ABF-1 is a human class ll basic helix-loop-helix transcription factor that is expressed predominately in EBV immortalized and activated B lymphocytes. A human cell line was stably transfected with a tetracycline regulated ABF-1 expression vector. The cell line revealed tight regulation of ABF-1 expression following stable incorporation of the vector into the genomic DNA. Upon induction of ABF-1 expression, the cell line exhibited a dramatic growth rate decrease. In order to monitor genes regulated by ABF-1, cells were collected both before and after induced ABF-1 expression and subjected to a microarray analysis. Early interpretations of the microarray data support the findings that ABF-1 may be regulating gene expression in a manner that facilitates withdrawal from the cell cycle.

Advancements in DNA microarray data sequencing have created the need for sophisticated machine learning algorithms and feature selection methods. Probabilistic graphical models, in particular, have been used to identify whether microarrays or genes cluster together in groups of individuals having a similar diagnosis. These clusters of genes are informative, but can be misleading when every gene is used in the calculation. First feature reduction techniques are explored, however the size and nature of the data prevents traditional techniques from working efficiently. Our method is to use the partial correlations between the features to create a precision matrix and predict which associations between genes are most important to predicting Leukemia diagnosis. This technique reduces the number of genes to a fraction of the original. In this approach, partial correlations are then extended into a spectral clustering approach. In particular, a variety of different Laplacian matrices are generated from the network of connections between features, and each implies a graphical network model of gene interconnectivity. Various edge and vertex weighted Laplacians are considered and compared against each other in a probabilistic graphical modeling approach. The resulting multivariate Gaussian distributed clusters are subsequently analyzed to determine which genes are activated in a patient with Leukemia. Finally, the results of this are compared against other feature engineering approaches to assess its accuracy on the Leukemia data set. The initial results show the partial correlation approach of feature selection predicts the diagnosis of a Leukemia patient with almost the same accuracy as using a machine learning algorithm on the full set of genes. More calculations of the precision matrix are needed to ensure the set of most important genes is correct. Additionally more machine learning algorithms will be implemented using the full and reduced data sets to further validate the current prediction accuracy of the partial correlation method.

Microarrays enable biologists to measure differences in gene expression in thousands of genes simultaneously. The data produced by microarrays present a statistical challenge, one which has been met both by new modifications of existing methods and by completely new approaches. One of the difficulties with a new approach to microarray analysis is validating the method's power and sensitivity. A simulation study could provide such validation by simulating gene expression data and investigating the method's response to changes in the data; however, due to the complex dependencies and interactions found in gene expression data, such a simulation would be complicated and time consuming. This thesis proposes a way to simulate gene expression data and validate a method by borrowing information from existing data. Analogous to the spike-in technique used to validate expression levels on an array, this simulation-based approach will add a simulated gene with known features to an existing data set. Analysis of this appended data set will reveal aspects of the method's sensitivity and power. The method and data on which this technique is illustrated come from Storey et al. (2005).

Early-onset diagnostics, or the detection of disease before clinical symptoms arise, has gained traction for its potential to improve patient quality of life and health outcomes. Biosensors, found in point-of-care (POC) devices, facilitate early-onset diagnostics and disease monitoring by addressing the limitations of current diagnostics strategies, which include timeliness, cost-effectiveness, and accessibility. Biosensors often incorporate microarrays within their design to allow for the detection of disease-associated biomolecules, known as biomarkers. Microarrays are composed of capture biomolecules, such as monoclonal antibodies, that are immobilized through either contact or non-contact printing techniques. In the following thesis, we investigated microarray designs within novel biosensing platforms for diagnostic and disease monitoring applications. First, we highlighted the advantages and challenges of implementing different types of biosensors, detection methods, and biomolecule immobilization strategies. Additionally, we proposed a novel 3D microarray incorporating hydrogels composed purely of crosslinked bovine serum albumin (BSA) proteins decorated with capture antibodies (CAbs). Utilizing industry-standard inkjet printing, we developed and optimized a two-step fabrication protocol, by which BSA proteins and CAbs are printed first, followed by the crosslinking agent, 1-Ethyl-3-[3-dimethylaminopropyl]carbodiimide (EDC). Characterization of the unique three-dimensional (3D) microstructure and hydrogel parameters and conducting comparisons with standard two-dimensional (2D) microdots, showed that increasing biosensor surface area led to a 3X increase in signal amplification. The limits of detection (LODs) for cytokine biomarkers were 0.3pg/mL for interleukin-6 (IL-6) and 1pg/mL for tumor necrosis factor receptor I (TNF RI), which were highly sensitive compared to reported LODs from literature. Alongside the investigation of novel printing protocols, proof-of-concepts for multiplex detection and distinguishing clinical patient samples from healthy donors were also presented. Overall, this thesis demonstrated the fabrication and optimization of microarray development shows promise in improving current biosensor designs, allowing for enhanced early-onset disease detection and monitoring. / Thesis / Master of Applied Science (MASc)

