Background: Rheumatoid arthritis (RA) and depression are prevalent diseases that have a negative impact on the quality of life and place a significant economic burden on society. There is increasing evidence that the two diseases are closely related, which could make the disease outcomes worse. In this study, we aimed to identify diagnostic markers and analyzed the therapeutic potential of key genes.Methods: We assessed the differentially expressed genes (DEGs) specific for RA and Major depressive disorder (MDD) and used weighted gene co-expression network analysis (WGCNA) to identify co-expressed gene modules by obtaining the Gene expression profile data from Gene Expression Omnibus (GEO) database. By using the STRING database, a protein–protein interaction (PPI) network constructed and identified key genes. We also employed two types of machine learning techniques to derive diagnostic markers, which were assessed for their association with immune cells and potential therapeutic effects. Molecular docking and in vitro experiments were used to validate these analytical results.Results: In total, 48 DEGs were identified in RA with comorbid MDD. The PPI network was combined with WGCNA to identify 26 key genes of RA with comorbid MDD. Machine learning-based methods indicated that RA combined with MDD is likely related to six diagnostic markers: AURKA, BTN3A2, CXCL10, ERAP2, MARCO, and PLA2G7. CXCL10 and MARCO are closely associated with diverse immune cells in RA. However, apart from PLA2G7, the expression levels of the other five genes were associated with the composition of the majority of immune cells in MDD. Molecular docking and in vitro studies have revealed that Aucubin (AU) exerts the therapeutic effect through the downregulation of CXCL10 and BTN3A2 gene expression in PC12 cells.Conclusion: Our study indicates that six diagnostic markers were the basis of the comorbidity mechanism of RA and MDD and may also be potential therapeutic targets. Further mechanistic studies of the pathogenesis and treatment of RA and MDD may be able to identify new targets using these shared pathways.

The combination of traditional Chinese medicine (TCM) and Western medicine is a promising method for treating rheumatoid arthritis (RA). Combining the two fully exploits the advantages of Western and TCM to treat RA and has the potential to greatly improve the therapeutic effect on RA. In this study, we developed a combination drug training set by using 16 characteristic variables based on the characteristics of small molecules of TCM ingredients and Food and Drug Administration‐certified combination drug data downloaded from the DrugCombDB database. Furthermore, we compared the prediction and classification abilities of five models: the k‐nearest neighbors, naive Bayes, support vector machine, random forest, and AdaBoost algorithms. The random forest model was selected as the classification and prediction model for Western and TCM and Western combination drugs. We collected data for 41 small molecules of TCM ingredients from the Traditional Chinese Medicine Systems Pharmacology database and 10 small molecule drugs commonly used in anti‐RA treatment from the DrugBank database. Combinations of Western and TCM for anti‐RA treatment were screened. Finally, the CellTiter‐Glo method was used to determine the synergy of these combinations, and the 15 most predicted drug combinations were carried out experimental verification. Myricetin, rhein, nobiletin, and fisetin had high synergy with celecoxib, and rhein had high synergy with hydroxychloroquine. The preliminary findings of this study can be further applied for practical clinical anti‐RA combined treatment strategies and serve as a reference for clinical treatment of RA with integrated Western and TCM.

Background: Achyranthes bidentata (AR) is a traditional Chinese herb used for the treatment of hypertension and cerebral ischemia, but its pharmacological effects are not known. Aim of study: We aimed to detect and accurately identify the components and metabolites of AR in the plasma and brain tissue of Sprague Dawley rats. Methods: We employed ultrahigh performance liquid chromatography–high-resolution mass spectrometry (UHPLC–HR-MS) to detect AR components in the plasma and brain tissue of rats. The absorption and metabolites in the plasma and brain tissue of normal control rats and rats that underwent middle cerebral artery occlusion (MCAO) were characterized and compared. Results: A total of 281 compounds, including alkaloids, flavonoids, terpenoids, phenylpropanes, sugars and glycosides, steroids, triterpenes, amino acids, and peptides, was identified in samples of Achyranthes bidentata (TCM-AR). Four types of absorbable prototype components and 48 kinds of metabolites were identified in rats in the normal control plasma group which were given AR (AR plasma group), and five kinds of metabolites were identified in rats of the normal control brain tissue group which were given AR (AR brain group). Three absorbed prototype components and 13 metabolites were identified in the plasma of rats which underwent MCAO and were given AR (MCAO + AR plasma group). Six absorbed prototype components and two metabolites were identified in the brain tissue of rats who underwent MCAO and were administered AR (MCAO + AR brain group). These results showed that, after the oral administration of AR, the number of identified components in plasma was more than that in brain tissue. The number of prototype components in the AR plasma group was higher than that in the MCAO + AR plasma group, which may indicate that metabolite absorption in rats undergoing MCAO was worse. The number of prototype components in the MCAO + AR brain group was higher than that in the AR brain group, indicating that the blood–brain barrier was destroyed after MCAO, resulting in more compounds entering brain tissue. Conclusions: UHPLC–HR-MS was used to rapidly analyze the components and metabolites of AR in the blood and brain of rats under normal and pathologic conditions, and to comprehensively characterize the components of TCM-AR. We also analyzed and compared the absorbable components and metabolites of normal rats under cerebral ischemia-reperfusion injury to explore the potential mechanism of action. This method could be applied to various Chinese herbs and disease models, which could promote TCM modernization.

