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American Journal of Translational Research logoLink to American Journal of Translational Research
. 2023 Jan 15;15(1):596–611.

Mechanism of Tianma Gouteng Decoction in the treatment of Parkinson’s disease based on network pharmacology and molecular docking

Pengyun Ni 1,*, Bingbing Zhao 2,*, Yu Pang 3, Kaiting Pan 4
PMCID: PMC9908455  PMID: 36777831

Abstract

Objective: To explore the pharmacological mechanism and molecular targets of Tianma Gouteng Decoction (TMGTD) in the treatment of Parkinson’s disease (PD). Methods: We applied network pharmacology to screen the active components of TMGTD and predict target genes in multiple Chinese herbal medicine databases and compound databases, and built a drug-ingredient-target network. Then, we used the CytoHubba plug-in to filter out the core components of TMGTD according to the order of degree value. We screened PD-related pathogenic targets in the DrugBank, Genecard and OMIM databases from high to low in Betweenness Centrality (BC) value and Closeness Centrality (CC) value. Subsequently, we determined the intersection target of TMGTD and PD by Venn diagram and performed protein-protein interaction (PPI) analysis, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on core molecules and intersection targets. Finally, molecular docking was performed to verify the binding of the top three core molecules of TMGTD with the top three core targets of PD. Results: The core components of TMGTD are quercetin, kaempferol and palmitic acid. The main targets of TMGTD in the treatment of PD are ALB, GAPDH and AKT1. GO analysis and KEGG analysis showed that the biological process of TMGTD in the treatment of PD is closely related to the activities of neurotransmitter receptors, G protein-coupled receptors and dopamine neurotransmitter receptors. TMGTD possesses therapeutic effects on PD mainly through the PI3K-Akt signaling pathway and MAPK signaling pathway. Molecular docking shows the high affinity of the quercetin, kaempferol and palmitic acid with PD core targets. Conclusion: TMGTD plays a pivotal role in the treatment of PD through multiple components, multiple targets and multiple pathways. The results provide a research direction for the subsequent exploration of the mechanism of TMGTD in PD treatment.

Keywords: Network pharmacology, Tianma Gouteng Decoction, Parkinson’s disease, molecular docking, mechanism

Introduction

The incidence rate of Parkinson’s disease (PD) is increasing annually [1]. Governments and organizations around the world have invested a lot of human and financial resources in the treatment of PD. PD is mainly characterized by motor symptoms such as static tremor, abnormal gait and posture, myotonia, and motor retardation, accompanied by non motor symptoms such as constipation, anosmia, mental and cognitive abnormalities. With the progression of PD, the patient’s motor and non motor symptoms gradually worsen, thereby severely restricting their daily life.

At present, the treatment of PD is mainly depends on drugs and surgery, of which drugs are the main means to improve the motor symptoms and delay the disease progress. Drugs with different mechanisms of action are selected according to the clinical characteristics of patients. Levodopa [2], dopamine receptor agonists [3], monoamine oxidase type B inhibitor [4], catechol-o-methyltransferase inhibitor [5], anticholinergic drugs [6] and amantadine [7] are the commonly used drugs. These drugs are effective in the early stages of PD, but with disease progression and increased drug dosage, the sensitivity of patients to drugs gradually decreases, and serious complications occur. Surgical treatment is a supplementary therapy for PD when the effect of drug treatment is not good. With the globus pallidus internus (GPI) and subthalamic nucleus [5] as the targets, Deep brain stimulation (DBS) [7] is the most common surgery for PD.

In addition, physiotherapy plays an active role in the treatment of PD through improving the gait and balance and other functional activities [8]. The treatment of non motor symptoms [9] and psychological intervention [10] of PD have also been given more and more attention. The treatment of PD is comprehensive, but it is still important to find effective and sustainable alternative therapies.

In China, many PD patients turn to traditional Chinese medicine (TCM). TCM, with its self-contained theory and treatment method, has been shown to be effective for PD patients In terms of alleviating clinical symptoms. Tianma Gouteng Decoction (TMGTD), a TCM formula, plays a neuroprotective role in PD modeled animals and cells [11]. It contains 11 ingredients including Gastrodia elata Bl, Uncariae Ramulus Cumuncis, Cassia obtusifolia, Scutellariae Radix, Gardeniae Fructus, Leonuri Herba, Cyathula officinalis Kuan, Eucommiae Cortex, Caulis Ploygoni multiflori, Poria cum radix pini and Taxillus herba. Modern pharmacological studies have shown that TMGTD inhibits the apoptosis of dopaminergic neurons by resisting oxidative stress, increasing BCL-2 expression and inhibiting Bax activation [12]. In addition, TMGTD could partially restore the protein levels of vasoactive substances TXB2, ET and 6-keto-PGF1alpha in PD mouse models [13]. Clinical studies have shown that TMGTD is effective in improving limb tremor in PD patients [14]. However, the molecular mechanism behind the role of TMGTD in PD treatment is still not clear.

