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.

Figure 1

Genomic signatures of innate immune responses to YF-17D. (a) Ingenuity Pathways Analysis of a subset of genes identified as being regulated significantly (Benjamini and Hochberg false-discovery rate, o0.05) in two independent trials and supplemented with transcription factor binding motif information from TOUCAN for IRF7 and IRF9 (complete network, Supplementary Fig. 3). (b) Heat map showing kinetics of changes in expression of common genes identified in two independent trials sorted into categories based on DAVID Bioinformatics Database gene descriptions. The heat map colors represent the average expression among the subjects for each time point (given in days at the bottom of each column). (c) Changes in relative gene expression have significant correlations between microarray and RT-PCR analysis. Each point represents a single gene at a given time point. (d) Analysis of 33 genes identified as being significantly modulated by microarray analysis reveals that 26 genes also have significant modulation as measured by RT-PCR (P < 0.05). The heat map represents the gene expression by RT-PCR on days 3 and 7 as a multiple of that on day 0. All genes and time points were first normalized to the average cycling threshold value of expression of the housekeeping genes for 18S ribosomal RNA, ACTB (β-actin) and B2M (β2-microglobulin). The gene expression on days 3 and 7 as a multiple of that on day 0 was then calculated and imported into GeneSpring for heat map production. Data from a,b are derived from trials 1 and 2, with 15 and 10 subjects, respectively. Data from c,d are from trial 1, with 15 subjects.

Figure 2

Variations in the magnitudes of the antigen-specific CD8⁺ T cell and neutralizing antibody responses to YF-17D. (a) Flow cytometry for expression of HLA-DR with CD38, on gated CD3⁺CD8⁺ T cells isolated from blood of YF-17D vaccinees. The red dots and numbers indicate the yellow-fever specific CD8⁺ T cells that stained with the HLA-A2-restricted tetramer (YF-Tet⁺). (b) Correlation between YF-Tet⁺ T cells and HLA-DR⁺CD38⁺CD3⁺CD8⁺ T cells. (c) Flow cytometry analysis of granzyme B, CD27, CD28, Bcl-2, Ki67, CD127, CCR5, CD45RA and CCR7 in the blood of YF-17D subjects from trial 1. HLA-DR⁺CD38⁺CD8⁺ T cells (in regions outlined for plots of days 0 and 15) have effector phenotype (red dots) on day 15. (d,e) Graph of flow cytometry data comparing day 15 and day 60 CD8⁺ T cell activation and neutralizing antibody titers from 15 subjects in trial 1.

Figure 3

Genomic signatures that correlate with the magnitude of the CD8⁺ T cell response. Genes with a log₂-fold change of >0.5 or −0.5 in more than 25% of the 15 subjects of trial 1 were first selected, for day 3 versus day 0 and separately for day 7 versus day 0. Next, the slope of the P-value of the percentage of activated CD8⁺ T cells versus the log₂-fold change in gene expression was calculated for each remaining gene. Those genes with P < 0.05 were identified as having a significant relationship between early gene expression changes and later CD8⁺ T cell responses. (a) Unsupervised principal component analysis of the gene expression for each subject on both days 3 and 7 revealed that subjects could be segregated on the basis of CD8⁺ T cell responses above and below 3%. (b) A standard correlation cluster analysis in GeneSpring confirmed the segregation of T cell responses into two groups with an approximate cutoff of 3–4% activation.

Figure 4

Genomic signatures that predict the magnitude of the CD8⁺ T cell responses, using the ClaNC model. The genes identified as having a relationship to the subsequent T cell responses, as described in Figure 3, were analyzed by ClaNC to develop a predictive model of CD8⁺ T cell responses based on a subset of genes. (a) A process of leave-one-out cross-validation testing the predictive strengths of subsets of genes for ClaNC gene models. (b) The ClaNC gene models developed through cross validation on the first trial of 15 subjects was tested on both trials of 15 and 10 subjects to determine the error rates.

Figure 5

YF-17D induces eIF2α phosphorylation and stress granule formation. (a) Immunoblot on lysates from human total PBMC or baby hamster kidney cells were treated with 0.5 mM arsenite for 30 min or YF-17D for the indicated lengths of time. Cell extracts were prepared and probed for eIF2α phosphorylation (top) as well as for total eIF2α abundance (bottom). (b) Fluorescence microscopy of baby hamster kidney cells treated with 0.5 mM arsenite for 30 min or YF-17D (multiplicity of infection 2) overnight before fixing and staining for cytotoxic granule-associated RNA-binding protein-like 1 (TIAR; green). Cells were counterstained with BODIPY 558/568 phalloidin for F-actin (red) and DAPI for nuclei (blue). Scale bars, 5 μm. Results are representative of two independent experiments.