Submitted by Guilherme Batista do Nascimento null (guilhermebn@msn.com) on 2018-03-16T12:24:54Z No. of bitstreams: 1 Tese_Guilherme_Batista_do_Nascimento.pdf: 1770231 bytes, checksum: ad03948ecc7b09b89d46d26b7c9e3bf8 (MD5) / Approved for entry into archive by Alexandra Maria Donadon Lusser Segali null (alexmar@fcav.unesp.br) on 2018-03-16T19:03:02Z (GMT) No. of bitstreams: 1 nascimento_gb_dr_jabo.pdf: 1770231 bytes, checksum: ad03948ecc7b09b89d46d26b7c9e3bf8 (MD5) / Made available in DSpace on 2018-03-16T19:03:02Z (GMT). No. of bitstreams: 1 nascimento_gb_dr_jabo.pdf: 1770231 bytes, checksum: ad03948ecc7b09b89d46d26b7c9e3bf8 (MD5) Previous issue date: 2018-02-22 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A implementação de dados de sequenciamento de nova geração - “next-generation sequence” (NGS) em programas de melhoramento genético animal representa a mais recente ferramenta na utilização de dados genotípicos nos modelos de associação genômica, tendo em vista que todo polimorfismo é considerado nas associações entre registros fenotípicos e dados de sequenciamento. Como em toda nova tecnologia, a prospecção das variantes ainda representa um desafio no sentido computacional e de viabilidade dos custos para sua implementação em larga escala. Diante desses desafios, neste trabalho buscou-se meios de explorar os benefícios na utilização da NGS nas predições genômicas e superar as limitações inerentes a esse processo. Registros fenotípicos e genotípicos (Illumina Bovine HD BeadChip) de 2.279 animais da raça Gir (Bos taurus indicus) foram disponibilizados pela Embrapa Gado de Leite (MG) e utilizados para as análises de associação genômica. Além disso, dados de sequenciamento de 53 animais do 1000 “Bulls Project” deram origem à população de referência de imputação. Visando verificar a eficiência de imputação, foram testados diferentes cenários quanto a sua acurácia de imputação por meio da análise “leave-one-out”, utilizando apenas os dados de sequenciamento, que apresentaram eficiências de até 84%, no cenário com todos os 51 animais disponíveis após o controle de qualidade. Também foram verificadas as influências das variantes em baixa frequência na acurácia de imputação em diferentes regiões do genoma. Com a escolha da melhor estrutura da população de referência de imputação e aplicação dos controles de qualidade nos dados de NGS e genômicos, foi possível imputar os 2.237 animais genotipados, que passaram pelo controle de qualidade para dados de sequenciamento e realizar análise de associação genômica para as características produção de leite (PL305), teor de gordura (PG305), proteína (PP305) e sólidos totais (PS305), mensuradas aos 305 dias em animais da raça Gir leiteiro. Para tal, foram utilizados os valores genéticos desregredidos (dEBV) como variável resposta no modelo de regressão múltipla. Regiões de 1Mb que contivessem 100 ou mais variantes com “False Discovery Rate” (FDR) inferior a 0,05, foram consideradas significativas e submetidas a análise de enriquecimento por meio dos termos MeSh (“Medical Subject Headings”). As três regiões significativas (FDR<0,05) para PS305 foram observadas nos cromossomos 11, 12 e 28 e a única região significativa em PG305 foi no cromossomo 6. Tais regiões apresentaram variantes associadas com vias metabólicas da produção de leite, ausentes nos painéis comerciais de genotipagem, podendo representar genes candidatos a seleção. / - Implementing "next-generation sequence" (NGS) data in animal breeding programs represents the latest tool in the use of genotypic data in genomic association models, since all polymorphisms are considered in the associations between phenotypic records and sequencing data. As with any new technology, variant prospecting still represents a computational and cost-effective challenge for large-scale implementation. Front to these challenges, this work sought ways to explore the benefits of using NGS in genomic predictions and overcome the inherent limitations of this process. Phenotypic and genotypic (Illumina Bovine HD BeadChip) records of 2,279 Gir animals (Bos taurus indicus) were made available by Embrapa Gado de Leite (MG) and used for genomic association analysis. In addition, sequence data of 53 animals from the 1000 Bulls Project gave rise to the imputation reference population. In order to verify the imputation efficiency, different scenarios were tested for their imputation accuracy through the leave-one-out analysis, using only the sequencing data, which presented efficiencies of up to 84%, in the scenario with all the 51 animals available after quality control. Influences from the low-frequency variants on the accuracy of imputation in different regions of the genome were also verified. After identifying the best reference