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- Genre: Doctoral Dissertation
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
Description
We propose a novel solution to prevent cancer by developing a prophylactic cancer. Several sources of antigens for cancer vaccines have been published. Among these, antigens that contain a frame-shift (FS) peptide or viral peptide are quite attractive for a variety of reasons. FS sequences, from either mistake in RNA processing or in genomic DNA, may lead to generation of neo-peptides that are foreign to the immune system. Viral peptides presumably would originate from exogenous but integrated viral nucleic acid sequences. Both are non-self, therefore lessen concerns about development of autoimmunity. I have developed a bioinformatical approach to identify these aberrant transcripts in the cancer transcriptome. Their suitability for use in a vaccine is evaluated by establishing their frequencies and predicting possible epitopes along with their population coverage according to the prevalence of major histocompatibility complex (MHC) types. Viral transcripts and transcripts with FS mutations from gene fusion, insertion/deletion at coding microsatellite DNA, and alternative splicing were identified in NCBI Expressed Sequence Tag (EST) database. 48 FS chimeric transcripts were validated in 50 breast cell lines and 68 primary breast tumor samples with their frequencies from 4% to 98% by RT-PCR and sequencing confirmation. These 48 FS peptides, if translated and presented, could be used to protect more than 90% of the population in Northern America based on the prediction of epitopes derived from them. Furthermore, we synthesized 150 peptides that correspond to FS and viral peptides that we predicted would exist in tumor patients and we tested over 200 different cancer patient sera. We found a number of serological reactive peptide sequences in cancer patients that had little to no reactivity in healthy controls; strong support for the strength of our bioinformatic approach. This study describes a process used to identify aberrant transcripts that lead to a new source of antigens that can be tested and used in a prophylactic cancer vaccine. The vast amount of transcriptome data of various cancers from the Cancer Genome Atlas (TCGA) project will enhance our ability to further select better cancer antigen candidates.
ContributorsLee, HoJoon (Author) / Johnston, Stephen A. (Thesis advisor) / Kumar, Sudhir (Committee member) / Miller, Laurence (Committee member) / Stafford, Phillip (Committee member) / Sykes, Kathryn (Committee member) / Arizona State University (Publisher)
Created2012
Description
Immunosignaturing is a technology that allows the humoral immune response to be observed through the binding of antibodies to random sequence peptides. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides in a multiplexed fashion. There are computational and statistical challenges to the analysis of immunosignaturing data. The overall aim of my dissertation is to develop novel computational and statistical methods for immunosignaturing data to access its potential for diagnostics and drug discovery. Firstly, I discovered that a classification algorithm Naive Bayes which leverages the biological independence of the probes on our array in such a way as to gather more information outperforms other classification algorithms due to speed and accuracy. Secondly, using this classifier, I then tested the specificity and sensitivity of immunosignaturing platform for its ability to resolve four different diseases (pancreatic cancer, pancreatitis, type 2 diabetes and panIN) that target the same organ (pancreas). These diseases were separated with >90% specificity from controls and from each other. Thirdly, I observed that the immunosignature of type 2 diabetes and cardiovascular complications are unique, consistent, and reproducible and can be separated by 100% accuracy from controls. But when these two complications arise in the same person, the resultant immunosignature is quite different in that of individuals with only one disease. I developed a method to trace back from informative random peptides in disease signatures to the potential antigen(s). Hence, I built a decipher system to trace random peptides in type 1 diabetes immunosignature to known antigens. Immunosignaturing, unlike the ELISA, has the ability to not only detect the presence of response but also absence of response during a disease. I observed, not only higher but also lower peptides intensities can be mapped to antigens in type 1 diabetes. To study immunosignaturing potential for population diagnostics, I studied effect of age, gender and geographical location on immunosignaturing data. For its potential to be a health monitoring technology, I proposed a single metric Coefficient of Variation that has shown potential to change significantly when a person enters a disease state.
