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. 2023 Nov 20;20(7):1087–1099. doi: 10.2174/0115734099266308231108112058

Mechanism of Polygala-Acorus in Treating Autism Spectrum Disorder Based on Network Pharmacology and Molecular Docking

Haozhi Chen 1, Changlin Zhou 1, Wen Li 1, Yaoyao Bian 1,2,*
PMCID: PMC11476213  PMID: 39354858

Abstract

Background

Recent epidemic survey data have revealed a globally increasing prevalence of autism spectrum disorders (ASDs). Currently, while Western medicine mostly uses a combination of comprehensive intervention and rehabilitative treatment, patient outcomes remain unsatisfactory. Polygala-Acorus, used as a pair drug, positively affects the brain and kidneys, and can improve intelligence, wisdom, and awareness; however, the underlying mechanism of action is unclear.

Objectives

We performed network pharmacology analysis of the mechanism of Polygala–Acorus in treating ASD and its potential therapeutic effects to provide a scientific basis for the pharmaceutical’s clinical application.

Methods

The chemical compositions and targets corresponding to Polygala–Acorus were obtained using the Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform, Chemical Source Website, and PharmMapper database. Disease targets in ASD were screened using the DisGeNET, DrugBank, and GeneCards databases. Gene Ontology functional analysis and metabolic pathway analysis (Kyoto Encyclopedia of Genes and Genomes) were performed using the Metascape database and validated via molecular docking using AutoDock Vina and PyMOL software.

Results

Molecular docking analysis showed that the key active components of Polygala-Acorus interacted with the following key targets: EGFR, SRC, MAPK1, and ALB. Thus, the key active components of Polygala-Acorus (sibiricaxanthone A, sibiricaxanthone B tenuifolin, polygalic acid, cycloartenol, and 8-isopentenyl-kaempferol) have been found to bind to EGFR, SRC, MAPK1, and ALB.

Conclusion

This study has preliminarily revealed the active ingredients and underlying mechanism of Polygala-Acorus in the treatment of ASD, and our predictions need to be proven by further experimentation.

Keywords: Network pharmacology, molecular docking, Polygala-Acorus, autism spectrum disorder, drugs and disease, molecular mechanism

1. INTRODUCTION

Autism spectrum disorders (ASDs) are a group of neurodevelopmental disorders characterized by socially obstructive communication, a narrow range of interests or activities, and repetitive stereotypical behaviors [1]. Patients diagnosed with ASD often have multiple comorbidities, such as intellectual disability, speech disorder, language disorder, attention deficit hyperactivity disorder, tic disorder, sleep disorder, gastrointestinal dysfunction, anxiety, and epilepsy [2]. Recent epidemic survey data have revealed that the prevalence of ASD is increasing worldwide. According to the screening data released by the US Centers for Disease Control and Prevention in 2021, the prevalence of ASD is as high as 2.3% [3]. In 2020, the results of the first national assessment survey in China showed the prevalence of ASD in children aged 6–12 years to be 0.7%, with a male-to-female ratio of 4.3:1 [4]. ASD has a serious negative impact on children, both physically and mentally, and leads to a high rate of social disability and low possibility of complete spontaneous recovery, which seriously affects their quality of life. Thus, ASD is a public health issue that requires urgent attention.

While there are no records of autism in ancient Chinese medical literature, the core symptoms of ASD include late speech, fetal-period weakness, lack of intelligence, and emotionlessness in the eyes [5]. However, the etiology and pathogenesis of ASD remain unclear. Currently, in Western medicine, a combination of comprehensive interventions and rehabilitative treatments is used for patients with ASD, but outcomes remain unsatisfactory. The Clinical Guidelines for the Treatment of Pediatrics published by the China Association of Chinese Medicine in 2020 recommend oral Chinese medicine as the main treatment for ASD [6]. Traditional Chinese medicine specializes in herbal pairings, and Polygala paired with Acorus has been widely used in clinical applications [7, 8]. Both of these medicines were first mentioned in the Agriculture God's Canon of Materia Medica. Notably, Polygala benefits the intellect and tranquilizes the mind, thus expelling phlegm and opening the orifices. Acorus opens the orifices and expels phlegm, waking up the mind and benefiting the intellect. Renowned doctor Mr. JinMo Shi often prescribes Polygala–Acorus pairings, which are believed to benefit the kidneys, enhance brain function and intelligence, and promote mental clarity and tranquility, particularly for conditions, such as dementia, memory loss, and indifferent facial expressions [9-14].

