Microarray technology provides a quantitative assessment of the number of gene transcripts gene using a high-throughput hybridization assay. Reliable detection of gene expression therefore requires reliable design of probes used in the hybridization assay. It is noted that microarray gene expression measurements are often characterized by variability, even among a series of technical replicate arrays. Therefore, sequence verification, used as a low-level filter to exclude probes exhibiting sequence inaccuracies, has previously been shown to reduce gene expression variability.Building on this work, the effects of sequence- and annotation-based filtering methods were quantified, and shown to be effective in reducing microarray variability among a set of technical replicates. Further, appropriate thresholds for filtering are recommended. A significant interaction in an analysis of variation model was found when a combination of sequence- and annotation-based filtering methods were explanatory factors, suggesting the use of the combination of filtering methods might be most beneficial.

Gene expression arrays are used to assess expression of exons and genes of orga- nisms. The design of expression arrays is based on a genome of laboratory strains of model organisms. The most frequent summarization algorithms used to pro- cess data from measurements are gcRMA, PLER and IterPLIER. When using expression arrays to research free living species, the measured values are influen- ced by differences in genomes of free living and model organisms. We propose a method to improve the results by removing parts of genomes influenced by known differences between species from the summarization. Removing influenced parts can improve summarization, especially on exon level. 1

The analysis of Affymetrix GeneChip® data is a complex, multistep process. Most often, methodscondense the multiple probe level intensities into single probeset level measures (such as RobustMulti-chip Average (RMA), dChip and Microarray Suite version 5.0 (MAS5)), which are thenfollowed by application of statistical tests to determine which genes are differentially expressed. An alternative approach is a probe-level analysis, which tests for differential expression directly using the probe-level data. Probe-level models offer the potential advantage of more accurately capturing sources of variation in microarray experiments. However, this has not been thoroughly investigated, since current research efforts have largely focused on the development of improved expression summary methods. This research project will review current approaches to analysis of probe-level data and discuss extensions of two examples, the S-Score and the Random Variance Model (RVM). The S-Score is a probe-level algorithm based on an error model in which the detected signal is proportional to the probe pair signal for highly expressed genes, but approaches a background level (rather than 0) for genes with low levels of expression. Initial results with the S-Score have been promising, but the method has been limited to two-chip comparisons. This project presents extensions to the S-Score that permit comparisons of multiple chips and "borrowing" of information across probes to increase statistical power. The RVM is a probeset-level algorithm that models the variance of the probeset intensities as a random sample from a common distribution to "borrow" information across genes. This project presents extensions to the RVM for probe-level data, using multivariate statistical theory to model the covariance among probes in a probeset. Both of these methods show the advantages of probe-level, rather than probeset-level, analysis in detecting differential gene expression for Afymetrix GeneChip data. Future research will focus on refining the probe-level models of both the S-Score and RVM algorithms to increase the sensitivity and specificity of microarray experiments.

Data management and analysis represent a major challenge for microarray studies. In this study, Affymetrix software was used to analyze an HIV-infection data. The microarray analysis shows remarkably different results when using different parameters provided by the software. This highlights the fact that a standardized analysis tool, incorporating biological information about the genes is needed in order to better interpret the microarray study. To address the data management problem, in-house programs, including scripts and a database, were designed. The in-house programs were also used to overcome problems and inconveniences discovered during the data analysis, including management of the gene lists. The database provides rapid connection to many online public databases, as well as the integration of the original microarray data, relevant publications and other useful information. The in-house programs allow investigators to process and analyze the full Affymetrix microarray data in a speedy manner.

