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Article

Integrating Metabolomics and Network Pharmacology: Investigating the Therapeutic Mechanism of Atractylodes Rhizome Against Rheumatoid Arthritis

by
Rou Wen
1,
Cheng Xu
1,
Hailian Zheng
1,
Chao Li
2,* and
Huan Yu
1,*
1
School of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang 330004, China
2
School of Chinese Medicine, Wenzhou Medical University, Wenzhou 325035, China
*
Authors to whom correspondence should be addressed.
Pharmaceuticals 2026, 19(2), 262; https://doi.org/10.3390/ph19020262
Submission received: 14 January 2026 / Revised: 29 January 2026 / Accepted: 30 January 2026 / Published: 3 February 2026
(This article belongs to the Topic From Plant to Pharmacology: Understanding the Metabolism of Natural Products)
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Abstract

Purpose: The purpose of this study is to investigate the bioactive constituents of Atractylodes Rhizome (AR) and to explore its mechanism of action in the treatment of rheumatoid arthritis (RA). Methods: The research mainly adopts the methods of tissue metabolomics and network pharmacology. Firstly, we employed a metabolomics strategy to obtain the metabolite profile and utilized PCA/OPLS-DA analyses to identify the differential metabolites involved in the treatment of RA by AR. Subsequently, we determined the key target metabolic pathways of AR in RA treatment. Next, a network pharmacology approach was employed to identify active compounds, potential targets, and signaling pathways for AR in RA treatment, with a PPI network constructed. These predictions were then validated through molecular docking simulations, followed by in vivo verification using a CFA-induced RA rat model. The anti-RA efficacy was evaluated through synovial histopathology and cytokine assays, with the key mechanistic insights being confirmed at the molecular level by RT-qPCR and WB. Results: The results of the metabolomics study showed that AR regulated 28 differential metabolites linked to glycerophospholipid, linoleic acid, and alpha-linolenic acid metabolism. Network pharmacology identified Wogonin, Atractyloyne, and Atractylenolide II as key active compounds, acting through pathways such as Pathways in cancer and PI3K-Akt signaling, combined with the metabolites to jointly analyze the metabolic pathways, and were verified by correlation analysis. Molecular docking confirmed the main active ingredients’ strong binding to core targets. In AIA rats, AR treatment reduced synovial inflammation and lowered serum levels of IL-6 and MMP-9. At the molecular level, AR up-regulated Bcl-2, down-regulated Bax, and inhibited the SRC/JAK2-STAT3 pathway by decreasing EGFR, SRC, JAK2, and p-STAT3 expression. Conclusion: These findings may illuminate the mechanism by which Atractylodes Rhizome exerts its effects via the JAK2/SRC-STAT3 axis, thereby revealing its potential mechanism of action against rheumatoid arthritis.

Graphical Abstract

1. Introduction

Rheumatoid arthritis (RA) is a chronic, symmetrical, inflammatory autoimmune disorder. Its primary characterization is joint pain, with common symptoms including morning stiffness lasting over 30 min in the affected joints, fatigue, joint tenderness and swelling, as well as subcutaneous rheumatoid nodules [1,2]. The precise pathogenesis of RA remains incompletely understood, likely arising from a complicated interplay between genetic susceptibility, environmental exposure, and microbial dysregulation, ultimately triggering a pathogenic cascade [3]. The global disease prevalence of RA is estimated to be approximately 0.5% to 1%, with its incidence gradually increasing with age [4]; moreover, the high relapse rate characteristic of RA not only severely impairs physical function and quality of life, potentially shortening life expectancy [4,5,6], This also results in the treatment options remaining limited. In the current clinical management of RA, treatment primarily relies on two classes of drugs: nonsteroidal anti-inflammatory drugs (NSAIDs) and disease-modifying antirheumatic drugs (DMARDs). Their primary objectives are to alleviate pain symptoms and slow disease progression. However, long-term use of these medications may lead to serious adverse reactions, poor patient dependence, and unsatisfactory therapeutic effects [6,7]. Therefore, it is imperative to investigate novel, effective, and feasible therapeutic strategies. Interestingly, a growing trend in Western countries shows RA patients increasingly seeking Traditional Chinese Medicine (TCM) or other complementary and alternative medicine (CAM) for treatment and health management [8,9]. Moreover, accumulating studies have highlighted the potential advantages and promising prospects of TCM in RA treatment [9,10]. Modern research reveals that the mechanisms of action of TCM in treating RA include anti-inflammatory effects, antioxidant properties, inhibition of angiogenesis, suppression of osteoclastogenesis, immunomodulation, inhibition of synovial hyperplasia, etc., which not only highlights the complexity of RA disease but also highlights the unique advantages of TCM in the treatment of RA.
Under the system of TCM, rheumatoid arthritis is categorized as “Bi syndrome,” and TCM-based interventions have been shown to be effective in alleviating RA. Atractylodes Rhizoma (Cangzhu), a traditional Chinese medicinal herb with a long history, is the dried rhizome of Atractylodes lancea (Thunb.) DC. or Atractylodes chinensis (DC.) Koidz. and is one of the herbs employed in TCM for RA treatment [11,12]; its primary functions are to dry dampness, strengthen the spleen, dispel wind, and scatter cold, and it is indicated for conditions like dampness obstructing the middle jiao and painful impediment due to wind-dampness [12]. Modern research has shown that AR has a clear therapeutic effect on RA-related diseases [11], and its main component, wogonin, has been proved to possess effective anti-RA activity [13]. Furthermore, the classical formula “Ermiao San” (Ermiao Powder), which features AR as the monarch drug, is a well-regarded prescription commonly used in clinical practice for RA [14]. Therefore, this collective evidence provides strong support for the efficacy of AR against RA; however, the underlying mechanisms involved remain to be fully elucidated.
Metabolomics and network pharmacology, acting as core methodologies in modern TCM research, provide a systems biology framework that combines computational and chemometric analysis. This integrated approach allows for the exploration of multiple mechanisms and the therapeutic potential of medicinal plants. It aids in the scientific verification of the efficacy and action mechanisms of herbs like AR against RA [15,16]. Notably, our previous research has already established the efficacy and mechanisms of AR in the context of gouty arthritis [17]. This work sets out to elucidate the mechanisms by which AR contributes to the pathogenesis of RA. We adopted an integration strategy combining metabolomics, network pharmacology, molecular docking, and in vivo validation. It is expected to provide new ideas for exploring the AR mechanism of action and contribute a theoretical basis for novel anti-RA drug development.

2. Results

2.1. A Metabolomic Analysis of Rat Swollen Tissue

2.1.1. PCA Analysis of Metabolite Differences in Rat Ankle Joint Tissue

Firstly, principal component analysis (PCA) served to characterize intergroup differences within rat swollen tissue samples. As shown in Figure 1, The tight clustering of all quality control (QC) samples (n = 6) indicated good reproducibility and stability of the mass spectrometry system. The blank control group (n = 6) exhibited distinct separation from the model group (n = 10), which confirmed the successful establishment of the RA model. Within the PCA scoring plot, the drug group (AR group, n = 10) exhibited distinct separation from the model group and clustered proximate to the methotrexate (MTX group, n = 9) distribution. This indicates that AR therapy possesses a certain degree of anti-RA efficacy.

2.1.2. OLPS-DA Analysis of Metabolite Differences in Rat Ankle Joint Tissue

As presented in Figure 2A–D, to further identify differences in endogenous metabolites within rat swelling tissues following drug administration, OPLS-DA analyses were performed between the model group and the blank control group, as well as between the model group and the AR-treated group. The OPLS-DA score plots showed that all samples fell within the 95% confidence interval, and the values of R2Y and Q2 were greater than 0.85, indicating the model’s suitability for determining intergroup differences. Subsequently, potential overfitting was evaluated through 200 iterations of permutation testing. The results revealed that the intercept of the Q2 regression line on the y-axis was below zero for all comparisons, demonstrating that the model was not overfitted and possessed good stability and predictive ability. The results are presented in Figure 2E–H.

2.1.3. Potential Biomarkers Exploring and Related Metabolic Pathway Analysis

Differential metabolites were screened using the criteria of VIP > 1, p < 0.05 (−log10(p-value) > 1.3), and |log2 FC| > 1. Volcano plots were constructed to visualize the results (Figure 3), where points in red represent metabolites satisfying all three criteria. The red-highlighted metabolites from the comparisons of A vs. B and C vs. D were subsequently intersected using the Venny 2.1 online platform to pinpoint biomarkers related to the anti-RA effect of AR. Finally, these metabolites were identified by searching the MS/MS spectra against the HMDB and KEGG databases. This process confirmed 28 differential metabolites (Table 1), with 26 showing a reversal in their abundance after AR administration. Supplementary File S1 includes the total ion chromatogram (TIC) of tissue samples from each group of rats.

2.2. Network Pharmacology Predicts the Mechanism of AR in Anti-RA

2.2.1. Screening of Drug-Disease Targets

A total of 347 drug-active ingredient-related targets and 1996 RA-related targets were obtained from databases. These were imported into the online tool Venny 2.1.0, yielding 151 overlapping genes (Figure 4A).

