As drought can impinge significantly on forest health and productivity, the mechanisms by which forest trees respond to drought is of interest. The research presented herein examined the intra-specific variation in the Populus balsamifera drought response, examining the potential role of the transcriptome to configure growth, metabolism and development in response to water deficit. Amassing evidence indicates that different species of Populus have divergent mechanisms and Three lines of inquiry were pursued to investigate the intraspecific variation the drought response in P. balsamifera. First, the transcriptome responses of six genotypes of P. balsamifera were examined using Affymetrix Poplar GeneChips under well-watered and water-deficit conditions. A core species-level transcriptome response was identified. Significantly, intraspecific variation in the drought transcriptome was also identified. The data support a role for genotype-derived variation in the magnitude of P. balsamifera transcriptome remodelling playing a role in conditioning drought responsiveness. Second, the impact of drought-stress induced declines in stomatal conductance, as well as an alteration in stomatal development in two genotypes was examined. Patterns of transcript abundance of genes hypothesised to underpin stomatal development had patterns congruent with their role in modulation of stomatal development. These results suggest that stomatal development may play a role as a long-term mechanism to limit water loss from P. balsamifera leaves under conditions of drought-stress. Finally, the drought-induced metabolome of six P. balsmaifera genotypes was interrogated. Metabolite profiling reveled amino acids such as isoleucine and proline and sugars such as galactinol and raffinose were found with increased abundance, whereas TCA intermediates succinic and malic acid were found with decreased abundance in response to drought. Comparative analysis of the metabolome and the transcriptome revealed genotypic-specific variation in energy and carbohydrate metabolism. Taken together, the findings reported in this thesis form a foundation to understand the basis of intraspecific variation in the drought response in P. balsamifera. Transcripts and metabolites that contribute to within-species differences in drought tolerance were defined. These molecular components are useful targets for both future study, as well as efforts aimed at protecting and growing trees of this important species under challenging environmental conditions.

As drought can impinge significantly on forest health and productivity, the mechanisms by which forest trees respond to drought is of interest. The research presented herein examined the intra-specific variation in the Populus balsamifera drought response, examining the potential role of the transcriptome to configure growth, metabolism and development in response to water deficit. Amassing evidence indicates that different species of Populus have divergent mechanisms and Three lines of inquiry were pursued to investigate the intraspecific variation the drought response in P. balsamifera. First, the transcriptome responses of six genotypes of P. balsamifera were examined using Affymetrix Poplar GeneChips under well-watered and water-deficit conditions. A core species-level transcriptome response was identified. Significantly, intraspecific variation in the drought transcriptome was also identified. The data support a role for genotype-derived variation in the magnitude of P. balsamifera transcriptome remodelling playing a role in conditioning drought responsiveness. Second, the impact of drought-stress induced declines in stomatal conductance, as well as an alteration in stomatal development in two genotypes was examined. Patterns of transcript abundance of genes hypothesised to underpin stomatal development had patterns congruent with their role in modulation of stomatal development. These results suggest that stomatal development may play a role as a long-term mechanism to limit water loss from P. balsamifera leaves under conditions of drought-stress. Finally, the drought-induced metabolome of six P. balsmaifera genotypes was interrogated. Metabolite profiling reveled amino acids such as isoleucine and proline and sugars such as galactinol and raffinose were found with increased abundance, whereas TCA intermediates succinic and malic acid were found with decreased abundance in response to drought. Comparative analysis of the metabolome and the transcriptome revealed genotypic-specific variation in energy and carbohydrate metabolism. Taken together, the findings reported in this thesis form a foundation to understand the basis of intraspecific variation in the drought response in P. balsamifera. Transcripts and metabolites that contribute to within-species differences in drought tolerance were defined. These molecular components are useful targets for both future study, as well as efforts aimed at protecting and growing trees of this important species under challenging environmental conditions.

