Cellular life is highly complex. In order to expand our understanding of the workings of human cells, in particular in the context of health and disease, detailed knowledge about the underlying molecular systems is needed. The unifying theme of this thesis concerns the use of data derived from sequencing of RNA, both within the field of transcriptomics itself and as a guide for further studies at the level of protein expression. In paper I, we showed that publicly available RNA-seq datasets are consistent across different studies, requiring only light processing for the data to cluster according to biological, rather than technical characteristics. This suggests that RNA-seq has developed into a reliable and highly reproducible technology, and that the increasing amount of publicly available RNA-seq data constitutes a valuable resource for meta-analyses. In paper II, we explored the ability to extrapolate protein concentrations by the use of RNA expression levels. We showed that mRNA and corresponding steady-state protein concentrations correlate well by introducing a gene-specific RNA-to-protein conversion factor that is stable across various cell types and tissues. The results from this study indicate the utility of RNA-seq also within the field of proteomics. The second part of the thesis starts with a paper in which we used transcriptomics to guide subsequent protein studies of the molecular mechanisms underlying malignant transformation. In paper III, we applied a transcriptomics approach to a cell model for defined steps of malignant transformation, and identified several genes with interesting expression patterns whose corresponding proteins were further analyzed with subcellular spatial resolution. Several of these proteins were further studied in clinical tumor samples, confirming that this cell model provides a relevant system for studying cancer mechanisms. In paper IV, we continued to explore the transcriptional landscape in the same cell model under moderate hypoxic conditions. To conclude, this thesis demonstrates the usefulness of RNA-seq data, from a transcriptomics perspective and beyond; to guide in analyses of protein expression, with the ultimate goal to unravel the complexity of the human cell, from a holistic point of view. / <p>QC 20161121</p>

Microarray technology allows measurement of the expression levels of thousand of genes simultaneously. Several gene set analysis (GSA) methods are widely used for extracting useful information from microarrays, for example identifying differentially expressed pathways associated with a particular biological process or disease phenotype. Though GSA methods like Gene Set Enrichment Analysis (GSEA) are widely used for pathway analysis, these methods are solely based on statistics. Such methods can be awkward to use if knowledge of specific pathways involved in particular biological processes are the aim of the study. Here we present a novel method (Knowledge Based Gene Set Analysis: KB-GSA) which integrates knowledge about user-selected pathways that are known to be involved in specific biological processes. The method generates an easy to understand graphical visualization of the changes in expression of the genes, complemented with some common statistics about the pathway of particular interest.

Genetic regulatory networks are of great importance in terms of scientific interests and practical medical importance. Since a number of high-throughput measurement devices are available, such as microarrays and sequencing techniques, regulatory networks have been intensively studied over the last decade. Based on these high-throughput data sets, statistical interpretations of these billions of bits are crucial for biologist to extract meaningful results. In this thesis, we compare a variety of existing regression models and apply them to construct regulatory networks which span trancription factors and microRNAs. We also propose an extended algorithm to address the local optimum issue in finding the Maximum A Posterjorj estimator. An E. coli mRNA expression microarray data set with known bona fide interactions is used to evaluate our models and we show that our regression networks with a properly chosen prior can perform comparably to the state-of-the-art regulatory network construction algorithm. Finally, we apply our models on a p53-related data set, NCI-60 data. By further incorporating available prior structural information from sequencing data, we identify several significantly enriched interactions with cell proliferation function. In both of the two data sets, we select specific examples to show that many regulatory interactions can be confirmed by previous studies or functional enrichment analysis. Through comparing statistical models, we conclude from the project that combining different models with over-representation analysis and prior structural information can improve the quality of prediction and facilitate biological interpretation. Keywords: regulatory network, variable selection, penalized maximum likelihood estimation, optimization, functional enrichment analysis.

