Metabolomics Signature in Prediabetes and Diabetes: Insights From Tandem Mass Spectrometry Analysis.

OBJECTIVE: This study investigates the metabolic differences between normal, prediabetic and diabetic patients with good and poor glycaemic control (GGC and PGC). DESIGN: In this study, 1102 individuals were included, and 50 metabolites were analysed using tandem mass spectrometry. The diabetes diagnosis and treatment standards of the American Diabetes Association (ADA) were used to classify patients. METHODS: The nearest neighbour method was used to match controls and cases in each group on the basis of age, sex and BMI. Factor analysis was used to reduce the number of variables and find influential underlying factors. Finally, Pearson's correlation coefficient was used to check the correlation between both glucose and HbAc1 as independent factors with binary classes. RESULTS: Amino acids such as glycine, serine and proline, and acylcarnitines (AcylCs) such as C16 and C18 showed significant differences between the prediabetes and normal groups. Additionally, several metabolites, including C0, C5, C8 and C16, showed significant differences between the diabetes and normal groups. Moreover, the study found that several metabolites significantly differed between the GGC and PGC diabetes groups, such as C2, C6, C10, C16 and C18. The correlation analysis revealed that glucose and HbA1c levels significantly correlated with several metabolites, including glycine, serine and C16, in both the prediabetes and diabetes groups. Additionally, the correlation analysis showed that HbA1c significantly correlated with several metabolites, such as C2, C5 and C18, in the controlled and uncontrolled diabetes groups. CONCLUSIONS: These findings could help identify new biomarkers or underlying markers for the early detection and management of diabetes.

The role of S477N mutation in the molecular behavior of SARS-CoV-2 spike protein: An in-silico perspective.

The attachment of SARA-CoV-2 happens between ACE2 and the receptor binding domain (RBD) on the spike protein. Mutations in this domain can affect the binding affinity of the spike protein for ACE2. S477N, one of the most common mutations reported in the recent variants, is located in the RBD. Today's computational approaches in biology, especially during the SARS-CoV-2 pandemic, assist researchers in predicting a protein's behavior in contact with other proteins in more detail. In this study, we investigated the interactions of the S477N-hACE2 in silico to find the impact of this mutation on its binding affinity for ACE2 and immunity responses using dynamics simulation, protein-protein docking, and immunoinformatics methods. Our computational analysis revealed an increased binding affinity of N477 for ACE2. Four new hydrogen and hydrophobic bonds in the mutant RBD-ACE2 were formed (with S19 and Q24 of ACE2), which do not exist in the wild type. Also, the protein spike structure in this mutation was associated with an increase in stabilization and a decrease in its fluctuations at the atomic level. N477 mutation can be considered as the cause of increased escape from the immune system through MHC-II.