Uncovering the mechanism of Radix Paeoniae Alba in the treatment of restless legs syndrome based on network pharmacology and molecular docking : Medicine

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Research Article: Observational Study

Uncovering the mechanism of Radix Paeoniae Alba in the treatment of restless legs syndrome based on network pharmacology and molecular docking

Liu, Jun MDa,b; Liu, Suxian MDa,b; Hao, Liansheng MMSCc; Liu, Fangfang MDd; Mu, Shengkai MMSCc; Wang, Tengteng MDe,*

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doi: 10.1097/MD.0000000000031791
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1. Introduction

Restless legs syndrome (RLS) is a common neurological motor disorder.[1,2] The main symptoms include sensory abnormalities of both calves, thighs, trunks, and arms (calves are the most affected body parts), which frequently occur at rest, especially at night.[1,3–8] Patients are usually forced to move the affected parts to alleviate their discomfort.[2,9] RLS can cause or coexist with cardiovascular, metabolic, sleep, and mental disorders (such as anxiety and depression)[1,2,10–12] and reduce quality of life.[5,11,13–16] The prevalence of RLS has been estimated to range from 4% to 29%,[11] and it has attracted wide attention in recent years. The etiology and pathogenesis of RLS still remain unclear. Dopamine agonists and gabapentinoids are currently used as first-line medications. However, their therapeutic effects are unsatisfactory.[5,12]

In Traditional Chinese Medicine (TCM), RLS is classified as “arthralgia” and “tibial acid.” In clinics, Radix Paeoniae Alba (RPA) can effectively relieve the discomfort of the legs and treat RLS. However, there are few studies on the efficacy and mechanism of RPA in treating RLS. RPA can effectively treat related brain disorder-related diseases, such as Parkinson’s disease (PD). PD is a neurological disorder in which there are disturbances in the movement including resting tremors, rigidity, bradykinesia or akinesia.[17] The pathogenesis of PD involves dysfunction of dopaminergic neurons, iron metabolism disorders, oxidative stress, and abnormal immune reactions.[17] Total glucosides of paeony (TGP) of RPA were extracted from dried roots of RPA, and paeoniflorin was the main active compound of TGP.[18] TGP could significantly increase the level of dopamine and its metabolites in striatum of PD mice and improve the motor coordination.[19] Paeoniflorin had neuroprotective effects and ameliorated motor dysfunction in both PD rats[20] and mouse models.[21–23] Dopaminergic dysfunction,[24,25] iron deficiency,[24–26] oxidative stress,[27] and immunological alterations[28] are also associated with the pathogenesis of RLS. Therefore, RPA should be effective in the treatment of RLS and the mechanism is worth to be elucidated.

Network pharmacology can reveal the synergistic effect of multi-molecule medications through big data analysis, providing a practical basis and effective way for the research and innovation of TCM. In this study, the active components, core targets, and main signaling pathways of RPA in the treatment of RLS were screened using network pharmacology. The binding of components and targets was performed through molecular docking. The mechanism of RPA in treating RLS was revealed, providing the basis for future research and clinical applications. The workflow of this study is depicted in Figure 1.

Figure 1.:
The workflow of this study. The active components and targets of RPA, as well as RLS-related targets were obtained. The overlapping targets of RPA and RLS were collected. The “components-targets” network was then built. Furthermore, the PPI network was constructed. The GO functions and KEGG signaling pathways were analyzed. Finally, the molecular docking was performed. GO = gene ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes, PPI = protein-protein interaction, RLS = restless legs syndrome, RPA = Radix Paeoniae Alba.

2. Methods

2.1. Screening active components of RPA

The components of RPA were collected in the Traditional Chinese Medicine System Pharmacology Database (TCMSP, https://old.tcmsp-e.com/tcmsp.php).[29] Based on the absorption, distribution, metabolism, and excretion (ADME) parameters provided by TCMSP, oral bioavailability ≥ 30% and drug likeness ≥ 0.18 were set as the limiting conditions for screening the active components. Furthermore, the components of RPA were supplemented by retrieving the published papers in CNKI (https://www.cnki.net/) and PubMed. The chemical structures of these components were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/)[30] for Swiss ADME prediction,[31] which was required to be equal to HIGH and at least 2 terms of drug likeness were YES.[32]

2.2. Gathering potential targets of active components of RPA

The mol2 files of active components of RPA were downloaded from the TCMSP database and uploaded to the PharmMapper server to search for potential targets.[33] The species were set as human protein, and the parameter value was set as default. Excel and UniProt database (https://www.uniprot.org/)[34] were used to merge, deduplicate, and standardize the data obtained from the targets. The targets that were not “reviewed” were deleted.

2.3. Collecting RLS-related targets

The search term “restless legs syndrome” was used to obtain the targets of RLS in GeneCards (https://www.genecards.org/),[35] OMIM (https://www.omim.org/),[36] DrugBank (https://go.drugbank.com/),[37] and DisGeNET databases (https://www.disgenet.org/).[38] The results were merged, deduplicated, and standardized.

2.4. Obtaining the overlapping targets of RPA and RLS

The overlapping targets of RPA and RLS were obtained after being processed by Excel and the Bioinformatics website (http://www.bioinformatics.com.cn/), which were the potential targets of RPA in treating RLS.

2.5. Construction of “active components-overlapping targets” network and screening key active components

The “active components-overlapping targets” network was built using Cytoscape 3.9.1.[38] The network topology was examined using its own Network Analyzer analysis tool, and the key components of RPA involved in treating RLS were screened based on the topological parameter degree.

