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Oxidative stress (OS) has been associated with aging and age-related diseases in humans, as well as with the decline in economic trait performance in poultry and other domesticated animals. However, the potential effects of OS on the poultry immune system are not well understood. In addition, the impact of bird genetic variation on redox balance remains to be elucidated. Thus, the central hypothesis of this dissertation is: The bird's adaptive immunocompetence is impacted by their OS level, which is not only influenced by environmental factors, but also related to genetic phenotype of either mitochondrial DNA (mtDNA) or nuclear DNA (nDNA). In the first phase of this study, White Leghorn chickens were provided ethanol at different concentrations in drinking water to induce OS. Biomarkers including malondialdehyde (MDA), glutathione (GSH), and plasma uric acid (PUA) were measured to assess OS before and after ethanol treatment. The adaptive immune response during an OS event was measured by plasma IgG and IgM levels, major lymphoid organ weights, CD4+/CD8+ cell ratio, and histopathological analysis of the immune organs. Results showed that when OS was induced by 10% ethanol, chicken adaptive immune responses decreased; however, when birds were exposed to 2% ethanol, there was an enhancement in antioxidant defense and immune response; These results would suggest a negative correlation between OS level and chicken adaptive immune response. In the second phase of the study, subsets of chickens were selected based on their high (H)- or low (L)-OS to assess for variations in their genetic phenotypes. Using MDA levels, 36 chickens were chosen to scan a 2734-bp region of mtDNA, but no definitive SNP was detected. In another experiment, 40 chickens were conversely selected according to three biomarkers for OS. Although no variation was found at eight SNP loci tested across the mitochondrial genome, mtDNA damage measured by 8-hydroxy-2′-deoxy-guanosine was shown to increase with time, and at higher levels in the high OS birds (p < 0.05). Thses results suggest that long-term high OS levels in chickens may increase the somatic mutation of their mtDNA. In the final phase of this dissertation, the effect of nDNA on OS, measured via a genome-wide association study was performed with 18 H and 18 L chickens using the latest chicken 60k SNP microarray for genotyping. Among 56,483 SNPs successfully genotyped, 13 SNPs across five independent loci were associated with OS at significance level of p ≤ 0.001, and another 144 SNPs were also associated with OS (p ≤ 0.01). These results indicate new loci and related genes for their genetic influence upon redox balance. In general, experiments carried out on White Leghorn chickens here have shown that adaptive immune response is tightly related to changes of OS. Further, genetic variance in nDNA is associated with the risk of high OS or the ability to better resist it. / Ph. D.

Nescient Helix Loop Helix 2 (Nhlh2) is a member of the basic helix-loop-helix transcription factor family. Mice with a targeted deletion of Nhlh2, called N2KO mice, show adult onset obesity in both males and females. Nhlh2 regulates other genes by binding to the E-box in the promoter region of these genes. This transcription factor regulates many other transcription factors including MC4R and PC1/3 which are associated with human obesity. The Nhlh2 promoter has been analyzed for putative transcription factors binding sites. These putative binding sites have been tested to be the regulators of Nhlh2 by transactivation assays with mutant promoters, Electrophoretic Shift Assay (EMSA), and Chromatin Immunoprecipitation Assay (ChIP) as methods to investigate the DNA-protein binding. The results of these experiments showed that the Nhlh2 promoter has five Signal Transducer and Activator of Transcription 3 (Stat3) binding site motifs at -47, -65, -80, -281, -294 and two Nuclear Factor Kappa-Light-Chain-Enhancer of Activated B Cells (NFκB) binding site motifs at -67 and -135. While NFκB acts as a negative regulator of Nhlh2, this research showed that Stat3 acts as a regulator for the Nhlh2 basal expression and leptin stimulation. The ChIP assay using chromatin from mouse hypothalamus and antibodies against Stat3 and the NFκB subunits P50, P65, and c-Rel demonstrated that all of these antibodies were able to pull down the part of the Nhlh2 promoter containing the binding sites of Stat3 and NFκB. The EMSA results not only demonstrated that NFκB and Stat3 binding site motifs are real binding sites, but also exists the possibility of a relationship between these transcription factors to regulate Nhlh2 expression with leptin stimulation. An effort in analyzing the human NHLH2 3'UTR showed that one of the SNPs located at position 1568 in the NHLH2 mRNA (NHLH2A^1568G) which converts adenosine to guanine might have the potential to decrease the mRNA stability. For more investigation about this SNP, the mouse Nhlh2 tail was cloned into 2 different vectors and these vectors were subjected to site directed mutagenesis to create the 3'UTR SNP that convert A to G. One of these vectors used luciferase as a reporter gene for expression while the other one was used to measure Nhlh2 mRNA stability. These vectors were transfected into hypothalamic cell line N29/2 to test the effect of this SNP on Nhlh2 expression. This study demonstrated that this SNP down regulated luciferase expression and also decreased Nhlh2 mRNA stability. Taken together, this study demonstrated that Nhlh2 could be regulated transcriptionally by both NFκB and Stat3 transcription factors and post-transcripitionally by the 3'UTR SNP that converts adenosine to guanine. / Ph. D.