<p>Microarray technology allows measurement of the expression levels of thousand of genes simultaneously. Several gene set analysis (GSA) methods are widely used for extracting useful information from microarrays, for example identifying differentially expressed pathways associated with a particular biological process or disease phenotype. Though GSA methods like Gene Set Enrichment Analysis (GSEA) are widely used for pathway analysis, these methods are solely based on statistics. Such methods can be awkward to use if knowledge of specific pathways involved in particular biological processes are the aim of the study. Here we present a novel method <strong><em>(Knowledge Based Gene Set Analysis: KB-GSA</em></strong>) which integrates knowledge about user-selected pathways that are known to be involved in specific biological processes. The method generates an easy to understand graphical visualization of the changes in expression of the genes, complemented with some common statistics about the pathway of particular interest.</p>

Genetic regulatory networks are of great importance in terms of scientific interests and practical medical importance. Since a number of high-throughput measurement devices are available, such as microarrays and sequencing techniques, regulatory networks have been intensively studied over the last decade. Based on these high-throughput data sets, statistical interpretations of these billions of bits are crucial for biologist to extract meaningful results. In this thesis, we compare a variety of existing regression models and apply them to construct regulatory networks which span trancription factors and microRNAs. We also propose an extended algorithm to address the local optimum issue in finding the Maximum A Posterjorj estimator. An E. coli mRNA expression microarray data set with known bona fide interactions is used to evaluate our models and we show that our regression networks with a properly chosen prior can perform comparably to the state-of-the-art regulatory network construction algorithm. Finally, we apply our models on a p53-related data set, NCI-60 data. By further incorporating available prior structural information from sequencing data, we identify several significantly enriched interactions with cell proliferation function. In both of the two data sets, we select specific examples to show that many regulatory interactions can be confirmed by previous studies or functional enrichment analysis. Through comparing statistical models, we conclude from the project that combining different models with over-representation analysis and prior structural information can improve the quality of prediction and facilitate biological interpretation. Keywords: regulatory network, variable selection, penalized maximum likelihood estimation, optimization, functional enrichment analysis.

Genetic regulatory networks are of great importance in terms of scientific interests and practical medical importance. Since a number of high-throughput measurement devices are available, such as microarrays and sequencing techniques, regulatory networks have been intensively studied over the last decade. Based on these high-throughput data sets, statistical interpretations of these billions of bits are crucial for biologist to extract meaningful results. In this thesis, we compare a variety of existing regression models and apply them to construct regulatory networks which span trancription factors and microRNAs. We also propose an extended algorithm to address the local optimum issue in finding the Maximum A Posterjorj estimator. An E. coli mRNA expression microarray data set with known bona fide interactions is used to evaluate our models and we show that our regression networks with a properly chosen prior can perform comparably to the state-of-the-art regulatory network construction algorithm. Finally, we apply our models on a p53-related data set, NCI-60 data. By further incorporating available prior structural information from sequencing data, we identify several significantly enriched interactions with cell proliferation function. In both of the two data sets, we select specific examples to show that many regulatory interactions can be confirmed by previous studies or functional enrichment analysis. Through comparing statistical models, we conclude from the project that combining different models with over-representation analysis and prior structural information can improve the quality of prediction and facilitate biological interpretation. Keywords: regulatory network, variable selection, penalized maximum likelihood estimation, optimization, functional enrichment analysis. / Science, Faculty of / Graduate