Network pharmacology is a high-throughput omics data analysis method based on information retrieval of network databases on a public platform. It combines biology and pharmacology theories and provides a new research strategy for studying the mechanism of action of TCM. In order to clarify the main pharmacological mechanism of TMGTD, this study used network pharmacology to predict the core active ingredients of TMGTD for PD treatment and built a drug-component-target network. In order to verify the main pharmacological action mechanism of TMGTD, the core molecules and targets of TMGTD for the treatment of PD was selected and its biological mechanism of action and signal pathway through protein-protein interaction (PPI) analysis, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were obtained. Finally, molecular docking was carried out to confirm its molecular mechanism in the treatment of PD. This study provides the direction and basis for the follow-up laboratory research of TMGTD in the treatment of PD. The framework of this study is illustrated in Figure 1.

Figure 1.

Figure 1

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Workflow of network pharmacology analysis.

Materials and methods

Screening of potential active components and related target genes in TMGTD

The compounds and corresponding target information of 11 components in TMGTD were collected from the following databases, namely Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (https://old.tcmsp-e.com/tcmsp.php) [15]. The Encyclopedia of Traditional Chinese Medicine (ETCM) (http://www.tcmip.cn/ETCM/index.php/Home/Index/) [16]. In order to consider the changes of drug absorption and utilization in the human body, pharmacokinetic data including oral bioavailability (OB) ≥30% and drug likeness (DL) ≥0.18 were set up. Then, the main components of TMGTD and its target proteins were collected.

In order to clarify the absorption and distribution of the main active components of TMGTD, the pharmacokinetic information of the core components was predicted by ADMETlab 2.0 (https://admetmesh.scbdd.com/), and the absorption, distribution, metabolism and excretion of the drug components were predicted. ADMETlab 2.0 is a public website providing pharmacokinetic and toxicity predictions [17].

Screening for Parkinson’s disease-related targets

PD-related targets were screened from the following 3 databases, namely Drugbank (https://go.drugbank.com/) [18], genecard (https://www.gene-cards.org/) [19] and OMIM (https://omim.org/) [20]. The keywords “Parkinson’s disease” were entered into the database search box, and targets of the three databases were pooled in each database, then, merged and de-duplicated. The processed targets were the PD targets used in this study.

Construction of drug-ingredient-target network and identification of core component target network

Before building the network, the drug targets and disease targets collected above were brought into the UniProt database [21], the species was set as “Homo sapiens”, and the target names were standardized to obtain the duplicate free UniProt ID and gene name. Subsequently, the drug and disease target data sets were brought into the Venn database (https://bioinfogp.cnb.csic.es/tools/venny/), and the obtained overlapping targets were the candidate active targets for TMGTD against PD. Then, Cytoscape software was used to construct the drug-active ingredients-target network, in which “drug” was set as a circle, “candidate active ingredient” was set as a triangle, drug common ingredient was set as a square, and “target” was set as a diamond. Then we calculated the node degree in network through the Cytohubba plug-in [22], screened the value which is twice the median degree value for the nodes, and found the core components and targets of TMGTD for PD treatment from high to low.

Protein-protein interaction (PPI) analysis

In order to obtain the information of protein-protein interaction network, the overlapping targets obtained from the above Venn database were imported into the string database (https://cn.string-db.org/) [23]. We set the organism as “Homo sapiens” and the required minimum interaction score as “highest confidence >0.9” [24]. The PPI network was constructed by Cytoscape 3.8.2. The degree, Betweenness Centrality (BC) and Closeness Centrality (CC) parameters of each node in the network were calculated, and the node attributes in the PPI network were analyzed [25]. In the study, the target nodes with high BC and CC values were selected to further study the interaction network relationship between core proteins and to explore the direct physical interaction and indirect functional relationship between core proteins.

Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis

In order to further explore the related biological processes (BP), molecular functions (MF), cellular components (CC) [26] and regulatory signal transduction pathways [27] involved in the core targets of TMGTD in the treatment of PD, the candidate core target information was brought into DAVID database (DAVID version 6.8, https://david.ncifcrf.gov/.2022-8-10) for GO analysis and KEGG enrichment analysis. The screening parameters were set as P < 0.01, FDR < 0.05 [28] and the obtained GO and KEGG data were adjusted to the required format. Then, we uploaded the data to Bioinformatics (http://www.bioinformatics.com.cn/) for visualization.

