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. 2011 Jul 10;12(8):786-95.
doi: 10.1038/ni.2067.

Systems biology of vaccination for seasonal influenza in humans

Helder I Nakaya  1 , Jens Wrammert, Eva K Lee, Luigi Racioppi, Stephanie Marie-Kunze, W Nicholas Haining, Anthony R Means, Sudhir P Kasturi, Nooruddin Khan, Gui-Mei Li, Megan McCausland, Vibhu Kanchan, Kenneth E Kokko, Shuzhao Li, Rivka Elbein, Aneesh K Mehta, Alan Aderem, Kanta Subbarao, Rafi Ahmed, Bali Pulendran
Affiliations

Systems biology of vaccination for seasonal influenza in humans

Helder I Nakaya et al. Nat Immunol. .

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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Conflict of interest statement

COMPETING INTERESTS STATEMENT

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1
Analysis of humoral immunity to influenza vaccination. (a) Antibody response determined by HAI titers on the plasma of TIV (blue bars) and LAIV (black bars) vaccinees on day 28 post-vaccination. The bars represent the highest HAI response (fold-change day 28/ day 0) among all 3 influenza strains contained in the vaccine. Subjects were classified as “Low responders” if no increase higher than 2-fold was observed in the HAI response and as “High responders” if the HAI titers on day 28 is ≥4 times higher than the titers at baseline. (b) PBMCs collected from all vaccinees were assayed for influenza-specific IgG secreting plasmablasts by ELISPOT assay at 0 and 7 days after vaccination. Each sample was measured in duplicate, averaged and plotted as plasmablasts per million PBMCs. Median values are shown. (c) Flow cytometry analysis of plasmablasts in blood. The frequency of plasmablast gate (CD3−CD20−/loCD19+CD27hiCD38hi) is shown for a representative TIV (left panel) and LAIV (right panel) vaccinee. (d) Statistically significant positive correlation (Pearson r = 0.58, P-value (two-tail) < 0.0001) between the frequencies of plasmablasts at day 7 determined by flow cytometry and the number of influenza-specific IgG-secreting plasmablasts by ELISPOT on the same day. TIV and LAIV vaccinees are represented by blue and black dots, respectively. (e) Statistically significant positive correlation (Pearson r = 0.43, P-value (two-tail) = 0.02) between influenza-specific IgG secreting plasmablasts at day 7 and the antibody response at day 28 on TIV vaccinees.
Figure 2
Figure 2
Molecular signature induced by LAIV vaccination. (a) Interferon (IFN)-related genes differentially expressed after LAIV vaccination. Solid and dashed lines represent respectively, direct and indirect interactions reported for the genes. The colors represent the mean fold-change in gene expression on days 3 or 7 compared to day 0 in all LAIV vaccinees. Genes with expression fold-change highest at day 3 or day 7 post-vaccination are shown on the left or on the right of the network, respectively. (b) Induction of key IFN-related genes was confirmed by quantitative RT-PCR. PBMCs of healthy subjects were stimulated in vitro with different vaccines for 24h. The GAPDH-normalized expression levels of OAS1, IRF7, Mx2 and STAT1 in stimulated PBMCs were compared to those of PBMCs non-stimulated.
Figure 3
Figure 3
Molecular signatures induced by TIV vaccination. (a) Heat map of gene signatures of immune cells identified by meta-analysis (see Methods). Expression level of each gene (in rows) is represented by the number of standard deviations above (red) or below (blue) the average value for that gene across all samples (in columns). (b) Spider graph showing the fold enrichment of TIV up-regulated genes among the genes highly expressed in any PBMC subset. Fold enrichment is calculated as described in Methods. Cell subsets with statistically significant enrichment (Fisher’s exact test two tailed P-value < 10−10) were marked with asterisks. (c) Spider graph showing the fold enrichment of TIV up-regulated genes among the genes highly expressed in B cells and also highly expressed in a specific B cell subset. (d) Heat map of genes up-regulated by TIV vaccination and also highly expressed in B cells and antibody-secreting cells. The official gene symbol for each probe set is shown on the bottom of the heat map. Probe sets that mapped to antibody variable regions are named ‘abParts’ and those ones not annotated are represented by the Affymetrix probe ID. (e) Spider graph showing the fold enrichment of LAIV up-regulated genes among the genes highly expressed in any PBMC subset.
Figure 4
Figure 4