In this study, we performed network pharmacology analysis to evaluate the mechanism of Polygala-Acorus in treating ASD (Fig. 1). This study was conducted to explore and validate the molecular mechanisms and pathways of the main bioactive components of Polygala-Acorus in treating ASD. Our results provide a basis for further studies on the pharmacological mechanisms of Polygala-Acorus and the potential therapeutic effects of this combination in the clinic.

Fig. (1).

Fig. (1)

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Flowchart of the mechanism of action of Polygala–Acorus in the treatment of ASD.

2. MATERIALS AND METHODS

2.1. Active Ingredients and Target Points Selection of Polygala-Acorus

Using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (https://old.tcmsp-e.com/tcmsp.php/), the chemical compositions of Acorus were searched under the selective condition of oral bioactivity ≥30% and drug-likeness properties ≥0.18 to obtain the chemical substance registration number corresponding to its chemical composition. The Chemical Source Website (https://www.chemsrc.com/) was used to search for Acorus, and the CAS number corresponding to its chemical composition was obtained. We used the Chemistry Professional Database of the Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, to search for Acorus and obtain its CAS number. PubChem (https://pubchem.ncbi.nlm.nih.gov/) was used to obtain the CAS numbers of Polygala–Acorus corresponding to the 2D structure in SDF format, and the 2D structures were used to obtain compounds corresponding to all individual genes in each herb with the data provided by PharmMapper (http://www.lilab-ecust.cn/pharmmapper/). After aggregation and deduplication, the gene targets of all the active ingredients of Polygala–Acorus were obtained for their action in the human body.

2.2. Screening of ASD Target Points

We used “autism spectrum disorder” as a search term to obtain aggregative information on the disease targets by searching OMIM (https://omim.org/), DisGeNET (https://www.disgenet.org/), DrugBank (https://go.drugbank.com/), and GeneCards (https://www.genecards.org/). Using the data from these four databases, we obtained the final ASD targets after deduplication.

2.3. Access to Drug-disease Intersection Target Points

The drug and disease target points were entered into the online tool Venny (version 2.1.0) (https://bioinfogp.cnb.csic.es/tools/venny/index.html) to obtain the intersection target points.

2.4. Protein-protein Interaction Network Construction and Network Topology Analysis

We entered the intersecting target into the String database (https://cn.string-db.org/), chose “multiple proteins” and organism option as “Homo sapiens,” under the minimum required interactive sub-selection “medium confidence (0.400)”, and network display selection was performed using “hide disconnected nodes in the network.” Using the default setting for the other options, we obtained protein-protein interaction (PPI) network data. The data were imported into Cytoscape (3.9.1) using the CytoNCA plug-in unit for topological analysis and charting of PPI network.

2.5. Gene Ontology Functional Analysis and Kyoto Encyclopedia of Genes and Genomes Metabolic Analysis

To further investigate the specific role of Polygala–Acorus in ASD intersecting targets in relevant pathways, we entered the intersecting targets into the Metascape database (https://metascape.org/gp/index.html#/main/step1) with the genetic species option “H. sapiens”, and performed Gene Ontology functional enrichment analysis, which included the following three levels: cellular component (CC), molecular function (MF), and biological process (BP); additionally, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis.

2.6. Selection of Major Active Ingredients and Core Targets

We used Cytoscape to conduct topological analysis while referring to the three main parameters, degree centrality, betweenness centrality, and compact centrality, with the highest parameter considered as the key node. Accordingly, we obtained the gene set of the core targets. Using CytoHubba, we selected maximal clique centrality (MCC) as the optimal algorithm for screening core gene sets. Based on the intersection of the gene set obtained using the two methods above, the main core gene was identified, and its drug component was predicted to be the main active ingredient.