2.6. Construction of the protein-protein interaction (PPI) network

To systematically explore the complicated network synergy between the potential targets of RPA and RLS, the overlapping targets were imported into the Search Tool for the Retrieval of Interacting Genes (https://string-db.org/).[39] Moreover, the PPI network with the species limited to “Homo sapiens” was built. Furthermore, the minimum interaction threshold was set to medium confidence (0.4) for analysis. The PPI network was then built, and the network topology was analyzed. The core targets with close interaction were screened based on the degree of the topological parameter.

2.7. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis

The overlapping targets were imported into R packages such as clusterProfiler[39] and pathview[40] to perform the GO and KEGG enrichment analysis. The GO function included biological process (BP), molecular function (MF), and cellular component (CC). The “gene-pathway” network was built to visualize the primary mechanism of RPA in the treatment of RLS.

2.8. Molecular docking

The structure of components and proteins were obtained from the TCMSP and Protein Data Bank databases, respectively (https://www.rcsb.org/).[41] The molecular docking of the components and targets was then performed using AutoDock4.2.6 software. Finally, the results were visualized using PyMoL 2.5.0 software.

2.9. Ethical review

This article didn’t contain any studies with human participants or animals performed by any of the authors. Hence, ethical review was not necessary.

3. Results

3.1. Active components and targets of RPA

In total, 85 components of RPA were collected from the TCMSP database and published articles. Following ADME parameter screening and Swiss ADME prediction,[31] 12 active components of RPA were obtained (Table 1). Figure 2A depicts the chemical structures of these components. Furthermore, 109 potential targets of 12 active components were obtained from the PharmMapper database.

Table 1 - The active components of Radix Paeoniae Alba.
Mol ID Molecule name Oral bioavailability (%) Drug likeness
MOL001910 11alpha,12alpha-epoxy-3beta-23-dihydroxy-30-norolean-20-en-28,12beta-olide 64.77 0.38
MOL001918 paeoniflorgenone 87.59 0.37
MOL001919 (3S,5R,8R,9R,10S,14S)-3,17-dihydroxy-4,4,8,10,14-pentamethyl-2,3,5,6,7,9-hexahydro-1H-cyclopenta9phenanthrene-15,16-dione 43.56 0.53
MOL001921 Lactiflorin 49.12 0.8
MOL001924 Paeoniflorin 53.87 0.79
MOL001925 paeoniflorin_qt 68.18 0.4
MOL001928 albiflorin_qt 66.64 0.33
MOL001930 benzoyl paeoniflorin 31.27 0.75
MOL000211 Mairin 55.38 0.78
MOL000359 Sitosterol 36.91 0.75
MOL000492 (+)-catechin 54.83 0.24
MOL000422 Kaempferol 41.88 0.24

Figure 2.:
The active components and targets. (A) The chemical structures of 12 active components of RPA. (B) The RLS-related targets. The green, blue, pink, and yellow ellipse represented the GeneCards, OMIM, DrugBank and DisGeNET databases, respectively. (C) The RPA-RLS overlapping targets. The pink, green, and brown circle represented the targets of RPA, the RLS-related targets and the overlapping targets, respectively. (D) The “active components-overlapping targets” network had 60 nodes and 174 edges. The blue, light blue, and yellow circular nodes represented RPA, the active components, and the overlapping targets, respectively. The target relationships were represented by edges between nodes. RLS = restless legs syndrome, RPA = Radix Paeoniae Alba.

3.2. The RLS-related targets

Using the keyword “restless legs syndrome,” 2193, 180, 71, and 113 targets associated with RLS were collected from the GeneCards, OMIM, DrugBank, and DisGeNET databases, respectively (Fig. 2B). Finally, 2387 RLS-related targets were obtained after removing the duplicates.

3.3. The overlapping targets of RPA and RLS

A total of 47 overlapping targets of the components and the disease were identified as potential targets of RPA in the treatment of RLS (Fig. 2C; Table 2).

Table 2 - The overlapping targets of Radix Paeoniae Alba and Restless legs syndrome.
Gene name Protein name Uniprot ID Degree
ALB Albumin P02768 32
AR Androgen receptor P10275 17
BCHE Butyrylcholinesterase P06276 4
BMP2 Bone morphogenetic protein 2 P12643 8
CASP7 Caspase 7 P55210 7
GC Vitamin D-binding protein P02774 4
KIF11 Kinesin-like protein KIF11 P52732 0
MAPK1 Mitogen-activated protein kinase 1 P28482 15
MAPK10 Mitogen-activated protein kinase 10 D6RBH2 7
STS Steryl-sulfatase P08842 3
THRB Thyroid hormone receptor β P10828 3
TREM1 Triggering receptor expressed on myeloid cells 1 Q38L15 0
TTR Transthyretin P02766 6
ADAM17 Disintegrin and metalloproteinase domain-containing protein 17 P78536 7
BACE1 Beta-secretase 1 P56817 10
CASP3 Caspase 3 P42574 24
CDK5R1 Cyclin-dependent kinase 5 activator 1 Q15078 5
CES1 Liver carboxylesterase 1 H3BSU0 3
CYP19A1 CYP19A1 protein Q7Z471 9
DDX6 Probable ATP-dependent RNA helicase DDX 6 P26196 1
EGFR Epidermal growth factor receptor P00533 21
ESR1 Estrogen Receptor 1 P03372 21
GBA Lysosomal acid glucosylceramidase P04062 3
MAOB Amine oxidase B P27338 3
MAPK14 Mitogen-activated protein kinase 14 Q16539 16
MAPK8 Mitogen-activated protein kinase 8 C9JWQ4 16
NQO1 NAD(P)H dehydrogenase 1 P15559 8
PDE4B cAMP-specific 3’,5’-cyclic phosphodiesterase 4B Q07343 0
PIK3CG Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit gamma isoform P48736 5
RORA Nuclear receptor ROR-alpha P35398 0
GSTP1 Glutathione S-transferase P1 P09211 10
AKR1B1 Aldo-keto reductase family 1 member B1 P15121 8
PDE5A cGMP-specific 3’,5’-cyclic phosphodiesterase O76074 3
AKT1 RAC-alpha serine/threonine-protein kinase P31749 28
AMY1C Alpha-amylase 1C P0DTE8 1
CTSD Cathepsin D P07339 7
CYP2C9 Cytochrome P450 2C9 P11712 5
GSK3B Glycogen synthase kinase-3 beta P49841 11
GSR Glutathione reductase, mitochondrial P00390 9
HSP90AA1 Heat shock protein HSP 90-alpha P07900 21
HSPA8 Heat shock cognate 71 kDa protein P11142 11
IGF1R Insulin-like growth factor 1 recepto P08069 15
NQO2 Ribosyldihydronicotinamide dehydrogenase P16083 0
NR3C2 Mineralocorticoid receptor P08235 5
PPARG Peroxisome proliferator-activated receptor gamma P37231 17
REN Renin P00797 9
RTN4R Reticulon-4 receptor Q9BZR6 0

