Exploring the antidepressant-like effects of stigmasterol using network pharmacology with molecular docking and in vivo experimental validation: Involvement of serotonin modulation

Exploring the antidepressant-like effects of stigmasterol using network pharmacology with molecular docking and in vivo experimental validation: Involvement of serotonin modulation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exploring the antidepressant-like effects of stigmasterol using network pharmacology with molecular docking and in vivo experimental validation: Involvement of serotonin modulation Robert Peter Biney, Isaac Blessed Mensah, Akua Afriyie Karikari, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7271649/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract With a global prevalence of 3.8% and affecting approximately 300 million people, depression remains a global burden calling for renewed efforts including novel antidepressants to address treatment gaps. Stigmasterol, an unsaturated naturally abundant phytosterol with reported neuropsychiatric activity in preclinical studies, was studied for potential antidepressant-like activity and mechanism of action using computational and experimental approaches. Using network pharmacology, we identified potential key targets of major depressive disorder and stigmasterol (STG) by analysing intersection genes for protein-protein interaction, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. We subsequently carried out molecular docking of stigmasterol with key targets identified to confirm antidepressant activity and potential mechanism(s) while the forced swim test (FST) and tail suspension test (TST) were used for experimental validation. To experimentally validate the involvement of monoaminergic mechanism(s) in STG’s action, mice were pretreated with selective inhibitors of monoamine synthesis and storage after which the antidepressant-like effects of STG was re-evaluated in FST. Forty intersection target genes were obtained with AKT1, TP53 and IL1B as well as MAO B , MAO A and SLC6A4 being identified as key targets. GO and KEGG enrichment analysis further reinforced the involvement of monoamine regulation, especially serotonin. Molecular docking showed STG interacts with SLC6A4 (-7.237 kcal/mol), HTR2A (-7.685 kcal/mol) and TPH2 (-8.073 kcal/mol) further buttressing serotonergic involvement. STG demonstrated significant antidepressant-like effects similar to fluoxetine-treated mice in both FST and TST. This antidepressant action was significantly influenced by serotonin neurotransmission. Overall, we confirmed the antidepressant-like activity of STG and its possible multitarget potential in depression management. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Drug discovery Biological sciences/Neuroscience Mood disorders Neuropsychiatry Drug discovery 5-hydroxytryptamine Computational binding prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Mental health disorders are among the leading causes of the global health-related burden. Anxiety and depression disorders are the two most incapacitating mental diseases according to the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) ( 1 ). Depression affects over 300 million people globally and is the leading cause of disability worldwide ( 2 ) with significant aspects of the cellular and molecular etiology of depression largely unknown. ( 3 ). Approximately 17% of men and 25% of women experience major depressive episodes during the course of their lifetimes, and recurrence is common, with up to 60% of persons experiencing more than one major depressive episode in their lifetime ( 4 ). Despite the availability of several treatment options ( 5 ) and the strong evidence of actions to lessen the impact, no significant decline in global prevalence has been recorded since 1990 ( 1 ). Even though modern antidepressants have saved lives and helped a number of people, they are still ineffective in about one-third of patients, failing to elicit at least a partial response, and only about 30% of patients experience complete remission ( 6 , 7 ). These current antidepressants take weeks to months to achieve full effect, sometimes even increasing the risk of suicidal thoughts and actions, and morbidity during this lag period ( 8 ). In addition, over 50% of people receiving antidepressants report negative side effects such weight gain, drowsiness, sexual difficulties, emotional numbness, and withdrawal symptoms despite their effectiveness ( 9 ). The complexities of depression and inadequacies of current antidepressants require innovative approaches in the search for novel antidepressants. That requires evaluating novel antidepressants in a battery of models that assess potential effect in various hypotheses of depression ( 10 ). The “one drug–one target–one disease” model in drug discovery which simplifies the screening of compounds and reduces unwanted side effects, has shown limited efficacy, particularly in complex heterogeneous diseases like major depressive disorder ( 11 ). Thus network pharmacology, which is the field of pharmacology that integrates and coordinates intricate networks involving drug targets and diseases to facilitate a comprehensive understanding of complex pharmacological interactions ( 12 ) remains a good approach in identifying multiple targets/pathways which may be working in concert to contribute to the pathophysiology of depression and can thus facilitate the discovery of novel antidepressants that can modulate multiple targets simultaneously and enhance therapeutic efficacy. The coupling of network pharmacology with molecular docking provides additional benefit of understanding the potential mechanisms underlying action of small molecules on targets and pathways implicated in a particular disorder ( 13 ). This research thus sought to use this approach in tandem with experimental validation using in-vivo studies to evaluate the antidepressant-like activity of stigmasterol, an unsaturated phytosterol. This compound has reported anti-inflammatory ( 14 ), neuroprotective ( 15 ) and acute antidepressant-like ( 16 ) properties in rodent models. RESULTS Potential Stigmasterol Targets and Depression Targets To examine the relationship between stigmasterol and major depressive disorders, a preliminary screening was conducted to retrieve possible targets of stigmasterol. From the PharmMapper database, 203 targets were retrieved, 137 from GeneCard database and 100 from SwissTargetPrediction. A total of 394 targets were subsequently obtained after unification and removal of duplicates (Fig. 2 a). To obtain the targets for major depressive disorder; GeneCard, OMIM, DisGenet and PharmGKB databases were searched with keywords “major depressive disorders”. 201 targets were retrieved From the GeneCard (Relevance score > 30.05), 29 from OMIM, 229 from Disgenet (Score_gda > 0.4) and 79 from PhamGKB (p < 0.05). After duplicates were removed, 445 targets were obtained for major depressive disorder. 