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No description available.

The number of children and young adolescents diagnosed with cancer is increasing, leading to a need for new therapeutic strategies with diminished neurodegenerative side- effects. This report presents preliminary observations on glioma-initiating cells (GICs) in the way to develop a strategy that induces cell-cycle arrest or quiescence in neural stem cells (NSCs). To test how changes in membrane potential due to pharmacological treatments have effects on localization and levels of REV-ERBα protein, proneural (PN) and mesenchymal (MES) cells were treated with varying concentrations of REV-ERBα agonist SR9009 drug and T-type calcium channel blocker mibefradil. Treatments showed that both drugs do not relocalize REV-ERBα to the nucleus. However, SR9009 decreases the levels of REV-ERBα protein, whereas mibefradil does not have a similar effect.  Our preliminary data on mouse NSCs showed they engage with REV-ERBα protein while going into contact inhibition. Therefore, we investigated whether high confluency put PN and MES GICs into quiescence and the role of the main molecular clock protein REV-ERBα in this process. Cells were grown up to certain confluency, and following qPCR gene expression analysis revealed PN cells go into contact inhibition whereas MES cells continue proliferating even after they are grown to confluency. Moreover, REV-ERBα protein does not have any role in both outcomes.

Gamma-ray burst light curves are notoriously irregular, yet a significant number consists of a single fast-rising, exponentially decaying pulse. These are called single-peaked light curves. The goal of this thesis is to analyse a sample of 2710 GRBs collected by the Fermi space telescope by identifying single-peaked bursts and comparing their properties to those of the multi-peaked bursts. Furthermore, the validity of the relativistic shock breakout theory as an explanation for single-peaked, low-luminosity GRBs is investigated using a closure relation. For this investigation, the Fermi sample wascomplemented by low-luminosity GRBs observed by other instruments. A criterion for selecting single-peaked bursts was successfully developed, yielding 48% long and 79% short, single-peaked GRBs. Significant differences between the populations were found in multiple properties. In general, single-peaked GRBs appear to be weaker and more slowly varying than multi-peaked ones; however, a larger sample of GRBs with redshift measurements is needed to draw conclusions about possible intrinsic differences in energy connected to the progenitor systems. The investigation of low-luminosity GRBs’ compliance with the shock breakout closure relation showed that 64% of the low-luminosity GRBs were within a factor 5 of fulfilling the relation as opposed to only 24% of high-luminosity GRBs. It was further shown that only a small number (< 5%) of Fermi GRBs without redshift measurements could be low-luminosity shock breakout GRBs according to this theory. In conclusion, while the shock breakout closure relation does hold for a greater proportion of low-luminosity GRBs than high-luminosity GRBs, there is still a large number of low-luminosity GRBs left unexplained by this theory. / Gammablixtljuskurvor är ökänt oregelbundna men en betydande andel består av en enda snabbt stigande och exponentiellt avtagande puls. Dessa kallas singelpeakade ljuskurvor. Målet med detta examensarbete är att analysera de 2710 gammablixtar som Fermirymdteleskopet har observerat genom att identifiera singelpeakade blixtar och jämföra deras egenskaper med multipeakade blixtars. Dessutom undersöks den relativistiska shockbreakoutteorin som förklaringsmodell för singelpeakade lågluminositetsgammablixtar. I denna undersökning kompletterades fermiblixtarna med lågluminositetsblixtar från andra instrument. Ett kriterium för identifikation av singelpeakade gammablixtar utvecklades och detta resulterade i 48% långa och 70% korta, singelpeakade gammablixtar. Flertalet egenskaper uppvisade signifikanta skillnader mellan populationerna. I allmänhet verkar singelpeakade gammablixtar vara svagare och variera långsammare än multipeakade. Dock behövs en större population av gammablixtar med uppmätta rödskift för att med säkerhet kunna avgöra om singelpeakade blixtar verkligen släpper ut mindre energi. Undersökningen av huruvida lågluminositetsgammablixtar kan förklaras med shockbreakoutteorin visade att 64% av lågluminositetsblixtarna uppfyllde kravet upp till en faktor fem medan bara 24% av högluminositetsblixtarna gjorde det. Vidare visades att endast ett litet antal (<5%) av fermiblixtarna utan uppmätta rödskift skulle kunna vara lågluminositetsshockbreakoutblixtar enligt denna teori. Även om shockbreakoutteorin kan förklara en större andel av lågluminositetsblixtarna än högluminositetsblixtarna återstår ett stort antal oförklarade lågluminositetsblixtar.

