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- Member of: ASU Electronic Theses and Dissertations
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
High throughput transcriptome data analysis like Single-cell Ribonucleic Acid sequencing (scRNA-seq) and Circular Ribonucleic Acid (circRNA) data have made significant breakthroughs, especially in cancer genomics. Analysis of transcriptome time series data is core in identifying time point(s) where drastic changes in gene transcription are associated with homeostatic to non-homeostatic cellular transition (tipping points). In Chapter 2 of this dissertation, I present a novel cell-type specific and co-expression-based tipping point detection method to identify target gene (TG) versus transcription factor (TF) pairs whose differential co-expression across time points drive biological changes in different cell types and the time point when these changes are observed. This method was applied to scRNA-seq data sets from a SARS-CoV-2 study (18 time points), a human cerebellum development study (9 time points), and a lung injury study (18 time points). Similarly, leveraging transcriptome data across treatment time points, I developed methodologies to identify treatment-induced and cell-type specific differentially co-expressed pairs (DCEPs). In part one of Chapter 3, I presented a pipeline that used a series of statistical tests to detect DCEPs. This method was applied to scRNA-seq data of patients with non-small cell lung cancer (NSCLC) sequenced across cancer treatment times. However, this pipeline does not account for correlations among multiple single cells from the same sample and correlations among multiple samples from the same patient. In Part 2 of Chapter 3, I presented a solution to this problem using a mixed-effect model. In Chapter 4, I present a summary of my work that focused on the cross-species analysis of circRNA transcriptome time series data. I compared circRNA profiles in neonatal pig and mouse hearts, identified orthologous circRNAs, and discussed regulation mechanisms of cardiomyocyte proliferation and myocardial regeneration conserved between mouse and pig at different time points.
ContributorsNyarige, Verah Mocheche (Author) / Liu, Li (Thesis advisor) / Wang, Junwen (Thesis advisor) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2022
Description
Beta-Amyloid(Aβ) plaques and tau protein tangles in the brain are now widely recognized as the defining hallmarks of Alzheimer’s disease (AD), followed by structural atrophy detectable on brain magnetic resonance imaging (MRI) scans. However, current methods to detect Aβ/tau pathology are either invasive (lumbar puncture) or quite costly and not widely available (positron emission tomography (PET)). And one of the particular neurodegenerative regions is the hippocampus to which the influence of Aβ/tau on has been one of the research projects focuses in the AD pathophysiological progress.
In this dissertation, I proposed three novel machine learning and statistical models to examine subtle aspects of the hippocampal morphometry from MRI that are associated with Aβ /tau burden in the brain, measured using PET images. The first model is a novel unsupervised feature reduction model to generate a low-dimensional representation of hippocampal morphometry for each individual subject, which has superior performance in predicting Aβ/tau burden in the brain. The second one is an efficient federated group lasso model to identify the hippocampal subregions where atrophy is strongly associated with abnormal Aβ/Tau. The last one is a federated model for imaging genetics, which can identify genetic and transcriptomic influences on hippocampal morphometry. Finally, I stated the results of these three models that have been published or submitted to peer-reviewed conferences and journals.
ContributorsWu, Jianfeng (Author) / Wang, Yalin (Thesis advisor) / Li, Baoxin (Committee member) / Liang, Jianming (Committee member) / Wang, Junwen (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2022
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
Immunosignature is a technology that retrieves information from the immune system. The technology is based on microarrays with peptides chosen from random sequence space. My thesis focuses on improving the Immunosignature platform and using Immunosignatures to improve diagnosis for diseases. I first contributed to the optimization of the immunosignature platform by introducing scoring metrics to select optimal parameters, considering performance as well as practicality. Next, I primarily worked on identifying a signature shared across various pathogens that can distinguish them from the healthy population. I further retrieved consensus epitopes from the disease common signature and proposed that most pathogens could share the signature by studying the enrichment of the common signature in the pathogen proteomes. Following this, I worked on studying cancer samples from different stages and correlated the immune response with whether the epitope presented by tumor is similar to the pathogen proteome. An effective immune response is defined as an antibody titer increasing followed by decrease, suggesting elimination of the epitope. I found that an effective immune response usually correlates with epitopes that are more similar to pathogens. This suggests that the immune system might occupy a limited space and can be effective against only certain epitopes that have similarity with pathogens. I then participated in the attempt to solve the antibiotic resistance problem by developing a classification algorithm that can distinguish bacterial versus viral infection. This algorithm outperforms other currently available classification methods. Finally, I worked on the concept of deriving a single number to represent all the data on the immunosignature platform. This is in resemblance to the concept of temperature, which is an approximate measurement of whether an individual is healthy. The measure of Immune Entropy was found to work best as a single measurement to describe the immune system information derived from the immunosignature. Entropy is relatively invariant in healthy population, but shows significant differences when comparing healthy donors with patients either infected with a pathogen or have cancer.
