In this Project 2 entitled """"""""T cell and General Immune Responses to Influenza,"""""""" we focus on general immunological biomarkers and antigen specific T cells in a continuing effort to define the immune response to influenza vaccination in volunteers from different age groups and especially utilizing the twin cohorts of SRI. We hope to establish benchmarks of immune proficiency in these different cohorts that will be predictive not just of an influenza response, but of immunological """"""""health,"""""""" or at least a component of it, in general.
In Aim 1, we will continue our ongoing program to survey the blood samples of volunteers being immunized for a broad array of serum cytokines, white blood cell subsets, lymphocyte proliferation, and whole genome gene expression. Using the twin cohorts, we will be able to ask what traits are determined genetically, and of those evident in young adults, which change in older adults. Also a high priority is what traits correlate with a robust or poor response.
in Aim 2 and 3, we will take advantage of recent advances in peptide-MHC tetramer technology together with our own efforts to probe more broadly and deeply into the nature of the T cell response to different influenza antigens. Thus, we will survey dozens of different T cell epitopes at once to ask whether or not there are specific changes in influenza specific T cell repertoires with age and to what extent does genetics contribute to the repertoire. We will also ask how the different influenza vaccine types influence the repertoire of responding T cells and whether or not there is a correlation between the repertoire used, and a robust or poor response. We have also been able recently to isolate antigen specific naive populations of T cells (which are in the cytokines produced in the signaling pathways utilized;in some cases, only 1 in one million cells of the CD8+ T cell pool). This gives us the ability to probe the naive repertoire of strain-specific T cells in young adults and characterize their response to different types of influenza vaccination. Lastly, in Aim 4, we propose to use """"""""humanized"""""""" mice to test hypotheses generated in the first three aims.
We seek to understand at multiple levels-molecular, cellular and organismal?how the immune systems of humans in different age groups are constituted and how their T lymphocytes respond or fail to defend against specific influenza strains. It is expected that the assays that will result from these studies will aid in the development of more effective vaccines and an understanding of immunological health.
|Ju, Chia-Hsin; Blum, Lisa K; Kongpachith, Sarah et al. (2018) Plasmablast antibody repertoires in elderly influenza vaccine responders exhibit restricted diversity but increased breadth of binding across influenza strains. Clin Immunol 193:70-79|
|Sweeney, Timothy E; Perumal, Thanneer M; Henao, Ricardo et al. (2018) A community approach to mortality prediction in sepsis via gene expression analysis. Nat Commun 9:694|
|Davis, Mark M; Tato, Cristina M (2018) Will Systems Biology Deliver Its Promise and Contribute to the Development of New or Improved Vaccines? Seeing the Forest Rather than a Few Trees. Cold Spring Harb Perspect Biol 10:|
|Gee, Marvin H; Han, Arnold; Lofgren, Shane M et al. (2018) Antigen Identification for Orphan T Cell Receptors Expressed on Tumor-Infiltrating Lymphocytes. Cell 172:549-563.e16|
|Keeffe, Jennifer R; Van Rompay, Koen K A; Olsen, Priscilla C et al. (2018) A Combination of Two Human Monoclonal Antibodies Prevents Zika Virus Escape Mutations in Non-human Primates. Cell Rep 25:1385-1394.e7|
|Wagar, Lisa E; DiFazio, Robert M; Davis, Mark M (2018) Advanced model systems and tools for basic and translational human immunology. Genome Med 10:73|
|Good, Zinaida; Sarno, Jolanda; Jager, Astraea et al. (2018) Single-cell developmental classification of B cell precursor acute lymphoblastic leukemia at diagnosis reveals predictors of relapse. Nat Med 24:474-483|
|Satpathy, Ansuman T; Saligrama, Naresha; Buenrostro, Jason D et al. (2018) Transcript-indexed ATAC-seq for precision immune profiling. Nat Med 24:580-590|
|Vallania, Francesco; Tam, Andrew; Lofgren, Shane et al. (2018) Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases. Nat Commun 9:4735|
|Bongen, Erika; Vallania, Francesco; Utz, Paul J et al. (2018) KLRD1-expressing natural killer cells predict influenza susceptibility. Genome Med 10:45|
Showing the most recent 10 out of 249 publications