<p>Alternative mRNA processing can result in the generation of multiple, qualitatively different RNA transcripts from the same gene and is a powerful engine of complexity in higher organisms. Recent deep sequencing studies have indicated that essentially all human genes containing more than a single exon generate multiple RNA transcripts. Functional roles of alternative processing have been established in virtually all areas of biological regulation, particularly in development and cancer. Changes in alternative mRNA processing can now be detected from over a billion dollars' worth of conventional gene expression microarray data archived over the past 20 years using a program we created called SplicerAV. Application of SplicerAV to publicly available microarray data has granted new insights into previously existing studies of oncogene over-expression and clinical cancer prognosis.</p> <p>Adaptation of SplicerAV to the new Affymetrix Human Exon arrays has resulted in the creation of SplicerEX, the first program that can automatically categorize microarray detected changes in alternative processing into biologically pertinent categories. We use SplicerEX's automatic event categorization to identify changes in global mRNA processing during B cell transformation and show that the conventional U133 platform is able to detect 3' located changes in mRNA processing five times more frequently than the Human Exon array.</p> / Dissertation

Aortic valve disease (AVD) affects millions of people of all ages around the world. Current treatment for AVD consists of valvular replacement with a non-living prosthetic valve, which is incapable of growth, self-repair, or remodeling. While tissue engineering has great promise to develop a living heart valve alternative, success in animal models has been limited. This may be attributed to the fact that understanding of valvular cell biology has not kept pace with advances in biomaterial development. Aortic valve leaflets are exposed to a complex and dynamic mechanical environment unlike any in the vasculature, and it is likely that native endothelial and interstitial cells respond to mechanical forces differently from other vascular cells. The objective of this thesis was to compare valvular cell phenotype to vascular cell phenotype, and assess the influence of steady shear stress on valvular cell biology. This thesis demonstrates that valvular endothelial cells respond differently to shear than vascular endothelial cells, by aligning perpendicular to the direction of steady shear stress, and by the differential regulation of hundreds of genes in both static and fluid flow environments. Valvular interstitial cells expressed a combination of contractile and synthetic phenotypes not mimicked by vascular smooth muscle cells. Two three-dimensional leaflet models were developed to assess cellular interactions and the influences of steady laminar shear stress. Valvular co-culture models exhibited a physiological response profile, while interstitial cell-only constructs behaved more pathologically. Steady shear stress enhanced physiological functions of valvular co-cultures, but increased pathological response of interstitial cell-only constructs. These results showed that valvular cells, whether cultured separately or together, behaved distinctly different from vascular cells. It was also determined that shear stress alone cannot induce tissue remodeling to more resemble native valve leaflets. The leaflet models developed in this thesis can be used in future experiments to explore valvular cell biology, assess the progression of certain forms AVD, and develop targeted diagnostic and therapeutic strategies to hopefully eliminate the need for valvular replacement entirely.

Because of the Human Genome Project, enormous quantities of biological data, e.g., microarray data, are generated. Since the amount of biological data is very large, data mining techniques can be used to help biologists efficiently analyze the biological data. For microarray data, biclustering, which performs simulataneous clustering of rows (e.g., genes) and columns (e.g., conditions), has proved of great value for finding interesting patterns. There were several types of biclusters proposed. To mine biclusters with coherent values, most of the previous methods need to compute Maximum Dimension Sets (MDSs) for every two genes in the microarray data. Since the number of genes is far larger than the number of conditions, this step is inefficient. On the other hand, to mine biclusters with coherent evolutions, the Co-gclustering method was proposed which could simultaneously find biclusters with both coregulated and negative-coregulated patterns. However, its time complexity is exponential to the number of conditions, which is not efficient. Therefore, in this dissertation, to efficiently solve the problem of biclustering for microarray databases, first, we propose a Condition Enumeration Tree (CE-Tree) method which mines biclusters with coherent values. Second, we propose an Up-Down Bit Pattern (UDB) method which mines biclusters with coherent evolutions. In the first proposed method, CE-Tree, to mine biclusters, instead of generating MDSs for every two genes, we generate only MDSs for every two conditions. Then, we expand the CE-Tree in a special local breadth-first within global depth-first manner to efficiently find the clustering result. From the experimental results on real data, we have shown that the CE-Tree method could mine biclusters more efficiently than several previous methods. In the second proposed method, UDB, we utilize up-down bit patterns to record the condition pairs where one gene is upregulated or downregulated. Then, we utilize bit operations and apply a heuristic idea on these up-down bit patterns to efficiently find the clustering result. As compared to the Co-gclustering method, the UDB method reduces the time complexity from exponential time to polynomial time. From the experimental results on real data, we have shown that the UDB method is more efficient than the Co-gclustering method.

