Limits...
Systems biology of vaccination for seasonal influenza in humans.

Nakaya HI, Wrammert J, Lee EK, Racioppi L, Marie-Kunze S, Haining WN, Means AR, Kasturi SP, Khan N, Li GM, McCausland M, Kanchan V, Kokko KE, Li S, Elbein R, Mehta AK, Aderem A, Subbarao K, Ahmed R, Pulendran B - Nat. Immunol. (2011)

Bottom Line: 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).Thus, systems approaches can be used to predict immunogenicity and provide new mechanistic insights about vaccines.

View Article: PubMed Central - PubMed

Affiliation: Emory Vaccine Center, Emory University, Atlanta, Georgia, USA.

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.

Show MeSH

Related in: MedlinePlus

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).
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3140559&req=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).

Mentions: In initial analyses, we classified the TIV vaccinees into 2 extreme groups: very low and very high HAI responders. The former consisted of subjects with 2-fold or lower increase in the HAI titers against any of the 3 influenza strains of the vaccine (Fig. 1a). The latter group consisted of subjects with 8-fold or higher increase in the HAI response for at least one of the 3 influenza strains of the vaccine. Subjects with intermediate HAI response (>= 2-fold and < 8-fold) and subjects for whom microarray data were not available at either day 3 or day 7 post-vaccination (total of 7 subjects) were not analyzed. This trial (named “2008–2009 Trial”) was used to train the DAMIP model to establish an unbiased estimate of correct classification. A second, independent trial was used to evaluate the predictive accuracy of the classification rules identified in the first trial (see Methods and Fig. 5a). The second trial (named “2007–2008 Trial”) consisted of microarray gene expression profile of 9 subjects vaccinated with TIV in the previous year. Using this approach, DAMIP model identified 12 sets of genes containing 2 to 4 genes each (each set associates with one predictive rule) from 2008–2009 trial with 10-fold cross validation accuracy over 90%. The resulting blind prediction accuracy on 2007–2008 trial (predicting low or high antibody responders) is over 90%. Further, some of the 271 set of discriminatory genes offer an accuracy of over 90% in both 10-fold cross validation in the training trial, and in blind prediction accuracy. (Fig. 5a and Supplementary Table 6)


Systems biology of vaccination for seasonal influenza in humans.

Nakaya HI, Wrammert J, Lee EK, Racioppi L, Marie-Kunze S, Haining WN, Means AR, Kasturi SP, Khan N, Li GM, McCausland M, Kanchan V, Kokko KE, Li S, Elbein R, Mehta AK, Aderem A, Subbarao K, Ahmed R, Pulendran B - Nat. Immunol. (2011)

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).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3140559&req=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).
Mentions: In initial analyses, we classified the TIV vaccinees into 2 extreme groups: very low and very high HAI responders. The former consisted of subjects with 2-fold or lower increase in the HAI titers against any of the 3 influenza strains of the vaccine (Fig. 1a). The latter group consisted of subjects with 8-fold or higher increase in the HAI response for at least one of the 3 influenza strains of the vaccine. Subjects with intermediate HAI response (>= 2-fold and < 8-fold) and subjects for whom microarray data were not available at either day 3 or day 7 post-vaccination (total of 7 subjects) were not analyzed. This trial (named “2008–2009 Trial”) was used to train the DAMIP model to establish an unbiased estimate of correct classification. A second, independent trial was used to evaluate the predictive accuracy of the classification rules identified in the first trial (see Methods and Fig. 5a). The second trial (named “2007–2008 Trial”) consisted of microarray gene expression profile of 9 subjects vaccinated with TIV in the previous year. Using this approach, DAMIP model identified 12 sets of genes containing 2 to 4 genes each (each set associates with one predictive rule) from 2008–2009 trial with 10-fold cross validation accuracy over 90%. The resulting blind prediction accuracy on 2007–2008 trial (predicting low or high antibody responders) is over 90%. Further, some of the 271 set of discriminatory genes offer an accuracy of over 90% in both 10-fold cross validation in the training trial, and in blind prediction accuracy. (Fig. 5a and Supplementary Table 6)

Bottom Line: 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).Thus, systems approaches can be used to predict immunogenicity and provide new mechanistic insights about vaccines.

View Article: PubMed Central - PubMed

Affiliation: Emory Vaccine Center, Emory University, Atlanta, Georgia, USA.

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.

Show MeSH
Related in: MedlinePlus