In this thesis the Gamma-Ray Bursts (GRBs) are studied, the brightest explosions in the universe. GRBs have been observed since year 1967, but there are several unsolved problems. In the first chapter there is an introduction to the issue of GRBs, and the history of observations are briefly described. The Fermi satellite, the latest satellite devoted to gamma-ray burst observations is described in chapter two. Characteristics of the Fermi instruments are also described. The observed data of GRBs are characterized in the third chapter. The distribution of GRB durations, distances, and spectral hardnesses are described. The characteristics of long and short GRBs (distance, isotropy of distribution, metalicity dependence, isotropic energy) are described. A chance of the appearance of a GRB in the Milky Way is discussed. New Fermi observations are described too. Fourth chapter is about models of GRBs. The fireball and canonball models are described. Fifth chapter is focused on the exposure function of CGRO-BATSE, Fermi-GBM, Swift. I have created the exposure function for GBM on Fermi satellite. It is quite difficult, and I have assumed some simplified hypotheses. Information of the satellite's position, position of detectors on the Fermi satellite, have been found on the Fermi web pages and in the article...

O glioblastoma (GBM) é o tumor primário do sistema nervoso central mais frequente no adulto, com sobrevida média de aproximadamente 12 meses. Múltiplas alterações genéticas e epigenéticas presentes neste tumor determinam sua biologia e fenótipo bastante agressivos. Assim, o presente estudo objetivou estudar a expressão dos genes envolvidos no Índice de Radiossensibilidade (RSI) e do gene MGMT em amostras de tumor primário humano de GBM, buscando identificar a associação destes com radiossensibilidade e sobrevida. Foram analisadas as características epidemiológicas, de evolução e desfecho clínico de 28 pacientes com GBM que fizeram uso de temozolamida (TMZ), cujos prontuários físicos e eletrônicos foram revisados no Serviço de Arquivo Médico (SAM) e Sistema Athos. Foi observado que 64,29% dos pacientes possuíam mais de 50 anos de idade e eram do sexo masculino; 86,36% possuíam o Karnofsky Performance Status acima ou igual a 80%; 57,14% possuíam localização não eloquente; 78,57% apresentavam lesão residual; 89,29% não foram reexpostos à radioterapia; 64,29% não fizeram uso de temozolamida metronômica; 96,43% não foram submetidos a outra quimioterapia; 78,57% não realizaram reabordagem cirúrgica; 60,71% tiveram progressão tardia e/ou não tiveram progressão da doença. Também foi evidenciado que tanto a regressão bruta de cada gene separadamente, quanto a regressão ajustada para os fatores clínicos mais relevantes (idade ao diagnóstico, localização do tumor e presença de lesão residual) não evidenciaram significância estatística em relação à progressão tardia. A relação entre o tempo de sobrevida global e a expressão dos genes ajustada para os mesmos fatores clínicos evidenciou significância estatística para os genes RELA (HR 1,265 / p = 0,01); c-Jun (HR 1,237 / p = 0,03) e c-ABL (HR 0,33 / p = 0,04). A análise de sobrevida livre de progressão ajustada mostrou diferença significativa (p = 0,04) entre os grupos com tumor de localização eloquente e não eloquente, assim como para a expressão do gene RELA (HR 1,107 / p = 0,03). Em relação à expressão gênica e a sobrevida global ou sobrevida livre de progressão dos pacientes pelo teste de Log-rank não foi observada diferença estatística. Na análise da expressão gênica com as variáveis clínico-epidemiológicas, a expressão do gene SUMO1 foi significativamente aumentada (p = 0,009) no grupo com progressão tardia com relação ao grupo com progressão precoce da doença, a expressão do AR apresentou aumento significativo (p = 0,021) no grupo em que a TMZ metronômica não foi administrada e as expressões dos genes RELA (p = 0,03),c-Jun (p = 0,005), STAT1 (p = 0.009) e HDAC1 (p = 0.044) estava elevada nos pacientes que apresentaram o exame neurológico pré-operatório normal. Esses dados indicam que o conjunto dos genes do RSI e o MGMT não demonstraram ser preditores de radiossensibilidade. Podemos inferir que RELA, c-Jun e c-ABL são potenciais marcadores de pior prognóstico e que SUMO1 pode ser um marcador de bom prognóstico / Glioblastoma (GBM) is the primary tumor of the central nervous system most common in adults, with a median survival of approximately 12 months. Multiple genetic and epigenetic alterations present in this tumor determine its biology and phenotype very aggressive. Thus, the present study aimed to study the expression of genes involved in radiosensitivity index (RSI) and the MGMT gene in GBM human primary tumor samples, seeking to identify the association of these with radiosensitivity and survival. The epidemiological evolution and clinical characteristics of 28 GBM patients who used temozolamide (TMZ), whose physical and electronic medical records were reviewed in the Medical File Service (SAM) and Athos System, were analyzed. It was observed that 64.29% of the patients were over 50 years of age and were male; 86.36% had Karnofsky Performance Status of 80 or more; 57.14% had no eloquent location; 78.57% had residual lesion; 89.29% did not have reexposure to radiotherapy; 64.29% did