<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

Duchenne muscular dystrophy (DMD) is a severe and fatal muscle wasting disease characterized by a high mutation rate in the gene that encodes the membrane-associated protein dystrophin that results in absence of expressed protein. Although the primary genetic defect for DMD is known, the mechanisms that initiate the onset of DMD are not currently understood. This study tested the hypothesis that pathophysiological processes involved in DMD could be identified by the global expression of mRNA in maturing dystrophin- and utrophin-deficient mouse (mdx:utrn-/-) muscles. Two potential dystrophic onset mechanisms targeted for analysis were (1) disrupted expression of calcium handling proteins; and, (2) increased expression of immune response markers. An mRNA expression profile was developed following isolation of total RNA from control and mdx:utrn-/- triceps surae (TS) muscles at ages 9-10 and 20-21 days using Affymetrix® Mu74Av2 GeneChips®. Compared to control, the mRNA expression profile in mdx:utrn-/- muscles revealed there was a 3-fold increase in the number of gene transcripts differentially expressed more than 2-fold (53 transcripts at ages 9-10 days; 153 at ages 20-21 days). However, there were no changes in the mRNA transcripts for calcium handling proteins. In distinct contrast, there was up-regulation of transcripts that corresponded to an immune response (40 transcripts), extracellular matrix activity (14), and proteolysis (8). Up-regulation of several transcripts corresponded to cytokines and their receptors (11), chemokines and their receptors (5), and lymphoid and myeloid markers (16) suggesting that dystrophic muscle is susceptible to invasion by macrophages, leukocytes, B- and T-cells. These results are consistent with several reports (Spencer et al., 1997; Chen et al., 2000; Porter et al., 2002; Porter et al., 2003a; Porter et al., 2003b; Porter et al., 2004) that indicate the immune system may play an important role in the early pathophysiology of DMD. Understanding the functional aspects of an immune response in DMD onset should lead to more effective therapeutics. / Master of Science