3.4. “Active components-overlapping targets” network and key active components

The data from 12 active components and 47 overlapping targets were imported into Cytoscape 3.9.1 software to build the “active components-overlapping targets” network (Fig. 2D). A total of 12 components were provided with a degree. The degree of 5 components was greater than ten, and these components were closely associated with RLS-related targets and might play a crucial role in the treatment of RLS (Table 3).

Table 3 - The key active components of Radix Paeoniae Alba in the treatment of Restless legs syndrome.
Mol ID MOL001924 MOL001930 MOL001918 MOL001921 MOL001910
Degree 39 32 24 15 13

3.5. The PPI network

The PPI network of the overlapping targets contained 47 nodes and 209 edges (Fig. 3A). The nodes represented the targets, while the edges represented their connection. The median degree was 7, and the targets with a degree ≥ 2 times the median were the core targets. There were 12 nodes and 64 edges in the PPI network of the core targets (Fig. 3B; Table 4). These targets might be crucial in the mechanism of RPA in treating RLS.

Table 4 - The core targets of Radix Paeoniae Alba in the treatment of Restless legs syndrome.
Degree 32 28 24 21 21 21 17 17 16 16 15 15

Figure 3.:
The PPI network. The nodes and edges represented the targets and their connection, respectively. (A) The PPI network of the overlapping targets with 47 nodes and 209 edges. (B) The PPI network of the core targets with 12 nodes and 64 edges. PPI = protein-protein interaction.

3.6. GO enrichment analysis

3.6.1. BP analysis.

There were 2368 BP enrichment results which primarily involved response to lipopolysaccharide, steroid metabolic process, chemical stress, and regulation of MAP kinase activity. The significance of these functions and the connection between them are depicted in Figure 4A and B. The higher the enrichment score and the smaller the P value, the more significant the function was. According to the enrichment score and P value, the top 10 enrichment results of BP were selected (Fig. 4A; Table 5).

Table 5 - The top 10 biological process analysis.
ID Biological process analysis Gene names
GO:0062197 Cellular response to chemical stress AKT1 AKR1B1 CASP3 NQO1 EGFR GSR MAPK1 MAPK8
GO:0070302 Stress-activated protein kinase signaling cascade BMP2 EGFR GSTP1 IGF1R MAPK1
GO:0008202 Steroid metabolic process AKR1B1 STS CES1 CYP2C9 CYP19A1 ESR1 GBA GC
GO:0031663 Response to lipopolysaccharide AKT1 MAPK14 MAPK1
GO:0043405 Regulation of MAP kinase activity BMP2 EGFR GBA GSTP1 PIK3CG PPARG PDE5A
GO:0071902 Positive regulation of protein serine/threonine kinase activity AKT1 BMP2 EGFR PIK3CG ADAM17 PDE5A CDK5R1
GO:0002237 Response to molecule of bacterial origin AKT1 CASP3 MAPK14 GSTP1 MAOB PDE4B MAPK1 MAPK8 REN ADAM17
GO:0031667 Response to nutrient levels AKT1 ALB BCHE MAPK14 NQO1 GBA HSPA8 IGF1R PPARG MAPK1 MAPK8
GO:0051403 Stress-activated MAPK cascade MAPK14 MAPK1 MAPK8 MAPK10
GO:0034614 Reactive oxygen species metabolic process AKT1 NQO1 EGFR MAPK1 MAPK8

Figure 4.:
The results of GO analysis. (A) The top 10 enrichment results of BP, MF and CC. (B) The significance of BP functions and their connection. The nodes represented GO functions while the edges represented their connection. The color of nodes represented the P value. The redder the color, the lower the P value was. The red nodes represented the following functions: steroid metabolic process, regulation of MAP kinase activity, positive regulation of protein serine/threonine kinase activity, kinase signaling cascade, response to lipopolysaccharide, chemical stress, nutrient levels, and molecule of bacterial origin. (C) The significance of MF analysis and their connection. The red nodes represented the following functions: steroid binding, nuclear receptor activity, MAP kinase activity, ligand-activated transcription factor activity, aspartic-type endopeptidase activity, aspartic-type peptidase activity, transcription coactivator binding, hormone binding and protein serine/threonine/tyrosine kinase activity. (D) The significance of CC functions and their connection. The red nodes represented the following functions: vesicle lumen, vacuolar lumen, lysosomal lumen, ficolin-1-rich granule, ficolin-1-rich granule lumen, cytoplasmic vesicle lumen and secretory granule lumen. BP = biological process, CC = cellular component, GO = gene ontology, MF = molecular function.

