Abstract

   Objective: The mechanism of action and potential targets of Paeoniae
   Radix Alba (Baishao, B) in the treatment of adjuvant-induced arthritis
   (AIA) rats are explained using metabolomics and network pharmacology
   techniques, and the research evidence for the development of
   anti-rheumatoid arthritis (RA) drugs is enriched. Methods: The rats
   were injected with Freund’s complete adjuvant (CFA) to induce
   arthritis. We then measured the general physical characteristics,
   examined their X-rays and histopathology to evaluate the pathological
   condition of the inflammation models, and conducted metabolomics
   studies on the change in urine metabolism caused by CFA. The
   lyophilized powder of B at a dose of 2.16 g/kg was orally administered
   to the rats continuously for 28 days, and the therapeutic effect was
   evaluated. Network pharmacology prediction shows that B contains the
   target action of the ingredient, and the simulation of the target
   molecular docking, in combination with the metabolomics analysis
   results, shows that B has a potential role in the treatment of AIA
   rats. Results: B can reduce the paw swelling and pathological changes
   in rats caused by CFA, reverse the levels of 12 urine biomarkers, and
   regulate histidine metabolism, phenylalanine metabolism, arginine,
   proline metabolism, pyrimidine metabolism, etc. The prediction of the
   active ingredient target in B indicates that it may act as an
   inflammatory signaling pathway in anti-RA, among them being
   paeoniflorin, palbinone, beta-sitosterol, kaempferol, and catechin,
   which are the significant active ingredients. Conclusion: The
   metabolomics results revealed the markers and metabolic mechanisms of
   urinary metabolic disorders in rats with AIA, demonstrated the efficacy
   of the therapeutic effect of B, and identified the key ingredients in
   B, providing theoretical support for the subsequent development and
   utilization of B.

   Keywords: Paeoniae Radix Alba, rheumatoid arthritis, metabolomics,
   paeoniflorin, kaempferol, amino acid metabolism

1. Introduction

   Paeoniae radix alba (Bai Shao, B) is the dried root of Paeoniae
   lactiflora Pall. The total glucosides of the paeony of terpenes, such
   as paeoniflorin, albiflorin, oxypaeoniflorin, benzoylpaeoniflorin, and
   benzoyloxypeoniflorin, are the major chemical ingredients of B. B is a
   valuable traditional medicine that is used to cure liver disease,
   rheumatism, and autoimmune diseases [[30]1,[31]2,[32]3,[33]4]. Numerous
   in vivo and in vitro reports on the pharmacological activity and mode
   of action of B and its extracts are currently available. As an example,
   B polysaccharides derived through improved extraction can successfully
   treat experimental autoimmune hepatitis [[34]5], and B’s active
   ingredient decreases the damaging reaction of nonalcoholic fatty liver
   disease [[35]6]. B and its extracts play a vital role in the treatment
   of immune diseases. First, in rats with arthritis, B and its extracts
   can limit the proliferation of fibroblast synoviocytes and lower the
   synovial inflammatory response [[36]7]. Additionally, the regulation of
   immune cells is a key function of B and its extracts in the treatment
   of immunological illnesses. B and its extracts suppress dendritic cell
   maturation and activation, which impairs Th1 and Th17 differentiation
   in vivo and drastically lowers the proportion and total number of Th1
   and Th17 cells in collagen-induced arthritic mice [[37]8]. Furthermore,
   the latest information indicates that the terpenoid active components
   in B significantly reduce rheumatoid arthritis (RA), with palbinone
   being confirmed to be an inhibitor responsive to crucial signals
   [[38]9]. In conclusion, B and its extracts profoundly regulate
   immunological abnormal disorders, particularly the complicated
   treatment of arthritis.

   Metabolomics technology with mass spectrometry as a tool has been
   valued and applied in the pharmacological efficacy research of
   traditional Chinese medicine and natural products. Network pharmacology
   further realized the huge chemical compositions in herbs for disease
   resistance and the treatment of multiple-target, multiple-pathway
   mechanism predictions for the biological activity of herbal natural
   products. In the face of huge, complex chemical compositions, mass
   spectrometry with a high sensitivity to identifying compounds expounds
   the molecular markers and metabolic mechanism of disease
   [[39]10,[40]11,[41]12,[42]13].

   This study will evaluate the effectiveness of B for RA based on
   high-resolution mass spectrometry, analyze the ability of B to regulate
   abnormal metabolism effectively, and reveal the potential active
   ingredients of B for RA using network pharmacology techniques. It aims
   to supplement the evidence for the mechanism of anti-RA action in B and
   promote the development of promising compounds of B ([43]Figure 1).

Figure 1.

   [44]Figure 1
   [45]Open in a new tab

   Flow chart of mass spectrometry-based metabolomics combined with
   network pharmacological analysis of the mechanism of action of Paeoniae
   Radix Alba in rheumatoid arthritis.

2. Materials and Methods

2.1. Drugs and Reagents

   The B was purchased from the Tongrentang Drug Store and authenticated
   by Professor Xijun Wang of the Department of Pharmacognosy of
   Heilongjiang University of Chinese Medicine (Harbin, China).
   High-performance liquid chromatography-grade methanol and acetonitrile
   were purchased from Thermo Fisher (Waltham, MA, USA), the leucine
   enkephalin and Freund’s complete adjuvant (CFA) were purchased from
   Sigma Aldrich (Darmstadt, Germany), and the distilled water was
   purchased from Watsons (Guangzhou, China). In this study, B was
   decocted in 10 volumes of ultrapure water for 30 min (twice) and then
   prepared into a freeze-dried powder and dissolved in appropriate water
   to form a dosing solution.

2.2. Drug Administration

   The Changsheng Animal Research Center (Liaoning, China) provided the
   8-week-old male Sprague Dawley rats for the experiment. The rats were
   adapted to being raised for 7 days in a stable environment (12:12-h
   light/dark schedule; temperature: 232 °C; relative humidity: 40–60%)
   with unlimited access to food (common grade feed: corn, wheat bran,
   soybean meal, grain flour, salt, etc.) and water. Twenty-four rats were
   randomly divided into a control group, model group, and treatment group
   (B). To create RA-related pathological models, the rats were injected
   with 0.1 mL of CFA at a concentration of 2.0 mg/mL. The control group
   received an equal volume of saline as an injection [[46]14]. On the
   14th day, the rats in group B were given a 2.16-g/kg B freeze-dried
   powder aqueous solution for 28 days, and the other rats were given an
   equal volume of distilled water.

2.3. Evaluation of Swelling and Pathological Characteristics

   To assess the pathological status of adjuvant-induced arthritis (AIA)
   during the study period, the claw volume for each group of rats was
   measured, and the arthritis index (AI) was calculated [[47]15]. The
   rats were sacrificed on the 28th day of B administration for ankle
   X-ray observation, joint synovium analysis, hematoxylin and eosin (H&E)
   examination, and organ index calculation to evaluate B’s therapeutic
   effect.

2.4. Urine Sample Collection and Preparation

   On day 28, we collected urine from the AIA rats to identify the
   biomarkers and monitor metabolism for assessing the treatment’s impact.
   The urine from the stage of 12 h was collected from all rats between
   the hours of 8:00 p.m. and 8:00 a.m. the following day in separate
   metabolic cages. The urine samples were centrifuged for 10 min at 4 °C
   and at 13,000 rpm. The supernatant was aspirated, filtered with a
   0.22-μm filter membrane, and analyzed by ultra-performance liquid
   chromatography-quadrupole time of flight mass spectrometry
   (UPLC-Q/TOF-MS, Waters, Milford, MA, USA).

2.5. Metabolomics Analysis Conditions

   The chromatography was equipped with an ACQUITY™ UPLC system and
   separated in an HSS T[3] column (100 mm × 2.1 mm id, 1.8 µm; Waters,
   Milford, MA, USA), where the column temperature was 35 °C, the flow
   rate was 0.4 mL/min, and the injection volume was 2 μL. Acetonitrile
   with 0.1% formic acid (A) and water with 0.1% formic acid (B) were the
   mobile phases. The optimized gradient elution conditions were 0–4.5 min
   and 1–21% A, 4.5–7 min and 21–40% A, 7–7.5 min and 40–50% A, 7.5–9.5
   min and 50–99% A, 9.5–10 min and 99–99% A, and 10–14.5 min and 99–1% A.

   The MS was equipped with an electrospray ion source. In positive ion
   mode, the capillary voltage was 3 kV, the cone voltage was 20 V, the
   ion source temperature was 110 °C, the dissolvent gas temperature was
   350 °C, and the flow rate was 800 L/h. The cone gas flow rate was 50
   L/h, a capillary voltage in negative ion mode was 2.7 kV, and other
   analysis conditions were the same as for positive ion mode. Leucine
   enkephalin with a concentration of 1 ng/μL and a flow rate of 10 μL/min
   was used for real-time mass correction in positive mode ([M + H]^+ =
   556.2771) and negative mode ([M − H]^− = 554.2615), with centroid mode
   at a 50–1200-m/z full scan to collect data.

2.6. Data Processing and Multivariate Data Analysis

   The raw data were acquired by UPLC-MS using Progenesis QI 2.0 software
   (Waters, Milford, MA, USA) to perform peak noise reduction, matching,
   normalization, and alignment to obtain a matrix with the retention
   time, molecular formula, normalized abundance, and m/z, SIMCA-P 14.0
   for principal ingredient analysis (PCA), and orthogonal partial least
   squares discriminant analysis (OPLS-DA) to analyze the differences
   between groups and determine the variable importance in projection
   value (VIP) (>1), combined with the student’s t-test statistics (p <
   0.05) difference ion for online database biological function analysis
   (HMDB, [48]http://www.hmdb.ca/, accessed on 19 November 2020; METLIN,
   [49]http://www.metlin.scripps.edu/, accessed on 19 November 2020; and
   Lipid Maps, [50]http://www.lipidmaps.org/, accessed on 19 November
   2020), and mass tolerances <10 ppm, and those with MS/MS fragmentation
   information were considered to be important biomarkers.

2.7. Network Pharmacology and Molecular Docking Analysis Methods

   B contains a compound information query from the traditional Chinese
   medicine systems pharmacology database and analysis platform (TCMSP,
   [51]http://lsp.nwsuaf.edu.cn/index.php/, accessed on 20 November 2020).
   We selected the compounds with the pharmacokinetics parameters of oral
   bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.18. We also
   utilized GeneCards ([52]https://www.genecards.org/, accessed on 20
   November 2020) and DisGeNET ([53]https://www.disgenet.org/home/,
   accessed on 20 November 2020) to retrieve the targets associated with
   RA and align them to those of the selected compounds described above
   for further identifying potentially acting RA targets for pathway
   analysis and molecular docking studies. Visualization of
   ingredient-target-pathway interaction was achieved using Cytoscape
   3.8.0 software [[54]16], while Autodock 1.5.6 software was used for
   molecular interaction pattern prediction and a molecular docking
   approach for exploring the binding modes of the key compounds [[55]17].

3. Results

3.1. Inhibitory Effect of B on Rat AIA Paw Swelling

   Compared with the control group, the paw volume of the rats in the
   model group after injection of CFA was significantly larger on days
   12–16 ([56]Figure 2a). As a vital immune organ, the thymus of the model
   group was atrophied, and the spleen was swollen ([57]Figure 2b). In
   addition, the X-ray examination results showed stenosis of the ankle
   joint space and soft tissue edema in the rats’ model group. The model
   group’s synovium, spleen, and thymus tissues displayed inflammatory
   pathological alterations according to the results of the histological
   analysis. However, B was able to endure this condition after 28 days of
   treatment ([58]Figure 2c).

Figure 2.

   [59]Figure 2
   [60]Open in a new tab

   The physical signs measured during model preparation, showing the
   results of histological pathology (×200) and X-ray examination after 28
   days of oral administration of B. (a) Claw volume. (b) Indexes of the
   spleen and thymus. (c) X-ray and histological pathology. * p < 0.05. **
   p < 0.01 vs. control group. ^# p < 0.05. ^## p < 0.01 vs. model group.

3.2. Multivariate Statistical Analysis and Biomarker Characterization of the
Metabolic Profile

   On day 14, the urine metabolic profiles of the model group and the
   control group were subjected to PCA pattern recognition ([61]Figure
   3a,b). The model group’s metabolism changed as a result of the CFA
   injection because there were distinct differences between the control
   and model groups. OPLS-DA was employed to screen potential biomarkers,
   and the R2Y and Q2 values of the model (R2Y(cum) = 0.995, Q2 (cum) =
   0.974 in positive mode and R2Y(cum) = 0.985, Q2 (cum) = 0.799 in
   negative mode) indicated that the model had good quality and
   predictability. An S-plot was used to screen for important metabolic
   difference ions where VIP > 1 ([62]Figure 3c,d), and the results of the
   permutation prediction (n = 200) showed that the OPLS statistical model
   did not overfit ([63]Figure 3e,f). A Student’s t-test was applied to
   analyze the difference ions where VIP > 1 and the normalized abundance
   was statistically significant (p < 0.05). These important difference
   ions were considered potential biomarkers, combining MS and MS/MS
   structural information with HMDB and KEGG biological function analysis,
   including betaine, N2-succinyl-L-ornithine, phenyl pyruvic acid,
   pyruvic acid, and taurine. Finally, 29 potential biomarkers were found
   to be closely related to AIA ([64]Table S1).

Figure 3.

   [65]Figure 3
   [66]Open in a new tab

   Multivariate statistical analysis of control groups and model groups.
   Note: (●) = control group and (●) = model group. (a) PCA scores plotted
   in positive mode. (b) PCA scores plotted in negative mode. (c) S-plot
   in positive mode. (d) S-plot in negative mode. (e) Permutation plot in
   positive mode. (f) Permutation plot in negative mode.

3.3. Metabolic Pathway Integration Enrichment Analysis

   The above 29 potential biomarkers were subjected to MetaboAnalyst
   ([67]https://www.metaboanalyst.ca/, accessed on 26 November 2020)
   metabolic pathway enrichment analysis to reveal the mechanism of the
   metabolic differences in the rats after receiving CFA injections. The
   results showed that these biomarkers were involved in multiple
   metabolic pathways, such as taurine and hypotaurine metabolism, starch
   and sucrose metabolism, alpha-linolenic acid metabolism, phenylalanine
   metabolism, and glycolysis or gluconeogenesis, which together lead to
   the development of inflammatory pathological conditions ([68]Figure
   4a–c).

Figure 4.

   [69]Figure 4
   [70]Open in a new tab

   Enrichment analysis of MetPA metabolic pathway of biomarkers. (a)
   Pathway impact enrichment. (1) Taurine and hypotaurine metabolism. (2)
   Starch and sucrose metabolism. (3) Alpha-linolenic acid metabolism. (4)
   Phenylalanine metabolism. (5) Retinol metabolism. (6) Pyruvate
   metabolism. (7) Histidine metabolism. (8) Glycolysis or
   gluconeogenesis. (9) Pyrimidine metabolism. (10) Porphyrin and
   chlorophyll metabolism. (11) Glycine, serine, and threonine metabolism.
   (12) Citrate cycle (TCA cycle). (13) Galactose metabolism. (14) Primary
   bile acid biosynthesis. (15) Steroid hormone biosynthesis. (b) The
   proportion of biomarkers in the overall pathway. (c) Association
   network of biomarker metabolic pathways.

3.4. B Regulates Biomarkers and Metabolic Trajectories to Play a Therapeutic
Role

   The PCA results after 28 days of treatment demonstrate that group B was
   between the control group and the model group, with a propensity to
   shift to the control group in the case where B could resist the
   pathological alterations generated by CFA in rats ([71]Figure 5a,b).
   The three groups of Euclidean cluster analysis also showed that B
   reversed the normalized abundance level of metabolites ([72]Figure 5c),
   making the levels of many biomarkers closer to the control group
   ([73]Figure 6), including 1H-indole-3-carboxaldehyde,
   4,6-dihydroxyquinoline, 7-methyladenine, lndoleacrylic acid,
   N2-succinyl-l-ornithine, phenyl pyruvic acid, porphobilinogen,
   semilepidinoside B, thymidine, urocanic acid,
   3,4-dihydroxyhydrocinnamic acid, and sepacic acid.

Figure 5.

   [74]Figure 5
   [75]Open in a new tab

   Metabolic regulation and cluster analysis of biomarkers in B treatment
   of AIA rats. Note: (●) = control group, (●) = model group, and (●) =
   Treatment group (B). (a) The 3D PCA scores plotted in positive mode.
   (b) The 3D PCA scores plotted in negative mode. (c) Euclidean Cluster
   analysis of biomarker levels.

Figure 6.

   [76]Figure 6
   [77]Open in a new tab

   Box plots of the normalized abundance of biomarkers in the control
   group, model group, and treatment group (B).

3.5. Predicting the Target and Binding Mode of B

   Network pharmacology is currently widely used in Chinese medicine
   research to predict the targets and potential mechanisms of herbs and
   prescriptions. The ingredients and targets with the pharmacokinetic
   parameters OB ≥ 30 and DL ≥ 0.18 were searched for in the TCMSP
   database for string ([78]https://string-db.org/cgi/input.pl/, accessed
   on 20 November 2020) KEGG pathway analysis, and it was found that
   palbinone, paeoniflorin, mairin, beta-sitosterol, sitosterol,
   kaempferol, and (+)-catechin in B can regulate 47 targets and then act
   on the TNF signaling pathway, HIF-1 signaling pathway, IL-17 signaling
   pathway, MAPK signaling pathway, RA, and other pathways related to
   inflammation ([79]Tables S2 and S3), which may be important potential
   biological mechanisms for B treatment of RA ([80]Figure 7a).

Figure 7.

   [81]Figure 7
   [82]Open in a new tab

   Network pharmacological target prediction and molecular docking
   simulation of active ingredients contained in B. (a)
   Ingredient-target-pathway network. (b) The binding mode of the
   representative ingredient paeoniflorin and the target TNF. (c) The
   binding mode of the significant ingredient kaempferol and the target
   AKT.

   Regarding network construction of the target pathway for the above
   ingredients and degree unDir, the results show that the average degree
   unDir was 7, while for kaempferol it was 19, the highest among the
   ingredients of B. AKT1 has the highest degree unDir, meaning it
   participated in most pathways ([83]Table S3) and could interact with
   kaempferol. Therefore, molecular docking of the two combinations was
   conducted, and paeoniflorin was a famous compound in B marked as a key
   indicator for detecting B quality by the Chinese pharmacopoeia.
   Therefore, the binding analysis of action targets was also carried out
   in this study. The results show that kaempferol can bind to the residue
   LYS-67 of the most relevant target AKT (PDB ID: 4GAH) with a binding
   energy of −3.24 kcal/mol ([84]Figure 7c), while for paeoniflorin and
   the target TNF (PDB ID: 1DU3), the docking binding energy of the
   residue CLY-245 was −0.99 kcal/mol ([85]Figure 7b).

   Palbinone, paeoniflorin, mairin, beta-sitosterol, sitosterol,
   kaempferol, and (+)-catechin can bind to the corresponding targets in
   different numbers, and they are sorted according to the degree unDir of
   binding from high to low as kaempferol, beta- sitosterol, (+)-catechin,
   paeoniflorin, mairin, sitosterol, and palbinone.

4. Discussion

   The chronic etiology of the AIA rat model is comparable to the clinical
   manifestations of RA, making it a classic and popular choice for
   pharmacological research and experimental evaluation. Synovitis and
   cartilage degeneration eventually develop from the initial inflammation
   [[86]18,[87]19]. Although the pathogenesis of RA is unclear, the
   majority of researchers concentrate on omics studies with inflammatory
   responses and immunological abnormalities. CFA was injected into the
   rats’ roots to construct an AIA animal model comparable to RA. The
   spleen and thymus, which are both crucial immune organs, have lesions,
   spleen tissue edema, intercellular spaces mashed with inflammatory
   cells, thymus atrophy, and cell gap narrowing. The rat ankle X-ray data
   showed that there was severe tissue edema in the joints of the model
   rats. Neutrophils and macrophages were prevalent in the pathophysiology
   of the synovial tissue. After 28 days of oral treatment with B, the
   aforementioned occurrences were diminished, and B had a positive
   therapeutic effect.

   Metabolomics technology can collect batch sample data information in a
   short time, especially with the high sensitivity of MS as the
   instrument to help in the discovery and identification of metabolites.
   The current metabolomic analysis strategy is widely employed in RA
   metabolic mechanism research and biomarker development
   [[88]20,[89]21,[90]22,[91]23]. In this study, UPLC-Q/TOF-MS was used to
   perform an untargeted metabolomics analysis of AIA rat urine to explore
   the overall metabolic changes and characterize the biomarkers. As a
   result, 29 biomarkers with a VIP > 1 and normalized abundance
   significance of p < 0.05 were found ([92]Table S1), which participated
   in 15 metabolic pathways. It is worth noting that the AIA rats reversed
   the levels of 12 metabolites after 28 days of oral administration of B,
   including 1H-indole-3-carboxaldehyde, 4,6-dihydroxyquinoline,
   7-methyladenine, lndoleacrylic acid, N2-succinyl-l-ornithine, phenyl
   pyruvic acid, porphobilinogen, semilepidinoside B, thymidine, urocanic
   acid, 3,4-dihydroxyhydrocinnamic acid, and sepacic acid. In addition to
   the above significantly reversed metabolites, after 28 days of oral
   administration of B, a concentration reversal occurred in the urine of
   the AIA rats for 13-L-hydroperoxylinoleic acid, 9-cis-retinoic acid,
   betaine, and pyruvic acid, but there was no significance, which may
   have been caused by a short cycle of medication. Other unresponsive
   metabolisms may not be the pharmacodynamic pathways for B to the AIA
   rats.

   RA is a chronic and progressive inflammatory disease closely related to
   the environment and autoimmunity. Previous reports have shown that
   there are inflammatory factors in the synovial tissue fluid, serum, and
   urine of RA patients and biological models, and some metabolic
   disorders are mainly related to energy metabolism, lipid metabolism,
   and amino acid metabolism. The phenotypes are joint swelling and
   synovial membrane inflammation, bone destruction, pain, and mobility
   impairment [[93]24,[94]25]. Phenylalanine is an essential amino acid
   and has important physiological effects, and its levels in the synovium
   are related to the expression of IL-1β and IL-8 [[95]25]. Metabolic
   abnormalities of phenylalanine were revealed in the patient-based
   metabolomics study samples. Ouyang’s study was based on ^1H NMR
   [[96]26], while Zhou applied GC-MS [[97]27] and Urbaniak applied LC-MS
   [[98]28] to illustrate low levels of phenylalanine in the patient’s
   serum, and Li applied LC-MS to find high levels of phenylalanine in the
   serum of patients [[99]29]. Although our study did not directly observe
   the statistical differences between the control group and model group
   of phenylalanine, it was found that B can significantly reverse the
   levels of phenyl pyruvic acid in the phenylalanine metabolic pathway,
   while high levels of phenyl pyruvic acid may also affect phenylalanine
   production through negative feedback from enzyme 1.4.1.20 [[100]30]. It
   is worth mentioning that arginine and proline metabolism crosstalk was
   also found in the serum of patients with abnormal phenylalanine
   metabolism, which was specifically manifested by a significant decrease
   in the level of arginine [[101]28]. Arginine is considered an important
   amino acid in nutrition, nitric oxide synthase regulation, or nerve
   transmission in the central nervous system, which affects the
   interference response by enhancing nitric oxide synthesis and in turn
   produces the goose inflammatory factors TNF and interleukin, and the
   serum arginine concentration decreases in the immune response
   [[102]31,[103]32,[104]33]. Similar to previous studies,
   N2-succinyl-L-ornithine in this study responded abnormally in the
   arginine and proline pathway, but B significantly regulated this
   metabolic condition ([105]Figure 6 and [106]Table S1). This study shows
   that in abnormal histidine metabolism, the significant decrease in the
   level of urocanic acid in the urine of AIA rats may be the cause of
   histamine conversion, and a decrease in the histamine level is a
   disadvantage to RA patients [[107]34]. The inflammatory environment of
   patients with RA stimulates the breakdown of histidine into histamine
   and stimulates the proliferation of chondrocytes [[108]35]. For the
   results of this study, in the same 1-methylhistidine and urocanic acid
   which belong to histidine metabolism, B significantly reversed the
   level of urocanic acid but did not regulate 1-methylhistidine or its
   trend. Clonal expansion of autoreactive T cells may cause RA onset, and
   pyrimidine metabolism of dihydroorotate dehydrogenase is the source
   pathogenic mechanism, so blocking pyrimidine metabolism may block
   lymphocyte proliferation and inhibit disease progression
   [[109]36,[110]37]. This study shows that B reduces the level of
   thymidine in the urine of AIA rats to regulate pyrimidine metabolism,
   which may protect against the inflammatory injury and lesion formation
   caused by CFA.