O objetivo deste trabalho foi quantificar as concentrações de heat shock protein (HSP) 27 e 70 no músculo Longissimus thoracis bovino e avaliar o perfil proteico das amostras no decorrer do processo de maturação, bem como, a relação com as características de qualidade da carne. Foram utilizados 191 animais ½ Simental Sul Africano x ½ Nelore (½ S x ½ N), castrados e terminados em confinamento. Para qualidade da carne mensurou-se os valores de pH, cor (L*, a* e b*), perda de água por cocção (PAC) e força de cisalhamento (FC) em dois tempos de maturação (um e 14 dias). Para a quantificação de HSP 27 e 70 foram selecionadas 40 amostras em função dos valores de FC, e separadas em dois grupos (mais e menos macia) com 20 amostras cada, dentro de cada tempo de maturação (48h e 14 dias). Para realização da análise proteômica foram selecionados três animais de cada grupo e tempo de maturação. A concentração de HSP 27 apresentou diferença significativa (P &lt; 0,05) entre os tempos de maturação dentro dos dois grupos estudados. Já para a HSP 70 verificou-se diferença significativa (P &lt; 0,05) apenas para as amostras do grupo mais macia. A análise de correlação mostrou que as HSP 27 e 70 não interferiram nas características de qualidade da carne. A análise proteômica identificou seis proteínas diferencialmente abundantes (P &lt; 0,05) e o enriquecimento funcional demonstrou que essas proteínas estão relacionadas com processos biológicos e componentes celulares. A desmina e a glicerol-3-fosfato desidrogenase demonstraram correlação com a FC e coloração da carne, respectivamente. Os resultados indicam que as HSP 27 e 70 não podem ser apontadas como biomarcadores eficientes para qualidade de carne em animais ½ S x ½ N. Já a glicerol-3-fosfato desidrogenase demonstrou forte potencial em predizer a estabilidade da cor da carne. Desmina pode ser apontada como um importante biomarcador candidato associado à maciez da carne em animais ½ S x ½ N. / The aim of this work was to quantify the concentrations of heat shock protein (HSP) 27 and 70 in Longissimus thoracis muscle and evaluate the protein profile of the samples during the aging process and its relationship with meat quality characteristics. A total of 191 steers ½ Simmental South African x ½ Nellore (½ S x ½ N) feedlot-finished, were used in this work. For meat quality, pH values, color values (L *, a * and b *), cooking loss (CL) and shear force (SF) were measured in two aging times (one and 14 days). For quantification of HSP 27 and 70, 40 samples were selected according to the SF values and separated into two groups (more and less tender) with 20 samples each, according to the aging time (one and 14 days). To perform the proteomic analysis were selected three animals from each group and aging time. The concentration of HSP 27 showed a significant difference (P &lt; 0.05) between aging times within both groups. As for the HSP 70, there was a significant difference (P &lt; 0.05) for the tenderness samples. Correlation analysis showed that the HSP 27 and 70 didn\'t affect the meat quality traits. The proteomic analysis identified six proteins differentially expressed (P &lt; 0.05) and functional enrichment analysis demonstrated that these proteins are associated with biological processes and cellular components. Desmin and glycerol-3-phosphate dehydrogenase showed correlation with meat traits of SF and color, respectively. The results indicate that HSP 27 and 70 could not be identified as effective biomarkers for meat quality in ½ S x ½ N. However, glycerol-3-phosphate dehydrogenase showed a strong potential in predicting meat color stability. Desmin can be shown as a candidate biomarker associated with meat tenderness in ½ S x ½ N.

O objetivo deste trabalho foi quantificar as concentrações de heat shock protein (HSP) 27 e 70 no músculo Longissimus thoracis bovino e avaliar o perfil proteico das amostras no decorrer do processo de maturação, bem como, a relação com as características de qualidade da carne. Foram utilizados 191 animais ½ Simental Sul Africano x ½ Nelore (½ S x ½ N), castrados e terminados em confinamento. Para qualidade da carne mensurou-se os valores de pH, cor (L*, a* e b*), perda de água por cocção (PAC) e força de cisalhamento (FC) em dois tempos de maturação (um e 14 dias). Para a quantificação de HSP 27 e 70 foram selecionadas 40 amostras em função dos valores de FC, e separadas em dois grupos (mais e menos macia) com 20 amostras cada, dentro de cada tempo de maturação (48h e 14 dias). Para realização da análise proteômica foram selecionados três animais de cada grupo e tempo de maturação. A concentração de HSP 27 apresentou diferença significativa (P &lt; 0,05) entre os tempos de maturação dentro dos dois grupos estudados. Já para a HSP 70 verificou-se diferença significativa (P &lt; 0,05) apenas para as amostras do grupo mais macia. A análise de correlação mostrou que as HSP 27 e 70 não interferiram nas características de qualidade da carne. A análise proteômica identificou seis proteínas diferencialmente abundantes (P &lt; 0,05) e o enriquecimento funcional demonstrou que essas proteínas estão relacionadas com processos biológicos e componentes celulares. A desmina e a glicerol-3-fosfato desidrogenase demonstraram correlação com a FC e coloração da carne, respectivamente. Os resultados indicam que as HSP 27 e 70 não podem ser apontadas como biomarcadores eficientes para qualidade de carne em animais ½ S x ½ N. Já a glicerol-3-fosfato desidrogenase demonstrou forte potencial em predizer a estabilidade da cor da carne. Desmina pode ser apontada como um importante biomarcador candidato associado à maciez da carne em animais ½ S x ½ N. / The aim of this work was to quantify the concentrations of heat shock protein (HSP) 27 and 70 in Longissimus thoracis muscle and evaluate the protein profile of the samples during the aging process and its relationship with meat quality characteristics. A total of 191 steers ½ Simmental South African x ½ Nellore (½ S x ½ N) feedlot-finished, were used in this work. For meat quality, pH values, color values (L *, a * and b *), cooking loss (CL) and shear force (SF) were measured in two aging times (one and 14 days). For quantification of HSP 27 and 70, 40 samples were selected according to the SF values and separated into two groups (more and less tender) with 20 samples each, according to the aging time (one and 14 days). To perform the proteomic analysis were selected three animals from each group and aging time. The concentration of HSP 27 showed a significant difference (P &lt; 0.05) between aging times within both groups. As for the HSP 70, there was a significant difference (P &lt; 0.05) for the tenderness samples. Correlation analysis showed that the HSP 27 and 70 didn\'t affect the meat quality traits. The proteomic analysis identified six proteins differentially expressed (P &lt; 0.05) and functional enrichment analysis demonstrated that these proteins are associated with biological processes and cellular components. Desmin and glycerol-3-phosphate dehydrogenase showed correlation with meat traits of SF and color, respectively. The results indicate that HSP 27 and 70 could not be identified as effective biomarkers for meat quality in ½ S x ½ N. However, glycerol-3-phosphate dehydrogenase showed a strong potential in predicting meat color stability. Desmin can be shown as a candidate biomarker associated with meat tenderness in ½ S x ½ N.