3.6.2. MF analysis.

There were 264 MF enrichment results which primarily included hormone binding, MAP kinase activity, and steroid binding (Fig. 4A and C). The top 10 enrichment results of MF are shown in Figure 4 and Table 6.

Table 6 - The top 10 molecular function analysis.
ID Molecular function analysis Gene names
GO:0004879 Nuclear receptor activity AR ESR1 NR3C2 PPARG RORA THRB
GO:0098531 Ligand-activated transcription factor activity AR ESR1 NR3C2 PPARG RORA THRB
GO:0004707 MAP kinase activity MAPK14 MAPK1 MAPK8 MAPK10
GO:0004190 Aspartic-type endopeptidase activity CASP3 CASP7 CTSD REN BACE1
GO:0070001 Aspartic-type peptidase activity CASP3 CASP7 CTSD REN BACE1
GO:0001223 Transcription coactivator binding AR ESR1 RORA THRB
GO:0042562 Hormone binding AR EGFR IGF1R THRB TTR
GO:0004712 Protein serine/threonine/tyrosine kinase activity AKT1 MAPK14 EGFR GSK3B IGF1R PIK3CG MAPK1 MAPK8 MAPK10
GO:0005496 Steroid binding AR ESR1 GC NR3C2 RORA
GO:0008134 Transcription region AR MAPK14 ESR1 GSK3B PPARG RORA THRB

3.6.3. CC analysis.

There were 164 CC enrichment results which consisted of vesicle lumen, cytoplasmic vesicle lumen, vacuolar lumen, and others (Fig. 4A and D). Figure 4A and Table 6 represent the top 10 enrichment results of CC.

All the results of GO enrichment analysis suggested that a variety of biological processes were involved in the mechanism of RPA in treating RLS.

3.7. KEGG pathway enrichment analysis

A total of 207 KEGG enrichment results were obtained, and the top 10 results were selected using enrichment score and P value (Fig. 5A). Many pathways, including lipid and atherosclerosis (Fig. 5B), endocrine resistance pathway (Fig. 5C), prolactin signaling pathway (Fig. 5D), IL-17 signaling pathway (Fig. 5E), and others were involved in the mechanism of RPA in treating RLS. The genes involved in the top 10 KEGG signaling pathways are listed in Table 7. The gene-pathway network (Fig. 5F) was built through the microbiot platform to visualize the relationship between the main target genes and the pathways of RPA in treating RLS.

Table 7 - The top 10 cellular component analysis.
ID Cellular component analysis Gene names
GO:0060205 Cytoplasmic vesicle lumen ALB MAPK14 CTSD GSTP1 HSPA8 HSP90AA1 MAPK1 TTR BACE1
GO:0005775 Vacuolar lumen CTSD GBA GC HSPA8 HSP90AA1 MAPK1 TTR
GO:0101002 Ficolin-1-rich granule MAPK14 CTSD GSTP1 HSPA8 HSP90AA1 MAPK1
GO:1904813 Ficolin-1-rich granule lumen MAPK14 CTSD GSTP1 HSPA8 HSP90AA1 MAPK1
GO:0034774 Secretory granule lumen ALB MAPK14 CTSD GSTP1 HSPA8 HSP90AA1 MAPK1 TTR
GO:0043202 Lysosomal lumen CTSD GBA GC HSPA8 HSP90AA1
GO:0045121 Membrane raft CASP3 CTSD EGFR IGF1R MAPK1 ADAM17 BACE1 RTN4R
GO:0098857 Membrane microdomain CASP3 CTSD EGFR IGF1R MAPK1 ADAM17 BACE1 RTN4R
GO:0098797 Membrane protein complex BMP2 CASP3 EGFR IGF1R PDE4B

Figure 5.:
The pathway analysis. (A) The top 10 enrichment results of KEGG. The color of nodes represented the P value. The redder the color, the lower the P value was. The higher the enrichment score and the smaller the P value, the more significant the function was. (B) The lipid and atherosclerosis pathway. The proteins marked red in this pathway were closely associated with the overlapping targets of RPA and RLS (AKT, JNK, p38, ERK, CYP, HSP, PPARγ, GSK3B, CASP3, and CASP7). (C) The endocrine resistance pathway. The proteins marked red were closely related to the overlapping targets (ER, JNK, p38, ERK1/2, AKT, EGFR, and IGF1R). (D) The prolactin signaling pathway. The proteins marked red were closely associated with the overlapping targets (AKT, JNK, p38, ERK, GSK3B, and ERA/B). (E) The IL-17 signaling pathway. The proteins marked red were closely associated with the overlapping targets (CASP, HSP90, MAPKS, ERK, and GSK3B). (F) The gene-pathway network. The red and brown nodes represented genes and pathways, respectively. The edges represented the connection between them. KEGG = Kyoto Encyclopedia of Genes and Genomes, RLS = restless legs syndrome, RPA = Radix Paeoniae Alba.

3.8. Molecular docking

The 2 key components with a higher degree (paeoniflorgenone and paeoniflorin) successfully docked with the 2 core targets with a higher degree (AKT1 and CASP3), respectively. Negative binding energy was the premise for successful docking. In general, the lower the binding energy of ligand and receptor is, the more stable the binding is. The key components of RPA had a high affinity for the core targets (Table 8). The area where the components bind to the protein is called the active pocket. The molecular docking results and the active pockets were visualized using PyMoL software (Fig. 6).

