Abstract: Solasonine, a steroidal alkaloid extracted from Solanum nigrum L., has been found to exert inhibitory effect on cancers. However, the underlying anticancer mechanisms of solasonine, particularly in urinary bladder cancer (BC), remain unclear. In this study, we identified the potential targets and biological functions associated with solasonine activity using a bioinformatics approach. Ingenuity pathway analysis revealed that neuropilin-1 (NRP1) and other signaling pathways, such as PI3K/AKT and ERK/MAPK pathways, were potentially involved in the therapeutic effects of solasonine. The ability of solasonine in inducing apoptosis and inhibiting proliferation in BC cells was confirmed experimentally, and the inhibition of ERK/MAPK, P38/MAPK, and PI3K/AKT pathways was validated by Western blot. Mechanistically, solasonine suppressed the expression of NRP1 protein, but not that of mRNA. Further results of molecular docking and molecular dynamics simulation analysis indicated that solasonine could directly bind to the b1 domain of NRP1 protein with a reasonable and stable docking conformation. We previously found that targeting NRP1 is a potential antitumor strategy. Combined with these findings, it can be speculated that the binding of solasonine with NRP1 on the cell membrane could prevent the formation of NRP1/VEGFA/VEGFR2 and NRP1/EGFR complexes, resulting in the inhibition of downstream signaling, including ERK/MAPK, P38/MAPK, and PI3K/AKT pathways. Additionally, intracellular solasonine could inhibit the membrane localization of NRP1 and provoke its cytoplasmic retention, facilitating the degradation of NRP1 protein in the cytoplasm. The dual effects induced by the binding of solasonine to NRP1 extracellularly and intracellularly could account for the antiproliferative and proapoptotic effects of solasonine on BC.

Abstract: The application of network pharmacology has greatly promoted the scientific interpretation of disease treatment mechanism of traditional Chinese medicine (TCM). However, the data required by network pharmacology analysis were scattered in different resources. In the present work, by integrating and reorganizing the data from multiple resources, we developed the intelligent network pharmacology platform unique for traditional Chinese medicine, called INPUT (http://cbcb.cdutcm.edu.cn/INPUT/), for automatically performing network pharmacology analysis. Besides the curated data collected from multiple resources, a series of bioinformatics tools for network pharmacology analysis were also embedded in INPUT, which makes it become the first automatic platform able to explore the disease treatment mechanisms of TCM. With the built-in tools, researchers can also analyze their own in-house data and obtain the results of pivotal ingredients, GO and KEGG pathway, protein-protein interactions, etc. In addition, as a proof-of-principle, INPUT was applied to decipher the antidepressant mechanism of a commonly used prescription. In summary, INPUT is a powerful platform for network pharmacology analysis and will facilitate the researches on drug discovery.

Abstract: Antibiotic resistance is a major public health concern. Antibiotic combinations, offering better efficacy at lower doses, are a useful way to handle this problem. However, it is difficult for us to find effective antibiotic combinations in the vast chemical space. Herein, we propose a graph learning framework to predict synergistic antibiotic combinations. In this model, a network proximity method combined with network propagation was used to quantify the relationships of drug pairs, and we found that synergistic antibiotic combinations tend to have smaller network proximity. Therefore, network proximity can be used for building an affinity matrix. Subsequently, the affinity matrix was fed into a graph regularization model to predict potential synergistic antibiotic combinations. Compared with existing methods, our model shows a better performance in the prediction of synergistic antibiotic combinations and interpretability.

Abstract: Increased uric acid levels and inflammatory reactions are the main factors considered responsible for the development of gouty arthritis. Phyllanthus emblica L. (PEL) has several promising pharmacological properties, including anti-inflammation and antioxidation. However, only a few studies have investigated its use for treating acute gouty arthritis (AGA), and the mechanism of action of PEL has not yet been clarified. The aim of this study was to verify the protective effects of PEL against gout and explore its underlying mechanism through network pharmacology and animal experiments. The main active components of the extract from PEL including mucic acid, mucic acid lactone, gallic acid, ethyl hexyl phthalate, and glucose were identified by UPLC-ESI-qTOF-MS. Network pharmacological analysis results revealed 13 active compounds in PEL and 85 related targets for the treatment of gout. The core mechanism of action of PEL is mainly associated with inflammation-related pathways, including the HIF-1, PI3K-Akt, TNF, and NOD-like receptor signaling pathways. Previous studies revealed that the NOD-like receptor signaling pathway, especially the NLRP3 inflammasome, plays an important role in the pathogenesis of AGA; therefore, we mainly investigated the effect of PEL on the NLRP3/ASC/caspase-1 pathway in gout rats. In the animal experiments, PEL was shown to have a satisfactory antigout effect, as it effectively reduced uric acid (UA) and xanthine oxidase (XOD) levels. In terms of inhibiting AGA-associated inflammatory reactions, our results showed that PEL significantly decreased the expression of NLRP3 and caspase-1 in ankle synoviocytes as well as the levels of downstream inflammatory factors, such as TNF-alpha, IL-10, and IL-1beta in serum. Moreover, the results of our study show that PEL reduced MMP13 expression in the ankle synovium. Overall, the results of this study indicate that PEL exerted a therapeutic effect against AGA. Reducing uric acid levels, inhibiting inflammation, and decreasing the expression of MMP13 may be responsible for the therapeutic effect of PEL, which suggests that PEL can be further developed as a drug for the treatment of gout.