Molecular docking verification

Through autodock 4.2.6 software, the molecular docking between active drug components and hub genes was verified, and the geometric matching and energy matching processes were simulated [29,30]. The suitable proteins structure of potential active targets were obtained from the PDB protein database [31] (https://www.rcsb.org/). Auto dock tools (version 1.5.7) and MOE were used to prepare proteins and ligands. The target proteins and ligands were treated by removing water molecules and replacing them with hydrogen, then the docking of the target proteins and ligands were simulated, and the minimum binding energy was calculated. Finally, the optimal position image of the docking of the protein and ligands were visualized in PyMOL software.

Results

Screening of TMGTD-active compounds and their potential targets

A total of 209 active ingredients were screened out from TMGTD, among which 28 were Gastrodia elata Bl, 32 were Uncariae Ramulus Cumuncis, 4 were Cassia obtusifolia, 36 were Scutellariae Radix, 20 were Gardeniae Fructus, 8 were Leonuri Herba, 4 were Cyathula officinalis Kuan, 33 were Eucommiae Cortex, 11 were Caulis Ploygoni multiflori, 31 were Poria cum radix pini and 2 were Taxillus herba. A total of 187 compounds with different components were found in the compound recipes, and 854 non-repetitive target genes were identified through the TCMSP database combined with the Swiss Target Prediction tool. Table 1 presents the overlapping active ingredient information from TMGTD.

Table 1.

Active Ingredient Information of Tianma Gouteng Decoction (TMGTD)

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We constructed a TMGTD drug-component-target network with 1147 nodes and 5440 edges, as shown in Figure 2. The light purple diamond in the middle represents the potential target gene; the dark blue square is the common component of the Chinese medicine in the compound; the yellow ellipse is the medicine of the TMGTD compound; the triangles surrounding 11 TCM represent the corresponding active ingredients of each medicine, of which the light pink is the active ingredient of Cyathula officinalis Kuan, the flesh pink is the active ingredient of Eucommiae cortex, the dark pink is the active ingredient of Poria cum Radix pini, the orange red is the active ingredient of Uncariae Ramulus cumuncis, and the dark green is the active ingredient of Scutellaria radix, the orange is the active ingredient of Cassia obtusifolia, the light green is the active ingredient of Gastrodia elata BI, the lake green is the active ingredient of Caulis ploygoni Multiflori, the sky blue is the active ingredient of Leonuri Herba, and the grass green is the active ingredient of Gardeniae Fructus. The active ingredients of Taxillus herba are the common ingredients quercetin and sitosterol. According to the degree value, BC value and CC value, the top 10 core active ingredients were determined in Table 2, indicating that quercetin, kaempferol, Palmitic acid are the main active ingredients in TMGTD.

Figure 2.

Figure 2

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Tianma Gouteng Decoction (TMGTD) drug-ingredient-target network diagram. (The light purple diamonds represent potential targets of TMGTD; the yellow circles represent TMGTD drug composition; the dark blue squares represent TMGTD drug common components; and the triangles represent active ingredients of each drug).

Table 2.

Top 10 active ingredients of TMGTD

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Screening of core targets of TMGTD in the treatment of PD

The PD genes were identified through Drugbank, genecard and OMIM databases. After removal of the duplicate values, a total of 453 PD-related target genes were found. Through the Venn diagram results, it was found that there are 104 common targets between TMGTD targets and PD targets (as shown in Figure 3).

Figure 3.

Figure 3

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Venn diagram of intersection target genes of TMGTD (drug) for PD (disease). There were 104 overlapping candidate targets between TMGTD and known targets associated with the pathological process of PD.

PPI network construction and topological clustering analysis of hub genes

We brought 104 TMGTD and PD intersection targets into the String database and obtained protein interaction network data. Then we imported them into Cytoscape 3.7.2 software for visualization and core network screening, as shown in Figure 4. There were 104 overlapping proteins to construct a PPI network. The main parameters of network contained the number of nodes (104) and edges (2404).

Figure 4.

Figure 4

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Identification of potential targets of TMGTD for the treatment of PD by protein-protein interaction (PPI) analysis. A. The PPI network of intersection targets between TMGTD and PD was analyzed by String database. B. Determine the core target network by screening greater than Degree value, Betweenness Centrality (BC) and Closeness Centrality (CC) median values. C. Through the cluster analysis of the MCODE plug-in, 7 groups of core action groups were identified.

