Systems Pharmacology and Molecular Docking Reveals the Mechanisms of Nux Vomica for the Prevention of Myasthenia Gravis

Background Myasthenia gravis (MG) is a rare autoimmune disease with clinical symptoms of fluctuating muscle weakness. Due to the side effects of current therapies, there is an urgent need for a new medication for MG treatment. Nux vomica is a traditional Chinese medicine used in various diseases. However, the mechanism of action of Nux vomica against MG remains unclear. Methods Network pharmacology was used to explore the underlying mechanisms of Nux vomica in MG treatment, which was validated using molecular docking and in vivo experiments in mice. Results Twelve bioactive compounds and 72 targets in Nux vomica were screened. Seventy-nine myasthenia-related targets were obtained from the GENECARD and DisGeNET databases. PPI networks of Nux vomica- and myasthenia-related targets were constructed using Bisogenet, and these two networks were subsequently merged into an intersection to establish a core-target PPI network that consisted of 204 nodes and 4,668 edges. KEGG enrichment analysis indicated that 132 pathways were enriched in 204 core targets. In addition, we obtained 50 docking pairs via molecular docking. In vivo experiments revealed that Nux vomica can improve the symptoms of MG. Conclusion Nux vomica is involved in the pathogenesis of MG through the “multicomponent-target-pathway” mechanism.


Introduction
Myasthenia gravis (MG) is an autoantibody-mediated neuromuscular disease that causes fluctuating muscle weakness predominantly in the ocular, bulbar, and proximal limb muscles [1]. e prevalence rate is approximately one in 5,000 worldwide, and the incidence is higher in younger women and older men [2]. Currently, patients with MG are mainly treated with corticosteroids, long-term immunosuppressive drugs, plasma exchange, or thymectomy [3]. However, their clinical application is limited due to their high costs and side effects, such as cardiac rhythm disorders and hypotension [4]. Hence, alternative therapies with higher efficacy and fewer side effects are needed for MG treatment.
Traditional Chinese medicines (TCMs) have received increasing attention because of their high efficacy, safety, and few adverse effects. According to the TCM theory, MG is classified as "Weizheng" and diagnosed as "flaccidity syndrome" and "Qi-deficiency pattern" [5]. us, Qi-supplementation therapy is important for MG treatment. An increasing number of studies have shown that TCMs as an alternative or adjuvant of Western medicines could significantly improve the clinical efficacy of MG treatment [6][7][8]. Jianpiyiqi granules have been shown to remarkably alleviate the clinical symptoms in children with ocular MG accompanied by a decrease in acetylcholine receptor autoantibodies [6]. In addition, the Qiangji Jianli decoction has been shown to alleviate oxidative stress injury and facilitate mitochondrial biogenesis via the AMPK/PGC-1α-dependent signaling pathway, which restored muscle energy supply and ameliorated gastrocnemius muscle contractility in experimental autoimmune MG rats [7]. Another well-known TCM treatment for MG, Bu Zhong Yi Qi decoction, has been reported to decrease simulated weightlessness-induced muscle atrophy and promote myogenesis by reducing nuclear receptor corepressor 1 (NCoR1)-associated gene expression [8]. Nux vomica, also called Semen Strychni or Ma qianzi (Chinese: 马钱子), is the seed of Strychnos Nuxvomica L, belonging to the genus Strychnos of the family Loganiaceae. Studies have illustrated that Nux vomica has numerous biological activities, such as analgesic, anti-inflammatory, antitumor, and immunomodulatory effects [9]. A previous study found that Nux vomica could alleviate inflammatory response and thereby ameliorates MG in experimental autoimmune MG rats [10]. However, the molecular mechanisms of Nux vomica against MG remain unclear. erefore, it is meaningful to explore the targets and further clarify the underlying mechanisms of action of Nux vomica on MG treatment based on network pharmacology.
Network pharmacology, first introduced by British pharmacologist Hopkins, has proven to be an effective strategy for revealing the complicated relationship between TCMs and diseases [11,12]. TCMs exert their therapeutic functions through a multicompound-target-pathway network.
us, the molecular mechanisms of TCMs can be clarified using network pharmacology systematically and integrally [13]. In this study, we screened the active components of Nux vomica and explored its underlying molecular mechanisms on MG based on network pharmacology. Our study provides a theoretical basis for the future clinical application of Nux vomica for treating MG.