2.2.2. Core Target Screening and Protein–Protein Interaction (PPI) Network Construction

A PPI network was constructed by uploading the 151 target genes to STRING 12.0 and visualizing it with Cytoscape 3.10.4 (Figure 4B). The resulting network contained 151 nodes and 2452 edges. Using CytoHubba for topology analysis, the top 10 genes and active components were identified and regarded as core genes, including IL1β, STAT3, EGFR, CASP3, MMP9, SRC, CTNNB1, ESR1, PTGS2, and MAPK3. Additionally, a compound-target network diagram was generated (Supplementary File S2). Ranking the components by their degree values revealed that Atractyloyne and Wogonin possessed the highest connectivity, suggesting their potential as the key active constituents in AR for treating RA. Relevant component details are provided in Table 2.

2.2.3. GO Function and KEGG Pathway Enrichment Analysis Reveal Multi-Dimensional Mechanisms of Action

The 151 intersecting genes were submitted to the DAVID online analysis platform for GO functional analysis and KEGG pathway enrichment analysis. GO functional enrichment encompassed three factors: biological process (BP), molecular function (MF), and cellular component (CC). The top 10 entries for each factor were selected for visualization (Figure 5A), revealing that AR exerts its effects by regulating processes such as signal transduction, protein phosphorylation, and apoptosis; acting at sites including the plasma membrane and cytoplasm; and also participating in anti-RA activity through protein binding and ATP binding.
KEGG pathway analysis revealed that the most enriched pathways included Pathways in cancer, the PI3K-Akt signaling pathway, metabolic pathways, lipid and atherosclerosis, and the MAPK signaling pathway (Figure 5B). Multiple research studies have found that individuals with RA face a higher risk of developing cancer compared to the general population and have demonstrated that delaying disease activity in RA can reduce this risk. In addition, various anti-cancer chemotherapy drugs are frequently employed in the treatment of RA to alleviate its symptoms [18,19]. In addition, the PI3K-Akt-mTOR pathway exerts a pivotal influence on the abnormal synovial cell proliferative activity, invasive behavior, and anti-apoptotic processes of synovial cells in patients with RA [20,21]. Notably, following inhibition of the PI3K/Akt signaling pathway, this pathway also exhibits anti-angiogenic effects in RA [22]. This pattern aligns with network pharmacology studies on AR, which also highlight its potential multi-target action on inflammation and immune-related pathways in RA.
Next, we constructed a tripartite network connecting the primary active components of AR, the top 10 core genes, and the associated signaling pathways (Figure 5C). The network visualization results show that multiple active ingredients, including Wogonin, Atractyloyne, 2-Hydroxyisoxypropyl-3-hydroxy-7-isopentene-2,3-dihydrobenzofuran-5-carboxylic acid, Costunolide, Atractylenolide II, and Stigmasterol 3-O-beta-D-glucopyranoside, can interact with these 10 core targets. This indicates their potential key roles in the therapeutic mechanism against RA by modulating the central target network.

2.3. A Metabolomics and Network Pharmacology Integration Analysis

The Online MetaboAnalyst 6.0 platform performed pathway enrichment analysis on key metabolites. Results indicated significant enrichment (p < 0.05) of differentially expressed metabolites in pathways related to lipid signaling, including Glycerophospholipid metabolism, Linoleic acid metabolism, and alpha-Linolenic acid metabolism (Figure 6A). Subsequently, an integrated pathway analysis was performed using MetScape, combining the 151 core targets from network pharmacology with the 28 plasma metabolites. The resulting Compound-Reaction-Enzyme-Gene network (Figure 6B) highlighted Glycerophospholipid metabolism as a major correlated pathway. This metabolic pathway involved in RA pathogenesis may be related to its regulation of the IL-6/JAK signaling axis [23].

2.4. Active Ingredient-Core Target Molecule Docking

To validate the network pharmacology predictions, molecular docking was conducted between 6 core components of AR (Section 2.2.3) and the 6 key targets (IL1β, STAT3, EGFR, CASP3, MMP-9, SRC). Typically, a lower (more negative) binding energy is indicative of a more favorable binding activity between the compound and its target protein [24,25,26]. A docking score <−5 kcal/mol is considered indicative of strong ligand-receptor affinity and favorable binding. The results demonstrated that the majority of the components showed strong or high binding affinities to these targets (Figure 7). This supports the potential of AR as an effective agent for RA treatment.

2.5. Study on the Efficacy and Mechanism of AR in Anti-RA

2.5.1. The Effect of AR on Synovial Tissue of Ankle Joint in AIA Rats

Representative hematoxylin–eosin (HE) staining images of rat ankle synovial tissue are presented in Figure 8A. The blank control group exhibited normal synovial architecture without significant inflammatory cell infiltration or synovial hyperplasia (yellow arrow). In stark contrast, the model group showed marked synovial structural abnormalities; a large number of synovial cell necrosis can be seen in the tissue (yellow arrow), and a small amount of inflammatory cell infiltration can be seen in the tissue (black arrow). In the MTX group, the overall structure of synovial tissue was basically normal, the synovial structure of tissue joints was clear and complete, the synovial cells were arranged regularly, and there was no obvious inflammatory cell infiltration in synovial tissue (yellow arrow). In the AR low-dose group, the synovial tissue structure was slightly abnormal, a small amount of synovial cells were exfoliated and necrotic (yellow arrow), and a small amount of inflammatory cells were infiltrated (black arrow). In the AR medium-dose group, the overall structure of synovial tissue was moderately abnormal, a small number of synovial cells were obviously necrotic (yellow arrow), and there was no obvious inflammatory cell infiltration. The synovial tissue structure of AR high-dose groups was basically normal, and the synovial structure of tissues and joints was clear and complete (yellow arrow). Collectively, these data indicate that AR treatment significantly alleviated pathological joint damage in AIA rats. Following relevant literature research [27,28], three independent observers evaluated the pathological characteristics of synovial tissue (synovial hyperplasia, synovial architecture, and inflammatory cell infiltration). The histopathological scoring criteria are shown in Table 3. The pathological scoring results are shown in Figure 8B.

2.5.2. ELISA Detection of IL-6 and MMP-9 Levels in Rat Serum

To further evaluate the efficacy of AR in AIA rats, serum levels of IL-6 and MMP-9 were quantified using ELISA. The model group showed a marked increase in these inflammatory cytokines compared to the control group (p < 0.0001). In contrast, all AR-treated groups exhibited a significant reduction in IL-6 and MMP-9 levels relative to the model group (p < 0.0001) (Figure 8C,D). These findings indicate a potent therapeutic effect of AR against adjuvant-induced arthritis.

2.5.3. RT-qPCR Detection of Related Gene Expression

The expression levels of Bax, Bcl-2, and STAT3 mRNA in rat ankle tissues were determined by RT-qPCR (Figure 9). Compared to the model group, AR treatment significantly downregulated the mRNA expression of Bax and STAT3 at all doses. Conversely, the expression of Bcl-2 was significantly up-regulated in the low-dose AR group.

2.5.4. Western Blotting (WB) Detection of Related Protein Expression

To further elucidate the therapeutic mechanism of AR against RA, the protein expression of key components in the JAK2/SRC-STAT3 signaling pathway was assessed by WB in AIA rat ankle tissue (Figure 10). The results indicated that, compared to the model group, AR treatment significantly down-regulated the expression of several key upstream regulators, notably EGFR, JAK2, phosphorylated STAT3 (p-STAT3), and SRC. And we can see that at high doses of AR, the expression of these proteins is closest to the blank group.