In this study, in order to screen out the core PPI-acting proteins, we set the parameters that proteins with degree value, BC value and CC value were all greater than the average. The parameters were degree value >21, BC value >0.004, and CC value >0.523. The core PPI network contained 41 nodes and 1554 edges.

To illustrate the effect of TMGTD on the core targets of PD, MCODE was used to analyze the core PPI network. There are and the identified targets were divided into 7 groups, representing potential key targets in PD therapy, as shown in Figure 4C. At the same time, we screened the top 10 targets according to the Degree value, BC value and CC value, as shown in Table 3. The node degree of proteins in the top 10 were ALB, GAPDH, AKT1, INS, TNF, APP, CASP3, IL6, TP53, IL1B.

Table 3.

Top 10 core target information of protein-protein interaction (PPI) network

Target name Protein name Degree Betweenness Centrality Closeness Centrality
ALB Albumin 65 0.08229507 0.73722628
GAPDH Glyceraldehyde-3-phosphate dehydrogenase 60 0.06320935 0.70629371
AKT1 RAC-alpha serine 57 0.03559661 0.69655172000
INS Insulin 55 0.03819642 0.68707483
TNF Tumor necrosis factor 53 0.0302108 0.67785235
APP Amyloid-beta precursor protein 52 0.03933287 0.66887417
CASP3 Caspase-3 51 0.01645894 0.66447368
IL6 Interleukin-6 50 0.02569663 0.66013072
TP53 Cellular tumor antigen p53 49 0.0180041 0.62732919
IL1B Interleukin-1beta 47 0.01460551 0.6474359
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GO enrichment and KEGG pathway analysis

Enrichment analysis was performed using DAVID, and the data generated by GO enrichment analysis were sorted by P < 0.01, FDR < 0.05 entries, including 250 biological processes (BP), 63 cellular components (CC) and 44 molecular functions (MF). Sorted by Lg P value, the top 10 entries in the BP, CC and MF data were selected for visualization, as shown in Figure 5.

Figure 5.

Figure 5

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The Gene Ontology (GO) function and KEGG pathway enrichments. Top-10 items of biological process (BP), cellular component (CC) and molecular function (MF) of GO analysis. Set the parameters “P value < 0.01, FDR value < 0.05”.

Biological processes are associated with modulation of neurotransmitter levels, neuronal death, chemical synaptic transmission and membrane potential modulation, cognitive learning and memory. Cellular components are closely related to synaptic membranes and neuronal cell bodies. Molecular functions are reflected in neurotransmitter receptor activity, G protein-coupled receptor activity, and dopamine neurotransmitter receptor activity, as well as ammonium ion binding, catecholamine binding, dopamine binding, neurotransmitter binding, hormone binding, amine binding, and serum element binding.

Through the KEGG pathway enrichment analysis data in the David database, a total of 55 entries with P < 0.01 and FDR < 0.05 were screened out. According to the P value, the top 20 entries were selected for bubble chart drawing, as shown in Figure 6. KEGG analysis showed that it was highly related to PI3K-Akt signaling pathway and MAPK signaling pathway.

Figure 6.

Figure 6

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Top 20 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of hub genes. The abscissa shows the enrichment factor, and the ordinate shows the KEGG pathways. The color of the dot represents the adjusted P value, and the size of the dot represents the number of core targets mapped to the reference pathways.

Molecular docking verification

According to the results of network pharmacology analysis, we analyzed the combination ability of top 3 active components of TMGTD, quercetin, kaempferol, and Palmitic acid, with the top 10 target proteins ALB (PDB ID: 4L8U), AKT1 (PDB ID: 1UNQ), APP (PDB ID: 1TKN), CASP3 (PDB ID: 3H0E), GAPDH (PDB ID: 6M61), IL1B (PDB ID: 5R8M), IL6 (PDB ID: 1ALU), INS (PDB ID: 1UZ9), TNF (PDB ID: 7JRA), and TP53 (PDB ID: 2J1Y) by molecular docking. The results showed that the binding energy is between -3.17 and -6.5 Kcal/mol, which means the binding was stable and feasible. The detailed docking results of proteins and ligands are shown in Table 4. The three core molecules quercetin, kaempferol and Palmitic acid could combine with the top 10 target proteins. The images of the optimal docking site between ligands and proteins were shown in Figure 7.

Table 4.

Molecular docking simulation of the top 3 TMGTD core active ingredients and the top 10 PD core targets

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Figure 7.