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Submitted by Priscila Arrigucci Bernardes (p.arrigucci@yahoo.com.br) on 2018-07-25T18:53:46Z No. of bitstreams: 1 Tese_Priscila_Arrigucci_Bernardes.pdf: 3025957 bytes, checksum: 866a988a470ab68070a7e7a922fc2993 (MD5) / Approved for entry into archive by Karina Gimenes Fernandes null (karinagi@fcav.unesp.br) on 2018-07-26T10:33:53Z (GMT) No. of bitstreams: 1 bernardes_pa_dr_jabo.pdf: 3025957 bytes, checksum: 866a988a470ab68070a7e7a922fc2993 (MD5) / Made available in DSpace on 2018-07-26T10:33:53Z (GMT). No. of bitstreams: 1 bernardes_pa_dr_jabo.pdf: 3025957 bytes, checksum: 866a988a470ab68070a7e7a922fc2993 (MD5) Previous issue date: 2018-06-29 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) / Dentre as informações fornecidas pelas metodologias que utilizam marcadores do tipo polimorfismo de nucleotídeo único (SNPs), as de segmentos de homozigose (ROH) e de desequilíbrio de ligação têm colaborado para estudos de aplicação direta da informação genômica em populações de bovinos de corte, como em estudos de associação com cobertura ampla do genoma, de seleção genômica e de estrutura da população, dentre outros. Atualmente a imputação vem sendo utilizada principalmente para reduzir custos com a genotipagem dos animais e pode ser utilizada combinando informações genômicas de diferentes painéis. Para que dados imputados sejam utilizados de forma eficiente, é necessário que a imputação tenha sido implementada de forma que todos os animais tenham seus genótipos inferidos com elevada acurácia. No entanto, esta é verificada apenas se houver o genótipo real para avaliar a confiabilidade do genótipo imputado. Dessa maneira, os objetivos deste trabalho foram: (1) estudar a imputação de painéis comercial e customizados de baixa densidade para painéis de alta densidade (Illumina e Affymetrix), assim como para um painel combinado (Illumina + Affymetrix) para bovinos da raça Nelore, e estudar o desequilíbrio de ligação e conformação de blocos de haplótipos antes e após a imputação; (2) estudar estratégias para predição da acurácia de imputação, utilizando redes neurais artificiais e regressão linear múltipla; (3) estudar os segmentos de homozigose e, com isso, a endogamia presente em uma população de bovinos da raça Nelore, assim como identificar os genes presentes nos segmentos de homozigose mais frequentes na população. Os estudos de ROH foram realizados com utilização de informações de 34 touros de diferentes linhagens e suas progênies, totalizando 809 animais genotipados da raça Nelore com informação de 509.107 SNPs (Illumina). Para as análises de imputação e de predição da acurácia de imputação foram utilizados os mesmos animais, sendo que 93 destes também foram genotipados com o painel “Axion Genome-Wide BOS 1 Array Plate”. A partir dos resultados das análises de imputação demonstrou-se que o uso combinado de painéis pode ser uma alternativa para aumentar a densidade e o número de bloco de haplótipos, aumentando a probabilidade de obter um marcador próximo a um QTL de interesse. Além disso, essa estratégia indica que a escolha de SNPs em comum entre os painéis de alta densidade (Ilumina e Affymetrix) pode ser utilizada para customizar um painel de menor densidade, permitindo elevar a acurácia de imputação do painel da Illumina e Affymetrix. Na análise de predição da acurácia de imputação, a utilização de redes neurais artificiais foi mais eficiente comparada ao modelo de regressão linear múltipla, podendo ser utilizada com esta finalidade. A partir dos resultados das análises de ROH observou-se que a população encontra-se com baixo nível de endogamia, no entanto os reprodutores apresentaram maior valor de endogamia comparado a progênie, o qual somado a presença de segmentos de homozigose mais longos nestes animais podem indicar que tenha ocorrido intensa utilização de poucos reprodutores nas gerações mais recentes em algumas famílias. / Among all the information provided by methodologies that use single nucleotide polymorphism (SNPs), the runs of homozygosity (ROH) and linkage disequilibrium have been used for studies that explore genomic information in beef cattle population, as the genome-wide association, genomic selection, the structure of population and others. Nowadays, the imputation is used in these studies to reduce genomic costs and this also can be used combining genomic information from different panels. The animals used to be imputed should present genotypes inferred with high accuracy to allow the use imputed genotypes in other studies. However, the accuracy is verified only if there is a real genotype to evaluate the imputed genotype. Therefore, this study aimed: (1) Evaluate imputation of commercial and customized low density panels to high density panels (Illumina and Affymetrix), as well as to a combined panel (Illumina + Affymetrix) in Nelore beef cattle, and estimating linkage disequilibrium and haplotype blocks conformation to high density panels individually and after imputation; (2) Study a strategy to predict imputation accuracy using artificial neural network and linear regression; (3) Study runs of homozygosity and inbreeding in a populations from Nelore beef cattle, as well as identify genes present in ROH with high frequency in population. For ROH studies were used 34 bulls from different lines and the progeny, totalizing 809 Nelore animals genotyped with information of 509.107 SNPs (Illumina). The imputation analysis and imputation accuracy prediction used the same animals, wherein 93 were also genotyped with Axion Genome-Wide BOS 1 Array Plate. The imputation analysis demonstrates that the combined panels used from different panels can be considered due to increasing the density and number of haplotype blocks, increasing the probability to find a marker close to an important QTL. Furthermore, this strategy indicates that the choice for common SNPs between high-density panels Illumina and Affymetrix to customize a lower density panel can increase the imputation accuracy to Illumina and Affymetrix. The prediction of imputation accuracy analysis showed that the neural network is more efficient compared to linear regression, and could be used for this purpose. The results from ROH analysis showed low population inbreeding, however the sires presented higher inbreeding compared to progenies and longer runs of homozigosity, which suggest that has occurred intense use of few sires in recent generations in some families. / FAPESP: 2015/25096-6 e 2016/22940-3.

The Affymetrix GeneChip® Canine Genome 2.0 microarray is re-annotated using AgBase tools, up-to-date ID mapping and GO annotations associated with publicly available gene products updated on this array. This re-annotation makes the array more useful for researchers using the canine microarray for biological discovery. We use flow cytometry to determine if liposomal clodronate (LC) is an acceptable alternative to surgical splenectomy to facilitate detection of subclinical infection with Babesia canis in potential blood donor greyhounds. Our study shows that LC is not a reliable means of exposing babesiosis in greyhounds with a recent history of infection. We evaluate the effect of depletion of antigen presenting cells on regulatory T cells (Tregs) in dogs treated with LC by multi-color flow cytometry. We demonstrate that LC promotes increases in the CD4+CD25+FOXP3+ Tregs affecting mostly the CD4+CD25lowFOXP3+ Tregs subset suggesting a role of monocytes in naïve T cell priming and differentiation into Tregs.

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