3. Discussion

We employed a prediction-integration-validation strategy, integrating metabolomics, network pharmacology, and experimental verification. This approach not only systematically elucidated its multi-dimensional mechanism of action, characterized by coordinated multi-component, multi-pathway, and multi-target interactions to enhance the therapeutic efficacy of AR in RA. It also provides a reference research paradigm for elucidating the complex scientific basis of TCM in preventing and treating diseases.
AR may exert its therapeutic effect on RA through its multicomponent. Studies have shown that Wogonin can effectively inhibit the migration, invasion, and pro-inflammatory cytokine production of RA-like fibroblasts through the PI3K/Akt/NF-κB pathway [13]; furthermore, it also exerts therapeutic effects on RA by targeting the NF-κB/MAPK signaling pathways [29]. Costunolide is a class of sesquiterpenes in AR; it shows strong anti-inflammatory properties and can reduce the paw oedema, myeloperoxidase, and N-acetyl-glucosaminidase activity induced by carrageenan in mice [30]. suggesting they may be the key material bases for AR to exert its efficacy in the treatment of RA.
AR may exert its anti-RA effect by modulating multipathway. Glycerophospholipid metabolism plays a central role in the development of RA disease, which may be related to the significantly increased expression of the acetylcholinesterase (ACHE) and diacylglycerol kinase iota (DGKI) genes in the pathway [31]; moreover, literature indicates that dysregulation of glycerophospholipid metabolism is the main metabolic pathway underlying differential IL-6 expression in RA patients [23]. Our experimental results have confirmed this point. The metabolite content of glycerophospholipid metabolism in the blank group has significantly changed compared with that in the model group. Following AR administration, these metabolite levels exhibited a reversal in trend, driving the systemic metabolic phenotype back towards a normal range, suggesting that the therapeutic action of AR may be initiated by regulating the host’s overall metabolic milieu, indirectly indicating a potential therapeutic effect of AR on RA.
The PI3K/Akt signaling pathway has been proven to be associated with RA. Inhibiting PI3K/Akt not only reduces the expression levels of TNF-α, IL-1β, and IL-6 in RA-FLS but also suppresses the proliferation and metastasis of RA-FLS [32]. The interaction between metabolomics and network pharmacology is crucial for a deeper understanding of how drugs influence metabolic networks in vivo [33]. The PI3K/Akt signaling pathway and glycerophospholipid metabolism exhibit a clear synergistic effect on the occurrence and treatment of various diseases, mainly through the PI3K/Akt pathway to reduce the inflammatory reaction and regulate the key metabolites in glycerophospholipid metabolism, including phosphatidylserines, phosphatidylethanolamines, and phosphatidylcholines, and so on [34,35]. Therefore, it is speculated that the therapeutic effect of AR against RA is closely related to the PI3K-Akt-regulated signal pathway and glycerophospholipid metabolism.
RA is characterized by chronic, destructive synovitis; the pathogenesis of RA is complex and may be associated with the abnormal response of immune regulatory signaling. AR can exert anti-RA action through the regulation of multi-targets. Studies have shown that the downstream transcriptional regulator STAT3 induced by JAK can promote rapid proliferation and long-term survival of synovial fibroblasts [36]. Furthermore, the literature indicates that IL-6, as an upstream regulator of the JAK2-STAT3 pathway, can mediate its involvement in the inflammation and pathogenesis of conditions like acute gout via a positive feedback mechanism [37]. Therefore, it is supposed that a reduction in IL-6 levels could inhibit the JAK2-STAT3 pathway, contributing to the therapeutic effect.
EGFR, a member of the epidermal growth factor receptor (HER) family, also plays a significant role in RA pathogenesis and treatment [38]. Studies have found that EGFR and SRC kinase have a synergistic effect. Subbasin (SbSn) can enhance the phosphorylation of EGFR and SRC kinase and then promote the phosphorylation and nuclear localization of STAT3 [39]. It can be seen that EGFR and IL-6 could synergistically regulate STAT3, thereby affecting the expression of downstream factors.
Rheumatoid arthritis fibroblast-like synoviocytes (RA-FLS) contribute to disease pathogenesis by promoting the expression of matrix metalloproteinases (MMPs) and inhibiting lymphocyte apoptosis, thereby exacerbating inflammation and joint destruction [40]. MMP-9 is a downstream factor of STAT3; several literatures have shown that inhibiting the activation of p-STAT3 can increase the expression of MMP-9 [41,42]. In synovial cells from RA patients, inhibition of STAT3 downregulates the anti-apoptotic protein Bcl-2 and upregulates the pro-apoptotic protein Bax, thereby promoting the apoptotic process in RA. Bcl-2 maintains mitochondrial membrane integrity by inhibiting Bax activity, thus preventing programmed cell death [43,44]. It is consistent with our RT-qPCR results: the downregulation of STAT3 following AR administration may promote its phosphorylation and form p-STAT3, subsequently leading to decreased Bax and increased Bcl-2 expression, which inhibits abnormal synovial cell activation and proliferation. These results suggest that AR can inhibit the abnormal activation of this pathway (JAK2/SRC-STAT3) at multiple levels, thereby directly suppressing downstream effectors such as MMP-9, which may contribute to reduced joint damage. Additionally, AR treatment downregulates the expression of the pro-apoptotic protein Bax and concurrently upregulates the expression of the anti-apoptotic protein Bcl-2, indicating its regulatory role in the apoptotic process.
However, it should be noted that this research has several limitations. Firstly, the specific active components within AR that mediate its core pharmacological effects have yet to be elucidated. Subsequently, future work will require the isolation and characterization of these key components through in vitro cellular experiments. Secondly, the relevant pathways suggested by metabolomics and network pharmacology analysis have not been experimentally verified. Subsequent studies can verify the expression changes of core differential metabolites by knocking out key targets or using inhibitors, thus providing experimental evidence for the underlying mechanisms.

4. Materials and Methods

4.1. Materials and Main Reagents

Atractylodes Rhizome (AR, 200319) was provided by Jiangxi Jiangzhong Chinese Herbal Pieces Co., Ltd.(Jiujiang, China). Methotrexate hydrate (MTX, purity >99%, C15743978) was purchased from Shanghai Macklin Biochemical Technology Co., Ltd. (Shanghai, China). Sodium carboxymethyl cellulose (CMC-Na, 20230602) was obtained from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). Complete Freund’s Adjuvant (CFA, Sigma-Aldrich, St. Louis, MO, USA). Nitrogen gas was supplied by Nanchang Jiangzhu Industrial Co., Ltd. (Nanchang, China). Purified water was prepared from Watson’s distilled water (Guangzhou, China).
IL-6 (MM-0190R1) and MMP-9 (MM-20918R1), all purchased from Jiangsu Enzyme Free Industrial Co., Ltd. (Jiangsu, China). The quantitative PCR kit was from Beijing ComWin Biotech Co., Ltd. (Beijing, China). GAPDH, STAT3, Bcl-2, and Bax primers were synthesized by Sangon Biotech (Shanghai, China) Co., Ltd. The Transzol Up Plus RNA Kit and cDNA synthesis (reverse transcription) kit were all provided by Beijing TransGen Biotech Co., Ltd. (Beijing, China). Eight-row qPCR reaction tubes (LABSELECT). JAK2 (AF6022), SRC (AF6161), and p-STAT3 (AF3293) were purchased from Affinity Biosciences (Jiangsu, China). EGFR (AF51071-2AP) and β-actin (66009-1-Ig) were purchased from Proteintech Group, Inc. (Wuhan, China). HRP-Goat anti-rabbit (SA00001-2) and HRP-Goat anti-mouse (SA00001-1) were both purchased from Proteintech Group Company, Wuhan, China. Freund’s complete adjuvant (F5881, Sigma Corporation, St. Louis, MO, USA).

4.2. Equipments

TripleTOF5600+ high-resolution mass spectrometer (ABSCIEX, Boston, MA, USA), tissue grinding instrument (Shanghai Huxi Industrial Co., Ltd., Shanghai, China), dry nitrogen blowing instrument JTN100-2 (Hangzhou Jutong Electronics Co., Ltd., Hangzhou, China), 16K-R benchtop high-speed freezing centrifuge (manufacturer GENIUS, Hunan, China), EPS600 gel electrophoresis instrument (manufacturer Tanon, Shanghai, China), JS-2012 gel imaging system (Peiqing Science and Technology, Shanghai, China), US850 ultra-micro spectrophotometer (manufacturer Novogene, Beijing, China), electrothermal blast drying oven (DHG-9070, Shanghai, China), Infinite F50 microplate reader (Tecan Laboratory Equipment Co., Ltd., Shanghai, China), automatic microplate washer (DKW-310, Shanghai, China), Micro high-speed centrifuge (TG16-W, Hunan, China), PCR gradient gene amplification instrument (SCI1000-G, SCILOGEX, Rocky Hill, NJ, USA), Real-time PCR instrument (Archimed-X4, ROCGENE, Beijing, China).

4.3. Drugs and Solutions Preparation

Preparation of administered solution: AR powder (passed through No.3 sieve) was soaked overnight (12 h) in a 10-fold amount of 80% ethanol solution. The next day, the mixture underwent three ultrasonic extractions, each lasting for 2 h. The combined extracts were mixed, heated, and concentrated using a rotary evaporator and then diluted to the desired concentration before use. All prepared extracts were stored at 4 °C.
0.5% CMC-Na solution: Dissolve 5 g of sodium carboxymethyl cellulose in 1000 mL of purified water via ultrasonication.
MTX solution (0.05 g/mL): Dissolve methotrexate in the prepared 0.5% CMC-Na solution to the target concentration.

4.4. Animal Experiments and Ethics Statement

Sixty SD female rats (body weight 180 ± 20 g) were procured from the Experimental Animal Science and Technology Centre of Jiangxi University of Traditional Chinese Medicine in Nanchang. The experimental animals were maintained in specific pathogen-free (SPF) conditions under standard 12 h light/dark cycles in rooms equipped with controls for 22–25 °C temperature and 45–65% humidity. They were acclimated for 7 days in the experimental environment. This experimental protocol and design have been approved by the Laboratory Animal Ethics Committee of Jiangxi University of Traditional Chinese Medicine, with the animal ethics review number is JZLLSC-20220321.

4.5. Experimental Designs and Process

Following randomization using a random number table, rats were assigned to a blank control group and an experimental group. An adjuvant-induced arthritis (AIA) model was established according to methods documented in the literature [45]. The RA model was induced by an intradermal injection of 0.15 mL of CFA into the plantar skin of the right hind toe of each group. 0.15 mL of normal saline was injected into the plantar skin of the right hind foot of the rats in the control group. All model-making groups were successfully established and randomly divided into a model group, a positive group, and 3 AR-administered groups (n = 10 per group).
The experimental rats received oral administration of AR once daily for 21 days at doses of 0.81 g/kg, 1.62 g/kg, and 3.24 g/kg. Positive control rats received oral administration of 0.5 mg/kg MTX solution twice weekly. Blank control and model groups received oral administration of 0.5% CMC-Na once daily. All drugs were administered via gastric intubation at a dosage of 1 mg/100 g body weight. Following the final administration on day 21, animals were fasted for 12 h with unrestricted water access. Rats were then anesthetized. Blood was collected from the abdominal aorta and stored at −80 °C for subsequent analysis. Synovial tissues from the ankle joint were also harvested for further study. Procedures for medication administration and sample collection are illustrated in Figure 11.

