DRAK2 suppresses autophagy by phosphorylating ULK1 at Ser56 to diminish pancreatic ß cell function upon overnutrition.

Impeded autophagy can impair pancreatic ß cell function by causing apoptosis, of which DAP-related apoptosis-inducing kinase-2 (DRAK2) is a critical regulator. Here, we identified a marked up-regulation of DRAK2 in pancreatic tissue across humans, macaques, and mice with type 2 diabetes (T2D). Further studies in mice showed that conditional knockout (cKO) of DRAK2 in pancreatic ß cells protected ß cell function against high-fat diet feeding along with sustained autophagy and mitochondrial function. Phosphoproteome analysis in isolated mouse primary islets revealed that DRAK2 directly phosphorylated unc-51-like autophagy activating kinase 1 (ULK1) at Ser56, which was subsequently found to induce ULK1 ubiquitylation and suppress autophagy. ULK1-S56A mutation or pharmacological inhibition of DRAK2 preserved mitochondrial function and insulin secretion against lipotoxicity in mouse primary islets, Min6 cells, or INS-1E cells. In conclusion, these findings together indicate an indispensable role of the DRAK2-ULK1 axis in pancreatic ß cells upon metabolic challenge, which offers a potential target to protect ß cell function in T2D.

Luteolin Protects Pancreatic ß Cells against Apoptosis through Regulation of Autophagy and ROS Clearance.

Diabetes, which is mainly characterized by increased apoptosis and dysfunction of beta (ß) cells, is a metabolic disease caused by impairment of pancreatic islet function. Previous studies have demonstrated that death-associated protein kinase-related apoptosis-inducing kinase-2 (Drak2) is involved in regulating ß cell survival. Since natural products have multiple targets and often are multifunctional, making them promising compounds for the treatment of diabetes, we identified Drak2 inhibitors from a natural product library. Among the identified products, luteolin, a flavonoid, was found to be the most effective compound. In vitro, luteolin effectively alleviated palmitate (PA)-induced apoptosis of ß cells and PA-induced impairment of primary islet function. In vivo, luteolin showed a tendency to lower blood glucose levels. It also alleviated STZ-induced apoptosis of ß cells and metabolic disruption in mice. This function of luteolin partially relied on Drak2 inhibition. Furthermore, luteolin was also found to effectively relieve oxidative stress and promote autophagy in ß cells, possibly improving ß cell function and slowing the progression of diabetes. In conclusion, our findings show the promising effect of Drak2 inhibitors in relieving diabetes and offer a potential therapeutic target for the protection of ß cells. We also reveal some of the underlying mechanisms of luteolin's cytoprotective function.

A novel information diffusion method based on network consistency for identifying disease related microRNAs.

The abnormal expression of miRNAs is directly related to the development of human diseases. Predicting the potential candidate miRNAs associated with diseases can contribute to the detection, diagnosis, treatment and prevention of human complex diseases. The effective inference of the calculation method of the relationship between miRNAs and diseases is an effective supplement to biological experiments. It is of great help in the prevention, treatment and prognosis of complex diseases. This paper proposes a novel information diffusion method based on network consistency (IDNC) for identifying disease related microRNAs. The model first synthesizes the miRNA family information and the miRNA function similarity to reconstruct the miRNA network, and reconstruct the disease network by using the known disease and miRNA-related information and the semantic score between diseases. Then the global similarity of the two networks is obtained by using the Laplacian score of graphs. The global similarity score is a measure of the similarity between diseases and miRNAs. The disease-miRNA relation network was reconstructed by integrating the global similarity relation. The network consistency diffusion seed is then obtained by combining the global similarity network with the reconstructed disease-miRNA association network. Thereafter, the stable diffusion spectrum is generated as the prediction score by using the restarted random walk algorithm. The AUC value obtained by performing the LOOCV in the gold benchmark dataset is 0.8814. The AUC value obtained by performing the LOOCV in the predictive dataset is 0.9512. Compared with other frontier methods, our method has higher accuracy, which is further illustrated by case studies of breast neoplasms and colon neoplasms to prove that IDNC is valuable.