Matching Items (30)
Description
Alzheimer's Disease (AD) is a progressive neurodegenerative disease accounting for 50-80% of dementia cases in the country. This disease is characterized by the deposition of extracellular plaques occurring in regions of the brain important for cognitive function. A primary component of these plaques is the amyloid-beta protein. While a natively unfolded protein, amyloid-beta can misfold and aggregate generating a variety of different species including numerous different soluble oligomeric species some of which are precursors to the neurofibrillary plaques. Various of the soluble amyloid-beta oligomeric species have been shown to be toxic to cells and their presence may correlate with progression of AD. Current treatment options target the dementia symptoms, but there is no effective cure or alternative to delay the progression of the disease once it occurs. Amyloid-beta aggregates show up many years before symptoms develop, so detection of various amyloid-beta aggregate species has great promise as an early biomarker for AD. Therefore reagents that can selectively identify key early oligomeric amyloid-beta species have value both as potential diagnostics for early detection of AD and as well as therapeutics that selectively target only the toxic amyloid-beta aggregate species. Earlier work in the lab includes development of several different single chain antibody fragments (scFvs) against different oligomeric amyloid-beta species. This includes isolation of C6 scFv against human AD brain derived oligomeric amyloid-beta (Kasturirangan et al., 2013). This thesis furthers research in this direction by improving the yields and investigating the specificity of modified C6 scFv as a diagnostic for AD. It is motivated by experiments reporting low yields of the C6 scFv. We also used the C6T scFv to characterize the variation in concentration of this particular oligomeric amyloid-beta species with age in a triple transgenic AD mouse model. We also show that C6T can be used to differentiate between post-mortem human AD, Parkinson's disease (PD) and healthy human brain samples. These results indicate that C6T has potential value as a diagnostic tool for early detection of AD.
ContributorsVenkataraman, Lalitha (Author) / Sierks, Michael (Thesis advisor) / Rege, Kaushal (Committee member) / Pauken, Christine (Committee member) / Arizona State University (Publisher)
Created2013
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
The shape of glucose response and one hour (1-hr) glucose during an oral glucose tolerance test (OGTT) are emerging biomarkers for type 2 diabetes. The purpose of this study was two-fold: (1) to investigate the utility of these novel biomakers to differentiate type 2 diabetes risk in Latino youth, and (2) to examine the genetic determinants in a Latino population.
Data from the ASU Arizona Insulin Registry (AIR) registry and the USC Study of Latino Adolescents at Risk for diabetes project were used to test the cross-sectional and prospective utility of novel biomarkers to identify youth at risk for type 2 diabetes. Pediatric and adult data from the ASU AIR registry were assessed to examine the association of single nucleotide polymorphisms (SNPs) with type 2 diabetes risk. Three KCNQ1 SNPs (rs151290; rs2237892; rs2237895) were examined as novel genetic variants for type 2 diabetes in Latinos.
Latino youth with a biphasic response in the AIR registry exhibited significantly better β-cell function (P < 0.05) compared to youth with a monophasic response. Additionally, Latino youth with a 1-hr glucose ≥155 mg/dL exhibited a significantly greater decline in β-cell function over 8 years compared with the <155 mg/dL group (β=-327.8±126.2, P = 0.01). Moreover, a 1-hr glucose ≥155 mg/dL was associated with a 2.5 times greater risk for developing prediabetes over time (P = 0.0001). 1-hr glucose was the most powerful predictor of prediabetes (area under the receiver operating characteristic curve=0.73) when compared to the traditional biomarkers including HbA1c (0.58), fasting (0.67), and 2-hr glucose (0.64). Two KCNQ1 SNPs (rs151290 and rs2237892) exhibited significant associations with type 2 diabetes risk factors. For the novel glycemic markers, 15 SNPs were associated with the glucose response curve, while 18 SNPs were associated with 1-hr glucose.
These data suggest that glucose response curve and 1-hr glucose during an OGTT independently differentiate type 2 diabetes risk among Latino youth. Furthermore, it was successful to replicate the association of type 2 diabetes risk with 2 KCNQ1 SNPs in a Latino population. Data suggest that novel glycemic biomarkers are influenced by genetic background in this high-risk population.