4.6. Metabolomics Analysis of Rat Ankle Tissue

4.6.1. Sample Preparation

Sample preparation for UPLC-Q-TOF-MS/MS: prior to analysis, frozen rat swollen tissue (0.1 g) was thawed at room temperature and homogenized using a tissue homogenizer (14,000 rpm, 20 min) with steel balls in 300 μL normal saline. Subsequently, take 200 μL of homogenate and mix with three-fold the volume of methanol. Vortex for 1 min, then centrifuge at 4 °C (12,000 r·min−1, 20 min). Collect the supernatant and dry it under a stream of nitrogen. Dissolve the residue in 200 μL of methanol, vortex for 1 min, and centrifuge at 4 °C (14,000 r·min−1, 20 min). Finally, 100 μL of the supersaturated solution obtained was subjected to LC-MS analysis. Moreover, 20 µL of each test solution was accurately pipetted and mixed as the tissue QC sample, respectively. During instrumental analysis, one QC injection was performed immediately after every six consecutive sample injections.

4.6.2. Chromatography and Mass Spectrometry Conditions

Chromatographic conditions were established as follows: sample volume of 3 µL, flow rate of 0.4 mL/min. The chromatographic column temperature of 30 °C and autosampler temperature were maintained at 4 °C. The mobile phase comprised 0.1% formic acid in water (A) and acetonitrile (B), eluted by a gradient program over 36 min: 0–3 min, 5% B; 3–5 min, 5–10% B; 5–8 min, 10–20% B; 8–18 min, 20–70% B; 18–29 min, 70–90% B; 29–30 min, 90–95% B; 30–33 min, 95% B; 33–36 min, 95–5% B.
Mass spectrometry conditions were established as follows: Detection was performed using an ESI source in both the positive and negative ion modes. Mass scan range, m/z 50–1500. ion spray voltage (ISVF), 5.5 kV; source temperature (TEM), 500 °C; curtain gas (CUR), 40 psi; nebulizer gas (GS1) and auxiliary gas (GS2), both 50 psi. For collision-induced dissociation, the declustering potential (DP) was set at 100 V and −100 V, and the collision energy (CE) was applied at 10, −10, 35, and −35 eV, with a collision energy spread (CES) of 15 eV. The total data acquisition time was 34 min.

4.6.3. Metabolomics Data Processing

The raw mass spectrometry data from the Blank control, Model, and Low-dose AR groups were processed separately using MarkerView software version 1.2.1.1 for peak detection, alignment, and normalization. The resultant data matrices were subsequently analyzed in SIMCA 14.1. PCA was first performed to overview the global metabolic profiles of the rat synovial tissue. It was followed by OPLS-DA to identify metabolites contributing to group separations. Metabolites with a variable importance in projection VIP ≥ 1 and a p-value < 0.05 were defined as differential metabolites [46]. The differential metabolites identified from the three-group comparison were considered potential biomarkers for the therapeutic effect of AR on RA. These candidate metabolites were identified by querying the Human Metabolome Database (HMDB) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database using their accurate mass and MS/MS spectra. Additionally, differential metabolite analysis and metabolic pathway analysis were conducted using the online platform MetaboAnalyst 6.0 (https://www.metaboanalyst.ca/, accessed on 20 December 2023).

4.7. Network Pharmacology Analysis [47]

4.7.1. Predicting Potential Gene Targets of AR

Building upon our prior research [48] and the TCMSP database (https://old.tcmsp-e.com/tcmsp.php, accessed on 24 December 2023), we have compiled the chemical composition of AR. Choose compounds with an oral bioavailability (OB) percentage > 30% and a drug-like property (DL) score > 0.18 as candidate components. The chemical formulas and SMILES structures of these screened compounds were retrieved from the PubChem database (https://pubchem.ncbi.nlm.nih.gov, accessed on 24 December 2023). Subsequently, the SMILES structures of the active compounds were submitted to the SwissADME web tool to evaluate their drug-likeness according to Lipinski’s Rule of Five. Only compounds meeting the criteria of high gastrointestinal (GI) absorption and at least three “yes” results in the drug-likeness evaluation were retained. Finally, all qualified active compounds were imported into the SwissTargetPrediction database (https://www.swisstargetprediction.ch, accessed on 24 December 2023) to predict their corresponding targets. With the setting of the species as Homo sapiens, targets with a probability > 0 were designated as potential constituent targets for AR [49].

4.7.2. Predicting Potential Gene Targets of RA

Retrieve disease targets related to RA from GeneCards (https://www.genecards.org/, accessed on 24 December 2023), OMIM (http://www.ncbi.nlm.nih.gov/omim, accessed on 24 December 2023), and PharmGKB (https://www.pharmgkb.org/, accessed on 24 December 2023) databases using “Rheumatoid Arthritis” as the search keyword. The top 1000 targets with the highest relevance scores from each database were collected and merged to form a consolidated RA target set, from which duplicates were removed. The potential targets of the active compounds in AR and the RA-related targets were then submitted to the Venny 2.1.0 online platform (https://bioinfogp.cnb.csic.es/tools/venny/index.html, accessed on 24 December 2023) to identify overlapping genes. A Venn diagram was generated to visualize the intersection, and these common genes were considered potential therapeutic targets through which AR exerts its pharmacological effects against RA.

4.7.3. Constructing PPI Networks and Identifying Candidate Targets

The common drug-disease targets were imported into the STRING database (https://string-db.org, accessed on 24 December 2023) under the “Multiple Proteins” option. The list of gene names was submitted, with “Homo sapiens” set as the organism, to generate a PPI network. Candidate targets were identified based on interactions with a probabilistic association confidence score of ≥ 0.4. Subsequently, the corresponding TSV file type was incorporated into Cytoscape version 3.9.1 for further analysis. The CentiScape2.2 plugin was then used to screen for key targets based on three topological parameters: degree, closeness, and betweenness [13].

4.7.4. GO Function and KEGG Pathway Enrichment Analysis

The list of key targets was uploaded to the DAVID online platform, with “Official Gene Symbol” selected as the identifier and “Homo sapiens” set as the species. The enrichment results for both GO terms and KEGG pathways were extracted. Terms exhibiting statistical significance at p < 0.05 were filtered and considered statistically significant. The filtered results were then visualized using the bioinformatics mapping website (http://www.bioinformatics.com.cn/, accessed on 26 December 2023).

4.8. Molecular Docking

We obtained the structural data for the AR component in mol2 format from the TCMSP database (https://www.tcmsp-e.com). The PDB format of the core target protein was retrieved from the RCSB Protein Data Bank (https://www.rcsb.org/). Then, CB-dock 2 (https://cadd.labshare.cn/cb-dock2/php/blinddock.php, accessed on 28 December 2023) [50] was used to assess the binding activity between the targets and compound, obtain the corresponding binding energy, and repeat the molecular docking 3 times.

4.9. In Vivo Experiment Validation

4.9.1. Histopathological Evaluation

Synovial tissue was harvested from rat ankle joints, and histopathological alterations in the synovial tissue were assessed via HE staining. Briefly, ankle joint synovial tissues were fixed in 4% paraformaldehyde for 48 h and dehydrated through a graded ethanol series (80%, 90%, 95%, 100%), spending 2 h at each concentration. The samples then underwent xylene clearing, paraffin embedding, and sectioning. Finally, the sections from each group were subjected to HE staining and observed with an optical microscope to evaluate pathological alterations in the synovial tissue across the experimental groups.

4.9.2. ELISA

According to the manufacturer’s instructions, the ELISA kit was employed to determine the concentrations of the inflammatory factors IL-6 and MMP-9 in rat serum samples. The actual concentrations for each group were calculated based on the linear regression equation derived from the standard curve.

4.9.3. RT-qPCR

Total RNA was extracted from frozen synovial tissue, which was pulverized under liquid nitrogen and processed according to the Trizol reagent kit instructions. Subsequently, the RNA was reverse-transcribed into cDNA. Primer sequences for STAT3, Bcl-2, and Bax were designed using the online tools provided by the National Center for Biotechnology Information (NCBI) and synthesized by Shanghai Sangon Biotech. GAPDH was selected as the internal control gene, and relative mRNA expression levels were quantified according to the 2–ΔΔCT method. Detailed primer sequences for each gene are provided in Table 4.

4.9.4. Western Blot

Synovial tissue samples were homogenized in liquid nitrogen, and total protein was extracted using RIPA lysis buffer supplemented with protease inhibitors. Protein concentration was determined by the BCA method. The proteins were then separated by electrophoresis, transferred onto a membrane, and blocked. The membrane was subsequently incubated with primary and secondary antibodies; specifically, the primary antibodies included EGFR (1:1000), JAK2 (1:1000), SRC (1:1000), p-STAT3 (1:1000), β-actin (1:5000); the secondary antibodies used were HRP Coat anti-rabbit and HRP Coat anti-mouse, both diluted at a ratio of 1:5000. The bands were then visualized using Image-Pro Plus 6.0 software. β-actin served as the internal control, and the expression level of each target protein was calculated as the ratio of its band intensity to that of the β-actin band.

4.10. Statistical Methodology

Data analysis was conducted using IBM SPSS Statistics software (version 27), with tests for normal distribution and homogeneity of variance performed. For normally distributed quantitative data, results are presented as the mean ± standard deviation (SD). When the data satisfy the assumption of homogeneity of variance and follow a normal distribution, one-way analysis of variance (ANOVA) and LSD t-tests shall be employed for data analysis. Where variances are heterogeneous, Tamarek’s test shall be utilized. p < 0.05 was considered statistically significant compared with the control group: # p < 0.05, ## p < 0.01, ### p < 0.001, and #### p < 0.0001; compared with the model group: * p < 0.05, ** p < 0.01, *** p < 0.001, and **** p < 0.0001. All data were plotted and graphed using GraphPad Prism 10.1.2 software.