O câncer gástrico está entre as neoplasias com a mais alta mortalidade no mundo, sendo que as modalidades de tratamento envolvem a cirurgia, a quimioterapia e também a radioterapia. Na quimioterapia, o uso de drogas isoladas ou em conjunto enfrenta um dilema frequente nesta patologia: a quimiorresistência. Assim, torna-se essencial, na clínica, a necessidade de encontrar novos compostos ou alvos protéicos que sejam capazes de manter uma resposta por longos períodos de tempo de tratamento. Dessa forma, este estudo utilizou ferramentas de farmacologia de sistemas, avaliando as interações entre proteínas e pequenos compostos a partir de dados proteômicos já existentes para a espécie humana. Neste contexto, as drogas estudadas foram o 5-fluorouracil, a Capecitabina, a Oxaliplatina, o Irinotecan e o Docetaxel. Uma rede de interações entre proteínas (physical protein-protein interactions; PPPI) e proteínas-quimioterápico (physical compound-protein interaction; PCPI) foi obtida, seguida da definição de cinco sub-redes, representando os diferentes processos biológicos observados. Além disso, foram empregadas análises de centralidade para buscar os principais componentes da rede e as suas interações, denominados de gargalos (bottlenecks), os quais regulam o fluxo de informação dentro da rede. Assim, o estudo demonstrou que as drogas atualmente em uso clínico convergem para processos biológicos semelhantes, o que explicaria o desenvolvimento de quimiorresistência a médio e longo prazo. Além disso, foram identificados sete novos gargalos, que representam novos alvos de proteínas e pequenos compostos capazes de interferir no controle e na comunicação entre outros vértices na própria rede. Esses dados poderão ser utilizados no desenvolvimento de novas combinações de drogas com o objetivo de melhorar os protocolos utilizados no tratamento quimioterápico do câncer gástrico. / Gastric cancer is among the cancers with the highest mortality in the world, and treatment modalities involve surgery, chemotherapy and radiotherapy. In chemotherapy, the drugs alone or in combination face a dilemma common in this disease: the chemoresistance. Thus, it becomes essential in the clinic, the need to find new protein target or compounds that are capable of maintaining a response for long periods of treatment. Thus, this studied used tools of systems pharmacology, evaluating the interactions between proteins and small compounds from existing proteomic data for the human species. In this context, the study drugs were 5-fluorouracil, capecitabine, oxaliplatin, irinotecan and docetaxel. A network of protein interactions and protein-chemotherapy (PPPI-PCPI, respectively) was obtained, followed by the definition of five sub-networks, representing different biological processes observed. Moreover, centrality analysis were used to search the main network components and their interactions, called bottlenecks , which regulate the flow of information within the network. Thus, the study showed that the drugs currently in clinical use converge to similar biological processes, which would explain the development of chemoresistance in the medium and long term. Furthermore, we identified seven new bottlenecks that represent new target proteins and small compounds able to interfere in the control and communication between other nodes in the network itself. These data could be used to develop new combinations of drugs with the aim of improving the protocols used in chemotherapy of gastric cancer.

O câncer gástrico está entre as neoplasias com a mais alta mortalidade no mundo, sendo que as modalidades de tratamento envolvem a cirurgia, a quimioterapia e também a radioterapia. Na quimioterapia, o uso de drogas isoladas ou em conjunto enfrenta um dilema frequente nesta patologia: a quimiorresistência. Assim, torna-se essencial, na clínica, a necessidade de encontrar novos compostos ou alvos protéicos que sejam capazes de manter uma resposta por longos períodos de tempo de tratamento. Dessa forma, este estudo utilizou ferramentas de farmacologia de sistemas, avaliando as interações entre proteínas e pequenos compostos a partir de dados proteômicos já existentes para a espécie humana. Neste contexto, as drogas estudadas foram o 5-fluorouracil, a Capecitabina, a Oxaliplatina, o Irinotecan e o Docetaxel. Uma rede de interações entre proteínas (physical protein-protein interactions; PPPI) e proteínas-quimioterápico (physical compound-protein interaction; PCPI) foi obtida, seguida da definição de cinco sub-redes, representando os diferentes processos biológicos observados. Além disso, foram empregadas análises de centralidade para buscar os principais componentes da rede e as suas interações, denominados de gargalos (bottlenecks), os quais regulam o fluxo de informação dentro da rede. Assim, o estudo demonstrou que as drogas atualmente em uso clínico convergem para processos biológicos semelhantes, o que explicaria o desenvolvimento de quimiorresistência a médio e longo prazo. Além disso, foram identificados sete novos gargalos, que representam novos alvos de proteínas e pequenos compostos capazes de interferir no controle e na comunicação entre outros vértices na própria rede. Esses dados poderão ser utilizados no desenvolvimento