Integrating sequence information in microarray data analysis by free energy modeling

Microarray technology is a high-throughput method for genotyping and gene expression profiling. Limited sensitivity and specificity are one of the essential problems for this technology. Most of existing methods of microarray data analysis have an apparent limitation for they merely deal with the numerical part of microarray data and have made little use of gene sequence information. Because it's the gene sequences that precisely define the physical objects being measured by a microarray, it is natural to make the gene sequences an essential part of the data analysis. This dissertation focused on the development of free energy models to integrate sequence information in microarray data analysis. The models were used to characterize the mechanism of hybridization on microarrays and enhance sensitivity and specificity of microarray measurements. ^ Cross-hybridization is a major obstacle factor for the sensitivity and specificity of microarray measurements. In this dissertation, we evaluated the scope of cross-hybridization problem on short-oligo microarrays. The results showed that cross hybridization on arrays is mostly caused by oligo fragments with a run of 10 to 16 nucleotides complementary to the probes. Furthermore, a free-energy based model was proposed to quantify the amount of cross-hybridization signal on each probe. This model treats cross-hybridization as an integral effect of the interactions between a probe and various off-target oligo fragments. Using public spike-in datasets, the model showed high accuracy in predicting the cross-hybridization signals on those probes whose intended targets are absent in the sample. ^ Several prospective models were proposed to improve Positional Dependent Nearest-Neighbor (PDNN) model for better quantification of gene expression and cross-hybridization. ^ The problem addressed in this dissertation is fundamental to the microarray technology. We expect that this study will help us to understand the detailed mechanism that determines sensitivity and specificity on the microarrays. Consequently, this research will have a wide impact on how microarrays are designed and how the data are interpreted. ^

STUDIES ON ENERGY METABOLISM IN THE HIPPOCAMPUS AFTER ISCHEMIA

The gerbil model of ischemia was used to determine the effect of carotid occlusion on energy metabolites in cellular layers of discrete regions of the hippocampus and dentate gyrus. Levels of glucose, glycogen, ATP and phosphocreatine (PCr) were unchanged after 1 minute of ischemia. However, 3 minutes of ischemia produced a dramatic decrease in net levels of all metabolites. No additional decrease was observed after 15 minutes of ischemia. Re-establishment of the blood flow for 5 minutes after a 15 minute ischemic episode returned all metabolites to pre-ischemia levels. Concentrations of glucose and glycogen were elevated in sham-operated animals as a function of the pentobarbital anesthetic employed. In other studies, elevated GABA levels (produced by inhibiting GABA-transaminase with (gamma)-vinyl-GABA (GVG)) were found to decrease the rate of utilization of the high-energy phosphate metabolites ATP and PCr in the mouse cortex. In addition, glucose and glycogen levels were increased. Thus, tonic inhibition by GABA produced decreased cellular activity. Additional experiments demonstrated the attenuation of ischemia-induced metabolite depletion in cellular layers of regions of the hippocampus, dentate gyrus and cortex after GVG administration. Under ether, 1 minute of bilateral carotid occlusion produced a dramatic decrease in metabolite levels. After GVG treatment, the decrease was blocked completely for glucose, glycogen and ATP, and partially for PCr. Therefore, GABA-transaminase inhibition produced increased levels of GABA which subsequently decreased cellular activity. The protection against ischemia may have been due to (a)decreased metabolic rate; the available energy stores were utilized at a slower rate, and (b)increased levels of energy substrates; additional supplies available to maintain viability. These data suggest that the functional state of neural tissue can determine the response to metabolic stress. ^

A LAPLACE TRANSFORM PAIR MODEL TO DETERMINE BREMSSTRAHLUNG SPECTRA FROM ATTENUATION DATA (RADIATION, X-RAY, SPECTRAL MODELING)

Every x-ray attenuation curve inherently contains all the information necessary to extract the complete energy spectrum of a beam. To date, attempts to obtain accurate spectral information from attenuation data have been inadequate.^ This investigation presents a mathematical pair model, grounded in physical reality by the Laplace Transformation, to describe the attenuation of a photon beam and the corresponding bremsstrahlung spectral distribution. In addition the Laplace model has been mathematically extended to include characteristic radiation in a physically meaningful way. A method to determine the fraction of characteristic radiation in any diagnostic x-ray beam was introduced for use with the extended model.^ This work has examined the reconstructive capability of the Laplace pair model for a photon beam range of from 50 kVp to 25 MV, using both theoretical and experimental methods.^ In the diagnostic region, excellent agreement between a wide variety of experimental spectra and those reconstructed with the Laplace model was obtained when the atomic composition of the attenuators was accurately known. The model successfully reproduced a 2 MV spectrum but demonstrated difficulty in accurately reconstructing orthovoltage and 6 MV spectra. The 25 MV spectrum was successfully reconstructed although poor agreement with the spectrum obtained by Levy was found.^ The analysis of errors, performed with diagnostic energy data, demonstrated the relative insensitivity of the model to typical experimental errors and confirmed that the model can be successfully used to theoretically derive accurate spectral information from experimental attenuation data. ^

Alterations in redox and energy metabolism in Ras-transformed cells: Mechanisms and therapeutic implications

Increasing attention has been given to the connection between metabolism and cancer. Under aerobic conditions, normal cells predominantly use oxidative phosphorylation for ATP generation. In contrast, increase of glycolytic activity has been observed in various tumor cells, which is known as Warburg effect. Cancer cells, compared to normal cells, produce high levels of Reactive Oxygen Species (ROS) and hence are constantly under oxidative stress. Increase of oxidative stress and glycolytic activity in cancer cells represent major biochemical alterations associated with malignant transformation. Despite prevalent upregulation of ROS production and glycolytic activity observed in various cancer cells, underlying mechanisms still remain to be defined. Oncogenic signals including Ras has been linked to regulation of energy metabolism and ROS production. Current study was initiated to investigate the mechanism by which Ras oncogenic signal regulates cellular metabolism and redox status. A doxycycline inducible gene expression system with oncogenic K-ras transfection was constructed to assess the role played by Ras activation in any given studied parameters. Data obtained here reveals that K-ras activation directly caused mitochondrial dysfunction and ROS generation, which appeared to be mechanistically associated with translocation of K-ras to mitochondria and the opening of the mitochondrial permeability transition pore. K-ras induced mitochondrial dysfunction led to upregulation of glycolysis and constitutive activation of ROS-generating NAD(P)H Oxidase (NOX). Increased oxidative stress, upregulation of glycolytic activity, and constitutive activated NOX were also observed in the pancreatic K-ras transformed cancer cells compared to their normal counterparts. Compared to non-transformed cells, the pancreatic K-ras transformed cancer cells with activated NOX exhibited higher sensitivity to capsaicin, a natural compound that appeared to target NOX and cause preferential accumulation of oxidative stress in K-ras transformed cells. Taken together, these findings shed new light on the role played by Ras in the road to cancer in the context of oxidative stress and metabolic alteration. The mechanistic relationship between K-ras oncogenic signals and metabolic alteration in cancer will help to identify potential molecular targets such as NAD(P)H Oxidase and glycolytic pathway for therapeutic intervention of cancer development. ^

Secondary data analysis of a pilot study of use of an instructional video for dietary assessment

Introduction. Food frequency questionnaires (FFQ) are used study the association between dietary intake and disease. An instructional video may potentially offer a low cost, practical method of dietary assessment training for participants thereby reducing recall bias in FFQs. There is little evidence in the literature of the effect of using instructional videos on FFQ-based intake. Objective. This analysis compared the reported energy and macronutrient intake of two groups that were randomized either to watch an instructional video before completing an FFQ or to view the same instructional video after completing the same FFQ. Methods. In the parent study, a diverse group of students, faculty and staff from Houston Community College were randomized to two groups, stratified by ethnicity, and completed an FFQ. The "video before" group watched an instructional video about completing the FFQ prior to answering the FFQ. The "video after" group watched the instructional video after completing the FFQ. The two groups were compared on mean daily energy (Kcal/day), fat (g/day), protein (g/day), carbohydrate (g/day) and fiber (g/day) intakes using descriptive statistics and one-way ANOVA. Demographic, height, and weight information was collected. Dietary intakes were adjusted for total energy intake before the comparative analysis. BMI and age were ruled out as potential confounders. Results. There were no significant differences between the two groups in mean daily dietary intakes of energy, total fat, protein, carbohydrates and fiber. However, a pattern of higher energy intake and lower fiber intake was reported in the group that viewed the instructional video before completing the FFQ compared to those who viewed the video after. Discussion. Analysis of the difference between reported intake of energy and macronutrients showed an overall pattern, albeit not statistically significant, of higher intake in the video before versus the video after group. Application of instructional videos for dietary assessment may require further research to address the validity of reported dietary intakes in those who are randomized to watch an instructional video before reporting diet compared to a control groups that does not view a video.^