Table 8 - The genes involved in the top 10 Kyoto Encyclopedia of Genes and Genomes signaling pathways.
ID Pathways Gene names
hsa05417 Lipid and atherosclerosis AKT1 CASP3 CASP7 MAPK14 CYP2C9 GSK3B HSPA8 HSP90AA1 PPARG MAPK1 MAPK8 MAPK10
hsa05215 Prostate cancer AKT1 AR EGFR GSK3B GSTP1 HSP90AA1 IGF1R MAPK1
hsa01522 Endocrine resistance AKT1 MAPK14 EGFR ESR1 IGF1R MAPK1 MAPK8 MAPK10
hsa04917 Prolactin signaling pathway AKT1 MAPK14 ESR1 GSK3B MAPK1 MAPK8 MAPK10
hsa05145 Toxoplasmosis AKT1 CASP3 MAPK14 HSPA8 PIK3CG MAPK1 MAPK8 MAPK10
hsa05210 Colorectal cancer AKT1 CASP3 EGFR GSK3B MAPK1 MAPK8 MAPK10
hsa04657 IL-17 signaling pathway CASP3 MAPK14 GSK3B HSP90AA1 MAPK1 MAPK8 MAPK10
hsa04914 Progesterone-mediated oocyte maturation AKT1 MAPK14 HSP90AA1 IGF1R MAPK1 MAPK8 MAPK10
hsa04932 Nonalcoholic fatty liver disease AKT1 CASP3 CASP7 MAPK14 GSK3B PPARG MAPK8 MAPK10
hsa05120 Epithelial cell signaling in Helicobacter pylori infection CASP3 MAPK14 EGFR MAPK8 MAPK10 ADAM17

Figure 6.:
The hydrogen bond lengths, amino acid residues, and the active pockets between active components and proteins. (A) Paeoniflorgenone-AKT1. (B) Paeoniflorgenone-CASP3. (C) Paeoniflorin-AKT1. (D). Paeoniflorin-CASP3.

4. Discussion

The pathogenesis of RLS still remains unclear. It is often considered to be associated with dopaminergic dysfunction in the central nervous system, iron deficiency, peripheral nerves, vascular diseases, oxidative stress, and immunological abnormality.[24–28,42–57] Weinstock et al[28] studied many RLS-related diseases and found that 95% of the 38 diseases which were significantly correlated with RLS had changes in inflammation and immunity. Furthermore, they speculated that inflammation might cause iron deficiency and induce RLS. The pathogenesis of RLS tends to be complicated, making it difficult for single-target medications to obtain better therapeutic effects. The exploration of treatment with multi-targets and multi-pathways is the common goal of traditional Chinese and western medicine. TCM believes that the basic pathogenesis of RLS is based on the lack of nourishment of yin and blood in tendons. RPA can nourish yin and blood while further relieving the discomfort of the tendon. It is frequently used in the clinical treatment of RLS.

In this study, several key active components of RPA in treating RLS were screened, including paeoniflorin and paeoniflorgenone. TGP was involved in immune regulation, anti-inflammatory effect, brain protection, and nerve protection.[18] Paeoniflorin has a wide range of anti-inflammatory and immunomodulatory effects.[58] It could restore the downregulation of dopamine D2 receptor protein expression in the pituitary and hypothalamus induced by olanzapine[59] and was a neuroprotective monoterpene glycoside with a good antidepressant effect. The mechanism was linked to upregulating the levels of monoaminergic neurotransmitters, inhibiting the hyperfunction of the hypothalamic-pituitary-adrenal axis, promoting neuroprotection and hippocampus neurogenesis, upregulating brain-derived neurotrophic factor level, inhibiting inflammatory reaction, and downregulating nitric oxide level.[60] Paeoniflorgenone was a depolarization neuromuscular blocker similar to succinic choline, but it did not produce any contraction, while succinic choline did.[61]

In this study, we obtained several core targets that might be critical in the mechanism of RPA in the treatment of RLS, such as ALB, AKT1, CASP3, and others. Iron deficiency in the brain was associated with the pathophysiology of RLS.[26,42,44–47,51,52] Serum ALB might interact with other serum factors (such as transferrin) to limit the supply of iron, thereby limiting the growth of invasive microorganisms.[62] Changes in the dopaminergic system caused by iron deficiency might lead to RLS.[53,54] The immune response to gastrointestinal bacteria or other antigens might cause RLS through direct immune attacks on the central or peripheral nervous system.[28] AKT disorders could lead to neurological diseases.[63] CASP3,[64,65] EGFR,[66] and AR[67] were associated with the pathogenesis of nervous system diseases and could be involved in the regulation of the central nervous system.

The KEGG enrichment results showed that the possible pathways involved in the treatment process included lipid and atherosclerosis, endocrine resistance, prolactin signaling pathway, and IL-17 signaling pathway. Oxidative stress might participate in the pathogenesis of RLS.[27,55–57] PPARG, one of the core targets, was confirmed to be involved in lipid metabolism and oxidative stress.[68–70] IGF1R was also associated with oxidative stress.[71] The pathogenesis of RLS was associated with the endocrine system.[72] AR,[73] ESR1,[74] EGFR,[75] and IGF1R[76] were involved in endocrine regulation. The prolactin signaling pathway was closely related to dopamine function. IL-17 was an inflammatory cytokine mainly produced by CD4+ T cells that played an important role in the pathogenesis of immune disorders.[77] HSP90AA1, MAPK1, MAPK8, and MAPK14 in the core targets were related to the IL-17 signaling pathway. HSP90AA1 was one of the proteins in the IL-17 signaling pathway.[78] MAPK signaling pathway was downstream of the IL-17 signaling pathway. Therefore, RPA may be used to treat RLS via the aforementioned pathways.