not use metronomic temozolamide; 96.43% did not undergo another chemotherapy; 78.57% did not undergo surgical reassessment; 60.71% had late progression and / or had no disease progression. It was also evidenced that both the gross regression of each gene separately and the regression adjusted for the most relevant clinical factors (age at diagnosis, tumor location and presence of residual lesion) did not show statistical significance in relation to the late progression. The relationship between overall survival time and gene expression adjusted for the same clinical factors showed statistical significance for RELA genes (HR 1.265 / p = 0.01), c-Jun (HR 1.237 / p = 0.03) and c-ABL (HR 0.33 / p = 0.04). Adjusted progression-free survival analysis showed a significant difference (p = 0.04) between the groups with eloquent and noneloquent localization, as well as RELA gene expression (HR 1,107 / p = 0.03). Regarding the gene expression and overall survival or progression-free survival of the patients by the Logrank test, no statistical difference was observed. In the analysis of the expression genes with the clinical-epidemiological variables the expression of the SUMO1 gene was significantly increased (p = 0.009) in the group with late progression in relation to the group with early disease progression, the expression of AR showed a significant increase (p = 0.021) in the group in which the metronomic TMZ was not administered and the RELA (p = 0.03), c-Jun (p = 0.005), STAT1 (p = 0.009) and HDAC1 (p = 0.044) gene expressions were elevated in patients who had normal preoperative neurologic examination. These data indicate that the set of RSIgenes and MGMT were not shown to be predictors of radiosensitivity. We can infer that RELA, c-Jun and c-ABL are potential markers of worse prognosis and that SUMO1 may be a marker of good prognosis

O glioblastoma (GBM) é o tumor primário do sistema nervoso central mais frequente no adulto, com sobrevida média de aproximadamente 12 meses. Múltiplas alterações genéticas e epigenéticas presentes neste tumor determinam sua biologia e fenótipo bastante agressivos. Assim, o presente estudo objetivou estudar a expressão dos genes envolvidos no Índice de Radiossensibilidade (RSI) e do gene MGMT em amostras de tumor primário humano de GBM, buscando identificar a associação destes com radiossensibilidade e sobrevida. Foram analisadas as características epidemiológicas, de evolução e desfecho clínico de 28 pacientes com GBM que fizeram uso de temozolamida (TMZ), cujos prontuários físicos e eletrônicos foram revisados no Serviço de Arquivo Médico (SAM) e Sistema Athos. Foi observado que 64,29% dos pacientes possuíam mais de 50 anos de idade e eram do sexo masculino; 86,36% possuíam o Karnofsky Performance Status acima ou igual a 80%; 57,14% possuíam localização não eloquente; 78,57% apresentavam lesão residual; 89,29% não foram reexpostos à radioterapia; 64,29% não fizeram uso de temozolamida metronômica; 96,43% não foram submetidos a outra quimioterapia; 78,57% não realizaram reabordagem cirúrgica; 60,71% tiveram progressão tardia e/ou não tiveram progressão da doença. Também foi evidenciado que tanto a regressão bruta de cada gene separadamente, quanto a regressão ajustada para os fatores clínicos mais relevantes (idade ao diagnóstico, localização do tumor e presença de lesão residual) não evidenciaram significância estatística em relação à progressão tardia. A relação entre o tempo de sobrevida global e a expressão dos genes ajustada para os mesmos fatores clínicos evidenciou significância estatística para os genes RELA (HR 1,265 / p = 0,01); c-Jun (HR 1,237 / p = 0,03) e c-ABL (HR 0,33 / p = 0,04). A análise de sobrevida livre de progressão ajustada mostrou diferença significativa (p = 0,04) entre os grupos com tumor de localização eloquente e não eloquente, assim como para a expressão do gene RELA (HR 1,107 / p = 0,03). Em relação à expressão gênica e a sobrevida global ou sobrevida livre de progressão dos pacientes pelo teste de Log-rank não foi observada diferença estatística. Na análise da expressão gênica com as variáveis clínico-epidemiológicas, a expressão do gene SUMO1 foi significativamente aumentada (p = 0,009) no grupo com progressão tardia com relação ao grupo com progressão precoce da doença, a expressão do AR apresentou aumento significativo (p = 0,021) no grupo em que a TMZ metronômica não foi administrada e as expressões dos genes RELA (p = 0,03),c-Jun (p = 0,005), STAT1 (p = 0.009) e HDAC1 (p = 0.044) estava elevada nos pacientes que apresentaram o exame neurológico pré-operatório normal. Esses dados indicam que o conjunto dos genes do RSI e o MGMT não demonstraram ser preditores de radiossensibilidade. Podemos inferir que RELA, c-Jun e c-ABL são potenciais marcadores de pior prognóstico e que SUMO1 pode ser um marcador de bom prognóstico / Glioblastoma (GBM) is the primary tumor of the central nervous system most common in adults, with a median survival of approximately 12 months. Multiple genetic and epigenetic alterations present in this tumor determine its biology and phenotype very aggressive. Thus, the present study aimed to study the expression of genes involved in radiosensitivity index (RSI) and the MGMT gene in GBM human primary tumor samples, seeking to identify the association of