de novas combinações de drogas com o objetivo de melhorar os protocolos utilizados no tratamento quimioterápico do câncer gástrico. / Gastric cancer is among the cancers with the highest mortality in the world, and treatment modalities involve surgery, chemotherapy and radiotherapy. In chemotherapy, the drugs alone or in combination face a dilemma common in this disease: the chemoresistance. Thus, it becomes essential in the clinic, the need to find new protein target or compounds that are capable of maintaining a response for long periods of treatment. Thus, this studied used tools of systems pharmacology, evaluating the interactions between proteins and small compounds from existing proteomic data for the human species. In this context, the study drugs were 5-fluorouracil, capecitabine, oxaliplatin, irinotecan and docetaxel. A network of protein interactions and protein-chemotherapy (PPPI-PCPI, respectively) was obtained, followed by the definition of five sub-networks, representing different biological processes observed. Moreover, centrality analysis were used to search the main network components and their interactions, called bottlenecks , which regulate the flow of information within the network. Thus, the study showed that the drugs currently in clinical use converge to similar biological processes, which would explain the development of chemoresistance in the medium and long term. Furthermore, we identified seven new bottlenecks that represent new target proteins and small compounds able to interfere in the control and communication between other nodes in the network itself. These data could be used to develop new combinations of drugs with the aim of improving the protocols used in chemotherapy of gastric cancer.

O câncer gástrico está entre as neoplasias com a mais alta mortalidade no mundo, sendo que as modalidades de tratamento envolvem a cirurgia, a quimioterapia e também a radioterapia. Na quimioterapia, o uso de drogas isoladas ou em conjunto enfrenta um dilema frequente nesta patologia: a quimiorresistência. Assim, torna-se essencial, na clínica, a necessidade de encontrar novos compostos ou alvos protéicos que sejam capazes de manter uma resposta por longos períodos de tempo de tratamento. Dessa forma, este estudo utilizou ferramentas de farmacologia de sistemas, avaliando as interações entre proteínas e pequenos compostos a partir de dados proteômicos já existentes para a espécie humana. Neste contexto, as drogas estudadas foram o 5-fluorouracil, a Capecitabina, a Oxaliplatina, o Irinotecan e o Docetaxel. Uma rede de interações entre proteínas (physical protein-protein interactions; PPPI) e proteínas-quimioterápico (physical compound-protein interaction; PCPI) foi obtida, seguida da definição de cinco sub-redes, representando os diferentes processos biológicos observados. Além disso, foram empregadas análises de centralidade para buscar os principais componentes da rede e as suas interações, denominados de gargalos (bottlenecks), os quais regulam o fluxo de informação dentro da rede. Assim, o estudo demonstrou que as drogas atualmente em uso clínico convergem para processos biológicos semelhantes, o que explicaria o desenvolvimento de quimiorresistência a médio e longo prazo. Além disso, foram identificados sete novos gargalos, que representam novos alvos de proteínas e pequenos compostos capazes de interferir no controle e na comunicação entre outros vértices na própria rede. Esses dados poderão ser utilizados no desenvolvimento de novas combinações de drogas com o objetivo de melhorar os protocolos utilizados no tratamento quimioterápico do câncer gástrico. / Gastric cancer is among the cancers with the highest mortality in the world, and treatment modalities involve surgery, chemotherapy and radiotherapy. In chemotherapy, the drugs alone or in combination face a dilemma common in this disease: the chemoresistance. Thus, it becomes essential in the clinic, the need to find new protein target or compounds that are capable of maintaining a response for long periods of treatment. Thus, this studied used tools of systems pharmacology, evaluating the interactions between proteins and small compounds from existing proteomic data for the human species. In this context, the study drugs were 5-fluorouracil, capecitabine, oxaliplatin, irinotecan and docetaxel. A network of protein interactions and protein-chemotherapy (PPPI-PCPI, respectively) was obtained, followed by the definition of five sub-networks, representing different biological processes observed. Moreover, centrality analysis were used to search the main network components and their interactions, called bottlenecks , which regulate the flow of information within the network. Thus, the study showed that the drugs currently in clinical use converge to similar biological processes, which would explain the development of chemoresistance in the medium and long term. Furthermore, we identified seven new bottlenecks that represent new target proteins and small compounds able to interfere in the control and communication between other nodes in the network itself. These data could be used to develop new combinations of drugs with the aim of improving the protocols used in chemotherapy of gastric cancer.