Data from the ASU Arizona Insulin Registry (AIR) registry and the USC Study of Latino Adolescents at Risk for diabetes project were used to test the cross-sectional and prospective utility of novel biomarkers to identify youth at risk for type 2 diabetes. Pediatric and adult data from the ASU AIR registry were assessed to examine the association of single nucleotide polymorphisms (SNPs) with type 2 diabetes risk. Three KCNQ1 SNPs (rs151290; rs2237892; rs2237895) were examined as novel genetic variants for type 2 diabetes in Latinos.
Latino youth with a biphasic response in the AIR registry exhibited significantly better β-cell function (P < 0.05) compared to youth with a monophasic response. Additionally, Latino youth with a 1-hr glucose ≥155 mg/dL exhibited a significantly greater decline in β-cell function over 8 years compared with the <155 mg/dL group (β=-327.8±126.2, P = 0.01). Moreover, a 1-hr glucose ≥155 mg/dL was associated with a 2.5 times greater risk for developing prediabetes over time (P = 0.0001). 1-hr glucose was the most powerful predictor of prediabetes (area under the receiver operating characteristic curve=0.73) when compared to the traditional biomarkers including HbA1c (0.58), fasting (0.67), and 2-hr glucose (0.64). Two KCNQ1 SNPs (rs151290 and rs2237892) exhibited significant associations with type 2 diabetes risk factors. For the novel glycemic markers, 15 SNPs were associated with the glucose response curve, while 18 SNPs were associated with 1-hr glucose.
These data suggest that glucose response curve and 1-hr glucose during an OGTT independently differentiate type 2 diabetes risk among Latino youth. Furthermore, it was successful to replicate the association of type 2 diabetes risk with 2 KCNQ1 SNPs in a Latino population. Data suggest that novel glycemic biomarkers are influenced by genetic background in this high-risk population.
ContributorsKim, Joon Young (Author) / Shaibi, Gabriel Q (Thesis advisor) / Mandarino, Lawrence J (Committee member) / Coletta, Dawn K (Committee member) / De Filippis, Elena A (Committee member) / Arizona State University (Publisher)
Created2015
Description
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
Demand for biosensor research applications is growing steadily. According to a new report by Frost & Sullivan, the biosensor market is expected to reach $14.42 billion by 2016. Clinical diagnostic applications continue to be the largest market for biosensors, and this demand is likely to continue through 2016 and beyond. Biosensor technology for use in clinical diagnostics, however, requires translational research that moves bench science and theoretical knowledge toward marketable products. Despite the high volume of academic research to date, only a handful of biomedical devices have become viable commercial applications. Academic research must increase its focus on practical uses for biosensors. This dissertation is an example of this increased focus, and discusses work to advance microfluidic-based protein biosensor technologies for practical use in clinical diagnostics. Four areas of work are discussed: The first involved work to develop reusable/reconfigurable biosensors that are useful in applications like biochemical science and analytical chemistry that require detailed sensor calibration. This work resulted in a prototype sensor and an in-situ electrochemical surface regeneration technique that can be used to produce microfluidic-based reusable biosensors. The second area of work looked at non-specific adsorption (NSA) of biomolecules, which is a persistent challenge in conventional microfluidic biosensors. The results of this work produced design methods that reduce the NSA. The third area of work involved a novel microfluidic sensing platform that was designed to detect target biomarkers using competitive protein adsorption. This technique uses physical adsorption of proteins to a surface rather than complex and time-consuming immobilization procedures. This method enabled us to selectively detect a thyroid cancer biomarker, thyroglobulin, in a controlled-proteins cocktail and a cardiovascular biomarker, fibrinogen, in undiluted human serum. The fourth area of work involved expanding the technique to produce a unique protein identification method; Pattern-recognition. A sample mixture of proteins generates a distinctive composite pattern upon interaction with a sensing platform consisting of multiple surfaces whereby each surface consists of a distinct type of protein pre-adsorbed on the surface. The utility of the "pattern-recognition" sensing mechanism was then verified via recognition of a particular biomarker, C-reactive protein, in the cocktail sample mixture.