Collection of Bioactive Ingredients and erapeutic Targets
for Nux Vomica. TCMSP (https://tcmspw.com/tcmsp.php) is an online platform for integrating pharmacokinetics, medicinal chemistry, therapeutic targets, and drug-targetdisease networks [16]. Based on the TCMSP platform, bioactive ingredients and targets of Nux vomica were obtained using the screening criteria of oral bioavailability (OB) > 30% and drug-likeness (DL) > 0.18 [17]. In addition, strychnine was considered as a candidate bioactive ingredient based on a previous study. e pharmacokinetics and DL properties of strychnine were further assessed using the online SwissADME database (https://www.swissadme.ch/ index.php). e therapeutic targets of strychnine were then predicted using the SwissTargetPrediction online tool (https://swisstargetprediction.ch/).

PPI Network Construction.
A PPI network was constructed and visually analyzed using the Cytoscape plugin Bisogenet, which contains the following six PPI databases: the IntAct Molecular Interaction Database (IntAct), Biomolecular Interaction Network Database (BIND), Biological General Repository for Interaction Datasets (BioGRID), Database of Interacting Proteins (DIP), Molecular INTeraction Database (MINT), and Human Protein Reference Database (HPRD) [18]. PPI networks of the Nux vomicarelated targets and MG-related targets were constructed using Cytoscape.

Central Network Evaluation.
Central network evaluation is considered a primary method for core protein screening in PPI networks. First, the PPI networks of the Nux vomica-related targets and MG-related targets were merged into an intersection. Next, the Cytoscape plugin CytoNCA was applied to assess the intersection from the following six centrality measures: betweenness centrality (BC), closeness centrality (CC), degree centrality (DC), eigenvector centrality (EC), network centrality (NC), and local average connectivity-based method (LAC) [19]. In addition, CytoHubba, a Cytoscape plugin, was used to select the hub genes [20].

GO and KEGG Pathway Enrichment Analyses.
Gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed based on the public database GO (https:// geneontology.org/) and KEGG (https://www.genome.jp/ kegg/). A hypergeometric distribution model was used to evaluate whether the target gene set was significantly Evidence-Based Complementary and Alternative Medicine associated with a specific gene ontology or biological pathway using the following formula: Here, N represents the total number of genes from the reference terms, M represents the number of genes annotated to specific GO terms or pathways, n represents the number of identified Nux vomica-target genes, and k represents the number of common genes between Nux vomicatarget genes and the reference set. e clusterProfiler package in R (4.0) (https://www.bioconductor.org/packages/ release/bioc/html/clusterProfiler.html) was adopted to perform the GO and KEGG pathway enrichment analyses with P values <0.01, adjusted by the Bonferroni correction.

Molecular Docking Analysis.
e zinc database (https:// zinc15.docking.org) was used to download two-dimensional (2D) structures of bioactive compounds [21]. Auto-DockTools-1.5.6 was used to add nonpolar hydrogen, calculate charges, and set rotatable keys for the structure and save it as a "pdbqt" file [22]. e 3D structures of the hub targets were downloaded from the Protein Data Bank (PDB) database (https://www.rcsb.org/) [23]. e hub targets and the original ligands were separated and the water molecules were removed using PyMol 2.3.0 [24]. Next, the ligand and receptor were imported into AutoDockTools-1.5.6 to add nonpolar hydrogen, calculate charges, and save as a "pdbqt" file.
e protein proligand was set as the center of the docking box; if no proligand was present in the target protein, the docking region was identified based on key amino acid residues near the active pocket of the target, as reported in previous studies, or the fpocket [25] online pocket parameter prediction website for analysis. e prepared protein files, ligand files, compound files, and scripts were placed in the appropriate folders, and molecular docking was performed using AutoDock Vina 1.1.2 [26]. Finally, the binding affinity was calculated using AutoDock Vina 1.1.2, and the docking results were visualized using the open-source version PyMol 2.3.0.  After anaesthesia, mice in the model group were emulsified with 40 μg MuSK in 100 μL phosphate-buffered saline (PBS) and 100 μL complete Freund's adjuvant (CFA; Becton, Dickinson and Company, NJ, USA), which were intraperitoneally injected into the hind foot. e control group was injected with 100 μL of PBS and 100 μL of CFA. e mice in both groups were injected with cyclophosphamide monohydrate (300 mg/kg, dissolved in 0.9% saline to a final concentration of 10 mg/mL; Sigma-Aldrich, MO, USA) within 24 h to suppress immune resistance. e aforementioned operation was repeated after 30 days. e body weight of the mice was measured weekly, and a clinical examination was performed every 2 weeks to determine the model-building cycle. After the MG mouse model was successfully constructed, the mice were randomly divided into five groups (n � 8). e mice in the low-, mid-, and high-dose Nux vomica groups were administered low (4 mg/ kg), mid (8 mg/kg), and high (16 mg/kg) doses of Nux vomica. e mice in the control and model groups were intragastrically administered equivalent doses of saline. e body weight of the mice was measured weekly, and a clinical examination was performed every 2 weeks. After 30 days, mice were sacrificed by cervical dislocation after injecting sodium pentobarbital (50 mg/kg), and tendon tissues were removed for subsequent experiments.