these with radiosensitivity and survival. The epidemiological evolution and clinical characteristics of 28 GBM patients who used temozolamide (TMZ), whose physical and electronic medical records were reviewed in the Medical File Service (SAM) and Athos System, were analyzed. It was observed that 64.29% of the patients were over 50 years of age and were male; 86.36% had Karnofsky Performance Status of 80 or more; 57.14% had no eloquent location; 78.57% had residual lesion; 89.29% did not have reexposure to radiotherapy; 64.29% did not use metronomic temozolamide; 96.43% did not undergo another chemotherapy; 78.57% did not undergo surgical reassessment; 60.71% had late progression and / or had no disease progression. It was also evidenced that both the gross regression of each gene separately and the regression adjusted for the most relevant clinical factors (age at diagnosis, tumor location and presence of residual lesion) did not show statistical significance in relation to the late progression. The relationship between overall survival time and gene expression adjusted for the same clinical factors showed statistical significance for RELA genes (HR 1.265 / p = 0.01), c-Jun (HR 1.237 / p = 0.03) and c-ABL (HR 0.33 / p = 0.04). Adjusted progression-free survival analysis showed a significant difference (p = 0.04) between the groups with eloquent and noneloquent localization, as well as RELA gene expression (HR 1,107 / p = 0.03). Regarding the gene expression and overall survival or progression-free survival of the patients by the Logrank test, no statistical difference was observed. In the analysis of the expression genes with the clinical-epidemiological variables the expression of the SUMO1 gene was significantly increased (p = 0.009) in the group with late progression in relation to the group with early disease progression, the expression of AR showed a significant increase (p = 0.021) in the group in which the metronomic TMZ was not administered and the RELA (p = 0.03), c-Jun (p = 0.005), STAT1 (p = 0.009) and HDAC1 (p = 0.044) gene expressions were elevated in patients who had normal preoperative neurologic examination. These data indicate that the set of RSIgenes and MGMT were not shown to be predictors of radiosensitivity. We can infer that RELA, c-Jun and c-ABL are potential markers of worse prognosis and that SUMO1 may be a marker of good prognosis

Uma das principais áreas de pesquisa em Biologia de Sistemas refere-se à descoberta de redes biológicas a partir de conjuntos de dados de microarrays. Estas redes consistem de um grande número de genes cujos níveis de expressão afetam os outros genes de vários modos. Nesta tese, apresenta-se uma nova maneira de analisar os conjuntos de dados de microarrays, com base nos diferentes tipos de ciclos encontrados entre os genes das redes de co-expressão construídas com dados quantificados obtidos a partir dos microarrays. A entrada do método de análise é formada pelos dados brutos, um conjunto de genes de interesse (por exemplo, genes de uma via conhecida) e uma função (ativador ou inibidor) destes genes. A saída do método é um conjunto de ciclos. Um ciclo é um caminho fechado com todos os vértices (exceto o primeiro e o último) distintos. Graças à nova forma de encontrar relações entre os genes, é possível uma interpretação mais robusta das correlações dos genes, porque os ciclos estão associados a mecanismos de feedback, que são muito comuns em redes biológicas. A hipótese é que feedbacks negativos permitem encontrar relações entre os genes que podem ajudar a explicar a estabilidade do processo regulatório dentro da célula. Ciclos de feedback positivo, por outro lado, podem mostrar a quantidade de desequilíbrio de uma determinada célula em um determinado momento. A análise baseada em ciclos permite identificar a relação estequiométrica entre os genes da rede. Esta metodologia proporciona uma melhor compreensão da biologia do tumor. Portanto, as principais contribuições desta tese são: (i) um novo método de análise baseada em ciclos; (ii) um novo método de classificação; (iii) e, finalmente, aplicação dos métodos e a obtenção de resultados práticos. A metodologia proposta foi utilizada para analisar os genes de quatro redes fortemente relacionadas com o câncer - apoptose, glicólise, ciclo celular e NF B - em tecidos do tipo mais agressivo de tumor cerebral (Gliobastoma multiforme - GBM) e em tecidos cerebrais saudáveis. A maioria dos pacientes com GBM morrem em menos de um ano, essencialmente nenhum paciente tem sobrevivência a longo prazo, por isso estes tumores têm atraído atenção significativa. Os principais resultados nesta tese mostram que a relação estequiométrica entre genes envolvidos na apoptose, glicólise, ciclo celular e NF B está desequilibrada em amostras de GBM em comparação as amostras de controle. Este desequilíbrio pode ser medido e explicado pela identificação de um percentual maior de ciclos positivos nas redes das primeiras amostras. Esta conclusão ajuda a entender mais sobre a biologia deste tipo de tumor. O método de classificação baseado no ciclo proposto obteve as mesmas métricas de desempenho como uma rede neural, um método clássico de classificação. No entanto, o método proposto tem uma vantagem significativa em relação às redes neurais. O método de classificação