We have been primarily utilizing human data generated by the trans-NIH Center for Human Immunology (CHI) to assess the immune phenotypes of healthy individuals at baseline and after flu vaccination. The CHI has generated multiple types of measurements of peripheral blood mononuclear cells (PBMC), including microarray data for measuring transcript abundance, multiple panels of 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 a number of data analysis and modeling challenges in the past year and initiated integrative modeling projects that involved both in-house and public data sets. These include: 1. By utilizing vaccine perturbation data, we developed a conceptual and methodological framework to quantify baseline and response variations at the level of genes, pathways, and cell populations in a cohort of individuals and to take advantage of these variations to systematically infer correlates, build predictive models of response quality after vaccination, and infer novel functional connectivities in the human immune system. By applying this framework using antibody titer response from the CHI flu study as an exemplar endpoint, we confirmed previously known post-vaccination correlates based on gene expression and plasmablasts frequency from day 7 samples. More importantly, using an approach that compensates for the influence of pre-existing serology, age and gender, we describe accurate predictive models of antibody responses using pre-vaccination non-antigen specific cell population frequencies alone. This latter finding of response predictors in baseline measurements has obvious implications for the design of future vaccine trials and for developing a deeper understanding of the molecular and cellular parameters that contribute to robust vaccine responses. The robustness and translational potential of these findings is further emphasized by our discovery that the parameters playing the greatest role in correct response prediction are those with the most stable baseline values across individuals. This raises the prospect of monitoring immune health and predicting the quality of immune responses in the clinic via the evaluation of these blood biomarkers. The conceptual and computational analysis framework we have developed can also be applied to systems and population level exploration in a number of medically relevant circumstances, including but not limited to the effects of drug intervention or natural disease history studies in humans. 2. We developed a novel method to integrate cytokine, gene expression and cell population data to infer functional linkages between cytokines, genes and cell populations. Cytokine functions are often studied in vitro;our approach provides direct inference of cytokine functions in vivo by utilizing human population variations. 3. Given the presence of African American individuals in our cohort, we developed a more accurate and robust approach to perform genetic imputation to infer the missing genotypes of admixed populations. Our approach achieves better accuracy, especially for more difficult to impute loci, by customizing the imputation panels for individual haplotype blocks across the genome. 4. We have conducted genetic analysis linking genetic variants to PBMC gene expression and cell populations. These include hypothesis driven analysis that involved well known variants such as PTPN22 (in collaboration with Erik Petersen and Robert Carter) as well as genome-wide associations. While signals tend to be weak given the relative small size of the cohort, we have been developing and applying network based approaches to infer the effects of multiple genetic variants on multiple phenotypes to boost power. We are also in the process of integrating numerous genome-wide association study data sets (GWAS) of immune relevant phenotypes (including autoimmune, metabolic and infectious diseases) with our data set to decipher the molecular, cellular and immunological underpinnings of a number of common diseases.
| Matsuyama, Masashi; Martins, Andrew J; Shallom, Shamira et al. (2018) Transcriptional Response of Respiratory Epithelium to Nontuberculous Mycobacteria. Am J Respir Cell Mol Biol 58:241-252 |
| Candia, Julián; Cheung, Foo; Kotliarov, Yuri et al. (2017) Assessment of Variability in the SOMAscan Assay. Sci Rep 7:14248 |
| Vodovotz, Yoram; Xia, Ashley; Read, Elizabeth L et al. (2017) Solving Immunology? Trends Immunol 38:116-127 |
| Lu, Yong; Biancotto, Angelique; Cheung, Foo et al. (2016) Systematic Analysis of Cell-to-Cell Expression Variation of T Lymphocytes in a Human Cohort Identifies Aging and Genetic Associations. Immunity 45:1162-1175 |
| Olnes, Matthew J; Kotliarov, Yuri; Biancotto, Angélique et al. (2016) Effects of Systemically Administered Hydrocortisone on the Human Immunome. Sci Rep 6:23002 |
| Crompton, Joseph G; Narayanan, Manikandan; Cuddapah, Suresh et al. (2016) Lineage relationship of CD8(+) T cell subsets is revealed by progressive changes in the epigenetic landscape. Cell Mol Immunol 13:502-13 |
| Shah, Naisha; Guo, Yongjian; Wendelsdorf, Katherine V et al. (2016) A crowdsourcing approach for reusing and meta-analyzing gene expression data. Nat Biotechnol 34:803-6 |
| Lau, William W; Tsang, John S (2016) Humoral Fingerprinting of Immune Responses: 'Super-Resolution', High-Dimensional Serology. Trends Immunol 37:167-169 |
| Lau, William W; Sparks, Rachel; OMiCC Jamboree Working Group et al. (2016) Meta-analysis of crowdsourced data compendia suggests pan-disease transcriptional signatures of autoimmunity. F1000Res 5:2884 |
| Sparks, Rachel; Lau, William W; Tsang, John S (2016) Expanding the Immunology Toolbox: Embracing Public-Data Reuse and Crowdsourcing. Immunity 45:1191-1204 |
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