Functioning of the living cell is controlled by a complex network of interactions among genes, proteins, and other molecules. A major goal of systems biology is to understand and explain the mechanisms by which these interactions govern the cell's response to various conditions. Molecular interaction networks have proven to be a powerful representation for studying cellular behavior. Numerous algorithms have been developed to unravel the complexity of these networks. Our work addresses the drawbacks of existing techniques. This thesis includes three related research efforts that introduce network-based approaches to bridge current methodological gaps in systems biology. i. Functional enrichment methods provide a summary of biological functions that are overrepresented in an interesting collection of genes (e.g., highly differentially expressed genes between a diseased cell and a healthy cell). Standard functional enrichment algorithms ignore the known interactions among proteins. We propose a novel network-based approach to functional enrichment that explicitly accounts for these underlying molecular interactions. Through this work, we close the gap between set-based functional enrichment and topological analysis of molecular interaction networks. ii. Many techniques have been developed to compute the response network of a cell. A recent trend in this area is to compute response networks of small size, with the rationale that only part of a pathway is often changed by disease and that interpreting small subnetworks is easier than interpreting larger ones. However, these methods may not uncover the spectrum of pathways perturbed in a particular experiment or disease. To avoid these difficulties, we propose to use algorithms that reconcile case-control DNA microarray data with a molecular interaction network by modifying per-gene differential expression p-values such that two genes connected by an interaction show similar changes in their gene expression values. iii. Top-down analyses in systems biology can automatically find correlations among genes and proteins in large-scale datasets. However, it is often difficult to design experiments from these results. In contrast, bottom-up approaches painstakingly craft detailed models of cellular processes. However, developing the models is a manual process that can take many years. These approaches have largely been developed independently. We present Linker, an efficient and automated data-driven method that analyzes molecular interactomes. Linker combines teleporting random walks and k-shortest path computations to discover connections from a set of source proteins to a set of target proteins. We demonstrate the efficacy of Linker through two applications: proposing extensions to an existing model of cell cycle regulation in budding yeast and automated reconstruction of human signaling pathways. Linker achieves superior precision and recall compared to state-of-the-art algorithms from the literature. / Ph. D.

