Matching Items (28)

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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

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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Date Created
  • 2013

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Mass spectrometric and molecular analyses of biological agents in environmental compartments

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

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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Date Created
  • 2012

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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

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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Date Created
  • 2011

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Statistical signal processing of ESI-TOF-MS for biomarker discovery

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

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.

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Date Created
  • 2012

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

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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

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

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Effects of coconut oil supplementation on biomarkers of inflammation and lipid peroxidation

Description

ABSTRACT

Objective: The purpose of this randomized, placebo-controlled trial was to investigate the effect a daily coconut oil supplement (2 grams) would have on a common serum marker of systemic inflammation

ABSTRACT

Objective: The purpose of this randomized, placebo-controlled trial was to investigate the effect a daily coconut oil supplement (2 grams) would have on a common serum marker of systemic inflammation (C-reactive protein) and an indicator of oxidative stress (TBARS) when compared to the control group receiving a placebo capsule (white flour) in healthy, sedentary adults between the ages of 18-40 in Phoenix, Arizona.

Design: This study was designed as secondary analyses of blood samples originally collected to study the effects of coconut oil supplementation on blood lipids and body composition. The original study consisted of 32 healthy, adult volunteers recruited from the Arizona State University campus in Phoenix, Arizona. Participants followed no food restrictions or special diets, exercised less than 150 minutes per week, had no diagnoses of chronic disease, were not taking statin medications, were non-smokers, and no female participants were pregnant. Participants were randomized into either the Coconut Oil group (CO) or the Placebo group (PL) at week 0, and baseline blood samples and anthropometric measurements were obtained. Each participant completed an 8-week protocol consisting of two supplement capsules daily (coconut oil or placebo). Final fasting blood samples and anthropometric measurements were taken at week 8. This study analyzed the blood samples for measurements of C-reactive protein (CRP) and thiobarbituric reactive substance (TBARS).

Results: Eight weeks of 2 grams per day coconut oil supplementation, in comparison to placebo treatment, did not significantly reduce serum CRP ( -13% and +51% respectively, p=0.183) but did significantly increase TBARS ( +16% and -27% respectively, p=0.049).

Conclusions: Coconut oil supplementation (2 g/day) may impact lipid peroxidation as indicated by an increase in plasma TBARS concentration. Future trials are necessary to corroborate these results using other indices of fatty peroxide formation.

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Date Created
  • 2017

Investigation of DNA methylation in obesity and its underlying insulin resistance

Description

Obesity and its underlying insulin resistance are caused by environmental and genetic factors. DNA methylation provides a mechanism by which environmental factors can regulate transcriptional activity. The overall goal of

Obesity and its underlying insulin resistance are caused by environmental and genetic factors. DNA methylation provides a mechanism by which environmental factors can regulate transcriptional activity. The overall goal of the work herein was to (1) identify alterations in DNA methylation in human skeletal muscle with obesity and its underlying insulin resistance, (2) to determine if these changes in methylation can be altered through weight-loss induced by bariatric surgery, and (3) to identify DNA methylation biomarkers in whole blood that can be used as a surrogate for skeletal muscle.

Assessment of DNA methylation was performed on human skeletal muscle and blood using reduced representation bisulfite sequencing (RRBS) for high-throughput identification and pyrosequencing for site-specific confirmation. Sorbin and SH3 homology domain 3 (SORBS3) was identified in skeletal muscle to be increased in methylation (+5.0 to +24.4 %) in the promoter and 5’untranslated region (UTR) in the obese participants (n= 10) compared to lean (n=12), and this finding corresponded with a decrease in gene expression (fold change: -1.9, P=0.0001). Furthermore, SORBS3 was demonstrated in a separate cohort of morbidly obese participants (n=7) undergoing weight-loss induced by surgery, to decrease in methylation (-5.6 to -24.2%) and increase in gene expression (fold change: +1.7; P=0.05) post-surgery. Moreover, SORBS3 promoter methylation was demonstrated in vitro to inhibit transcriptional activity (P=0.000003). The methylation and transcriptional changes for SORBS3 were significantly (P≤0.05) correlated with obesity measures and fasting insulin levels. SORBS3 was not identified in the blood methylation analysis of lean (n=10) and obese (n=10) participants suggesting that it is a muscle specific marker. However, solute carrier family 19 member 1 (SLC19A1) was identified in blood and skeletal muscle to have decreased 5’UTR methylation in obese participants, and this was significantly (P≤0.05) predicted by insulin sensitivity.

These findings suggest SLC19A1 as a potential blood-based biomarker for obese, insulin resistant states. The collective findings of SORBS3 DNA methylation and gene expression present an exciting novel target in skeletal muscle for further understanding obesity and its underlying insulin resistance. Moreover, the dynamic changes to SORBS3 in response to metabolic improvements and weight-loss induced by surgery.