Figure 7

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Visualization of molecular docking results. The top-three ingredients are Kaempferol, Palmitic acid and quercetin. A. The molecular docking between top-three ingredients and Protein structure of ALB (PDB ID: 4L8U). B. The molecular docking between top-three ingredients and Protein structure of GAPDH (PDB ID: 6M61). C. The molecular docking between top-three ingredients and Protein structure of AKT1 (PDB ID: 1UNQ). D. The molecular docking between top-three ingredients and Protein structure of INS (PDB ID: 1UZ9). E. The molecular docking between top-three ingredients and Protein structure of TNF (PDB ID: 7JRA). F. The molecular docking between top-three ingredients and Protein structure of APP (PDB ID: 1TKN).

Discussion

PD seriously affects the physical and mental health of elderly patients, and greatly reduces the quality of life. At present, it is still challenging to explore a curable treatment method for PD. TCM provides a complementary treatment, in which, TMGTD has been shown to be effective in improving PD symptoms [11,32]. However, its mechanism of action needs further study. In this study, network pharmacology approach and molecular docking were used jointly to investigate the possible mechanism of action of TMGTD.

A total of 187 unique active chemical components of TMGTD were collected via the TCMSP and ETCM databases. The drug-component-target network was constructed and the active ingredients such as quercetin, kaempferol and palmitic acid were screened out according to the degree value, BC value and CC value. Previous studies have confirmed that quercetin inhibits the expression of a-synuclein protein by controlling mitochondrial mass, reducing oxidative stress, and alleviating neuronal death [33]. Quercetin also improves the motor symptoms of PD by regulating the autophagy pathway, and reverses the oxidative and apoptotic responses of neurons [34]. Strong evidence revealed that kaemperfol can inhibit the activation of microglia and astrocytes, reduce the expression of inflammatory cytokines, and improve the behavioral deficits in PD rats [35]. Furthermore, kaempferol exerts a strong neuroprotective effect and delays the occurrence and development of neurodegenerative diseases through scavenging free radicals while retaining the activity of antioxidant substances, and crossing the blood-brain barrier [36]. Additionally, palmitic acid stimulates PGC-1α promoter methylation in cortical neurons, microglia, and astrocytes [37], thereby protects astrocytes under inflammatory conditions, reduces the effects of lipotoxicity on neurons as well as alleviates neurodegenerative processes [38]. The above studies showed the active effects of quercetin, kaempferol and Palmitic acid on the treatment of PD, and its mechanism is worthy of further exploration.

In this study, a total of 104 common targets of TMGTD were found and these molecules are potential targets of TMGTD in the treatment of PD. In order to identify the core targets, we constructed a PPI network, ranked according to degree value, BC value and DC value from high to low. The main therapeutic targets are ALB, GAPDH, AKT1, INS, TNF, APP, CASP3, IL6, TP53 and IL1B. Among them, ALB, GAPDH and AKT1 ranked the top three. Recent study showed that ALB is the core active target in PD [39]. The significant accumulation of GAPDH in the nuclei of neurons in PD patients plays a key role in the pathophysiology of neuronal cell death. Reducing such death signaling markers like GAPDH may attenuate neurodegeneration and help therapeutically control the development of PD [40]. Another study showed that activation of Akt1-Creb signaling pathway involved in neurodegeneration in PD tissue [41]. The AKT1 gene is a potential risk factor for PD, and it plays a protective role in the development of PD [42].

This study explored the mechanism of TMGTD in the treatment of PD through GO analysis and KEGG enrichment analysis. GO analysis showed that, BC are associated with modulation of neurotransmitter levels, neuronal death, chemical synaptic transmission and membrane potential modulation, cognitive learning and memory. CC are closely related to synaptic membranes and neuronal cell bodies. MF are mainly reflected in neurotransmitter receptor activity, G protein-coupled receptor activity, and dopamine neurotransmitter receptor activity. KEGG analysis showed that treatment of PD by TMGTD involves multiple signaling pathways including PI3K-Akt signaling pathway and MAPK signaling pathway. Activation of PI3K/AKT signaling pathway can alleviate dopaminergic neuron damage, reduce oxidative stress and inflammatory responses, and relieve PD symptoms [43,44]. MAPK signaling pathway inhibition exerts anti-autophagy, anti-apoptotic and anti-inflammatory effects, and reduces the movement and sensory disorders of PD [45,46].

Based on the above network pharmacology research results, the potential molecular mechanism of TMGTD in the treatment of PD was simulated and verified. The predicted results of network pharmacology in this study were validated by molecular docking. When the binding energy is less than 1.2 Kcal/mol (-5.0 KJ/mol), the molecular docking simulation binding is stable and feasible. The results showed that these compounds can stably bind to targets, providing a material basis for the TMGTD’s treatment on PD.