2.7. Molecular Docking Verification

We further evaluated the reliability of the interactions between the core components of Polygala–Acorus and their corresponding targets. We searched the molecular structures of the main active ingredients obtained from the PubChem database through molecular docking, carried out energy minimization using Chemoffice 3D (2021 version), and saved the results in mol2 format. By searching the UniProt database (https://legacy.uniprot.org/) and setting the limited conditions as validated human-derived genes, we obtained the protein structure of the core genes. Next, we downloaded the protein structure data from the RCSB Protein Data Bank in the RCSB database (https://www.pdbus.org/). To dehydrate and de-ligand the protein molecules using PyMOL, AutoDock tools (1.5.7) were used to dehydrate and hydrogenate the obtained small molecules and protein molecules, and the semi-flexible molecular docking function of AutoDock Vina software was used to set up a grid point with the target protein proto-ligand as the center. Decoding the minimum acquired binding energy between small molecules and target proteins revealed that a smaller numerical value indicated stronger binding. The Lamarckian genetic algorithm was chosen as the docking algorithm, and the rest of the parameters were set by default. To verify the reliability of the AutoDock program for docking in this study system, the original ligand of the target protein complex was abstracted and re-docked to the active pocket of the target protein, and the root-mean-square deviation (RMSD) value of the conformation of the docked ligand from that of the ligand in the original crystal structure was calculated. When the RMSD value is ≤2.0Å, the docking method can better reproduce the original binding pattern of the ligand receptor, which indicates that the docking parameters are set reasonably. The results have been visualized in PyMOL.

3. RESULTS

3.1. Active Chemical Compositions of Polygala–Acorus

Using chemistry data identified by searching the Traditional Chinese Medicine Systems Pharmacology database and Analysis Platform, Chemical Source website, and Chemistry Professional Database Originated from the Shanghai Institute of Organic Chemistry, we obtained 48 active ingredients in Polygala and 1120 corresponding targets, with 110 target points remaining after deduplication. Acorus contained 5 active chemical components, 103 corresponding target points, and 61 targets remaining after deduplication. The specific active ingredients are listed in Table 1.

Table 1. Active chemical constituents of Polygala–Acorus.

Medicine Name Code Chemical Compound CAS Number PubChem CID Targets
Polygala YZ1 1,2,3,6,7-pentamethoxyxanthone 64756-86-1 15693541 3
YZ2 1,2,3,7-tetramethoxyxanthone 22804-52-0 14528828 11
YZ3 1,6-dihydroxy-3,5,7-trimethoxyxanthone 65008-17-5 5316837 21
YZ4 1,7-dihydroxy-2,3-dimethoxyxanthone 78405-33-1 10039726 16
YZ5 1,7-dihydroxy-3-methoxyxanthone 437-50-3 5281636 8
YZ6 1,7-dihydroxyxanthone 529-61-3 5281631 9
YZ7 1,7-dimethoxy-2,3-methylenedioxyxanthone 145523-71-3 85670503 13
YZ8 1-hydroxy-3,6,7-trimethoxyxanthone 2054-36-6 5318373 11
YZ9 1-hydroxy-3,7-dimethoxyxanthone 13379-35-6 5488808 11
YZ10 3,4,5-trimethoxycinnamic acid 90-50-6 735755 7
YZ11 3,6-disinapoylsucrose 139891-98-8 11968389 37
YZ12 6-hydroxy-1,2,3,7-tetramethoxyxanthone 64756-87-2 71378875 14
YZ13 Arillanin A 154287-47-5 11968790 21
YZ14 Bernardioside A 121368-52-3 14699964 46
YZ15 Chromeno[4,3-b]chromene-6,7-dione 38210-27-4 441965 15
YZ16 Ferulic acid 1135-24-6 445858 5
YZ17 Glomeratose A 202471-84-9 73157754 28
YZ18 N-acetyl-glucosamine 7512-17-6 1738118 6
YZ19 Onjisaponin A 82410-33-1 21669941 20
YZ20 Onjisaponin B 35906-36-6 21669942 27
YZ21 Onjisaponin F 79103-90-5 10701737 24
YZ22 Onjisaponin Z 1078708-72-1 134715185 22
YZ23 Onjixanthone I 136083-92-6 5320290 16
YZ24 Polygalacin D 66663-91-0 46173909 29
YZ25 Polygalaxanthone IV N/A 11972435 43
YZ26 Polygalaxanthone XI 857859-82-6 101740054 36
YZ27 Polygalic acid 1260-04-4 12442765 41
YZ28 Presenegenin 2163-40-8 21594224 43
YZ29 Senegenin 2469-34-3 12442762 33
YZ30 Sibiricaxanthone A 241125-76-8 21581292 37
YZ31 Sibiricaxanthone B 241125-81-5 21581293 36
YZ32 Sibiricose A6 241125-75-7 6326021 32
YZ33 Sinapinic acid 530-59-6 637775 5
YZ34 Tenuifolin 20183-47-5 21588226 51
YZ35 Tenuifoliose J 147742-14-1 145865805 8
YZ36 Tenuifoliside A 139726-35-5 46933844 20
YZ37 Tenuifoliside B 139726-36-6 10055215 34
YZ38 Tenuifoliside C 139726-37-7 11968391 24
YZ39 Tenuifoliside D 139726-38-8 5321809 22
YZ40 Virgaureagenin G 22338-71-2 161388 36
YZ41 α-spinasterol 54352-47-5 5315190 27
YZ42 Polygalitol 154-58-5 64960 10
YZ43 1,6-dihydroxy-3,7-dimethoxyxanthone 69618-09-3 5316766 14
YZ44 Sibiricose A5 107912-97-0 6326020 26
YZ45 1,3,6-trihydroxy-2,7-dimethoxy xanthone 136083-93-7 5320291 24
YZ46 Polygalaxanthone III 162857-78-5 11169063 40
YZ47 6-O-p-hydroxybenzoyl sucrose 139726-39-9 10813903 33
YZ48 Tenuifoliose A 139682-01-2 145865806 21
Acorus SCP1 Cycloartenol 469-38-5 92110 31
SCP2 Kaempferol 520-18-3 5280863 13
SCP3 Marmesin 13849-08-6 334704 5
SCP4 8-isopentenyl-kaempferol 28610-31-3 5318624 33
SCP5 (1R,3aS,4R,6aS)-1,4-bis(3,4-methoxyphenyl)-1,3,3a,4,6,6a-hexahydrofuro[4,3-c]furan 526-06-7 234823 21
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3.2. Ways of Operation