7 common target proteins were obtained; serotonin transporter (SLC6A4), serotonin receptor 2A (HTR2A), FK506 binding protein 5 (FKBP5), Methylenetetrahydrofolate reductase (MTHFR), Tryptophan hydroxylase 2 (TPH2), Catechol-O-methyltransferase (COMT) and Dopamine D3 receptor (DRD3) (Fig. 2 b). The intersection of stigmasterol and major depressive disorder targets resulted in 40 common targets (Fig. 2 c) Protein-protein interaction network and core target screening Interactions with medium confidence was chosen after the 40 intersection targets were imported into the STRING database. As seen in Fig. 2 d, the resultant PPI network had 40 nodes and 240 edges, each of which represented a unique protein-protein interaction (PPI). The average node degree, which shows how many targets are connected to the network, was found to be 12. The average local clustering coefficient, which shows how many targets are connected to the network, was 0.701. The network was imported to Cytoscape 3.10.0 and the cytohubba plugin was used to compute the degree with the top 25 hub genes presented in Fig. 2 e. Function enrichment analysis The 40 intersection targets associated with both stigmasterol and major depressive disorders were further used for gene ontology (GO) enrichment and KEGG pathway analysis using ShinyGO. Targets were analyzed under three levels, namely biological processes (BP), molecular function (MF) and cellular components (CC). Most BPs enriched were associated with propagation of stimuli and cell signalling (Fig. 3 A). For targets analysed under molecular function (MF), monoamine transmembrane transporter activity and also kinase activity were significantly highlighted (Fig. 3 B). According to the CC of GO analysis, stigmasterol targets were primarily located around neuronal cells (Fig. 3 C). Although further KEGG analysis revealed the involvement of the intersection genes in multiple cancer pathways, the analyses also show significant involvement of genes involved in disorders with dysfunction of the serotonergic synapse as the pathway relevant in depression (Fig. 3 D). MOLECULAR DOCKING Six common target genes of major depressive disorder: SLC6A4, HTR2A, TPH2, COMT, MAOA and DRD3, were selected for the molecular docking study based on the compound-target network construction, protein‒protein interactions (PPIs) and GO & KEGG enrichment analysis. These targets play crucial roles in the pathophysiology of depression, especially relating to the monoamine hypothesis. Stigmasterol showed affinity for MAOA (PDB:2Z5X), SLC6A4 (5i73), TPH2 (1MLW), HTR2A (6a93), COMT (4XUC) and DRD3 (3PBL) (Fig. 4 ). Table 1 Docking scores of Stigmasterol -Targets interactions Protein target Docking score (kcal/mol) TPH2 (1MLW) -8.073 MAOA (PDB:2Z5X) -7.767 HTR2A (6A93) -7.685 SLC6A4 (PDB: 5i73) -7.237 DRD3 (3PBL) -6.919 COMT (4XUC) -3.401 IN VIVO EXPERIMENTAL VALIDATION Antidepressant-like activity Forced swim test As shown in Fig. 5 b, a dose-dependent antidepressant-like effect is exhibited by fluoxetine-treated mice with doses at 10 and 30 mg kg − 1 showing the most significant reduction in duration of immobility ( p < 0.001). Though not significant, a marginal reduction of immobility duration was observed in mice treated with stigmasterol 1, 3 and 10 mg kg − 1 in a dose-dependent manner. At doses of 30 and 100 mg kg − 1 stigmasterol exhibited significant antidepressant-like effects (p < 0.05 and p < 0.01 respectively) (Fig. 5 a). Dose response curve indicated marginal difference in the efficacy of stigmasterol and fluoxetine (STG: EC 50 = 3.385; E max = 58.47 and FLX: EC 50 = 2.711; E max = 78.55 mg kg − 1 respectively) in reducing immobility time with fluoxetine being more potent (Fig. 5 c). Tail suspension test A dose-dependent antidepressant-like effect was exhibited by stigmasterol (Fig. 5 d). Mice treated with 100 mg kg − 1 significantly reduced duration of immobility similar to fluoxetine 1–10 mg kg − 1 (p < 0.01). Mice treated with 30 mg kg − 1 of fluoxetine showed the most significant reduction in duration of immobility ( p < 0.001) (Fig. 5 e). Dose response analyses revealed differences in the efficacy and potency between stigmasterol and fluoxetine in reducing immobility time in TST (Fig. 5 f). Stigmasterol had EC 50 = 8.847 and E max = 72.33 while fluoxetine had EC 50 = 2.334 and E max = 96.53 mg kg − 1 . Mechanisms Underlying Antidepressant-like Activity Influence of monoamines As indicated in Fig. 6 a the observed antidepressant-like effect of fluoxetine 30 mg kg − 1 and imipramine 30 mg kg − 1 in forced swim test was reversed significantly when mice were pre-treated with a single dose of vesicular monoamine transporter inhibitor, reserpine (1 mg kg − 1 ). A marginal reversal of antidepressant-like effect is observed in the stigmasterol 30 mg kg − 1 group (Fig. 6 a). Pre-treatment with the tryptophan hydroxylase inhibitor pCPA (100 mg kg − 1 i.p. for 4 days) significantly reversed ( p < 0.01) the observed antidepressant-like effects of stigmasterol and fluoxetine but not imipramine (Fig. 6 c). However, in the presence of AMPT (100 mg kg − 1 ), the observed antidepressant-like effects of stigmasterol and fluoxetine are preserved while that of imipramine were reversed (Fig. 6 b). Figure 6 e shows that antagonizing 5-HT 3 receptors using 5-HT 3 selective receptor antagonist, ondansetron, reverses the antidepressant-like effects of stigmasterol significantly but only marginally in fluoxetine-treated animals. Also, while marginal reversal of antidepressant-like effect is observed in the fluoxetine-treated mice when pretreated with pizotifen, a 5-HT 2 receptor antagonist, no such reversal was observed in the stigmasterol-treated mice. Furthermore, the antidepressant-like behaviors of stigmasterol were preserved in mice pretreated with beta 2 adrenoceptor blocker, propranolol. DISCUSSION The World Health Organization (WHO) projects that by 2030, depression will be the leading cause of disease worldwide ( 20 ). In terms of treatment, less than half of MDD patients achieve remission with currently prescribed antidepressants while one-third of MDD patients fail to respond to treatment ( 7 ). The dominant view for decades has been that depression is caused by abnormalities in monoamine neurotransmitters: serotonin, norepinephrine and dopamine, which serves as a significant rationale for the use of antidepressants ( 21 ). Despite these challenges with current treatment regimen, the development of new drugs to treat MDD has been surprisingly slow for the past few decades ( 22 ). One strategy to curb this current dearth of novel antidepressants is the pharmacological characterization of scaffold molecules that feed the pipeline of lead compounds for development as novel antidepressants with superior therapeutic property. This study thus sought to investigate the antidepressant-like potential of the plant sterol, stigmasterol, as a scaffold, and the possible mechanism(s) that may contribute to its effects. As an unsaturated phytosterol, stigmasterol is a member of the tetracyclic triterpene class. It can be found in vegetable oils ( 15 ). Pharmacologic properties such as antinociceptive, anti-inflammatory, antiarthritic, neuroprotective, antitumor and others have already been demonstrated ( 23 ). Stigmasterol is also known to modulate reactive oxygen species (ROS) levels, which adds to its neuroprotective effects ( 15 ). Previous studies by Ghosh et al., (2022) reported an acute antidepressant-like activity of stigmasterol in the tail suspension test at a single dose of 20 mg kg − 1 . Further findings suggested the antidepressant-like activity observed was via modulation of the glutamatergic system ( 16 ). While this study points to the potential antidepressant activity of stigmasterol, further gaps in knowledge needed to be filled with regards to the pharmacological properties of stigmasterol. We first used network pharmacology to further analyze the antidepressant activity of stigmasterol in a bid to accurately predict all potential mechanism(s) of action ( 24 ). Screening for common targets of stigmasterol and major depressive disorders yielded forty gene targets that undergo intricate interactions to modulate depression through multiple pathways. Top three targets based on their degree were AKT serine/threonine kinase 1 (AKT1), Tumor Protein P53 (TP53) and interleukin-1β (IL-1b). Notably, MAO A , MOA B and SLC6A4 which are also strongly implicated in serotonergic and dopaminergic signaling