population structure of imputation and applying the quality controls in the NGS and genomic data, it was possible to impute the 2 237 genotyped animals that passed in the quality control to sequencing data and perform genomic association analysis for (PL305), fat content (PG305), protein (PP305) and total solids (PS305), measured at 305 days in dairy Gir animals. For this, unregulated genetic values (dEBV) were used as response variable in the multiple regression model. Regions of 1Mb containing 100 or more variants with a False Discovery Rate (FDR) lower than 0.05 were considered statistically significant and submitted to pathways enrichment analysis using the MeSh (Medical Subject Headings) terms. The three significant regions (FDR <0.05) for PS305 were observed on chromosomes 11, 12 and 28 and only one significant region in PG305, was on chromosome 6. These regions presented variants associated with metabolic pathways of milk production, absent in the panels genotyping, and may represent genes that are candidates for selection / convênio Capes/Embrapa (edital 15/2014)

Submitted by RENATA ZUGAIB null (rzugaib@gmail.com) on 2017-01-20T15:02:38Z No. of bitstreams: 1 Dissertação Renata - Defesa.pdf: 1638667 bytes, checksum: f166690438b6f00c2e9fe2b0da55497d (MD5) / Approved for entry into archive by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br) on 2017-01-24T18:09:13Z (GMT) No. of bitstreams: 1 zugaib_r_me_bot.pdf: 1638667 bytes, checksum: f166690438b6f00c2e9fe2b0da55497d (MD5) / Made available in DSpace on 2017-01-24T18:09:13Z (GMT). No. of bitstreams: 1 zugaib_r_me_bot.pdf: 1638667 bytes, checksum: f166690438b6f00c2e9fe2b0da55497d (MD5) Previous issue date: 2017-01-13 / O vírus da hepatite C (VHC) constitui a principal causa de doença hepática crônica, que representa um dos maiores problemas de saúde pública. O carcinoma hepatocelular (CHC), altamente associado a infecção crônica pelo vírus da hepatite C (VHC), é um dos tumores malignos mais comuns no mundo, com um alto índice de causa de óbito. Com o avanço das técnicas moleculares, tornou-se possível uma nova abordagem nos estudos gênicos para um melhor entendimento molecular de processos infecciosos e crônicos. Estudos evidenciando uma associação da transcrição gênica ao processo patológico e a importância de análises mais abrangentes. O sequenciamento de nova geração fornece uma maneira poderosa para a avaliação global do transcriptoma com alta resolução e um menor custo, possibilitando uma análise do perfil transcricional da doença. Assim, este estudo teve por objetivo avaliar o perfil de expressão gênica diferencial de pacientes infectados pelo VHC com CHC e comparar com amostras de tecidos não tumorais através do sequenciamento em larga escala do transcriptoma, a fim de identificar potenciais biomarcadores de diagnóstico e prognóstico de CHC. Foram analisados três fragmentos de tecido tumoral e três fragmentos de tecido hepático não tumoral como controle através do sequenciamento de RNAs (RNA-Seq). Os resultados obtidos demonstraram uma expressão diferencial de 4.792 genes. Avaliando os 10 genes mais e menos expressos, foi observada uma grande associação de variações nesses genes em diversos tipos de tumores. Também foram observados, entre os menos expressos, genes intimamente relacionados a função hepática ou relacionados a componentes produzidos pelo fígado. Esses achados sugerem que COL11A1, SFRP4, SFRP2, LRRC15, CCL18, ADAMDEC1, COL1A1, COL10A1, CTHRC1 e OLR1, superexpressos, possam atuar juntos no processo tumoral servindo como marcadores moleculares tumorais, e que a presença do tumor possa provocar uma desregulação nos genes associados ao fígado aqui encontrados, contudo, estudos mais específicos devem ser conduzidos para a confirmação dessa hipótese. / Hepatitis C virus (HCV) is the leading cause of chronic liver disease, one of the major public health problems. Hepatocellular carcinoma (HCC), highly associated with chronic hepatitis C virus (HCV) infection, is one of the most common malignant tumors in the world, with a high cause of death. With the advancement of molecular techniques, a new approach in gene studies has become possible for a better molecular understanding of infectious and chronic processes. Studies evidencing an association of the gene transcription to the pathological process and the importance of more comprehensive analyzes. Next generation sequencing provides a powerful way for the global evaluation of the transcriptome with high resolution and a lower cost, allowing an analysis of the transcriptional profile of the disease. Thus, this study aimed to evaluate the differential gene expression profile of HCV infected patients in their highest degree of chronicity (HCC) and to compare with non-tumor