proposto não só classifica as amostras, fornecendo diagnóstico, mas também explica porque as amostras foram classificadas de uma certa maneira em termos dos mecanismos de feedback que estão presentes/ausentes. Desta forma, o método fornece dicas para bioquímicos sobre possíveis experiências laboratoriais, bem como sobre potenciais genes alvo de terapias. / One of the main research areas in Systems Biology concerns the discovery of biological networks from microarray datasets. These networks consist of a great number of genes whose expression levels affect each other in various ways. We present a new way of analyzing microarray datasets, based on the different kind of cycles found among genes of the co-expression networks constructed using quantized data obtained from the microarrays. The input of the analysis method is formed by raw data, a set of interest genes (for example, genes from a known pathway) and a function (activator or inhibitor) of these genes. The output of the method is a set of cycles. A cycle is a closed walk, in which all vertices (except the first and last) are distinct. Thanks to the new way of finding relations among genes, a more robust interpretation of gene correlations is possible, because cycles are associated with feedback mechanisms that are very common in biological networks. Our hypothesis is that negative feedbacks allow finding relations among genes that may help explaining the stability of the regulatory process within the cell. Positive feedback cycles, on the other hand, may show the amount of imbalance of a certain cell in a given time. The cycle-based analysis allows identifying the stoichiometric relationship between the genes of the network. This methodology provides a better understanding of the biology of tumors. As a consequence, it may enable the development of more effective treatment therapies. Furthermore, cycles help differentiate, measure and explain the phenomena identified in healthy and diseased tissues. Cycles may also be used as a new method for classification of samples of a microarray (cancer diagnosis). Compared to other classification methods, cycle-based classification provides a richer explanation of the proposed classification, that can give hints on the possible therapies. Therefore, the main contributions of this thesis are: (i) a new cycle-based analysis method; (ii) a new microarray samples classification method; (iii) and, finally, application and achievement of practical results. We use the proposed methodology to analyze the genes of four networks closely related with cancer - apoptosis, glucolysis, cell cycle and NF B - in tissues of the most aggressive type of brain tumor (Gliobastoma multiforme – GBM) and in healthy tissues. Because most patients with GBMs die in less than a year, and essentially no patient has long-term survival, these tumors have drawn significant attention. Our main results show that the stoichiometric relationship between genes involved in apoptosis, glucolysis, cell cycle and NF B pathways is unbalanced in GBM samples versus control samples. This dysregulation can be measured and explained by the identification of a higher percentage of positive cycles in these networks. This conclusion helps to understand more about the biology of this tumor type. The proposed cycle-based classification method achieved the same performance metrics as a neural network, a classical classification method. However, our method has a significant advantage with respect to neural networks. The proposed classification method not only classifies samples, providing diagnosis, but also explains why samples were classified in a certain way in terms of the feedback mechanisms that are present/absent. This way, the method provides hints to biochemists about possible laboratory experiments, as well as on potential drug target genes.

Uma das principais áreas de pesquisa em Biologia de Sistemas refere-se à descoberta de redes biológicas a partir de conjuntos de dados de microarrays. Estas redes consistem de um grande número de genes cujos níveis de expressão afetam os outros genes de vários modos. Nesta tese, apresenta-se uma nova maneira de analisar os conjuntos de dados de microarrays, com base nos diferentes tipos de ciclos encontrados entre os genes das redes de co-expressão construídas com dados quantificados obtidos a partir dos microarrays. A entrada do método de análise é formada pelos dados brutos, um conjunto de genes de interesse (por exemplo, genes de uma via conhecida) e uma função (ativador ou inibidor) destes genes. A saída do método é um conjunto de ciclos. Um ciclo é um caminho fechado com todos os vértices (exceto o primeiro e o último) distintos. Graças à nova forma de encontrar relações entre os genes, é possível uma interpretação mais robusta das correlações dos genes, porque os ciclos estão associados a mecanismos de feedback, que são muito comuns em redes biológicas. A hipótese é que feedbacks negativos permitem encontrar relações entre os genes que podem ajudar a explicar a estabilidade do processo regulatório dentro da célula. Ciclos de feedback positivo, por outro lado, podem mostrar a quantidade de desequilíbrio de uma determinada célula em um determinado momento. A análise baseada em ciclos permite identificar a relação estequiométrica entre os