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Date Created
  • 2017

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The investigation of sucrose and fructose in spot versus 24-hour urine as biomarkers of sugars intake

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Background: Twenty-four hour urinary sucrose and fructose (24uSF) has been developed as a dietary biomarker for total sugars intake. Collection of 24-h urine is associated with high costs and

Background: Twenty-four hour urinary sucrose and fructose (24uSF) has been developed as a dietary biomarker for total sugars intake. Collection of 24-h urine is associated with high costs and heavy participant burden, while collection of spot urine samples can be easily implemented in research protocols. The aim of this thesis is to investigate the utility of uSF biomarker measured in spot urine. Methods: 15 participants age 22 to 49 years completed a 15-day feeding study in which they consumed their usual diet under controlled conditions, and recorded the time each meal was consumed. Two nonconsecutive 24-hour urines, where each urine void was collected in a separate container, were collected. Four timed voids (morning, afternoon, evening, and next day) were identified based on time of void and meal time. Urine samples were measured for sucrose, fructose and creatinine. Variability of uSF excretion was assessed by coefficient of variation (%CV) and variance ratios. Pearson correlation coefficient and multiple linear regression were used to investigate the association between uSF in each timed void and corresponding 24uSF excretion. Results: The two-day mean uSF was 50.6 mg (SD=29.5) for the 24-h urine, and ranged from 4.5 to 7.5 mg/void for the timed voids. The afternoon void uSF had the lowest within-subject variability (49.1%), and lowest within- to between-subject variance ratio (0.2). The morning and afternoon void uSF had the strongest correlation with 24-h uSF for both mg/void (r=0.80 and r=0.72) and mg/creatinine (r=0.72 and r=0.67), respectively. Finally, the afternoon void uSF along with other covariates had the strongest predictive ability of 24-h uSF excretion (mg/void) (Adjusted R2= 0.69; p=0.002), whereas the morning void had the strongest predictive ability of 24-h uSF excretion (mg/g creatinine) (adjusted R2= 0.58; p=0.008). Conclusions: The afternoon void uSF had the most favorable reproducibility estimates, strong correlation with 24uSF excretion, and explained greatest proportion of the variability in 24uSF. USF in mg/void may be better to use than uSF in mg/g creatinine as a biomarker in spot urine. These findings need to be confirmed in a larger study, and in a study population with a wide range of sugars intake.

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Date Created
  • 2018

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Sleep-related mediators of the physical activity and sedentary behavior-cardiometabolic biomarker relationship in middle age adults

Description

Physical activity, sedentary behaviors, and sleep are often associated with cardiometabolic biomarkers commonly found in metabolic syndrome. These relationships are well studied, and yet there are still questions on how

Physical activity, sedentary behaviors, and sleep are often associated with cardiometabolic biomarkers commonly found in metabolic syndrome. These relationships are well studied, and yet there are still questions on how each activity may affect cardiometabolic biomarkers. The objective of this study was to examine data from the BeWell24 studies to evaluate the relationship between objectively measured physical activity and sedentary behaviors and cardiometabolic biomarkers in middle age adults, while also determining if sleep quality and duration mediates this relationship. A group of inactive participants (N = 29, age = 52.1 ± 8.1 years, 38% female) with increased risk for cardiometabolic disease were recruited to participate in BeWell24, a trial testing the impact of a lifestyle-based, multicomponent smartphone application targeting sleep, sedentary, and more active behaviors. During baseline, interim (4 weeks), and posttest visits (8 weeks), biomarker measurements were collected for weight (kg), waist circumference (cm), glucose (mg/dl), insulin (uU/ml), lipids (mg/dl), diastolic and systolic blood pressures (mm Hg), and C reactive protein (mg/L). Participants wore validated wrist and thigh sensors for one week intervals at each time point to measure sedentary behavior, physical activity, and sleep outcomes. Long bouts of sitting time (>30 min) significantly affected triglycerides (beta = .15 (±.07), p<.03); however, no significant mediation effects for sleep quality or duration were present. No other direct effects were observed between physical activity measurements and cardiometabolic biomarkers. The findings of this study suggest that reductions in long bouts of sitting time may support reductions in triglycerides, yet these effects were not mediated by sleep-related improvements.

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Date Created
  • 2017

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Effects of a lifestyle intervention on brain-derived neurotrophic factor (BDNF) in obese Latino youth with pre-diabetes

Description

Latino youth have substantially higher rates of obesity and T2D than their white peers. The higher prevalence of obesity and T2D among Latino youth places them at greater risk for

Latino youth have substantially higher rates of obesity and T2D than their white peers. The higher prevalence of obesity and T2D among Latino youth places them at greater risk for cognitive dysfunction, an urgent and serious health threat to the United States. Exercise has been the cornerstone to combat the negative effects of obesity, diabetes and recent research also supports this effects for preventing cognitive dysfunction. A wealth of evidence suggests that a mediating mechanism linking exercise with brain health is BDNF, a cognitive biomarker that increases in the brain with exercise. BDNF is the most abundant neurotrophic factor that supports growth, survival and synaptic plasticity of neurons, all vital for cognitive function and brain health. The present study sought to investigate the effects of a 12-week lifestyle intervention of physical activity and lifestyle education on serum BDNF, in obese pre diabetic Latino youth.

A total of twelve obese pre diabetic Latino youth were selected from a larger RCT sample to be the focus for this analysis. After an overnight fast, a serum concentration was collected from all youth to be used for the BDNF analysis. In addition, the following cardio metabolic measures were also at taken at baseline and post intervention: Submaximal VO2max, medical and family history questionnaire, anthropometric, fasting glucose and a 2-hour oral glucose tolerance test (OGTT). A 12-weeks Lifestyle Intervention that involved a progressive moderate to high intensity exercise component and lifestyle education program did not significantly change serum BDNF levels in obese pre diabetic Latino youth. In conclusion, the variation of our serum BDNF results are highly speculative at this time, therefore the need for future investigations is crucial.

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Created

Date Created
  • 2016