By searching for information in the OMIM, DisGeNET, and GeneCards databases using the search term “autism spectrum disorder,” we screened disease targets of ASD; 5660 targets remained after deduplication.

3.3. Access to Drug-disease Intersection Targets

We entered the collected drug-disease targets into Venny (version 2.1.0), an online tool, and obtained 71 drug-disease intersection targets. The results are shown in Fig. (2), and the specific genes are listed in Table 2.

Fig. (2).

Fig. (2)

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Venn diagram of common genes between Polygala–Acorus and autism spectrum disorder.

Table 2. Specific gene names.

Gene Names Entry ID Gene Names Entry ID Gene Names Entry ID
AR P10275 CHEK1 O14757 THRB P10828
BCHE P06276 ESR1 P03372 AMY1B P0DTE7
GSTP1 P09211 ESR2 Q92731 AMY1C P0DTE8
CA2 P00918 MAPK1 P28482 CASP7 P55210
CTSD P07339 PDE4B Q07343 EPHB4 P54760
CYP19A1 P11511 RTN4R Q9BZR6 SELP P16109
FKBP1A P62942 TTR P02766 ANXA5 P08758
PIK3CG P48736 IMPA1 P29218 BRAF P15056
PPIA P62937 LGALS7 P47929 KIF5B P33176
EGFR P00533 PLAU P00749 PPARG P37231
ESRRG P62508 AKR1B1 P15121 PTPN1 P18031
GSR P00390 ALB P02768 SULT2A1 Q06520
HSP90AA1 P07900 APOA2 P02652 SRC P12931
PDE4D Q08499 CASP3 P42574 NR1I3 Q14994
KDR P35968 F10 P00742 RORA P35398
MAPK10 P53779 F2 P00734 MIF P14174
NR1H2 P55055 ITGAL P20701 IGF1R P08069
PGR P06401 KIF11 P52732 RXRA P19793
BACE1 P56817 MAOB P27338 NR3C2 P08235
DDX6 P26196 MAPK14 Q16539 CALM1 P0DP23
FAP Q12884 MAPK8 P45983 FGFR1 P11362
AMY1A P0DUB6 MMP3 P08254 CDK6 Q00534
CFB P00751 SHBG P04278 WAS P42768
LGALS7B P47929 STS P08842
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3.4. PPI Network Construction

We inputted the 71 obtained intersection target proteins into the STRING platform, set the confidence degree to medium confidence ≥0.400, and used hidden targets independent of the network to build a PPI network. This network had 71 nodes, 352 edges, and an average node degree of 9.92. The details are shown in Fig. (3). We imported the data into Cytoscape (version 3.9.1) and used CytoNCA to topologically analyze the PPI network data. According to the ranked degrees, a larger circle and darker color indicate a high genetic value (Fig. 4).