ContributorsKukreja, Muskan (Author) / Johnston, Stephen Albert (Thesis advisor) / Stafford, Phillip (Committee member) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Peptides offer great promise as targeted affinity ligands, but the space of possible peptide sequences is vast, making experimental identification of lead candidates expensive, difficult, and uncertain. Computational modeling can narrow the search by estimating the affinity and specificity of a given peptide in relation to a predetermined protein target. The predictive performance of computational models of interactions of intermediate-length peptides with proteins can be improved by taking into account the stochastic nature of the encounter and binding dynamics. A theoretical case is made for the hypothesis that, because of the flexibility of the peptide and the structural complexity of the target protein, interactions are best characterized by an ensemble of possible bound configurations rather than a single “lock and key” fit. A model incorporating these factors is proposed and evaluated. A comprehensive dataset of 3,924 peptide-protein interface structures was extracted from the Protein Data Bank (PDB) and descriptors were computed characterizing the geometry and energetics of each interface. The characteristics of these interfaces are shown to be generally consistent with the proposed model, and heuristics for design and selection of peptide ligands are derived. The curated and energy-minimized interface structure dataset and a relational database containing the detailed results of analysis and energy modeling are made publicly available via a web repository. A novel analytical technique based on the proposed theoretical model, Virtual Scanning Probe Mapping (VSPM), is implemented in software to analyze the interaction between a target protein of known structure and a peptide of specified sequence, producing a spatial map indicating the most likely peptide binding regions on the protein target. The resulting predictions are shown to be superior to those of two other published methods, and support the validity of the stochastic binding model.
ContributorsEmery, Jack Scott (Author) / Pizziconi, Vincent B (Thesis advisor) / Woodbury, Neal W (Thesis advisor) / Guilbeau, Eric J (Committee member) / Stafford, Phillip (Committee member) / Taylor, Thomas (Committee member) / Towe, Bruce C (Committee member) / Arizona State University (Publisher)
Created2010
Description
ABSTRACT Peptide microarrays may prove to be a powerful tool for proteomics research and clinical diagnosis applications. Fodor et al. and Maurer et al. have shown proof-of-concept methods of light- and electrochemically-directed peptide microarray fabrication on glass and semiconductor microchips respectively. In this work, peptide microarray fabrication based on the abovementioned techniques were optimized. In addition, MALDI mass spectrometry based peptide synthesis characterization on semiconductor microchips was developed and novel applications of a CombiMatrix (CBMX) platform for electrochemically controlled synthesis were explored. We have investigated performance of 2-(2-nitrophenyl)propoxycarbonyl (NPPOC) derivatives as photo-labile protecting group. Specifically, influence of substituents on 4 and 5 positions of phenyl ring of NPPOC group on the rate of photolysis and the yield of the amine was investigated. The results indicated that substituents capable of forming a π-network with the nitro group enhanced the rate of photolysis and yield. Once such properly substituted NPPOC groups were used, the rate of photolysis/yield depended on the nature of protected amino group indicating that a different chemical step during the photo-cleavage process became the rate limiting step. We also focused on electrochemically-directed parallel synthesis of high-density peptide microarrays using the CBMX technology referred to above which uses electrochemically generated acids to perform patterned chemistry. Several issues related to peptide synthesis on the CBMX platform were studied and optimized, with emphasis placed on the reactions of electro-generated acids during the deprotection step of peptide synthesis. We have developed a MALDI mass spectrometry based method to determine the chemical composition of microarray synthesis, directly on the feature. This method utilizes non-diffusional chemical cleavage from the surface, thereby making the chemical characterization of high-density microarray features simple, accurate, and amenable to high-throughput. CBMX Corp. has developed a microarray reader which is based on electro-chemical detection of redox chemical species. Several parameters of the instrument were studied and optimized and novel redox applications of peptide microarrays on CBMX platform were also investigated using the instrument. These include (i) a search of metal binding catalytic peptides to reduce overpotential associated with water oxidation reaction and (ii) an immobilization of peptide microarrays using electro-polymerized polypyrrole.
