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In an effort to begin validating the large number of discovered candidate biomarkers, proteomics is beginning to shift from shotgun proteomic experiments towards targeted proteomic approaches that provide solutions to automation and economic concerns. Such approaches to validate biomarkers necessitate the mass spectrometric analysis of hundreds to thousands of human samples. As this takes place, a serendipitous opportunity has become evident. By the virtue that as one narrows the focus towards "single" protein targets (instead of entire proteomes) using pan-antibody-based enrichment techniques, a discovery science has emerged, so to speak. This is due to the largely unknown context in which "single" proteins exist in blood (i.e. polymorphisms, transcript variants, and posttranslational modifications) and hence, targeted proteomics has applications for established biomarkers. Furthermore, besides protein heterogeneity accounting for interferences with conventional immunometric platforms, it is becoming evident that this formerly hidden dimension of structural information also contains rich-pathobiological information. Consequently, targeted proteomics studies that aim to ascertain a protein's genuine presentation within disease- stratified populations and serve as a stepping-stone within a biomarker translational pipeline are of clinical interest. Roughly 128 million Americans are pre-diabetic, diabetic, and/or have kidney disease and public and private spending for treating these diseases is in the hundreds of billions of dollars. In an effort to create new solutions for the early detection and management of these conditions, described herein is the design, development, and translation of mass spectrometric immunoassays targeted towards diabetes and kidney disease. Population proteomics experiments were performed for the following clinically relevant proteins: insulin, C-peptide, RANTES, and parathyroid hormone. At least thirty-eight protein isoforms were detected. Besides the numerous disease correlations confronted within the disease-stratified cohorts, certain isoforms also appeared to be causally related to the underlying pathophysiology and/or have therapeutic implications. Technical advancements include multiplexed isoform quantification as well a "dual- extraction" methodology for eliminating non-specific proteins while simultaneously validating isoforms. Industrial efforts towards widespread clinical adoption are also described. Consequently, this work lays a foundation for the translation of mass spectrometric immunoassays into the clinical arena and simultaneously presents the most recent advancements concerning the mass spectrometric immunoassay approach.
ContributorsOran, Paul (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Ros, Alexandra (Committee member) / Williams, Peter (Committee member) / Arizona State University (Publisher)
Created2011
Description
Signal processing techniques have been used extensively in many engineering problems and in recent years its application has extended to non-traditional research fields such as biological systems. Many of these applications require extraction of a signal or parameter of interest from degraded measurements. One such application is mass spectrometry immunoassay (MSIA) which has been one of the primary methods of biomarker discovery techniques. MSIA analyzes protein molecules as potential biomarkers using time of flight mass spectrometry (TOF-MS). Peak detection in TOF-MS is important for biomarker analysis and many other MS related application. Though many peak detection algorithms exist, most of them are based on heuristics models. One of the ways of detecting signal peaks is by deploying stochastic models of the signal and noise observations. Likelihood ratio test (LRT) detector, based on the Neyman-Pearson (NP) lemma, is an uniformly most powerful test to decision making in the form of a hypothesis test. The primary goal of this dissertation is to develop signal and noise models for the electrospray ionization (ESI) TOF-MS data. A new method is proposed for developing the signal model by employing first principles calculations based on device physics and molecular properties. The noise model is developed by analyzing MS data from careful experiments in the ESI mass spectrometer. A non-flat baseline in MS data is common. The reasons behind the formation of this baseline has not been fully comprehended. A new signal model explaining the presence of baseline is proposed, though detailed experiments are needed to further substantiate the model assumptions. Signal detection schemes based on these signal and noise models are proposed. A maximum likelihood (ML) method is introduced for estimating the signal peak amplitudes. The performance of the detection methods and ML estimation are evaluated with Monte Carlo simulation which shows promising results. An application of these methods is proposed for fractional abundance calculation for biomarker analysis, which is mathematically robust and fundamentally different than the current algorithms. Biomarker panels for type 2 diabetes and cardiovascular disease are analyzed using existing MS analysis algorithms. Finally, a support vector machine based multi-classification algorithm is developed for evaluating the biomarkers' effectiveness in discriminating type 2 diabetes and cardiovascular diseases and is shown to perform better than a linear discriminant analysis based classifier.