ContributorsWang, Lu (Author) / Johnston, Stephen (Thesis advisor) / Stafford, Phillip (Committee member) / Buetow, Kenneth (Committee member) / McFadden, Grant (Committee member) / Arizona State University (Publisher)
Created2018
Description
This work advances structural and biophysical studies of three proteins important in disease. First protein of interest is the Francisella tularensis outer membrane protein A (FopA), which is a virulence determinant of tularemia. This work describes recombinant expression in Escherichia coli and successful purification of membrane translocated FopA. The purified protein was dimeric as shown by native polyacrylamide gel electrophoresis and small angle X-ray scattering (SAXS) analysis, with an abundance of β-strands based on circular dichroism spectroscopy. SAXS data supports the presence of a pore. Furthermore, protein crystals of membrane translocated FopA were obtained with preliminary X-ray diffraction data. The identified crystallization condition provides the means towards FopA structure determination; a valuable tool for structure-based design of anti-tularemia therapeutics.
Next, the nonstructural protein μNS of avian reoviruses was investigated using in vivo crystallization and serial femtosecond X-ray crystallography. Avian reoviruses infect poultry flocks causing significant economic losses. μNS is crucial in viral factory formation facilitating viral replication within host cells. Thus, structure-based targeting of μNS has the potential to disrupt intracellular viral propagation. Towards this goal, crystals of EGFP-tagged μNS (EGFP-μNS (448-605)) were produced in insect cells. The crystals diffracted to 4.5 Å at X-ray free electron lasers using viscous jets as crystal delivery methods and initial electron density maps were obtained. The resolution reported here is the highest described to date for μNS, which lays the foundation towards its structure determination.
Finally, structural, and functional studies of human Threonine aspartase 1 (Taspase1) were performed. Taspase1 is overexpressed in many liquid and solid malignancies. In the present study, using strategic circular permutations and X-ray crystallography, structure of catalytically active Taspase1 was resolved. The structure reveals the conformation of a 50 residues long fragment preceding the active side residue (Thr234), which has not been structurally characterized previously. This fragment adopted a straight helical conformation in contrast to previous predictions. Functional studies revealed that the long helix is essential for proteolytic activity in addition to the active site nucleophilic residue (Thr234) mediated proteolysis. Together, these findings enable a new approach for designing anti-cancer drugs by targeting the long helical fragment.
Next, the nonstructural protein μNS of avian reoviruses was investigated using in vivo crystallization and serial femtosecond X-ray crystallography. Avian reoviruses infect poultry flocks causing significant economic losses. μNS is crucial in viral factory formation facilitating viral replication within host cells. Thus, structure-based targeting of μNS has the potential to disrupt intracellular viral propagation. Towards this goal, crystals of EGFP-tagged μNS (EGFP-μNS (448-605)) were produced in insect cells. The crystals diffracted to 4.5 Å at X-ray free electron lasers using viscous jets as crystal delivery methods and initial electron density maps were obtained. The resolution reported here is the highest described to date for μNS, which lays the foundation towards its structure determination.
Finally, structural, and functional studies of human Threonine aspartase 1 (Taspase1) were performed. Taspase1 is overexpressed in many liquid and solid malignancies. In the present study, using strategic circular permutations and X-ray crystallography, structure of catalytically active Taspase1 was resolved. The structure reveals the conformation of a 50 residues long fragment preceding the active side residue (Thr234), which has not been structurally characterized previously. This fragment adopted a straight helical conformation in contrast to previous predictions. Functional studies revealed that the long helix is essential for proteolytic activity in addition to the active site nucleophilic residue (Thr234) mediated proteolysis. Together, these findings enable a new approach for designing anti-cancer drugs by targeting the long helical fragment.
ContributorsNagaratnam, Nirupa (Author) / Fromme, Petra (Thesis advisor) / Johnston, Stephen (Thesis advisor) / Van Horn, Wade (Committee member) / Liu, Wei (Committee member) / Arizona State University (Publisher)
Created2020
Description
This dissertation presents three novel algorithms with real-world applications to genomic oncology. While the methodologies presented here were all developed to overcome various challenges associated with the adoption of high throughput genomic data in clinical oncology, they can be used in other domains as well. First, a network informed feature ranking algorithm is presented, which shows a significant increase in ability to select true predictive features from simulated data sets when compared to other state of the art graphical feature ranking methods. The methodology also shows an increased ability to predict pathological complete response to preoperative chemotherapy from genomic sequencing data of breast cancer patients utilizing domain knowledge from protein-protein interaction networks.
Second, an algorithm that overcomes population biases inherent in the use of a human reference genome developed primarily from European populations is presented to classify microsatellite instability (MSI) status from next-generation-sequencing (NGS) data. The methodology significantly increases the accuracy of MSI status prediction in African and African American ancestries.
Finally, a single variable model is presented to capture the bimodality inherent in genomic data stemming from heterogeneous diseases. This model shows improvements over other parametric models in the measurements of receiver-operator characteristic (ROC) curves for bimodal data. The model is used to estimate ROC curves for heterogeneous biomarkers in a dataset containing breast cancer and cancer-free specimen.
ContributorsSaul, Michelle (Author) / Dinu, Valentin (Thesis advisor) / Liu, Li (Committee member) / Wang, Junwen (Committee member) / Arizona State University (Publisher)
Created2021