Implying Analytic Measures for Unravelling Rheumatoid Arthritis Significant Proteins Through Drug–Target Interaction

The role of cellular senescence in the pathogenesis of Rheumatoid Arthritis: Focus on IL-6 as a target gene

BACKGROUND

Rheumatoid arthritis is a chronic autoimmune disease. However, the specific role of senescence in rheumatoid arthritis (RA) is unknown. This study aimed to identify potential aging-related genes that have diagnostic and therapeutic value for RA.

METHODS

The GSE89408 dataset was downloaded from the Gene Expression Omnibus (GEO). Aging-related genes were downloaded from the HAGR database. Differentially expressed genes (DEGs) were subsequently identified with the "edgeR" tool. Next, hub genes were identified with a PPI network and CytoHubba analysis. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic value of these hub genes. Immune infiltration analysis was performed with the CIBERSORT algorithm. Additionally, molecular docking was performed with CB-Dock2. Finally, correlation experiments were performed to validate the bioinformatics and molecular docking results.

RESULTS

A total of 22 ADEGs were identified. Combined PPI network and CytoHubba analyses identified a total of 7 hub genes, including IL-6, IL7R, IL2RG, CDK1, PTGS2, and LEP, which are associated mainly with inflammation and immune responses. ROC analysis revealed that the hub genes were highly predictive of RA. Analysis of immune infiltration revealed that the 6 hub genes were positively associated with M1 macrophages. Validation experiments revealed that the inhibition of IL-6 significantly decreased the degree of synovial fibroblast (FLS) senescence. Furthermore, molecular docking and validation experiments revealed that IL-6 is a potential target for drug therapy.

CONCLUSION

This study demonstrated that RA-FLS senescence may promote the development of RA via inflammatory and immune mechanisms. Seven hub genes were identified, of which IL-6 is a reliable biomarker for the diagnosis and treatment of RA.

Classifying Integrated Signature Molecules in Macrophages of Rheumatoid Arthritis, Osteoarthritis, and Periodontal Disease: An Omics-Based Study

Rheumatoid arthritis (RA), osteoarthritis (OA), and periodontal disease (PD) are chronic inflammatory diseases that are globally prevalent, and pose a public health concern. The search for a potential mechanism linking PD to RA and OA continues, as it could play a significant role in disease prevention and treatment. Recent studies have linked RA, OA, and PD to Porphyromonas gingivalis (PG), a periodontal bacterium, through a similar dysregulation in an inflammatory mechanism. This study aimed to identify potential gene signatures that could assist in early diagnosis as well as gain insight into the molecular mechanisms of these diseases. The expression data sets with the series IDs GSE97779, GSE123492, and GSE24897 for macrophages of RA, OA synovium, and PG stimulated macrophages (PG-SM), respectively, were retrieved and screened for differentially expressed genes (DEGs). The 72 common DEGs among RA, OA, and PG-SM were further subjected to gene–gene correlation analysis. A GeneMANIA interaction network of the 47 highly correlated DEGs comprises 53 nodes and 271 edges. Network centrality analysis identified 15 hub genes, 6 of which are DEGs (API5, ATE1, CCNG1, EHD1, RIN2, and STK39). Additionally, two significantly up-regulated non-hub genes (IER3 and RGS16) showed interactions with hub genes. Functional enrichment analysis of the genes showed that “apoptotic regulation” and “inflammasomes” were among the major pathways. These eight genes can serve as important signatures/targets, and provide new insights into the molecular mechanism of PG-induced RA, OA, and PD.