INTRODUCTION: Breast cancer is the most common cancer in women worldwide and metastatic dissemination is the principal factor related to death by this disease. Breast cancer stem cells (bCSC), defined in this work as the ALDH1high/LIN-/ESA+ population, are thought to be responsible for metastasis and chemoresistance. The objective of this work is to find gene master regulators, in particular transcription factors (TFs), which are controlling the bCSC phenotype. METHODS: We used in this work two groups of datasets with transcriptome data, the discovery dataset group contains one dataset obtained by ourselves containing three paired samples comparing the bCSC and the bulk of the tumor (My Data - bCSC/Bulk dataset), a dataset with eight paired samples comparing the bCSC and cancer cells (Wicha - bCSC/CC dataset) and a dataset with 115 samples of breast cancer tissue (clinical response dataset). The second group, validation datasets, contains the BRCA-TCGA dataset with information of 621 samples, 4142 breast cancer samples of the Kmplot tool, 17 primary samples of BasL subtype and their information of grafting in patient derived xenografts and analyzes of cell lines (MF10A and HMLE). For the analyzes we used the paired t-test in the Limma R package, the ARACNE algorithm for the inference of regulons in the clinical response dataset, MRA-FET to define the master regulators of the bCSC phenotype, and GSEA to identify the biological meaning of the findings in the different datasets. RESULTS: We identified 12 TFs as master regulators of the bCSC phenotype, with nine of them forming two highly interconnected networks, one positively related with the bCSC phenotype formed by SNAI2, TWIST, PRRX1, BNC2 and TBX5 with its regulons, defined here as the mesenchymal transcription network and one negative correlated to the phenotype formed by SCML4, ZNF831, SP140 and IKZF3, defined as the immune response transcription network, totally unknown in the context of breast cancer in the literature. Although still with weak evidence, ZEB1 seems to control the two networks and can be responsible for the expression of ALDH1 and of the three remaining TFs: ID4, HOXA5 and TEAD1. As their names portray, our data showed in the different datasets, and independently of the molecular subtype and of the platform used, that the mesenchymal transcription network seems to be responsible for the bCSC phenotype and the immune response transcription network to the adaptive immune response in the tumor and a better prognosis for the patients. We also defined 10 membrane proteins as new markers and/or therapeutic targets of the bCSC. CONCLUSION: We found and described two TF networks that seem to control the bCSC phenotype, one of them totally unknown until now and correlated to a good prognosis. Our findings have a clear potential for clinical use. / INTRODUÇÃO: O cancer de mama é no mundo o câncer mais comum em mulheres e a disseminação metastática é o principal fator relacionado com a morte pela doença. Acreditasse que as células tronco do câncer de mama - bCSC, na sigla em inglês e definida neste trabalho com a população ALDH1high/LIN-/ESA+ - é responsável pela metástase e pela quimioresistência. O objetivo deste trabalho é encontrar genes que são essenciais para o controle do fenótipo das bCSC, em particular fatores de transcrição. MATERIAIS E MÉTODOS: Nesse trabalho nós utlizamos dois grupos de datasets com dados do transcriptoma, o grupo de datasets de descoberta contém um dataset gerado por nós com 3 amostras pareadas comparando as bCSC com o tumor total (My Data - bCSC/Bulk dataset), um dataset com 8 amostras pareadas comparando as bCSC com as células cancerígenas (Wicha - bCSC/CC dataset) e um dataset com 115 amostras de tecido de câncer de mama (Clinical Response dataset). O segundo grupo, grupo de validação, contém o dataset BRCA-TCGA com 621 amostras, as 4142 amostras de câncer de mama da ferramenta Kmplot, as 17 amostras humanas primárias do subtipo BasL e sua