Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans

Abstract

A major challenge in vaccinology is to prospectively determine vaccine efficacy. Here we have used a systems biology approach to identify early gene 'signatures' that predicted immune responses in humans vaccinated with yellow fever vaccine YF-17D. Vaccination induced genes that regulate virus innate sensing and type I interferon production. Computational analyses identified a gene signature, including complement protein C1qB and eukaryotic translation initiation factor 2 alpha kinase 4—an orchestrator of the integrated stress response—that correlated with and predicted YF-17D CD8+ T cell responses with up to 90% accuracy in an independent, blinded trial. A distinct signature, including B cell growth factor TNFRS17, predicted the neutralizing antibody response with up to 100% accuracy. These data highlight the utility of systems biology approaches in predicting vaccine efficacy.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Genomic signatures of innate immune responses to YF-17D.
Figure 2: Variations in the magnitudes of the antigen-specific CD8+ T cell and neutralizing antibody responses to YF-17D.
Figure 3: Genomic signatures that correlate with the magnitude of the CD8+ T cell response.
Figure 4: Genomic signatures that predict the magnitude of the CD8+ T cell responses, using the ClaNC model.
Figure 5: YF-17D induces eIF2α phosphorylation and stress granule formation.

Accession codes

Accessions

Gene Expression Omnibus

References

  1. 1

    Monath, T.P. Yellow fever vaccine. Expert Rev. Vaccines 4, 553–574 (2005).

  2. 2

    Theiler, M. & Smith, H.H. The use of yellow fever virus modified by in vitro cultivation for human immunization. J. Exp. Med. 65, 787–800 (1937); Rev. Med. Virol. 10, 6–16; discussion 13–15 (2000).

  3. 3

    Monath, T.P., Cetron, M. & Teuwen, D.E. Yellow fever vaccine. in Vaccines 5th ed. (Saunders Elsevier, Philadelphia, 2008).

  4. 4

    Takeuchi, O. & Akira, S. Recognition of viruses by innate immunity. Immunol. Rev. 220, 214–224 (2007).

  5. 5

    Steinman, R.M. & Banchereau, J. Taking dendritic cells into medicine. Nature 449, 419–426 (2007).

  6. 6

    Barba-Spaeth, G., Longman, R.S., Albert, M.L. & Rice, C.M. Live attenuated yellow fever 17D infects human DCs and allows for presentation of endogenous and recombinant T cell epitopes. J. Exp. Med. 202, 1179–1184 (2005).

  7. 7

    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).

  8. 8

    Alizadeh, A.A. et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403, 503–511 (2000).

  9. 9

    Potti, A. et al. Genomic signatures to guide the use of chemotherapeutics. Nat. Med. 12, 1294–1300 (2006).

  10. 10

    Sorlie, T. et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. USA 98, 10869–10874 (2001).

  11. 11

    Ramilo, O. et al. Gene expression patterns in blood leukocytes discriminate patients with acute infections. Blood 109, 2066–2077 (2007).

  12. 12

    Diebold, S.S., Kaisho, T., Hemmi, H., Akira, S. & Reis e Sousa, C. Innate antiviral responses by means of TLR7-mediated recognition of single-stranded RNA. Science 303, 1529–1531 (2004).

  13. 13

    Rothenfusser, S. et al. The RNA helicase Lgp2 inhibits TLR-independent sensing of viral replication by retinoic acid-inducible gene-I. J. Immunol. 175, 5260–5268 (2005).

  14. 14

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

  15. 15

    Dabney, A.R. Classification of microarrays to nearest centroids. Bioinformatics 21, 4148–4154 (2005).

  16. 16

    Lee, E.K. Large-scale optimization-based classification models in medicine and biology. Ann. Biomed. Eng. 35, 1095–1109 (2007).

  17. 17

    Brooks, J.P. & Lee, E.K. Analysis of the consistency of a mixed integer programming-based multi-category constrained discriminant model. Ann. Oper. Res. 164, 1–20 (2008).

  18. 18

    Gallagher, R.J., Lee, E.K. & Patterson, D.A. Constrained discriminant analysis via 0/1 mixed integer programming. Ann. Oper. Res. 74, 65–88 (1997).

  19. 19

    Richter, J.D. & Sonenberg, N. Regulation of cap-dependent translation by eIF4E inhibitory proteins. Nature 433, 477–480 (2005).

  20. 20

    Kedersha, N. & Anderson, P. Mammalian stress granules and processing bodies. Methods Enzymol. 431, 61–81 (2007).

  21. 21

    Ron, D. & Walter, P. Signal integration in the endoplasmic reticulum unfolded protein response. Nat. Rev. Mol. Cell Biol. 8, 519–529 (2007).

  22. 22

    Kaufman, R.J. Stress signaling from the lumen of the endoplasmic reticulum: coordination of gene transcriptional and translational controls. Genes Dev. 13, 1211–1233 (1999).

