A probabilistic knowledge graph for target identification.

Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process.

Improving target-disease association prediction through a graph neural network with credibility information.

Identifying effective target-disease associations (TDAs) can alleviate the tremendous cost incurred by clinical failures of drug development. Although many machine learning models have been proposed to predict potential novel TDAs rapidly, their credibility is not guaranteed, thus requiring extensive experimental validation. In addition, it is generally challenging for current models to predict meaningful associations for entities with less information, hence limiting the application potential of these models in guiding future research. Based on recent advances in utilizing graph neural networks to extract features from heterogeneous biological data, we develop CreaTDA, an end-to-end deep learning-based framework that effectively learns latent feature representations of targets and diseases to facilitate TDA prediction. We also propose a novel way of encoding credibility information obtained from literature to enhance the performance of TDA prediction and predict more novel TDAs with real evidence support from previous studies. Compared with state-of-the-art baseline methods, CreaTDA achieves substantially better prediction performance on the whole TDA network and its sparse sub-networks containing the proteins associated with few known diseases. Our results demonstrate that CreaTDA can provide a powerful and helpful tool for identifying novel target-disease associations, thereby facilitating drug discovery.

Quercetin protects against palmitate-induced pancreatic ß-cell apoptosis by restoring lysosomal function and autophagic flux.

Quercetin, a natural flavonoid, has been reported to prevent pancreatic ß-cell apoptosis in animal models of diabetes. However, the underlying mechanism remains unclear. We investigated the mechanisms through which quercetin protects ß cells from palmitate-induced apoptosis and determined whether autophagy is involved in this process. We found that quercetin treatment partially reduced palmitate-induced ß-cell apoptosis. This protective effect was abolished by pharmacologic inhibition of autophagy and by silencing a key autophagy gene. Further analysis revealed that palmitate treatment promoted the expression of LC3 II, a marker of autophagosomes, but resulted in the blockade of autophagic flux due to lysosome dysfunction. Defective lysosome accumulation can cause lysosomal membrane permeabilization and the release of cathepsins from lysosome into the cytosol that triggers apoptosis. Treatment with quercetin reversed lysosomal dysfunction and promoted autophagosome-lysosome fusion, which restored defective autophagic flux and provoked autophagy. Overall, our results indicate that lysosomal dysfunction is a major factor that contributes to ß-cell apoptosis and demonstrates that quercetin improves cell survival by restoring lysosomal function and autophagic flux. This study provides new evidence regarding the anti-apoptotic mechanism of quercetin in the treatment of type 2 diabetes.