Abstract: Background: Clinical research found that TCM is therapeutic in treating gastric cancer. Clearing heat is the most common method, while some antirheumatic medicines are widely used in treatment as well. To explore the pharmacological mechanism, we researched the comparison between heat-clearing medicine and antirheumatic medicine in treating gastric cancer. Methods: First, related ingredients and targets were searched, respectively, and are shown in an active ingredient-target network. Combining the relevant targets of gastric cancer, we constructed a PPI network and MCODE network. Then, GO and KEGG enrichment analyses were conducted. Molecular docking experiments were performed to verify the affinity of targets and ligands. Finally, we analyzed the tumor immune infiltration on gene expression, somatic CNA, and clinical outcome. Results: A total of 31 ingredients and 90 targets of heat-clearing medicine, 31 ingredients and 186 targets of antirheumatic medicine, and 12,155 targets of gastric cancer were collected. Antirheumatic medicine ranked the top in all the enrichment analyses. In the KEGG pathway, both types of medicines were related to pathways in cancer. In the KEGG map, AR, MMP2, ERBB2, and TP53 were the most crucial targets. Key targets and ligands were docked with low binding energy. Analysis of tumor immune infiltration showed that the expressions of AR and ERBB2 were correlated with the abundance of immune infiltration and made a difference in clinical outcomes. Conclusions: Quercetin is an important ingredient in both heat-clearing medicine and antirheumatic medicine. AR signaling pathway exists in both types of medicines. The mechanism of the antitumor effect in antirheumatic medicine was similar to trastuzumab, a targeted drug aimed at ERBB2. Both types of medicines were significant in tumor immune infiltration. The immunology of gastric tumor deserves further research.

Abstract: Background: Vascular calcification (VC) constitutes subclinical vascular burden and increases cardiovascular mortality. Effective therapeutics for VC remains to be procured. We aimed to use a deep learning-based strategy to screen and uncover plant compounds that potentially can be repurposed for managing VC. Methods: We integrated drugome, interactome, and diseasome information from Comparative Toxicogenomic Database (CTD), DrugBank, PubChem, Gene Ontology (GO), and BioGrid to analyze drug-disease associations. A deep representation learning was done using a high-level description of the local network architecture and features of the entities, followed by learning the global embeddings of nodes derived from a heterogeneous network using the graph neural network architecture and a random forest classifier established for prediction. Predicted results were tested in an in vitro VC model for validity based on the probability scores. Results: We collected 6,790 compounds with available Simplified Molecular-Input Line-Entry System (SMILES) data, 11,958 GO terms, 7,238 diseases, and 25,482 proteins, followed by local embedding vectors using an end-to-end transformer network and a node2vec algorithm and global embedding vectors learned from heterogeneous network via the graph neural network. Our algorithm conferred a good distinction between potential compounds, presenting as higher prediction scores for the compound categories with a higher potential but lower scores for other categories. Probability score-dependent selection revealed that antioxidants such as sulforaphane and daidzein were potentially effective compounds against VC, while catechin had low probability. All three compounds were validated in vitro. Conclusions: Our findings exemplify the utility of deep learning in identifying promising VC-treating plant compounds. Our model can be a quick and comprehensive computational screening tool to assist in the early drug discovery process.

Abstract: Materials and Methods: Active compounds of LJP were examined established on the analysis platform, Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. DrugBank identified drug targets and annotated them on UniPort and GeneCards. Besides, the COM-related genes were identified on GeneCards. The network of the drug, main active compounds, targets, and diseases was built utilizing Cytoscape. STRING was utilized to build the protein-protein interaction network. Moreover, the KEGG and GO pathway enrichment analysis were applied to analyze biological function. Results: 23 active compounds of LJP were screened, and 204 drug targets and 686 COM-related genes were identified. Forty-five intersection genes were overlapped from 204 drug targets and 686 COM-related genes. The drug-active compounds-target protein-diseases network was established based on 23 active compounds of LJP and 45 intersection genes. Moreover, the interaction of 45 intersection genes was explored by the PPI network, and the drug-active compounds-target protein-diseases network was formed grounded by 23 active compounds of LJP, 45 intersection genes, and PPI network. The KEGG and GO pathway enrichment analysis specified that 45 intersection genes primarily enriched in immune-related pathways and oxidative stress-related pathways. Conclusions: In the research done, the main active compounds of LJP and drug targets in the treatment of COM were identified. Our findings might provide the ingredient option of LJP and drug targets of LJP in COM treatment.