5. Conclusions

In short, Atractylodes Rhizome alleviates RA by inhibiting the JAK2/SRC-STAT3 axis at multiple targets. This integration mechanism is initiated by reducing the levels of IL-6 and EGFR, effectively regulating the balance of cell apoptosis, limiting joint injury, and delaying disease progression (Figure 12). This study provides a comprehensive pharmacological basis for AR to become a multi-target TCM preparation against rheumatoid arthritis. This offers a scientific foundation for its clinical application and the development of natural anti-RA drugs.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ph19020262/s1, Figure S1: TIC of Atractylodes Rhizome in positive and negative ion modes; Figure S2: Compound-target network diagram.

Author Contributions

R.W.: writing—review and editing, software, investigation, formal analysis; C.X.: writing–original draft, validation, data curation; H.Z.: validation, resources; C.L.: writing–original draft, visualization, investigation, conceptualization; H.Y.: writing—review and editing, funding acquisition, project administration. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by funds from the National TCM Characteristic Technology Inheritance Talent Training Program (National TCM Human Education Letter [2023] No. 96); Jiangxi Provincial TCM Young and Middle-aged Backbone Talents (the fourth batch) (Gan TCM Science and Education Word [2022] No. 7); National Natural Science Foundation of China (81960716); Jiangxi Provincial Natural Science Foundation (No.20232BAB216109).

Institutional Review Board Statement

The Experimental Animal Ethics Committee of Jiangxi University of Traditional Chinese Medicine, JZLLSC-2022-0321, 21 March 2022.

Informed Consent Statement

Not applicable.

Data Availability Statement

We have provided Supplementary Figures in the Supplementary Materials and supporting data in the GitHub repository (https://github.com/gentle1399/gentle-20260122.git, accessed on 22 January 2026). Any additional data can be obtained by contacting the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

AR, Atractylodes Rhizome; RA, Rheumatoid arthritis; TCM, Traditional Chinese Medicine; CFA, Complete Freund’s Adjuvant; AIA, Adjuvant-Induced Arthritis; MTX, Methotrexate; ELISA, Enzyme-linked immunosorbent assay; RT-qPCR, Real-Time Quantitative Polymerase Chain Reaction; WB, Western blot; IL-6, Interleukin-6; MMP-9, Matrix metalloproteinase-9; Bcl-2, B-cell lymphoma 2; Bax, Bcl-2 associated X, apoptosis regulator; EGFR, Epidermal growth factor receptor; JAK2, Janus kinase 2; STAT3, Signal transducer and activator of transcription 3; TIC, Total ion chromatogram; ESI, electrospray ionization; FC, Fold change; PCA, Principal Component Analysis; OPLS-DA, Orthogonal Partial Least Squares Discriminant Analysis; PPI, protein–protein interaction; GO, Gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; BP, Biological process; CC, Cell component.

