Matching Items (95)
Description
Multicellular organisms use precise gene regulation, executed throughout development, to build and sustain various cell and tissue types. Post-transcriptional gene regulation is essential for metazoan development and acts on mRNA to determine its localization, stability, and translation. MicroRNAs (miRNAs) and RNA binding proteins (RBPs) are the principal effectors of post-transcriptional gene regulation and act by targeting the 3'untranslated regions (3'UTRs) of mRNA. MiRNAs are small non-coding RNAs that have the potential to regulate hundreds to thousands of genes and are dysregulated in many prevalent human diseases such as diabetes, Alzheimer's disease, Duchenne muscular dystrophy, and cancer. However, the precise contribution of miRNAs to the pathology of these diseases is not known.
MiRNA-based gene regulation occurs in a tissue-specific manner and is implemented by an interplay of poorly understood and complex mechanisms, which control both the presence of the miRNAs and their targets. As a consequence, the precise contributions of miRNAs to gene regulation are not well known. The research presented in this thesis systematically explores the targets and effects of miRNA-based gene regulation in cell lines and tissues.
I hypothesize that miRNAs have distinct tissue-specific roles that contribute to the gene expression differences seen across tissues. To address this hypothesis and expand our understanding of miRNA-based gene regulation, 1) I developed the human 3'UTRome v1, a resource for studying post-transcriptional gene regulation. Using this resource, I explored the targets of two cancer-associated miRNAs miR-221 and let-7c. I identified novel targets of both these miRNAs, which present potential mechanisms by which they contribute to cancer. 2) Identified in vivo, tissue-specific targets in the intestine and body muscle of the model organism Caenorhabditis elegans. The results from this study revealed that miRNAs regulate tissue homeostasis, and that alternative polyadenylation and miRNA expression patterns modulate miRNA targeting at the tissue-specific level. 3) Explored the functional relevance of miRNA targeting to tissue-specific gene expression, where I found that miRNAs contribute to the biogenesis of mRNAs, through alternative splicing, by regulating tissue-specific expression of splicing factors. These results expand our understanding of the mechanisms that guide miRNA targeting and its effects on tissue-specific gene expression.
MiRNA-based gene regulation occurs in a tissue-specific manner and is implemented by an interplay of poorly understood and complex mechanisms, which control both the presence of the miRNAs and their targets. As a consequence, the precise contributions of miRNAs to gene regulation are not well known. The research presented in this thesis systematically explores the targets and effects of miRNA-based gene regulation in cell lines and tissues.
I hypothesize that miRNAs have distinct tissue-specific roles that contribute to the gene expression differences seen across tissues. To address this hypothesis and expand our understanding of miRNA-based gene regulation, 1) I developed the human 3'UTRome v1, a resource for studying post-transcriptional gene regulation. Using this resource, I explored the targets of two cancer-associated miRNAs miR-221 and let-7c. I identified novel targets of both these miRNAs, which present potential mechanisms by which they contribute to cancer. 2) Identified in vivo, tissue-specific targets in the intestine and body muscle of the model organism Caenorhabditis elegans. The results from this study revealed that miRNAs regulate tissue homeostasis, and that alternative polyadenylation and miRNA expression patterns modulate miRNA targeting at the tissue-specific level. 3) Explored the functional relevance of miRNA targeting to tissue-specific gene expression, where I found that miRNAs contribute to the biogenesis of mRNAs, through alternative splicing, by regulating tissue-specific expression of splicing factors. These results expand our understanding of the mechanisms that guide miRNA targeting and its effects on tissue-specific gene expression.
