Analyzing the Therapeutic Mechanism of Mongolian Medicine Zhonglun-5 in Rheumatoid Arthritis Using a Bagging Algorithm with Serum Metabonomics

Abstract

Rheumatoid arthritis (RA) is a complex autoimmune disorder. Zhonglun-5 (ZL), a traditional Mongolian medicine, exhibits an excellent clinical effect on RA; however, its molecular mechanism remains unclear. In this study, rat serum metabolomic analysis was performed to identify potential biomarkers for RA and investigate its treatment mechanism. A Dionex Ultimate 3000 ultrahigh-performance liquid chromatography system coupled with a Q-Exactive Focus Orbitrap mass spectrometer was used for metabonomics analysis. Bootstrap aggregation (bagging) classification algorithm was applied to process data from control (CG), model (MG), and treatment administration groups. The classification accuracy was 100.00% (6/6) in the decision tree model and 83.33% (5/6) in the K-nearest neighbor (KNN) model, accompanied by 18 training samples and 6 testing samples. Using volcanic map analysis, 24 biomarkers were identified between CG and MG, including those related to glycosphingolipid biosynthesis, arachidonic acid, fatty acids, amino acids, bile acids, vitamins, and sphingolipids. A set diagram of the heatmap and drug-biomarker network of potential biomarkers was constructed. After ZL administration, the levels of these biomarkers returned to normal, indicating that ZL had a therapeutic effect in rats with RA. This study established a solid theoretical foundation to promote further research on the clinical applicability of ZL.

Figure 1

Bagging classification model.

Figure 2

Indices of oxidant stress (a) and cytokine levels (b) in rat serum. Data are shown as mean ± standard deviation (n = 8). CG, control group; MG, model group; ZL, ZL administration group. ^#P < 0.05 compared with MG.

Figure 3

Histopathological change in rat arthrodial cartilage. Photomicrographs show representative cartilage sections stained with hematoxylin and eosin (100×). (a) Control group; (b) model group; (c) ZL administration group.

Figure 4

Serum total ion flow chromatograms of various groups. (a) Control group, (b) model group, and (c) ZL administration group in the switching mode between positive and negative spectra.

Figure 5

Volcano figure of serum metabolic profiling of CG and MG groups. Dots in green-shaded areas represent the biomarkers with decreased content (MG vs CG), and dots in red-shaded areas represent the biomarkers with increased content (MG vs CG).

Figure 6

Set diagram of heatmap and drug-biomarker network of potential biomarkers regulated by ZL. (PG1, prostaglandin G1; 10-HHP, 10-hydroperoxy-H4-neuroprostane; GA2, ganglioside GA2 (d18 : 1/18 : 0); GD3, ganglioside GD3 (d18 : 1/23 : 0); GM2, ganglioside GM2 (d18 : 0/18 : 1 (11Z)); PA, palmitoleic acid; ESA, eicosapentaenoic acid; α-LA, α-linolenic acid; SA, stearic acid; OTC, octadecanamide; ECA, eicosadienoic acid; PLG, phenylacetyl glycine; L-V, L-valine; MLC, myristoyl glycine; N-U, N-undecanoylglycine; L-P, L-proline; TDC, tauroursodeoxycholic acid; GTS, gytosine; CTP, 13′-carboxy-γ-tocopherol; α-TT, α-tocotrienol; SP, sphinganine; PC, PC (22 : 5 (7Z, 10Z, 13Z, 16Z, 19Z)/16 : 0); Cer, cer (d18 : 0/16 : 0); PS, phytosphingosine).