and significant contributors to the monoamine hypothesis of depression were identified as pivotal genes modifiable by stigmasterol based on the degree analysis ( 25 , 26 ). The pathogenesis of depression is complex and involves multiple signal transduction pathways and molecular changes ( 27 ). AKT is a serine/threonine protein kinase that plays a central role in the signaling network involving MAPK and mTOR, and which regulates multiple cellular processes including glucose metabolism, apoptosis, cell proliferation, transcription and cell migration ( 24 ). The AKT pathway is an essential signaling pathway in neurons, which is widely expressed in emotion-related regions of the brain, playing a key role in the survival, proliferation, and apoptosis of neurons ( 28 ). The involvement of AKT in neuropsychiatric disorders has been recently established showing that the depletion of AKT evokes depressant-like behavior ( 24 , 28 ). AKT mainly consists of AKT1, AKT2, and AKT3 subtypes. AKT1 is the most widely expressed ( 29 ) and its activation increases neuronal survival against hypoxic stress and nitric oxide toxicity especially in the hippocampal regions, a region highly implicated in major depressive disorder ( 30 ). Thus the finding that stigmasterol modifies the gene provides novel mechanistic insights that are required for novel antidepressants to be superior therapeutics. Additionally, accumulating evidence suggests the involvement of neuroinflammatory processes in the pathophysiology of major depressive disorder and resistance to antidepressant treatment ( 31 , 32 ). Indeed, stigmasterol has been shown to possess anti-inflammatory effects ( 14 ). This finding coupled with the fact that we found significant potential gene modulation of interleukin-1β, a key pro-inflammatory cytokine, suggest that stigmasterol will possess additional mechanisms that would enhance the therapeutic efficacy in managing the gaps in current depression treatment. IL-1b serves as one of the main mediators in the cross-talk between the central nervous system and the immune ( 33 , 34 ) and its link with MDD is underscored by meta-analyses confirming higher concentrations of IL1B in individuals with depression ( 34 ). Moreover, IL-1β levels have been associated not only with the presence of depressive symptoms but also with their severity and the degree of resistance to conventional antidepressant treatments especially in elderly patients ( 35 ). Therefore, antidepressants with multimodal action including modulation of IL-1b would be significant in the search of novel antidepressants that address current treatment gaps. To highlight the multimodal potential of stigmasterol, we observed its potential modulatory effect on SLC6A4 (Solute Carrier Family 6 Member 4), which encodes the serotonin transporter protein which regulates serotonin levels by reuptaking serotonin from the synaptic cleft ( 12 , 36 ). The serotonin transporter has received a lot of attention in MDD research, in part since it is the primary mechanism to decrease extracellular 5-HT, but also because it is the target of the most commonly used antidepressant drugs ( 37 ). The serotonin transporter is one of the most investigated parameters of the serotonergic system in depression research. The interest in this transporter stems down from antidepressants such as selective serotonin reuptake inhibitors (SSRIs) directly binding to serotonin transporter inhibiting serotonin reuptake serving as the most efficacious pharmacological treatment target for depressive disorders ( 37 , 38 ). Thus, while we find stigmasterol to be interacting with unconventional targets described above, it also interacts with known antidepressant targets like the serotonin transporter. This points to the multimodal utility of this small molecule as a great scaffold. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was employed to further analyze the 40 intersecting genes, revealing numerous pathways as well as highlighting associations with other diseases. The GO enrichment analysis revealed that cellular response to stimuli, signal transduction, nitric oxide synthase regulation, monoamine transmembrane transporter activity and tyrosine kinase activity pathway are associated with antidepressant activity of stigmasterol. Furthermore, the KEGG pathway analysis highlighted the critical role played by the serotonergic synapse within the designated network. This corroborates the network pharmacology assessment that shows significant modulation of serotonergic transporters. In summary, our findings from the network pharmacology indicated that stigmasterol may exhibit antidepressant activity through increasing neuronal survival, attenuating neuroinflammation and modulation of monoamines, especially serotonin and confirms indicates a promising, multifaceted approach leveraging stigmasterol as potential multitarget therapeutic agent. Armed by the network pharmacology results, we conducted further studies via molecular docking to explore the binding affinity and pose of stigmasterol to core intersecting targets of major depressive disorders. Stigmasterol was docked against SLC6A4, MAO A , HTR2A, COMT, DRD3 and TPH2. MAO A (monoamine oxidase A), SLC6A4 (serotonin transporter), HTR2A (serotonin receptor 2A) and TPH2 (Tryptophan hydroxylase 2) had high docking scores above − 7.0; -7.767, -7.237, -7.685 and − 8.073 kcal/mol respectively. These were relatively good affinities compared to their respective co-crystalized structures (Co-crystalized ligands; MAO A : -9.108, SLC6A4: -9.726, HTR2A: -8.593 and TPH2: -7.921) further buttressing the possible involvement of the serotonergic pathway in the antidepressant-like activity of stigmasterol. Results obtained from prior in-silico study pointed to the involvement of monoamine modulation in the antidepressant activity of stigmasterol. To confirm this in in vivo study, synthesis or storage of specific neurotransmitters were inhibited prior to drug treatment to diminish antidepressant action of drug acting via their modulation. But first, two murine behavioral despair models of depression were used to experimentally validate the antidepressant-like activity of depression as well as evaluate the dose-response of stigmasterol. The forced swim test and tail suspension test have long been used to evaluate the antidepressant-like activity of novel compounds and crude extracts due to their good predictive validity ( 39 ). Stigmasterol exhibited an antidepressant-like effect similar to fluoxetine by reducing the level of despair presented by the reduction in duration of immobility compared to saline treated group. Nevertheless, fluoxetine showed to be more potent and efficacious than stigmasterol. To confirm monoamine involvement in this observed antidepressant-like activity, pre-treatment with Reserpine, a vesicular mononine transportor (VMAT) inhibtor marginally reversed the activity of stigmasterol. Depletion of norepinephrine stores by AMPT seem to have potentiated the antidepressant-like activity of stigmasterol while depletion of serotonin stores by pCPA significantly reversed the observed antidepressant-like activity of stigmasterol in the forced swim test. Serotoninergic role in stigmasterol's antidepressant-like effects was further confirmed when ondansetron and pizotifen were used to selectively block 5HT 3 and 5HT 2 receptors respectively. Reversal of antidepressant-like activity was observed in ondansetron pretreated groups but not in pizotifen pretreated group suggesting more involvement of 5-HT receptors but Propranolol, a non-selective beta adrenoceptor antagonist, marginally reversed the observed antidepressant-like activity of stigmasterol. Similar observation was reported by Ghosh et