References

  1. Weyand, C.M.; Goronzy, J.J. The immunology of rheumatoid arthritis. Nat. Immunol. 2021, 22, 10–18. [Google Scholar] [CrossRef] [Scilit]
  2. Bullock, J.; Rizvi Syed, A.A.; Saleh Ayman, M.; Ahmed Sultan, S.; Do Duc, P.; Ansari Rais, A.; Ahmed, J. Rheumatoid Arthritis: A Brief Overview of the Treatment. Med. Princ. Pract. 2018, 27, 501–507. [Google Scholar] [CrossRef] [Scilit]
  3. Alivernini, S.; Firestein, G.S.; McInnes, I.B. The pathogenesis of rheumatoid arthritis. Immunity 2022, 55, 2219–2255. [Google Scholar] [CrossRef] [Scilit]
  4. Gabriel, S.E. The epidemiology of rheumatoid arthritis. Rheum. Dis. Clin. N. Am. 2001, 27, 269–281. [Google Scholar] [CrossRef] [Scilit]
  5. Smith, M.H.; Berman, J.R. What is rheumatoid arthritis? JAMA 2022, 327, 1194. [Google Scholar] [CrossRef] [Scilit]
  6. Smolen, J.S.; Steiner, G. Therapeutic strategies for rheumatoid arthritis. Nat. Rev. Drug Discov. 2003, 2, 473–488. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  7. Brown, P.; Pratt, A.G.; Hyrich, K.L. Therapeutic advances in rheumatoid arthritis. BMJ 2024, 384, e070856. [Google Scholar] [CrossRef] [Scilit]
  8. Moudgil, K.D.; Berman, B.M. Traditional Chinese medicine: Potential for clinical treatment of rheumatoid arthritis. Expert. Rev. Clin. Immunol. 2014, 10, 819–822. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  9. Pan, H.-D.; Xiao, Y.; Wang, W.-Y.; Ren, R.-T.; Leung, E.L.-H.; Liu, L. Traditional Chinese Medicine as a Treatment for Rheumatoid Arthritis: From Empirical Practice to Evidence-Based Therapy. Engineering 2019, 5, 895–906. [Google Scholar] [CrossRef] [Scilit]
  10. Zhang, C.; Jiang, M.; Chen, G.; Lu, A. Incorporation of traditional Chinese medicine pattern diagnosis in the management of rheumatoid arthritis. Eur. J. Integr. Med. 2012, 4, e245. [Google Scholar] [CrossRef] [Scilit]
  11. Pang, J.; Ma, S.; Xu, X.; Zhang, B.; Cai, Q. Effects of rhizome of Atractylodes koreana (Nakai) Kitam on intestinal flora and metabolites in rats with rheumatoid arthritis. J. Ethnopharmacol. 2021, 281, 114026. [Google Scholar] [CrossRef] [Scilit]
  12. Commission, N.P. Pharmacopoeia of the People’s Republic of China; China Medical Science and Technology Press: Beijing, China, 2025. [Google Scholar]
  13. Yang, H.; Liu, C.; Lin, X.; Li, X.; Zeng, S.; Gong, Z.; Xu, Q.; Li, D.; Li, N. Wogonin inhibits the migration and invasion of fibroblast-like synoviocytes by targeting PI3K/AKT/NF-κB pathway in rheumatoid arthritis. Arch. Biochem. Biophys. 2024, 755, 109965. [Google Scholar] [CrossRef] [Scilit]
  14. Tang, R.; Qin, Z.-F.; Yin, J.-H.; Wang, J.-Y.; Su, W.-R.; Xuan, Z.-H.; Chen, B.; Jia, X.-Y. Er Miao San and its main components phellodendrine and atractylenolide-I exert anti-rheumatoid arthritis effects by inhibiting PAD4 and thereby reducing the formation of NETs. Fitoterapia 2025, 185, 106771. [Google Scholar] [CrossRef] [Scilit]
  15. Sharma, B.; Yadav, D.K. Metabolomics and network pharmacology in the exploration of the multi-targeted therapeutic approach of traditional medicinal plants. Plants 2022, 11, 3243. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  16. Xu, W.; Wu, J.; Lu, M.; Yu, H.; Li, C. Plantaginis Semen, a Novel Natural S1PR1 Agonist, Inhibits Oxidative Stress and Inflammation to Treat Lipopolysaccharide-Induced Acute Lung Injury. Phytother. Res. 2026, 40, 351–369. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  17. Li, C.; Wang, C.; Guo, Y.; Wen, R.; Yan, L.; Zhang, F.; Gong, Q.; Yu, H. Research on the effect and underlying molecular mechanism of Cangzhu in the treatment of gouty arthritis. Eur. J. Pharmacol. 2022, 927, 175044. [Google Scholar] [CrossRef] [Scilit]
  18. Beydon, M.; Pinto, S.; De Rycke, Y.; Fautrel, B.; Mariette, X.; Seror, R.; Tubach, F. Risk of cancer for patients with rheumatoid arthritis versus general population: A national claims database cohort study. Lancet Reg. Health 2023, 35, 100768. [Google Scholar] [CrossRef] [Scilit]
  19. Jayashree, S.; Nirekshana, K.; Guha, G.; Bhakta-Guha, D. Cancer chemotherapeutics in rheumatoid arthritis: A convoluted connection. Biomed. Pharmacother. 2018, 102, 894–911. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  20. Bobkova, T.; Bobkov, A.; Li, Y. Pharmacological Inhibition of the PI3K/AKT/mTOR Pathway in Rheumatoid Arthritis Synoviocytes: A Systematic Review and Meta-Analysis (Preclinical). Pharmaceuticals 2025, 18, 1152. [Google Scholar] [CrossRef] [Scilit]
  21. Chen, K.; Lin, Z.-W.; He, S.-M.; Wang, C.-Q.; Yang, J.-C.; Lu, Y.; Xie, X.-B.; Li, Q. Metformin inhibits the proliferation of rheumatoid arthritis fibroblast-like synoviocytes through IGF-IR/PI3K/AKT/m-TOR pathway. Biomed. Pharmacother. 2019, 115, 108875. [Google Scholar] [CrossRef] [Scilit]
  22. Liu, C.; He, L.; Wang, J.; Wang, Q.; Sun, C.; Li, Y.; Jia, K.; Wang, J.; Xu, T.; Ming, R.; et al. Anti-angiogenic effect of Shikonin in rheumatoid arthritis by downregulating PI3K/AKT and MAPKs signaling pathways. J. Ethnopharmacol. 2020, 260, 113039. [Google Scholar] [CrossRef] [Scilit]
  23. Su, J.; Li, S.; Chen, J.; Jian, C.; Hu, J.; Du, H.; Hai, H.; Wu, J.; Zeng, F.; Zhu, J.; et al. Glycerophospholipid metabolism is involved in rheumatoid arthritis pathogenesis by regulating the IL-6/JAK signaling pathway. Biochem. Biophys. Res. Commun. 2022, 600, 130–135. [Google Scholar] [CrossRef] [Scilit]
  24. Li, C.; Wen, R.; Liu, D.; Yan, L.; Gong, Q.; Yu, H. Assessment of the potential of Sarcandra glabra (thunb.) nakai. In treating ethanol-induced gastric ulcer in rats based on metabolomics and network analysis. Front. Pharmacol. 2022, 13, 810344. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  25. Zhang, Y.; Yang, J.; Qu, B.; Huang, J.; Wang, Y.; Yan, J. Enhanced Anti-Lung Cancer Effects of Steamed Panacis Japonici Rhizoma: Insights from Metabolomics, Network Pharmacology and Molecular Dynamics Simulation. Int. J. Mol. Sci. 2025, 26, 11999. [Google Scholar] [CrossRef] [Scilit]
  26. Liu, Y.; Lu, W.; Xiang, Z.; Shen, Y.; Ni, B.; Zou, H.; Shao, X. Integrated bioinformatics and machine learning reveal pan-apoptosis and immune infiltration signatures in diabetic nephropathy. Front. Immunol. 2025, 16, 1659065. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  27. Obeidat, A.M.; Kim, S.Y.; Burt, K.G.; Hu, B.; Li, J.; Ishihara, S.; Xiao, R.; Miller, R.E.; Little, C.; Malfait, A.-M.; et al. Recommendations For a Standardized Approach to Histopathologic Evaluation of Synovial Membrane in Murine Models of Experimental Osteoarthritis. bioRxiv 2023. [Google Scholar] [CrossRef] [Scilit]
  28. Pearce-Fisher, D.; Smith, M.H.; Mehta, B.Y.; Spolaore, E.; DiCarlo, E.; Sun, D.; Goodman, S.M. Patient-Reported Fatigue Associated with Joint Histopathology in Rheumatoid Arthritis. ACR Open Rheumatol. 2025, 7, e11772. [Google Scholar] [CrossRef] [Scilit]
  29. Huang, Y.; Guo, L.; Chitti, R.; Sreeharsha, N.; Mishra, A.; Gubbiyappa, S.K.; Singh, Y. Wogonin ameliorate complete Freund’s adjuvant induced rheumatoid arthritis via targeting NF-κB/MAPK signaling pathway. Biofactors 2020, 46, 283. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  30. Kassuya, C.A.L.; Cremoneze, A.; Barros, L.F.L.; Simas, A.S.; da Rocha Lapa, F.; Mello-Silva, R.; Stefanello, M.É.A.; Zampronio, A.R. Antipyretic and anti-inflammatory properties of the ethanolic extract, dichloromethane fraction and costunolide from Magnolia ovata (Magnoliaceae). J. Ethnopharmacol. 2009, 124, 369. [Google Scholar] [CrossRef] [Scilit]
  31. Jian, C.; Wei, L.; Wu, T.; Li, S.; Wang, T.; Chen, J.; Chang, S.; Zhang, J.; He, B.; Wu, J.; et al. Comprehensive multi-omics analysis reveals the core role of glycerophospholipid metabolism in rheumatoid arthritis development. Arthritis Res. Ther. 2023, 25, 246. [Google Scholar] [CrossRef] [Scilit]
  32. Li, X.; Wang, Y. Cinnamaldehyde Attenuates the Progression of Rheumatoid Arthritis through Down-Regulation of PI3K/AKT Signaling Pathway. Inflammation 2020, 43, 1729–1741. [Google Scholar] [CrossRef] [Scilit]
  33. Chen, T.; Wang, T.; Shi, Y.; Deng, J.; Yan, X.; Zhang, C.; Yin, X.; Liu, W. Integrated network pharmacology, metabolomics and molecular docking analysis to reveal the mechanisms of quercetin in the treatment of hyperlipidemia. J. Pharm. Biomed. Anal. 2025, 252, 116507. [Google Scholar] [CrossRef] [Scilit]
  34. Lu, L.; Huang, C.; Zhou, Y.; Jiang, H.; Chen, C.; Du, J.; Zhou, T.; Wen, F.; Pei, J.; Wu, Q. Tinosporae Radix attenuates acute pharyngitis by regulating glycerophospholipid metabolism and inflammatory responses through PI3K-Akt signaling pathway. Front. Pharmacol. 2024, 15, 1491321. [Google Scholar] [CrossRef] [Scilit]
  35. Hou, J.-Y.; Wang, L.-H.; Ni, L.-Q.; Hou, B.-W.; Wang, K.; Zhu, N.-Q.; Yang, H.-J. Chuanxiong Qingnao Granules (CQG) alleviates nitroglycerin-induced migraine-like pain in rats by glycerophospholipid metabolism and PI3K/Akt signaling pathway. Phytomedicine 2025, 136, 156336. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  36. Gao, Y.; Zhang, Y.; Liu, X. Rheumatoid arthritis: Pathogenesis and therapeutic advances. MedComm 2024, 5, e509. [Google Scholar] [CrossRef] [Scilit]
  37. Zhang, Z.; Wang, P.; Lei, T.; Guo, J.; Jiang, Y.; Li, Y.; Zheng, J.; Wang, S.; Xu, H.; Jian, G.; et al. The role and impact of the IL-6 mediated JAK2-STAT1/3 signaling pathway in the pathogenesis of gout. Front. Pharmacol. 2025, 16, 1480844. [Google Scholar] [CrossRef] [Scilit]