Genetic variability and the hypothalamic control of energy balance

Background. Obesity is a major health problem throughout the industrialized world. Despite numerous attempts to curtail the rapid growth of obesity, its incidence continues to rise. Therefore, it is crucial to better understand the etiology of obesity beyond the concept of energy balance.^ Aims. The first aim of this study was to first investigate the relationship between eating behaviors and body size. The second goal was to identify genetic variation associated with eating behaviors. Thirdly, this study aimed to examine the joint relationships between eating behavior, body size and genetic variation.^ Methods. This study utilized baseline data ascertained in young adults from the Training Interventions and Genetics of Exercise (TIGER) Study. Variables assessed included eating behavior (Emotional Eating Scale, Eating Attitudes Test-26, and the Block98 Food Frequency Questionnaire), body size (body mass index, waist and hip circumference, waist/hip ratio, and percent body fat), genetic variation in genes implicated related to the hypothalamic control of energy balance, and appropriate covariates (age, gender, race/ethnicity, smoking status, and physical activity. For the genetic association analyses, genotypes were collapsed by minor allele frequency, and haplotypes were estimated for each gene. Additionally, Bayesian networks were constructed in order to determine the relationships between genetic variation, eating behavior and body size.^ Results. We report that the EAT-26 score, Caloric intake, percent fat, fiber intake, HEAT index, and daily servings of vegetables, meats, grains, and fats were significantly associated with at least one body size measure. Multiple SNPs in 17 genes and haplotypes from 12 genes were tested for their association with body size. Variation within both DRD4 and HTR2A was found to be associated with EAT-26 score. In addition, variation in the ghrelin gene (GHRL) was significantly associated with daily Caloric intake. A significant interaction between daily servings of grains and the HEAT index and variation within the leptin receptor gene (LEPR) was shown to influence body size.^ Conclusion. This study has shown that there is a substantial genetic component to eating behavior and that genetic variation interacts with eating behavior to influence body size.^

EVALUATION OF THE EFFECTIVENESS OF ANISOTROPIC ANALYTICAL ALGORITHM IN FLATTENED AND FLATTENING-FILTER-FREE BEAMS FOR HIGH ENERGY LUNG DOSE DELIVERY USING THE RADIOLOGICAL PHYSICS CENTER LUNG PHANTOM

The effectiveness of the Anisotropic Analytical Algorithm (AAA) implemented in the Eclipse treatment planning system (TPS) was evaluated using theRadiologicalPhysicsCenteranthropomorphic lung phantom using both flattened and flattening-filter-free high energy beams. Radiation treatment plans were developed following the Radiation Therapy Oncology Group and theRadiologicalPhysicsCenterguidelines for lung treatment using Stereotactic Radiation Body Therapy. The tumor was covered such that at least 95% of Planning Target Volume (PTV) received 100% of the prescribed dose while ensuring that normal tissue constraints were followed as well. Calculated doses were exported from the Eclipse TPS and compared with the experimental data as measured using thermoluminescence detectors (TLD) and radiochromic films that were placed inside the phantom. The results demonstrate that the AAA superposition-convolution algorithm is able to calculate SBRT treatment plans with all clinically used photon beams in the range from 6 MV to 18 MV. The measured dose distribution showed a good agreement with the calculated distribution using clinically acceptable criteria of ±5% dose or 3mm distance to agreement. These results show that in a heterogeneous environment a 3D pencil beam superposition-convolution algorithms with Monte Carlo pre-calculated scatter kernels, such as AAA, are able to reliably calculate dose, accounting for increased lateral scattering due to the loss of electronic equilibrium in low density medium. The data for high energy plans (15 MV and 18 MV) showed very good tumor coverage in contrast to findings by other investigators for less sophisticated dose calculation algorithms, which demonstrated less than expected tumor doses and generally worse tumor coverage for high energy plans compared to 6MV plans. This demonstrates that the modern superposition-convolution AAA algorithm is a significant improvement over previous algorithms and is able to calculate doses accurately for SBRT treatment plans in the highly heterogeneous environment of the thorax for both lower (≤12 MV) and higher (greater than 12 MV) beam energies.

