Abstract

Here we have used a systems biology approach to study innate and adaptive responses to vaccination against influenza in humans during three consecutive influenza seasons. We studied healthy adults vaccinated with trivalent inactivated influenza vaccine (TIV) or live attenuated influenza vaccine (LAIV). TIV induced higher antibody titers and more plasmablasts than LAIV did. In subjects vaccinated with TIV, early molecular signatures correlated with and could be used to accurately predict later antibody titers in two independent trials. Notably, expression of the kinase CaMKIV at day 3 was inversely correlated with later antibody titers. Vaccination of CaMKIV-deficient mice with TIV induced enhanced antigen-specific antibody titers, which demonstrated an unappreciated role for CaMKIV in the regulation of antibody responses. Thus, systems approaches can be used to predict immunogenicity and provide new mechanistic insights about vaccines.

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Acknowledgements

We thank B.T. Rouse and R. Compans for discussion and comments on the manuscript, and H. Oluoch for technical assistance. Supported the US National Institutes of Health (U19AI090023, HHSN266200700006C, U54AI057157, R37AI48638, R01DK057665, U19AI057266 and N01 AI50025 for the B.P. laboratory; AI30048 and AI057266 for the R.A. laboratory; DK074701 for the A.R.M. laboratory; Intramural Research Program of the National Institute of Allergy and Infectious Diseases for the K.S. laboratory; and UL1 RR025008 from the Clinical and Translational Science Award program, National Center for Research Resources for clinical work), the Bill & Melinda Gates Foundation (Collaboration for AIDS Vaccine Discovery 38645 to the R.A. and B.P. laboratories), the National Science Foundation (E.K.L. laboratory) and the Centers for Disease Control (E.K.L. laboratory).

Author information

Affiliations

  1. Emory Vaccine Center, Emory University, Atlanta, Georgia, USA.

    • Helder I Nakaya
    • , Jens Wrammert
    • , Stephanie Marie-Kunze
    • , Sudhir P Kasturi
    • , Nooruddin Khan
    • , Gui-Mei Li
    • , Megan McCausland
    • , Vibhu Kanchan
    • , Shuzhao Li
    • , Rafi Ahmed
    •  & Bali Pulendran
  2. Yerkes National Primate Research Center, Emory University, Atlanta, Georgia, USA.

    • Helder I Nakaya
    • , Stephanie Marie-Kunze
    • , Sudhir P Kasturi
    • , Nooruddin Khan
    • , Shuzhao Li
    •  & Bali Pulendran
  3. Department of Microbiology and Immunology, Emory University, Atlanta, Georgia, USA.

    • Jens Wrammert
    • , Gui-Mei Li
    • , Megan McCausland
    • , Vibhu Kanchan
    •  & Rafi Ahmed
  4. Center for Operations Research in Medicine & Healthcare, School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.

    • Eva K Lee
  5. Department of Pharmacology and Cancer Biology, Duke University, Durham, North Carolina, USA.

    • Luigi Racioppi
  6. Department of Cellular and Molecular Biology and Pathology, University of Naples Federico II, Naples, Italy.

    • Luigi Racioppi
    •  & Anthony R Means
  7. Dana-Farber Cancer Institute, Boston, Massachusetts, USA.

    • W Nicholas Haining
  8. Department of Medicine, Division of Nephrology, Emory University School of Medicine, Atlanta, Georgia, USA.

    • Kenneth E Kokko
  9. Division of Infectious Diseases, Department of Medicine, School of Medicine, Emory University, Atlanta, Georgia, USA.

    • Rivka Elbein
    •  & Aneesh K Mehta
  10. Institute for Systems Biology, Seattle, Washington, USA.

    • Alan Aderem
  11. Laboratory of Infectious Diseases, National Institute for Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA.

    • Kanta Subbarao
  12. Department of Pathology, Emory University School of Medicine, Atlanta, Georgia, USA.

    • Bali Pulendran

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Contributions

H.I.N. did all the experiments and analyses in Figures 2,3,4,5 and 6 and Supplementary Figures 2–8; J.W., G.-M.L., M.M. and V.K. did the analyses in Figure 1 and Supplementary Figure 1; E.K.L. did the DAMIP model analyses in Figure 5; L.R., A.R.M., S.P.K. and N.K. did the mouse experiments in Figure 6; W.N.H. helped with the microarray analyses in Supplementary Figure 4; S.L. assisted with the bioinformatics analyses of the data in Figure 3; A.A. did the microarray analysis of samples from the 2007 influenza annual season; S.M.-K., K.E.K., R.E. and A.K.M. assisted with the collection and processing of samples; K.S. measured HAI titers; R.A. helped conceive of and design the study and supervised the studies in Figure 1 and Supplementary Figure 1; B.P. conceived of the study and designed and supervised the experiments and analyses in Figures 1,2,3,4,5 and 6 and Supplementary Figures 1–8; and B.P. and H.I.N. wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Bali Pulendran.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–8 and Supplementary Methods

Excel files

  1. 1.

    Supplementary Table 1

    All the differentially expressed genes identified in PBMCs of TIV or LAIV vaccinees

  2. 2.

    Supplementary Table 2

    All the differentially expressed genes identified in monocytes, mDCs, pDCs and B cells from TIV or LAIV vaccinees

  3. 3.

    Supplementary Table 3

    Microarray study ID (from NCBI GEO) and microarray sample ID used in the meta analysis of PBMC subsets and of B cell subsets

  4. 4.

    Supplementary Table 4

    Genes identified in our meta-analysis as highly expressed in a given PBMC cell subset or in a given B cell subset

  5. 5.

    Supplementary Table 5

    All the genes whose expression (d3/d0 or d7/d0) correlates to the fold increase in HAI titers (d28/d0)

  6. 6.

    Supplementary Table 6

    Sets of 2-4 genes identified by DAMIP model (first analysis) as predictors of HAI response and Number of appearances in the DAMIP model (first analysis)

  7. 7.

    Supplementary Table 7

    Microarray expression and RT-qPCR values of selected genes from TIV vaccinees of 2007-2008 and 2008-2009 Influenza seasons and Genes selected for RT-qPCR validation

  8. 8.

    Supplementary Table 8

    Sets of 2-4 genes identified by DAMIP model (second analysis) as predictors of HAI response and Number of appearances in the DAMIP model (second analysis)

  9. 9.

    Supplementary Table 9

    Sets of 2-4 genes identified by DAMIP model (third analysis) as predictors of HAI response and Number of appearances in the DAMIP model (third analysis)

  10. 10.

    Supplementary Table 10

    This table shows the Influenza vaccine composition and the gender and age of vaccinees in each Influenza season

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DOI

https://doi.org/10.1038/ni.2067

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