Clinical Evaluation.
For clinical examination, mice were left for 3 min on a flat platform and observed for signs of MG. e clinical muscle weakness was graded as follows: Grade 0.5, the mouse exhibited slightly lower activity than  normal after the standard exercise state, and the hanging time was >60 s; Grade 1, the mouse exhibited normal movement, lying prone, staying still, and/or hanging time <60 s; Grade 2, before and after exercise, the mouse was lying prone, staying still, and/or hanging test <30 s; Grade 3, severe weakness, dehydration and paralysis, weight loss >15%, and/or hanging time 0 s; Grade 4, the mouse died. e grip of the mice was observed every 2 weeks. First, the tail of each mouse was gently grabbed and the mouse was suspended above a metal grid. When the mouse grasped the metal grid, the mouse was lifted and the tension was measured 20 times. One end was fixed on the tension device,  Evidence-Based Complementary and Alternative Medicine and the front end of the force measurement device was made into a grid shape. e tail of the mouse was grasped, the front end of the force measuring device was grasped, and the tail of the mouse was pulled forcefully. When the mouse was not strong enough to grasp the front end of the force measuring device, the force measuring device showed the grip force at this time. In addition, clinical progress was assessed using an inverted screening test every 2 weeks. In this test, the mouse was placed in the center of the grid and immediately rotated to the upside-down position, and stably maintained 50 cm above the soft cushion. e time when the mouse fell was recorded and the observation time was limited to 300 s.  Table S1. GAPDH was used as an internal control.

Western Blot
Analysis. e predicted target protein expression was detected by western blotting. Briefly, total proteins were extracted from tendon tissue using RIPA lysis buffer (Applied Biosystems, MA, USA). en, the proteins were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred to a polyvinylidene fluoride membrane (Millipore, MA, USA). e membranes were blocked with 5% skimmed milk and incubated with primary antibodies anti-EGFR, anti-TP53, anti-AKT1, anti-MYC, and anti-GAPDH (1 : 1,000, Abcam, UK) at 4°C overnight. After washing with PBS, the membranes were incubated with a secondary antibody (1 : 1,000, Abcam). Enhanced chemiluminescent reagents (Millipore, MA, USA)   studies have shown that strychnine is one of the major bioactive and principal toxic compounds of Nux vomica [9]. erefore, strychnine was considered as a candidate in this study.
e DL and pharmacokinetics of strychnine were subsequently evaluated using the online tool SwissADME. e results indicated that strychnine exhibited good gastrointestinal absorption and high DL properties, confirming that the strain was an active compound of Nux vomica. e targets of strychnine were predicted by Swis-sTargetPrediction, and the top 25 were viewed as key targets of strychnine. Collectively, we found 12 bioactive compounds and 72 targets in Nux vomica, and the details are shown in Tables 1 and 2. Subsequently, we constructed a Nux vomica bioactive compound-target network using Cytoscape (Figure 1).

GO and KEGG Pathway Enrichment Analysis of Nux
Vomica. To further investigate the 72 targets of the Nux vomica bioactive compounds, GO enrichment analysis was      Figure 2(d) and Table 3.

Target Genes of MG.
irty-seven MG-related targets were obtained from the GENECARD database, and 42 were from the DisGeNET database. Seventy-nine disease target genes were retrieved after combining the results of both databases, and the duplicates were deleted. e details are listed in Table 4.

Nux Vomica-MG PPI Network.
e PPI networks of Nux vomica-and MG-related targets were constructed using Bisogenet. e PPI network of Nux vomica-related genes was constructed with 2,275 nodes and 92,252 edges (Figure 3(a)).
In addition, the MG-related target network was constructed with 3,480 nodes and 121,919 edges (Figure 3(b)). Both networks were subsequently merged into an intersection with 1,517 nodes and 50,040 edges (Figure 3(c)). CytoNCA was used to assess the intersection of the PPI network by topological analysis. A Nux vomica on the myasthenia PPI network was first screened based on the criteria of "BC ≥ 902.385, CC ≥ 0.471, DC ≥ 52" (Figure 3(d)). A coretarget PPI network was further screened using the criteria of "BC ≥ 1985.705, CC ≥ 0.500, DC ≥ 90," which consists of 204 nodes and 4,668 edges (Figure 3(e)).