Conclusion

Integrating network pharmacology and molecular docking technologies, this study explored the main chemical components of TMGTD in the treatment of PD and its molecular mechanism. The result shows that quercetin, kaempferol and palmitic acid may be the main active ingredients of TMGTD. Protein target genes ALB, GAPDH and AKT1 may be potential target genes for the treatment of PD, and it may play a therapeutic role through PI3K-Akt signaling pathway and MAPK signaling pathway. In general, this study provides a scientific basis and reference for further research on the potential mechanism of TMGTD in the treatment of PD.

Acknowledgements

We thank post-doc LiTao for his guidance and manuscript proofreading. We would also like to thank all of the authors in the references.

Disclosure of conflict of interest

None.

References

  • 1.Samii A, Nutt JG, Ransom BR. Parkinson’s disease. Lancet. 2004;363:1783–1793. doi: 10.1016/S0140-6736(04)16305-8. [DOI] [PubMed] [Google Scholar]
  • 2.Connolly BS, Lang AE. Pharmacological treatment of Parkinson disease: a review. JAMA. 2014;311:1670–1683. doi: 10.1001/jama.2014.3654. [DOI] [PubMed] [Google Scholar]
  • 3.Hauser RA. New considerations in the medical management of early Parkinson’s disease: impact of recent clinical trials on treatment strategy. Parkinsonism Relat Disord. 2009;15(Suppl 3):S17–21. doi: 10.1016/S1353-8020(09)70772-2. [DOI] [PubMed] [Google Scholar]
  • 4.Poewe W, Mahlknecht P. Pharmacologic treatment of motor symptoms associated with Parkinson disease. Neurol Clin. 2020;38:255–267. doi: 10.1016/j.ncl.2019.12.002. [DOI] [PubMed] [Google Scholar]
  • 5.Antonini A, Moro E, Godeiro C, Reichmann H. Medical and surgical management of advanced Parkinson’s disease. Mov Disord. 2018;33:900–908. doi: 10.1002/mds.27340. [DOI] [PubMed] [Google Scholar]
  • 6.Fox SH, Katzenschlager R, Lim SY, Barton B, de Bie RMA, Seppi K, Coelho M, Sampaio C Movement Disorder Society Evidence-Based Medicine Committee. International Parkinson and movement disorder society evidence-based medicine review: update on treatments for the motor symptoms of Parkinson’s disease. Mov Disord. 2018;33:1248–1266. doi: 10.1002/mds.27372. [DOI] [PubMed] [Google Scholar]
  • 7.Zhu GY, Geng XY, Zhang RL, Chen YC, Liu YY, Wang SY, Zhang JG. Deep brain stimulation modulates pallidal and subthalamic neural oscillations in Tourette’s syndrome. Brain Behav. 2019;9:e01450. doi: 10.1002/brb3.1450. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Domingos J, Keus SHJ, Dean J, de Vries NM, Ferreira JJ, Bloem BR. The European physiotherapy guideline for Parkinson’s disease: implications for neurologists. J Parkinsons Dis. 2018;8:499–502. doi: 10.3233/JPD-181383. [DOI] [PubMed] [Google Scholar]
  • 9.Armstrong MJ, Okun MS. Diagnosis and treatment of Parkinson disease: a review. JAMA. 2020;323:548–560. doi: 10.1001/jama.2019.22360. [DOI] [PubMed] [Google Scholar]
  • 10.Koychev I, Okai D. Cognitive-behavioural therapy for non-motor symptoms of Parkinson’s disease: a clinical review. Evid Based Ment Health. 2017;20:15–20. doi: 10.1136/eb-2016-102574. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Liu LF, Song JX, Lu JH, Huang YY, Zeng Y, Chen LL, Durairajan SS, Han QB, Li M. Tianma Gouteng Yin, a traditional chinese medicine decoction, exerts neuroprotective effects in animal and cellular models of Parkinson’s disease. Sci Rep. 2015;5:16862. doi: 10.1038/srep16862. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.He JC, Wang WW. Effect of Tianma Gouteng Yin on apoptosis of dopaminergic neurons in Parkinson’s disease model rats. J Tradit Chin Med. 2010;51:1024–1027. [Google Scholar]
  • 13.Ran Q, He JC. The influence of different therapeutic methods in TCM on ET, TXB-2 and 6-keto-PGF-(1alpha) of PD rats. Chinese Journal of Gerontology. 2010;30:2771–2773. [Google Scholar]