   Predictive analysis of the ingredients in B by network pharmacology
   showed that the TNF signaling pathway, HIF-1 signaling pathway, IL-17
   signaling pathway, osteoclast differentiation, and MAPK signaling
   pathway were the main pathways of action. These pathways harm the
   disease of RA. After their activation, the production of independent or
   interoperable inflammatory factors including TNF and interleukin and
   hypoxia-inducing factors, triggering inflammatory storms to exacerbate
   the immune response, promote synovial cell proliferation and fibrosis,
   gradually forming vascular fins and damaged cartilage and promoting the
   development of RA [[111]38,[112]39,[113]40,[114]41]. Fortunately, the
   ingredients in B may modulate the pathway and have the potential for
   prevention. The total glucosides of paeony were extracted from the root
   of Paeonia lactiflora Pall, where more than 90% of the composition is
   paeoniflorin, which is the active ingredient against RA according to
   previous reports [[115]7,[116]42]. Additionally, the anti-inflammatory
   abilities of palbinone [[117]9], beta-sitosterol [[118]43], kaempferol
   [[119]44], and (+)-catechin [[120]45] have been verified in
   experimental conditions, and the authors believe there is a
   considerable prospect for the development of anti-RA lead compounds.

5. Conclusions

   This study examined the anti-RA effects of B and investigated the
   metabolic processes in experimental rats with MS. B inhibited
   CFA-induced claw swelling in rats with RA and alleviated the
   pathological conditions of the spleen and thymus. The model rat’s
   disordered metabolism, which included histidine metabolism,
   phenylalanine metabolism, arginine and proline metabolism, and
   pyrimidine metabolism, was corrected after 28 days of continuous
   administration. Paeoniflorin, palbinone, beta-sitosterol, kaempferol,
   and catechin may be important components. In sum, this research focused
   on the experimental data of B’s drug development and screening projects
   and conducted preliminary pharmacodynamic and pharmacological studies
   on B.

Abbreviations

   B: Paeoniae Radix Alba; AIA: adjuvant-induced arthritis; RA: rheumatoid
   arthritis; CFA: Freund’s complete adjuvant; AI: arthritis index;
   UPLC-Q/TOF-MS: ultra-performance liquid chromatography quadrupole time
   of flight mass spectrometry; PCA: principal ingredient analysis;
   OPLS-DA: orthogonal partial least squares discriminant analysis; VIP:
   variable importance in projection.

Supplementary Materials

   The following supporting information can be downloaded at:
   [121]https://www.mdpi.com/article/10.3390/molecules27207014/s1, Table
   S1: Identification and pathways of potential biomarkers of RA based on
   urine metabolic profiling; Table S2: The chemical constituents of Radix
   Paeoniae Rubra in TCMSP 2.3 database effect on RA related targets;
   Table S3: Target predicts the top 25 KEGG pathways of significance.
   [122]Click here for additional data file.^ (110.3KB, zip)

Author Contributions

   X.W. designed the experiments; L.L. conducted the experiments; H.D.
   analyzed the data; T.L. wrote the paper. All authors have read and
   agreed to the published version of the manuscript.

Institutional Review Board Statement

   The Drug Safety Evaluation Center of Heilongjiang University of
   Traditional Chinese Medicine has passed review to meet national GLP
   requirements. The animal experiments involved in this study are all
   done here. Therefore, this study was exempted from ethical review and
   approval.

Informed Consent Statement

   Not applicable.

Data Availability Statement

   The data that support the findings of this study are available from the
   corresponding author upon reasonable request.

Conflicts of Interest

   The authors declare no conflict of interest.

Sample Availability

   Samples of the compounds are not available from the authors.

Funding Statement

   This study was supported by grants from the Key Program of Natural
   Science Foundation of State (Grant No. 81830110), Young Talent Lift
   Engineering Project of China Association of Traditional Chinese
   Medicine (QNRC2-B06), and Heilongjiang Touyan Innovation Team Program.

Footnotes

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References