As crescentes preocupações com questões ambientais têm despertado interesse global pelo uso de combustíveis alternativos, e o uso da biomassa vegetal surge como uma alternativa viável para a geração de biocombustíveis. Diferentes materiais orgânicos têm sido utilizados, e dentre eles destaca-se o sorgo biomassa (Sorghum bicolor L. Moench). A seleção genômica apresenta grande potencial e pode, em médio prazo, reestruturar os programas de melhoramento de plantas, promovendo maiores ganhos genéticos quando comparada a outros métodos, além de reduzir significativamente o tempo necessário para o desenvolvimento de novas cultivares, através da seleção precoce. Este trabalho teve como objetivo avaliar modelos de seleção genômica e aplicá-los para a predição dos valores genéticos de indivíduos do painel de sorgo biomassa da Embrapa/Milho e Sorgo. Tal painel inclui materiais do banco de germoplasma e materiais utilizados em programas de melhoramento de sorgo dessa instituição, bem como coleções núcleo do CIRAD e ICRISAT, sendo, portanto, subdividido em dois sub-painéis. As 100 linhagens do sub-painel 1 foram avaliadas fenotipicamente por dois anos (2011 e 2012) e as 100 linhagens do sub-painel 2 por um ano (2011), ambas no município de Sete Lagoas-MG, para as seguintes características fenotípicas: tempo até o florescimento, altura de plantas, produção de massa verde e massa seca, proporções de fibra ácida e neutra, celulose, hemicelulose e lignina. Posteriormente, as 200 linhagens integrantes do painel foram genotipadas através da técnica de genotipagem por sequenciamento. A partir desses dados genotípicos e fenotípicos, os modelos de seleção genômica Bayes A, Bayes B, Bayes C&pi;, Bayes Lasso, Bayes Ridge Regression e Random Regression BLUP (RRBLUP) foram ajustados e comparados. As capacidades preditivas obtidas foram elevadas e pouco variaram entre os diversos modelos, variando de 0,61 para o caráter florescimento a 0,85 para a proporção de fibra ácida, quando o modelo RRBLUP foi empregado na análise conjunta dos dois sub-painéis. Por outro lado, a predição cruzada entre sub-painéis resultou em capacidades preditivas substancialmente menores, nunca superiores a 0,66 e em alguns cenários virtualmente iguais a zero, além de apresentar maiores variações entre os modelos ajustados. Simulações do uso de subconjuntos dos marcadores moleculares são apresentadas e indicam possibilidades de obtenção de capacidades preditivas mais elevadas. Análises de enriquecimento funcional realizadas a partir dos efeitos preditos dos marcadores sugeriram associações interessantes, as quais devem ser investigadas com maiores detalhes em estudos futuros, com potencial de elucidação da arquitetura genética dos caracteres quantitativos. / Increased concerns about environmental issues have aroused global interest in the use of alternative fuels, and the use of plant biomass emerges as a viable alternative for the generation of biofuels. Different organic materials have been used, including high biomass sorghum (Sorghum bicolor L. Moench). Genomic selection has great potential and could, in the medium term, restructure plant breeding programs, promoting greater genetic gains when compared to other methods and significantly reducing the time required for the development of new cultivars through early selection. This work aimed at evaluating models of genomic selection and applying them to the prediction of breeding values for a panel of high biomass sorghum genotypes of Embrapa / Milho e Sorgo. This panel includes materials from the gene bank and materials used in sorghum breeding programs of this institution, as well as core collections from CIRAD and ICRISAT, and is therefore divided into two sub-panels. The 100 lines of sub-panel 1 were evaluated phenotypically for two years (2011 and 2012) and the 100 lines of sub-panel 2 for one year (2011), both in the city of Sete Lagoas, Minas Gerais, for the following phenotypic traits: days to flowering, plant height, fresh and dry matter yield and fiber, cellulose, hemicellulose and lignin proportions. Subsequently, the 200 lines were genotyped by via the genotyping by sequencing technique. From these genotypic and phenotypic data, genomic selection models Bayes A, Bayes B, Bayes C&pi;, Bayes Lasso, Bayes Ridge Regression and Random Regression BLUP (RRBLUP) were fitted and compared. The predictive capabilities obtained were high and varied little between the different models, ranging from 0.61 for days to flowering to 0.85 for acid fiber, when the RRBLUP model was used on the combined analysis of the two sub-panels. On the other hand, cross prediction between sub-panels resulted in substantially lower predictive capability, never above 0.66 and in some scenarios virtually equal to zero, with greater variations between the fitted models. Simulations of using subsets of molecular markers are presented and indicate possibilities of achieving higher predictive capabilities. Functional enrichment analyses performed with the marker predicted effects suggested interesting associations, which should be investigated in more detail in future studies, with potential for elucidating the genetic architecture of quantitative traits.