The role of neutrophils in the pathogenesis of Salmonella typhimurium-induced ruminant and human enteritis and diarrhea remains incompletely understood. To address this question, the in vivo bovine ligated ileal loop model of non-typhoidal salmonellosis was used in calves with the naturallyoccurring Bovine Leukocyte Adhesion Deficiency (BLAD) mutation whose neutrophils are unable to extravasate and infiltrate the extravascular matrix. Data obtained from BLAD calves were compared to those from genetically normal calves negative for the BLAD mutation. Morphologic studies showed that the absence of significant tissue influx of neutrophils in intestine infected by S. typhimurium resulted in less tissue damage, reduced luminal fluid accumulation, and increased bacterial invasion compared to regular calves. Study of gene expression profile of cytokines by quantitative Real-Time PCR (qRTPCR) revealed that the massive tissue influx of neutrophils during acute infection is mainly driven by the CXC chemokine GRO- α especially in the last stages of acute infection and to a lesser extent, IL-8. In contrast, the pro-inflammatory cytokines IL-1 β and TNF- α were not significantly correlated with the presence or absence of tissue neutrophils. The precise in situ localization of gene expression of these major cytokines and chemokines was investigated by qRTCPR from specific groups of intestinal cells captured by Laser Capture Microdissection in S. typhimuriuminfected ileal loops from BLAD animals. Our data confirmed that gene expression of IL-8, GRO- α, and IL-1 β was predominantly localized to enterocytes of crypts with less expression in enterocytes of villi tips and cells that form the domed villi were not an important source of TNF- α gene expression. Microarray technology was used to determine the global transcriptional profile of bovine intestinal loops inoculated with S. typhimurium. The host samples were hybridized on a 13K bovine-specific oligoarray and microarray data was analyzed using a suite of gene expression analysis and modeling tools. Analysis of our data revealed that the tissue influx of neutrophils in ileal loops greatly influenced the host gene expression. Major differences in gene expression in relevant fields of Salmonella research including inflammation and immune response, Toll-like receptor signaling, cytokine profiles, apoptosis, and intracellular defense against infection are discussed.

Light is the fundamental source of energy and information throughout the plant life cycle. Light signals regulate plant architecture and branching, key processes that determine biomass production and grain yield. Low red (R) to far-red (FR) light ratios (R:FR) perceived by phytochromes serve as a warning signal about impending competition for light resources and lead to shade avoidance responses (SARs), including reduced branching. The R:FR regulates branching in both a bud autonomous and non-bud autonomous manner, however a detailed mechanistic understanding of the process remains unclear. We hypothesized that high R:FR promotes bud outgrowth by differentially regulating branching-related genes (transcriptome) within the axillary bud and that increased apical dominance under low R:FR or with phyB deficiency is mediated by auxin or other novel signal/s. We analyzed the branching phenotype of Arabidopsis Columbia-60000 ecotype in response to different R:FR treatments and conducted a microarray study to identify early (within 3 hours) changes in the transcriptome of buds from different rosette positions in response to altered R:FR. Physiological experiments were also conducted to determine if auxin concentration, transport rate, sensitivity, and establishment of an auxin transport stream were important in determining the branching phenotype of shade avoiding plants. The results revealed that the duration of low R:FR determines plant architecture and the branching phenotype and that bud outgrowth is regulated by the R:FR in a spatial and temporal manner. Low R:FR promoted the elongation of branches at top rosette nodes while it suppressed the outgrowth of axillary buds at lower nodes. High R:FR could reverse the effects of previous low R:FR by promoting the outgrowth of buds from lower axils within 24 hours of treatment. Transcriptomic analysis revealed that the R:FR differentially regulated the expression of genes related to hormone biosynthesis/transport/signaling, cell-cycle regulation and cell wall modification. Cis-elements responsive to light and hormone signaling pathways were overrepresented in several gene clusters. Apical dominance related studies discovered that loss of phyB function results in a slower auxin transport rate, fewer xylem parenchyma cells, and reduced sensitivity to auxin. These results, in addition to estimates of correlative inhibition, suggested that auxin is at least partially responsible for increased apical dominance under low R:FR or with phyB deficiency, but may be acting in conjunction with other undefined regulators.

