published_or_final_version / abstract / toc / Clinical Oncology / Doctoral / Doctor of Philosophy

Microarray expression data which contains the expression levels of a large number of simultaneously observed genes have been used in many scientific research and clinical studies. Due to its high dimensionalities, selecting a small number of genes has shown to be beneficial for many tasks such as building prediction models from the microarray expression data or gene regulatory network discovery. Traditional gene selection methods, however, fail to take the class distribution into the selection process. In biomedical science, it is very common to have microarray expression data which is severely biased with one class of examples (e.g., diseased samples) significantly less than other classes (e.g., normal samples). These sample sets with biased distributions require special attention from researchers for identification of genes responsible for a particular disease. In this thesis, we propose three filtering techniques, Higher Weight ReliefF, ReliefF with Differential Minority Repeat and ReliefF with Balanced Minority Repeat to identify genes responsible for fatal diseases from biased microarray expression data. Our solutions are evaluated on five well-known microarray datasets, Colon, Central Nervous System, DLBCL Tumor, Lymphoma and ECML Pancreas. Experimental comparisons with the traditional ReliefF filtering method demonstrate the effectiveness of the proposed methods in selecting informative genes from microarray expression data with biased sample distributions. / by Abu Hena Mustafa Kamal. / Thesis (M.S.C.S.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

The identification of drug treatments that are useful in diverse therapeutic settings is a significant driving force in biomedical research [Macarron et al., 2011], [Poureetezadi et al., 2014], [Lamb, 2007]. Typical means for measuring the efficacy of a drug for a given clinical application include protein-protein interactions, cell death, mitochondrial respiration and cell growth as well as broader measurements of absorption, distribution, metabolism, excretion and toxicity (ADMET), specifically related the the drug or drugs being tested [Szakcs et al., 2008]. A wide array of methods are routinely employed to perform these screens, from ligand binding assays [Wagner et al., 2016] to high-throughput proteomics [Verheul, 2014]. One method that is currently underutilized in small-molecule drug screens and drug discovery is high-throughput transcriptome sequencing, such as RNA-Seq. Although RNA-Seq is routinely used to profile patterns of genetic changes following perturbations such as drug treatment [Young et al., 2014], it has not, to my knowledge, yet been used as the primary readout of a drug screen.

Triticale (x Triticosecale Whitm.) is a cereal with favorable agronomic traits for a Canadian bioproduction platform crop. Appropriate tissue sampling times were determined and gene expression profiles were evaluated in five triticale seed tissues and eleven vegetative tissues using the Affymetrix Wheat GeneChip®. Genes that were expressed, not expressed, tissue-specific, tissue-enriched and developmentally regulated were identified. The percentage of probe sets on the wheat GeneChip with gene ontology annotations was improved from less than 3% to over 76% using homologous sequence identification and annotation transfer. This information was used to determine functions and processes over-represented within the identified gene lists and provide biological meaning to the results. Expression of candidate genes was further evaluated using qRT-PCR, RNA in situ hybridization and promoter characterization. This study has provided a comprehensive triticale gene expression atlas; knowledge regarding triticale development, gene function, expression and regulation; and tools enabling further triticale research and development. / xxiii, 425 leaves : col. ill. ; 29 cm

Gene expression is random owing to the low copy numbers of molecules in a living cell and the best way to study it is by use of a stochastic method, specifically the chemical master equation. The method is used here to derive analytically the invariant probability distributions, and expressions for the moments and noise strength for a simple gene model without feedback. Sensitivity analysis, emphasizing particularly the dependence of the probability distributions, the moments, and noise strength is carried out using Metabolic Control Analysis, which uses control coefficients that measure the response of observables when parameters change. Bifurcation analysis is also carried out. The results show that the number of mRNA molecules follows a hypergeometric probability distribution, and that noise decreases as the number of these molecules increases. Metabolic Control Analysis was successfully extended to genetic control mechanisms, with the obtained control coefficients satisfying a summation theorem. The system undergoes stochastic bifurcations as parameters change. / xii, 86 leaves : ill. ; 29 cm

