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. 2013 Apr 16:9:659.
doi: 10.1038/msb.2013.15.

Apoptosis and other immune biomarkers predict influenza vaccine responsiveness

David Furman  1 , Vladimir Jojic, Brian Kidd, Shai Shen-Orr, Jordan Price, Justin Jarrell, Tiffany Tse, Huang Huang, Peder Lund, Holden T Maecker, Paul J Utz, Cornelia L Dekker, Daphne Koller, Mark M Davis
Affiliations

Apoptosis and other immune biomarkers predict influenza vaccine responsiveness

David Furman (V体育ios版) et al. Mol Syst Biol. .

Erratum in

  • Mol Syst Biol. 2013;9:680
  • Mol Syst Biol. 2014;10:750

V体育安卓版 - Abstract

Despite the importance of the immune system in many diseases, there are currently no objective benchmarks of immunological health. In an effort to identifying such markers, we used influenza vaccination in 30 young (20-30 years) and 59 older subjects (60 to >89 years) as models for strong and weak immune responses, respectively, and assayed their serological responses to influenza strains as well as a wide variety of other parameters, including gene expression, antibodies to hemagglutinin peptides, serum cytokines, cell subset phenotypes and in vitro cytokine stimulation. Using machine learning, we identified nine variables that predict the antibody response with 84% accuracy. Two of these variables are involved in apoptosis, which positively associated with the response to vaccination and was confirmed to be a contributor to vaccine responsiveness in mice. The identification of these biomarkers provides new insights into what immune features may be most important for immune health. VSports手机版.

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Conflict of interest statement (V体育安卓版)

The authors declare that they have no conflict of interest.

Figures

Figure 1
Figure 1
Study design. Blood samples are obtained before (d0) and 28±7 days (d28) after a single intramuscular inoculation of the seasonal inactivated influenza vaccine. Samples from d0 are used for gene expression analysis, hemagglutinin 20-mer peptide microarrays, determination of serum cytokines and chemokines, cell subset phenotyping and signaling responses to cytokine stimulations on CD4+ and CD8+ T cells, B cells and monocytes, as well as the phosphorylation of PLC-γ and Akt upon BCR crosslinking on B cells. Serum samples from d0 and d28 are utilized for determination of anti-influenza antibody titers by the HAI assay. Measurements from d0 are processed and subjected to computational modeling for prediction of the antibody response.
Figure 2
Figure 2
Antibody responses to the influenza vaccine in young and older subjects. Serum samples obtained from 89 vaccine recipients before and ∼3 weeks after vaccination are assayed for HAI against each vaccine strain of the influenza virus to determine the antibody response to the vaccine. Seroconversion (⩾4-fold increase) is calculated and the number of strains that subjects seroconverted against is used to define response categories; seroconversion to 0 or 1 strain is considered as a poor response (PR), whereas seroconversion to 2 or all 3 strains is considered as a good response (GR) to the vaccine (A). (B) Percentage of PR and GR for young and older subjects. (C) Pre-vaccine geometric mean titer for all strains in young versus older subjects. **P-value <0.01.
Figure 3
Figure 3
Pre-existing antibodies to HA peptides that correlate with pre-vaccine HAI antibody titers (pre-GMT). Individuals were sorted by pre-GMT levels and divided into high or low pre-GMT (A) and reactivities against HA peptides were compared (B). A significantly higher fraction of samples reactive to peptides H1_23, H3_5, H3_8 and BH14 were found in individuals with high pre-GMT compared with low pre-GMT (P<0.05 by Student’s t-test). Red circles=young, green circles=older.
Figure 4
Figure 4
Hemagglutinin peptides targeted by pre-existing antibodies that predict the antibody response to the influenza vaccine. Pre-vaccine serum reactivity to HA peptides for each of the three strains was subjected to feature selection and prediction of the HAI response. The crossvalidated area under the ROC curve (cvAUROC) (A) and regression coefficients for each feature (B) from Model 1 were obtained from the cross-validation procedure. Excluding age from the learning procedure results in incorporation of adjacent peptide reactivities to Model 3 (C); however, the accuracy of the prediction drops to 0.711. Asterisks in (C) indicate significant age associations.
Figure 5
Figure 5
Baseline blood measurements that predict the antibody response to the influenza vaccine. Diverse measurements from pre-vaccine samples were used for feature selection and prediction of the HAI response. The cross-validated area under the ROC curve (cvAUROC) for Model 1 (A) and regression coefficients for each feature (B) were obtained from the cross-validation procedure. mod_047, gene module APO; CD8/U/STAT1, pSTAT1 in unstimulated CD8+ T cells; CD4.CM, central-memory CD4+ T cells; CD8.EM, effector-memory CD8+ T cells; mod_085, gene module PROL; pre-GMT, geometric mean titer for all three virus strains.
Figure 6
Figure 6
Pre-existing antibodies to HA peptides that correlate with expression of apoptosis module. Individuals were sorted and divided by expression levels of genes in the APO module (A) and reactivity to HA peptides was compared between individuals with high or low gene expression (B). Various HA peptides’ reactivities were significantly different between the APO high and low groups. Peptides H1_1, H1_6, H1_16, H3_15, H3_21, BH21 and BH23 (asterisks) were found to be predictive of the HAI titer in response to the vaccine. Red circles=young, Green circles=older.
Figure 7
Figure 7
Immune features from Model 3 that associate with and predict the HAI response to TIV. The cross-validated area under the ROC curve (cvAUROC) for Model 3 is depicted in A and the selected immune features that associate with and predict the HAI response to TIV in B. mod_047, gene module APO; CD8/U/STAT1, pSTAT1 in unstimulated CD8+ T cells; CD4.CM, central-memory CD4+ T cells; CD8.EM, effector-memory CD8+ T cells; mod_085, gene module PROL; pre-GMT, geometric mean titer for all three virus strains; CD8.NA, naive CD8 T cell frequency; mod_054, gene module 54 (cell death by apoptosis; P<0.01); mod_034, gene module 34 (cell-to-cell signaling and interaction; P<0.01); mod_043, gene module 43 (RNA post-transcriptional modification; P<0.01); CD8 CD28-, CD8+ CD28− cell frequency; NK cells, NK cell frequency; mod_079 and mod_092, gene modules 79 and 92 (metabolism of carbohydrates (P<0.01)).
Figure 8
Figure 8
Antibody responses to vaccination in apoptosis-deficient mice. Fas-deficient (squares) or control (circles) mice were vaccinated with a single intramuscular injection of the seasonal trivalent inactivated influenza vaccine and specific anti-vaccine IgG levels were measured by ELISA before and ∼4 weeks after vaccination. Results from four MRL/Mpj-Faslpr/J (Mpj/lpr) and four MRL/MpJ (Mpj) mice (A); and from six B6.MRL-Faslpr/J (B6/lpr) and three C57BL/6 (B6) (B) mice are shown. **P=0.006, ***P=0.0001 by Student’s t-test.
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