genes da rede. Esta metodologia proporciona uma melhor compreensão da biologia do tumor. Portanto, as principais contribuições desta tese são: (i) um novo método de análise baseada em ciclos; (ii) um novo método de classificação; (iii) e, finalmente, aplicação dos métodos e a obtenção de resultados práticos. A metodologia proposta foi utilizada para analisar os genes de quatro redes fortemente relacionadas com o câncer - apoptose, glicólise, ciclo celular e NF B - em tecidos do tipo mais agressivo de tumor cerebral (Gliobastoma multiforme - GBM) e em tecidos cerebrais saudáveis. A maioria dos pacientes com GBM morrem em menos de um ano, essencialmente nenhum paciente tem sobrevivência a longo prazo, por isso estes tumores têm atraído atenção significativa. Os principais resultados nesta tese mostram que a relação estequiométrica entre genes envolvidos na apoptose, glicólise, ciclo celular e NF B está desequilibrada em amostras de GBM em comparação as amostras de controle. Este desequilíbrio pode ser medido e explicado pela identificação de um percentual maior de ciclos positivos nas redes das primeiras amostras. Esta conclusão ajuda a entender mais sobre a biologia deste tipo de tumor. O método de classificação baseado no ciclo proposto obteve as mesmas métricas de desempenho como uma rede neural, um método clássico de classificação. No entanto, o método proposto tem uma vantagem significativa em relação às redes neurais. O método de classificação proposto não só classifica as amostras, fornecendo diagnóstico, mas também explica porque as amostras foram classificadas de uma certa maneira em termos dos mecanismos de feedback que estão presentes/ausentes. Desta forma, o método fornece dicas para bioquímicos sobre possíveis experiências laboratoriais, bem como sobre potenciais genes alvo de terapias. / One of the main research areas in Systems Biology concerns the discovery of biological networks from microarray datasets. These networks consist of a great number of genes whose expression levels affect each other in various ways. We present a new way of analyzing microarray datasets, based on the different kind of cycles found among genes of the co-expression networks constructed using quantized data obtained from the microarrays. The input of the analysis method is formed by raw data, a set of interest genes (for example, genes from a known pathway) and a function (activator or inhibitor) of these genes. The output of the method is a set of cycles. A cycle is a closed walk, in which all vertices (except the first and last) are distinct. Thanks to the new way of finding relations among genes, a more robust interpretation of gene correlations is possible, because cycles are associated with feedback mechanisms that are very common in biological networks. Our hypothesis is that negative feedbacks allow finding relations among genes that may help explaining the stability of the regulatory process within the cell. Positive feedback cycles, on the other hand, may show the amount of imbalance of a certain cell in a given time. The cycle-based analysis allows identifying the stoichiometric relationship between the genes of the network. This methodology provides a better understanding of the biology of tumors. As a consequence, it may enable the development of more effective treatment therapies. Furthermore, cycles help differentiate, measure and explain the phenomena identified in healthy and diseased tissues. Cycles may also be used as a new method for classification of samples of a microarray (cancer diagnosis). Compared to other classification methods, cycle-based classification provides a richer explanation of the proposed classification, that can give hints on the possible therapies. Therefore, the main contributions of this thesis are: (i) a new cycle-based analysis method; (ii) a new microarray samples classification method; (iii) and, finally, application and achievement of practical results. We use the proposed methodology to analyze the genes of four networks closely related with cancer - apoptosis, glucolysis, cell cycle and NF B - in tissues of the most aggressive type of brain tumor (Gliobastoma multiforme – GBM) and in healthy tissues. Because most patients with GBMs die in less than a year, and essentially no patient has long-term survival, these tumors have drawn significant attention. Our main results show that the stoichiometric relationship between genes involved in apoptosis, glucolysis, cell cycle and NF B pathways is unbalanced in GBM samples versus control samples. This dysregulation can be measured and explained by the identification of a higher percentage of positive cycles in these networks. This conclusion helps to understand more about the biology of this tumor type. The proposed cycle-based classification method achieved the same performance metrics as a neural network, a classical classification method. However, our method has a significant advantage with respect to neural networks. The proposed classification method not only classifies samples, providing diagnosis, but also explains why samples were classified in a certain way in terms of the feedback mechanisms that are present/absent. This way, the method provides hints to biochemists about possible laboratory experiments, as well as on potential drug target genes.