Fig. (3).

Fig. (3)

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Protein-protein interaction network of potential target genes of Polygala–Acorus in treating autism spectrum disorder.

Fig. (4).

Fig. (4)

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Topological analysis of protein-protein interaction network. A larger circle and darker color indicate a high genetic value.

3.5. GO Function Analysis and KEGG Metabolic Pathway Analysis

The common target points obtained from Polygala–Acorus and ASD were entered into the Metascape database, and GO functional and KEGG pathway enrichment analyses were performed. The former analysis revealed 538 BP, 50 CC, and 80 MF processes; the top 10 enriched entries for visual display are shown in Fig. (5). Those involving BP enrichment were response to hormones, intracellular receptor signaling pathways, cellular response to organic cyclic compounds, hormone-mediated signaling pathways, cellular responses to lipids, response to steroid hormones, cellular response to hormone stimulus, steroid hormone-mediated signaling pathways, cellular response to steroid hormone stimulus, and epithelial cell development. Further, high-ranked CC enrichment included the vesicle lumen, cytoplasmic vesicle lumen, ficolin-1-rich granule lumen, secretory granule lumen, ficolin-1-rich granules, lytic vacuoles, lysosomes, vacuolar lumen, membrane rafts, and membrane microdomains. Frontal-ranked MF enrichment involved nuclear receptor activity, ligand-activated transcription factor activity, ATPase binding, transcription co-regulator binding, transcription co-activator binding, transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, nuclear receptor binding, DNA-binding transcription factor binding, and DNA-binding transcription activator activity. KEGG analysis showed 140 enriched pathways, and 15 results were chosen for visual display: pathways in cancer, PI3K-Akt signaling, lipid and atherosclerosis, endocrine resistance, progesterone-mediated oocyte maturation, prolactin signaling, MAPK signaling, GnRH signaling, IL-17 signaling, Th17 cell differentiation, pathways of neurodegeneration, multiple diseases, Th1 and Th2 cell differentiation, EGFR tyrosine kinase inhibitor resistance, mTOR signaling, and estrogen signaling. The results are shown in Fig. (6).

Fig. (5).

Fig. (5)

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Gene ontology function analysis.

Fig. (6).

Fig. (6)

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Kyoto encyclopedia of genes and genomes pathway enrichment.

3.6. Screening of Major Active Ingredients and Core Target Points

The CytoNCA results were analyzed using Cytoscape (version 3.9.1) software. The medians of the obtained centrality, betweenness centrality, and compact centrality were 7, 6.354, and 0.247, respectively, and the screened nodes above the medians were considered as key nodes. MCC was used as the optimal algorithm to select the core gene set, the top 10 gene targets were selected by using CytoHubba, and the final core genes (EGFR, SRC, MAPK1, ALB, HSP90AA1, CASP3, ANXA5, ESR1, and MAPK8) were obtained by overlapping the gene sets of the above two methods. The corresponding drug components were considered as the main active ingredients. The details are presented in Table 3.

Table 3. Core target points’ properties.

