Identification of drug-target interactions (DTIs) is an important step in drug discovery and drug repositioning. In recent years, graph-based methods have attracted great attention and show advantages on predicting potential DTIs. However, these methods face the problem that the known DTIs are very limited and expensive to obtain, which decreases the generalization ability of the methods. Self-supervised contrastive learning is independent of labeled DTIs, which can mitigate the impact of the problem. Therefore, we propose a framework SHGCL-DTI for predicting DTIs, which supplements the classical semi-supervised DTI prediction task with an auxiliary graph contrastive learning module. Specifically, we generate representations for the nodes through the neighbor view and meta-path view, and define positive and negative pairs to maximize the similarity between positive pairs from different views. Subsequently, SHGCL-DTI reconstructs the original heterogeneous network to predict the potential DTIs. The experiments on the public dataset show that SHGCL-DTI has significant improvement in different scenarios, compared with existing state-of-the-art methods. We also demonstrate that the contrastive learning module improves the prediction performance and generalization ability of SHGCL-DTI through ablation study. In addition, we have found several novel predicted DTIs supported by the biological literature. The data and source code are available at: https://github.com/TOJSSE-iData/SHGCL-DTI.
Objective: People suffering from coronavirus disease 2019 (COVID-19) are prone to develop pulmonary fibrosis (PF), but there is currently no definitive treatment for COVID-19/PF co-occurrence. Kaempferol with promising antiviral and anti-fibrotic effects is expected to become a potential treatment for COVID-19 and PF comorbidities. Therefore, this study explored the targets and molecular mechanisms of kaempferol against COVID-19/PF co-occurrence by bioinformatics and network pharmacology. Methods: Various open-source databases and Venn Diagram tool were applied to confirm the targets of kaempferol against COVID-19/PF co-occurrence. Protein-protein interaction (PPI), MCODE, key transcription factors, tissue-specific enrichment, molecular docking, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to clarify the influential molecular mechanisms of kaempferol against COVID-19 and PF comorbidities. Results: 290 targets and 203 transcription factors of kaempferol against COVID-19/PF co-occurrence were captured. Epidermal growth factor receptor (EGFR), proto-oncogene tyrosine-protein kinase SRC (SRC), mitogen-activated protein kinase 3 (MAPK3), mitogen-activated protein kinase 1 (MAPK1), mitogen-activated protein kinase 8 (MAPK8), RAC-alpha serine/threonine-protein kinase (AKT1), transcription factor p65 (RELA) and phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform (PIK3CA) were identified as the most critical targets, and kaempferol showed effective binding activities with the above critical eight targets. Further, anti-COVID-19/PF co-occurrence effects of kaempferol were associated with the regulation of inflammation, oxidative stress, immunity, virus infection, cell growth process and metabolism. EGFR, interleukin 17 (IL-17), tumor necrosis factor (TNF), hypoxia inducible factor 1 (HIF-1), phosphoinositide 3-kinase/AKT serine/threonine kinase (PI3K/AKT) and Toll-like receptor signaling pathways were identified as the key anti-COVID-19/PF co-occurrence pathways. Conclusion: Kaempferol is a candidate treatment for COVID-19/PF co-occurrence. The underlying mechanisms may be related to the regulation of critical targets (EGFR, SRC, MAPK3, MAPK1, MAPK8, AKT1, RELA, PIK3CA and so on) and EGFR, IL-17, TNF, HIF-1, PI3K/AKT and Toll-like receptor signaling pathways. This study contributes to guiding development of new drugs for COVID-19 and PF comorbidities.
