Rosavin improves insulin resistance and alleviates hepatic and kidney damage via modulating the cGAS-STING pathway and autophagy signaling in HFD/STZ-induced T2DM animals.

Background: Inflammation-mediated insulin resistance in type 2 diabetes mellitus (T2DM) increases complications, necessitating investigation of its mechanism to find new safe therapies. This study investigated the effect of rosavin on the autophagy and the cGAS-STING pathway-related signatures (ZBP1, STING1, DDX58, LC3B, TNF-α) and on their epigenetic modifiers (miR-1976 and lncRNA AC074117.2) that were identified from in silico analysis in T2DM animals. Methods: A T2DM rat model was established by combining a high-fat diet (HFD) and streptozotocin (STZ). After four weeks from T2DM induction, HFD/STZ-induced T2DM rats were subdivided into an untreated group (T2DM group) and three treated groups which received 10, 20, or 30 mg per kg of R. rosea daily for 4 weeks. Results: The study found that rosavin can affect the cGAS-STING pathway-related RNA signatures by decreasing the expressions of ZBP1, STING1, DDX58, and miR-1976 while increasing the lncRNA AC074117.2 level in the liver, kidney, and adipose tissues. Rosavin prevented further weight loss, reduced serum insulin and glucose, improved insulin resistance and the lipid panel, and mitigated liver and kidney damage compared to the untreated T2DM group. The treatment also resulted in reduced inflammation levels and improved autophagy manifested by decreased immunostaining of TNF-α and increased immunostaining of LC3B in the liver and kidneys of the treated T2DM rats. Conclusion: Rosavin has shown potential in attenuating T2DM, inhibiting inflammation in the liver and kidneys, and improving metabolic disturbances in a T2DM animal model. The observed effect was linked to the activation of autophagy and suppression of the cGAS-STING pathway.

Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats.

Introduction: With the increasing prevalence of type 2 diabetes mellitus (T2DM), there is an urgent need to discover effective therapeutic targets for this complex condition. Coding and non-coding RNAs, with traditional biochemical parameters, have shown promise as viable targets for therapy. Machine learning (ML) techniques have emerged as powerful tools for predicting drug responses. Method: In this study, we developed an ML-based model to identify the most influential features for drug response in the treatment of type 2 diabetes using three medicinal plant-based drugs (Rosavin, Caffeic acid, and Isorhamnetin), and a probiotics drug (Z-biotic), at different doses. A hundred rats were randomly assigned to ten groups, including a normal group, a streptozotocin-induced diabetic group, and eight treated groups. Serum samples were collected for biochemical analysis, while liver tissues (L) and adipose tissues (A) underwent histopathological examination and molecular biomarker extraction using quantitative PCR. Utilizing five machine learning algorithms, we integrated 32 molecular features and 12 biochemical features to select the most predictive targets for each model and the combined model. Results and discussion: Our results indicated that high doses of the selected drugs effectively mitigated liver inflammation, reduced insulin resistance, and improved lipid profiles and renal function biomarkers. The machine learning model identified 13 molecular features, 10 biochemical features, and 20 combined features with an accuracy of 80% and AUC (0.894, 0.93, and 0.896), respectively. This study presents an ML model that accurately identifies effective therapeutic targets implicated in the molecular pathways associated with T2DM pathogenesis.