One of the major protocols of the CHI is to assess the immune phenotypes of normal individuals at baseline and after flu vaccination. Our collaborators have generated multiple data sets from high-throughput measurements (herein refer to as the immunome) of peripheral blood mononuclear cells (PBMC), including microarray data for measuring transcript abundance, 15-color flow cytometry for assessing cell populations (and abundance of key markers), luminex assays for measuring serum cytokine concentrations, genome-wide genotyping, and immunological endpoints such as virus-specific antibody titers and B cell elispots. We have successfully tackled several data analysis challenges in the past year and are in the process of constructing and refining predictive models, including: 1. Based on statistical models that take into account of both known and hidden confounding variables, we have inferred and are in the process of validating (using an independent cohort) the vaccine-induced changes in the abundance of transcripts, cell populations, and cytokines. Pathway and network enrichment analyses have also been conducted based on gene expression data, revealing both expected (e.g., pattern recognition receptor pathways during the innate-response phase) and novel network perturbed by the vaccine. We have also analyzed the correlation between initial conditions, such as age and gender, and vaccine-induced changes and found transcripts and pathways that are strongly dependent on these initial conditions. Our colleagues are in the process of validating these by conducting the same experiments on an independent cohort. 2. We have conducted correlation and cross-validation based predictive analysis on the primary end points (antibody titers) and identified predictive genes, pathways and cell populations. In addition to the expected signature of plasmablast expansion during the adaptive phase of the response, we have also identified several putative innate (e.g., dendritic cells) and baseline signatures. We are currently in the process of confirming these findings using a separate cohort. 3. Using methods robust to batch effects, we have integrated cytokine and gene expression data to infer how cytokines affect genes and cell populations. This provides direct in vivo evidence of the functions of human cytokine and how they relate to one another on the basis of their signatures. 4. We have conducted preliminary genetic analysis linking genetic variants to PBMC gene expression. Using our cohort data, we are also developing a genetic imputation method that takes into account individual variation (i.e., personal imputation).
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