Analysis of Hepatic Lipid Metabolism and Immune Function During the Development of Collagen-Induced Arthritis

The liver is essential for metabolic and immune functions and has been linked to systemic inflammatory diseases. However, the role of the liver is still elusive during the development of rheumatoid arthritis (RA), although there have been indeed some reports. We used label-free quantitative proteomics and experimental verification in this study to reveal the hepatic lipid metabolism and immune function during collagen-induced arthritis (CIA) development. The proteomics results revealed that the role of the liver differs in different phases of CIA rats. In terms of specific performance, hepatic lipid metabolism, which is primarily concerned with cholesterol, triacylglycerol, and phospholipid, was significantly influenced in the CIA induction phase, whereas the immune function, which includes binding of granulocytes, adhesion of immune cells, etc., was affected considerably at the peak phase of CIA rats compared to normal rats. Finally, the hepatic dynamic changes in CIA rats were further confirmed using targeted metabolomics and ELISA. We found that most fatty acids of the liver in the CIA induction phase were significantly decreased, and proteins related to complement activation and migration or adhesion of immune cells including C3, MMP-8, CTSZ, and S100A9 were significantly increased in the liver of CIA rats in the peak phase. Our findings indicated that the lipid metabolism and immune function of the liver were influenced in CIA rats. Thus, the conditions of the liver during RA development should be considered in therapeutic and nutritional interventions.

Exploration of hanshi zufei prescription for treatment of COVID-19 based on network pharmacology

Objective

Network pharmacology combines drug and disease targets with biological information networks based on the integrity and systematicness of the interactions between drugs and disease targets. This study aims to explore the molecular basis of Hanshi Zufei formula for treatment of COVID-19 based on network pharmacology and molecular docking techniques.

Methods

Using TCMSP, the chemical constituents and molecular targets of Atractylodis Rhizoma, Citri Reticulatae Pericarpium, Magnoliae Officinalis Cortex, Pogostemonis Herba, Tsaoko Fructus, Ephedrae Herba, Notopterygii Rhizoma et Radix, Zingiberis Rhizoma Recens, and Arecae Semen were investigated. The predicted targets of novel coronavirus were screened using the NCBI and GeneCards databases. To further screen the drug-disease core targets network, the corresponding target proteins were queried using multiple databases (Biogrid, DIP, and HPRD), a protein interaction network graph was constructed, and the network topology was analyzed. The molecular docking studies were also performed between the network’s top 15 compounds and the coronavirus (SARS-CoV-2) 3CL hydrolytic enzyme and angiotensin conversion enzyme II (ACE2).

Results

The herb-active ingredient-target network contained nine drugs, 86 compounds, and 49 drug-disease targets. Gene ontology (GO) enrichment analysis resulted in 1566 GO items (P < 0.05), among which 1438 were biological process items, 35 were cell composition items, and 93 were molecular function items. Fourteen signal pathways were obtained by enrichment screening of the KEGG pathway database (P < 0.05). The molecular docking results showed that the affinity of the core active compounds with the SARS-CoV-2 3CL hydrolase was better than for the other compounds.

Conclusion

Several core compounds can regulate multiple signaling pathways by binding with 3CL hydrolase and ACE2, which might contribute to the treatment of COVID-19.

A network pharmacology approach to explore the potential targets underlying the effect of sinomenine on rheumatoid arthritis

OBJECTIVE

To explore the potential targets underlying the effect of sinomenine (SIN) on rheumatoid arthritis (RA) by utilizing a network pharmacology approach.

METHODS

SIN and its drug targets were identified using network analysis followed by experimental validation. First, the Pharmmapper, UniProt and GeneCards databases were mined for information relevant to the prediction of SIN targets and RA-related targets. Second, the SIN-target gene and SIN-RA target gene networks were created in Cytoscape software followed by the collection of the candidate targets of each component by R software. Eventually, the key targets and enriched pathways were examined by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.

RESULTS

Sixty-seven potential targets of SIN and 3797 related targets involved in RA were subjected to network analysis, and the 20 intersection targets indicated the principal pathways linked to RA. Additionally, 16 key targets, which were linked to more than three genes, were determined to be crucial genes. GO analysis showed that 14 biological processes, 5 cellular components and 2 molecular functions were identified, when corrected by a P value ≤ 0.01. Seven related signaling pathways were identified by KEGG analysis, when corrected according to a Bonferroni P value ≤ 0.05.

CONCLUSION

The present study explored the potential targets and signaling pathways of SIN during the treatment of RA, which may help to illustrate the mechanism (s) involved in the action of SIN and may provide a better understanding of its anti-rheumatoid arthritis effects in terms of inhibiting angiogenesis, synovial hyperplasia, and bone destruction.