informação sobre a geração, ou não, de tumores em camundongos imunosuprimidos e a análise de linhagens celulares (MF10A e HMLE). Para a análise dos dataset utilizamos o test-t pareado no pacote Limma da liguagem R, o algoritmo ARACNE para a inferência de regulons no dataset Clinical Response, a análise MRA-FET para definir os Reguladores Mestres para o fenótipo das bCSC e a análise GSEA para identificar o significado biológico de nosso achados nos diferentes datasets. RESULTADOS E DISCUSSÃO: Nós identificamos 12 TFs como reguladores mestres, com 9 deles formando duas redes altamente conectadas, uma positivamente relacionada ao fenótipo bCSC formada por SNAI2, TWIST, PRRX1, BNC2 e TBX5 com seus regulons, e definida aqui como a rede de transcrição mesenquimal, e uma rede correlacionada negativamente, formada por SCML4, ZNF831, SP140 e IKZF3, definida aqui como a rede de transcrição da resposta imune e totalmente desconhecida da literatura no contexto do câncer de mama. Embora ainda com fraca evidencia, ZEB1 para controlar as duas redes e ser responsável pela expressão de ALDH1 e dos 3 TFs restantes: ID4, HOXA5 e TEAD1. Como mostram seus nomes, e independente do dataset, do subtipo molecular ou da plataforma utilizada, a rede de transcrição mesenquimal, parece ser responsável pela manutenção do fenótipo de células tronco cancerígenas e a rede de transcrição da resposta imune pela resposta imune adaptativa ao tumor e a um bom prognóstico para as pacientes. CONCLUSÃO: Nós encontramos e descrevemos duas redes de fatores de transcrição que parecem controlar o fenótipo das bCSC, uma delas totalmente desconhecida até agora e relacionada a um bom prognóstico. Nosso achados possuem um claro potencial para uso clínico.

Many diseases are caused by proteins or peptides folding incorrectly and aggregating into fibrils or plaques, including Alzheimer’s disease, Parkinson's disease and type II diabetes. Amyloid formation in the human pancreas occurs via the aggregation of a 37 amino acid peptide called amylin or IAPP which is shown to be toxic to pancreatic β cells. Amylin (IAPP) aggregation initiates a large number of events, leading ultimately to cell death. However the exact cytotoxic action of human IAPP and also the underlying molecular events leading from amylin (IAPP) aggregation to β cell death is still unknown. The toxic effect of human amylin (IAPP) is thought to involve changes in the expression of several genes and proteins. Further transcriptional and proteomics studies in this field can therefore facilitate the identifications of new targets whose expression are affected by amylin (IAPP). These information could be further used to construct an integrated model of the signalling and regulatory pathways through which amylin (IAPP) interacts with cellular metabolism.To investigate the effects of amylin (IAPP) aggregation on the islets proteome in this study, rat Rin-5F cell line, reported as a model of pancreatic β cell, was used. MTT assay was initially performed to determine the effect of IAPP on the cell viability at different time points. The isolated proteins form the untreated and IAPP treated Rin-5F cells were then fractionated by off gel electrophoresis and analysed by quantitative label free LC- MS/MS approach.Label free quantification of IAPP treated Rin-5F cells has identified the altered expression of many proteins, some of which were previously suggested in the literature to be involved in the pathogenesis of type 2 diabetes. These proteins were map to several pathways (including glycolysis and proteasome) whose expressions were significantly affected upon amylin (IAPP) exposure. The IAPP responsive proteins were also structured into a well connected network. Some of the hub proteins identified in this network were greatly affected as the result of IAPP treatments of RIN-5F cells. Our data therefore revealed the effect of IAPP on several proteins and pathways that might be important in the pathogenesis of type 2 diabetes.