DNA microarrays are widely used techniques in molecular biology and DNA computing area. Before performing the microarray experiment, a set of subsequences of DNA called probes which are complementary to the target genes of interest must be found. And its reliability seriously depends on the quality of the probe sequences. Therefore, one must carefully choose the probe set in target sequences. A new method for probe design strategy using multi-objective genetic algorithm is proposed. The proposed algorithm is able to find a set of suitable probes more efficient and uses a model based on suffix tree to speed up the specificity constraint checking. The dry dock experimental results show that the proposed algorithm finds several probes for DNA microarray that not only obey the design properties, but also have specificity.

A DNA microarray is a very good tool to study the gene expression level in different situations. Mining association rules in DNA microarray datasets can help us know how genes affect each other, and what genes are usually co-expressed. Mining closed large itemsets can be useful for reducing the size of the mining result from the DNA microarray datasets, where a closed itemset is an itemset that there is no superset whose support value is the same as the support value of this itemset. Since the number of genes stored in columns is much larger than the number of samples stored in rows in a DNA microarray dataset, traditional mining methods which use column enumeration face a great challenge, where the column enumeration means that enumerating itemsets from the combinations of items stored in columns. Therefore, several row enumeration methods, e.g., RERII, have been proposed to solve this problem, where row enumeration means that enumerating itemsets from the combinations of items stored in rows. Although the RERII method saves more memory space and has better performance than the other row enumeration methods, it needs complex intersection operations at each node of the row enumeration tree to generate the closed itemsets. In this thesis, we propose a new method, UMiner, which is based on the union operations to mine the closed large itemsets in the DNA microarray datasets. Our approach is a row enumeration method. First, we add all tuples in the transposed table to a prefix tree, where a transposed table records the information about where an item appears in the original table. Next, we traverse this prefix tree to create a row-node table which records the information about a node and the related range of its child nodes in the prefix tree created from the transposed table. Then we generate the closed itemset by using the union operations on the itemsets in the item groups stored in the row-node table. Since we do not use the intersection operations to generate the closed itemset for each enumeration node, we can reduce the time complexity that is needed at each node of the row enumeration tree. Moreover, we develop four pruning techniques to reduce the number of candidate closed itemsets in the row enumeration tree. By replacing the complex intersection operations with the union operations and designing four pruning techniques to reduce the number of branches in the row enumeration tree, our method can find closed large itemsets very efficiently. In our performance study, we use three real datasets which are the clinical data on breast cancer, lung cancer, and AML-ALL. From the experiment results, we show that our UMiner method is always faster than the RERII method in all support values, no matter what the average length of the closed large itemsets is. Moreover, in our simulation result, we also show that the processing time of our method increases much more slowly than that of the RERII method as the average number of items in the rows of a dataset increases.