al., (2022) using prazosin, a α1 -adrenoceptor antagonist, to assess the role of the adrenergic system in the antidepressant activity of stigmasterol. Multi-targets therapeutics are promising approaches for management of depressive disorders as it helps in overcoming significant limitations of monotherapies through modulation of multiple cellular pathways ( 40 ). Stigmasterol, from our study, is presented as a promising multitarget compound for treatment of depression, targeting neuronal survival, neuroinflammation and serotonergic modulation. METHOD NETWORK PHARMACOLOGY Screening for Stigmasterol-related Targets and Depression-associated Targets We screened for all possible biological targets of stigmasterol using online the databases GeneCard ( https://www.genecards.org/ ) , SwissTarget ( http://www.swisstargetprediction.ch/ ) and PharmMapper ( https://www.lilab-ecust.cn/pharmmapper/ ). The SMILES and SDF of stigmasterol were acquired from the PubChem database and submitted to SwissTarget and PharmMapper respectively for identification of potential targets. Depression-associated genes were obtained from the OMIM ( http://www.omim.org ) , PharmGKB ( https://www.pharmgkb.org ) , DisGeNET ( http://www.disgenet.org/ ) and GeneCards ( https://www.genecards.org/ ) databases using the term s ‘Major Depressive Disorder’ as search keyword. The UniProt database ( https://www.UniProt.org/UniProt/ ) was employed to unify gene abbreviations. The intersection targets between stigmasterol-related genes and depression-associated genes were computed and the resultant venn diagram constructed using the bioinformatics tool Venn 2.1( https://bioinfogp.cnb.csic.es/tools/venny/ ). Protein-protein interaction network construction and core target screening Analysis of protein–protein interactions (PPIs) is a crucial tool for understanding the complex involvement of proteins in various biochemical cascades. This approach aids in obtaining a comprehensive understanding of cellular architecture, biological processes, and functional modalities ( 12 ). Intersection targets for stigmasterol and depression identified via network pharmacology were uploaded into the STRING database ( https://string-db.org ) to obtain the PPI network. The species type was limited to “homo sapiens,” with the interaction confidence score threshold set to 0.4. The resulting PPI network was visualized and a network analysis was performed using Cytoscape 3.10.0 software. The topological properties of targets were analyzed using the CytoHubba tool (a plug-in of Cytoscape), in order to identify key target genes with high connectivity based on their degree of centrality (DC). Targets with higher degree indicate they might be the primary targets in the molecular mechanism. Function enrichment analysis We further examined the intersecting gene targets for Gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Gene Ontology (GO) analysis is an important method for annotating genes and determining biological properties such as cellular component (CC), molecular function (MF), and biological process (BP) ( 41 ). The Kyoto Encyclopedia of Genes and Genomes (KEGG) anaylses explores the functions of various pathways of the intersecting genes that have been identified ( 30 ). The gene ontology tool, Shiny GO ( http://bioinformatics.sdstate.edu/go/ ) was used for CC, MF, BP and KEGG pathway analysis of hub genes ( 17 – 19 ). MOLECULAR DOCKING Molecular docking was conducted using Maestro V13.5 of Schrödinger Suite 2023-1 to confirm the connection and underlying mechanism of action between candidate protein targets and stigmasterol. The crystal structures of candidate proteins were obtained from RCSB Protein Data Bank (RCSB PDB, https://www.rcsb.org/ ). Candidate protein target was selected based on the intersecting genes from the four databases for major depressive disorder targets and results obtained from the PPI analysis. The ligand, stigmasterol, was obtained by drawing the structure in ChemDraw with the CAS number (83-48-7). The structure was copied as SMILES into maestro and prepared with the Ligprep module. Docking protocol described by Abebe et al., (2024) with some modification was employed for the molecular docking. Docking scores in kcal/mol were used to analyze the binding affinity. All the analyses and procedures were performed using the Schrodinger suite of programs. IN VIVO EXPERIMENTAL VALIDATION Animals Male and female inbred Institute of Cancer Research (ICR) mice (20–30 g; 8–10 weeks old) purchased from the Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Ghana, were used for all behavioural assays. They were housed in groups of 7 in mouse housing cages with wood shaving as bedding. All experimental mice were housed in controlled conditions of 25 ± 2°C temperature and 60–80% humidity while light was alternated in 12 h light/dark cycle. Food was normal mouse pellet diet and tap water was given ad libitum . The National Institute of Health Guidelines for the Care and Use of Laboratory Animals was followed for all procedures and technique. Ethical clearance was obtained from the IRB of the Ghana Council for Scientific Research into Plant Medicine (CSIR) (RP001/CSIR-IACUC/2024). All experiments were also performed and reported in accordance with ARRIVE guidelines. Antidepressant-like activity To confirm the antidepressant potential of stigmasterol, two acute mouse models of depression were employed. Mice were randomly assigned to groups (n = 7), and received either stigmasterol (1, 3, 10, 30, 100 mg kg − 1 ) i.p or fluoxetine (1, 3, 10, 30 mg kg − 1 ) p.o or saline 10 ml kg − 1 . The mice were then subjected to the tail suspension test 30 minutes after i.p administration or 60 minutes after p.o administration. This was followed 30 minutes later by the forced swim test. The tail suspension test was done as described by Steru et al., (1985) with slight modifications. Mice were held by their tail (1 cm from the tip) on a horizontal bar raised 50 cm from the benchtop using an adhesive tape. Every mouse was individually recorded for 6 min for escape-oriented behaviour using a camera. Videos were later scored for the duration of immobility with JWatcher™. The forced swim test with slight modifications as described in Biney et al., (2016) was used. Each mouse was allowed to swim for 6 minutes in cylindrical tanks filled with water to a depth of 15 cm. The session was recorded and the duration of immobility during the last 4 minutes of the test quantified using JWatcher™. Evaluating possible mechanism(s) of antidepressant-like action of stigmasterol Influence of monoamines Results obtained from the network pharmacology and molecular docking implicated monoaminergic pathways as potential targets for stigmasterol’s biological effects. To experimentally validate the possible involvement of monoamines in the antidepressant-like effect of stigmasterol, storage and/or synthesis of monoamines was inhibited based on previous studies ( 45 , 46 ). Mice were assigned to two groups; one group pre-treated with a single dose of reserpine (1 mg kg − 1 ) and the other with 10 ml kg − 1 distilled water. Mice in both groups subsequently received either the vehicle or equipotent doses of stigmasterol (30 mg kg − 1 ), fluoxetine (30 mg kg − 1 ) or imipramine (30 mg kg − 1 ) 18 hours after reserpine pretreatment before repeating the forced swim test. Selective inhibition of 5-HT synthesis was achieved by pretreatment with p-chlorophenylalanine (pCPA), a tryptophan hydroxylase inhibitor. This paradigm was used to assess the role of 5-HT in the observed antidepressant-like effect of stigmasterol. Mice were assigned to two groups: one group receiving pCPA (100 mg kg − 1 i.p.) pre-treatment, daily for 4 consecutive