No description available.

Curiosity of human nature drives us to explore the origins of what makes each of us different. From ancient legends and mythology, Mendel's law, Punnett square to modern genetic research, we carry on this old but eternal question. Thanks to technological revolution, today's scientists try to answer this question using easily measurable gene expression and other profiling data. However, the exploration can easily get lost in the data of growing volume, dimension, noise and complexity. This dissertation is aimed at developing new machine learning methods that take data from different classes as input, augment them with knowledge of feature relationships, and train classification models that serve two goals: 1) class prediction for previously unseen samples; 2) knowledge discovery of the underlying causes of class differences. Application of our methods in genetic studies can help scientist take advantage of existing biological networks, generate diagnosis with higher accuracy, and discover the driver networks behind the differences. We proposed three new graph-based regularization algorithms. Graph Connectivity Constrained AdaBoost algorithm combines a connectivity module, a deletion function, and a model retraining procedure with the AdaBoost classifier. Graph-regularized Linear Programming Support Vector Machine integrates penalty term based on submodular graph cut function into linear classifier's objective function. Proximal Graph LogisticBoost adds lasso and graph-based penalties into logistic risk function of an ensemble classifier. Results of tests of our models on simulated biological datasets show that the proposed methods are able to produce accurate, sparse classifiers, and can help discover true genetic differences between phenotypes.

A Febre de Chikungunya é uma doença sistêmica causada por arbovírus e estima-se que afete cerca de 1 milhão de pessoas anualmente. Os principais sintomas associados com esta doença são febre e poliartralgia, podendo esta última assumir um caráter crônico em cerca de metade dos casos. Devido aos inúmeros surtos que ocorreram nos últimos 50 anos, diversos estudos tiveram como objetivo determinar os mecanismos de replicação do vírus e da resposta imune. Mesmo assim, pouco se sabe sobre quais moléculas possibilitam o processo de infecção. Já foi demonstrada a importância de interferons, principalmente de tipo I, citocinas e quimiocinas na diminuição da replicação viral. Além disso, há uma preferência do vírus por tipos celulares específicos como células endoteliais e epiteliais. Porém, estudos mostram informações contraditórias referentes ao papel de células mononucleares do sangue periférico (PBMC), principalmente com relação a monócitos e células B e T. Diante deste contexto, o tratamento utilizado atualmente é direcionado apenas ao alívio de sintomas uma vez que não existem drogas ou vacinas licenciadas específicas para esta doença. Logo, o objetivo deste trabalho é estudar as modificações transcricionais que ocorrem em amostras humanas durante o processo de infecção pelo vírus de Chikungunya de modo a esclarecer os mecanismos utilizados pelo sistema imune em resposta a infecção. Além disso, este estudo tem como finalidade apontar possíveis diferenças transcricionais entre amostras crônicas e não-crônicas. / The Chikungunya Fever is a systemic disease transmitted by arboviruses and is estimated to affect 1 million people annually. The main symptoms associated with this disease are fever and polyarthralgia, which can develop to chronic features in about half of the cases. Due to outbreaks that occurred in the last 50 years, many studies had the goal of determining the mechanisms of virus replication and immune response. Nevertheless, it is still poorly understood which molecules enable the ocurrence of the infection process. It has already been shown the importance of interferons, mainly type I, cytokines and chemokines in restricting the viral replication. In addition, the Chikungunya virus shows a preference for specific cell types such as endothelial and epithelial cells. However, studies display contradictory information regarding the role of peripheral blood mononuclear cells (PBMC), mainly in relation to monocytes and B and T cells. Given this context, the treatment currently used is directed only to alleviate the symptoms since there are no specific licensed drugs or vaccines for this disease. Therefore, the objective of this work was to study the transcriptional modifications that occur in humans during the process of Chikungunya virus infection in order to clarify the mechanisms used by the immune system. In addition, it aims to point out possible transcriptional differences between sample from the Chronic and Non-Chronic acute phase of the infection.