 

  57. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Urata, M., Koga-Wada, Y., Kayamori, Y. & Kang, D. Platelet contamination causes large variation as well as overestimation of mitochondrial DNA content of peripheral blood mononuclear cells. Ann. Clin. Biochem. 45, 513–514 (2008).

    CAS  PubMed  Google Scholar 

  59. Liu, C. et al. Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19. Cell 184, 1836–1857.e1822 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e1821 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Wilk, A. J. et al. A single-cell atlas of the peripheral immune response in patients with severe COVID-19. Nat. Med. 26, 1070–1076 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Price, M. J., Patterson, D. G., Scharer, C. D. & Boss, J. M. Progressive upregulation of oxidative metabolism facilitates plasmablast differentiation to a T-Independent antigen. Cell Rep. 23, 3152–3159 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Waters, L. R., Ahsan, F. M., Wolf, D. M., Shirihai, O. & Teitell, M. A. Initial B Cell activation induces metabolic reprogramming and Mitochondrial remodeling. iScience 5, 99–109 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Angenieux, C. et al. Time-dependent decay of mRNA and ribosomal RNA during platelet aging and its correlation with translation activity. PLoS ONE 11, e0148064 (2016).

    PubMed  PubMed Central  Google Scholar 

  65. Davizon-Castillo, P., Rowley, J. W. & Rondina, M. T. Megakaryocyte and platelet transcriptomics for discoveries in human health and disease. Arterioscler. Thromb. Vasc. Biol. 40, 1432–1440 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Winter, O. et al. Megakaryocytes constitute a functional component of a plasma cell niche in the bone marrow. Blood 116, 1867–1875 (2010).

    CAS  PubMed  Google Scholar 

  67. Gurney, A. L., Carver-Moore, K., de Sauvage, F. J. & Moore, M. W. Thrombocytopenia in c-mpl-deficient mice. Science 265, 1445–1447 (1994).

    CAS  PubMed  Google Scholar 

  68. Mazzi, S., Lordier, L., Debili, N., Raslova, H. & Vainchenker, W. Megakaryocyte and polyploidization. Exp. Hematol. 57, 1–13 (2018).

    CAS  PubMed  Google Scholar 

  69. de Graaf, C. A. & Metcalf, D. Thrombopoietin and hematopoietic stem cells. Cell Cycle 10, 1582–1589 (2011).

    PubMed  PubMed Central  Google Scholar 

  70. Amanna, I. J. & Slifka, M. K. Successful vaccines. Curr. Top. Microbiol. Immunol. 428, 1–30 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Sandberg, W. J. et al. The tumour necrosis factor superfamily ligand APRIL (TNFSF13) is released upon platelet activation and expressed in atherosclerosis. Thromb. Haemost. 102, 704–710 (2009).

    CAS  PubMed  Google Scholar 

  72. Aradottir Pind, A. A. et al. LT-K63 enhances B cell activation and survival factors in neonatal mice that translates into long-lived humoral immunity. Front. Immunol. 11, 527310 (2020).

    PubMed  PubMed Central  Google Scholar 

  73. Aradottir Pind, A. A. et al. A comparative study of adjuvants effects on neonatal plasma cell survival niche in bone marrow and persistence of humoral immune responses. Front. Immunol. 13, 904415 (2022).

    PubMed  PubMed Central  Google Scholar 

  74. Rao, J. et al. Platelets correlate with false negative T-SPOT.TB results by inhibiting interferon-γ production in T cells via degranulation. Front. Cell Infect. Microbiol. 12, 937416 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Handtke, S. & Thiele, T. Large and small platelets—(When) do they differ? J. Thromb. Haemost. 18, 1256–1267 (2020).

    PubMed  Google Scholar 

  76. Cortese, M., Sherman, A. C., Rouphael, N. G. & Pulendran, B. Systems biological analysis of immune response to influenza vaccination. Cold Spring Harb. Perspect. Med. 11, a038596 (2020).

    Google Scholar 

  77. Hagan, T. & Pulendran, B. Will systems biology deliver its promise and contribute to the development of new or improved vaccines? From data to understanding through systems biology. Cold Spring Harb. Perspect. Biol. 10, a028894 (2018).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Khurana, S. et al. Properly folded bacterially expressed H1N1 hemagglutinin globular head and ectodomain vaccines protect ferrets against H1N1 pandemic influenza virus. PLoS ONE 5, e11548 (2010).

