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BackgroundLiver fibrosis is a reversible wound-healing response that can lead to end-stage liver diseases without effective treatment, in which HBV infection is a major cause. However, the underlying mechanisms for the development of HBV-induced fibrosis remains elusive, and efficacious therapies for this disease are still lacking. In present investigation, we investigated the effect and mechanism of green tea polyphenol epigallocatechin-3-gallate (EGCG) on HBV-induced liver injury and fibrosis.MethodsThe effect of EGCG on liver fibrosis was examined in a recombinant cccDNA (rcccDNA) chronic HBV mouse model by immunohistochemical staining, Sirius red and Masson’s trichrome staining. The functional relevance between high mobility group box 1 (HMGB1) and inflammasome activation and the role of EGCG in it were analyzed by Western blotting. The effect of EGCG on autophagic flux was determined by Western blotting and flow cytometric analysis.ResultsEGCG treatment efficiently was found to alleviate HBV-induced liver injury and fibrosis in a recombinant cccDNA (rcccDNA) chronic HBV mouse model, a proven suitable research platform for HBV-induced fibrosis. Mechanistically, EGCG was revealed to repress the activation of macrophage NLRP3 inflammasome, a critical trigger of HBV-induced liver fibrosis. Further study revealed that EGCG suppressed macrophage inflammasome through downregulating the level of extracellular HMGB1. Furthermore, our data demonstrated that EGCG treatment downregulated the levels of extracellular HMGB1 through activating autophagic degradation of cytoplasmic HMGB1 in hepatocytes. Accordingly, autophagy blockade was revealed to significantly reverse EGCG-mediated inhibition on extracellular HMGB1-activated macrophage inflammasome and thus suppress the therapeutic effect of EGCG on HBV-induced liver injury and fibrosis.ConclusionEGCG ameliorates HBV-induced liver injury and fibrosis via autophagic degradation of cytoplasmic HMGB1 and the subsequent suppression of macrophage inflammasome activation. These data provided a new pathogenic mechanism for HBV-induced liver fibrosis involving the extracellular HMGB1-mediated macrophage inflammasome activation, and also suggested EGCG administration as a promising therapeutic strategy for this disease.

Molecular representation is a critical part of various prediction tasks for physicochemical properties of molecules and drug design. As graph notations are common in expressing the structural information of chemical compounds, graph neural networks (GNNs) have become the mainstream backbone model for learning molecular representation. However, the scarcity of task-specific labels in the biomedical domain limits the power of GNNs. Recently, self-supervised pretraining for GNNs has been leveraged to deal with this issue, while the existing pretraining methods are mainly designed for graph data in general domains without considering the specific data properties of molecules. In this paper, we propose a representation learning method for molecular graphs, called ReLMole, which is featured by a hierarchical graph modeling of molecules and a contrastive learning scheme based on two-level graph similarities. We assess the performance of ReLMole on two types of downstream tasks, namely, the prediction of molecular properties (MPs) and drug-drug interaction (DDIs). ReLMole achieves promising results for all the tasks. It outperforms the baseline models by over 2.6% on ROC-AUC averaged across six MP prediction tasks, and it improves the F1 value by 7-18% in DDI prediction for unseen drugs compared with other self-supervised models.

The identification of drug-target interaction (DTI) plays a key role in drug discovery and development. Benefitting from large-scale drug databases and verified DTI relationships, a lot of machine-learning methods have been developed to predict DTIs. However, due to the difficulty in extracting useful information from molecules, the performance of these methods is limited by the representation of drugs and target proteins. This study proposes a new model called EmbedDTI to enhance the representation of both drugs and target proteins, and improve the performance of DTI prediction. For protein sequences, we leverage language modeling for pretraining the feature embeddings of amino acids and feed them to a convolutional neural network model for further representation learning. For drugs, we build two levels of graphs to represent compound structural information, namely the atom graph and substructure graph, and adopt graph convolutional network with an attention module to learn the embedding vectors for the graphs. We compare EmbedDTI with the existing DTI predictors on two benchmark datasets. The experimental results show that EmbedDTI outperforms the state-of-the-art models, and the attention module can identify the components crucial for DTIs in compounds.