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Integrative genomic analysis of the human immune response to influenza vaccination.

Franco LM, Bucasas KL, Wells JM, Niño D, Wang X, Zapata GE, Arden N, Renwick A, Yu P, Quarles JM, Bray MS, Couch RB, Belmont JW, Shaw CA - Elife (2013)

Bottom Line: Identification of the host genetic factors that contribute to variation in vaccine responsiveness may uncover important mechanisms affecting vaccine efficacy.We carried out an integrative, longitudinal study combining genetic, transcriptional, and immunologic data in humans given seasonal influenza vaccine.More broadly, we demonstrate that an integrative study design is an efficient alternative to existing methods for the identification of genes involved in complex traits.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular and Human Genetics , Baylor College of Medicine , Houston , United States ; Department of Medicine , Baylor College of Medicine , Houston , United States.

ABSTRACT
Identification of the host genetic factors that contribute to variation in vaccine responsiveness may uncover important mechanisms affecting vaccine efficacy. We carried out an integrative, longitudinal study combining genetic, transcriptional, and immunologic data in humans given seasonal influenza vaccine. We identified 20 genes exhibiting a transcriptional response to vaccination, significant genotype effects on gene expression, and correlation between the transcriptional and antibody responses. The results show that variation at the level of genes involved in membrane trafficking and antigen processing significantly influences the human response to influenza vaccination. More broadly, we demonstrate that an integrative study design is an efficient alternative to existing methods for the identification of genes involved in complex traits. DOI:http://dx.doi.org/10.7554/eLife.00299.001.

No MeSH data available.


Related in: MedlinePlus

Gene expression at specific loci correlates with the antibody response to vaccination.(A) Examples of positive (DYNLT1) and negative (ANKRD33) correlation between gene expression on day 1 and the magnitude of the antibody response to the vaccine. Data points and regression lines in the scatterplots display the results for the discovery (blue) and validation (magenta) cohorts. (B) A total of 301 genes showed evidence of significant correlation between gene expression and the antibody response to the vaccine in both cohorts. Of these, 281 showed evidence of positive correlation and 83 of negative correlation. Each individual is represented by a column in the heatmaps. The top heatmaps display the magnitude of the antibody response (titer response index). The bottom heatmaps display the deviations around the expression mean for each gene. Individual gene identifiers and correlation coefficients are presented in the Interactive Results Tool.DOI:http://dx.doi.org/10.7554/eLife.00299.006
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fig4: Gene expression at specific loci correlates with the antibody response to vaccination.(A) Examples of positive (DYNLT1) and negative (ANKRD33) correlation between gene expression on day 1 and the magnitude of the antibody response to the vaccine. Data points and regression lines in the scatterplots display the results for the discovery (blue) and validation (magenta) cohorts. (B) A total of 301 genes showed evidence of significant correlation between gene expression and the antibody response to the vaccine in both cohorts. Of these, 281 showed evidence of positive correlation and 83 of negative correlation. Each individual is represented by a column in the heatmaps. The top heatmaps display the magnitude of the antibody response (titer response index). The bottom heatmaps display the deviations around the expression mean for each gene. Individual gene identifiers and correlation coefficients are presented in the Interactive Results Tool.DOI:http://dx.doi.org/10.7554/eLife.00299.006

Mentions: We and others have shown that for some transcripts there is significant correlation between the magnitude of the transcriptional and antibody responses to the vaccine stimulus (Zhu et al., 2010; Bucasas et al., 2011; Nakaya et al., 2011) In a combined analysis of the two cohorts in the present study, 301 transcripts were found to correlate with the magnitude of the antibody response (Figure 4). Additional details of these 301 transcripts, including correlation coefficients and days of maximum correlation, are provided in Table 3 via the Interactive Results Tool (which is also available to download from Zenodo and shown within Supplementary file 1). We imposed an additional selection threshold based on this correlation, and identified 20 genes that show evidence of significant genotype-expression association (genotype effect p<5 × 10−8), a significant correlation between the transcriptional and antibody responses (expression-antibody effect p<0.05), and either a transcriptional response to the vaccine (day effect p<0.01) or evidence of a change in the magnitude of the genetic effect after vaccination (top 1% of the delta-Rg2 distribution) in the two independent cohorts. These loci have the strongest evidence of genetic variation influencing the immune response to the vaccine, and include TAP2, SNX29, FGD2, NAPSA, NAPSB, GM2A, C1orf85, JUP, FBLN5, CHST13, DIP2A, PAM, D4S234E, C3AR1, HERC2, LST1, LRRC37A4, OAS1, RPL14, and DYNLT1. Remarkably, seven of these encode proteins involved in intracellular antigen transport and processing (Figure 5).10.7554/eLife.00299.006Figure 4.Gene expression at specific loci correlates with the antibody response to vaccination.


Integrative genomic analysis of the human immune response to influenza vaccination.

Franco LM, Bucasas KL, Wells JM, Niño D, Wang X, Zapata GE, Arden N, Renwick A, Yu P, Quarles JM, Bray MS, Couch RB, Belmont JW, Shaw CA - Elife (2013)

Gene expression at specific loci correlates with the antibody response to vaccination.(A) Examples of positive (DYNLT1) and negative (ANKRD33) correlation between gene expression on day 1 and the magnitude of the antibody response to the vaccine. Data points and regression lines in the scatterplots display the results for the discovery (blue) and validation (magenta) cohorts. (B) A total of 301 genes showed evidence of significant correlation between gene expression and the antibody response to the vaccine in both cohorts. Of these, 281 showed evidence of positive correlation and 83 of negative correlation. Each individual is represented by a column in the heatmaps. The top heatmaps display the magnitude of the antibody response (titer response index). The bottom heatmaps display the deviations around the expression mean for each gene. Individual gene identifiers and correlation coefficients are presented in the Interactive Results Tool.DOI:http://dx.doi.org/10.7554/eLife.00299.006
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3713456&req=5

fig4: Gene expression at specific loci correlates with the antibody response to vaccination.(A) Examples of positive (DYNLT1) and negative (ANKRD33) correlation between gene expression on day 1 and the magnitude of the antibody response to the vaccine. Data points and regression lines in the scatterplots display the results for the discovery (blue) and validation (magenta) cohorts. (B) A total of 301 genes showed evidence of significant correlation between gene expression and the antibody response to the vaccine in both cohorts. Of these, 281 showed evidence of positive correlation and 83 of negative correlation. Each individual is represented by a column in the heatmaps. The top heatmaps display the magnitude of the antibody response (titer response index). The bottom heatmaps display the deviations around the expression mean for each gene. Individual gene identifiers and correlation coefficients are presented in the Interactive Results Tool.DOI:http://dx.doi.org/10.7554/eLife.00299.006
Mentions: We and others have shown that for some transcripts there is significant correlation between the magnitude of the transcriptional and antibody responses to the vaccine stimulus (Zhu et al., 2010; Bucasas et al., 2011; Nakaya et al., 2011) In a combined analysis of the two cohorts in the present study, 301 transcripts were found to correlate with the magnitude of the antibody response (Figure 4). Additional details of these 301 transcripts, including correlation coefficients and days of maximum correlation, are provided in Table 3 via the Interactive Results Tool (which is also available to download from Zenodo and shown within Supplementary file 1). We imposed an additional selection threshold based on this correlation, and identified 20 genes that show evidence of significant genotype-expression association (genotype effect p<5 × 10−8), a significant correlation between the transcriptional and antibody responses (expression-antibody effect p<0.05), and either a transcriptional response to the vaccine (day effect p<0.01) or evidence of a change in the magnitude of the genetic effect after vaccination (top 1% of the delta-Rg2 distribution) in the two independent cohorts. These loci have the strongest evidence of genetic variation influencing the immune response to the vaccine, and include TAP2, SNX29, FGD2, NAPSA, NAPSB, GM2A, C1orf85, JUP, FBLN5, CHST13, DIP2A, PAM, D4S234E, C3AR1, HERC2, LST1, LRRC37A4, OAS1, RPL14, and DYNLT1. Remarkably, seven of these encode proteins involved in intracellular antigen transport and processing (Figure 5).10.7554/eLife.00299.006Figure 4.Gene expression at specific loci correlates with the antibody response to vaccination.

Bottom Line: Identification of the host genetic factors that contribute to variation in vaccine responsiveness may uncover important mechanisms affecting vaccine efficacy.We carried out an integrative, longitudinal study combining genetic, transcriptional, and immunologic data in humans given seasonal influenza vaccine.More broadly, we demonstrate that an integrative study design is an efficient alternative to existing methods for the identification of genes involved in complex traits.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular and Human Genetics , Baylor College of Medicine , Houston , United States ; Department of Medicine , Baylor College of Medicine , Houston , United States.

ABSTRACT
Identification of the host genetic factors that contribute to variation in vaccine responsiveness may uncover important mechanisms affecting vaccine efficacy. We carried out an integrative, longitudinal study combining genetic, transcriptional, and immunologic data in humans given seasonal influenza vaccine. We identified 20 genes exhibiting a transcriptional response to vaccination, significant genotype effects on gene expression, and correlation between the transcriptional and antibody responses. The results show that variation at the level of genes involved in membrane trafficking and antigen processing significantly influences the human response to influenza vaccination. More broadly, we demonstrate that an integrative study design is an efficient alternative to existing methods for the identification of genes involved in complex traits. DOI:http://dx.doi.org/10.7554/eLife.00299.001.

No MeSH data available.


Related in: MedlinePlus