Targets Name Degree Central Number in Mediation Level of Tight Centrality
ALB Albumin 45 1218.3943 0.3018018
EGFR Epidermal growth factor receptor 35 443.92847 0.28879312
SRC Proto-oncogene tyrosine-protein kinase Src 32 347.50858 0.2863248
HSP90AA1 Heat shock protein HSP 90-alpha 30 493.08432 0.2838983
ESR1 Estrogen receptor 29 159.86342 0.28033474
MAPK1 Mitogen-activated protein kinase 1 29 291.0724 0.28270042
CASP3 Caspase-3 28 202.23653 0.28033474
ANXA5 Annexin A5 23 70.59635 0.27459016
MAPK8 Mitogen-activated protein kinase 8 19 64.82308 0.26907632
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The Chinese medicine-active ingredient-core target network contained 197 nodes and 1474 effective relationships. The two V-shaped icons on both sides represent the Chinese herbs Polygala and Acorus, and the surrounding diamonds and triangles represent the drug ingredients. The red hexagonal icon in the middle represents ASD. Based on the degree of ranking, a darker color indicated the node as more important. 3,6-disinapoylsucrose, bernardioside A, polygalic acid, presenegenin, sibiricaxanthone A, sibiricaxanthone B, tenuifolin, virgaureagenin G in Polygala and cycloartenol and 8-isopentenyl-kaempferol in Acorus may be crucial components in the treatment of ASD. The corresponding results are shown in Fig. (7).

Fig. (7).

Fig. (7)

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Active components-targets-pathways-disease networks.

3.7. Molecular Docking Verification

Molecular docking of small molecules and target proteins was carried out using AutoDock Vina software. The 10 major compounds of Polygala-Acorus were combined with 9 core target proteins, EGFR (PDB ID, 3POZ), SRC (PDB ID, 1O42), MAPK1 (PDB ID, 2OJG), ALB (PDB ID, 1HK4), HSP90AA1 (PDB ID, 1OSF), CASP3 (PDB ID, 1RHJ), ANXA5 (PDB ID, 1HAK), ESR1 (PDB ID, 1SJ0), and MAPK8 (PDB ID, 3ELJ) for docking calculations. The minimum binding energy of small molecules to target proteins showed that a smaller number indicated a stronger binding capacity (Fig. 8). A docking score binding energy of less than -4.25 kcal/mol was considered to indicate high binding activity between the ligand and target-point proteins. Less than -5.0 kcal/mol indicated better binding and less than -7.0 kcal/mol implied a vibrant docking state between the ligand and target-point proteins [15]. Among the 90 docking results, only one showed a value higher than -5.0 kcal/mol, 13 values were between -6.0 and -7.0 kcal/mol, and the remaining values were below -7.0 kcal/mol. 8-isopentenyl-kaempferol and ALB of Acorus showed the highest binding energy of -10.8 kcal/mol; superimposition of the ligand conformation in the original crystal structure of the target protein and the conformation of the ligand after docking clearly showed that the conformation of the ligand after docking overlapped well with that in the original crystal structure, and the RMSD values before and after docking were both 0.013 Å. This value indicated that the present docking method and the docking parameter settings were reasonable and had high credibility. Strong binding was observed between the screened major compounds and core targets. These results provide a basis for further experimental screening and design of herbal medicines. Finally, the first six positions of the docking results were visualized using PyMOL, as shown in Fig. (9).

Fig. (8).

Fig. (8)

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Molecular docking results (kcal·mol-1).

Fig. (9).

Fig. (9)

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Molecular docking details.

4. DISCUSSION

In this study, 48 active ingredients were screened from the traditional Chinese medicine pair Polygala–Acorus based on network pharmacology, including 3,6-disinapoylsucrose, bernardioside A, onjisaponin, senegenin, sibiricaxanthone A, sibiricaxanthone B, tenuifolin, polygalic acid, cycloartenol, kaempferol, and 8-prenyl kaempferol. Some studies have shown cycloartenol to be a precursor of phytosterol compounds, which is implicated to have various activities, such as anti-inflammatory, anti-tumor, antioxidant, anti-bacterial, and anti-Alzheimer's disease effects. It also strongly impacts the growth and development of most plants [16]. Kaempferol has been shown to have a neuroprotective effect in rats with cerebral ischemia/reperfusion, possibly via its anti-inflammatory, antioxidant, and anti-apoptotic activities [17]. Tenuifolin may exert neuroprotective effects on hippocampal neurons by improving the body's ability to resist oxidative stress, stabilizing mitochondrial membrane potential in the hippocampus, and inhibiting apoptosis [18].