Abstract: As a heterogeneous and wide inflammation, osteoarticular infection (OAI) shows an increasing incidence in recent years. Staphylococcus aureus is the most important pathogen causing OAI. The antibiotic treatment will affect the outcomes of OAI patients, and the drug selection and dosage regimen highly rely on patients' variability, pathogen susceptibility, and drug property like bone permeability. Model-informed precision dosing (MIPD) provides options to describe and quantify the pharmacokinetic (PK) variability of the OAI population using different models, such as the population pharmacokinetic (PPK) model and physiological-based pharmacokinetic (PB/PK) model. In the present review, we highlighted that the MIPD of antibiotics played a critical role in OAI and listed the dose regimen recommended by the model. Collectively, our current study provided a valuable reference for the treatment of patients and improved the safety and efficiency of drug use.

Abstract: Purpose: The network pharmacology approach and validation experiment were performed to investigate the potential mechanisms of Agrimonia pilosa Ledeb. (APL) extract against acute myocardial infarction (AMI). Methods: The primary compounds of APL extract were identified by High-Performance Liquid Chromatography (HPLC) analysis. The intersecting targets of active compounds and AMI were determined via network pharmacology analysis. A mouse model of AMI was established by subcutaneous injection of isoproterenol (Iso). Mice were treated with APL extract by intragastric administration. We assessed the effects of APL extract on the electrocardiography (ECG), cardiac representative markers, representative indicators of oxidative stress, pathological changes, and phosphatidylinositol-3-kinase (PI3K)/protein kinase B (Akt) signaling pathway, as well as apoptosis-related indicators in the mice. Results: Five candidate compounds were identified in APL extract. Enrichment analyses indicated that APL extract could exert myocardial protective effects via the PI3K/Akt pathway. ST segment elevation and increased heart rate were obviously reversed in APL extract groups compared to Iso group. We also detected significant decreases in lactate dehydrogenase (LDH), creatine kinase (CK), creatine kinase MB (CK-MB), malondialdehyde (MDA), and reactive oxygen species (ROS), as well as a significant increase in superoxide dismutase activities (SOD) after APL extract treatment. In addition, APL extract markedly decreased the number of apoptotic cardiomyocytes after AMI. In the APL extract groups of AMI mice, there were increased expression levels of p-PI3K, p-Akt, and B-cell lymphoma-2 (Bcl-2) protein, and there were decreases in Bcl-2-associated X (Bax), cysteinyl aspartate-specific proteases-3 (caspase-3), and cleaved-caspase-3 protein expression levels, as well as the Bax/Bcl-2 ratio. Conclusion: APL extract had a protective effect against Iso-induced AMI. APL extract could ameliorate AMI through antioxidant and anti-apoptosis actions which may be associated with the activation of the PI3K/Akt signaling pathway.

Abstract: Untargeted metabolomics aims at measuring the entire set of metabolites in a wide range of biological samples. However, due to the high chemical diversity of metabolites that range from small to large and more complex molecules (i.e., amino acids/carbohydrates vs. phospholipids/gangliosides), the identification and characterization of the metabolome remain a major bottleneck. The first step of this process consists of searching the experimental monoisotopic mass against databases, thus resulting in a highly redundant/complex list of candidates. Despite the progress in this area, researchers are still forced to manually explore the resulting table in order to prioritize the most likely identifications for further biological interpretation or confirmation with standards. Here, we present TurboPutative (https://proteomics.cnic.es/TurboPutative/), a flexible and user-friendly web-based platform composed of four modules (Tagger, REname, RowMerger, and TPMetrics) that streamlines data handling, classification, and interpretability of untargeted LC-MS-based metabolomics data. Tagger classifies the different compounds and provides preliminary insights into the biological system studied. REname improves putative annotation handling and visualization, allowing the recognition of isomers and equivalent compounds and redundant data removal. RowMerger reduces the dataset size, facilitating the manual comparison among annotations. Finally, TPMetrics combines different datasets with feature intensity and relevant information for the researcher and calculates a score based on adduct probability and feature correlations, facilitating further identification, assessment, and interpretation of the results. The TurboPutative web application allows researchers in the metabolomics field that are dealing with massive datasets containing multiple putative annotations to reduce the number of these entries by 80%-90%, thus facilitating the extrapolation of biological knowledge and improving metabolite prioritization for subsequent pathway analysis. TurboPutative comprises a rapid, automated, and customizable workflow that can also be included in programmed bioinformatics pipelines through its RESTful API services. Users can explore the performance of each module through demo datasets supplied on the website. The platform will help the metabolomics community to speed up the arduous task of manual data curation that is required in the first steps of metabolite identification, improving the generation of biological knowledge.