Molecular signatures that correlate with antibody titers to TIV. (a) Heat map of probe sets (in lines) whose baseline normalized expression at day 3 (top) or day 7 (bottom) correlates (Pearson, P-value < 0.05) to baseline-normalized antibody response at day 28 post-TIV vaccination. Blue and red bars on the right of the heat map indicate the probe sets with negative and positive correlation, respectively (number of probe sets shown). The colors represent the individual fold-change in gene expression at days 3 or 7 compared to day 0 in TIV vaccinees. Probe sets that correlate on both day 3 and 7 to HAI response were counted as “day 7” and the day7-day0 expression was used to represent the expression on heat map. (b) HAI response-correlated genes (Pearson, P-value < 0.05) associated with the Unfolded protein response (purple area), Antibody-secreting cell differentiation (light brown area) and/or regulated by the transcription factor XBP-1. Solid and dashed lines represent, respectively direct and indirect interactions reported for the genes. (c) Spider graph showing the fold enrichment of genes (among those highly expressed in any PBMC subset), whose expression on either day 3 or day 7 post-TIV vaccination is positively (red line) or negatively (blue line) correlated to HAI titers (Pearson, P-value < 0.05). Fold enrichment is calculated as described in Methods. Cell subsets with statistically significant enrichment (Fisher’s exact test two tailed P-value < 10−10) are marked with asterisks. (d) Heat map of probe sets highly expressed in B cells and antibody-secreting cells whose baseline normalized expression correlates (Pearson, P-value < 0.05) to baseline-normalized HAI response.
Figure 5
Figure 5
Signatures that predict the antibody response induced by TIV. (a) Schematic representation of the experimental design used to identify the early gene signatures that predict antibody responses to TIV vaccination. The 2008–2009 Trial was used as a “training set to identify predictive signatures, using the Discriminant Analysis of Mixed Integer Programming (DAMIP) model. These signatures were then tested on the data from the 2007–2008 trial, which represents the “testing set.” The expression of a subset of genes contained within the DAMIP predictive signatures using the 2007–2008 and 2008–2009 trials was then quantified by RT-PCR in a third independent trial (2009–2010 trial). The DAMIP model was again used to confirm the predictive signatures. (b) The expression of a subset of genes contained within the predictive signatures generated by the DAMIP model was validated using RT-PCR. There was a statistically significant positive correlation (2,897 XY pairs, Pearson r = 0.68, P-value < 10−11) between the changes in relative gene expression determined by microarray and RT-PCR analysis. Each point represents a single gene at a given time point. (c) Some of the DAMIP gene signatures identified using 2008–2009 trial as training set and 2007–2008 and 2009–2010 trials as validation sets (i.e. DAMIP model 3). The accuracy represents the number of subjects correctly classified as “low responders” or “high responders” (see legend of Fig. 1a).
Figure 6
Figure 6
CAMKIV regulates the antibody response to influenza vaccine. (a) Statistically significant negative correlation between the HAI response at day 28 and the levels of CaMKIV mRNA on PBMCs of vaccinees at day 3 post-vaccination. The left graph represents the TIV vaccinees of 2008–2009 trial (Pearson r = −0.47, P-value (two-tail) = 0.016) and the right graph represents the TIV vaccinees of 2007–2008 trial (Pearson r = −0.73, P-value (two-tail) = 0.024). (b) Statistically significant negative correlation between the number of influenza-specific IgG secreting plasmablasts by ELISPOT at day 7 and the levels of CAMKIV mRNA on PBMCs of vaccinees at day 3 post-vaccination. (c) Phosphorylation of mouse CaMKIV protein after in vitro stimulation of splenocytes with TIV, as determined by western blot. (d) Phosphorylation of CaMKIV protein after in vitro stimulation of human PBMCs treated with TIV for different time points, as determined by western blot. (e) Serum antigen-specific IgG1 (top) and IgG2c (bottom) responses of wild-type (black line) and CamkIV −/− (blue line) mice at day 7, 14 and 28 post-TIV immunization. Student t-test method was used to calculate the statistical significance of each comparison (* = p-value < 0.05, ** = p-value < 0.01). Individual wild type and CamkIV −/− mice are represented by black squares and blue triangles, respectively.
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