Enrichment Analysis of the Core-Target PPI Network.
In the GO enrichment analysis, a total of 144 molecular functions, 1,570 biological processes, and 151 cell components were identified in 204 core targets of Nux vomica following MG treatment (P < 0.01). e top 15 significant GO terms, including MF, BP, and CC terms, are shown in Figures 4(a)-4(c). KEGG pathway enrichment analysis demonstrated that 132 pathways were enriched in 204 core targets (P < 0.01). e top 15 KEGG pathways enriched for the targets of bioactive compounds from Nux vomica are shown in Figure 4(d).   Evidence-Based Complementary and Alternative Medicine 3.6. Enrichment Analysis of the Hub Genes. Hub genes were selected using CytoHubba based on the core-target PPI network. e results showed that TP53, EGFR, UBC, MYC, HSP90AA1, EP300, HSPA8, RPS27A, AKT1, and GAPDH were the major hub nodes in the core-target PPI network, and the details are shown in Table 5. e PPI network of the hub genes is shown in Figure 5. e results of the most representative GO terms included regulation of protein modification by small protein conjugation or removal, cellular response to drugs, activation of the innate immune response, intrinsic apoptotic signaling pathway, regulation of protein ubiquitination, regulation of the innate immune response, positive regulation of cellular protein localization, regulation of protein stability, positive regulation of the innate immune response, regulation of the mRNA metabolic process, positive regulation of establishment of protein localization, regulation of binding, regulation of sequence-specific DNA binding transcription factor activity, regulation of apoptotic signaling pathway, and rhythmic process ( Figure 6).
In addition, the top 15 pathways in which the core targets were enriched were identified (Figure 7), mainly including the neurotrophin signaling pathway, PI3K-Akt signaling pathway, cell cycle, apoptosis, viral carcinogenesis, prostate cancer, hepatitis C, pancreatic cancer, Epstein-Barr (EB) virus infection, human cytomegalovirus infection, proteoglycans in cancer, hepatitis B, chronic myeloid leukemia, Kaposi sarcoma-associated herpes, and microRNAs (miRNAs) in cancer.

Molecular Docking Analysis.
e five bioactive compounds (strychnine, stigmasterol, (S)-stylopine, brucine-Noxide, and isostrychnineN-oxide (II)) are shown in Table 6. e top ten hug targets (TP53, EGFR, UBC, MYC, HSP90AA1, EP300, HSPA8, RPS27A, AKT1, and GAPDH) were selected as the core compounds and core targets for molecular docking. AutoDock Vina assesses the binding of small molecules mainly by binding energy (affinity) to assess the binding strength of a small molecule to a protein. If the affinity is <0, the ligand can spontaneously bind to the receptor, and the smaller the value, the easier it is for the active ingredient to bind to the receptor. erefore, the interaction between the active compound and its corresponding target was analyzed by the binding energy, and a

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Evidence-Based Complementary and Alternative Medicine threshold affinity <-5 kcal/mol was set in this study. A total of 50 docking pairs were obtained, as shown in Table 7. Figure 8 shows the binding patterns of the top ten binding core targets and their corresponding compounds. Figure 9, the body weight of the mice increased every week, and after 42 days, the body weight of the mice in the model group was significantly lower than that of the mice in the control group (P < 0.01). Clinical examinations were performed every 2 weeks. After 42 days, compared to the control group, the clinical scores were increased, the grip strengths were weakened, and the inverted screen times were shortened in the model group (P < 0.01).

Nux Vomica Improves Clinical Examination Indexes of MG Model Mice.
After treatment with Nux vomica, the clinical examination indicators of the MG model (weight, clinical score, grip strength, and inverted screen time) significantly improved. After 28 days of treatment, the weight of the MG mice increased significantly, clinical scores reduced, grip strength increased, and the inverted screen times were increased with the increased dose of Nux vomica ( Figure 10).

Expression of Hub Targets.
To further verify the effect of Nux vomica against MG, we detected the expression of hub targets by qRT-PCR and western blotting. e results showed that compared to the model group, Nux vomica treatment significantly reduced EGFR and TP53 expression and increased MYC expression in a dose-dependent manner. Moreover, HSPA8 expression was not significantly changed, while AKT1 expression was significantly reduced, and the effect was particularly pronounced in the high-dose group. e qRT-PCR results also showed that the mRNA expression of RPS27, UBC, HSP90AA1, and EP300 was not significantly different between the groups ( Figure 11).