ContributorsKotagama, Kasuen Indrajith Bandara (Author) / Mangone, Marco (Thesis advisor) / LaBaer, Joshua (Committee member) / Newbern, Jason (Committee member) / Rawls, Alan (Committee member) / Arizona State University (Publisher)
Created2019
Description
Biomarkers find a wide variety of applications in oncology from risk assessment to diagnosis and predicting and monitoring recurrence and response to therapy. Developing clinically useful biomarkers for cancer is faced with several challenges, including cancer heterogeneity and factors related to assay development and biomarker performance. Circulating biomarkers offer a rapid, cost-effective, and minimally-invasive window to disease and are ideal for population-based screening. Circulating immune biomarkers are stable, measurable, and can betray the underlying antigen when present below detection levels or even no longer present. This dissertation aims to investigate potential circulating immune biomarkers with applications in cancer detection and novel therapies. Over 600,000 cancers each year are attributed to the human papillomavirus (HPV), including cervical, anogenital and oropharyngeal cancers. A key challenge in understanding HPV immunobiology and developing immune biomarkers is the diversity of HPV types and the need for multiplexed display of HPV antigens. In Project 1, nucleic acid programmable protein arrays displaying the proteomes of 12 HPV types were developed and used for serum immunoprofiling of women with cervical lesions or invasive cervical cancer. These arrays provide a valuable high-throughput tool for measuring the breadth, specificity, heterogeneity, and cross-reactivity of the serologic response to HPV. Project 2 investigates potential biomarkers of immunity to the bacterial CRISPR/Cas9 system that is currently in clinical trials for cancer. Pre-existing B cell and T cell immune responses to Cas9 were detected in humans and Cas9 was modified to eliminate immunodominant epitopes while preserving its function and specificity. This dissertation broadens our understanding of the immunobiology of cervical cancer and provides insights into the immune profiles that could serve as biomarkers of various applications in cancer.
ContributorsEwaisha, Radwa Mohamed Emadeldin Mahmoud (Author) / Anderson, Karen S (Thesis advisor) / LaBaer, Joshua (Committee member) / Lake, Douglas F (Committee member) / Stout, Valerie (Committee member) / Arizona State University (Publisher)
Created2018
Description
Biological fluids, in particular blood plasma, provide a vital source of information on the state of human health. While specific detection of biomarker species can aid in disease diagnostics, the complexity of plasma makes analysis challenging. Despite the challenge of complex sample analysis, biomarker quantification has become a primary interest in biomedical analysis. Due to the extremely specific interaction between antibody and analyte, immunoassays are attractive for the analysis of these samples and have gained popularity since their initial introduction several decades ago. Current limitations to diagnostics through blood testing include long incubation times, interference from non-specific binding, and the requirement for specialized instrumentation and personnel. Optimizing the features of immunoassay for diagnostic testing and biomarker quantification would enable early and accurate detection of disease and afford rapid intervention, potentially improving patient outcomes. Improving the limit of quantitation for immunoassay has been the primary goal of many diverse experimental platforms. While the ability to accurately quantify low abundance species in a complex biological sample is of the utmost importance in diagnostic testing, models illustrating experimental limitations have relied on mathematical fittings, which cannot be directly related to finite analytical limits or fundamental relationships. By creating models based on the law of mass action, it is demonstrated that fundamental limitations are imposed by molecular shot noise, creating a finite statistical limitation to quantitative abilities. Regardless of sample volume, 131 molecules are necessary for quantitation to take place with acceptable levels of uncertainty. Understanding the fundamental limitations of the technique can aid in the design of immunoassay platforms, and assess progress toward the development of optimal diagnostic testing. A sandwich-type immunoassay was developed and tested on three separate human protein targets: myoglobin, heart-type fatty acid binding protein, and cardiac troponin I, achieving superior limits of quantitation approaching ultimate limitations. Furthermore, this approach is compatible with upstream sample separation methods, enabling the isolation of target molecules from a complex biological sample. Isolation of target species prior to analysis allows for the multiplex detection of biomarker panels in a microscale device, making the full optimization of immunoassay techniques possible for clinical diagnostics.
ContributorsWoolley, Christine F (Author) / Hayes, Mark A. (Thesis advisor) / Ros, Alexandra (Committee member) / LaBaer, Joshua (Committee member) / Arizona State University (Publisher)
Created2015
Description
No two cancers are alike. Cancer is a dynamic and heterogeneous disease, such heterogeneity arise among patients with the same cancer type, among cancer cells within the same individual’s tumor and even among cells within the same sub-clone over time. The recent application of next-generation sequencing and precision medicine techniques is the driving force to uncover the complexity of cancer and the best clinical practice. The core concept of precision medicine is to move away from crowd-based, best-for-most treatment and take individual variability into account when optimizing the prevention and treatment strategies. Next-generation sequencing is the method to sift through the entire 3 billion letters of each patient’s DNA genetic code in a massively parallel fashion.
The deluge of next-generation sequencing data nowadays has shifted the bottleneck of cancer research from multiple “-omics” data collection to integrative analysis and data interpretation. In this dissertation, I attempt to address two distinct, but dependent, challenges. The first is to design specific computational algorithms and tools that can process and extract useful information from the raw data in an efficient, robust, and reproducible manner. The second challenge is to develop high-level computational methods and data frameworks for integrating and interpreting these data. Specifically, Chapter 2 presents a tool called Snipea (SNv Integration, Prioritization, Ensemble, and Annotation) to further identify, prioritize and annotate somatic SNVs (Single Nucleotide Variant) called from multiple variant callers. Chapter 3 describes a novel alignment-based algorithm to accurately and losslessly classify sequencing reads from xenograft models. Chapter 4 describes a direct and biologically motivated framework and associated methods for identification of putative aberrations causing survival difference in GBM patients by integrating whole-genome sequencing, exome sequencing, RNA-Sequencing, methylation array and clinical data. Lastly, chapter 5 explores longitudinal and intratumor heterogeneity studies to reveal the temporal and spatial context of tumor evolution. The long-term goal is to help patients with cancer, particularly those who are in front of us today. Genome-based analysis of the patient tumor can identify genomic alterations unique to each patient’s tumor that are candidate therapeutic targets to decrease therapy resistance and improve clinical outcome.
The deluge of next-generation sequencing data nowadays has shifted the bottleneck of cancer research from multiple “-omics” data collection to integrative analysis and data interpretation. In this dissertation, I attempt to address two distinct, but dependent, challenges. The first is to design specific computational algorithms and tools that can process and extract useful information from the raw data in an efficient, robust, and reproducible manner. The second challenge is to develop high-level computational methods and data frameworks for integrating and interpreting these data. Specifically, Chapter 2 presents a tool called Snipea (SNv Integration, Prioritization, Ensemble, and Annotation) to further identify, prioritize and annotate somatic SNVs (Single Nucleotide Variant) called from multiple variant callers. Chapter 3 describes a novel alignment-based algorithm to accurately and losslessly classify sequencing reads from xenograft models. Chapter 4 describes a direct and biologically motivated framework and associated methods for identification of putative aberrations causing survival difference in GBM patients by integrating whole-genome sequencing, exome sequencing, RNA-Sequencing, methylation array and clinical data. Lastly, chapter 5 explores longitudinal and intratumor heterogeneity studies to reveal the temporal and spatial context of tumor evolution. The long-term goal is to help patients with cancer, particularly those who are in front of us today. Genome-based analysis of the patient tumor can identify genomic alterations unique to each patient’s tumor that are candidate therapeutic targets to decrease therapy resistance and improve clinical outcome.
ContributorsPeng, Sen (Author) / Dinu, Valentin (Thesis advisor) / Scotch, Matthew (Committee member) / Wallstrom, Garrick (Committee member) / Arizona State University (Publisher)
Created2015
Description
Quiescin sulfhydryl oxidase 1 (QSOX1) is a highly conserved disulfide bond-generating enzyme that represents the ancient fusion of two major thiol-disulfide oxidoreductase gene families: thioredoxin and ERV. QSOX1 was first linked with cancer after being identified as overexpressed in pancreatic ductal adenocarcinoma (but not in adjacent normal ductal epithelia, infiltrating lymphocytes, or chronic pancreatitis). QSOX1 overexpression has been confirmed in a number of other histological tumor types, such as breast, lung, kidney, prostate, and others. Expression of QSOX1 supports a proliferative and invasive phenotype in tumor cells, and its enzymatic activity is critical for promoting an invasive phenotype. An in vivo tumor growth study utilizing the pancreatic tumor cell line MIAPaCa-2 containing a QSOX1-silencing shRNA construct revealed that QSOX1 expression supports a proliferative phenotype. These preliminary studies suggest that suppressing the enzymatic activity of QSOX1 could represent a novel therapeutic strategy to inhibit proliferation and invasion of malignant neoplasms.
The goal of this research was to identify and characterize biologically active small molecule inhibitors for QSOX1. Chemical inhibition of QSOX1 enzymatic activity was hypothesized to reduce growth and invasion of tumor cells. Recombinant QSOX1 was screened against libraries of small molecules using an enzymatic activity assay to identify potential QSOX1 inhibitors. Two lead QSOX1 inhibitors were confirmed, 2-phenyl-1, 2-benzisoselenazol-3-one (ebselen), and 3-methoxy-n-[4-(1 pyrrolidinyl)phenyl]benzamide. The biological activity of these compounds is consistent with QSOX1 knockdown in tumor cell lines, reducing growth and invasion in vitro. Treatment of tumor cells with these compounds also resulted in specific ECM defects, a phenotype associated with QSOX1 knockdown. Additionally, these compounds were shown to be active in pancreatic and renal cancer xenografts, reducing tumor growth with daily treatment. For ebselen, the molecular mechanism of inhibition was determined using a combination of biochemical and mass spectrometric techniques. The results obtained in these studies provide proof-of-principle that targeting QSOX1 enzymatic activity with chemical compounds represents a novel potential therapeutic avenue worthy of further investigation in cancer. Additionally, the utility of these small molecules as chemical probes will yield future insight into the general biology of QSOX1, including the identification of novel substrates of QSOX1.
The goal of this research was to identify and characterize biologically active small molecule inhibitors for QSOX1. Chemical inhibition of QSOX1 enzymatic activity was hypothesized to reduce growth and invasion of tumor cells. Recombinant QSOX1 was screened against libraries of small molecules using an enzymatic activity assay to identify potential QSOX1 inhibitors. Two lead QSOX1 inhibitors were confirmed, 2-phenyl-1, 2-benzisoselenazol-3-one (ebselen), and 3-methoxy-n-[4-(1 pyrrolidinyl)phenyl]benzamide. The biological activity of these compounds is consistent with QSOX1 knockdown in tumor cell lines, reducing growth and invasion in vitro. Treatment of tumor cells with these compounds also resulted in specific ECM defects, a phenotype associated with QSOX1 knockdown. Additionally, these compounds were shown to be active in pancreatic and renal cancer xenografts, reducing tumor growth with daily treatment. For ebselen, the molecular mechanism of inhibition was determined using a combination of biochemical and mass spectrometric techniques. The results obtained in these studies provide proof-of-principle that targeting QSOX1 enzymatic activity with chemical compounds represents a novel potential therapeutic avenue worthy of further investigation in cancer. Additionally, the utility of these small molecules as chemical probes will yield future insight into the general biology of QSOX1, including the identification of novel substrates of QSOX1.
ContributorsHanavan, Paul D (Author) / Lake, Douglas (Thesis advisor) / LaBaer, Joshua (Committee member) / Mangone, Marco (Committee member) / Borges, Chad (Committee member) / Arizona State University (Publisher)
Created2015
Description
Detection of molecular interactions is critical for understanding many biological processes, for detecting disease biomarkers, and for screening drug candidates. Fluorescence-based approach can be problematic, especially when applied to the detection of small molecules. Various label-free techniques, such as surface plasmon resonance technique are sensitive to mass, making it extremely challenging to detect small molecules. In this thesis, novel detection methods for molecular interactions are described.
First, a simple detection paradigm based on reflectance interferometry is developed. This method is simple, low cost and can be easily applied for protein array detection.
Second, a label-free charge sensitive optical detection (CSOD) technique is developed for detecting of both large and small molecules. The technique is based on that most molecules relevant to biomedical research and applications are charged or partially charged. An optical fiber is dipped into the well of a microplate. It detects the surface charge of the fiber, which does not decrease with the size (mass) of the molecule, making it particularly attractive for studying small molecules.
