Integrative multi-target analysis of Urtica dioica for gout arthritis treatment: a network pharmacology and clustering approach

Abstract

Urtica dioica (stinging nettle) has been traditionally used in Chinese medicine for the treatment of joint pain and rheumatoid arthritis. This study aims to elucidate the active compounds and mechanisms by which it acts against gout arthritis (GA). Gout-related genes were identified from the DisGeNet, GeneCards, and OMIM databases. These genes may play a role in inhibiting corresponding proteins targeted by the active compounds identified from the literature, which have an oral bioavailability of ≥ 30% and a drug-likeness score of ≥ 0.18. A human protein-protein interaction network was constructed, resulting in sixteen clusters containing plant-targeted genes, including ABCG2, SLC22A12, MAP2K7, ADCY10, RELA, and TP53. The key bioactive compounds, apigenin-7-O-glucoside and kaempferol, demonstrated significant binding to SLC22A12 and ABCG2, suggesting their potential to reduce uric acid levels and inflammation. Pathway enrichment analysis further identified key metabolic pathways involved, highlighting a dual mechanism of anti-inflammatory and urate-lowering effects. These findings underscore the potential of U. dioica in targeting multiple pathways involved in GA, combining traditional medicine with modern pharmacology. This integrated approach provides a foundation for future research and the development of multi-target therapeutic strategies for managing gout arthritis.

Supplementary information: The online version contains supplementary material available at 10.1007/s40203-024-00254-9.

Keywords: Gout arthritis; Leiden algorithm; Network pharmacology; OMIM; Urtica dioica.

Fig. 1

Gout-affected Big Toe  (https://www.biorender.com/). This figure illustrates a big toe affected by gout, highlighting the typical symptoms including inflammation, swelling, and the presence of urate crystal deposits. The visual representation emphasizes the characteristic redness and swelling around the joint, which are common indicators of gout. The detailed depiction provides a clear view of the affected area, making it a useful reference for understanding the clinical manifestations of gout in the big toe

Fig. 2

The workflow explores the therapeutic potential of Urtica dioica L. (stinging nettle) in gout arthritis (GA). Gout-related targets were collected from DisGeNet, GeneCards, and OMIM, while U. dioica chemical constituents were sourced from literature and PubChem. SwissTargetPrediction predicted targets of these constituents, selecting compounds with oral bioavailability ≥ 30% and drug-likeness ≥ 0.18 using SwissADME and Molsoft. A human PPI network was constructed with NetworkX, mapping relevant genes. The Leiden algorithm identified gene clusters, visualized with Graphistry. Disease relevance was assessed using Enrichr, and common genes were identified using Venny 2.1. Intersecting genes were analyzed with STRING, and PPI networks were visualized with Cytoscape. Molecular docking studies were performed using PyRx and visualized by ChimeraX. Key bioactive compounds identified were apigenin-7-O-glucoside and kaempferol. Apigenin-7-O-glucoside showed significant binding to SLC22A12 and ABCG2, potentially reducing uric acid levels and inflammation. Kaempferol also effectively binds to these targets, suggesting its role in modulating uric acid excretion and inflammation. Targeting SLC22A12 can decrease uric acid levels, alleviating gout symptoms, while enhancing ABCG2 function can improve uric acid elimination, reducing gout attacks

Fig. 3

Overall cluster visualization of 9684 nodes and 665,444 edges

Fig. 4

Cluster 1 (a) has 3164 nodes and 21,053 edges, Cluster 2 (b) has 2662 nodes and 10,813 edges, Cluster 3 (c) has 1182 nodes and 2748 edges, Cluster 4 (d) has 1875 nodes and 6936 edges, Cluster 5 (e) has 1274 nodes and 3966 edges, Cluster 6 (f) has 1149 nodes and 2323 edges, Cluster 7 (g) has 1069 nodes and 2993 edges, Cluster 8 (h) has 1321 nodes and 3082 edges, Cluster 9 (i) has 815 nodes and 1594 edges, and Cluster 10 (j) has 1003 nodes and 2736 edges

Fig. 5

Cluster 11 (k) has 895 nodes and 2011 edges, Cluster 12 (l) has 605 nodes and 2222 edges, Cluster 13 (m) has 597 nodes and 1178 edges, Cluster 14 (n) has 458 nodes and 980 edges, Cluster 15 (o) has 361 nodes and 866 edges, Cluster 16 (p) has 341 nodes and 705 edges, Cluster 17 (q) has 109 nodes and 197 edges, Cluster 18 (r) has 45 nodes and 74 edges, Cluster 19 (s) has 38 nodes and 49 edges, and Cluster 20 (t) has 19 nodes and 18 edges

Fig. 6

Venn diagram of intersecting genes. (a) Cluster 1 has 23 genes, (b) Cluster 3 has 12 genes, (c) Cluster 4 has 17 genes, (d) Cluster 5 has 8 genes, (e) Cluster 6 has 15 genes, (f) Cluster 7 has 6 genes, (g) Cluster 8 has 4 genes, (h) Cluster 9 has 16 genes, (i) Cluster 10 has 6 genes, (j) Cluster 11 has 26 genes, (k) Cluster 12 has 19 genes, and (l) Cluster 13 has 2 genes

Fig. 7

(a) Cluster 1 has 23 nodes, 76 edges, (b) Cluster 3 has 12 nodes, 13 edges, (c) Cluster 4 has 17 nodes, 25 edges, (d) Cluster 5 has 8 nodes, 4 edges, (e) Cluster 6 has 15 nodes, 62 edges, (f) Cluster 7 has 6 nodes, 6 edges, (g) Cluster 8 has 4 nodes, 2 edges, (h) Cluster 9 has 16 nodes, 32 edges, (i) Cluster 10 has 6 nodes, 6 edges, (j) Cluster 11 has 26 nodes, 91 edges, (k) Cluster 12 has 19 nodes, 34 edges, and (l) Cluster 13 has 2 nodes, 1 edge

Fig. 8

(a) Cluster (b) Cluster 3, (c) Cluster 4, (d) Cluster 5, (e) Cluster 6 and (f) Cluster 7

Fig. 9

(g) Cluster 8, (h) Cluster 9, (i) Cluster 10 (j) Cluster 11, (k) Cluster 12 and (l) Cluster 13

Fig. 10

Green octagons show compounds, blue diamonds show targets, and pink triangles show pathways

Fig. 11

(a) Reactome pathway and (b) KEGG pathway analysis of gout patients treated with U. dioica

Fig. 12

Diagram of the binding of compounds with (a) TP53, (b) MAP2K7, (c) SLC22A12, (d) SLC22A12, (e) ABCG2, (f) ABCG2, (g) ADCY10 and (h) RELA

Fig. 13

The binding modes of (a) TP53, (b) MAP2K7, (c) SLC22A12, (d) SLC22A12, (e) ABCG2, (f) ABCG2, (g) ADCY10 and (h) RELA. (visualized by drug discovery tool and drugs are obtained from PubChem Database: https://pubchem.ncbi.nlm.nih.gov/)