Deciphering the pharmacological mechanism of Guan-Jie-Kang in treating rat adjuvant-induced arthritis using omics analysis

doi:10.1007/s11684-018-0676-2

Front. Med.

2019, Vol. 13

Issue (5) : 564-574 https://doi.org/10.1007/s11684-018-0676-2

RESEARCH ARTICLE

Deciphering the pharmacological mechanism of Guan-Jie-Kang in treating rat adjuvant-induced arthritis using omics analysis

Hudan Pan¹, Yanfang Zheng^1,², Zhongqiu Liu³, Zhongwen Yuan¹, Rutong Ren¹, Hua Zhou¹, Ying Xie¹(

), Liang Liu¹(

)

¹. State Key Laboratory of Quality Research in Chinese Medicine/Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, China
². Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
³. International Institute for Translational Research of Traditional Chinese Medicine of Guangzhou University of Chinese Medicine, Guangzhou 510006, China

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Abstract

Traditional Chinese medicine (TCM) formulas have attracted increasing attention worldwide in the past few years for treating complex disease including rheumatoid arthritis. However, their mechanisms are complex and remain unclear. Guan-Jie-Kang (GJK), a prescription modified from “Wu Tou Decoction,” was found to significantly relieve arthritis symptoms in rats with adjuvant-induced arthritis after 30-day treatment, especially in the 24 g/kg/day group. By analyzing 1749 targets related to 358 compounds in the five herbs of GJK, we identified the possible anti-arthritis pathways of GJK, including the calcium signaling and metabolic pathways. Bone damage levels were assessed by micro-computed tomography, and greater bone protective effect was observed with GJK treatment than with methotrexate. Receptor activator of nuclear factor κB ligand (RANKL)–RANK signaling, which is related to calcium signaling, was significantly regulated by GJK. Moreover, a target metabolomics assay of serum was conducted; 17 metabolic biomarkers showed significant correlations with treatment. An integrated pathway analysis revealed that pyruvate metabolism, purine metabolism, and glycolysis metabolism were significantly associated with the effects of GJK in arthritis treatment. Thus, this study establishes a new omics analytical method integrated with bioinformatics analysis for elucidating the multi-pathway mechanisms of TCM.

Keywords rheumatoid arthritis traditional Chinese medicine pharmacological mechanism metabolism adjuvant-induced arthritis omics analysis

Corresponding Author(s): Ying Xie,Liang Liu

Just Accepted Date: 01 March 2019 Online First Date: 15 May 2019 Issue Date: 14 October 2019

Cite this article:

Hudan Pan,Yanfang Zheng,Zhongqiu Liu, et al. Deciphering the pharmacological mechanism of Guan-Jie-Kang in treating rat adjuvant-induced arthritis using omics analysis[J]. Front. Med., 2019, 13(5): 564-574.

URL:

https://academic.hep.com.cn/fmd/EN/10.1007/s11684-018-0676-2
https://academic.hep.com.cn/fmd/EN/Y2019/V13/I5/564

Fig.1 Study overview.

Fig.2 Pharmacological effects of GJK in AIA rats. Rats were randomly divided into five groups: normal group (N), vehicle disease control group (M), positive control group (MTX), 16 g/kg GJK group, and 24 g/kg GJK group. (A) Changes in body weight in each group (n = 12). (B) Arthritis score changes in the five groups during the 30-day study period (n = 12). (C) Effects of GJK on increased paw volume (n = 12). Group differences in (A), (B), and (C) were analyzed by two-way ANOVA (*P<0.05, **P<0.01, ***P<0.001 vs. model). (D) ESR measurements on the day of sacrifice (n = 11–12). (E) Spleen index assessments in the five groups (n = 12). (F) Effects of GJK on pro-inflammatory cytokine expression (n = 8–12 in IL-1β, IL-17A, and IL-6; n = 5–6 in TNF-α). Group differences in (D), (E), and (F) analyzed by one-way ANOVA (*P<0.05, **P<0.01, ***P<0.001 vs. model).

Fig.3 Bone protective effects of GJK in AIA rats. (A) Representative micro-CT image of the ankles. (B) Inter-group micro-CT score assessments (n = 5–6). (C) Bone destruction level alteration based on micro-CT score. Levels were divided according to micro-CT score. Very severe: 0–0.2; severe: 0.2–0.4; moderate: 0.4–0.6; mild: 0.6–0.8; normal: 0.8–1. (D) MMP-1 mRNA expression of the upper tibia (n = 6). (E) MMP-3 mRNA expression of the upper tibia (n = 5). (F) MMP-13 mRNA expression of the upper tibia (n = 6). (G) MMP-9 mRNA expression of the upper tibia (n = 5). (H) OPG/RANKL rate improvement in the GJK group (n = 5). (I) TNF-α mRNA expression of the upper tibia (n = 5). ^###P<0.001 vs. control, *P<0.05 vs. model, **P<0.01 vs. model, ***P<0.001 vs. model.

Fig.4 Metabolic alterations in AIA rats. (A) Multiple pattern recognition analysis of plasma biomarkers between normal control group and model group on day 30. OPLS-DA score plot (R2X= 0.987, R2Y= 0.992, Q2= 0.192, n = 8) of control, model, MTX, and GJK group. (B) Potential biomarkers in response to RA and their metabolic pathways. (C) Potential biomarkers in response to AIA detected by cluster analysis (heatmap).

Fig.5 Correlations between RA parameters and serum metabolite levels with P<0.05 based on correlation analysis. Abbreviation: GSSG, glutathione disulfide; GSH, glutathione; LPC, lysophosphatidylcholine; GDCA, glycodeoxycholic acid; GCDCA, glycochenodeoxycholic acid.

Fig.6 Effects of GJK on regulating metabolic disorder in AIA rats. (A) Integrated analysis of the targets of GJK and the disease-related metabolites. (B) Quantification of the identified metabolites in serum. ^#P<0.05 vs. control, ^##P<0.01 vs. control, *P<0.05 vs. model, **P<0.01 vs. model.

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