Abstract: Porphyromonas gingivalis is a major pathogenic bacterium involved in the pathogenesis of periodontitis. Citrullination has been reported as the underlying mechanism of the pathogenesis, which relies on the interplay between two virulence factors of the bacterium, namely gingipain R and the bacterial peptidyl arginine deiminase. Gingipain R cleaves host proteins to expose the C-terminal arginines for peptidyl arginine deiminase to citrullinate and generate citrullinated proteins. Apart from carrying out citrullination in the periodontium, the bacterium is found capable of citrullinating proteins present in the host synovial tissues, atherosclerotic plaques and neurons. Studies have suggested that both virulence factors are the key factors that trigger distal effects mediated by citrullination, leading to the development of some non-communicable diseases, such as rheumatoid arthritis, atherosclerosis, and Alzheimer's disease. Thus, inhibition of these virulence factors not only can mitigate periodontitis, but also can provide new therapeutic solutions for systematic diseases involving bacterial citrullination. Herein, we described both these proteins in terms of their unique structural conformations and biological relevance to different human diseases. Moreover, investigations of inhibitory actions on the enzymes are also enumerated. New approaches for identifying inhibitors for peptidyl arginine deiminase through drug repurposing and virtual screening are also discussed.

Abstract: Objective: Neonatal hyperbilirubinemia is caused by the excessive production of bilirubin and decreased excretion ability in the neonatal period. It leads to a concentration of blood bilirubin that exceeds a certain threshold. Yinzhihuang oral liquid (YZH) is a traditional Chinese medicine mixture used in the treatment of neonatal hyperbilirubinemia in China. This article systematically explores the pharmacological mechanisms by which YZH acts in the treatment of neonatal hyperbilirubinemia through network pharmacology at the molecular level. Methods: We adopted the method of network pharmacology, which includes active component prescreening, target gene prediction, gene enrichment analysis, and network analysis. Results: According to the network pharmacological analysis, 8 genes (STAT3, AKT1, MAPK14, JUN, TP53, MAPK3, ESR1, and RELA) may be targets of YZH in the treatment of neonatal hyperbilirubinemia. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that YZH may regulate antioxidation, modulate lipid metabolism, and have anti-infective properties. Conclusion: In this study, the pharmacological action and molecular mechanisms of YZH were predicted as a whole. It was found that YZH is a promising drug for treating oxidative stress due to bilirubin, as it reduces immunosuppression and helps to eliminate virus infection.

Abstract: Background: Glucocorticoid-induced osteoporosis (GIOP) is a common form of secondary osteoporosis caused by the protracted or a large dosage of glucocorticoids (GCs). Total flavonoids of Drynariae rhizoma (TFDR) have been widely used in treating postmenopausal osteoporosis (POP). However, their therapeutic effects and potential mechanism against GIOP have not been fully elucidated. Methods: Ultra-high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESIQ-TOF-MS) experiments were performed for qualitative analysis. We performed hematoxylin-eosin (HE) staining and microcomputed tomography (micro-CT) analysis to detect the changes in bone microstructure. The changes in biochemical parameters in the serum samples were determined by performing an enzyme-linked immunosorbent assay (ELISA). The prediction results of network pharmacology were verified via quantitative real-time polymerase chain reaction (qRT-PCR) to elucidate the potential mechanism of TFDR against GIOP. Results: A total of 191 ingredients were identified in vitro and 48 ingredients in vivo. In the in-vivo experiment, the levels of the serum total cholesterol (TC), the serum triglyceride (TG), Leptin (LEP), osteocalcin (OC), osteoprotegerin (OPG), bone morphogenetic protein-2 (BMP-2), propeptide of type I procollagen (PINP), tartrate-resistant acid phosphatase (TRACP) and type-I collagen carboxy-terminal peptide (CTX-1) in the TFDR group significantly changed compared with those in the GIOP group. Moreover, the TFDR group showed an improvement in bone mineral density and bone microstructure. Based on the results of network pharmacology analysis, 67 core targets were selected to construct the network and perform PPI analysis as well as biological enrichment analysis. Five of the targets with high "degree value" had differential gene expression between groups using qRT-PCR. Conclusion: TFDR, which may play a crucial role between adipose metabolism and bone metabolism, may be a novel remedy for the prevention and clinical treatment of GIOP.

Abstract: The rapid and accurate evaluation of chemical toxicity is of great significance for estimation of chemical safety. In the past decades, a great number of excellent computational models have been developed for chemical toxicity prediction. But most machine learning models tend to be "black box", which bring about poor interpretability. In the present study, we focused on the identification and collection of structural alerts (SAs) responsible for a series of important toxicity endpoints. Then, we carried out effective storage of these structural alerts and developed a web-server named SApredictor (www.sapredictor.cn) for screening chemicals against structural alerts. People can quickly estimate the toxicity of chemicals with SApredictor, and the specific key substructures which cause the chemical toxicity will be intuitively displayed to provide valuable information for the structural optimization by medicinal chemists.