The analysis has revealed Polygala–Acorus to mainly involve the nine core genes EGFR, SRC, MAPK1, ALB, HSP90AA1, CASP3, ANXA5, ESR1, and MAPK8. A related study indicated that phosphorylated SRC kinase may boost oligodendrocytes to form myelin sheaths by mediating brain-derived neurotrophic factors, whereas myelin dysplasia can cause multiple complications, such as mental retardation, hearing, and speech impairment [19]. Specific deletion of CASP3 in catecholaminergic neurons leads to hypodopamine function and affects the nigrostriatal dopaminergic pathway, resulting in all core symptoms of ASD [20]. In addition to improving cognitive function, miR-129-5p reduced inflammation in the mouse hippocampus by downregulating MAPK1 expression in chronically mildly stressed mice [21]. miR-132 also improved cognitive function in rats with AD by inhibiting nitric oxide synthase and oxidative stress in the hippocampus by inhibiting MAPK1 expression [22].

The molecular docking results showed that 8-prenyl kaempferol, onjisaponin, and sibiricaxanthone strongly bound to EGFR and ALB. Epidermal growth factor receptor (EGFR) plays a vital role in early brain development, although its expression pattern decreases as the active nervous system matures [23]. However, during neurological decline and brain atrophy, EGFR reappears in brain cells to maintain a balanced neuron bank. It is also involved in regulating learning and memorization, and activated EGFR initiates several signaling pathways simultaneously, such as the PI3K-Akt signaling pathway and MAPK signaling pathway, which are associated with brain plasticity and memory. Disturbance of local receptor tyrosine kinase activity in the brain can affect memory capacity [24].

KEGG analysis showed that the main active components of Polygala–Acorus function through the PI3K-Akt signaling pathway, MAPK signaling pathway, interleukin-17 signaling pathway, pathways of neurodegeneration in multiple diseases, EGFR tyrosine kinase inhibitor resistance, mTOR signaling pathway, estrogen signaling pathway, and other pathways. Human recombinant PGRN reduced cerebellar neuronal apoptosis, rescued synapse formation, and protected neurodevelopment in rats with ASD via the PI3K/Akt/GSK-3β pathway [25]. Inhibition of mTOR increased PI3K/AKT/mTOR-mediated autophagic activity and improved social interactions in valproate-induced ASD [26]. Another study revealed eIF4E, which is downstream of mTOR, to control and regulate the synthesis of neural proteins and maintain a balance between excitation and inhibition, and that its dysregulation can lead to ASD-like manifestations [27]. Inhibition of the central mTORC1 signaling pathway in the autism model BTBR mice significantly improved social dysfunction and stereotypical behavior, demonstrating that the autistic performance of mice is closely associated with abnormal activation of the mTORC1 signaling pathway [28].

CONCLUSION

In summary, our study has utilized network pharmacology and molecular docking to predict the key components, target points, and pathways involved in the intervention of ASD by Polygala–Acorus. The strong binding activity observed between the core target points and compounds supported the reliability of their network connections. In further studies, we will conduct animal experiments to further evaluate the findings described here.

ACKNOWLEDGEMENTS

Declared none.

LIST OF ABBREVIATIONS

ASD

Autism Spectrum Disorder

BP

Biological Process

CC

Cellular Component

EGFR

Epidermal Growth Factor Receptor

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

MCC

Maximal Clique Centrality

MF

Molecular Function

PDB

RCSB Protein Data Bank

PPI

Protein-protein Interaction

AUTHORS’ CONTRIBUTIONS

Haozhi Chen and Yaoyao Bian conceptualized the study; Haozhi Chen and Changlin Zhou designed the methodology; Haozhi Chen, Changlin Zhou, and Wen Li analyze the data; Changlin Zhou, Wen Li, and Yaoyao Bian validated the findings; Yaoyao Bian supervised the study; Haozhi Chen, Changlin Zhou, Wen Li, and Yaoyao Bian contributed to writing, review and editing. All authors read and approved the final manuscript.

ETHICS APPROVAL AND CONSENT TO PARTICIPATE

Not applicable.

HUMAN AND ANIMAL RIGHTS

Not applicable.

CONSENT FOR PUBLICATION

Not applicable.

AVAILABILITY OF DATA AND MATERIALS

The data and supportive information are available within the article.

FUNDING

This study was financially supported by the National Natural Science Foundation of China (No. 81603529) and the Natural Science Foundation of Jiangsu Province (No. BK20211297).

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

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