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The DNA microarray is a powerful tool to study expression levels of thousands of genes simultaneously. Often, cDNA libraries representing expressed genes of an organism are available, along with expressed sequence tags (ESTs). ESTs are widely used as the probes for microarrays. Designing custom microarrays, rich in genes relevant to the experimental objectives, requires selection of probes based on their sequence. We have designed a probe selection method, called GeneSieve, to select EST probes for custom microarrays. To assign annotations to the ESTs, we cluster them into contigs using PHRAP. The larger contig sequences are then used for similarity search against known proteins in model organism such as Arabidopsis thaliana. We have designed three different methods to assign annotations to the contigs: bidirectional hits (BH), bidirectional best hits (BBH), and unidirectional best hits (UBH). We apply these methods to pine and potato EST sets. Results show that the UBH method assigns unambiguous annotations to a large fraction of contigs in an organism. Hence, we use UBH to assign annotations to ESTs in GeneSieve. To select a single EST from a contig, GeneSieve assigns a quality score to each EST based on its protein homology (PH), cross hybridization (CH), and relative length (RL). We use this quality score to rank ESTs according to seven different measures: length, 3' proximity, 5' proximity, protein homology, cross hybridization, relative length, and overall quality score. Results for pine and potato EST sets indicate that EST probes selected by quality score are relatively long and give better values for protein homology and cross hybridization. Results of the GeneSieve protocol are stored in a database and linked with sequence databases and known functional category schemes such as MIPS and GO. The database is made available via a web interface. A biologist is able to select large number of EST probes based on annotations or functional categories in a quick and easy way. / Master of Science

Microarrays provide biologists an opportunity to find the expression profiles of thousands of genes simultaneously. Biologists try to understand the mechanisms underlying the life processes by finding out relationships between gene-expression and their functional categories. Fusion is a software system that aids the biologists in performing microarray data analysis by providing them with both visual data exploration and data mining capabilities. Its multiple view visual framework allows the user to choose different views for different types of data. Fusion uses Proteus, an Inductive Logic Programming (ILP) rule finding algorithm to mine relationships in the microarray data. Fusion allows the user to explore the data interactively, choose biases, run the data mining algorithms and visualize the discovered rules. Fusion has the capability to smoothly switch across interactive data exploration and batch data mining modes. This optimizes the knowledge discovery process by facilitating a synergy between the interactivity and usability of visualization process with the pattern-finding abilities of ILP rule mining algorithms. Fusion was successful in helping biologists better understand the mechanisms underlying the acclimatization of certain varieties of Arabidopsis to ozone exposure. / Master of Science