  • 14.Huang JF, Liu JH, Tan CF, Wang TL, Xu Q. Clinical study of acupuncture combined with Bazhen Decoction and Tianma Gouteng Decoction in the treatment of Parkinson’s disease with syndrome of qi and Blood deficiency. Chinese Journal of Basic Medicine in Traditional Chinese Medicine. 2020;26:1134–1137. [Google Scholar]
  • 15.Ru J, Li P, Wang J, Zhou W, Li B, Huang C, Li P, Guo Z, Tao W, Yang Y, Xu X, Li Y, Wang Y, Yang L. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J Cheminform. 2014;6:13. doi: 10.1186/1758-2946-6-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Xu HY, Zhang YQ, Liu ZM, Chen T, Lv CY, Tang SH, Zhang XB, Zhang W, Li ZY, Zhou RR, Yang HJ, Wang XJ, Huang LQ. ETCM: an encyclopaedia of traditional Chinese medicine. Nucleic Acids Res. 2019;47:D976–D982. doi: 10.1093/nar/gky987. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Xiong G, Wu Z, Yi J, Fu L, Yang Z, Hsieh C, Yin M, Zeng X, Wu C, Lu A, Chen X, Hou T, Cao D. ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res. 2021;49:W5–W14. doi: 10.1093/nar/gkab255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, Maciejewski A, Gale N, Wilson A, Chin L, Cummings R, Le D, Pon A, Knox C, Wilson M. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46:D1074–D1082. doi: 10.1093/nar/gkx1037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Xia W, Hu S, Wang M, Xu F, Han L, Peng D. Exploration of the potential mechanism of the Tao Hong Si Wu Decoction for the treatment of postpartum blood stasis based on network pharmacology and in vivo experimental verification. J Ethnopharmacol. 2021;268:113641. doi: 10.1016/j.jep.2020.113641. [DOI] [PubMed] [Google Scholar]
  • 20.Amberger JS, Bocchini CA, Schiettecatte F, Scott AF, Hamosh A. OMIM.org: online mendelian inheritance in man (OMIM(R)), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 2015;43:D789–798. doi: 10.1093/nar/gku1205. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2021;49:D480–D489. doi: 10.1093/nar/gkaa1100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. CytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8(Suppl 4):S11. doi: 10.1186/1752-0509-8-S4-S11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, Jensen LJ, Mering CV. The string database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 2017;45:D362–D368. doi: 10.1093/nar/gkw937. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, Jensen LJ, Mering CV. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–D613. doi: 10.1093/nar/gky1131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Gan XX, Zhong LK, Shen F, Feng JH, Li YY, Li SJ, Cai WS, Xu B. Network pharmacology to explore the molecular mechanisms of prunella vulgaris for treating Hashimoto’s thyroiditis. Front Pharmacol. 2021;12:700896. doi: 10.3389/fphar.2021.700896. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.The Gene Ontology Consortium. Expansion of the gene ontology knowledgebase and resources. Nucleic Acids Res. 2017;45:D331–D338. doi: 10.1093/nar/gkw1108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30. doi: 10.1093/nar/28.1.27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57. doi: 10.1038/nprot.2008.211. [DOI] [PubMed] [Google Scholar]
  • 29.Trott O, Olson AJ. AutoDock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31:455–461. doi: 10.1002/jcc.21334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem. 2009;30:2785–2791. doi: 10.1002/jcc.21256. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The Protein data bank. Nucleic Acids Res. 2000;28:235–242. doi: 10.1093/nar/28.1.235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Jiang YN, Guo YZ, Lu DH, Pan MH, Liu HZ, Jiao GL, Bi W, Kurihara H, Li YF, Duan WJ, He RR, Yao XS. Tianma Gouteng granules decreases the susceptibility of Parkinson’s disease by inhibiting ALOX15-mediated lipid peroxidation. J Ethnopharmacol. 2020;256:112824. doi: 10.1016/j.jep.2020.112824. [DOI] [PubMed] [Google Scholar]
