Gene signatures related to B-cell proliferation predict influenza vaccine-induced antibody response - PubMed (original) (raw)

Yan Tan et al. Eur J Immunol. 2014 Jan.

Abstract

Vaccines are very effective at preventing infectious disease but not all recipients mount a protective immune response to vaccination. Recently, gene expression profiles of PBMC samples in vaccinated individuals have been used to predict the development of protective immunity. However, the magnitude of change in gene expression that separates vaccine responders and nonresponders is likely to be small and distributed across networks of genes, making the selection of predictive and biologically relevant genes difficult. Here we apply a new approach to predicting vaccine response based on coordinated upregulation of sets of biologically informative genes in postvaccination gene expression profiles. We found that enrichment of gene sets related to proliferation and immunoglobulin genes accurately segregated high responders to influenza vaccination from low responders and achieved a prediction accuracy of 88% in an independent clinical trial. Many of the genes in these gene sets would not have been identified using conventional, single-gene level approaches because of their subtle upregulation in vaccine responders. Our results demonstrate that gene set enrichment method can capture subtle transcriptional changes and may be a generally useful approach for developing and interpreting predictive models of the human immune response.

Keywords: B-Cell proliferation; Gene expression; Immune response; Systems biology; Vaccine efficacy.

© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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Figures

Figure 1

Figure 1. YF-17 vaccination induces upregulation of gene sets related to interferon response

(A) Heatmap of the top 20 gene sets enriched in Day 7 samples compared to Day 0 samples, with color indicating ssGSEA enrichment scores for each gene set in each sample. Gene sets are ranked by the normalized mutual information score. DAVID annotations of gene sets indicated in the bar on the left; orange indicates a signature enriched for the GO term “Response to Virus”; purple “Response to Stimulus”. (B) Constellation Map of the top scoring 20 gene sets. Purple arc indicates gene-sets with overlapping features. Numbers correspond to gene-sets in (A).

Figure 2

Figure 2. Antibody response to TIV correlates with enrichment of proliferation and immunoglobulin gene sets

(A) Heatmap of the top 13 gene sets (FDR < 0.25) enriched in high responders (yellow) compared to low responders (green). Gene sets are ranked by the mutual information score. Membership of the clusters detected in the Constellation Map (B) is shown on the left of the heatmap. (B) Constellation Map of the top 13 gene sets. Two connected clusters of gene sets are detected in the constellation map, indicated by orange and lilac arcs. (C and D) Protein-Protein interaction network of two connected clusters in (B). Significant physical connectivity is shown for genes within the antibody cluster (C, orange) and proliferation cluster (D, lilac).

Figure 3

Figure 3. Model fit of response to TIV using proliferation and immunoglobulin gene sets

(A and B) Logistical Regression Model of probability of vaccine response for proliferation (A, CHIANG_LIVER_CANCER_SUBCLASS_PROLIFERATION_UP) and immunoglobulin (B, REACTOME_INITIAL_TRIGGERING_OF_COMPLEMENT) gene set enrichment scores. (C) Combined model using Bayes rule.

Figure 4

Figure 4. Predictive gene sets capture genes with subtle changes in expression

Rank of genes identified in a single-gene predictor of TIV response (i), compared to the rank of genes contained in the proliferation (ii) and immunoglobulin (iii) gene-sets. Each gene indicated by a vertical line and its relative rank on the list of differentially expressed genes comparing TIV responders compared to non-responders indicated by the line graph below.

Figure 5

Figure 5. Enrichment of proliferation and immunoglobulin gene sets correlate with the frequency of antibody spot forming cells

(A and B) Enrichment scores of the proliferation gene set (A, CHIANG_LIVER_CANCER_SUBCLASS_PROLIFERATION_UP) and immunoglobulin gene set (B, REACTOME_INITIAL_TRIGGERING_OF_COMPLEMENT and frequency of IgG secreting cells (ASC). Significance is calculated by comparison to the null distribution, calculated by correlations derived from random gene sets.

References

    1. Pulendran B, Ahmed R. Immunological mechanisms of vaccination. Nature Immunology. 2011;131:509–517. -PMC -PubMed
    1. Pulendran B, Li S, Nakaya HI. Systems vaccinology. Immunity. 2010;33:516–529. -PMC -PubMed
    1. Nakaya HI, Li S, Pulendran B. Systems vaccinology: learning to compute the behavior of vaccine induced immunity. Wiley interdisciplinary reviews Systems biology and medicine. 2011 -PMC -PubMed
    1. Querec TD, Akondy RS, Lee EK, Cao W, Nakaya HI, Teuwen D, Pirani A. Systems biology approach predicts immunogenicity of the yellow fever vaccine in humans. Nature Immunology. 2009;10:116–125. -PMC -PubMed
    1. Gaucher D, Therrien R, Kettaf N, Angermann B, Boucher G, Filali-Mouhim A, Moser J. Yellow fever vaccine induces integrated multilineage and polyfunctional immune responses. The Journal of Experimental Medicine. 2008 jem.20082292. -PMC -PubMed

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