Astrocytoma is defined as the neoplasia of astrocytes, the most abundant non-neuronal glial cells in brain. According to the WHO classification, the different grades of astrocytoma are- gradeI/pilocytic astrocytoma (benign form), grade II/diffuse astrocytoma (DA), grade III/anaplastic astrocytoma (AA) and grade IV/Glioblastoma (GBM). Patients with grade II astrocytoma have median survival time of 6-8 years after surgical intervention. While the more aggressive grade III has a median survival of 2-3 years. Grade IV is the most malignant form and has a median survival of 15 months approximately. In spite of all the progress in the fields of diagnosis and therapy, the prognosis of GBM still remains very poor. The aggressiveness and poor survival of GBM is due to the recurrence which is primarily because of intratumoral heterogeneity, presence of glioma stem cells and infiltration of the tumor cells into the normal brain parenchyma. Apart from the role of genetic mechanisms in triggering tumorigenesis, epigenetic modifications particularly the DNA methylation and histone modifications, are now recognized as frequent alterations playing a crucial role in the development and progression of human malignancies. There are two distinct DNA methylation abnormalities. The first is the reduction in genome-wide DNA methylation levels (global hypomethylation) and the second is the hypermethylation in the CpG island of specific gene promoters. Hypomethylation is believed to induce proto-oncogene activation and chromosomal instability, whereas hypermethylation is strongly associated with silencing of tumor suppressor genes. Thus, DNA methylation can function as a “switch” to activate or repress gene transcription, providing an essential mechanism for overexpressed or silenced genes involved in the regulation of cell cycle, DNA repair, growth signalling, angiogenesis, apoptosis, migration, invasion and thus in the initiation and progression of astrocytoma. Recent studies have identified biomarkers with prognostic impact which would include promoter methylation of O⁶-methyl guanine-DNA methyltransferase methylation (MGMT), isocitrate dehydrogenase 1(IDH1) mutation and a glioma CpG-island methylator (G-CIMP) phenotype. In the current study, we have characterized the DNA methylation profile for the different grades of astrocytoma and analysed the significance of methylation events occurring commonly in all the grades or uniquely only in grade IV. One of the GBM-specific hypomethylated and upregulated genes, Fibromodulin (FMOD), was extensively investigated in terms of its role in glioma pathogenesis and its regulation. FMOD was found to induce F-actin stress fibre formation and promote glioma cell migration. We also found that FMOD-mediated glioma cell migration is dependent on Integrin/FAK/Src/Small Rho GTPases signalling cascade. We further found that TGFβ pathway regulates FMOD expression through a process involving active demethylation and chromatin state transitions on FMOD promoter. This work has been divided into three parts: Part I: Characterization of DNA methylome during progression of Astrocytoma To investigate the aberrant methylation pattern on a genome-wide scale, 17 Grade II, 16 Grade III and 36 Grade IV tumor samples as well as 9 control brain tissues were analysed using Infinium Human Methylation 450K Bead Array on Illumina platform. The analysis was carried out in two parts. Firstly, we validated the dataset with already existing TCGA dataset. Upon comparison, the methylation profile of our dataset was highly correlated to the TCGA dataset with correlation coefficient of 0.99. In addition, we also checked the methylation status of few known hypermethylated and hypomethylated genes which showed the similar type of differential methylation. Then, we characterized the differentially methylated CpGs based on their spatial distribution in the human genome, for different grades of astrocytoma. CpG-rich regions show more of hypermethylation while the non-CpG rich regions, like open sea or gene body, are observed to be hypomethylated. Secondly, we also analysed the differentially methylated genes which contribute to physiological events in gliomagenesis. We hypothesized that the methylation specific events that occur in grade II and remain similarly methylated in grade IV are the ones probably contributing to the initial astrocyte transformation. However, the methylation specific events responsible for the aggressive nature of grade IV may occur as differentially methylated genes only in grade IV (and not in grade II). In this analysis, we have identified differentially methylated genes that play a role in initial transformation process (293 genes hypermethylated and downregulated while 23 genes were hypomethylated and upregulated) and also those that play a role in tumor aggressiveness (459 genes hypermethylated and downregulated while 350 genes were hypomethylated and upregulated). The differentially methylated genes that were common in both grade II and grade IV showed an enrichment of cell proliferation pathways while the differentially methylated genes uniquely present in grade IV showed enrichment in pathways related to the aggressiveness phenotype of tumorigenesis like cell motility and angiogenesis. Part II: Fibromodulin (FMOD), a GBM-specific hypomethylated and upregulated gene, is essential for glioma cell migration Among differentially methylated genes specifically in GBM, fibromodulin (FMOD) is one of the top most hypomethylated genes. FMOD is a member of leucine – rich repeat proteoglycan that is widely distributed in interstitial connective tissues. We found that FMOD is hypomethylated and upregulated only in grade IV/GBM, not in the grade II. FMOD promoter methylation status is significantly negatively correlated to its transcript