Acebutolol, a Cardioselective Beta Blocker, Promotes Glucose Uptake in Diabetic Model Cells by Inhibiting JNK-JIP1 Interaction

The phosphorylation of JNK is known to induce insulin resistance in insulin target tissues. The inhibition of JNK-JIP1 interaction, which interferes JNK phosphorylation, becomes a potential target for drug development of type 2 diabetes. To discover the inhibitors of JNK-JIP1 interaction, we screened out 30 candidates from 4320 compound library with In Cell Interaction Trap method. The candidates were further confirmed and narrowed down to five compounds using the FRET method in a model cell. Among those five compounds, Acebutolol showed notable inhibition of JNK phosphorylation and elevation of glucose uptake in diabetic models of adipocyte and liver cell. Structural computation showed that the binding affinity of Acebutolol on the JNK-JIP1 interaction site was comparable to the known inhibitor, BI-78D3. Our results suggest that Acebutolol, an FDA-approved beta blocker for hypertension therapy, could have a new repurposed effect on type 2 diabetes elevating glucose uptake process by inhibiting JNK-JIP1 interaction.

Drug Similarity Search Based on Combined Signatures in Gene Expression Profiles / 대한의료정보학회지

OBJECTIVES: Recently, comparison of drug responses on gene expression has been a major approach to identifying the functional similarity of drugs. Previous studies have mostly focused on a single feature, the expression differences of individual genes. We provide a more robust and accurate method to compare the functional similarity of drugs by diversifying the features of comparison in gene expression and considering the sample dependent variations. METHODS: For differentially expressed gene measurement, we modified the conventional t-test to normalize variations in diverse experimental conditions of individual samples. To extract significant differentially co-expressed gene modules, we searched maximal cliques among the co-expressed gene network. Finally, we calculated a combined similarity score by averaging the two scaled scores from the above two measurements. RESULTS: This method shows significant performance improvement in comparison to other approaches in the test with Connectivity Map data. In the test to find the drugs based on their own expression profiles with leave-one-out cross validation, the proposed method showed an area under the curve (AUC) score of 0.99, which is much higher than scores obtained with previous methods, ranging from 0.71 to 0.93. In the drug networks, we could find well clustered drugs having the same target proteins and novel relations among drugs implying the possibility of drug repurposing. CONCLUSIONS: Inclusion of the features of a co-expressed module provides more implications to infer drug action. We propose that this method be used to find collaborative cellular mechanisms associated with drug action and to simply identify drugs having similar responses.

Correction: Drug Similarity Search Based on Combined Signatures in Gene Expression Profiles / 대한의료정보학회지

We have noticed an inadvertent error in our article.