ABSTRACT
Introduction: Nutrient-stimulated hormones (NUSH) play a critical role in regulating energy metabolism. While dysregulation of NUSH signalling is associated with obesity, there is a lack of quantitative models to investigate the complex dynamics of NUSH signalling and its impact on obesity development. Objective: This study aims to explore the relationship between NUSH and body weight using mathematical modelling. Methods: Data on elevated body mass index (BMI) were collected from meta-analysis studies available on PubMed, focusing on incretin-based therapies. A mathematical model was developed using software to integrate the interactions between NUSH levels and changes in BMI. The model captured the complex dynamics and feedback loops involved in obesity-related hormonal regulation, employing differential equations and statistical techniques. Parameter estimation was performed using meta-analysis results to minimize the discrepancy between model predictions and observed data. Results: This study included 15 meta-analysis studies on liraglutide, semaglutide, and tirzepatide for the treatment of obesity. A mathematical model was developed to understand NUSH dynamics in relation to obesity. The model derived the formula: NUSH(t) = N0 * (1 - e
Introdução: Os hormônios estimulados por nutrientes (NUSH) desempenham um papel fundamental na regulação do metabolismo energético. A desregulação da sinalização dos NUSH está associada à obesidade, entretanto faltam modelos quantitativos para investigar a dinâmica complexa da sinalização do NUSH e seu impacto no desenvolvimento da obesidade. Objetivo: Explorar a relação entre NUSH e o peso corporal utilizando modelagem matemática. Método: Dados sobre o índice de massa corporal (IMC) elevada foram coletados de estudos de meta-análises disponíveis no banco de dados Pubmed, utilizando terapias baseadas em incretinas. Um modelo matemático foi desenvolvido utilizando softwares para integrar as interações entre os níveis de NUSH e alterações no IMC. O modelo capturou a dinâmica complexa do NUSH e os "loops" de "feedback" envolvidos na regulação hormonal relacionada à obesidade, utilizando equações diferenciais e técnicas estatísticas. A estimativa dos parâmetros foi realizada por meio dos resultados de meta-análises para minimizar a discrepância entre as previsões do modelo e os dados observados. Resultados: Este estudo incluiu 15 meta-análises sobre liraglutida, semaglutida e tirzepatida para o tratamento da obesidade. Foi desenvolvido um modelo matemático para entender a dinâmica do NUSH em relação à obesidade. O modelo deduziu a fórmula: NUSH(t) = N0 * (1 - e
ABSTRACT
Bile acids (BAs) are steroid molecules that have a hydrophilic and a hydrophobic end, and are synthesized exclusively in the liver, being end product of cholesterol catabolism. Type 2 diabetes mellitus (DM2) is a chronic degenerative disease, with a pathophysiology characterized by insulin resistance (IR), insulin deficiency due to insufficient production of pancreatic ß-cells, and elevated serum glucose levels leading to multiple complications. BAs are related to several metabolic alterations, including metabolic syndrome and DM2. It is currently known that BAs act as a ligand for the nuclear farnesoid X receptor, a receptor with an important role in glucose metabolism, lipids and cellular energy production, as well as in the regulation of production, elimination and mobilization of BAs. BAs have also been reported to act as a signaling pathway through of Takeda G protein-coupled receptor 5. In this manuscript, we describe the interface between BAs and metabolic disorders, in particular DM2, including discussing possibilities in the development of therapeutic procedures targeting BAs as an optional pathway in the treatment of DM2.
ABSTRACT