ContributorsChoi, Seokheun (Author) / Chae, Junseok (Thesis advisor) / Tao, Nongjian (Committee member) / Yu, Hongyu (Committee member) / Forzani, Erica (Committee member) / Arizona State University (Publisher)
Created2011
Description
This thesis discusses the use of mass spectrometry and polymerase chain reaction (PCR), among other methods, to detect biomarkers of microorganisms in the environment. These methods can be used to detect bacteria involved in the degradation of environmental pollutants (bioremediation) or various single-celled pathogens, including those posing potential threats as bioterrorism agents. The first chapter introduces the hurdles in detecting in diverse environmental compartments in which they could be found, a select list of single-celled pathogens representing known or potential bioterrorism agents. These hurdles take the form of substances that interfere either directly or indirectly with the detection method. In the case of mass spectrometry-based detection, many of these substances (interferences) can be removed via effective sample pretreatment. Chapters 2 through 4 highlight specific methods developed to detect bioremediation or bioterrorism agents in environmental matrices. These methods are qualitative mass spectrometry, quantitative PCR, and quantitative mass spectrometry, respectively. The targeted organisms in these methods include several bioremediation agents, e.g. Pseudomonas putida F1 and Sphingomonas wittichii RW1, and bioterrorism agents, e.g. norovirus and Cryptosporidium parvum. In Chapter 2, I identify using qualitative mass spectrometry, biomarkers for three bacterial species involved in bioremediation. In Chapter 3, I report on a new quantitative PCR method suitable for monitoring of a key gene in yet another bioremediation agent, Sphingomonas wittichii RW1; furthermore, I apply this method to track the efficacy of bioremediation in bioaugmented environmental microcosms. In Chapter 4, I report on the development of new quantitative mass spectrometry methods for two organisms, S. wittichii RW1 and Cryptosporidium parvum, and evaluate two previously published methods for their applicability to the analysis of complex environmental samples. In Chapter 5, I review state-of-the-art methods for the detection of emerging biological contaminants, specifically viruses, in environmental samples. While this summary deals exclusively with viral pathogens, the advantages and remaining challenges identified are also applicable to all single-celled organisms in environmental settings. The suggestions I make at the end of this chapter are expected to be valid not only for future needs for emerging viruses but also for bacteria, eukaryotic pathogens, and prions. In general, it is advisable to continue the trend towards quantification and to standardize methods to facilitate comparison of results between studies.
ContributorsHartmann, Erica Marie (Author) / Halden, Rolf U. (Thesis advisor) / Ghirlanda, Giovanna (Committee member) / Nelson, Randall W. (Committee member) / Arizona State University (Publisher)
Created2012
Description
Biosensors offer excellent diagnostic methods through precise quantification of bodily fluid biomarkers and could fill an important niche in diagnostic screening. The long term goal of this research is the development of an impedance immunosensor for easy-to-use, rapid, sensitive and selective simultaneously multiplexed quantification of bodily fluid disease biomarkers. To test the hypothesis that various cytokines induce empirically determinable response frequencies when captured by printed circuit board (PCB) impedance immunosensor surface, cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) methods were used to test PCB biosensors versus multiple cytokine biomarkers to determine limits of detection, background interaction and response at all sweep frequencies. Results indicated that sensors for cytokine Interleukin-12 (IL-12) detected their target over three decades of concentration and were tolerant to high levels of background protein. Further, the hypothesis that cytokine analytes may be rapidly detected via constant frequency impedance immunosensing without sacrificing undue sensitivity, CV, EIS, impedance-time (Zt) methods and modeling were used to test CHITM gold electrodes versus IL-12 over different lengths of time to determine limits of detection, detection time, frequency of response and consistent cross-platform sensor performance. Modeling and Zt studies indicate interrogation of the electrode with optimum frequency could be used for detection of different target concentrations within 90 seconds of sensor exposure and that interrogating the immunosensor with fixed, optimum frequency could be used for sensing target antigen. This informs usability of fixed-frequency impedance methods for biosensor research and particularly for clinical biosensor use. Finally, a multiplexing impedance immunosensor prototype for quantification of biomarkers in various body fluids was designed for increased automation of sample handling and testing. This enables variability due to exogenous factors and increased rapidity of assay with eased sensor fabrication. Methods were provided for simultaneous multiplexing through multisine perturbation of a sensor, and subsequent data processing. This demonstrated ways to observe multiple types of antibody-antigen affinity binding events in real time, reducing the number of sensors and target sample used in the detection and quantification of multiple biomarkers. These features would also improve the suitability of the sensor for clinical multiplex detection of disease biomarkers.