Discussion
MG is an autoimmune disease of the neuromuscular junction, which is mainly mediated by acetylcholine receptor (AChR) antibody and muscle-specific kinase (MuSK) [27,28]. Presently, traditional therapies for long-term MG treatment have several adverse effects in some populations. erefore, it is vital to explore new methods for treating MG. Owing to their high efficiency and few side effects, TCMs have been widely used in MG treatment as an alternative or adjuvant therapy. In addition, a previous meta-analysis indicated that compared to using Western medication alone, TCMs could function as adjuvant therapies to improve the efficacy and decrease the side effects and relapse rate in treating MG [29,30]. As a TCM, Nux vomica is warm in nature, with a slightly bitter flavor due to its various pharmacological activities. However, the effects and mechanism of action of Nux vomica on MG treatment remain unclear. erefore, our study systematically explored the underlying mechanism of Nux vomica treatment for MG based on network pharmacology and further validated the therapeutic effect of Nux vomica on MG in combination with in vivo experiments.
Based on the public databases, 12 bioactive compounds of Nux vomica were collected by setting OB ≥ 30% and DL ≥ 0.18 (Table 1). Studies have isolated many chemical compounds from Nux vomica, including alkaloids, glycosides, steroids, and organic acids [9]. Among them, alkaloids, including strychnine, brucine-N-oxide, isobrucine, vomicine, and brucine N-oxide, are the primary chemical components of Nux vomica and have been reported to have anticancer effects. Additionally, strychnine has been shown to exhibit significant anti-inflammatory activity [31], while brucine-N-oxide possesses a strong antinociceptive potential [32]. Moreover, stigmasterol can inhibit the activity of acetylcholinesterase (AChE) and has antioxidant, anti-inflammatory, and neuroprotective effects [33][34][35]. us, many compounds from Nux vomica possess favorable biological activity, including inhibiting AChE and immunomodulation and may exert a mitigative effect on MG.
MG occurrence is closely related to continuous viral infections, genetic factors, and abnormal immune responses. Growing evidence has demonstrated that EBV infection is correlated with the occurrence of MG [36,37]. For example, EBV infection could affect TLR7/9-dependent innate immune responses, which enhance the inflammatory response, B-cell dysfunction, and autoimmunity in the thymus of patients with MG [36,37]. Moreover, a recent pilot study showed that hepatitis E infection was relevant to MG, at least in China, and may contribute to MG by inducing replication in the thymus, bystander activation, and stimulation of antigen-specific T cells [38]. Target identification is a critical step for the mechanism investigation on Nux vomica treating MG. According to the PPI network of active compound targets, Nux vomica has the characteristics of multiple components and targets in the treatment of MG. We also identified the miRNA pathway in cancer. An increasing number of studies have suggested that miRNAs are involved in the occurrence and development of MG [39]. e miRNA profile of peripheral blood from MG patients was sequenced using a next-generation sequencing approach, and 41 miRNAs were found to be differentially expressed in nonresponders versus responders to immunosuppressive therapies. More importantly, miR-181d-5p, -323b-3p, -340-3p, -409-3p, and -485-3p can modulate the immune response and drug metabolism processes in patients with MG [40]. In another study, miR-612, miR-3651, and miR-3653 were found to be highly expressed in the peripheral blood mononuclear cells of patients with AChRpositive early-onset MG (AChR-EOMG), suggesting that these miRNAs may be involved in the pathogenesis of AChR-EOMG [41]. It was predicted that the active ingredients of Nux vomica may regulate the immune response by regulating the miRNA signaling pathway, thereby playing a role in the treatment of MG.  (d) inverted screen test. * P < 0.05, * * P < 0.01, and * * * P < 0.001 vs. control group; # P < 0.05, ## P < 0.01, and ### P < 0.001 vs. model group; Δ P < 0.05, and ΔΔ P < 0.01 vs. low-dose Nux vomica group.

Conclusions
Our study systematically analyzed the potential effects of Nux vomica in the treatment of MG based on network pharmacology and validated its effects in combination with in vivo experiments. e findings showed that the bioactive compounds of Nux vomica exerted therapeutic effects on MG by acting on multiple targets and multiple pathways. Based on the network pharmacology, this study confirmed the therapeutic effects of Nux vomica on MG probably via downregulating the expression of EGFR, TP53, and AKT1 and upregulating MYC level. Our data may provide a valuable theoretical basis for future MG diagnosis and treatment and also serve as a reference for further mechanistic research on Nux vomica treating MG. However, we also found that the mechanisms of Nux vomica against are involved in the PI3K-Akt signaling pathway and miRNAs, so further experiments are needed to prove these predictions.
Besides, the therapeutic efficacy of Nux vomica on MG is needed to be verified in a clinical trial.