  38. Yuan, F.-L.; Li, X.; Lu, W.-G.; Sun, J.-M.; Jiang, D.-L.; Xu, R.-S. Epidermal growth factor receptor (EGFR) as a therapeutic target in rheumatoid arthritis. Clin. Rheumatol. 2013, 32, 289–292. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  39. Zhou, Z.; Zhang, Z.; Chen, H.; Bao, W.; Kuang, X.; Zhou, P.; Gao, Z.; Li, D.; Xie, X.; Yang, C.; et al. SBSN drives bladder cancer metastasis via EGFR/SRC/STAT3 signalling. Br. J. Cancer 2022, 127, 211–222. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  40. Zhang, Q.; Liu, J.; Zhang, M.; Wei, S.; Li, R.; Gao, Y.; Peng, W.; Wu, C. Apoptosis induction of fibroblast-like synoviocytes is an important molecular-mechanism for herbal medicine along with its active components in treating rheumatoid arthritis. Biomolecules 2019, 9, 795. [Google Scholar] [CrossRef] [Scilit]
  41. Zhang, M.; Xue, Y.; Chen, H.; Meng, L.; Chen, B.; Gong, H.; Zhao, Y.; Qi, R. Resveratrol inhibits MMP3 and MMP9 expression and secretion by suppressing TLR4/NF-κB/STAT3 activation in Ox-LDL-treated HUVECs. Oxidative Med. Cell. Longev. 2019, 2019, 9013169. [Google Scholar] [CrossRef] [Scilit]
  42. Matsui, F.; Babitz, S.A.; Rhee, A.; Hile, K.L.; Zhang, H.; Meldrum, K.K. Mesenchymal stem cells protect against obstruction-induced renal fibrosis by decreasing STAT3 activation and STAT3-dependent MMP-9 production. Am. J. Physiol. Ren. Physiol. 2017, 312, F25. [Google Scholar] [CrossRef] [Scilit]
  43. Nielsen, M.; Kæstel, C.G.; Eriksen, K.W.; Woetmann, A.; Stokkedal, T.; Kaltoft, K.; Geisler, C.; Röpke, C.; Ødum, N. Inhibition of constitutively activated Stat3 correlates with altered Bcl-2/Bax expression and induction of apoptosis in mycosis fungoides tumor cells. Leukemia 1999, 13, 735–738. [Google Scholar] [CrossRef] [Scilit]
  44. Liu, H.; Pope, R.M. The role of apoptosis in rheumatoid arthritis. Curr. Opin. Pharmacol. 2003, 3, 317–322. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  45. Youssef, M.E.; Abdel-Reheim, M.A.; Morsy, M.A.; El-Daly, M.; Atwa, G.M.; Yahya, G.; Cavalu, S.; Saber, S.; Ahmed Gaafar, A.G. Ameliorative effect of dabigatran on CFA-induced rheumatoid arthritis via modulating kallikrein-kinin system in rats. Int. J. Mol. Sci. 2022, 23, 10297. [Google Scholar] [CrossRef] [Scilit]
  46. Li, C.; Wen, R.; Liu, D.W.; Liu, Q.; Yan, L.P.; Wu, J.X.; Li, S.Y.; Gong, Q.F.; Yu, H. Diuretic Effect and Metabolomics Analysis of Crude and Salt-Processed Plantaginis Semen. Front. Pharmacol. 2020, 11, 563157. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  47. Kumar, A.; Patil, C. Network Pharmacology: Principles, Processes, and Applications; CRC Press: Boca Raton, FL, USA, 2025. [Google Scholar]
  48. Wang, C.; Xiang, Q.; Zhang, W. Analysis of chemical compositions in Atractylodes lancea Rhizoma before and after processing with rice-washed water by UPLC-Q-TOF-MS. Chin. J. Exp. Tradit. Med. Form 2022, 28, 164–173. [Google Scholar]
  49. Zhao, M.-J.; Yin, J.-L.; Luo, J.-H.; Ge, Y.-S.; Huang, C.-M.; Meng, T.-T.; Wang, X.-Z.; Huang, X.-H.; Chen, L.-L.; Zhai, Y.-Q.; et al. Integration of Network Pharmacology, Molecular Docking, and Experimental Validation to Identify the Effect of EGCG on Alleviating Chondrocyte Inflammatory Damage by Targeting ER Stress-STAT3 Crosstalk. J. Inflamm. Res. 2025, 18, 15165–15185. [Google Scholar] [CrossRef] [Scilit]
  50. Liu, Y.; Yang, X.; Gan, J.; Chen, S.; Xiao, Z.-X.; Cao, Y. CB-Dock2: Improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Res. 2022, 50, 159–164. [Google Scholar] [CrossRef] [Scilit]
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Figure 1. PCA scores plots of synovial tissue samples in rats; (A) positive ion mode; (B) negative ion mode.
Figure 1. PCA scores plots of synovial tissue samples in rats; (A) positive ion mode; (B) negative ion mode.
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Figure 2. OPLS-DA scores plots and OPLS-DA permutation test; (A) OPLS-DA score diagram of Control and Model in positive ion mode; (B) OPLS-DA score diagram of Model and AR in positive ion mode; (C) OPLS-DA score diagram of Control and Model in negative ion mode; (D) OPLS-DA score diagram of Model and AR in negative ion mode; (E) OPLS-DA permutation test results of Control to Model in positive ion mode; (F) OPLS-DA permutation test results of AR to Model in positive ion mode; (G) OPLS-DA permutation test results of Control to Model in negative ion mode; (H) OPLS-DA permutation test results of AR to Model in negative ion mode.
Figure 2. OPLS-DA scores plots and OPLS-DA permutation test; (A) OPLS-DA score diagram of Control and Model in positive ion mode; (B) OPLS-DA score diagram of Model and AR in positive ion mode; (C) OPLS-DA score diagram of Control and Model in negative ion mode; (D) OPLS-DA score diagram of Model and AR in negative ion mode; (E) OPLS-DA permutation test results of Control to Model in positive ion mode; (F) OPLS-DA permutation test results of AR to Model in positive ion mode; (G) OPLS-DA permutation test results of Control to Model in negative ion mode; (H) OPLS-DA permutation test results of AR to Model in negative ion mode.
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Figure 3. Volcanic plots of differential metabolites in different groups. (A) Metabolite volcanic map of Control to Model in positive ion mode; (B) Metabolite volcanic map of AR to Model in positive ion mode; (C) Metabolite volcanic map of Control to Model in negative ion mode; (D) Metabolite volcanic map of AR to Model in negative ion mode. Red represents the metabolites of VIP > 1, p < 0.05 (−log10 (p-value) > 1.3), and | log2 FC | > 1; Blue represents the metabolites of VIP > 1, p < 0.05 (−log10 (p-value) > 1.3), and | log2 FC | < 1; Gray represents the metabolites of VIP < 1, p < 0.05 (−log10 (p-value) > 1.3), and | log2 FC | < 1; Black represents the metabolites of VIP < 1, p > 0.05 (−log10 (p-value) < 1.3), and | log2 FC | < 1.
Figure 3. Volcanic plots of differential metabolites in different groups. (A) Metabolite volcanic map of Control to Model in positive ion mode; (B) Metabolite volcanic map of AR to Model in positive ion mode; (C) Metabolite volcanic map of Control to Model in negative ion mode; (D) Metabolite volcanic map of AR to Model in negative ion mode. Red represents the metabolites of VIP > 1, p < 0.05 (−log10 (p-value) > 1.3), and | log2 FC | > 1; Blue represents the metabolites of VIP > 1, p < 0.05 (−log10 (p-value) > 1.3), and | log2 FC | < 1; Gray represents the metabolites of VIP < 1, p < 0.05 (−log10 (p-value) > 1.3), and | log2 FC | < 1; Black represents the metabolites of VIP < 1, p > 0.05 (−log10 (p-value) < 1.3), and | log2 FC | < 1.
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Figure 4. (A), Venn diagram of intersecting genes between AR and RA targets; (B), PPI network of 151 intersected target genes.
Figure 4. (A), Venn diagram of intersecting genes between AR and RA targets; (B), PPI network of 151 intersected target genes.
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Figure 5. (A,B), Enrichment analysis of GO and KEGG pathways; (C) interaction diagram of the primary active ingredient AR with the top 10 core targets and associated signaling pathways.
Figure 5. (A,B), Enrichment analysis of GO and KEGG pathways; (C) interaction diagram of the primary active ingredient AR with the top 10 core targets and associated signaling pathways.
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Figure 6. Metabolomics combined with network pharmacology analysis. (A) Metabolic pathway bubble plot; (B) the compound-reaction-enzyme-gene networks of the key metabolites and targets. The pink hexagons, grey diamonds, green rectangles, purple circles represent the compounds, reactions, enzymes, genes, respectively; the red hexagon and blue circle represent active compounds and hub genes.
Figure 6. Metabolomics combined with network pharmacology analysis. (A) Metabolic pathway bubble plot; (B) the compound-reaction-enzyme-gene networks of the key metabolites and targets. The pink hexagons, grey diamonds, green rectangles, purple circles represent the compounds, reactions, enzymes, genes, respectively; the red hexagon and blue circle represent active compounds and hub genes.
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Figure 7. (A), Heat map of binding energy between AR active ingredients and core targets; 1, Atractyloyne; 2, wogonin; 3, 2-Hydroxyisoxypropyl-3-hydroxy-7-isopentene-2,3-dihydrobenzofuran-5-carboxylic; 4, Stigmasterol 3-O-beta-D-glucopyranoside; 5, Atractylenolide II; 6, Costunolide; (B), Visualization of partial molecular docking.
Figure 7. (A), Heat map of binding energy between AR active ingredients and core targets; 1, Atractyloyne; 2, wogonin; 3, 2-Hydroxyisoxypropyl-3-hydroxy-7-isopentene-2,3-dihydrobenzofuran-5-carboxylic; 4, Stigmasterol 3-O-beta-D-glucopyranoside; 5, Atractylenolide II; 6, Costunolide; (B), Visualization of partial molecular docking.
Pharmaceuticals 19 00262 g007
Pharmaceuticals 19 00262 g008
Figure 8. (A,B) HE staining results of ankle tissue and pathological score (n = 3): The yellow arrow in the pathological image points towards the synovial tissue, while the black arrow points towards the inflammatory cells; (C,D) the effect of AR on the levels of IL-6 and MMP-9 in rat serum. Data presented as means ± SD (n ≥ 9). p < 0.05 was considered statistically significant; compared with the control group: #### p < 0.0001; compared with the model group: ** p < 0.01, *** p < 0.001, **** p < 0.0001.