days and the other, 10 ml kg − 1 normal saline for the same period. Twenty-four hours after the last pCPA pretreatment, the groups received distilled water or equipotent doses of stigmasterol (30 mg kg − 1 ) i.p, fluoxetine (30 mg kg − 1 ) or imipramine (30 mg kg − 1 ) o.s. The forced swim test was repeated one hour after stigmasterol, fluoxetine or imipramine administration Lastly, a single dose of the tyrosine hydroxylase inhibitor, α-methyl-p-tyrosine (AMPT) (100 mg kg − 1 i. p.), was used to deplete catecholamine stores to assess the influence of catecholamines on the antidepressant-like activity of stigmasterol. Mice were grouped into pre-treated and untreated groups. The pretreated group received a single dose of AMPT while the untreated group received 10 ml kg − 1 distilled water. Four hours later both groups received equipotent doses of either stigmasterol (30 mg kg − 1 ) i.p, fluoxetine (30 mg kg − 1 ) or imipramine (30 mg kg − 1 ) p.o. Behavioral assessment was repeated using the FST one hour post drug treatment. Influence on 5-HT receptors To examine the implication of 5-HT receptors, mice were either pre-treated with ondansetron (3 mg kg − 1 ) or pizotifen (10 mg kg − 1 ) p.o. or distilled water before administration of equipotent doses of stigmasterol (30 mg kg − 1 ), or fluoxetine (30 mg kg − 1 ) after which the forced swim test was repeated. Data Analysis Behavioural data collected were analysed using GraphPad Prism 9.0 (GraphPad Software, San Diego, CA, USA). Data is organized as bar graphs with individual data points showing bars that represent mean ± standard error of mean. Data was analysed for normality and when normally distributed, bar graphs are further analysed by one-way analysis of variance (ANOVA) following which post hoc tests for multiple comparisons were conducted using the Tukey’s honest significant difference (HSD) test. In pre-treatment using pharmacological antagonism assays, results were analysed by 2-way ANOVA (mixed effects model) followed by Sidak multiple comparison test with antagonist pre-treatment and drug treatment as dependent variables. Functional enrichment analyses were computed with gene ontology tool, Shiny GO. Enriched terms were selected after a threshold of p ≤ 0.05, following the false discovery rate (FDR) determined via the Benjamini–Hochberg method. In molecular docking experiments, all the analyses were performed using the Schrodinger suite of programs. CONCLUSION This study presents a novel perspective concerning the molecular mechanism of stigmasterol against depression. Further pharmacological experiments should be conducted to explore antidepressant-like activity in chronic models of depression including depression as a result of neuroinflammation as well as assaying of neurogenesis and neuronal survival. Declarations Author contributions statement Robert Peter Biney – Project conceptualization, experimental design, data collection and analyses, manuscript writing Isaac Blessed Mensah – Experimental design, data collection and analyses, manuscript writing Akua Afriyie Karikari- Project conceptualization, Experimental design, manuscript writing Aaron Opoku Antwi- Project conceptualization, Experimental design, manuscript writing Abigail Wortsi – Data collection and experimental analyses Albert Dwamena Nkansah- Data collection Gyan Nana Kyere Barnieh – Data collection Isaac Tabiri Henneh – Experimental design and data analyses All authors reviewed and approved the drafted manuscript FUNDING We received no direct fund for this project. This work was conducted with authors’ funds Author Contribution R.B.P, A.A.K and A.O.A conceptualized the project and designed the experiment with I.B.M and I.T.H. Data collection was done by R.B.P, I.B.M, A.W, A.D.N and G.N.K.B. 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Effects of IL1B single nucleotide polymorphisms on depressive and anxiety symptoms are determined by severity and type of life stress. Brain Behav Immun [Internet]. ;56:96–104. (2016). Available from: http://dx.doi.org/10.1016/j.bbi.2016.02.012 Farooq, R. K., Asghar, K., Kanwal, S. & Zulqernain, A. Role of inflammatory cytokines in depression: Focus on interleukin-1β (Review). Biomed. Rep. 6 (1), 15–20 (2017). Miozzo, R., Eaton, W. W., Joseph Bienvenu, O., Samuels, J. & Nestadt, G. The serotonin transporter gene polymorphism (SLC6A4) and risk for psychiatric morbidity and comorbidity in the Baltimore ECA follow-up study. Compr. Psychiatry . 102 , 1–19 (2020). Svensson, J. E. et al. Serotonin transporter availability increases in patients recovering from a depressive episode. Transl Psychiatry [Internet]. ;11(1). (2021). Available from: http://dx.doi.org/10.1038/s41398-021-01376-w Sanwald, S., Widenhorn-Müller, K., Schönfeldt-Lecuona, C., Montag, C. & Kiefer, M. Correction to: Factors related to age at depression onset: the role of SLC6A4 methylation, sex, exposure to stressful life events and personality in a sample of inpatients suffering from major depression (BMC Psychiatry, 21, 1, (167), (2021). 10.1186/s128 . BMC Psychiatry. 2021;21(1):1–14. Castagné, V., Moser, P., Roux, S. & Porsolt, R. D. Rodent Models of Depression: Forced Swim and Tail Suspension Behavioral Despair Tests in Rats and Mice. Curr Protoc Neurosci [Internet]. 2011 Apr 1 [cited 2025 May 6];55(1):8.10A.1-8.10A.14. Available from: /doi/pdf/10.1002/0471142301.ns0810as55 Halder, A. K., Mitra, S. & Cordeiro, M. N. D. S. Designing multi-target drugs for the treatment of major depressive disorder. Expert Opin Drug Discov [Internet]. 2023 [cited 2025 Jul 31];18(6):643–58. Available from: https://pubmed.ncbi.nlm.nih.gov/37183604/ Shaikh, S., Yadav, D. K., Bhadresha, K. & Rawal, R. M. Integrated computational screening and liquid biopsy approach to uncover the role of biomarkers for oral cancer lymph node metastasis. Sci Rep [Internet]. ;13(1):1–14. (2023). Available from: https://doi.org/10.1038/s41598-023-41348-2 Abebe, T., Hymete, A., Giday, M. & Bisrat, D. Antidepressant-Like Activity and Molecular Docking Analysis of a Sesquiterpene Lactone Isolated from the Root Bark of Ximenia americana (L.). ;2024. (2024). Steru, L., Chermat, R., Thierry, B. & Simon, P. The tail suspension test: A new method for screening antidepressants in mice. Psychopharmacology (Berl) [Internet]. 1985 Mar [cited 2025 Apr 24];85(3):367–70. Available from: https://link.springer.com/article/ 10.1007/BF00428203 Biney, R. P., Benneh, C. K., Ameyaw, E. O., Boakye-Gyasi, E. & Woode, E. Xylopia aethiopica fruit extract exhibits antidepressant-like effect via interaction with serotonergic neurotransmission in mice. J Ethnopharmacol [Internet]. ;184(December 2018):49–57. (2016). Available from: http://dx.doi.org/10.1016/j.jep.2016.02.023 O’Leary, O. F. et al. Depletion of serotonin and catecholamines block the acute behavioral response to different classes of antidepressant drugs in the mouse tail suspension test. Psychopharmacology (Berl) [Internet]. 2007 Jun [cited 2025 Jul 7];192(3):357–71. Available from: https://pubmed.ncbi.nlm.nih.gov/17318507/ Biney, R. P., Benneh, C. K., Adongo, D. W., Ameyaw, E. O. & Woode, E. Evidence of an antidepressant-like effect of xylopic acid mediated by serotonergic mechanisms. Psychopharmacol. (Berl) . 238 (8), 2105–2120 (2021). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7271649","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":502527066,"identity":"47a1aa65-9e5c-4e8e-8d24-33c463f35411","order_by":0,"name":"Robert Peter Biney","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYFACHjCZwAYiP0CEDID4AAEtCRAtjDNI0gJiMvMQo8W8vffg48ofDHl80s3HpG3b7OQZ2Ju3STD8uoNTi8yZc8mGZxIYitlkjqVJ57YlGzbwHCuTYOx7hlOLhESOmWRDAkNiG5AB1HIggQHIkGDsOYxPi/lPiJb8b9KWIC3ybwhqMWOE2sImzQi2hcdMguEHHi0855IlG9Ikitkk0owte4Aea+NJK7ZIbMCjhb334McGG5s8+RnJD2/8KLOT52c/vPHGhz+4tcB0gggWMAlOBolthHRAAPMHBPsPcVpGwSgYBaNgRAAAtVlNWcMGW5gAAAAASUVORK5CYII=","orcid":"","institution":"University of Cape Coast","correspondingAuthor":true,"prefix":"","firstName":"Robert","middleName":"Peter","lastName":"Biney","suffix":""},{"id":502527067,"identity":"bba2f5f9-4d6d-4242-abde-4de68f76616a","order_by":1,"name":"Isaac Blessed Mensah","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Isaac","middleName":"Blessed","lastName":"Mensah","suffix":""},{"id":502527068,"identity":"252784b4-b58b-4605-be9e-1df26133baa4","order_by":2,"name":"Akua Afriyie Karikari","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Akua","middleName":"Afriyie","lastName":"Karikari","suffix":""},{"id":502527069,"identity":"38d9f348-77bb-406f-be4a-a5ccbadaecf4","order_by":3,"name":"Aaron Opoku Antwi","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Aaron","middleName":"Opoku","lastName":"Antwi","suffix":""},{"id":502527070,"identity":"913fd89b-fccc-439b-918f-b282cf4842ca","order_by":4,"name":"Abigail Wortsi","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Abigail","middleName":"","lastName":"Wortsi","suffix":""},{"id":502527071,"identity":"c909ab84-49df-4b66-9513-752cc0a89be0","order_by":5,"name":"Albert Dwamena Nkansah","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Albert","middleName":"Dwamena","lastName":"Nkansah","suffix":""},{"id":502527072,"identity":"c458a453-993d-4b7e-b7a2-efe0ae50e6d1","order_by":6,"name":"Gyan Nana Kyere Barnieh","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Gyan","middleName":"Nana Kyere","lastName":"Barnieh","suffix":""},{"id":502527073,"identity":"8774eabf-b2d2-4468-94ad-cc95346fd175","order_by":7,"name":"Isaac Tabiri Henneh","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Isaac","middleName":"Tabiri","lastName":"Henneh","suffix":""}],"badges":[],"createdAt":"2025-08-01 13:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7271649/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7271649/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-27533-5","type":"published","date":"2025-12-12T15:58:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89575383,"identity":"87c24231-8803-43a0-a44b-d116ee5459c6","added_by":"auto","created_at":"2025-08-21 12:55:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":573480,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic presentation of experimental procedures\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7271649/v1/c1414efbab10904bc71aecf8.png"},{"id":89575384,"identity":"b2e4fff7-0861-4543-b26e-5339f8c613c4","added_by":"auto","created_at":"2025-08-21 12:55:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":934253,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork construction; Targets of stigmasterol (a) and major depression disorder (b), identified by network pharmacology using the databases: GenCard, OMIM, DisGenet and PharmKGB. (c) Venn diagram identifying forty intersecting targets of stigmasterol and major depression disorder. (d) Protein-protein interaction (PPI) network of 40 intersecting targets. (e) Histogram of 25 core targets based on degree of centrality. Insert: PPI of top 25 targets. The colour of the nodes represented by the circular shape varied based on the value of their degree\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7271649/v1/235fcc84666894b6700886a8.png"},{"id":89576961,"identity":"b6d93168-9b09-4ba5-9bba-74e56bc15d31","added_by":"auto","created_at":"2025-08-21 13:11:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":461635,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG pathway analysis using ShinyGO tool. A-C shows results from GO analysis. D-E shows results obtained from the KEGG pathway analysis (17–19).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7271649/v1/0538133f535660f30d5c7401.png"},{"id":89575958,"identity":"daf92cbe-39ad-4d81-ab52-762d6ba5cb45","added_by":"auto","created_at":"2025-08-21 13:03:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1106528,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking results; MAOA (a), SLC6A4 (b), TPH2 (c), HTR2A (d), COMT (e) and DRD3 (f)\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7271649/v1/149dd87615c9b0734daf56a0.png"},{"id":89575388,"identity":"fd7693cc-dc42-4c93-a4fa-c0a026f6f3d8","added_by":"auto","created_at":"2025-08-21 12:55:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":193433,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of stigmasterol and fluoxetine on the duration of immobility in mice in the forced swim test (a) and tail suspension test (b). Dose response curves comparing efficacy and potency in FST (c) and TST (f). Data are presented as mean ± SEM (n=7), * = \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** = \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01, *** =\u003cem\u003e p\u003c/em\u003e\u0026lt;0.001.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7271649/v1/961ff1fc741ccf104c32e3d5.png"},{"id":89575389,"identity":"ae310d3b-1bd6-4acf-ac5b-e6bd4b515b0b","added_by":"auto","created_at":"2025-08-21 12:55:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":127189,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of reserpine (a), AMPT (b) pCPA (c) pizotifen (d), ondansetron (e) and propranolol (d) on immobility time in STG (30 mg kg\u003csup\u003e−1\u003c/sup\u003e), FLX (30 mg kg\u003csup\u003e−1\u003c/sup\u003e) and IMI (30 mg kg\u003csup\u003e−1\u003c/sup\u003e) treatments. Results indicate mean ± SEM. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 and ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. Two-way ANOVA followed by Sidak multiple comparison test.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7271649/v1/90d4d5d6c4666ed882d9f3e9.png"},{"id":98243984,"identity":"8bb452a0-5724-4d93-930d-b2f9114a97f3","added_by":"auto","created_at":"2025-12-15 16:11:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4154214,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7271649/v1/5c3df6fc-ffa4-499a-a100-ca54ba6f7953.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the antidepressant-like effects of stigmasterol using network pharmacology with molecular docking and in vivo experimental validation: Involvement of serotonin modulation","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMental health disorders are among the leading causes of the global health-related burden. Anxiety and depression disorders are the two most incapacitating mental diseases according to the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Depression affects over 300\u0026nbsp;million people globally and is the leading cause of disability worldwide (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) with significant aspects of the cellular and molecular etiology of depression largely unknown. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Approximately 17% of men and 25% of women experience major depressive episodes during the course of their lifetimes, and recurrence is common, with up to 60% of persons experiencing more than one major depressive episode in their lifetime (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the availability of several treatment options (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) and the strong evidence of actions to lessen the impact, no significant decline in global prevalence has been recorded since 1990 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Even though modern antidepressants have saved lives and helped a number of people, they are still ineffective in about one-third of patients, failing to elicit at least a partial response, and only about 30% of patients experience complete remission (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). These current antidepressants take weeks to months to achieve full effect, sometimes even increasing the risk of suicidal thoughts and actions, and morbidity during this lag period (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In addition, over 50% of people receiving antidepressants report negative side effects such weight gain, drowsiness, sexual difficulties, emotional numbness, and withdrawal symptoms despite their effectiveness (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe complexities of depression and inadequacies of current antidepressants require innovative approaches in the search for novel antidepressants. That requires evaluating novel antidepressants in a battery of models that assess potential effect in various hypotheses of depression (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The \u0026ldquo;one drug\u0026ndash;one target\u0026ndash;one disease\u0026rdquo; model in drug discovery which simplifies the screening of compounds and reduces unwanted side effects, has shown limited efficacy, particularly in complex heterogeneous diseases like major depressive disorder (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Thus network pharmacology, which is the field of pharmacology that integrates and coordinates intricate networks involving drug targets and diseases to facilitate a comprehensive understanding of complex pharmacological interactions (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) remains a good approach in identifying multiple targets/pathways which may be working in concert to contribute to the pathophysiology of depression and can thus facilitate the discovery of novel antidepressants that can modulate multiple targets simultaneously and enhance therapeutic efficacy. The coupling of network pharmacology with molecular docking provides additional benefit of understanding the potential mechanisms underlying action of small molecules on targets and pathways implicated in a particular disorder (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis research thus sought to use this approach in tandem with experimental validation using in-vivo studies to evaluate the antidepressant-like activity of stigmasterol, an unsaturated phytosterol. This compound has reported anti-inflammatory (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), neuroprotective (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and acute antidepressant-like (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) properties in rodent models.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePotential Stigmasterol Targets and Depression Targets\u003c/h2\u003e\u003cp\u003eTo examine the relationship between stigmasterol and major depressive disorders, a preliminary screening was conducted to retrieve possible targets of stigmasterol. From the PharmMapper database, 203 targets were retrieved, 137 from GeneCard database and 100 from SwissTargetPrediction. A total of 394 targets were subsequently obtained after unification and removal of duplicates (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003eTo obtain the targets for major depressive disorder; GeneCard, OMIM, DisGenet and PharmGKB databases were searched with keywords \u0026ldquo;major depressive disorders\u0026rdquo;. 201 targets were retrieved From the GeneCard (Relevance score\u0026thinsp;\u0026gt;\u0026thinsp;30.05), 29 from OMIM, 229 from Disgenet (Score_gda\u0026thinsp;\u0026gt;\u0026thinsp;0.4) and 79 from PhamGKB (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After duplicates were removed, 445 targets were obtained for major depressive disorder. 7 common target proteins were obtained; serotonin transporter (SLC6A4), serotonin receptor 2A (HTR2A), FK506 binding protein 5 (FKBP5), Methylenetetrahydrofolate reductase (MTHFR), Tryptophan hydroxylase 2 (TPH2), Catechol-O-methyltransferase (COMT) and Dopamine D3 receptor (DRD3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eThe intersection of stigmasterol and major depressive disorder targets resulted in 40 common targets (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec)\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eProtein-protein interaction network and core target screening\u003c/h3\u003e\n\u003cp\u003eInteractions with medium confidence was chosen after the 40 intersection targets were imported into the STRING database. As seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, the resultant PPI network had 40 nodes and 240 edges, each of which represented a unique protein-protein interaction (PPI). The average node degree, which shows how many targets are connected to the network, was found to be 12. The average local clustering coefficient, which shows how many targets are connected to the network, was 0.701. The network was imported to Cytoscape 3.10.0 and the cytohubba plugin was used to compute the degree with the top 25 hub genes presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eFunction enrichment analysis\u003c/h3\u003e\n\u003cp\u003eThe 40 intersection targets associated with both stigmasterol and major depressive disorders were further used for gene ontology (GO) enrichment and KEGG pathway analysis using ShinyGO. Targets were analyzed under three levels, namely biological processes (BP), molecular function (MF) and cellular components (CC). Most BPs enriched were associated with propagation of stimuli and cell signalling (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). For targets analysed under molecular function (MF), monoamine transmembrane transporter activity and also kinase activity were significantly highlighted (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). According to the CC of GO analysis, stigmasterol targets were primarily located around neuronal cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Although further KEGG analysis revealed the involvement of the intersection genes in multiple cancer pathways, the analyses also show significant involvement of genes involved in disorders with dysfunction of the serotonergic synapse as the pathway relevant in depression (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eMOLECULAR DOCKING\u003c/h3\u003e\n\u003cp\u003eSix common target genes of major depressive disorder: SLC6A4, HTR2A, TPH2, COMT, MAOA and DRD3, were selected for the molecular docking study based on the compound-target network construction, protein‒protein interactions (PPIs) and GO \u0026amp; KEGG enrichment analysis. These targets play crucial roles in the pathophysiology of depression, especially relating to the monoamine hypothesis. Stigmasterol showed affinity for MAOA (PDB:2Z5X), SLC6A4 (5i73), TPH2 (1MLW), HTR2A (6a93), COMT (4XUC) and DRD3 (3PBL) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDocking scores of Stigmasterol -Targets interactions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProtein target\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDocking score (kcal/mol)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTPH2 (1MLW)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-8.