Contact with viruses which have an aminoacid (AA) sequence simile to that of the auto-antigens can lead to autoimmune diseases in genetically susceptible individuals. SARS-CoV-2 has been implied as a possible causer of new-onset type 1 diabetes mellitus (DM1), however, no consistent evidence yet that SARS-CoV-2 take to DM1 on your own initiative. Objective: Evaluate the possible similarity between the AA sequences of human insulin and human glutamic acid decarboxylase-65 (GAD65) with SARS-CoV-2/COVID proteins, to explain the possible trigger of DM1. Methods: AA sequences of the human insulin (4F0N), GAD65 (2OKK), and SARS-CoV-2 (SARS-Cov2 S protein at open state (7DDN), SARS-Cov2 S protein at close state (7DDD), SARS CoV-2 Spike protein (6ZB5), Crystal structure of SARS-CoV-2 nucleocapsid protein N-terminal RNA binding domain (6M3M), Crystal structure of SARS-CoV-2 nucleocapsid protein C-terminal RNA binding domain (7DE1), Crystal Structure of NSP1 from SARS-CoV-2 (7K3N), and SARS-CoV-2 S trimer (7DK3)) available in the Protein Data Bank were compared using the Pairwise Structure Alignment. Results: Sequence identity percentage (SI%) and sequence similarity percentage (SS%) were found among the 4F0N, 2OKK and SARS-CoV-2. The SI% between the 4F0N and SARS-CoV-2 ranged from 4.76% to 14.29% and SS% ranged from 5.00% to 45.45%, distributed like this: 4F0N and 7DDN = SI% 4.76 and SS% 28.57; 4F0N and 7DDD = SI% 14.39 and SS% 23.81; 4F0N and 6ZB5 = SI% 4.76 and SS% 28.57; 4F0N and 6M3M = SI% 5.00 and SS% 5;00; 4F0N and 7DE1 = SI% 4.76 and SS% 9.21; 4F0N and 7K3N = SI% 9.09 and SS% 45.45; 4F0N and 7DK3 = SI% 4.76 and SS% 28.57. The SI% between the between the 2OKK and SARS-CoV-2 ranged from 3.19% to 6,70% and SS% ranged from 10.45 % to 22.22%, distributed like this: 2OKK and 7DDN = SI% 6.70 and SS% 15.64; 2OKK and 7DDD = SI% 7.53 and SS% 18.84; 2OKK and 6ZB5 = SI% 6.68 and SS% 17.38; 2OKK and 6M3M = SI% 4.48 and SS% 10.45; 2OKK and 7DE1 = SI% 6.67 and SS% 22.22; 2OKK and 7K3N = SI% 3.19 and SS% 15.97; 2OKK and 7DK3 = SI% 3.95 and 17.98. Conclusion: Immunoinformatics data suggest a potential pathogenic link between DM1 and SARS-CoV-2/COVID. Thus, by means of molecular mimicking we check that sequences similarity among SARS-CoV-2/COVID and human insulin and human glutamic acid decarboxylase-65 may lead to production of an immune cross-response to self-antigens, with breakage of self-tolerance that can trigger DM1.
O contato com vírus que têm uma sequência de aminoácidos (AA) semelhante à dos autoantígenos podem desencadear doenças autoimunes em indivíduos geneticamente suscetíveis. SARS-CoV-2 foi sugerido como um possível causador de diabetes mellitus tipo 1 de início recente (DM1), no entanto, não há evidências consistentes de que o SARS-CoV-2 possa desencadear DM1. Objetivo: Avaliar a possível semelhança entre as sequências AA da insulina humana e da descarboxilase-65 do ácido glutâmico humano (GAD65) com as proteínas SARS-CoV-2 / COVID, para explicar o possível desencadeamento do DM1. Métodos: Sequências de AA da insulina humana (4F0N), GAD65 (2OKK) e SARS-CoV-2 SARS-Cov2 S protein at open state (7DDN), SARS-Cov2 S protein at close state (7DDD), SARS CoV-2 Spike protein (6ZB5), Crystal structure of SARS-CoV-2 nucleocapsid protein N-terminal RNA binding domain (6M3M), Crystal structure of SARS-CoV-2 nucleocapsid protein C-terminal RNA binding domain (7DE1), Crystal Structure of NSP1 from SARS-CoV-2 (7K3N), and SARS-CoV-2 S trimer (7DK3)) disponíveis no Protein Data Bank foram comparadas utilizando o Pairwise Structure Alignment. Resultados: O percentual de identidade de sequências (SI%) e o percentual de similaridade de sequências (SS%) foram encontrados entre o 4F0N, 2OKK e o SARS-CoV-2. O SI% entre o 4F0N e o SARS-CoV-2 variou de 4,76% a 14,29% e o SS% variou de 5,00% a 45,45%, assim distribuídos: 4F0N e 7DDN = SI% 4,76 e SS% 28,57; 4F0N e 7DDD = SI% 14,39 e SS% 23,81; 4F0N e 6ZB5 = SI% 4,76 e SS% 28,57; 4F0N e 6M3M = SI% 5,00 e SS% 5; 00; 4F0N e 7DE1 = SI% 4,76 e SS% 9,21; 4F0N e 7K3N = SI% 9,09 e SS% 45,45; 4F0N e 7DK3 = SI% 4,76 e SS% 28,57. O SI% entre o 2OKK e o SARS-CoV-2 variou de 3,19% a 6,70% e o SS% variou de 10,45% a 22,22%, assim distribuídos: 2OKK e 7DDN = SI% 6,70 e SS% 15,64; 2OKK e 7DDD = SI% 7,53 e SS% 18,84; 2OKK e 6ZB5 = SI% 6,68 e SS% 17,38; 2OKK e 6M3M = SI% 4,48 e SS% 10,45; 2OKK e 7DE1 = SI% 6,67 e SS% 22,22; 2OKK e 7K3N = SI% 3,19 e SS% 15,97; 2OKK e 7DK3 = SI% 3,95 e 17,98. Conclusão: Os dados de imunoinformática sugerem uma potencial ligação patogênica entre SARS-CoV-2 / COVID e o DM1. Assim, por meio de mimetização molecular, verificamos que a similaridade das sequências de AA entre SARS-CoV-2 / COVID e insulina humana e a descarboxilase-65 do ácido glutâmico humano pode levar à produção de uma resposta cruzada imunológica para autoantígenos, com quebra de auto-tolerância, podendo desencadear o DM1.