    PubMed  PubMed Central  Google Scholar 

  80. Schmitt, N. & Ueno, H. Blood Tfh cells come with colors. Immunity 39, 629–630 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Arunachalam, P. S. et al. Durable protection against the SARS-CoV-2 Omicron variant is induced by an adjuvanted subunit vaccine. Sci. Transl. Med. 14, eabq4130 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Pariser, D. N. et al. Lung megakaryocytes are immune modulatory cells. J. Clin. Invest. 131, e137377 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Kauffmann, A., Gentleman, R. & Huber, W. arrayQualityMetrics—a bioconductor package for quality assessment of microarray data. Bioinformatics 25, 415–416 (2009).

    CAS  PubMed  Google Scholar 

  84. Irizarry, R. A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).

    PubMed  Google Scholar 

  85. Patel H. et al. nf-core/rnaseq: nf-core/rnaseq v3.11.2. Resurrected Radium Rhino (3.11.2) (2023).

  86. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Zhang, X. et al. CellMarker: a manually curated resource of cell markers in human and mouse. Nucleic Acids Res. 47, D721–D728 (2019).

    CAS  PubMed  Google Scholar 

  88. Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank A. Barrie for help with PBMC processing, N. Patel at the Yerkes NHP Genomics Core for RNA extraction and microarray preparation, S. Bentebibel at the MD Anderson Cancer Center for FACS analysis of T follicular cells, and C. Li, A. Dinasarapu, J. Dan, S. Crotty, R. Ahmed and A. Ellebedy for discussion and valuable feedback. This work was supported by the NIH (R01 AI048638, U19 AI057266 and U19 AI167903), DARPA (81414-BB-DRP), Bill and Melinda Gates Foundation, Open Philanthropy, Anonymous Donor and the Violetta L. Horton and Soffer Endowments to B.P., the Intramural Research Program of NIAID, NIH and NIH intramural support of the Trans-NIH CHI. The Yerkes NHP Genomics Core is supported in part by ORIP/OD P51OD011132 and NIH S10 OD026799. Additional funding for this study was provided by GlaxoSmithKline Biologicals SA (NCT01910519). GlaxoSmithKline Biologicals SA was provided the opportunity to review a preliminary version of this paper for factual accuracy, but the authors are solely responsible for final content and interpretation. The authors received no financial support or other form of compensation related to the development of the paper.

Author information

Author notes

  1. Hideki Ueno

    Present address: Department of Immunology, Kyoto University, Kyoto, Japan

  2. These authors contributed equally: Mario Cortese, Thomas Hagan.

Authors and Affiliations

  1. Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA, USA

    Mario Cortese, Sheng-Yang Wu, Xia Xie, Dmitri Kazmin, Florian Wimmers, Prabhu S. Aranuchalam & Bali Pulendran

  2. Division of Infectious Diseases, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    Thomas Hagan

  3. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA

    Thomas Hagan

  4. Hope Clinic of the Emory Vaccine Center, Decatur, GA, USA

    Nadine Rouphael, Mary Bower, Aneesh Mehta & Clifford Gunthel

  5. Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA

    Shakti Gupta & Shankar Subramaniam

  6. GSK, Rixensart, Belgium

    Robbert van der Most & Margherita Coccia

  7. Hospital Israelita Albert Einstein, São Paulo, Brazil

    Helder I. Nakaya

  8. The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA

    Yating Wang & Shuzhao Li

  9. Division of Viral Products, Center for Biologics Evaluation and Research, Food and Drug Administration, Silver Spring, MD, USA

    Elizabeth Coyle, Surender Khurana & Hana Golding

  10. Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Shu Horiuchi, Hanchih Wu, Randy Albrecht & Hideki Ueno

  11. Emory Vaccine Center, Yerkes National Primate Research Center, Atlanta, GA, USA

    Steve E. Bosinger

  12. Yerkes Genomics Core Laboratory, Yerkes National Primate Research Center, Atlanta, GA, USA

    Steve E. Bosinger

  13. Department of Pathology & Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, USA

    Steve E. Bosinger

  14. NIH Center for Human Immunology (CHI), National Institutes of Health, Bethesda, MD, USA

    Yuri Kotliarov, Foo Cheung, Pamela L. Schwartzberg, Ronald N. Germain & John Tsang

  15. Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health, Bethesda, MD, USA

    Pamela L. Schwartzberg, Ronald N. Germain & John Tsang

  16. Division of Infectious Diseases and Immunology, Department of Medicine and NYU Langone Vaccine Center, NYU Grossman School of Medicine, New York, NY, USA

