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- Genre: Doctoral Dissertation
Description
The advent of new high throughput technology allows for increasingly detailed characterization of the immune system in healthy, disease, and age states. The immune system is composed of two main branches: the innate and adaptive immune system, though the border between these two states is appearing less distinct. The adaptive immune system is further split into two main categories: humoral and cellular immunity. The humoral immune response produces antibodies against specific targets, and these antibodies can be used to learn about disease and normal states. In this document, I use antibodies to characterize the immune system in two ways: 1. I determine the Antibody Status (AbStat) from the data collected from applying sera to an array of non-natural sequence peptides, and demonstrate that this AbStat measure can distinguish between disease, normal, and aged samples as well as produce a single AbStat number for each sample; 2. I search for antigens for use in a cancer vaccine, and this search results in several candidates as well as a new hypothesis. Antibodies provide us with a powerful tool for characterizing the immune system, and this natural tool combined with emerging technologies allows us to learn more about healthy and disease states.
ContributorsWhittemore, Kurt (Author) / Sykes, Kathryn (Thesis advisor) / Johnston, Stephen A. (Committee member) / Jacobs, Bertram (Committee member) / Stafford, Phillip (Committee member) / Stout, Valerie (Committee member) / Arizona State University (Publisher)
Created2014
Description
Peptide microarrays are to proteomics as sequencing is to genomics. As microarrays become more content-rich, higher resolution proteomic studies will parallel deep sequencing of nucleic acids. Antigen-antibody interactions can be studied at a much higher resolution using microarrays than was possible only a decade ago. My dissertation focuses on testing the feasibility of using either the Immunosignature platform, based on non-natural peptide sequences, or a pathogen peptide microarray, which uses bioinformatically-selected peptides from pathogens for creating sensitive diagnostics. Both diagnostic applications use relatively little serum from infected individuals, but each approaches diagnosis of disease differently. The first project compares pathogen epitope peptide (life-space) and non-natural (random-space) peptide microarrays while using them for the early detection of Coccidioidomycosis (Valley Fever). The second project uses NIAID category A, B and C priority pathogen epitope peptides in a multiplexed microarray platform to assess the feasibility of using epitope peptides to simultaneously diagnose multiple exposures using a single assay. Cross-reactivity is a consistent feature of several antigen-antibody based immunodiagnostics. This work utilizes microarray optimization and bioinformatic approaches to distill the underlying disease specific antibody signature pattern. Circumventing inherent cross-reactivity observed in antibody binding to peptides was crucial to achieve the goal of this work to accurately distinguishing multiple exposures simultaneously.
ContributorsNavalkar, Krupa Arun (Author) / Johnston, Stephen A. (Thesis advisor) / Stafford, Phillip (Thesis advisor) / Sykes, Kathryn (Committee member) / Jacobs, Bertram (Committee member) / Arizona State University (Publisher)
Created2014
Description
Immunosignaturing is a new immunodiagnostic technology that uses random-sequence peptide microarrays to profile the humoral immune response. Though the peptides have little sequence homology to any known protein, binding of serum antibodies may be detected, and the pattern correlated to disease states. The aim of my dissertation is to analyze the factors affecting the binding patterns using monoclonal antibodies and determine how much information may be extracted from the sequences. Specifically, I examined the effects of antibody concentration, competition, peptide density, and antibody valence. Peptide binding could be detected at the low concentrations relevant to immunosignaturing, and a monoclonal's signature could even be detected in the presences of 100 fold excess naive IgG. I also found that peptide density was important, but this effect was not due to bivalent binding. Next, I examined in more detail how a polyreactive antibody binds to the random sequence peptides compared to protein sequence derived peptides, and found that it bound to many peptides from both sets, but with low apparent affinity. An in depth look at how the peptide physicochemical properties and sequence complexity revealed that there were some correlations with properties, but they were generally small and varied greatly between antibodies. However, on a limited diversity but larger peptide library, I found that sequence complexity was important for antibody binding. The redundancy on that library did enable the identification of specific sub-sequences recognized by an antibody. The current immunosignaturing platform has little repetition of sub-sequences, so I evaluated several methods to infer antibody epitopes. I found two methods that had modest prediction accuracy, and I developed a software application called GuiTope to facilitate the epitope prediction analysis. None of the methods had sufficient accuracy to identify an unknown antigen from a database. In conclusion, the characteristics of the immunosignaturing platform observed through monoclonal antibody experiments demonstrate its promise as a new diagnostic technology. However, a major limitation is the difficulty in connecting the signature back to the original antigen, though larger peptide libraries could facilitate these predictions.
ContributorsHalperin, Rebecca (Author) / Johnston, Stephen A. (Thesis advisor) / Bordner, Andrew (Committee member) / Taylor, Thomas (Committee member) / Stafford, Phillip (Committee member) / Arizona State University (Publisher)
Created2011