Resistance to targeted drugs is now a challenging clinical problem in the treatment of non-small cell lung cancer (NSCLC). So far, there are no approved targeted therapeutic drugs for patients with disease progression after the third-generation epidermal growth factor receptor-tyrosine kinase inhibitor osimertinib resistance (OR). Super-enhancers (SEs) are large clusters of transcriptional enhancers that drive gene expression. In this study, we aimed to explore the potential pathogenic SEs and their driven genes in OR NSCLC. OR cell line was established by exposure of H1975 cells to incremental dosing of osimertinib. RNA-sequencing and H3K27ac ChIP-sequencing were used to identify the differential expressed genes (DEGs) and SEs in parental and resistant cells. Gene ontology analysis for the OR-specific SEs-associated genes showed that histone citrullination, protein citrullination, and peptidyl-arginine modification are the top three biological processes, and the DEGs involved in these biological processes, including peptidyl arginine deiminase 1 (PADI1), PADI2, and PADI3. Realtime-PCR and western blot detections confirmed these genes were highly expressed in OR cells. SE inhibitor decreases their expression, ensuring that SEs regulate their transcriptional expressions. The PADI inhibitor inhibited OR cells’ proliferation, invasion, and colony formation. This study demonstrates that SE-driven PADI family genes are potential biomarkers and targets for OR NSCLC.
This study aimed to explore the effect of Jatrorrhizine on treating GR NSCLC and its possible mechanism of action. Cell viability and migration were performed to preliminarily evaluate the anti-tumor effect of Huanglian extract and Jatrorrhizine on inhibiting NSCLC cell. Swiss Target Prediction and Traditional Chinese Medicine Database, GeneCards and Online Mendelian Inheritance in Man databases and molecular docking were carried out to explore the targets of Jatrorrhizine. Western Blot was conducted to detect the effect of Jatrorrhizine on inhibiting PI3K/mTOR signaling pathway. Jatrorrhizine has similar anti-tumor effect on inhibiting the proliferation and migration of H1975 cells. Jatrorrhizine could dose-dependently inhibit the proliferation, invasion and promote the apoptosis of human NSCLC cell. The PI3K-Akt signaling pathway was preliminarily predicted (Kyoto Encyclopedia of Genes and Genomes) and verified (Molecular docking) to be an critical pathway of jatrorrhizine against NSCLC. The cytotoxic assay of PI3K/mTOR inhibitor PKI-402 on H1975 cells and ADP-Glo Kinase assay of the inhibitory effect of Jatrorrhizine on PI3K kinase activity. Western Blot verified that Jatrorrhizine down-regulates the phosphorylation of PI3K/ mTOR in H1975 cells. Conclusion: Our results revealed that jatrorrhizine is a potentially novel compound that inhibits GR NSCLC by inhibiting PI3K/mTOR phosphorylation.
Abnormal fibroblast differentiation into myofibroblast is a crucial pathological mechanism of pulmonary fibrosis (PF). Super-enhancers, a newly discovered cluster of regulatory elements, are regarded as the regulators of cell identity. We speculate that abnormal activation of super-enhancers must be involved in the pathological process of PF. This study aims to identify potential pathogenic super-enhancer-driven genes in PF. Differentially expressed genes (DEGs) in PF mouse lungs were identified from a GEO dataset (GDS1492). We collected super-enhancers and their associated genes in human lung fibroblasts and mouse embryonic fibroblasts from SEA version 3.0, a network database that provides comprehensive information on super-enhancers. We crosslinked upregulated DEGs and super-enhancer-associated genes in fibroblasts to predict potential super-enhancer-driven pathogenic genes in PF. A total of 25 genes formed an overlap, and the protein-protein interaction network of these genes was constructed by the STRING database. An interaction network of transcription factors (TFs), super-enhancers, and associated genes was constructed using the Cytoscape software. Gene enrichment analyses, including KEGG pathway and GO analysis, were performed for these genes. Latent transforming growth factor beta (TGF-β) binding protein 2 (LTBP2), one of the predicted super-enhancer-driven pathogenic genes, was used to verify the predicted network’s accuracy. LTBP2 was upregulated in the lungs of the bleomycin-induced PF mouse model and TGF-β1-stimulated mouse and human fibroblasts. Myc is one of the TFs binding to the LTBP2 super-enhancer. Knockout of super-enhancer sequences with a CRISPR/Cas9 plasmid or inhibition of Myc all decreased TGF-β1-induced LTBP2 expression in NIH/3 T3 cells. Identifying and interfering super-enhancers might be a new way to explore possible therapeutic methods for PF.
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