Dissertation/Thesis Abstract

High-content protein arrays for characterizing immune responses and pathophysiology at the molecular level
by Kattah, Michael George, Ph.D., Stanford University, 2008, 201; 3313820
Abstract (Summary)

Proteomics and genomics technologies offer tremendous promise for comprehensively characterizing immune responses as well as generating and testing new hypotheses related to pathophysiology and basic cell biology. In the area of genomics, researchers routinely profile thousands of mRNA species using DNA microarrays. In the relatively new field of proteomics, however, improved methods for analyzing specific proteins in a high-throughput manner are still needed. One rapidly evolving branch of biased proteomics screening technologies is the protein array platform. There are many varieties of protein arrays designed to target different proteomes. Here we report three independent studies that focus both on developing new protein array platforms as well as extending existing technology to investigate innate and adaptive immune responses.

In the first study we describe a new multi-analyte fluid-phase protein array termed high-throughput immunophenotyping using transcription (HIT). Although there are many potential applications for the HIT platform, here we report suitability for profiling cytokines, intracellular signal-transduction molecules, and cell-surface markers. Using HIT we successfully profiled 90 surface markers on human naive T helper (TH) cells activated in vitro under various conditions to identify both known and previously unrecognized surface marker changes. Due to the flexibility and multiplexing capacity of this technique, HIT arrays are an ideal platform for rapidly identifying markers for further characterization.

The second study relates to profiling autoantibodies on antigen microarrays. Antigen microarrays are a powerful method of profiling the humoral immune response in the setting of autoimmunity, allergy, and cancer. We evaluated a new two-color Fab labeling method that allows two samples to be applied simultaneously to the same antigen array. Using this technique we profiled serum from a mouse model of systemic lupus erythematosus (SLE) and detected a previously unrecognized reactivity to Ribosomal P. This straightforward labeling approach improves reproducibility and reliably detects changes in autoantibody levels.

Finally, in the third study we employed a different multiplexed technology for studying human IL-17 secreting T helper (TH17) cells. T H17 cells are implicated in the pathogenesis of rheumatoid arthritis and other autoimmune diseases, yet the soluble factors that influence human TH17 differentiation have yet to be fully elucidated. Supernatants from human peripheral blood mononuclear cells (PBMCs) treated with a panel of TLR agonists were tested for the ability to induce de novo IL-17 production in naive TH cells. Multiplexed analysis of 22 cytokines and chemokines identified a 6-factor cytokine signature that significantly correlated with IL-17 inducing activity. Activation in the presence of a subset of these cytokines reconstituted robust IL-17 production. We conclude that ligation of a subset of TLRs generates pro-inflammatory cytokines that combine to potentiate human TH17 differentiation.

Taken together, these data demonstrate the utility of protein array platforms for analyzing immune responses, identifying biomarkers, and generating hypotheses. High-content screens using protein arrays require small amounts of biological material and generate large amounts of data. As a result, these methods promise to accelerate the identification of potential molecular targets for therapeutic interventions.

Indexing (document details)
Advisor: Utz, Paul J.
Commitee:
School: Stanford University
School Location: United States -- California
Source: DAI-B 69/05, Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Immunology
Keywords: Arrays, Autoimmunity, IL-17, Protein arrays, Proteomics, T cells, foxp3
Publication Number: 3313820
ISBN: 9780549629917
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