Vias de Sinalização da Insulina

Insulin is an anabolic hormone with powerful metabolic effects. The events after insulin binds to its receptor are highly regulated and specific. Defining the key steps that lead to the specificity in insulin signaling presents a major challenge to biochemical research, but the outcome should offer new therapeutic approaches for treatment of patients suffering from insulin-resistant states, including type 2 diabetes. The insulin receptor belongs to the large family of growth factor receptors with intrinsic tyrosine kinase activity. Following insulin binding, the receptor undergoes autophosphorylation on multiple tyrosine residues. This results in activation of the receptor kinase and tyrosine phosphorylation of a family of insulin receptor substrate (IRS) proteins. Like other growth factors, insulin uses phosphorylation and the resultant protein-protein interactions as essential tools to transmit and compartmentalize its signal. These intracellular protein-protein interactions are pivotal in transmitting the signal from the receptor to the final cellular effect, such as translocation of vesicles containing GLUT4 glucose transporters from the intracellular pool to the plasma membrane, activation of glycogen or protein synthesis, and initiation of specific gene transcription.A insulina é um hormônio anabólico com efeitos metabólicos potentes. Os eventos que ocorrem após a ligação da insulina são específicos e estritamente regulados. Definir as etapas que levam à especificidade deste sinal representa um desafio para as pesquisas bioquímicas, todavia podem resultar no desenvolvimento de novas abordagens terapêuticas para pacientes que sofrem de estados de resistência à insulina, inclusive o diabetes tipo 2. O receptor de insulina pertence a uma família de receptores de fatores de crescimento que têm atividade tirosina quinase intrínseca. Após a ligação da insulina o receptor sofre autofosforilação em múltiplos resíduos de tirosina. Isto resulta na ativação da quinase do receptor e conseqüente fosforilação em tirosina de um a família de substratos do receptor de insulina (IRS). De forma similar a outros fatores de crescimento, a insulina usa fosforilação e interações proteína-proteína como ferramentas essenciais para transmitir o sinal. Estas interações proteína-proteína são fundamentais para transmitir o sinal do receptor em direção ao efeito celular final, tais como translocação de vesículas contendo transportadores de glicose (GLUT4) do pool intracelular para a membrana plasmática, ativação da síntese de glicogênio e de proteínas, e transcrição de genes específicos.41942

Physical Exercise Reduces Circulating Lipopolysaccharide and TLR4 Activation and Improves Insulin Signaling in Tissues of DIO Rats

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)OBJECTIVE-Insulin resistance in diet-induced obesity (DIO) is associated with a chronic systemic low-grade inflammation, and Toll-like receptor 4 (TLR4) plays an important role in the link among insulin resistance, inflammation, and obesity. The current study aimed to analyze the effect of exercise on TLR4 expression and activation in obese rats and its consequences on insulin sensitivity and signaling. RESEARCH DESIGN AND METHODS-The effect of chronic and acute exercise was investigated on insulin sensitivity, insulin signaling, TLR4 activation, c-Jun NH(2)-terminal kinase (JNK) and I kappa B kinase (IKK beta) activity, and lipopolysaccharide (LPS) serum levels in tissues of DIO rats. RESULTS-The results showed that chronic exercise reduced TLR4 mRNA and protein expression in liver, muscle, and adipose tissue. However, both acute and chronic exercise blunted TLR4 signaling in these tissues, including a reduction in JNK and IKK beta phosphorylation and IRS-1 serine 307 phosphorylation, and, in parallel, improved insulin-induced IR, IRS-1 tyrosine phosphorylation, and Akt serine phosphorylation, and reduced LPS serum levels. CONCLUSIONS-Our results show that physical exercise in DIO rats, both acute and chronic, induces an important suppression in the TLR4 signaling pathway in the liver, muscle, and adipose tissue, reduces LPS serum levels, and improves insulin signaling and sensitivity. These data provide considerable progress in our understanding of the molecular events that link physical exercise to an improvement in inflammation and insulin resistance. Diabetes 60:784-796, 2011603784796Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de PesquisaINCT-Obesidade e DiabetesFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Construction of a nomogram for predicting COVID-19 in-hospital mortality: A machine learning analysis

Background and objectives: We aim to verify the use of ML algorithms to predict patient outcome using a relatively small dataset and to create a nomogram to assess in-hospital mortality of patients with COVID-19. Methods: A database of 200 COVID-19 patients admitted to the Clinical Hospital of State University of Campinas (UNICAMP) was used in this analysis. Patient features were divided into three categories: clinical, chest abnormalities, and body composition characteristics acquired by computerized tomography. These features were evaluated independently and combined to predict patient outcomes. To minimize performance fluctuations due to low sample number, reduce possible bias related to outliers, and evaluate the uncertainties generated by the small dataset, we developed a shuffling technique, a modified version of the Monte Carlo Cross Validation, creating several subgroups for training the algorithm and complementary testing subgroups. The following ML algorithms were tested: random forest, boosted decision trees, logistic regression, support vector machines, and neural networks. Performance was evaluated by analyzing Receiver operating characteristic (ROC) curves. The importance of each feature in the determination of the outcome predictability was also studied and a nomogram was created based on the most important features selected by the exclusion test. Results: Among the different sets of features, clinical variables age, lymphocyte number and weight were the most valuable features for prognosis prediction. However, we observed that skeletal muscle radiodensity and presence of pleural effusion were also important for outcome determination. Integrating these independent predictors was successfully developed to accurately predict mortality in COVID-19 in hospital patients. A nomogram based on these five features was created to predict COVID-19 mortality in hospitalized patients. The area under the ROC curve was 0.86 ± 0.04. Conclusion: ML algorithms can be reliable for the prediction of COVID-19-related in-hospital mortality, even when using a relatively small dataset. The success of ML techniques in smaller datasets broadens the applicability of these methods in several problems in the medical area. In addition, feature importance analysis allowed us to determine the most important variables for the prediction tasks resulting in a nomogram with good accuracy and clinical utility in predicting COVID-19 in-hospital mortality