ContributorsBuddi, Sai (Author) / Taylor, Thomas (Thesis advisor) / Cochran, Douglas (Thesis advisor) / Nelson, Randall (Committee member) / Duman, Tolga (Committee member) / Arizona State University (Publisher)
Created2012
Description
Cancer claims hundreds of thousands of lives every year in US alone. Finding ways for early detection of cancer onset is crucial for better management and treatment of cancer. Thus, biomarkers especially protein biomarkers, being the functional units which reflect dynamic physiological changes, need to be discovered. Though important, there are only a few approved protein cancer biomarkers till date. To accelerate this process, fast, comprehensive and affordable assays are required which can be applied to large population studies. For this, these assays should be able to comprehensively characterize and explore the molecular diversity of nominally "single" proteins across populations. This information is usually unavailable with commonly used immunoassays such as ELISA (enzyme linked immunosorbent assay) which either ignore protein microheterogeneity, or are confounded by it. To this end, mass spectrometric immuno assays (MSIA) for three different human plasma proteins have been developed. These proteins viz. IGF-1, hemopexin and tetranectin have been found in reported literature to show correlations with many diseases along with several carcinomas. Developed assays were used to extract entire proteins from plasma samples and subsequently analyzed on mass spectrometric platforms. Matrix assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) mass spectrometric techniques where used due to their availability and suitability for the analysis. This resulted in visibility of different structural forms of these proteins showing their structural micro-heterogeneity which is invisible to commonly used immunoassays. These assays are fast, comprehensive and can be applied in large sample studies to analyze proteins for biomarker discovery.
ContributorsRai, Samita (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Borges, Chad (Committee member) / Ros, Alexandra (Committee member) / Arizona State University (Publisher)
Created2012
Description
Background: Human papillomavirus (HPV) is the cause of 99.7% of cervical cancers. Research of cervical cancer has made this disease mostly curable in the developing world. Head and neck cancer, which is increasingly caused by HPV, still is associated with a mortality rate of 50,000 in the US annually. This study proposed to evaluate the biology of HPV-16 in head and neck tumors by using RT-qPCR to measure the RNA expression and its relation to physical status of the virus. Methods: This study was to develop an assay that uses RT-qPCR to determine the quantitative expression of HPV-16 RNA coding for proteins E1, E2, E4, E5, E6, and E7 in tumor samples. The assay development started with creation of primers. It went on to test the primers on template DNA through traditional PCR and then on DNA from HPV-16 positive cell lines, SiHa and CaSki, using RT-qPCR. This paper also describes the troubleshooting methods taken for the PCR reaction. Once the primers are verified, the RT-qPCR process can be carried out on RNA purified from tumor samples. Results: No primer sets have been confirmed to produce a product through PCR or RT-qPCR. The primer sequences match up correctly with known sequences for HPV-16 E1, E2, E4, E5, E6, and E7. RT-qPCR showed results consistent with the hypothesis. Conclusion: The RT-qPCR protocol must be optimized to confirm the primer sequences work as desired. Then primers will be used to study physical status and RNA expression in HPV-positive and HPV-negative head and neck tumor samples. This assay can help shed light on which proteins are expressed most in tumors of the head and neck and will aid in the development of future screening and treatment options.
ContributorsKhazanovich, Jakob (Author) / Anderson, Karen (Thesis director) / Mangone, Marco (Committee member) / Sundaresan, Sri Krishna (Committee member) / Barrett, The Honors College (Contributor)
Created2015-05
Description
Duchenne Muscular Dystrophy (DMD) is an X-linked recessive disease characterized by progressive muscle loss and weakness. This disease arises from a mutation that occurs on a gene that encodes for dystrophin, which results in observable muscle death and inflammation; however, the genetic changes that result from dystrophin's dysfunctionality remain unknown. Current DMD research uses mdx mice as a model, and while very useful, does not allow the study of cell-autonomous transcriptome changes during the progression of DMD due to the strong inflammatory response, perhaps hiding important therapeutic targets. C. elegans, which has a very weak inflammatory response compared to mdx mice and humans, has been used in the past to study DMD with some success. The worm ortholog of the dystrophin gene has been identified as dys-1 since its mutation phenocopies the progression of the disease and a portion of the human dystrophin gene alleviates symptoms. Importantly, the extracted RNA transcriptome from dys-1 worms showed significant change in gene expression, which needs to be further investigated with the development of a more robust model. Our lab previously published a method to isolate high-quality muscle-specific RNA from worms, which could be used to study such changes at higher resolution. We crossed the dys-1 worms with our muscle-specific strain and demonstrated that the chimeric strain exhibits similar behavioral symptoms as DMD patients as characterized by a shortened lifespan, difficulty in movement, and a decrease in speed. The presence of dys-1 and other members of the dystrophin complex in the body muscle were supported by the development of a resulting phenotype due to RNAi knockdown of each component in the body muscle; however, further experimentation is needed to reinforce this conclusion. Thus, the constructed chimeric C. elegans strain possesses unique characteristics that will allow the study of genetic changes, such as transcriptome rearrangements and dysregulation of miRNA, and how they affect the progression of DMD.