Non-alcoholic fatty liver disease (NAFLD) is usually associated with obesity. However, it is crucial to recognize that NAFLD can also occur in lean individuals, which is frequently overlooked. Without an approved pharmacological therapy for lean NAFLD, we aimed to investigate whether the Ganjianglingzhu (GJLZ) decoction, a representative traditional Chinese medicine (TCM), protects against lean NAFLD and explore the potential mechanism underlying these protective effects. The mouse model of lean NAFLD was established with a methionine-choline-deficient (MCD) diet in male C57BL/6 mice to be compared with the control group fed the methionine-choline-sufficient (MCS) diet. After four weeks, physiological saline, a low dose of GJLZ decoction (GL), or a high dose of GJLZ decoction (GH) was administered daily by gavage to the MCD group; the MCS group was given physiological saline by gavage. Untargeted metabolomics techniques were used to explore further the potential mechanism of the effects of GJLZ on lean NAFLD. Different doses of GJLZ decoction were able to ameliorate steatosis, inflammation, fibrosis, and oxidative stress in the liver; GL performed a better effect on lean NAFLD. In addition, 78 candidate differential metabolites were screened and identified. Combined with metabolite pathway enrichment analysis, GL was capable of regulating the glucose and lipid metabolite pathway in lean NAFLD and regulating the glycerophospholipid metabolism by altering the levels of sn-3-O-(geranylgeranyl)glycerol 1-phosphate and lysoPC(P-18:0/0:0). GJLZ may protect against the development of lean NAFLD by regulating glucose and lipid metabolism, inhibiting the levels of sn-3-O-(geranylgeranyl)glycerol 1-phosphate and lysoPC(P-18:0/0:0) in glycerophospholipid metabolism.

This study is aimed at screening potential therapeutic ingredients in traditional Chinese medicine (TCM) and identifying the key rheumatoid arthritis (RA) targets using computational simulations. Data for TCM-active ingredients with clear pharmacological effects were collected. Absorption, distribution, metabolism, excretion, and toxicity were evaluated. Potential RA targets were identified using the Gene Expression Omnibus (GEO) database, proteinprotein interaction network, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and potential TCM ingredients using AutoDock Vina. To examine the mechanisms underlying small molecules, target prediction, Gene Ontology, KEGG, and network modeling analyses were conducted; the effects were verified in rat synovial cells using cell proliferation assay. The activities of tumor necrosis factor TNF-α and IL-1β and alterations in cellular target protein levels were detected by ELISA and Western blotting, respectively. In total, data for 432 TCM active ingredients with clear pharmacological effects were obtained. Five critical RA-related genes were identified; CCL5 and CXCL10 were selected for molecular docking. Target prediction and network-based proximity analysis showed that dioscin could modulate 22 known RA clinical targets. Dioscin, asiaticoside, and ginsenoside Re could effectively inhibit in vitro cell proliferation and secretion of TNF-α and IL-1β in RA rat synovial cells. Using bioinformatics and computer-aided drug design, the potential small anti-RA molecules and their mechanisms of action were comprehensively identified. Dioscin could significantly inhibit proliferation and induce apoptosis in RA rat synovial cells by reducing TNF-α and IL-1β secretion and inhibiting abnormal CCL5, CXCL10, CXCR2, and IL2 expression.

Objective: The aim of this study was to identify potentially important Rheumatoid arthritis (RA) targets related to immune cells based on bioinformatics analysis, and to identify small molecules of traditional Chinese medicine (TCM) associated with these targets that have potential therapeutic effects on RA. Methods: Gene expression profile data related to RA were downloaded from the Gene Expression Omnibus (GSE55235, GSE55457, and GSE77298), and datasets were merged by the batch effect removal method. The RA key gene set was identified by protein-protein interaction network analysis and machine learning-based feature extraction. Furthermore, immune cell infiltration analysis was carried out on all DEGs to obtain key RA markers related to immune cells. Batch molecular docking of key RA markers was performed on our previously compiled dataset of small molecules in TCM using AutoDock Vina. Moreover, in vitro experiments were performed to examine the inhibitory effect of screened compounds on the synovial cells of an RA rat model. Results: The PPI network and feature extraction with machine learning classifiers identified eight common key RA genes: MYH11, CFP, LY96, IGJ, LPL, CD48, RAC2, and CSK. RAC2 was significantly correlated with the infiltration and expression of five immune cells, with significant differences in these immune cells in the normal and RA samples. Molecular docking and in vitro experiments also showed that sanguinarine, sesamin, and honokiol could effectively inhibit the proliferation of RA rat synovial cells, also could all effectively inhibit the secretion of TNF-α and IL-1β in synovial cells, and had a certain inhibitory effect on expression of the target protein RAC2. Conclusions: The core gene set of RA was screened from a new perspective, revealing biomarkers related to immune cell infiltration. Using molecular docking, we screened out TCM small molecules for the treatment of RA, providing methods and technical support for the treatment of RA with TCM.