Many neuronal patterning genes are expressed in distinct populations of cells in the nervous system, leading researchers to analyze their function in specific isolated cellular contexts that often obscure broader, themes of gene function. In this thesis, I aim to make clearer those overlooked common functional themes. I show that the C. elegans homeobox gene unc-42 is expressed in 15 out of a total of 118 distinct sensory, inter, and motor neuron classes throughout the C. elegans nervous system. Of these 15 unc-42(+) synaptically interconnected neuron classes, I show the extent to which unc-42 controls their identities and assembly into functional circuitry. I find that unc-42 defines the routes of communication between these interconnected neurons by controlling the expression of neurotransmitter pathway genes, neurotransmitter receptors, neuropeptides and neuropeptide receptors. I also show that unc-42 controls the expression of molecules involved in axon pathfinding and cell-cell recognition. Consequently, I show how the loss of unc-42 has effects on axon pathfinding and chemical synaptic connectivity, as determined by electron microscopical reconstruction of serial sections of unc-42 mutants. I conclude that unc-42 plays a critical role in establishing functional circuitry by acting as a terminal selector of functionally connected neuron types. I speculate that in other parts of the nervous system “circuit transcription factors” may also control assembly of functional circuitry and propose that such organizational properties of transcription factors may be reflective of not only an ontogenetic, but perhaps also phylogenetic trajectory of neuronal circuit establishment.

Gene expression is regulated by local genetic sequence, and researchers have identified thousands of common genetic variants in the human population that associate with altered gene expression. These expression quantitative trait loci (eQTLs) often co-localize with genome wide association study (GWAS) loci, suggesting that they may hold the key to understanding genetic effects on human phenotype and cause disease. eQTLs are enriched in cis-regulatory elements, suggesting that many affect gene expression via non-coding mechanisms. However, many of the discovered loci lie in noncoding regions of the genome for which we lack understanding, and determining their mechanisms of action remains a challenge. To complicate matters further, genetic variants may have varied effects in different tissues or under different environmental conditions. The research presented here uses statistical methods to investigate genetic variants’ mechanisms of actions and context specificity. In Chapter 1, we introduce eQTLs and discuss challenges associated with their discovery and analysis. In Chapter 2, we investigate cross-tissue eQTL and gene expression patterns, including for GWAS genes. We find that eQTL effects show increasing, decreasing, and non-monotonic relationships with gene expression levels across tissues, and we observe higher eQTL effects and eGene expression for GWAS genes in disease-relevant tissues. In Chapter 3, we use the natural variation of transcription factor activity among tissues and between individuals to elucidate mechanisms of action of eQTL regulatory variants and understand context specificity of eQTL effects. We discover thousands of potential transcription factor mechanisms of eQTL effects, and we investigate the transcription factors’ roles with orthogonal datasets and experimental approaches. Finally, in Chapter 4, we focus on a locus implicated in coronary artery disease risk and unravel the likely causal variants and functional mechanisms of the locus’s effects on gene expression and disease. We confirm the locus’s colocalization with an eQTL for the LIPA gene, and using statistical, functional, and experimental approaches, we highlight two potential causal variants in partial linkage disequilibrium. Taken together, this work develops a framework for understanding eQTL context variability and highlights the complex genetic and environmental contributions to gene regulation. It provides a deeper understanding of gene regulation and of genetic and environmental contributions to complex traits and disease, enabling future research surrounding the context variability of genetic effects on gene expression and disease.