Targeting AKT/mTOR Signaling Pathways During Murine Skin Tumor Promotion and the Impact of Dietary Energy Balance Manipulation

The prevalence of obesity has continued to rise over the last several decades in the United States lending to overall increases in risk for chronic diseases including many types of cancer. In contrast, reduction in energy consumption via calorie restriction (CR) has been shown to be a potent inhibitor of carcinogenesis across a broad range of species and tumor types. Previous data has demonstrated differential signaling through Akt and mTOR via the IGF-1R and other growth factor receptors across the diet-induced obesity (DIO)/CR spectrum. Furthermore, mTORC1 is known to be regulated directly via nutrient availability, supporting its role in the link between epithelial carcinogenesis and diet-induced obesity. In an effort to better understand the importance of mTORC1 in the context of both positive and negative energy balance during epithelial carcinogenesis, we have employed the use of specific pharmacological inhibitors, rapamycin (mTORC1 inhibitor) and metformin (AMPK activator) to target mTORC1 or various components of this pathway during skin tumor promotion. Two-stage skin carcinogenesis studies demonstrated that mTORC1 inhibition via rapamycin, metformin or combination treatments greatly inhibited skin tumor development in normal, overweight and obese mice. Furthermore, mechanisms by which these chemopreventive agents may be exerting their anti-tumor effects were explored. In addition, the effect of these compounds on the epidermal proliferative response was analyzed and drastic decreases in epidermal hyperproliferation and hyperplasia were found. Rapamycin also inhibited dermal inflammatory cell infiltration in a dose-dependent manner. Both compounds also blocked or attenuated TPA-induced signaling through epidermal mTORC1 as well as several downstream targets. In addition, inhibition of this pathway by metformin appeared to be, at least in part, dependent on AMPK activation in the skin. Overall, the data indicate that pharmacological strategies targeting this pathway offset the tumor-enhancing effects of DIO and may serve as possible CR mimetics. They suggest that mTORC1 contributes significantly to the process of skin tumor promotion, specifically during dietary energy balance effects. Exploiting the mechanistic information underlying dietary energy balance responsive pathways will help translate decades of research into effective strategies for prevention of epithelial carcinogenesis.

Regression with autocorrelated data: A study of time trend of global infant mortality rate

The infant mortality rate (IMR) is considered to be one of the most important indices of a country's well-being. Countries around the world and other health organizations like the World Health Organization are dedicating their resources, knowledge and energy to reduce the infant mortality rates. The well-known Millennium Development Goal 4 (MDG 4), whose aim is to archive a two thirds reduction of the under-five mortality rate between 1990 and 2015, is an example of the commitment. ^ In this study our goal is to model the trends of IMR between the 1950s to 2010s for selected countries. We would like to know how the IMR is changing overtime and how it differs across countries. ^ IMR data collected over time forms a time series. The repeated observations of IMR time series are not statistically independent. So in modeling the trend of IMR, it is necessary to account for these correlations. We proposed to use the generalized least squares method in general linear models setting to deal with the variance-covariance structure in our model. In order to estimate the variance-covariance matrix, we referred to the time-series models, especially the autoregressive and moving average models. Furthermore, we will compared results from general linear model with correlation structure to that from ordinary least squares method without taking into account the correlation structure to check how significantly the estimates change.^