tissue samples through large-scale sequencing of the transcriptome in order to identify potential biomarkers for diagnosis and prognosis of HCC. Three fragments of tumor tissue and three fragments of non-tumor liver tissue were analyzed through the sequencing of RNAs (RNA-Seq). The results obtained demonstrated a differential expression of 4.792 genes. Evaluating the 10 over and down regulated genes, a high association of variations in these genes was observed in several types of tumors. Among the least expressed, genes closely related to liver function or related to components produced by the liver were also observed. These findings suggest that COL11A1, SFRP4, SFRP2, LRRC15, CCL18, ADAMDEC1, COL1A1, COL10A1, CTHRC1 and OLR1, overexpressed, may act together in the tumor process serving as molecular tumor markers, and that the presence of the tumor may lead to dysregulation in the genes associated with the liver found here, however, more specific studies should be conducted to confirm this hypothesis.

Large-scale bisulfite treatment and short reads sequencing technology allows comprehensive estimation of methylation states of Cs in the genomes of different tissues, cell types, and developmental stages. Accurate characterization of DNA methylation is essential for understanding genotype phenotype association, gene and environment interaction, diseases, and cancer. The thesis work first evaluates the performance of several commonly used bisulfite short read mappers and investigates how pre-processing data might affect the performance. Aligning bisulfite short reads to a reference genome remains a challenging task. In practice, only a limited proportion of bisulfite treated DNA reads can be mapped uniquely (around 50-70%) while a significant proportion of reads (called multireads) are aligned to multiple genomic locations. The thesis outlines a strategy to improve the mapping efficiencies of the existing bisulfite short reads software by finding unique locations for multireads. Analyses of both simulated data and real hairpin bisulfite sequencing data show that our strategy can effectively assign approximately 70% of the multireads to their best locations with up to 90% accuracy, leading to a significant increase in the overall mapping efficiency. The most common and essential downstream task in DNA methylation analysis is to detect differential methylated cytosines (DMCs). Although many statistical methods have been applied to detect DMCs, inconsistency in detecting differential methylated sites among statistical tools remains. We adapt the wavelet-based functional mixed models (WFMM) to detect DMCs. Analyses of simulated Arabidopsis data show that WFMM has higher sensitivities and specificities in detecting DMCs compared to existing methods especially when methylation differences are small. Analyses of monozygotic twin data who have different pain sensitivity also show that WFMM can find more relevant DMCs related to pain sensitivity compared to methylKit. In addition, we provide a strategy to modify the default settings in both WFMM and methylKit to be more tailored to a given methylation profile, thus improving the accuracy of detecting DMCs. Population growth and climate change leave billions of people around the world living in water scarcity conditions. Therefore, utility of reclaimed water (treated wastewater) is pivotal for water sustainability. Recently, researchers discovered microbial regrowth problems in reclaimed water distribution systems (RWDs). The third part of the thesis involves: 1) identifying fundamental conditions that affect proliferation of antibiotic resistance genes (ARGs), 2) identifying the effect of water chemistry and water age on microbial regrowth, and 3) characterizing co-occurrence of ARGs and/or mobile genetics elements (MGEs), i.e., plasmids in simulated RWDs. Analyses of preliminary results from simulated RWDs show that biofilms, bulk water environment, temperature, and disinfectant types have significant influence on shaping antibiotic resistant bacteria (ARB) communities. In particular, biofilms create a favorable environment for ARGs to diversify but with lower total ARG populations. ARGs are the least diverse at 300C and the most diverse at 220C. Disinfectants reduce ARG populations as well as ARG diversity. Chloramines keep ARG populations and diversity at the lowest rate. Disinfectants work better in bulk water environment than in biofilms in terms of shaping resistome. Network analysis on assembly data is done to determine which ARG pairs are the most co-occurred. Bayesian network is more consistent with the co-occurrence network constructed from assembly data than the network based on Spearman's correlation network of ARG abundance profiles. / Ph. D.