    Mark J. Mulligan

  17. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

    Bali Pulendran

  18. Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA

    Bali Pulendran

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  1. Mario Cortese
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  2. Thomas Hagan
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Contributions

Conceptualization and formulation of original project, B.P., M.J.M. and N.R.; intellectual contributions throughout project, M. Cortese., T.H. and B.P.; formal analysis, T.H., M. Cortese, F.W., S.G., D.K., H.I.N., H.U., Y.K. and F.C.; investigation, M. Cortese., S.-Y.W., X.X., F.W., P.S.A., R.A., Y.W., E.C., S.H., H.W., H.U. and S.K.; visualization, M. Cortese. and T.H.; writing – original draft, M. Cortese and T.H.; writing – review and editing, M. Cortese, T.H. and B.P.; supervision, B.P., N.R., S.L., S.S., M.J.M. and H.G.; project administration, M. Cortese.; resources, R.v.d.M., M. Coccia, M.B., A.M., C.G., S.E.B., P.L.S., R.N.G. and J.T.; funding acquisition, B.P.

Corresponding author

Correspondence to Bali Pulendran.

Ethics declarations

Competing interests

R.v.d.M., M. Cortese and M. Coccia are or were employees of GSK. B.P. serves or has served on the External Immunology Board of GSK and on the Scientific Advisory Board of Sanofi, Medicago, Boehringer Ingelheim, Pharmajet, Icosavax, Imu Biosciences and Ed-Jen, and holds shares at CircBio and Orbital Therapeutics. R.v.d.M. and T.H. hold shares in the GSK group of companies. The other authors declare no competing interests.

Peer review

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Nature Immunology thanks the anonymous reviewers 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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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Analysis of kinetics of transcriptional responses.

(a) Scatterplot of the mean log2 FC of all BTMs in adjuvanted participants on day 1 (x axis) and in nonadjuvanted participants on day 3 (y axis). The Pearson correlation coefficient and p value are reported. (b) Kinetics of differential BTMs between day 3 prime and boost. Lines represent average module fold change among adjuvanted participants. The 10 BTMs with the greatest fold change on day 24 are plotted (same as those labeled in Fig. 1g).

Extended Data Fig. 2 Analysis of circulating Tfh cells post-vaccination.

(a) Gating strategy for sorting of four different CD4+ CXCR5+ Tfh populations: quiescent Tfh1, quiescent Tfh2, activated Tfh1, and activated Tfh2. (b) Frequency of activated Tfh cells post-vaccination in adjuvanted (orange) and non-adjuvanted participants (green), defined as percentage of PD1+ICOS+ cells within the CXCR5+ CD4+ T cell population. n=34 (H5N1+AS03) and 16 (H5N1) participants. Orange/green p values represent post-vaccination changes within each group (two-sided Wilcoxon test), gray p values represent between group comparisons (two-sided Mann-Whitney test). (c) Correlation of the day 28/21 fold change in activated Tfh cell frequencies with the day 42/21 fold increase in MN titers. The Pearson correlation coefficient and p value are reported. (d) Average log2 fold change of genes in activated (PD-1+ICOS+) versus non-activated (PD-1- ICOS-) Tfh1 (x-axis) and Tfh2 (y-axis) cells. The top 20 genes with the greatest average absolute fold change are annotated in red. (e) Boxplot of estimated frequency of monocytes in non-activated and activated Tfh based on digital cytometry of transcriptional profiles using CIBERSORT. Lines represent means, shaded area represents 95% confidence interval, and lines represent standard deviation. n=27 (unactivated) and 28 (activated), unpaired two-sided t-test. (f) BTMs significantly enriched (FDR<0.05) in activated versus non-activated Tfh cells. CIBERSORTx was used to estimate CD4 T cell specific expression in sorted Tfh transcriptional profiles, and then GSEA was used to identify enriched BTMs using genes ranked by their fold change between activated and non-activated Tfh. See Methods section for further details. (g) Genes in BTM M219; each ‘edge’’ (gray line) represents a coexpression relationship; colors represent the fold change in activated versus non-activated Tfh. (h) Genes in BTM M4.2; each ‘edge’’ (gray line) represents a coexpression relationship; colors represent the fold change in activated versus non-activated Tfh. (i) Clustered heatmap of the top 40 genes by fold change between activated and non-activated Tfh. Colors represent row-wise z-scores. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.

Extended Data Fig. 3 Relationship between peak and durable antibody responses.

(a) Kinetics of HAI titers in response to H5N1+AS03 and IIV vaccination. Lines represent the geometric mean, and shaded areas represent the geometric standard deviation. IIV titers are from young adults (<65 years old) vaccinated with the 2010 and 2011 seasonal influenza vaccine (Nakaya et al.13). n=34 (H5N1+AS03) and 42 (IIV). (b) Scatterplot of the day 100 and day 42 HAI titers. The day 100/42 HAI residual is defined here as the vertical distance between a given point and the regression line, with the regression line representing the average day 100 titer expected given a particular day 42 response. Participants above/below the regression line are considered ‘persistent’ and ‘temporary’ responders, respectively. The Pearson correlation coefficient and p value are reported. (c) Heatmap of the Pearson correlation coefficients between expression of plasma cell and cell cycle BTMs and peak antibody titers (prime - day 21, boost - day 42) in adjuvanted participants. (d) Scatterplot of the day 28/day 21 mean log2 FC of M156.0 (x axis) versus the day100/day42 HAI residual in adjuvanted participants. The Pearson correlation coefficient and p value are reported.

Extended Data Fig. 4 Platelet-associated signatures of antibody response durability in a NHP vaccination model.

(a) Study design for AS03-adjuvanted COVID-19 vaccination in NHPs. (b) Kinetics of pseudotyped lentivirus neutralization antibody titers following vaccination in NHPs. (c) Bar plot of BTMs associated with antibody persistence on day 7 post-boost in the NHP COVID-19 vaccine study. GSEA was performed on genes ranked by correlation of their day 7 post-boost expression with the day 180/42 antibody residual. Modules shown are those with FDR < 0.05, NES ≥ 2, platelet-associated modules are highlighted in red.

Extended Data Fig. 5 CITEseq quality control data.

(a) Per-cluster cell proportions from day 21 and 28 samples before QC filtering. (b) Per-cluster cell proportions from each subject before QC filtering. (c) Scatterplots of day 28/21 FCs among day 28 DEGs via microarray (x axis) and pseudobulk estimates via CITEseq (y axis) for each subject. The Pearson correlation coefficient and p value are reported. (d) CITE-seq antibody abundance in each cell before QC filtering. (e) DEGs in each cluster compared to all other clusters before QC filtering. (f) Per-cell total reads and number of detected transcripts by cluster before QC filtering.

Extended Data Fig. 6 Effect of TPO administration on the bone marrow compartment.

(a) Study design for TPO administration in mice. Recombinant mouse TPO was injected intraperitoneally at 12.5 μg/kg daily for 5 days. Bone marrow cells were analyzed by flow cytometry on days 4, 7 and 11 following the initial TPO injection. (b-d) Flow cytometry gating strategy (b), megakaryocytes frequency in bone marrow live cells (c), and megakaryocytes ploidy stages (d) after TPO injection were analyzed. n = 6-15, Dunnett’s multiple comparisons test for panel C, unpaired t test for panel D. (e-f) Flow cytometry gating strategy and frequencies of bone marrow immune cell populations. n = 6-15, Dunnett’s multiple comparisons test. (g-h) Flow cytometry gating strategy and frequencies of bone marrow stem and progenitor cells. n = 6-15, Dunnett’s multiple comparisons test. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.

Extended Data Fig. 7 Analysis of platelet-associated durability signature across vaccines and cell types.

(a) Scatterplots of Day 7 vs Day 0 log2 fold changes (relative to last vaccination) of BTM M85 versus antibody residual in each vaccine dataset. Pearson correlation coefficient and p values are reported. (b) Heatmap of expression of platelet-associated BTMs across cell clusters in the CITE-seq data (Fig. 4). Colors represent row-wise z-scores of average expression of all genes in each module. (c) Scatterplots of Day 7 vs Day 0 log2 fold changes of platelet-expressed BTMs from PBMC (x-axis) and Paxgene (y-axis) samples in a cohort of healthy adults vaccinated with seasonal influenza vaccine. Pearson correlation coefficients and p values are reported.

Extended Data Table 1 Demographics information
Full size table
Extended Data Table 2 Summary of antibody responses
Full size table
Extended Data Table 3 Summary of vaccine datasets used for prediction of antibody response durability
Full size table

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Cortese, M., Hagan, T., Rouphael, N. et al. System vaccinology analysis of predictors and mechanisms of antibody response durability to multiple vaccines in humans. Nat Immunol 26, 116–130 (2025). https://doi.org/10.1038/s41590-024-02036-z

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