Alzheimer’s disease (AD) affects tens of millions of people worldwide. Its cause is unknown, with no cure, and disease-modifying treatment options have only recently become available. Emerging research has made a strong case for the involvement of immune cells, such as microglia and T cells, in modulating AD pathology. Newer technologies in RNA-sequencing have detailed specific phenotypic changes to microglia and T cells over the course of neurodegenerative disease. Some researchers have also used whole-genome sequencing to correlate genetic variants with changes in gene expression. However, no studies thus far have conducted this type of genotype-phenotype correlation in immune cells from aged individuals or AD patients. We have collected gene expression data from four sorted T cell subtypes in peripheral blood samples from 96 subjects in ROSMAP, a cohort of AD patients and age-matched controls. 78 of these subjects also have whole-genome sequencing data, which we used to detect genetic variants associated with changes in T cell gene expression. These are known as expression quantitative trait loci (eQTL). We found genes related to T cell cytotoxicity and immunosenescence in gene co-expression modules, among the eQTL, and in correlation with AD neuropathological traits or risk variants for several disease traits. We extended our findings related to disease association by calculating polygenic risk scores (PRSs) in our cohort from whole-genome sequencing data for 19 traits related to immune function and disease, including AD. Genes associated with the PRS for one or more disease traits often were in biological pathways related to downstream cytokine signaling, regulation of T cell receptor signaling, and T cell migration and trafficking. Overall, our findings indicate that the use of aged and AD patients in T cell genotype-phenotype correlation studies highlights genetic variants and differentially expressed genes that are not seen in studies using young, healthy individuals.

Transcriptome studies disentangle functional mechanisms of gene expression regulation and may lend key insights into disease mechanisms. However, the cost of RNA-sequencing and types of tissues currently assayed pose major limitations to study expansion and disease-relevant discovery. This thesis develops methods for sampling noninvasive biospecimens for transcriptome studies, investigating their technical and biological characteristics, and assessing the feasibility of using noninvasive samples in transcriptomic and clinical applications. Chapter 1 explores the technical and biological features of four potential noninvasive sample types (buccal swabs, hair follicles, saliva, and urine cell pellets) in a pilot study of 19 individuals whereby four separate collections of each tissue were performed (i.e. 76 samples/tissue, 304 samples in total). From this data, consistency of library preparation, cell type content, replication of GTEx cis-eQTLs, and disease applications were assessed. In all, hair follicles and urine cell pellets were found to be most promising for future applications. Chapter 2 investigates the scaling potential of noninvasive sampling in SPIROMICS, a COPD clinical cohort. To do so, 140 hair follicle and 110 buccal swab samples were collected from seven different clinical sites. Consistency of sample quality was observed to be high for hair follicles, and hair cell type abundance estimates were consistent within SPIROMICS and compared to the 19 subject pilot study. Mapping of cis-eQTLs in hair revealed 339 associations not identified in any prior study. These cis-eQTLs show higher replication in GTEx tissues that share cell types with hair follicles, indicating hair follicles may indeed capture gene expression regulatory mechanisms found in more invasive tissue types of the body. This thesis suggests future use of noninvasive sampling will facilitate discovery by increasing sample sizes in more diverse populations and in tissues with greater cell type diversity and biological relatedness to disease mechanisms. Moreover, the nature of noninvasive sampling enables complex, longitudinal study designs with greater ability to capture context-dependent mechanisms of genetic regulation not currently able to be interrogated.

Adrenalectomy (ADX) has been shown to cause selective degeneration of granule cells in the dentate gyrus (DG). This occurs due to the reduction of corticosterone (CORT) and behavioural deficits are associated with the loss of these neurons. Dentate lesions and cell loss associated with ADX have been shown to effect behaviour in a number of spatial tasks. In contras, it has been shown granule cell loss does not affect the specificity of place cells in CA3 and CA1. We used the ADX model to examine the role of DG granule cells plays in representing space using immediate early gene (IEG) activation in the principal hippocampal subfields after exploration of novel environments. Rats were allowed to free explore multiple novel environments and then the mRNA for the IEG Homer 1a (H1a) was used as a marker of neural activity. After degeneration of approximately half of the DG granule cells we found a significant increase in number of active cells in the DG, CA3 and CA1 in ADX animals. The results indicate a reduction in granule cells causes a dramatic increase in the proportion of remaining DG granule cells in response to exploration. The change in DG activation disrupts the representations in CA3 and CA1 and thereby affects behaviour. / vii, 60 leaves : ill. (some col.) ; 29 cm