ContributorsFairchild, Aaron (Author) / La Belle, Jeffrey T (Thesis advisor) / Muthuswamy, Jitendran (Committee member) / Nagaraj, Vinay (Committee member) / Pizziconi, Vince (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2012
Description
Cancer is a disease that affects millions of people worldwide each year. The metastatic progression of cancer is the number one reason for cancer related deaths. Cancer preventions rely on the early identification of tumor cells as well as a detailed understanding of cancer as a whole. Identifying proteins specific to tumor cells provide an opportunity to develop noninvasive clinical tests and further our understanding of tumor biology. Using liquid chromatography-mass spectrometry (LC-MS/MS) a short peptide was identified in pancreatic cancer patient plasma that was not found in normal samples, and mapped back to QSOX1 protein. Immunohistochemistry was performed probing for QSOX1 in tumor tissue and discovered that QSOX1 is highly over-expressed in pancreatic and breast tumors. QSOX1 is a FAD-dependent sulfhydryl oxidase that is extremely efficient at forming disulfide bonds in nascent proteins. While the enzymology of QSOX1 has been well studied, the tumor biology of QSOX1 has not been studied. To begin to determine the advantage that QSOX1 over-expression provides to tumors, short hairpin RNA (shRNA) were used to reduce the expression of QSOX1 in human tumor cell lines. Following the loss of QSOX1 growth rate, apoptosis, cell cycle and invasive potential were compared between tumor cells transduced with shQSOX1 and control tumor cells. Knock-down of QSOX1 protein suppressed tumor cell growth but had no effect on apoptosis and cell cycle regulation. However, shQSOX1 dramatically inhibited the abilities of both pancreatic and breast tumor cells to invade through Matrigel in a modified Boyden chamber assay. Mechanistically, shQSOX1-transduced tumor cells secreted MMP-2 and -9 that were less active than MMP-2 and -9 from control cells. Taken together, these results suggest that the mechanism of QSOX1-mediated tumor cell invasion is through the post-translational activation of MMPs. This dissertation represents the first in depth study of the role that QSOX1 plays in tumor cell biology.
ContributorsKatchman, Benjamin A (Author) / Lake, Douglas F. (Thesis advisor) / Rawls, Jeffery A (Committee member) / Miller, Laurence J (Committee member) / Chang, Yung (Committee member) / Arizona State University (Publisher)
Created2012
Description
Alzheimer's Disease (AD) is the most common form of dementia observed in elderly patients and has significant social-economic impact. There are many initiatives which aim to capture leading causes of AD. Several genetic, imaging, and biochemical markers are being explored to monitor progression of AD and explore treatment and detection options. The primary focus of this thesis is to identify key biomarkers to understand the pathogenesis and prognosis of Alzheimer's Disease. Feature selection is the process of finding a subset of relevant features to develop efficient and robust learning models. It is an active research topic in diverse areas such as computer vision, bioinformatics, information retrieval, chemical informatics, and computational finance. In this work, state of the art feature selection algorithms, such as Student's t-test, Relief-F, Information Gain, Gini Index, Chi-Square, Fisher Kernel Score, Kruskal-Wallis, Minimum Redundancy Maximum Relevance, and Sparse Logistic regression with Stability Selection have been extensively exploited to identify informative features for AD using data from Alzheimer's Disease Neuroimaging Initiative (ADNI). An integrative approach which uses blood plasma protein, Magnetic Resonance Imaging, and psychometric assessment scores biomarkers has been explored. This work also analyzes the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Performance of feature selection algorithm is evaluated using the relevance of derived features and the predictive power of the algorithm using Random Forest and Support Vector Machine classifiers. Performance metrics such as Accuracy, Sensitivity and Specificity, and area under the Receiver Operating Characteristic curve (AUC) have been used for evaluation. The feature selection algorithms best suited to analyze AD proteomics data have been proposed. The key biomarkers distinguishing healthy and AD patients, Mild Cognitive Impairment (MCI) converters and non-converters, and healthy and MCI patients have been identified.
ContributorsDubey, Rashmi (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Wu, Tong (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