Abstract: Quercetin (QUE), a health supplement, can improve renal function in diabetic nephropathy (DN) rats by ameliorating podocyte injury. Its clinical trial for renal insufficiency in advanced diabetes (NCT02848131) is currently underway. This study aimed to investigate the mechanism of QUE protecting against podocyte injury to attenuate DN through network pharmacology, microarray data analysis, and molecular docking. QUE-associated targets, genes related to both DN, and podocyte injury were obtained from different comprehensive databases and were intersected and analyzed to obtain mapping targets. Candidate targets were identified by constructing network of protein-protein interaction (PPI) of mapping targets and ranked to obtain key targets. The major pathways were obtained from Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) term enrichment analysis of candidate targets via ClueGO plug-in and R project software, respectively. Potential receptor-ligand interactions between QUE and key targets were evaluated via Autodocktools-1.5.6. 41. Candidate targets, of which three key targets (TNF, VEGFA, and AKT1), and the major AGE-RAGE signaling pathway in diabetic complications were ascertained and associated with QUE against podocyte injury in DN. Molecular docking models showed that QUE could closely bind to the key targets. This study revealed that QUE could protect against podocyte injury in DN through the following mechanisms: downregulating inflammatory cytokine of TNF, reducing VEGF-induced vascular permeability, inhibiting apoptosis by stimulating AKT1 phosphorylation, and suppressing the AGE-induced oxidative stress via the AGE-RAGE signaling pathway.

Abstract: MOTIVATION: Convolutional neural networks have enabled unprecedented breakthroughs in a variety of computer vision tasks. They have also drawn much attention from other domains, including drug discovery and drug development. In this study, we develop a computational method based on convolutional neural networks to tackle a fundamental question in drug discovery and development, i.e. the prediction of compound-protein interactions based on compound structure and protein sequence. We propose a hierarchical graph convolutional network (HGCN) to encode small molecules. The HGCN aggregates a molecule embedding from substructure embeddings, which are synthesized from atom embeddings. As small molecules usually share substructures, computing a molecule embedding from those common substructures allows us to learn better generic models. We then combined the HGCN with a one-dimensional convolutional network to construct a complete model for predicting compound-protein interactions. Furthermore we apply an explanation technique, Grad-CAM, to visualize the contribution of each amino acid into the prediction. RESULTS: Experiments using different datasets show the improvement of our model compared to other GCN-based methods and a sequence based method, DeepDTA, in predicting compound-protein interactions. Each prediction made by the model is also explainable and can be used to identify critical residues mediating the interaction.

Abstract: Purpose: Jiawei-Huang Lian-Gan Jiang decoction (JWHLGJD) was developed to treat and prevent the patients with colorectal adenomas (CRA) in China. This study is aimed to discover JWHLGJD's active compounds and demonstrate mechanisms of JWHLGJD against CRA through network pharmacology and molecular docking techniques. Methods: All the components of JWHLGJD were retrieved from the pharmacology database of Traditional Chinese Medicine Systems Pharmacology (TCMSP). The GeneCards database, the Online Mendelian Inheritance in Man database (OMIM), the DrugBank database, and PharmGKB were used to obtain the genes matching the targets. Cytoscape created the compound-target network. The network of target protein-protein interactions (PPI) was constructed using the STRING database. Gene Ontology (GO) functional and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways involved in the targets were analyzed by using the DAVID database. Cytoscape created the component-target-pathway (C-T-P) network. AutoDock Vina software was used to verify the molecular docking of JWHLGJD components and key targets. Core genes linked with survival and tumor microenvironment were analyzed through the Kaplan-Meier plotter and TIMER 2.0 databases, respectively. Results: Compound-target network mainly contained 38 compounds and 130 targets of the JWHLGJD associated with CRA. TP53, MAPK1, JUN, HSP90AA1, and AKT1 were identified as core targets by the PPI network. KEGG pathway shows that the pathways in cancer, lipids, and atherosclerosis, PI3K-Akt signaling pathway and MAPK signaling pathway, are the most relevant pathways to CRA. The C-T-P network suggests that the active component in JWHLGJD is capable of regulating target genes of these related pathways. The results of molecular docking showed that berberine and stigmasterol were the top two compounds of JWHLGJD, which had high affinity with TP53 and MAPK1, respectively. And, MAPK1 exerted a more significant effect on the prognosis of adenocarcinoma, for it was highly associated with various immune cells. Conclusion: Findings in this study provided light on JWHLGJD's active components, prospective targets, and molecular mechanism. It also gave a potential way to uncovering the scientific underpinning and therapeutic mechanism of traditional Chinese medicine (TCM) formulas.