Figure 8. (A,B) HE staining results of ankle tissue and pathological score (n = 3): The yellow arrow in the pathological image points towards the synovial tissue, while the black arrow points towards the inflammatory cells; (C,D) the effect of AR on the levels of IL-6 and MMP-9 in rat serum. Data presented as means ± SD (n ≥ 9). p < 0.05 was considered statistically significant; compared with the control group: #### p < 0.0001; compared with the model group: ** p < 0.01, *** p < 0.001, **** p < 0.0001.
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Pharmaceuticals 19 00262 g009
Figure 9. RT-qPCR analysis of AR samples (n = 3): (A) Effects of AR on the expression of Bax mRNA in AIA rat ankle tissue; (B) Effects of AR on the expression of Bcl-2 mRNA in AIA rat ankle tissue; (C) Effects of AR on the expression of STAT3 mRNA in AIA rat ankle tissue. p < 0.05 was considered statistically significant; compared with the control group: #### p < 0.0001; compared with the model group: * p < 0.05, **** p < 0.0001.
Figure 9. RT-qPCR analysis of AR samples (n = 3): (A) Effects of AR on the expression of Bax mRNA in AIA rat ankle tissue; (B) Effects of AR on the expression of Bcl-2 mRNA in AIA rat ankle tissue; (C) Effects of AR on the expression of STAT3 mRNA in AIA rat ankle tissue. p < 0.05 was considered statistically significant; compared with the control group: #### p < 0.0001; compared with the model group: * p < 0.05, **** p < 0.0001.
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Pharmaceuticals 19 00262 g010
Figure 10. WB analysis of AR samples (n = 3): (A) WB band plot of different proteins; (B) Effects of AR on EGFR protein expression in AIA rat ankle tissue; (C) Effects of AR on JAK2 protein expression in AIA rat ankle tissue; (D) Effects of AR on p-STAT3 protein expression in AIA rat ankle tissue; (E) Effects of AR on SRC protein expression in AIA rat ankle tissue. p < 0.05 was considered statistically significant; compared with the control group: ## p <0.01, #### p < 0.0001; compared with the model group: ** p < 0.01, *** p <0.001, **** p < 0.0001.
Figure 10. WB analysis of AR samples (n = 3): (A) WB band plot of different proteins; (B) Effects of AR on EGFR protein expression in AIA rat ankle tissue; (C) Effects of AR on JAK2 protein expression in AIA rat ankle tissue; (D) Effects of AR on p-STAT3 protein expression in AIA rat ankle tissue; (E) Effects of AR on SRC protein expression in AIA rat ankle tissue. p < 0.05 was considered statistically significant; compared with the control group: ## p <0.01, #### p < 0.0001; compared with the model group: ** p < 0.01, *** p <0.001, **** p < 0.0001.
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Pharmaceuticals 19 00262 g011
Figure 11. The drug administration process and sample collection process.
Figure 11. The drug administration process and sample collection process.
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Pharmaceuticals 19 00262 g012
Figure 12. Potential mechanisms of Atractylodes Rhizome treatment for rheumatoid arthritis. ↓, down-regulated; ↑, up-regulation.
Figure 12. Potential mechanisms of Atractylodes Rhizome treatment for rheumatoid arthritis. ↓, down-regulated; ↑, up-regulation.
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Table 1. Differential metabolite information table identified.
Table 1. Differential metabolite information table identified.
tR/
min		MetaboliteFormulaObserved ValueTheoretical ValueError
(ppm)		ModeMS/MS SpectraVariation
M vs. C AR vs. M
2.012-amino-3-(4-hydroxyphenyl)propanoic acidC9H11NO3182.0806181.07392[M + H]+100.0758, 56.0498↓↓
2.432-Ethyl-2-oxazolineC5H9NO100.075599.06843[M + H]+91.0520, 136.0737↓↓
2.95Isoketocamphoric acidC10H16O5217.1048216.099810[M + H]+217.1049↑↓
18.27LysoPC(18:4(6Z,9Z,12Z,15Z)/0:0)C26H46NO7P516.3065515.30121[M + H]+524.2762, 164.0077, 383.2572, 361.2739↑↓
18.282-Methoxyestradiol-3-methyletherC20H28O3317.2105316.20382[M + H]+339.1948↓↑
18.37LysoPC(16:1/0:0)C24H48NO7P494.324493.31680[M + H]+494.3292, 184.0730, 86.0966↑↓
18.394-Chloro-2-nitrobenzylalcoholC29H37N3O6524.2754523.26828[M + H]+612.4368, 566.2078, 498.2351, 421.2147↑↓
20.78DihydroergocristineC35H41N5O5612.3231611.31081[M + H]+367.3310, 385.2195, 324.3305↑↓
24.36PC(P-18:1(9Z)/18:3(6Z,9Z,12Z))C44H80NO7P766.5687765.56721[M + H]+788.6183, 184.0732↑↓
26.89CholestenoneC27H44O385.347384.33920[M + H]+516.3062, 457.2306, 104.1075, 311.2548, 146.9822↑↓
27.48PC(20:2(11Z,14Z)/14:1(9Z))C42H78NO8P778.5369755.54653[M + H]+862.6283, 184.0737↑↓
27.5SM(d18:2(4E,14Z)/24:0)C47H93N2O6P813.6825812.67716[M + H]+762.5983, 184.0728↓↑
27.52PC(18:0/18:1(9Z))C44H86NO8P788.6172787.60915[M + H]+816.5928, 757.5151, 184.0730, 633.5222↓↑
27.62PC(22:4(7Z,10Z,13Z,16Z)/20:2(11Z,14Z))C50H88NO8P862.6291861.62486[M + H]+540.5328, 522.5247, 185.1535↓↑
27.7PC(14:1(9Z)/24:0)C46H90NO8P816.6436815.64041[M + H]+703.5719, 184.0730↑↓
29.79PC(20:0/14:0)C42H84NO8P762.5959761.59342[M + H]+813.6849, 184.0738, 207.9991↓↑
29.85SM(d18:0/16:1(9Z))C39H79N2O6P703.5739702.56762[M + Na]+778.5490, 389.3005, 184.0724↑↓
30.07Cer(d18:0/16:0)C34H69NO3540.5316539.52778[M + H]+766.5669, 184.0733, 625.5195, 577.5235↑↓
10.992-(4-Methoxyphenyl)naphthalic anhydrideC19H12O4303.0664304.0736 0[M − H]−303.0660, 259.0773, 215.0872↑↓
14.86PlantacyaninC36H24N2483.1915484.1939 10[M − H]−483.2169↓↑
15.31(9S,10E,12S,13S)-9,12,13-Trihydroxy-10-octadecenoic acidC18H34O5329.2318330.2406 5[M − H]−329.2347, 201.1121, 299.0233, 171.1016↓↑
15.78Glycerophospho-N-Arachidonoyl EthanolamineC25H44NO7P500.276501.2855 5[M − H]−500.2807, 303.2320, 214.0484↑↓
17.623-EpiaphidicolinC20H34O4337.2386338.2457 0[M − H]−337.2328, 206.9736↑↓
17.8116-Hydroxy-10-oxohexadecanoic acidC16H30O4285.2068286.2144 1[M − H]−285.2059, 169.1610, 223.2076↑↓
19.21LysoPG(18:2(9Z,12Z)/0:0)C24H45O9P507.2732508.2801 0[M − H]−279.2321, 507.2730, 152.9949, 245.0400↑↓
19.42LysoPE(0:0/20:2(11Z,14Z))C25H48NO7P504.3095505.3168 0[M − H]−436.2837, 504.2740, 196.0375, 239.2376, 375.2329↑↓
21.57LysoPG(18:1(9Z)/0:0)C24H47O9P509.2873510.2958 2[M − H]−281.2488, 152.9952, 509.2830↑↓
21.6LysoPE(18:1(11Z)/0:0)C23H46NO7P478.2938479.3012 0[M − H]−281.2488, 478.2944, 196.0380↑↓
Note: C, Control group; M, Model group; AR, Atractylodes Rhizome group (AR group). C vs. M: Compared with the blank control group, the differential metabolites in the model group showed a downward (↓) or upward (↑) trend; AR vs. M: Compared with the model group, the differential metabolites in AR administration group showed a downward (↓) or upward (↑) trend.
Table 2. Top 10 AR component information.
Table 2. Top 10 AR component information.
NameChemical FormulaMolecular WeightDegree
AtractyloyneC15H20O216.3255
WogoninC16H12O5284.2649
2-Hydroxyisoxypropyl-3-hydroxy-7-isopentene-2,3-dihydrobenzofuran-5-carboxylicC17H22O5306.445
Stigmasterol 3-O-beta-D-glucopyranosideC35H58O6574.820
Atractylenolide IIC15H20O2232.3220
Atractylenolide IIIC15H20O3248.3216
CostunolideC15H20O2232.3214
Isochlorogenic acid AC25H24O12516.4611
AtractylodinC13H10O182.228
3β-acetoxy atractylonC17H22O3274.353
Table 3. Histopathological scoring criteria.
Table 3. Histopathological scoring criteria.
ScoreSynovial Hyperplasia (A)Synovial
Architecture (B)		Inflammatory Cell
Infiltration (C)
0
(Normal)		No synovial hyperplasiaThe synovial tissue architecture is clearly defined and intact, with synovial cells well-aligned.No inflammatory cells
1
(Mild)		1 to 3 cells thickThe overall synovial architecture remains intact; the synovial cells are relatively orderly.Occasionally scattered inflammatory cells
2
(Moderate)		3 to 4 cells thickThe synovial architecture is moderately disordered, the synovial cells are relatively orderlyFocal areas of dense subsynovial inflammatory cells infiltrate
3
(Severe)		more than 4 cells thickThe synovial architecture is abnormal, with disorganized arrangement of synovial cells.Visible obvious inflammatory cell infiltration
Pathological score = A + B + C
Table 4. Primer sequence list for primary antibody.
Table 4. Primer sequence list for primary antibody.
Gene NamePrimer NamePrimer Sequences (5′-3′)Product Length (bp)
GAPDHGAPDH-FGCCCAGAACATCATCCCTGCAT188
GAPDH-RGCCTGCTTCACCACCTTCTTGA
STAT3STAT3 FGTCAGCAATGGAGTACGTGCAGA164
STAT3 RGTTGGCGGGTCTGAAGTTGAGAT
Bcl-2Bcl-2 FGATGACTGAGTACCTGAACCGGC116
Bcl-2 RGCCAGGAGAAATCAAACAGAGGC
BaxBax FTGTCGCCCTTTTCTACTTTGCCA75
Bax RGTTCTGATCAGTTCCGGCACCTT
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Wen, R.; Xu, C.; Zheng, H.; Li, C.; Yu, H. Integrating Metabolomics and Network Pharmacology: Investigating the Therapeutic Mechanism of Atractylodes Rhizome Against Rheumatoid Arthritis. Pharmaceuticals 2026, 19, 262. https://doi.org/10.3390/ph19020262

AMA Style

Wen R, Xu C, Zheng H, Li C, Yu H. Integrating Metabolomics and Network Pharmacology: Investigating the Therapeutic Mechanism of Atractylodes Rhizome Against Rheumatoid Arthritis. Pharmaceuticals. 2026; 19(2):262. https://doi.org/10.3390/ph19020262

Chicago/Turabian Style

Wen, Rou, Cheng Xu, Hailian Zheng, Chao Li, and Huan Yu. 2026. "Integrating Metabolomics and Network Pharmacology: Investigating the Therapeutic Mechanism of Atractylodes Rhizome Against Rheumatoid Arthritis" Pharmaceuticals 19, no. 2: 262. https://doi.org/10.3390/ph19020262

APA Style

Wen, R., Xu, C., Zheng, H., Li, C., & Yu, H. (2026). Integrating Metabolomics and Network Pharmacology: Investigating the Therapeutic Mechanism of Atractylodes Rhizome Against Rheumatoid Arthritis. Pharmaceuticals, 19(2), 262. https://doi.org/10.3390/ph19020262

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