Data classification is one of important techniques in data mining. This technique has been applied widely in many applications, e.g., disease diagnosis. Recently, the data classification technique has been be used for microarray datasets, where a microarray is a very good tool to study the gene expression levels in Bioinformatics. In the part of data classification problem for microarray datasets, we consider two biology datasets which reflect two extreme different classes for the given same sets of tests. Basically, the classification process contains two phases: (1) the training phase, and (2) the testing phase. The propose of the training phase is to find the representative Emerging Patterns (EPs) in each of these two datasets, where an EP is an itemset which satisfies some conditions of the growth rate from one dataset to another dataset. Note that the growth rate represents the differences between these two datasets. After the training phase, we take the collections of EPs in each dataset as a classifier. A test sample in the testing phase will be predicted to one of the two datasets based on the result of a similarity function, which takes the growth rate and the support into consideration. The evaluating criteria of a classifier is the accuracy. Obviously, the higher the accuracy of a classifier is, the better the performance is. Therefore, several EP-based classifiers, e.g., the EJEP and the NEP strategies, have been proposed to achieve this goal. The EJEP strategy considers only those itemsets whose growth rates are infinite, since it claims that the high growth rates may result in the high accuracy. However, the EJEP strategy will not keep those useful EPs whose growth rates are very high but not infinite. On the other hand, the real-world data always contains noises. The NEP strategy considers noises and provides the higher accuracy than the EJEP strategy. However, it still may miss some itemsets with high growth rates, which may result in the low accuracy. Therefore, in this thesis, we propose a High Growth-rate EP (HGEP) strategy to improve the disadvantages of the NEP and the EJEP strategies. In addition to considering itemsets whose growth rates are infinite in the EJEP strategy and noise patterns in the NEP strategy, our HGEP strategy considers those itemsets which have the growth rate higher than all its proper subsets when the growth rates are finite. In this way, the itemsets with high growth rates could result in high similarity, and the high similarity predicts the sets of tests into the correct class. Therefore, our HGEP can provide high accuracy. In our performance study, we use several real datasets to evaluate the average accuracy of them. Moreover, we also do simulation study of increasing noises. From the experiment results, we show that the average accuracy of our HGEP strategy is higher than that of the NEP strategy.

The aim of this study was cloning of wheat (Triticum aestivum L. cv. Bayraktar) Trehalose-6-phosphate synthase gene and examining of gene expression pattern of wheat seedlings in response to salt and drought stress conditions using Wheat GeneChip (Affymetrix). In this study, 10-days old wheat seedlings were subjected to the salt (350 mM NaCl) and drought stress (20% PEG) for 24 hours, then root and leaf tissues were used for wheat TPS gene cloning and microarray studies. RACE (Rapid Amplification of cDNA Ends) was used to determine cDNA sequence of wheat TPS gene, TaTPS. The ORF of TaTPS encodes a putative protein of 859 amino acids with a predicted molecular weight (MW) of 96.7 kDa and an isoelectric point (pI) of 5.97. Based on tblastx, TaTPS showed great similarity with other plants TPS genes. In root tissue, expression of TaTPS increased under drought stress while no change was observed under salt stress. In leaf tissue, both salt and drought treatments repressed the expression of TaTPS. Microarray study was used to monitor transcript abundance in salt and drought stressed wheat. Data analyses were determined by using GCOS 1.4 and GeneSpring GX10. The genes encoding ferritin, Lipid transfer protein, LEA/Dehydrin, early nodulin, cold regulated protein and germin like proteins were upregulated at least 10-fold under salt and drought stress conditions. In addition, salt and drought stresses induced the expression of genes identified as DREB, ERF, NAC, MYB, and HSF, suggesting existence of various transcriptional regulatory pathways under salt and drought stresses.

The emergence of drug-resistance of tumor cells is a major complication for succesful chemotherapy. In this study, the molecular mechanisms of resistance to prednisone, vincristine and melphalan in multiple myeloma cell lines, RPMI-8226 and U-266 were investigated. Drug resistance was induced by application of the drugs by stepwise dose increments and confirmed by XTT cytotoxicity assay. Gene expression analysis demostrated that MDR1 gene is one of the most important factor causing the multidrug resistance phenotype in prednisone, vincristine and melphalan resistant multiple myeloma cell lines. According to microarray analysis alterations in laminin, integrin and collagen genes were detected. Additionally, upregulation of some oncogenes and growth factors (Rho family of GTPases, YES1, ACT2, TGFBR, EPS15, PDGF) was shown to have a role in MDR in multiple myeloma. Significant downregulation of suppressors of cytokine signalling gene expressions and upregulation of different types of interleukine and interferon gene expressions (IL3 and interferon-gamma receptor) which are related to JAK-STAT signalling pathay was shown. Alterations in expression levels of genes related to ceramide metabolism were shown especially for melphalan resistance in multiple myeloma. The data from vincristine/prednisone and vincristine/melphalan drug combination studies were shown that the usage of vincristine on prednisone and melphalan resistant multiple myeloma cell lines increase the efficacy of the chemotherapy. On the other hand the cross-resistance development of prednisone and melphalan resistant sublines to irradiation was detected. These results may help to understand the molecular mechanisms of prednisone, vincristine and melphalan resistance in multiple myeloma model cell lines RPMI-8226 and U-266.