ContributorsNguyen, Thuy-Duyen Cao (Author) / Mangone, Marco (Thesis director) / Newbern, Jason (Committee member) / Duchaine, Thomas (Committee member) / School of Social Transformation (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
microRNAs (miRNAs) are short ~22nt non-coding RNAs that regulate gene output at the post-transcriptional level. Via targeting of degenerate elements primarily in 3'untranslated regions (3'UTR) of mRNAs, miRNAs can target thousands of varying genes and suppress their protein translation. The precise mechanistic function and bio- logical role of miRNAs is not fully understood and yet it is a major contributor to a pleth- ora of diseases, including neurological disorders, muscular disorders, and cancer. Cer- tain model organisms are valuable in understanding the function of miRNA and there- fore fully understanding the biological significance of miRNA targeting. Here I report a mechanistic analysis of miRNA targeting in C. elegans, and a bioinformatic approach to aid in further investigation of miRNA targeted sequences. A few of the biologically significant mechanisms discussed in this thesis include alternative polyadenylation, RNA binding proteins, components of the miRNA recognition machinery, miRNA secondary structures, and their polymorphisms. This thesis also discusses a novel bioinformatic approach to studying miRNA biology, including computational miRNA target prediction software, and sequence complementarity. This thesis allows a better understanding of miRNA biology and presents an ideal strategy for approaching future research in miRNA targeting.
ContributorsWeigele, Dustin Keith (Author) / Mangone, Marco (Thesis director) / Katchman, Benjamin (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor) / School of Life Sciences (Contributor)
Created2014-12
Description
The Cannabis plant has historical roots with human beings. The plant produces compounds called cannabinoids, which are responsible for the physiological affects of Cannabis and make it a research candidate for medicinal use. Analysis of the plant and its components will help build a better database that could be used to develop a complete roster of medicinal benefits. Research regarding the cellular protein receptors that bind the cannabinoids may not only help provide reasons explaining why the Cannabis plant could be medicinally relevant, but will also help explain how the receptors originated. The receptors may have been present in organisms before the present day Cannabis plant. So why would there be receptors that bind to cannabinoids? Searching for an endocannabinoid system could help explain the purpose of the cannabinoid receptors and their current structures in humans. Using genetic technologies we are able to take a closer look into the evolutionary history of cannabinoids and the receptors that bind them.
ContributorsSalasnek, Reed Samuel (Author) / Capco, David (Thesis director) / Mangone, Marco (Committee member) / Stump, Edmund (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2014-05
Description
A coincidence reporter construct, consisting of the p21-promoter and two luciferase genes (Firefly and Renilla), was constructed for the screening of drugs that might inhibit Olig2's tumorigenic role in glioblastoma. The reporter construct was tested using an Olig2 inhibitor, HSP990, as well as short hairpin RNA targeting Olig2. Further confirmatory analysis is needed before the reporter cell line is ready for high-throughput screening at the NIH and lead compound selection.
ContributorsCusimano, Joseph Michael (Author) / LaBaer, Joshua (Thesis director) / Mangone, Marco (Committee member) / Mehta, Shwetal (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor)
Created2014-05
Description
Background: Breast cancer is the most frequently diagnosed cancer and the leading cause of cancer deaths in females worldwide, accounting for 23% of all new cancer cases and 14% of all total cancer deaths in 2008. Five tumor-normal pairs of primary breast epithelial cells were treated for infinite proliferation by using a ROCK inhibitor and mouse feeder cells. Methods: Raw paired-end, 100x coverage RNA-Seq data was aligned to the Human Reference Genome Version 19 using BWA and Tophat. Gene differential expression analysis was completed using Cufflinks and Cuffdiff. Interactive Genome Viewer was used for data visualization. Results: 15 genes were found to be down-regulated by at least one log-fold change in 4/5 of tumor samples. 75 genes were found to be down-regulated in 3/5 of our tumor samples by at least one log-fold change. 11 genes were found to be up-regulated in 4/5 of our tumor samples, and 68 genes were identified to be up-regulated in 3/5 of the tumor samples by at least one-fold change. Conclusion: Expression changes in genes such as AZGP1, AGER, ALG11, and S1007 suggest a disruption in the glycosylation pathway. No correlation was found between Cufflink's Her2 gene-expression and DAKO score classification.
ContributorsHernandez, Fernando (Author) / Anderson, Karen (Thesis director) / Mangone, Marco (Committee member) / Park, Jin (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor)
Created2013-05
Description
miRNAs are short non-coding regulatory RNAs that have an important roles in a wide range of biological processes. Dysfunction of miRNA regulation has also been shown to occur in diseases such as cancer. Despite the widespread influence of miRNAs in these contexts, the vast majority of miRNA targets are poorly characterized. The aim of this research project was to gain a better understating of miRNA targeting by using the model organism C. elegans. In order to do this I adapted a novel high-throughput assay to detect miRNA targets for use with the C. elegans 3`UTRome. As a proof of principle I performed this assay on 96 C. elegans 3`UTRs using high-throughput techniques. The results revealed miRNA interactions with two predicted 3`UTR targets for the miRNA lin-4 and ten unpredicted targets. The results also corroborated previous findings that certain worm miRNAs require special modifications to be expressed in human cells.
ContributorsKotagama, Kasuen Indrajith Bandara (Author) / Mangone, Marco (Thesis director) / Anderson, Karen (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor)
Created2013-12