Recent advances in biotechnologies have enabled multiplatform and large-scale quantitative measurements of biomedical events. The need to analyze the produced vast amount of imaging and genomic data stimulates various novel applications of statistical machine learning methods in many areas of biomedical research. The main objective is to assist biomedical investigators to better interpret, analyze, and understand the biomedical questions based on the acquired data. Given the computational challenges imposed by these high-dimensional and complex data, machine learning research finds its new opportunities and roles. In this dissertation thesis, we propose to develop, test and apply novel statistical machine learning methods to analyze the data mainly acquired by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and single nucleotide polymorphism (SNP) microarrays. The research work focuses on: (1) tissue-specific compartmental analysis for dynamic contrast-enhanced MR imaging of complex tumors; (2) computational Analysis for detecting DNA SNP interactions in genome-wide association studies. DCE-MRI provides a noninvasive method for evaluating tumor vasculature patterns based on contrast accumulation and washout. Compartmental analysis is a widely used mathematical tool to model dynamic imaging data and can provide accurate pharmacokinetics parameter estimates. However partial volume effect (PVE) existing in imaging data would have profound effect on the accuracy of pharmacokinetics studies. We therefore propose a convex analysis of mixtures (CAM) algorithm to explicitly eliminate PVE by expressing the kinetics in each pixel as a nonnegative combination of underlying compartments and subsequently identifying pure volume pixels at the corners of the clustered pixel time series scatter plot. The algorithm is supported by a series of newly proved theorems and additional noise filtering and normalization preprocessing. We demonstrate the principle and feasibility of the CAM approach together with compartmental modeling on realistic synthetic data, and compare the accuracy of parameter estimates obtained using CAM or other relevant techniques. Experimental results show a significant improvement in the accuracy of kinetic parameter estimation. We then apply the algorithm to real DCE-MRI data of breast cancer and observe improved pharmacokinetics parameter estimation that separates tumor tissue into sub-regions with differential tracer kinetics on a pixel-by-pixel basis and reveals biologically plausible tumor tissue heterogeneity patterns. This method has combined the advantages of multivariate clustering, convex optimization and compartmental modeling approaches. Interactions among genetic loci are believed to play an important role in disease risk. Due to the huge dimension of SNP data (normally several millions in genome-wide association studies), the combinatorial search and statistical evaluation required to detect multi-locus interactions constitute a significantly challenging computational task. While many approaches have been proposed for detecting such interactions, their relative performance remains largely unclear, due to the fact that performance was evaluated on different data sources, using different performance measures, and under different experimental protocols. Given the importance of detecting gene-gene interactions, a thorough evaluation of the performance and limitations of available methods, a theoretical analysis of the interaction effect and the genetic factors it depends on, and the development of more efficient methods are warranted. Therefore, we perform a computational analysis for detect interactions among SNPs. The contributions are four-fold: (1) developed simulation tools for evaluating performance of any technique designed to detect interactions among genetic variants in case-control studies; (2) used these tools to compare performance of five popular SNP detection methods; and (3) derived analytic relationships between power and the genetic factors, which not only support the experimental results but also gives a quantitative linkage between interaction effect and these factors; (4) based on the novel insights gained by comparative and theoretical analysis, developed an efficient statistically-principled method, namely the hybrid correlation-based association (HCA) to detect interacting SNPs. The HCA algorithm is based on three correlation-based statistics, which are designed to measure the strength of multi-locus interaction with three different interaction types, covering a large portion of possible interactions. Moreover, to maximize the detection power (sensitivity) while suppressing false positive rate (or retaining moderate specificity), we also devised a strategy to hybridize these three statistics in a case-by-case way. A heuristic search strategy is also proposed to largely decrease the computational complexity, especially for high-order interaction detection. We have tested HCA in both simulation study and real disease study. HCA and the selected peer methods were compared on a large number of simulated datasets, each including multiple sets of interaction models. The assessment criteria included several power measures, family-wise type I error rate, and computational complexity. The experimental results of HCA on the simulation data indicate its promising performance in terms of a good balance between detection accuracy and computational complexity. By running on multiple real datasets, HCA also replicates plausible biomarkers reported in previous literatures. / Ph. D.