Third, a method for mechanically amplification detection of molecular interactions (MADMI) is developed. It provides quantitative analysis of small molecules interaction with membrane proteins in intact cells. The interactions are monitored by detecting a mechanical deformation in the membrane induced by the molecular interactions. With this novel method small molecules and membrane proteins interaction in the intact cells can be detected. This new paradigm provides mechanical amplification of small interaction signals, allowing us to measure the binding kinetics of both large and small molecules with membrane proteins, and to analyze heterogeneous nature of the binding kinetics between different cells, and different regions of a single cell.
Last, by tracking the cell membrane edge deformation, binding caused downstream event – granule secretory has been measured. This method focuses on the plasma membrane change when granules fuse with the cell. The fusion of granules increases the plasma membrane area and thus the cell edge expands. The expansion is localized at the vesicle release location. Granule size was calculated based on measured edge expansion. The membrane deformation due to the granule release is real-time monitored by this method.
First, a simple detection paradigm based on reflectance interferometry is developed. This method is simple, low cost and can be easily applied for protein array detection.
Second, a label-free charge sensitive optical detection (CSOD) technique is developed for detecting of both large and small molecules. The technique is based on that most molecules relevant to biomedical research and applications are charged or partially charged. An optical fiber is dipped into the well of a microplate. It detects the surface charge of the fiber, which does not decrease with the size (mass) of the molecule, making it particularly attractive for studying small molecules.
Third, a method for mechanically amplification detection of molecular interactions (MADMI) is developed. It provides quantitative analysis of small molecules interaction with membrane proteins in intact cells. The interactions are monitored by detecting a mechanical deformation in the membrane induced by the molecular interactions. With this novel method small molecules and membrane proteins interaction in the intact cells can be detected. This new paradigm provides mechanical amplification of small interaction signals, allowing us to measure the binding kinetics of both large and small molecules with membrane proteins, and to analyze heterogeneous nature of the binding kinetics between different cells, and different regions of a single cell.
Last, by tracking the cell membrane edge deformation, binding caused downstream event – granule secretory has been measured. This method focuses on the plasma membrane change when granules fuse with the cell. The fusion of granules increases the plasma membrane area and thus the cell edge expands. The expansion is localized at the vesicle release location. Granule size was calculated based on measured edge expansion. The membrane deformation due to the granule release is real-time monitored by this method.
ContributorsGuan, Yan (Author) / Tao, Nongjian (Thesis advisor) / LaBaer, Joshua (Committee member) / Goryll, Michael (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2015
Description
Advances in chemical synthesis have enabled new lines of research with unnatural genetic polymers whose modified bases or sugar-phosphate backbones have potential therapeutic and biotechnological applications. Maximizing the potential of these synthetic genetic systems requires inventing new molecular biology tools that can both generate and faithfully replicate unnatural polymers of significant length. Threose nucleic acid (TNA) has received significant attention as a complete replication system has been developed by engineering natural polymerases to broaden their substrate specificity. The system, however, suffers from a high mutational load reducing its utility. This thesis will cover the development of two new polymerases capable of transcribing and reverse transcribing TNA polymers with high efficiency and fidelity. The polymerases are identified using a new strategy wherein gain-of-function mutations are sampled in homologous protein architectures leading to subtle optimization of protein function. The new replication system has a fidelity that supports the propagation of genetic information enabling in vitro selection of functional TNA molecules. TNA aptamers to human alpha-thrombin are identified and demonstrated to have superior stability compared to DNA and RNA in biologically relevant conditions. This is the first demonstration that functional TNA molecules have potential in biotechnology and molecular medicine.
