Matching Items (28)

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Mass spectrometric and molecular analyses of biological agents in environmental compartments

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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)

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.

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2012

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Caveolin-1: a potential biomarker of aggressive triple-negative breast cancer in African American women

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In the U.S., breast cancer (BC) incidences among African American (AA) and CA (CA) women are similar, yet AA women have a significantly higher mortality rate. In addition, AA women often present with tumors at a younger age, with a

In the U.S., breast cancer (BC) incidences among African American (AA) and CA (CA) women are similar, yet AA women have a significantly higher mortality rate. In addition, AA women often present with tumors at a younger age, with a higher tumor grade/stage and are more likely to be diagnosed with the highly aggressive triple-negative breast cancer (TNBC) subtype. Even within the TNBC subtype, AA women have a worse clinical outcome compared to CA. Although multiple socio-economic and lifestyle factors may contribute to these observed health disparities, it is essential that the underlying biological differences between CA and AA TNBC are identified. In this study, gene expression profiling was performed on archived FFPE samples, obtained from CA and AA women diagnosed with early stage TNBC. Initial analysis revealed a pattern of differential expression in the AA cohort compared to CA. Further molecular characterization results showed that the AA cohort segregated into 3-TNBC molecular subtypes; Basal-like (BL2), Immunomodulatory (IM) and Mesenchymal (M). Gene expression analyses resulted in 190 differentially expressed genes between the AA and CA cohorts. Pathway enrichment analysis demonstrated that differentially expressed genes were over-represented in cytoskeletal remodeling, cell adhesion, tight junctions, and immune response in the AA TNBC -cohort. Furthermore, genes in the Wnt/β-catenin pathway were over-expressed. These results were validated using RT-qPCR on an independent cohort of FFPE samples from AA and CA women with early stage TNBC, and identified Caveolin-1 (CAV1) as being significantly expressed in the AA-TNBC cohort. Furthermore, CAV1 was shown to be highly expressed in a cell line panel of TNBC, in particular, those of the mesenchymal and basal-like molecular subtype. Finally, silencing of CAV1 expression by siRNA resulted in a significant decrease in proliferation in each of the TNBC cell lines. These observations suggest that CAV1 expression may contribute to the more aggressive phenotype observed in AA women diagnosed with TNBC.

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2015

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Optimization and ultimate limitations for immunoassay and clinical diagnostics

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

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.

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2015

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Machine learning methods for biosignature discovery

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

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.

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2012

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Oligomeric amyloid-beta as a potential biomarker for Alzheimer's disease

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

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.

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2013

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Advancing microfluidic-based protein biosensor technology for use in clinical diagnostics

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

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.

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2011

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Targeted proteomics studies: design, development and translation of mass spectrometric immunoassays for diabetes and kidney disease

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

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.

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2011

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Novel biomarkers and genetic variants for type 2 diabetes in Latinos

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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

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.

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2015

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A multiplexing immunosensor for the quantification of cytokine biomarkers

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

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.

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2012

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From plasma peptide to phenotype: the emerging role of quiescin sulfhydryl oxidase 1 in tumor cell biology

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

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.

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Date Created
2012