ContributorsKumar, Pallav (Author) / Woodbury, Neal (Thesis advisor) / Allen, James (Committee member) / Johnston, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
Description
Particulate Guanylyl Cyclase Receptor A (pGC-A) is an atrial natriuretic peptide receptor, which plays a vital role in controlling cardiovascular, renal, and endocrine functions. The extracellular domain of pGC-A interacts with natriuretic peptides and triggers the intracellular guanylyl cyclase domain to convert GTP to cGMP. To effectively develop a method that can regulate pGC-A, structural information regarding its intact form is necessary. Currently, only the extracellular domain structure of rat pGC-A has been determined. However, structural data regarding the transmembrane domain, as well as functional intracellular domain regions, need to be elucidated.This dissertation presents detailed information regarding pGC-A expression and optimization in the baculovirus expression vector system, along with the first purification method for purifying functional intact human pGC-A. The first in vitro evidence of a purified intact human pGC-A tetramer was detected in detergent micellar solution. Intact pGC-A is currently proposed to function as a homodimer. Upon analyzing my findings and acknowledging that dimer formation is required for pGC-A functionality, I proposed the first tetramer complex model composed of two functional subunits (homodimer). Forming tetramer complexes on the cell membrane increases pGC-A binding efficiency and ligand sensitivity.
Currently, a two-step mechanism has been proposed for ATP-dependent pGC-A signal transduction. Based on cGMP functional assay results, it can be suggested that the binding ligand also moderately activates pGC-A, and that ATP is not crucial for the activation of guanylyl cyclase. Instead, three modulators can regulate different activation levels in intact pGC-A.
Crystallization of purified intact pGC-A was performed to determine its structure. During the crystallization condition screening process, I successfully selected seven promising initial crystallization conditions for intact human pGC-A crystallization. One selected condition led to the formation of excellent needle-shaped crystals. During the serial crystallography diffraction experiment, five diffraction patterns were detected. The highest diffraction resolution spot reached 3 Å.
This work will allow the determination of the intact human pGC-A structure while also providing structural information on the protein signal transduction mechanism. Further structural knowledge may potentially lead to improved drug design. More precise mutation experiments could help verify the current pGC-A signal transduction and activation mechanism.
ContributorsZhang, Shangji (Author) / Fromme, Petra (Thesis advisor) / Johnston, Stephen (Committee member) / Mazor, Yuval (Committee member) / Arizona State University (Publisher)
Created2021
Description
Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis, is the 10th leading cause of death, worldwide. The prevalence of drug-resistant clinical isolates and the paucity of newly-approved antituberculosis drugs impedes the successful eradication of Mtb. Bacteria commonly use two-component systems (TCS) to sense their environment and genetically modulate adaptive responses. The prrAB TCS is essential in Mtb, thus representing an auspicious drug target; however, the inability to generate an Mtb ΔprrAB mutant complicates investigating how this TCS contributes to pathogenesis. Mycobacterium smegmatis, a commonly used M. tuberculosis genetic surrogate was used here. This work shows that prrAB is not essential in M. smegmatis. During ammonium stress, the ΔprrAB mutant excessively accumulates triacylglycerol lipids, a phenotype associated with M. tuberculosis dormancy and chronic infection. Additionally, triacylglycerol biosynthetic genes were induced in the ΔprrAB mutant relative to the wild-type and complementation strains during ammonium stress. Next, RNA-seq was used to define the M. smegmatis PrrAB regulon. PrrAB regulates genes participating in respiration, metabolism, redox balance, and oxidative phosphorylation. The M. smegmatis ΔprrAB mutant is compromised for growth under hypoxia, is hypersensitive to cyanide, and fails to induce high-affinity respiratory genes during hypoxia. Furthermore, PrrAB positively regulates the hypoxia-responsive dosR TCS response regulator, potentially explaining the hypoxia-mediated growth defects in the ΔprrAB mutant. Despite inducing genes encoding the F1F0 ATP synthase, the ΔprrAB mutant accumulates significantly less ATP during aerobic, exponential growth compared to the wild-type and complementation strains. Finally, the M. smegmatis ΔprrAB mutant exhibited growth impairment in media containing gluconeogenic carbon sources. M. tuberculosis mutants unable to utilize these substrates fail to establish chronic infection, suggesting that PrrAB may regulate Mtb central carbon metabolism in response to chronic infection. In conclusion, 1) prrAB is not universally essential in mycobacteria; 2) M. smegmatis PrrAB regulates genetic responsiveness to nutrient and oxygen stress; and 3) PrrAB may provide feed-forward control of the DosRS TCS and dormancy phenotypes. The data generated in these studies provide insight into the mycobacterial PrrAB TCS transcriptional regulon, PrrAB essentiality in Mtb, and how PrrAB may mediate stresses encountered by Mtb during the transition to chronic infection.