  • 33.Wang WW, Han R, He HJ, Li J, Chen SY, Gu Y, Xie C. Administration of quercetin improves mitochondria quality control and protects the neurons in 6-OHDA-lesioned Parkinson’s disease models. Aging (Albany NY) 2021;13:11738–11751. doi: 10.18632/aging.202868. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Chakraborty J, Pakrashi S, Sarbajna A, Dutta M, Bandyopadhyay J. Quercetin attenuates copper-induced apoptotic cell death and endoplasmic reticulum stress in SH-SY5Y cells by autophagic modulation. Biol Trace Elem Res. 2022;200:5022–5041. doi: 10.1007/s12011-022-03093-x. [DOI] [PubMed] [Google Scholar]
  • 35.Cai M, Zhuang W, Lv E, Liu Z, Wang Y, Zhang W, Fu W. Kaemperfol alleviates pyroptosis and microglia-mediated neuroinflammation in Parkinson’s disease via inhibiting p38MAPK/NF-kappaB signaling pathway. Neurochem Int. 2022;152:105221. doi: 10.1016/j.neuint.2021.105221. [DOI] [PubMed] [Google Scholar]
  • 36.Rahul , Siddique YH. Neurodegenerative diseases and flavonoids: special reference to kaempferol. CNS neurol disord drug targets. 2021;20:327–342. doi: 10.2174/1871527320666210129122033. [DOI] [PubMed] [Google Scholar]
  • 37.Vesga-Jimenez DJ, Martin C, Barreto GE, Aristizabal-Pachon AF, Pinzon A, Gonzalez J. Fatty acids: an insight into the pathogenesis of neurodegenerative diseases and therapeutic potential. Int J Mol Sci. 2022;23:2577. doi: 10.3390/ijms23052577. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Cabezas R, Martin-Jimenez C, Zuluaga M, Pinzon A, Barreto GE, Gonzalez J. Integrated metabolomics and lipidomics reveal high accumulation of glycerophospholipids in human astrocytes under the lipotoxic effect of palmitic acid and tibolone protection. Int J Mol Sci. 2022;23:2474. doi: 10.3390/ijms23052474. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Hang W, Fan HJ, Li YR, Xiao Q, Jia L, Song LJ, Gao Y, Jin XM, Xiao BG, Yu JZ, Ma CG, Chai Z. Wuzi Yanzong pill attenuates MPTP-induced Parkinson’s disease via PI3K/Akt signaling pathway. Metab Brain Dis. 2022;37:1435–1450. doi: 10.1007/s11011-022-00993-8. [DOI] [PubMed] [Google Scholar]
  • 40.Sekar S, Taghibiglou C. Nuclear accumulation of GAPDH, GluA2 and p53 in post-mortem substantia nigral region of patients with Parkinson’s disease. Neurosci Lett. 2020;716:134641. doi: 10.1016/j.neulet.2019.134641. [DOI] [PubMed] [Google Scholar]
  • 41.Kim H, Park J, Kang H, Yun SP, Lee YS, Lee YI, Lee Y. Activation of the Akt1-CREB pathway promotes RNF146 expression to inhibit PARP1-mediated neuronal death. Sci Signal. 2020;13:eaax7119. doi: 10.1126/scisignal.aax7119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Xiromerisiou G, Hadjigeorgiou GM, Papadimitriou A, Katsarogiannis E, Gourbali V, Singleton AB. Association between AKT1 gene and Parkinson’s disease: a protective haplotype. Neurosci Lett. 2008;436:232–234. doi: 10.1016/j.neulet.2008.03.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Yao Z, Li J, Bian L, Li Q, Wang X, Yang X, Wei X, Wan G, Wang Y, Shi J, Guo J. Nootkatone alleviates rotenone-induced Parkinson’s disease symptoms through activation of the PI3K/Akt signaling pathway. Phytother Res. 2022;36:4183–4200. doi: 10.1002/ptr.7552. [DOI] [PubMed] [Google Scholar]
  • 44.Cai L, Tu L, Li T, Yang X, Ren Y, Gu R, Zhang Q, Yao H, Qu X, Wang Q, Tian J. Downregulation of lncRNA UCA1 ameliorates the damage of dopaminergic neurons, reduces oxidative stress and inflammation in Parkinson’s disease through the inhibition of the PI3K/Akt signaling pathway. Int Immunopharmacol. 2019;75:105734. doi: 10.1016/j.intimp.2019.105734. [DOI] [PubMed] [Google Scholar]
  • 45.Yang J, Jia M, Zhang X, Wang P. Calycosin attenuates MPTP-induced Parkinson’s disease by suppressing the activation of TLR/NF-kappaB and MAPK pathways. Phytother Res. 2019;33:309–318. doi: 10.1002/ptr.6221. [DOI] [PubMed] [Google Scholar]
  • 46.Wang Y, Ren Q, Zhang X, Lu H, Chen J. Neuroprotective mechanisms of calycosin against focal cerebral ischemia and reperfusion injury in rats. Cell Physiol Biochem. 2018;45:537–546. doi: 10.1159/000487031. [DOI] [PubMed] [Google Scholar]

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