  23. 23

    Cancro, M.P. The BLyS/BAFF family of ligands and receptors: key targets in the therapy and understanding of autoimmunity. Ann. Rheum. Dis. 65 (suppl. 3), iii34–iii36 (2006).

  24. 24

    Chen, J.P. et al. Dengue virus induces expression of CXC chemokine ligand 10/IFN-gamma-inducible protein 10, which competitively inhibits viral binding to cell surface heparan sulfate. J. Immunol. 177, 3185–3192 (2006).

  25. 25

    Shirato, K., Kimura, T., Mizutani, T., Kariwa, H. & Takashima, I. Different chemokine expression in lethal and non-lethal murine West Nile virus infection. J. Med. Virol. 74, 507–513 (2004).

  26. 26

    Atrasheuskaya, A.V., Fredeking, T.M. & Ignatyev, G.M. Changes in immune parameters and their correction in human cases of tick-borne encephalitis. Clin. Exp. Immunol. 131, 148–154 (2003).

  27. 27

    Wong, J.J., Pung, Y.F., Sze, N.S. & Chin, K.C. HERC5 is an IFN-induced HECT-type E3 protein ligase that mediates type I IFN-induced ISGylation of protein targets. Proc. Natl. Acad. Sci. USA 103, 10735–10740 (2006).

  28. 28

    Arimoto, K., Konishi, H. & Shimotohno, K. UbcH8 regulates ubiquitin and ISG15 conjugation to RIG-I. Mol. Immunol. 45, 1078–1084 (2008).

  29. 29

    Staub, O. Ubiquitylation and isgylation: overlapping enzymatic cascades do the job. Sci. STKE 2004, pe43 (2004).

  30. 30

    Mehlhop, E. & Diamond, M.S. Protective immune responses against West Nile virus are primed by distinct complement activation pathways. J. Exp. Med. 203, 1371–1381 (2006).

  31. 31

    Zhao, F.Q. & Keating, A.F. Functional properties and genomics of glucose transporters. Curr. Genomics 8, 113–128 (2007).

  32. 32

    Frauwirth, K.A. et al. The CD28 signaling pathway regulates glucose metabolism. Immunity 16, 769–777 (2002).

  33. 33

    Berlanga, J.J. et al. Antiviral effect of the mammalian translation initiation factor 2alpha kinase GCN2 against RNA viruses. EMBO J. 25, 1730–1740 (2006).

  34. 34

    Woodland, R.T., Schmidt, M.R. & Thompson, C.B. BLyS and B cell homeostasis. Semin. Immunol. 18, 318–326 (2006).

  35. 35

    Pantaleo, G. HIV-1 T-cell vaccines: evaluating the next step. Lancet Infect. Dis. 8, 82–83 (2008).

Download references

Acknowledgements

We thank J. Connolly at the Luminex Core at Baylor Institute for Immunology Research for doing Luminex analyses, and N. Kozyr (CFAR Virology Core, Emory University) for doing RT-PCR. Supported by the US National Institutes of Health (U19 AI057266, R01 AI048638, R01 DK057665, U54 AI057157, N01 AI50019, N01 AI50025), Sanofi Pasteur and the Bill & Melinda Gates Foundation.

Author information

T.D.Q. performed all the experiments and analyses in Tables 1 and 3, Figures 1, 3 and 4 and Supplementary Figures 1, 2, 3, 4, 6 and 7; R.S.A. together with J.M., performed the analyses in Figure 2; E.K.L. performed the DAMIP model analyses in Tables 2 and 4; W.C. performed the experiments in Figure 5; D.T. facilitated the design and execution of trial 2; A.P., K.G., H.I.N. and R.Z.V. assisted with the bioinformatics analyses of the data; H.W. assisted with the statistical analyses; J.D. performed the experiment in Supplementary Figure 5; B.M. and K.K. performed the hybridization analyses; S.B. performed the experiments in Supplementary Figure 1c; H.O. assisted with the processing of samples; M.M. organized the clinical trials; A.A. coordinated computational and gene expression analysis and helped design the study; and R.A. helped conceive and design the study and supervised the studies in Figure 2. B.P. conceived the study and designed and supervised the experiments and analyses in Figures 1, 3, 4, 5 and Supplementary Figures 1, 2, 3, 4, 5, 6, 7. B.P. and T.D.Q. wrote the paper.

Correspondence to Bali Pulendran.

Ethics declarations

Competing interests

Part of this study was funded by a research grant from Sanofi Pasteur, which makes one of the yellow fever vaccines. D.T. was, until recently, an employee of Sanofi Pasteur.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Tables 1–4, Supplementary Methods (PDF 634 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Querec, T., Akondy, R., Lee, E. et al. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nat Immunol 10, 116–125 (2009). https://doi.org/10.1038/ni.1688

Download citation

Further reading