ContributorsDunn, Matthew Ryan (Author) / Chaput, John C (Thesis advisor) / LaBaer, Joshua (Committee member) / Lake, Douglas (Committee member) / Mangone, Marco (Committee member) / Arizona State University (Publisher)
Created2015
Description
The processes of a human somatic cell are very complex with various genetic mechanisms governing its fate. Such cells undergo various genetic mutations, which translate to the genetic aberrations that we see in cancer. There are more than 100 types of cancer, each having many more subtypes with aberrations being unique to each. In the past two decades, the widespread application of high-throughput genomic technologies, such as micro-arrays and next-generation sequencing, has led to the revelation of many such aberrations. Known types and subtypes can be readily identified using gene-expression profiling and more importantly, high-throughput genomic datasets have helped identify novel sub-types with distinct signatures. Recent studies showing usage of gene-expression profiling in clinical decision making in breast cancer patients underscore the utility of high-throughput datasets. Beyond prognosis, understanding the underlying cellular processes is essential for effective cancer treatment. Various high-throughput techniques are now available to look at a particular aspect of a genetic mechanism in cancer tissue. To look at these mechanisms individually is akin to looking at a broken watch; taking apart each of its parts, looking at them individually and finally making a list of all the faulty ones. Integrative approaches are needed to transform one-dimensional cancer signatures into multi-dimensional interaction and regulatory networks, consequently bettering our understanding of cellular processes in cancer. Here, I attempt to (i) address ways to effectively identify high quality variants when multiple assays on the same sample samples are available through two novel tools, snpSniffer and NGSPE; (ii) glean new biological insight into multiple myeloma through two novel integrative analysis approaches making use of disparate high-throughput datasets. While these methods focus on multiple myeloma datasets, the informatics approaches are applicable to all cancer datasets and will thus help advance cancer genomics.
ContributorsYellapantula, Venkata (Author) / Dinu, Valentin (Thesis advisor) / Scotch, Matthew (Committee member) / Wallstrom, Garrick (Committee member) / Keats, Jonathan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Patients with malignant brain tumors have a median survival of approximately 15 months following diagnosis, regardless of currently available treatments which include surgery followed by radiation and chemotherapy. Improvement in the survival of brain cancer patients requires the design of new therapeutic modalities that take advantage of common phenotypes. One such phenotype is the metabolic dysregulation that is a hallmark of cancer cells. It has therefore been postulated that one approach to treating brain tumors may be by metabolic alteration such as that which occurs through the use of the ketogenic diet (KD). The KD is high-fat, low-carbohydrate diet that induces ketosis and has been utilized for the non-pharmacologic treatment of refractory epilepsy. It has been shown that this metabolic therapy enhances survival and potentiates standard therapy in mouse models of malignant gliomas, yet the anti-tumor mechanisms are not fully understood.
The current study reports that KetoCal® (KC; 4:1 fat:protein/carbohydrates), fed ad libitum, alters hypoxia, angiogenic, and inflammatory pathways in a mouse model of glioma. Tumors from animals maintained on KC showed reduced expression of the hypoxia marker carbonic anhydrase 9 (CA IX), a reduction in hypoxia inducible factor 1-alpha (HIF-1α) and decreased activation of nuclear factor kappa B (NF-κB). Animals maintained on KC also showed a reduction in expression of vascular endothelial growth factor receptor 2 (VEGFR2) and decreased microvasculature in their tumors. Further, peritumoral edema was significantly reduced in animals fed the KC and protein analysis showed significantly altered expression of the tight junction protein zona occludens-1 (ZO-1) and the water channeling protein aquaporin-4 (AQP4), both of which have been implicated in malignant processes in glioma, including the formation of peritumoral edema in patients. Taken together the data suggests that KC alters multiple processes involved in malignant progression of gliomas. A greater understanding of the effects of the ketogenic diet as an adjuvant therapy will allow for a more rational approach to its clinical use.
The current study reports that KetoCal® (KC; 4:1 fat:protein/carbohydrates), fed ad libitum, alters hypoxia, angiogenic, and inflammatory pathways in a mouse model of glioma. Tumors from animals maintained on KC showed reduced expression of the hypoxia marker carbonic anhydrase 9 (CA IX), a reduction in hypoxia inducible factor 1-alpha (HIF-1α) and decreased activation of nuclear factor kappa B (NF-κB). Animals maintained on KC also showed a reduction in expression of vascular endothelial growth factor receptor 2 (VEGFR2) and decreased microvasculature in their tumors. Further, peritumoral edema was significantly reduced in animals fed the KC and protein analysis showed significantly altered expression of the tight junction protein zona occludens-1 (ZO-1) and the water channeling protein aquaporin-4 (AQP4), both of which have been implicated in malignant processes in glioma, including the formation of peritumoral edema in patients. Taken together the data suggests that KC alters multiple processes involved in malignant progression of gliomas. A greater understanding of the effects of the ketogenic diet as an adjuvant therapy will allow for a more rational approach to its clinical use.
