Efeito do resveratrol sobre parâmetros de ativação de células GRX

A fibrose e/ou a cirrose hepática são doenças crônicas do fígado e representam uma das maiores causas de mortalidade humana. As células estreladas hepáticas (HSC) são protagonistas desse processo e estão associadas ao desenvolvimento da fibrose que, em último estágio, acarreta em cirrose. No fígado saudável, estas células apresentam um fenótipo quiescente ou lipocítico, caracterizado pela sua capacidade de armazenar gotas lipídicas. Entretanto, danos contínuos ao fígado desencadeiam uma resposta que gera estímulos autócrinos e parácrinos mediados por citocinas e espécies reativas de oxigênio. Este quadro induz uma modulação das HSC ao fenótipo ativado ou miofibroblastóide, caracterizado pelo aumento da capacidade de produzir componentes de matriz extracelular, cuja deposição exagerada configura o estado patológico da fibrose. A fitoalexina Resveratrol (3,5,4'-tetrahidroxistilbeno; RSV) tem sido relacionada a inúmeros efeitos benéficos à saúde por suas atividades de citoproteção e quimioprevenção. Há evidencias que suas propriedades que podem ser benéficas no tratamento da fibrose hepática. Nesta tese, avaliamos os efeitos do RSV em alguns marcadores de ativação em células da linhagem GRX. Na sequência, exploramos, por meio de recursos integrados de bioinformática e da utilização de bancos de dados públicos, as possíveis proteínas alvo do RSV e seus papéis potenciais na modulação fenotípica de HSC. Por fim, com ferramentas de bioinformática buscamos identificar variações na expressão genica e rotas metabólicas que atuam na transdiferenciação das HSC. Nossos resultados demonstram que o tratamento com 50 μM de RSV por 24 horas aumentou o conteúdo dessas proteínas relacionadas à ativação. Além disso, o RSV não alterou a morfologia semelhante a miofibroblastos de GRX. Curiosamente, o RSV a 10 e 50 μM diminuiu a migração de GRX e a contração do gel de colágeno I. Mostramos que o RSV desencadeou o aumento do conteúdo de TNF-α e IL-10 nos meios de cultura de GRX, enquanto o contrário ocorreu para o conteúdo de IL-6. A maioria dos genes alvos do RSV estão associados a várias vias celulares como: Via de transcrição genérica, Transcrição de RNA polimerase II, Interleucina-4 e Interleucina-13. Ainda, foram identificados 26 DTPs (Direct Target Proteins) no banco de dados DRUGBANK. Em seguida, a rede de interação proteína-proteína (PPI) e as vias Reactome foram analisadas. Da mesma forma, foram buscados artigos científicos sobre os genes-alvo do RSV na base de dados PUBMED. Foram encontrados 26 DTPs de RSV. Descobrimos que apenas 7 DTPs já foram associados a estudos no banco de dados PUBMED. Bem como, na análise de expressão diferencial foram encontrados 411 genes sendo 155 superexpressos e 256 subexpressos. Em resumo, nossos resultados sugerem que o RSV não diminuiu o estado de ativação da GRX; ao contrário, desencadeou um efeito de pró-ativação. Na sequência por meio de recursos integrados de bioinformática e da utilização de bancos de dados públicos, analisamos as possíveis proteínas alvo do RSV e seus papéis potenciais na modulação fenotípica de HSC. Também, os principais genes regulados positivamente foram descritos na literatura como estando envolvidos na ativação de HSC ou no desenvolvimento de fibrose hepática.Fibrosis and / or liver cirrhosis are chronic liver diseases and represent the major cause of human mortality. Hepatic stellate cells (HSC) are protagonists in this process and are associated to the development of fibrosis, which, at the last stage, causes cirrhosis. In healthy liver, these cells present a quiescent or lipocytic phenotype, characterized by their ability to store lipid droplets. However, continuous damage to the liver triggers a response that generates autocrine and paracrine stimuli mediated by cytokines and reactive oxygen species. This condition induces the HSC modulation to the activated or myofibroblastoid phenotype, characterized by the increased capacity to produce components of extracellular matrix, whose excessive deposition configures the pathological state of fibrosis. The phytoalexin Resveratrol (3,5,4'-tetrahydroxystilbene; RSV) has been linked to numerous beneficial health effects due to its cytoprotection and chemoprevention activities. There is evidence that RSV properties may be beneficial in the treatment of liver fibrosis. In this thesis, we evaluated the effects of RSV on some activation markers of the GRX cell line. Further, we explored, using integrated bioinformatics resources, the possible RSV target proteins and their potential roles in the phenotypic modulation of HSC. Finally, with bioinformatics tools, we seek to identify variations in gene expression and metabolic routes that act in the transdifferentiation of HSC. Our results demonstrated that treatment with 50 μM of RSV for 24 hours increased the content of these proteins related to activation. In addition, RSV did not alter the GRX myofibroblast-like morphology. Interestingly, RSV at 10 and 50 μM decreased GRX migration and contraction of collagen I gel. We showed that RSV triggered an increase in the content of TNF-α and IL-10 in GRX culture media, while the opposite occurred for IL-6 content. Most of the target genes for RSV are associated with several cellular pathways such as: Generic transcription pathway, RNA polymerase II transcription, Interleukin-4 and Interleukin-13. In addition, 26 DTPs (Direct Target Proteins) were identified in the DRUGBANK database. Then, the protein-protein interaction network (PPI) and the Reactome pathways were analyzed. Likewise, scientific articles on RSV target genes were searched for in the PUBMED database. 26 RSV DTPs were found. We found that only 7 DTPs have been associated with studies in the PUBMED database. As well as the analysis of differential expression, 411 genes were found, 155 overexpressed and 256 underexpressed. In summary, our results suggest that RSV did not decrease the GRX activation state; on the contrary, it triggered a pro-activation effect. Following through integrated bioinformatics resources, the possible RSV target proteins and their potential roles in the phenotypic modulation of HSC. Also, the main positively regulated genes have been described mainly in the literature as being involved in the activation of HSC or in the development of liver fibrosis

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost