Exploration of Mechanism of Withania somnifera (L.) Dunal in the Treatment of Huntington Disease: A Network Pharmacology Approaches Integrated with Molecular Docking and Dynamics

Introduction

The therapeutic properties of Withania somnifera (L.) Dunal, often known as Indian ginseng (Ashwagandha in Sanskrit), or Indian winter cherry, have drawn a lot of attention in recent years.^1-3 Over thousands of years, Indian medical systems have utilised it extensively as a nerve tonic, adaptogen, memory enhancer, anti-stress, and to treat gout, rheumatoid arthritis, infertility, infectious infections, sleeplessness, anxiety, and cognitive deficiencies. The primary medical systems that use its compositions are Ayurvedic and Unani.^{4, 5} A wonderful herbal Rasayana that has been utilised for generations to treat neurological disorders is called “Sattvic Kapha Rasayana.”⁶ Ashwagandha’s potential health benefits have drawn more attention in recent years, especially in relation to stress management, cognitive function, and physical performance. Supplementing with ashwagandha may have neuroprotective effects, assist treat obsessive-compulsive disorder, and have anti-inflammatory, immunomodulatory, anxiolytic, and anti-convulsive properties, according to a number of studies.^7-10

Studies have shown that because of the brain’s high oxygen consumption and the high amount of polyunsaturated fatty acids in their membranes, neuronal cells are susceptible to oxidative damage.¹¹ By controlling various antioxidant enzymes, amyloid beta clearance, calcium influx, neurite outgrowth, lipid peroxidation, inflammation, and other debilitating processes implicated in Alzheimer’s, Parkinson’s, Amyotrophic lateral sclerosis (ALS), and Huntington’s disease [HD], W. somnifera extract and its active constituents have been shown to have anti-oxidant qualities and protect neuronal cells from toxins. ^{12, 13}

The degeneration of neurones in the basal ganglia causes HD, a fatal neurodegenerative illness that is incurable. Present-day drugs simply treat the symptoms and slow the disease’s progression.^{14, 15} According to statistics, half of the children will inherit the allele that causes the disease because it is inherited in an autosomal dominant fashion. Huntingtin undergoes a conformational change into its insoluble form when the IT15 gene, which codes for the huntingtin (htt) protein on chromosome 4, is mutated. Rapid neuronal death results from the accumulation of the mutant huntingtin protein’s N-terminal region, which contains enlarged polyglutamine repeats. Acetylcholine, serotonin, GABA, and dopamine become unbalanced as a result. It is widely acknowledged that the GABAergic system plays a part in the pathophysiology of HD and that W. somnifera operates via this system.¹⁶

Though its potential health benefits are encouraging, further study is required to completely understand ashwagandha’s mechanisms of action and assess how well it treats Huntington disease. Therefore, employing network pharmacological techniques in combination with molecular docking and dynamics studies, we have investigated the potential mechanism of W. somnifera in the treatment of Huntington disease in the current study.

Materials and Methods

Exploring potential targets and active ingredients

The Web of Science and PubMed databases were used to find the main phytoconstituents of W. somnifera. To get the CAS number, each of the gathered chemical components was separately put into the PubChem database.¹⁷ Some of the substances were included even though they did not meet the aforementioned requirements because a literature review verified their good pharmacological qualities.¹⁸ The appropriate target genes were then obtained by entering the canonical SMILES of the gathered active components into the SwissTargetPrediction database.¹⁹ A probability higher than 0.1 served as the threshold for target screening. The targets were extracted in the MS Excel file.

Identifying targets with different expression

The genecard database provided the microarray platform and the expression profile data (relevance score >20).²⁰ In the search box of the gene card, Huntington disease was added and search for the targets involved in HD. Further, DisGeNet database was also used to search the targets involved in HD after creating account in the database.  The targets from Gene card and DisGeNet were extracted in the excel file. The duplicates were detected and excluded using MS Excel. The common targets between compound targets and targets involved in HD were identified using VennDiagram (both targets were added in the separate columns).²¹

Building networks of protein-protein interactions (ppi) and identifying important genes

The interaction among common targets obtained from VennDiagram was checked using STRING database with Default settings.²² After that, Cytoscape 3.10.1 was used to import the PPI network from the STRING database to investigate the crucial genes. Cytoscape plugins to guarantee the accuracy of the results.²³

Constructing the component-target-pathway network

A network of connections between active components, common targets for Huntington disease and W. somnifera, and pathways were constructed using Cytoscape. “Nodes” stand for components, targets, and pathways in the component-target-pathway network, while “edges” show the connections between them. The number of connections between nodes in the network is represented by the “degree” parameter, which is used to evaluate the key elements and objectives.²⁴

Molecular docking

The RCSB PDB database provided the target protein’s three-dimensional (3D) structure, while the PubChem database provided the molecular ligand’s two-dimensional structure.²⁵ The target was selected based on literature studies and the X-ray crystallographic structure of MAPK1 PDB ID- 4QTA, NOS2 PDB ID-3E7G, PDE10A PDB ID-4LM3, MAP2K1 PDB ID-7B7R, BRAF PDB ID-8C7X, GSK3B PDB ID-7SXJ, AKT1, PDB ID- 4GV1, PTGS2 PDB ID-5F19, HMGCR PDB ID-2R4F were retrieved from the protein data bank.

Ligand and protein preparation

After the extraction of phytoconstituents and protein, they underwent additional minimization and preparation steps to ensure their suitability for subsequent molecular modelling studies. “Ligprep” module was used to minimize and prepare the sdf formatted ligands. To confirm the cleanliness and appropriateness of the target crystal structure for further analysis, co-crystallized solvents and ions were removed from the original structure. Chain A was selected and chosen whereas chain B was removed from the crystal structure of targets before proceeding the protein preparation. Therefore, protein structure underwent preparation through a series of steps, including the addition of hydrogen atoms, adjustment of bond orders, elimination of water molecules, substitution of missing atoms, and incorporation of side chains followed by energy minimization using the “Protein Preparation wizard” module of Schrodinger suite. Following this, the structure of both the phytoconstituents and protein was ready for further investigations.

Receptor grid

After optimizing and preparing the protein, a critical procedure ensued, wherein a receptor grid was generated to delineate the precise space for the subsequent docking of the ligand. The generation of the grid involved the selection of the co-crystallized ligand, which served to illuminate the active site of the protein, pinpointed by the coordinates (X= -6.86, Y= -21.08, Z= 7.33). The Site Map module was used to create grid in targets where internal ligand was not available.

Molecular docking study

The investigation involved a docking study using the standard precision (SP) parameters aiming to identify the energetically preferred binding conformation of the phytoconstituents within the active site of targets. The docking study utilized the Ligand Docking module from Schrödinger.

The doc score was computed by the Maestro module of Schrödinger. Lower the doc score, more energetically favoured binding conformation of ligand in the active site of target. Doc score less than -0.7 considered as cut off vale for further molecular dynamics studies.

Molecular dynamics (MD) simulation

As previously mentioned, molecular dynamics (MD) simulation was utilised to ascertain the docked complex’s stability. DESMOND was used for the 100 ns MD simulation of the protein-ligand combination with the lowest MM-GBSA binding energy. The orthorhombic simulation box was constructed using a TIP3P explicit water model and a system builder panel. The distance of 10 Å was maintained between the edge of the simulation box and the protein surface. To maintain a steady isosmotic salt environment, 150 mM NaCl was administered after the system had been neutralised. The system was minimised through 2000 iterations. Using the NPT (normal pressure and temperature) ensemble at 300 K and 1.01 bars, the minimised system was run through a 100 ns MD simulation using the default relaxation prior to simulation. The Nose-Hoover Chain thermostat and the Martyna-Tobias-Klein barostat were used to maintain the temperature and pressure, respectively. The energy and structure were recorded and saved in the trajectory file at 10-ps intervals during the simulation, which was run with a time step of 2 fs. Trajectories and three-dimensional structures were inspected using MAESTRO.

Results

Selection of phytoconstituents

A total of 30 main phytoconstituents of W. somnifera were selected based on the literature review which have neuroprotective potential (Table 1).

Table 1: Phytoconstituents of W. somnifera

Network pharmacology

Targets of selected phytoconstituents

The Swiss Target Prediction database and SEA database identified 1059 targets of W. somnifera.

Target involved in Huntington’s disease

624 targets associated with Huntington’s disease have been identified using GeneCards, (relevance score >20) and DisGeNet database.

Common targets between phytoconstituents and huntington’s disease

Using the Venny 2.1.0 tool, we have identified 111 common targets between the phytoconstituents of W. somnifera and Huntington’s disease. The common targets were compiled in Table 2 and shown in Figure 1.

Table 2: List of common targets between W. somnifera and Huntington’s disease

Figure 1: Common targets between W. somnifera and Huntington’s disease. Click here to View Figure

PPI, network construction, and pathway analysis

The STRING database investigated the protein-protein interactions of 111 possible therapeutic targets of W. somnifera with Huntington disease and regulated 181 pathways. After a peer review of the literature, 16 pathways associated with Huntington disease were selected. Alzheimer’s disease (hsa05010) scored the lowest false discover rate (FDR) of 6.48E-25 by triggering 31 genes in Huntington disease. Likewise, the HIF-1 signaling pathway (hsa04066) scored a low false discover rate (FDR) of 2.73E-13 by triggering 14 genes in Huntington disease. The neurotrophin signalling route, PIK3-AKT, MAPK, and NF-Kappa B signalling pathways had the lowest FDR and were thought to be involved in Huntington disease (Table 3).

Table 3: 16 KEGG pathways involved in Huntington disease ranked by FDR (p-value)

The network between phytoconstituents -targets- pathways (Figure 2) was constructed by treating edge count topological parameters using Cytoscape ver 3.10.1.

Figure 2: W. somnifera bioactives’ interactions with their targets and altered pathways.  Click here to View Figure

The top 10 genes implicated in pathways were determined by network analysis to be MAPK1, HMGCR, PTGS2, AKT1, MAP2K1, GSK3B, NOS2, BRAF, PDE10A, and RAF1 (Table 4).

Table 4: Network analysis of W. somnifera

 KEGG pathway analysis

The Metascape platform was used to conduct GO enrichment analysis on core targets. The best 16 significant items were then chosen for visual analysis on the LogP (p <0.01) value shown in bubble diagrams (Figure 3). 

Figure 3: GO enrichment analysis of GG target. Bubble plot representing top 16 pathways that were involved. Click here to View Figure

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The findings demonstrated that the biological processes involved were primarily neurotrophine signaling, Alzheimer disease, chemical synaptic transmission, synaptic signalling, and cellular responses to organonitrogen compounds. These results implied that W. somnifera mode of action for treating Huntington disease was the consequence of several molecular biological mechanisms working in concert.

Molecular docking findings

To validate the findings, Schrodinger was used to perform molecular docking on ten core target proteins with top GG compounds (Table 5). Molecular docking studies showed good docking score between ligands and phytoconstituents.

Table 5: Docking score of W. somnifera

Molecular simulation findings

Molecular dynamics parameters (RMSD, RMSF, Target-ligand contacts histogram, PL-Contacts, Ligand-target contacts and Target-ligand contacts) have shown the stability of selected phytoconstituents of W. somnifera in the active site of the targets.

Discussion

The coding region of the Huntington’s disease gene, which is found on the short arm of chromosome 4, repeats a cytosine-adenine-guanine (CAG) trinucleotide, which causes Huntington disease, an autosomal dominant neurological illness. When the number of CAG repeats rises to more than 35, HTT is more likely to misfold and form insoluble aggregates in the cytoplasm and nucleus of neurones. The accumulated aggregates cause apoptosis and cell malfunction, which ultimately results in the affected brain regions atrophying severely. The FDA has only approved tetrabenazine (TBZ) as a treatment for chorea in Huntington disease. The central nervous system’s vesicular monoamine transporter 2 (VMAT-2) is reversibly inhibited by TBZ.²⁶ Therefore, it is anticipated that the clinical use of ashwagandha and its ingredients will benefit neurodegenerative illnesses in light of their anti-degenerative properties. Ashwagandha and its components are safe by several groups in the past. The behavioural, biochemical, and enzymatic alterations brought on by 3-NP were ameliorated by the long-term administration of W. somnifera root extracts. According to biochemical analysis, systemic 3-NP administration markedly raised levels of lipid peroxidation, nitrite, and lactate dehydrogenase enzymes, decreased levels of antioxidant enzymes, and prevented ATP synthesis by preventing mitochondrial complex activity in various brain regions. There have also been reports of the GABAergic system’s role in Huntington disease aetiology. Ashwagandha is a viable option for treating Huntington disease since it works through the GABAergic system, restores acetylcholinesterase and glutathione enzyme levels, and enhances cognitive function. These findings suggest that the neuroprotective effects of W. somnifera are mediated via its antioxidant properties.^{27, 7}

Withaferin A, which is extracted from ashwagandha, has been shown to have positive benefits in mice in another study. A hallmark of several neurodegenerative illnesses, including Huntington disease, and an indication of ageing is the incapacity of cells to maintain proteostasis. To improve compromised proteostasis and slow the progression of the disease, it has been demonstrated that low doses of withaferin A treatment in the R6/2 transgenic mouse model of Huntington disease and HD150Q cells strongly activates the heat shock response (HSR) by activating heat shock factor 1 (HSF1) by thiol oxidation. ^{2, 3} Withaferin A has been demonstrated to suppress proteasomal malfunction and autophagy induction at higher levels; these actions may be connected to its impact on thiol modification within the cell. Understanding the conformational dynamics, stability, and hydrogen bond interactions of these complexes is made possible by the molecular dynamics simulations used to investigate the interaction withanolide derivatives.  Understanding the possible therapeutic effects of withanolides on biological targets such as 8DNS requires knowledge of these discoveries.²⁸

When Huntington’s disease mice were given withaferin A, their body weight decreased, their behavioural and motor abnormalities were rectified, and they lived noticeably longer. Heat shock activation decreased mutant huntingtin aggregates, and enhanced striatal function in the mouse brain were all validated by biochemical investigations. Additionally, as seen by decreased microglial activity, withaferin A dramatically decreased inflammatory processes. It has also been demonstrated that W. somnifera root extract and its component withanolide. A greatly enhances motor activity and cognitive function. This improvement has been ascribed to the effects of W. somnifera supplementation on acetylcholinesterase enzyme activity, antioxidant status restoration, and oxidative stress inhibition. Therefore, extracts from W. somnifera or their refined form, withaferin A, may be useful as a treatment for Huntington disease. ^{2, 3, 29}

Therefore, our goal in this study was to find more phytoconstituents and then use computational analysis to find assuming from W. somnifera as a potential GABA-A receptor agonist for the treatment of Huntington’s disease. This work offers a viable substitute for synthetic medications by utilising natural chemicals derived from W. somnifera, utilising historic medical expertise to promote contemporary therapeutics. These drugs’ promising clinical translation is further supported by their favourable pharmacokinetic and safety profiles, which provide a more secure and convenient solution for insomnia sufferers.

It is important to recognise several limitations even if this computational analysis offers insightful information about the potential of hygrine, tropine, and withasomnine as GABA-A receptor agonists. First off, the results are predicated on molecular simulations and in silico predictions, which require experimental verification to verify the drugs’ true safety and effectiveness. Furthermore, the study ignored possible interactions with other molecular targets implicated in the pathogenesis of insomnia by concentrating only on the GABA-A receptor. To investigate the wider pharmacological effects and methods of action of these phytochemicals, more investigation is necessary. The study has following limitations: safety and pharmacokinetic profile of phytoconstituents were not predicted.

Conclusion

Utilising natural chemicals from W. somnifera, this study offers a viable substitute for manufactured medications, utilising ancient medical expertise to promote contemporary therapeutics. W. somnifera could plays an important role in Huntington’s disease through modulation of inflammatory and apoptogenic signalling pathways as indicated by network pharmacological and molecular docking along with molecular dynamics studies. However, further experimental studies are required to confirm the safety and efficacy of various phytoconstituents of W. somnifera in Huntington’s disease.

Acknowledgement

The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number “NBU-FFR-2025-2042-04.”

Funding source

Northern Border University, Arar, KSA.

Conflict of interest

The authors declare no conflicts of interest relevant to this article.

Data availability statement

This statement does not apply to this article.

Ethics Statement

This research did not involve human participants, animal subjects, or any material that requires ethical approval.

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