ContributorsWoolf, Eric C (Author) / Scheck, Adrienne C (Thesis advisor) / Lake, Douglas F (Committee member) / LaBaer, Joshua (Committee member) / Arizona State University (Publisher)
Created2014
Description
Type 1 diabetes (T1D) is a chronic autoimmune disease characterized by progressive autoimmune destruction of insulin-producing pancreatic β-cells. Genetic, immunological and environmental factors contribute to T1D development. The focus of this dissertation is to track the humoral immune response in T1D by profiling autoantibodies (AAbs) and anti-viral antibodies using an innovative protein array platform called Nucleic Acid Programmable Protein Array (NAPPA).
AAbs provide value in identifying individuals at risk, stratifying patients with different clinical courses, improving our understanding of autoimmune destructions, identifying antigens for cellular immune response and providing candidates for prevention trials in T1D. A two-stage serological AAb screening against 6,000 human proteins was performed. A dual specificity tyrosine-phosphorylation-regulated kinase 2 (DYRK2) was validated with 36% sensitivity at 98% specificity by an orthogonal immunoassay. This is the first systematic screening for novel AAbs against large number of human proteins by protein arrays in T1D. A more comprehensive search for novel AAbs was performed using a knowledge-based approach by ELISA and a screening-based approach against 10,000 human proteins by NAPPA. Six AAbs were identified and validated with sensitivities ranged from 16% to 27% at 95% specificity. These two studies enriched the T1D “autoantigenome” and provided insights into T1D pathophysiology in an unprecedented breadth and width.
The rapid rise of T1D incidence suggests the potential involvement of environmental factors including viral infections. Sero-reactivity to 646 viral antigens was assessed in new-onset T1D patients. Antibody positive rate of EBV was significantly higher in cases than controls that suggested a potential role of EBV in T1D development. A high density-NAPPA platform was demonstrated with high reproducibility and sensitivity in profiling anti-viral antibodies.
This dissertation shows the power of a protein-array based immunoproteomics approach to characterize humoral immunoprofile against human and viral proteomes. The identification of novel T1D-specific AAbs and T1D-associated viruses will help to connect the nodes in T1D etiology and provide better understanding of T1D pathophysiology.
AAbs provide value in identifying individuals at risk, stratifying patients with different clinical courses, improving our understanding of autoimmune destructions, identifying antigens for cellular immune response and providing candidates for prevention trials in T1D. A two-stage serological AAb screening against 6,000 human proteins was performed. A dual specificity tyrosine-phosphorylation-regulated kinase 2 (DYRK2) was validated with 36% sensitivity at 98% specificity by an orthogonal immunoassay. This is the first systematic screening for novel AAbs against large number of human proteins by protein arrays in T1D. A more comprehensive search for novel AAbs was performed using a knowledge-based approach by ELISA and a screening-based approach against 10,000 human proteins by NAPPA. Six AAbs were identified and validated with sensitivities ranged from 16% to 27% at 95% specificity. These two studies enriched the T1D “autoantigenome” and provided insights into T1D pathophysiology in an unprecedented breadth and width.
The rapid rise of T1D incidence suggests the potential involvement of environmental factors including viral infections. Sero-reactivity to 646 viral antigens was assessed in new-onset T1D patients. Antibody positive rate of EBV was significantly higher in cases than controls that suggested a potential role of EBV in T1D development. A high density-NAPPA platform was demonstrated with high reproducibility and sensitivity in profiling anti-viral antibodies.
This dissertation shows the power of a protein-array based immunoproteomics approach to characterize humoral immunoprofile against human and viral proteomes. The identification of novel T1D-specific AAbs and T1D-associated viruses will help to connect the nodes in T1D etiology and provide better understanding of T1D pathophysiology.
ContributorsBian, Xiaofang (Author) / LaBaer, Joshua (Thesis advisor) / Mandarino, Lawrence (Committee member) / Chang, Yung (Committee member) / Arizona State University (Publisher)
Created2015