O diabetes mellitus gestacional (DMG) é o distúrbio metabólico mais comum da gravidez. A definição padrão do DMG consiste no metabolismo anormal da glicose diagnosticado pela primeira vez durante a gestação. Mulheres que têm história de DMG geralmente apresentam diabetes pós-parto, resistência à insulina, síndrome metabólica, hipertensão e dislipidemia. A detecção precoce deste estado metabólico anormal é importante para eventual intervenção na tentativa de impedir ou mesmo retardar o aparecimento dos outros tipos de diabetes. Alguns estudos têm apontado, em mulheres com DMG, indução de genes envolvidos com resposta imune, particularmente aqueles associados com inflamação. A identificação de genes de inflamação induzidos em gestantes com DMG tem fornecido a base para elucidar a ligação entre vias inflamatórias e DMG. Para testar esta hipótese foi realizada a comparação do perfil transcricional de células mononucleares de sangue periférico (PBMCs) de pacientes com DMG e controles. As amostras de RNA total foram hibridadas utilizando oligo microarrays Agilent ® 4 x 44 K englobando o genoma funcional humano total. Os mRNAs diferencialmente expressos foram identificados aplicando-se a análise de Rank Products, e posteriormente submetidos ao agrupamento hierárquico de Pearson por meio do software Cluster. Utilizando o programa TreeView, foi realizada a construção dos dendrogramas com as representações espaciais dos mRNAs, classificados de acordo com suas funções moleculares e vias biológicas. A partir do banco de dados DAVID, foram identificados 130 processos biológicos significantes (P<0.05) incluindo os de resposta imune e defesa, resposta inflamatória, regulação de citocinas, apoptose, desenvolvimento de vasos sanguíneos e proliferação celular. Entre as vias de maior relevância destacamos a via de interação entre receptores de citocinas e a de sinalização do receptor NOD-like, além das vias de câncer, lúpus e asma. Adicionalmente, encontramos os transcritos dos genes IGFBP2, TCF3, OLR1, TCF7L2, previamente associados a alterações metabólicas, diferencialmente expressos nas gestantes com DMG. Também observamos que genes do complexo principal de histocompatibilidade (MHC), HLA-DRB6, HLA-DQA2, HLA-DQB2, HLA-DQB1, HLA-DOA, apresentaram mRNAs induzidos nas pacientes com DMG. A partir deste estudo, constatamos que vias relacionadas ao sistema imunológico e categorias funcionais associadas à inflamação participam da patogenia do DMG. Além disso, evidenciamos que transcritos de genes que pertencem ao MHC e aqueles envolvidos em processos metabólicos, estiveram diferencialmente expressos no DMG. Estes resultados confirmam nossa hipótese inicial e contribuem para o melhor entendimento das bases genéticas desta doença. / Gestational Diabetes Mellitus (GDM) is the most common metabolic disorder found during pregnancy. The standard definition of GDM is the abnormal glucose metabolism first diagnosed during pregnancy. Women who have a history of GDM usually present postpartum diabetes, insulin resistance, metabolic syndrome, hypertension and dyslipidemia. Early detection of this abnormal metabolic status may permit early intervention to prevent or even delay the development of other types of diabetes. The induction of genes involved in immune response in women with GDM has been reported, particularly those associated with inflammatory pathways, providing basis proposing that inflammation genes might be associated to GDM. To test this hypothesis, we compared the transcriptome profiling of peripheral blood mononuclear cells (PBMCs) of GDM patients and controls. The total RNA samples were hybridized to Agilent ® 4 x 44 K oligo microarrays covering the whole human functional genome. Differentially expressed mRNAs were obtained by Rank Product analysis and then submitted to hierarchical clustering using the Cluster software . Dendrograms and spatial representations of mRNAs were constructed through the TreeView software . These mRNAs were classified according to their molecular functions and biological pathways using the DAVID database. We observed 130 significant biological processes (P<0.05), including immune and defense response, inflammatory response, regulation of cytokines, apoptosis, blood vessels development and cell proliferation. Among the most relevant pathways, we highlighted the interaction between cytokine receptors, NOD-like receptor signaling and cancer, lupus and asthma pathways. Additionally, we found transcripts of the genes IGFBP2, TCF3, OLR1, TCF7L2, which were previously associated with metabolic abnormalities, differentially expressed in pregnant women with GDM. Some major histocompatibility complex (MHC) genes (HLA-DRB6, HLA-DQA2, HLA-DQB2, HLA-DQB1, HLA-DOA) also presented mRNAs induced in patients with GDM. In conclusion, we found that immunerelated pathways and functional categories associated with inflammation participate in the pathogenesis of DMG. Furthermore, we showed that transcripts of genes belonging to MHC and those involved in metabolic processes were differentially expressed in DMG. These results confirmed our initial hypothesis and contribute to a better understanding of the genetics basis of this disease.