ContributorsMaarsingh, Jason (Author) / Haydel, Shelley E (Thesis advisor) / Roland, Kenneth (Committee member) / Sandrin, Todd (Committee member) / Bean, Heather (Committee member) / Arizona State University (Publisher)
Created2019
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
Invasive salmonellosis caused by Salmonella enterica serovar Typhimurium ST313 is a major health crisis in sub-Saharan Africa, with multidrug resistance and atypical clinical presentation challenging current treatment regimens and resulting in high mortality. Moreover, the increased risk of spreading ST313 pathovars worldwide is of major concern, given global public transportation networks and increased populations of immunocompromised individuals (as a result of HIV infection, drug use, cancer therapy, aging, etc). While it is unclear as to how Salmonella ST313 strains cause invasive disease in humans, it is intriguing that the genomic profile of some of these pathovars indicates key differences between classic Typhimurium (broad host range), but similarities to human-specific typhoidal Salmonella Typhi and Paratyphi. In an effort to advance fundamental understanding of the pathogenesis mechanisms of ST313 in humans, I report characterization of the molecular genetic, phenotypic and virulence profiles of D23580 (a representative ST313 strain). Preliminary studies to characterize D23580 virulence, baseline stress responses, and biochemical profiles, and in vitro infection profiles in human surrogate 3-D tissue culture models were done using conventional bacterial culture conditions; while subsequent studies integrated a range of incrementally increasing fluid shear levels relevant to those naturally encountered by D23580 in the infected host to understand the impact of biomechanical forces in altering these characteristics. In response to culture of D23580 under these conditions, distinct differences in transcriptional biosignatures, pathogenesis-related stress responses, in vitro infection profiles and in vivo virulence in mice were observed as compared to those of classic Salmonella pathovars tested.
Collectively, this work represents the first characterization of in vivo virulence and in vitro pathogenesis properties of D23580, the latter using advanced human surrogate models that mimic key aspects of the parental tissue. Results from these studies highlight the importance of studying infectious diseases using an integrated approach that combines actions of biological and physical networks that mimic the host-pathogen microenvironment and regulate pathogen responses.
Collectively, this work represents the first characterization of in vivo virulence and in vitro pathogenesis properties of D23580, the latter using advanced human surrogate models that mimic key aspects of the parental tissue. Results from these studies highlight the importance of studying infectious diseases using an integrated approach that combines actions of biological and physical networks that mimic the host-pathogen microenvironment and regulate pathogen responses.
ContributorsYang, Jiseon (Author) / Nickerson, Cheryl A. (Thesis advisor) / Chang, Yung (Committee member) / Stout, Valerie (Committee member) / Ott, C Mark (Committee member) / Roland, Kenneth (Committee member) / Barrila, Jennifer (Committee member) / Arizona State University (Publisher)
Created2015
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