5. Conclusion

RLS studies were mostly carried out in clinics. Few RLS studies involved animal or cell experiments, and the ideal animal model of RLS is still under exploration.[79,80] Therefore, further experiments were not carried out in this study. Network pharmacology and molecular docking are rarely used to study RLS. However, based on network pharmacology and molecular docking, this study analyzed the possible mechanism of RPA in treating RLS with multiple components, multiple targets, and multiple pathways, which laid the foundation for future research, making this study innovative.

Author contributions

Conceptualization: Jun Liu, Shengkai Mu, Suxian Liu, Tengteng Wang.

Data curation: Jun Liu, Shengkai Mu, Liansheng Hao, Fangfang Liu.

Investigation: Jun Liu, Shengkai Mu.

Methodology: Jun Liu, Shengkai Mu, Liansheng Hao, Fangfang Liu.

Project administration: Jun Liu.

Resources: Jun Liu, Shengkai Mu.

Software: Jun Liu, Shengkai Mu.

Visualization: Jun Liu, Shengkai Mu.

Writing – original draft: Jun Liu, Shengkai Mu.

Writing – review & editing: Suxian Liu, Tengteng Wang.


    [1]. Hornyak M, Trenkwalder C. Restless legs syndrome and periodic limb movement disorder in the elderly. J Psychosom Res. 2004;56:543–8.
    [2]. Allen RP, Picchietti DL, Garcia-Borreguero D, et al. International Restless Legs Syndrome Study Group. Restless legs syndrome/Willis-Ekbom disease diagnostic criteria: updated International Restless Legs Syndrome Study Group (IRLSSG) consensus criteria – history, rationale, description, and significance. Sleep Med. 2014;15:860–73.
    [3]. Earley CJ. Clinical practice. Restless legs syndrome. N Engl J Med. 2003;348:2103–9.
    [4]. Ekbom K, Leissner L, Olsson JE, et al. [Restless legs – common but often missed disease. Successful treatment possibilities are now available]. Lakartidningen. 2006;103:207–11.
    [5]. Earley CJ, Silber MH. Restless legs syndrome: understanding its consequences and the need for better treatment. Sleep Med. 2010;11:807–15.
    [6]. Birinyi PV, Allen RP, Lesage S, et al. Investigation into the correlation between sensation and leg movement in restless legs syndrome. Mov Disord. 2005;20:1097–103.
    [7]. Garcia-Borreguero D, Kohnen R, Boothby L, et al. Validation of the multiple suggested immobilization test: a test for the assessment of severity of restless legs syndrome (Willis-Ekbom Disease). Sleep. 2013;36:1101–9.
    [8]. Michaud M, Lavigne G, Desautels A, et al. Effects of immobility on sensory and motor symptoms of restless legs syndrome. Mov Disord. 2002;17:112–5.
    [9]. Steensland I, Koskinen LD, Lindvall P. Treatment of restless legs syndrome with a pump; efficacy and complications. Acta Neurol Scand. 2020;141:368–73.
    [10]. During EH, Winkelman JW. Drug treatment of restless legs syndrome in older adults. Drugs Aging. 2019;36:939–46.
    [11]. Innes KE, Selfe TK, Agarwal P. Prevalence of restless legs syndrome in North American and Western European populations: a systematic review. Sleep Med. 2011;12:623–34.
    [12]. Costales B, Vouri SM, Brown JD, et al. Treatment initiation and utilization patterns of pharmacotherapies for early-onset idiopathic restless legs syndrome. Sleep Med. 2022;96:70–8.
    [13]. Gabaldón Torres L, Salas Felipe J, Fernández Domínguez J, et al. [Restless legs syndrome. Features and impact on sleep]. Neurologia. 2009;24:230–4.
    [14]. Winkelman JW, Redline S, Baldwin CM, et al. Polysomnographic and health-related quality of life correlates of restless legs syndrome in the sleep heart health study. Sleep. 2009;32:772–8.
    [15]. Allen RP, Stillman P, Myers AJ. Physician-diagnosed restless legs syndrome in a large sample of primary medical care patients in western Europe: prevalence and characteristics. Sleep Med. 2010;11:31–7.
    [16]. McCrink L, Allen RP, Wolowacz S, et al. Predictors of health-related quality of life in sufferers with restless legs syndrome: a multi-national study. Sleep Med. 2007;8:73–83.
    [17]. Amro MS, Teoh SL, Norzana AG, et al. The potential role of herbal products in the treatment of Parkinson’s disease. Clin Ter. 2018;169:e23–33.
    [18]. Xu JX, Xu J, Cao Y, et al. [Modern research progress of traditional Chinese medicine Paeoniae Radix Alba and prediction of its Q-markers]. Zhongguo Zhong Yao Za Zhi. 2021;46:5486–95.
    [19]. Zheng M, Liu C, Fan Y, et al. Total glucosides of paeony (TGP) extracted from Radix Paeoniae alba exerts neuroprotective effects in MPTP-induced experimental parkinsonism by regulating the cAMP/PKA/CREB signaling pathway. J Ethnopharmacol. 2019;245:112182.
    [20]. Liu DZ, Zhu J, Jin DZ, et al. Behavioral recovery following sub-chronic paeoniflorin administration in the striatal 6-OHDA lesion rodent model of Parkinson’s disease. J Ethnopharmacol. 2007;112:327–32.
    [21]. Liu HQ, Zhang WY, Luo XT, et al. Paeoniflorin attenuates neuroinflammation and dopaminergic neurodegeneration in the MPTP model of Parkinson’s disease by activation of adenosine A1 receptor. Br J Pharmacol. 2006;148:314–25.
    [22]. Zheng M, Liu C, Fan Y, et al. Neuroprotection by paeoniflorin in the MPTP mouse model of Parkinson’s disease. Neuropharmacology. 2017;116:412–20.
    [23]. Guo K, Zhang Y, Li L, et al. Neuroprotective effect of paeoniflorin in the mouse model of Parkinson’s disease through α-synuclein/protein kinase C δ subtype signaling pathway. Neuroreport. 2021;32:1379–87.
    [24]. Allen R. Dopamine and iron in the pathophysiology of restless legs syndrome (RLS). Sleep Med. 2004;5:385–91.
    [25]. Frauscher B, Gschliesser V, Brandauer E, et al. The severity range of restless legs syndrome (RLS) and augmentation in a prospective patient cohort: association with ferritin levels. Sleep Med. 2009;10:611–5.
    [26]. Connor JR, Ponnuru P, Wang XS, et al. Profile of altered brain iron acquisition in restless legs syndrome. Brain. 2011;134(Pt 4):959–68.
    [27]. Baskol G, Korkmaz S, Erdem F, et al. Assessment of nitric oxide, advanced oxidation protein products, malondialdehyde, and thiol levels in patients with restless legs syndrome. Sleep Med. 2012;13:414–8.
    [28]. Weinstock LB, Walters AS, Paueksakon P. Restless legs syndrome – theoretical roles of inflammatory and immune mechanisms. Sleep Med Rev. 2012;16:341–54.
    [29]. Ru J, Li P, Wang J, et al. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13.
    [30]. Kim S, Chen J, Cheng T, et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2019;47:D1102–9.
    [31]. Daina A, Michielin O, Zoete V. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res. 2019;47:W357–64.
    [32]. Liu F, Zhao Q, Liu S, et al. Revealing the pharmacological mechanism of acorus tatarinowii in the treatment of ischemic stroke based on network pharmacology. Evid Based Complement Alternat Med. 2020;2020:3236768.
    [33]. Wang X, Shen Y, Wang S, et al. PharmMapper 2017 update: a web server for potential drug target identification with a comprehensive target pharmacophore database. Nucleic Acids Res. 2017;45:W356–60.
    [34]. ; UniProt Consortium T. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 2018;46:2699.
    [35]. Stelzer G, Rosen N, Plaschkes I, et al. The GeneCards suite: from gene data mining to disease genome sequence analyses. Curr Protoc Bioinformatics. 2016;54:1.30.31–31.30.33.
    [36]. Amberger JS, Bocchini CA, Schiettecatte F, et al. OMIM.org: online mendelian inheritance in man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 2015;43:D789–98.
    [37]. Wishart DS, Feunang YD, Guo AC, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46:D1074–82.
    [38]. Piñero J, Saüch J, Sanz F, et al. The DisGeNET cytoscape app: exploring and visualizing disease genomics data. Comput Struct Biotechnol J. 2021;19:2960–7.
    [39]. Szklarczyk D, Gable AL, Nastou KC, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49:D605–12.
    [40]. Luo W, Brouwer C. Pathview: an R/Bioconductor package for pathway-based data integration and visualization. Bioinformatics. 2013;29:1830–1.
    [41]. Burley SK, Bhikadiya C, Bi C, et al. RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res. 2021;49:D437–51.
    [42]. Connor JR, Boyer PJ, Menzies SL, et al. Neuropathological examination suggests impaired brain iron acquisition in restless legs syndrome. Neurology. 2003;61:304–9.
    [43]. Stiasny-Kolster K, Haeske H, Tergau F, et al. Cortical silent period is shortened in restless legs syndrome independently from circadian rhythm. Suppl Clin Neurophysiol. 2003;56:381–9.
    [44]. Mizuno S, Mihara T, Miyaoka T, et al. CSF iron, ferritin and transferrin levels in restless legs syndrome. J Sleep Res. 2005;14:43–7.
    [45]. Earley CJ, Connor JR, Beard JL, et al. Abnormalities in CSF concentrations of ferritin and transferrin in restless legs syndrome. Neurology. 2000;54:1698–700.
    [46]. Earley CJ, Peter BB, Horská A, et al. MRI-determined regional brain iron concentrations in early- and late-onset restless legs syndrome. Sleep Med. 2006;7:458–61.
    [47]. Allen RP, Barker PB, Wehrl FW, et al. MRI measurement of brain iron in patients with restless legs syndrome. Neurology. 2001;56:263–5.
    [48]. Sun ER, Chen CA, Ho G, et al. Iron and the restless legs syndrome. Sleep. 1998;21:371–7.
    [49]. Konofal E, Cortese S, Marchand M, et al. Impact of restless legs syndrome and iron deficiency on attention-deficit/hyperactivity disorder in children. Sleep Med. 2007;8:711–5.
    [50]. Michaud M, Dumont M, Selmaoui B, et al. Circadian rhythm of restless legs syndrome: relationship with biological markers. Ann Neurol. 2004;55:372–80.
    [51]. Allen RP, Picchietti DL, Auerbach M, et al. International Restless Legs Syndrome Study Group (IRLSSG). Evidence-based and consensus clinical practice guidelines for the iron treatment of restless legs syndrome/Willis-Ekbom disease in adults and children: an IRLSSG task force report. Sleep Med. 2018;41:27–44.
    [52]. Quinn C, Uzbeck M, Saleem I, et al. Iron status and chronic kidney disease predict restless legs syndrome in an older hospital population. Sleep Med. 2011;12:295–301.
    [53]. Lv Q, Wang X, Asakawa T, et al. Pharmacologic treatment of restless legs syndrome. Curr Neuropharmacol. 2021;19:372–82.
    [54]. Sun YM, Hoang T, Neubauer JA, et al. Opioids protect against substantia nigra cell degeneration under conditions of iron deprivation: a mechanism of possible relevance to the restless legs syndrome (RLS) and Parkinson’s disease. J Neurol Sci. 2011;304:93–101.
    [55]. Larsson BW, Kadi F, Ulfberg J, et al. Skeletal muscle morphology in patients with restless legs syndrome. Eur Neurol. 2007;58:133–7.
    [56]. Patton SM, Ponnuru P, Snyder AM, et al. Hypoxia-inducible factor pathway activation in restless legs syndrome patients. Eur J Neurol. 2011;18:1329–35.
    [57]. Wåhlin-Larsson B, Ulfberg J, Aulin KP, et al. The expression of vascular endothelial growth factor in skeletal muscle of patients with sleep disorders. Muscle Nerve. 2009;40:556–61.
    [58]. Zhang L, Wei W. Anti-inflammatory and immunoregulatory effects of paeoniflorin and total glucosides of paeony. Pharmacol Ther. 2020;207:107452.
    [59]. Huang X, Ren L, Hou L, et al. Paeoniflorin ameliorates antipsychotic-induced hyperprolactinemia in rats by attenuating impairment of the dopamine D2 receptor and TGF-β1 signaling pathways in the hypothalamus and pituitary. J Ethnopharmacol. 2020;257:112862.
    [60]. Wang XL, Feng ST, Wang YT, et al. A neuroprotective monoterpenoid glycoside with promising anti-depressive properties. Phytomedicine. 2021;90:153669.
    [61]. Kimura M, Kimura I, Nojima H, et al. Blocking effects of a new component, paeoniflorigenone, in paeony root on neuromuscular junctions of frogs and mice. Jpn J Pharmacol. 1984;35:61–6.
    [62]. Konopka K, Neilands JB. Effect of serum albumin on siderophore-mediated utilization of transferrin iron. Biochemistry. 1984;23:2122–7.
    [63]. Hers I, Vincent EE, Tavaré JM. Akt signalling in health and disease. Cell Signal. 2011;23:1515–27.
    [64]. D’Amelio M, Cavallucci V, Cecconi F. Neuronal caspase-3 signaling: not only cell death. Cell Death Differ. 2010;17:1104–14.
    [65]. Springer JE, Nottingham SA, McEwen ML, et al. Caspase-3 apoptotic signaling following injury to the central nervous system. Clin Chem Lab Med. 2001;39:299–307.
    [66]. Plata-Salamán CR. Epidermal growth factor and the nervous system. Peptides. 1991;12:653–63.
    [67]. Mhaouty-Kodja S. Role of the androgen receptor in the central nervous system. Mol Cell Endocrinol. 2018;465:103–12.
    [68]. Zhao T, Wu K, Hogstrand C, et al. Lipophagy mediated carbohydrate-induced changes of lipid metabolism via oxidative stress, endoplasmic reticulum (ER) stress and ChREBP/PPARγ pathways. Cell Mol Life Sci. 2020;77:1987–2003.
    [69]. Ament Z, West JA, Stanley E, et al. PPAR-pan activation induces hepatic oxidative stress and lipidomic remodelling. Free Radic Biol Med. 2016;95:357–68.
    [70]. Das NR, Vaidya B, Khare P, et al. Combination of peroxisome proliferator-activated receptor Gamma (PPARγ) agonist and PPAR Gamma co-activator 1α (PGC-1α) activator ameliorates cognitive deficits, oxidative stress, and inflammation in rodent model of Parkinson’s Disease. Curr Neurovasc Res. 2021;18:497–507.
    [71]. Riedemann J, Macaulay VM. IGF1R signalling and its inhibition. Endocr Relat Cancer. 2006;13(Suppl 1):S33–43.
    [72]. Khan FH, Ahlberg CD, Chow CA, et al. Iron, dopamine, genetics, and hormones in the pathophysiology of restless legs syndrome. J Neurol. 2017;264:1634–41.
    [73]. Puente Vazquez J, Grande Pulido E, Anton Aparicio LM. Cytokine and endocrine signaling in prostate cancer. Med Oncol. 2012;29:1956–63.
    [74]. Hamilton KJ, Hewitt SC, Arao Y, et al. Estrogen hormone biology. Curr Top Dev Biol. 2017;125:109–46.
    [75]. Márquez DC, Lee J, Lin T, et al. Epidermal growth factor receptor and tyrosine phosphorylation of estrogen receptor. Endocrine. 2001;16:73–81.
    [76]. Osher E, Macaulay VM. Therapeutic targeting of the IGF axis. Cells. 2019;8:895.
    [77]. Jin W, Dong C. IL-17 cytokines in immunity and inflammation. Emerg Microbes Infect. 2013;2:e60.
    [78]. Zhou G, Kong WS, Li ZC, et al. Effects of Qing Chang suppository powder and its ingredients on IL-17 signal pathway in HT-29 cells and DSS-induced Mice. Phytomedicine. 2021;87:153573.
    [79]. Salminen AV, Silvani A, Allen RP, et al. International Restless Legs Syndrome Study Group (IRLSSG). Consensus guidelines on rodent models of restless legs syndrome. Mov Disord. 2021;36:558–69.
    [80]. Earley CJ, Jones BC, Ferré S. Brain-iron deficiency models of restless legs syndrome. Exp Neurol. 2022;356:114158.

    mechanism; molecular docking; network pharmacology; Radix Paeoniae Alba; restless legs syndrome

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