Abstract: The standard therapy administered to patients with advanced esophageal cancer remains uniform, despite its two main histological subtypes, namely esophageal squamous cell carcinoma (SCC) and esophageal adenocarcinoma (AC), are being increasingly considered to be different. The identification of potential drug target genes between SCC and AC is crucial for more effective treatment of these diseases, given the high toxicity of chemotherapy and resistance to administered medications. Herein we attempted to identify and rank differentially expressed genes (DEGs) in SCC vs. AC using ensemble feature selection methods. RNA-seq data from The Cancer Genome Atlas and the Fudan-Taizhou Institute of Health Sciences (China). Six feature filters algorithms were used to identify DEGs. We built robust predictive models for histological subtypes with the random forest (RF) classification algorithm. Pathway analysis also be performed to investigate the functional role of genes. 294 informative DEGs (87 of them are newly discovered) have been identified. The areas under receiver operator curve (AUC) were higher than 99.5% for all feature selection (FS) methods. Nine genes (i.e., ERBB3, ATP7B, ABCC3, GALNT14, CLDN18, GUCY2C, FGFR4, KCNQ5, and CACNA1B) may play a key role in the development of more directed anticancer therapy for SCC and AC patients. The first four of them are drug targets for chemotherapy and immunotherapy of esophageal cancer and involved in pharmacokinetics and pharmacodynamics pathways. Research identified novel DEGs in SCC and AC, and detected four potential drug targeted genes (ERBB3, ATP7B, ABCC3, and GALNT14) and five drug-related genes.

Abstract: Background: Inflammatory bowel disease (IBD) is a major cause of morbidity and mortality due to its repetitive remission and relapse. The Jian-Wei-Yu-Yang (JW) formula has a historical application in the clinic to combat gastrointestinal disorders. The investigation aimed to explore the molecular and cellular mechanisms of JW. Methods: 2% dextran sodium sulfate (DSS) was diluted in drinking water and given to mice for 5 days to establish murine models of experimental colitis, and different doses of JW solution were administered for 14 days. Network pharmacology analysis and weighted gene co-expression network analysis (WGCNA) were utilized to predict the therapeutic role of JW against experimental colitis and colitis-associated colorectal cancer (CAC). 16S rRNA sequencing and untargeted metabolomics were conducted using murine feces. Western blotting, immunocytochemistry, and wound healing experiments were performed to confirm the molecular mechanisms. Results: (1) Liquid chromatography with mass spectrometry was utilized to confirm the validity of the JW formula. The high dose of JW treatment markedly attenuated DSS-induced experimental colitis progression, and the targets were enriched in inflammation, infection, and tumorigenesis. (2) The JW targets were related to the survival probability in patients with colorectal cancer, underlying a potential therapeutic value in CRC intervention. (3) Moreover, the JW therapy successfully rescued the decreased richness and diversity of microbiota, suppressed the potentially pathogenic phenotype of the gut microorganisms, and increased cytochrome P450 activity in murine colitis models. (4) Our in vitro experiments confirmed that the JW treatment suppressed caspase3-dependent pyroptosis, hypoxia-inducible factor 1alpha (HIF1alpha), and interleukin-1b (IL-1b) in the colon; facilitated the alternative activation of macrophages (Mphis); and inhibited tumor necrosis factor-alpha (TNFalpha)-induced reactive oxygen species (ROS) level in intestinal organoids (IOs). Conclusion: The JW capsule attenuated the progression of murine colitis by a prompt resolution of inflammation and bloody stool and by re-establishing a microbiome profile that favors re-epithelization and prevents carcinogenesis.

Abstract: (4E)-7-(4-Hydroxy-3-methoxyphenyl)-1-phenylhept-4-en-3-one (DPHB) derived from A. officinarum Hance has been reported to exert anti-inflammatory and anti-insulin resistance (IR) effects. We explored the molecular mechanism of DPHB ameliorating IR through network pharmacological prediction and in vitro analysis. The PI3K/AKT and TNF signaling pathways are the core pathways for DPHB to exert anti-IR, and the key proteins of this pathway were confirmed by molecular docking. In the IR-3T3-L1 adipocyte model, DPHB significantly promoted glucose uptake and the glucose transporter type 4 (GLUT4) translocation. In addition, DPHB significantly improved lipid accumulation, triglyceride content, and the mRNA expression of key adipokines [such as peroxisome proliferator-activated receptors-gamma (PPARgamma), CCAAT enhancer-binding protein alpha (C/EBPalpha), and sterol regulatory element-binding protein-1 (SREBP-1)]. DPHB inhibited the protein expression of tumor necrosis factor-alpha (TNF-alpha), interleukin-6 (IL-6), and phosphorylated nuclear factor-kappaB (NF-kB), as well as promoted the expression of phosphatidylinositol 3-kinase (PI3K), protein kinase B (AKT), phosphorylated PI3K, and phosphorylated AKT. More interestingly, validation of the PI3K inhibitor LY294002 revealed that these changes were dependent on the activation of PI3K. Our cumulative findings thereby validate the potential of DPHB to alleviate and treat IR and the related diseases by regulating the PI3K/AKT and TNF-alpha signaling pathways.