Abstract
Background: Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease. Rosae Multiflorae Fructus (RMF), the dried fruits (rosehips) of Rosa multiflora Thunb., has long been used to treat RA in China; but its anti-RA compounds and mechanisms are not fully understood.
Objectives: To predict the active components and molecular mechanisms of RMF for treating RA using network pharmacology.
Methods: SciFinder and Traditional Chinese Medicine Systems Pharmacology (TCMSP) databases were used to gather information about the compounds occuring in the herb. Active compound filtering was carried out using TCMSP (oral bioavailability ≥ 0.3 and drug-likeness ≥0.18). Targets of the filtered compounds were predicted using the SwissTargetPrediction website tool, TCMSP, and Similarity ensemble approach (SEA) platform. RA-related genes were collected from the GeneCards database. Gene ontology (GO) enrichment analysis and Kyoto
Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the KOBAS platform. A compound-target network was constructed and analyzed using Cytoscape Software, and the main targets were filtered by Cytohubba (Degree score ≥ 80). Molecular docking was performed using SwissDock and Chimera.
Results: Through databases searching and consequent filtering, eight eligible compounds, including ar-curcumene, beta-sitosterol, ethyl linolenate, kaempferol, mairin, mandenol, quercetin, and stigmastane-3,6-dione, were obtained. These compounds had 226 targets. Intersection of the 226 compound targets and 4853 RA-related genes resulted in 122 potential anti-RA targets of RMF. Results of GO enrichment indicated that RMF affects RA-related biological processes including response to inorganic substance, cellular response to nitrogen compound, cellular response to organonitrogen compounds, cellular response to organic cyclic
compound, and cellular response to lipid. KEGG pathway enrichment revealed that TNF signaling, IL-17 signaling, PI3K-Akt signaling, and cellular senescence pathways are the main pathways involved in the anti-RA effects of RMF. By constructing the compound-target network, four main anti-RA targets of RMF (JUN, AKT1, TP53, FOS) were obtained. Molecular docking confirmed that the eight compounds in RMF can bind the four main anti-RA targets of RMF.
Conclusions: Multi-compounds and multi-targets are involved in the anti-RA effects of RMF. The present study provides a basis for further experimental investigations of the anti-RA compounds and mechanisms of RMF, and provides a basis for developing modern anti-RA agents based on compounds that occur in RMF.
Objectives: To predict the active components and molecular mechanisms of RMF for treating RA using network pharmacology.
Methods: SciFinder and Traditional Chinese Medicine Systems Pharmacology (TCMSP) databases were used to gather information about the compounds occuring in the herb. Active compound filtering was carried out using TCMSP (oral bioavailability ≥ 0.3 and drug-likeness ≥0.18). Targets of the filtered compounds were predicted using the SwissTargetPrediction website tool, TCMSP, and Similarity ensemble approach (SEA) platform. RA-related genes were collected from the GeneCards database. Gene ontology (GO) enrichment analysis and Kyoto
Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the KOBAS platform. A compound-target network was constructed and analyzed using Cytoscape Software, and the main targets were filtered by Cytohubba (Degree score ≥ 80). Molecular docking was performed using SwissDock and Chimera.
Results: Through databases searching and consequent filtering, eight eligible compounds, including ar-curcumene, beta-sitosterol, ethyl linolenate, kaempferol, mairin, mandenol, quercetin, and stigmastane-3,6-dione, were obtained. These compounds had 226 targets. Intersection of the 226 compound targets and 4853 RA-related genes resulted in 122 potential anti-RA targets of RMF. Results of GO enrichment indicated that RMF affects RA-related biological processes including response to inorganic substance, cellular response to nitrogen compound, cellular response to organonitrogen compounds, cellular response to organic cyclic
compound, and cellular response to lipid. KEGG pathway enrichment revealed that TNF signaling, IL-17 signaling, PI3K-Akt signaling, and cellular senescence pathways are the main pathways involved in the anti-RA effects of RMF. By constructing the compound-target network, four main anti-RA targets of RMF (JUN, AKT1, TP53, FOS) were obtained. Molecular docking confirmed that the eight compounds in RMF can bind the four main anti-RA targets of RMF.
Conclusions: Multi-compounds and multi-targets are involved in the anti-RA effects of RMF. The present study provides a basis for further experimental investigations of the anti-RA compounds and mechanisms of RMF, and provides a basis for developing modern anti-RA agents based on compounds that occur in RMF.
| Original language | English |
|---|---|
| Pages | 174-175 |
| Number of pages | 2 |
| Publication status | Published - 22 Jun 2023 |
| Event | The 9th Annual Meeting of The Specialty Committee on Immunology of Traditional Chinese Medicine of the World Federation of Chinese Medicine Societies cum The 6th International Forum on Triplet Therapy for Rheumatism and Pain = 世界中医药学会联合会中医药免疫专业委员会第九届学术年会暨第六届国际风湿与疼痛三联序贯疗法高峰论坛, 2023 - Guiyang, China Duration: 22 Jun 2023 → 24 Jun 2023 |
Conference
| Conference | The 9th Annual Meeting of The Specialty Committee on Immunology of Traditional Chinese Medicine of the World Federation of Chinese Medicine Societies cum The 6th International Forum on Triplet Therapy for Rheumatism and Pain = 世界中医药学会联合会中医药免疫专业委员会第九届学术年会暨第六届国际风湿与疼痛三联序贯疗法高峰论坛, 2023 |
|---|---|
| Country/Territory | China |
| City | Guiyang |
| Period | 22/06/23 → 24/06/23 |
User-Defined Keywords
- Rosae Multiflorae Fructus
- Network Pharmacology
- Rheumatoid arthritis