levels.Towards identifying functions of FMOD in glioma cells, total RNA derived from U251 cells transfected with either non-targeting siRNA or FMOD siRNA was subjected to transcriptome profiling. There were 872 genes upregulated and 299 genes downregulated in FMOD silenced cells than in control cells. PANTHER pathway analysis using the differentially regulated genes identified several pathways to be associated with FMOD. Cytoskeleton regulation by Rho GTPase, which is known to be involved in cell motility and migration, is enriched with highest significance. In coherence with the pathway analysis, modulating FMOD levels in glioma cells affected in glioma cell migration. Upon FMOD overexpression, there was significant increase in migration than in control cells. Conversely, when FMOD is silenced, there was delay in migration than in control cells and the delayed migration was rescued by the addition of recombinant purified FMOD protein. Prior neutralization with FMOD specific antibody inhibited cell migration suggesting that secreted FMOD promotes glioma cell migration. Overexpression of FMOD in glioma cells induced actin stress fibre formation required for the migration of cells. On the contrary, FMOD silencing resulted in the loss of F-actin stress fibres which was restored upon addition of FMOD purified protein exogenously to the media. To investigate further the role and the requirement of specific Rho GTPase in FMOD-mediated migration, each of members of Rho GTPase family was silenced and their effect on FMOD-induced silencing was studied. FMOD mediated glioma cell migration was delayed when RhoA, Rac1 and Cdc42 were silenced. In order to understand whether FMOD activates Integrin mediated signalling pathway, we performed western blot analysis to check the levels of phospho-FAK in either FMOD overexpressing or knockdown condition. We observed phospho-FAK levels increased upon FMOD overexpression and decreased upon FMOD silencing compared to the respective controls. Additional experiments revealed that inhibitors to Integrin, FAK and Src were able to abrogate the FMOD induced glioma cell migration. These results suggest that FMOD utilizes a pathway that involves Integrins, FAK, Src and Rho GTPases in promoting glioma cell migration. To comprehend the effect of FMOD promoter methylation status and its expression in GBM patient scenario, we stratified the patients into either high or low FMOD expression and promoter hypermethylation or hypomethylation. The GBM patients with low FMOD transcript levels and promoter hypermethylation showed better survival than the other group. Part III: Regulation of FMOD expression through TGFβ-dependent epigenetic remodelling in glioma To study how FMOD is regulated in glioma, we investigated the promoter sequence of FMOD by MatInspector. Several Smad-binding sites were located in FMOD promoter which indicated that FMOD might be regulated via TGFβ signalling pathway. Firstly, we checked active TGFβ signalling in glioma cell lines – LN229, U87 and U251. TGFβ-dependent signalling was active in U251 and U87 cells compared to LN229 cells as seen by the levels of phospho-Smad2. Moreover, FMOD transcript level was found to be high in U251 compared to LN229 cells. Further, TGFβ treatment increased FMOD promoter luciferase activity as well as FMOD transcript level in LN229 cells. In contrast, U251 cells that were treated with TGFβ RI inhibitor showed a significant decrease in FMOD promoter luciferase activity as well as FMOD transcript level. We correlated these findings with Smad2 occupancy at FMOD promoter by chromatin immunoprecipitation (ChIP). Smad2 association at FMOD promoter is found to be relatively higher in U251 cells than in LN229 cells which suggested that TGFβ induced transcription factor, Smad2, drives FMOD expression in U251 cells. Next, we investigated the role of TGFβ in FMOD promoter demethylation and chromatin state transition. Upon TGFβ treatment in LN229 cells, we found that there was gradual demethylation of FMOD promoter in a time-dependent manner. TGFβ treatment also altered the chromatin state by increasing the active marks (H3K4me3 and H3K9Ac) and decreasing the repressive mark (H3K27me3) with a simultaneous increase in Smad2 occupancy in the FMOD promoter. In contrast, TGFβ RI inhibitor treatment of U251 cells resulted in methylation of FMOD promoter in a time-dependent manner. Further, we observed a significant enrichment of repressive histone marks (H3K27me3) and loss of active chromatin marks (H3K4me3 and H3K9Ac) with a concomitant decrease in Smad2 occupancy at FMOD promoter. DNMT3A/B and EZH2 enzymes play a key role in DNA methylation and H3K27 trimethylation respectively. Accordingly, we examined the transcript levels of DNMT3A/B and EZH2 in LN229 cells treated with TGFβ as well as U251 cells treated with TGFβ RI inhibitor. In presence of TGFβ, DNMT3A/B and EZH2 transcript levels were significantly downregulated than in untreated cells in a time-dependent manner. Conversely, in U251 cells treated with TGFβ RI inhibitor, there was a significant increase in DNMT3A/B and EZH2 transcript levels when compared to untreated cells. TGFβ is known to promote glioma cell migration. In order to understand whether TGFβ-mediated glioma cell migration occurs via FMOD, we performed migration assay in U251 cells with or without TGFβ RI inhibitor followed by addition of either BSA control or FMOD purified protein. Upon TGFβ RI inhibitor treatment, there was delay in the migration of U251 cells than in untreated control cells which was rescued when purified FMOD protein was added, indicating that FMOD is essential for TGFβ signalling cascade to induce glioma cell migration. Therefore, we conclude from these results that epigenetically regulated FMOD is essential for TGFβ mediated glioma cell migration.