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMAOA (PDB:2Z5X)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-7.767\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHTR2A (6A93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-7.685\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSLC6A4 (PDB: 5i73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-7.237\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDRD3 (3PBL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.919\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOMT (4XUC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-3.401\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIN VIVO\u003c/b\u003e \u003cb\u003eEXPERIMENTAL VALIDATION\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003eAntidepressant-like activity\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eForced swim test\u003c/h2\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb, a dose-dependent antidepressant-like effect is exhibited by fluoxetine-treated mice with doses at 10 and 30 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e showing the most significant reduction in duration of immobility (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Though not significant, a marginal reduction of immobility duration was observed in mice treated with stigmasterol 1, 3 and 10 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in a dose-dependent manner. At doses of 30 and 100 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e stigmasterol exhibited significant antidepressant-like effects (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Dose response curve indicated marginal difference in the efficacy of stigmasterol and fluoxetine (STG: EC\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.385; E\u003csub\u003emax\u003c/sub\u003e = 58.47 and FLX: EC\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.711; E\u003csub\u003emax\u003c/sub\u003e = 78.55 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e respectively) in reducing immobility time with fluoxetine being more potent (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eTail suspension test\u003c/h3\u003e\n\u003cp\u003eA dose-dependent antidepressant-like effect was exhibited by stigmasterol (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). Mice treated with 100 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e significantly reduced duration of immobility similar to fluoxetine 1\u0026ndash;10 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e \u003cem\u003e(p\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Mice treated with 30 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of fluoxetine showed the most significant reduction in duration of immobility (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). Dose response analyses revealed differences in the efficacy and potency between stigmasterol and fluoxetine in reducing immobility time in TST (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). Stigmasterol had EC\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;8.847 and E\u003csub\u003emax\u003c/sub\u003e = 72.33 while fluoxetine had EC\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.334 and E\u003csub\u003emax\u003c/sub\u003e = 96.53 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eMechanisms Underlying Antidepressant-like Activity\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eInfluence of monoamines\u003c/h2\u003e\u003cp\u003eAs indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea the observed antidepressant-like effect of fluoxetine 30 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and imipramine 30 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in forced swim test was reversed significantly when mice were pre-treated with a single dose of vesicular monoamine transporter inhibitor, reserpine (1 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). A marginal reversal of antidepressant-like effect is observed in the stigmasterol 30 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea).\u003c/p\u003e\u003cp\u003ePre-treatment with the tryptophan hydroxylase inhibitor pCPA (100 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e i.p. for 4 days) significantly reversed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) the observed antidepressant-like effects of stigmasterol and fluoxetine but not imipramine (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). However, in the presence of AMPT (100 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), the observed antidepressant-like effects of stigmasterol and fluoxetine are preserved while that of imipramine were reversed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee shows that antagonizing 5-HT\u003csub\u003e3\u003c/sub\u003e receptors using 5-HT\u003csub\u003e3\u003c/sub\u003e selective receptor antagonist, ondansetron, reverses the antidepressant-like effects of stigmasterol significantly but only marginally in fluoxetine-treated animals. Also, while marginal reversal of antidepressant-like effect is observed in the fluoxetine-treated mice when pretreated with pizotifen, a 5-HT\u003csub\u003e2\u003c/sub\u003e receptor antagonist, no such reversal was observed in the stigmasterol-treated mice. Furthermore, the antidepressant-like behaviors of stigmasterol were preserved in mice pretreated with beta 2 adrenoceptor blocker, propranolol.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe World Health Organization (WHO) projects that by 2030, depression will be the leading cause of disease worldwide (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In terms of treatment, less than half of MDD patients achieve remission with currently prescribed antidepressants while one-third of MDD patients fail to respond to treatment (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The dominant view for decades has been that depression is caused by abnormalities in monoamine neurotransmitters: serotonin, norepinephrine and dopamine, which serves as a significant rationale for the use of antidepressants (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Despite these challenges with current treatment regimen, the development of new drugs to treat MDD has been surprisingly slow for the past few decades (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOne strategy to curb this current dearth of novel antidepressants is the pharmacological characterization of scaffold molecules that feed the pipeline of lead compounds for development as novel antidepressants with superior therapeutic property. This study thus sought to investigate the antidepressant-like potential of the plant sterol, stigmasterol, as a scaffold, and the possible mechanism(s) that may contribute to its effects. As an unsaturated phytosterol, stigmasterol is a member of the tetracyclic triterpene class. It can be found in vegetable oils (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Pharmacologic properties such as antinociceptive, anti-inflammatory, antiarthritic, neuroprotective, antitumor and others have already been demonstrated (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Stigmasterol is also known to modulate reactive oxygen species (ROS) levels, which adds to its neuroprotective effects (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Previous studies by Ghosh et al., (2022) reported an acute antidepressant-like activity of stigmasterol in the tail suspension test at a single dose of 20 mg kg\u003csup\u003e− 1\u003c/sup\u003e. Further findings suggested the antidepressant-like activity observed was via modulation of the glutamatergic system (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). While this study points to the potential antidepressant activity of stigmasterol, further gaps in knowledge needed to be filled with regards to the pharmacological properties of stigmasterol.\u003c/p\u003e\u003cp\u003eWe first used network pharmacology to further analyze the antidepressant activity of stigmasterol in a bid to accurately predict all potential mechanism(s) of action (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Screening for common targets of stigmasterol and major depressive disorders yielded forty gene targets that undergo intricate interactions to modulate depression through multiple pathways. Top three targets based on their degree were AKT serine/threonine kinase 1 (AKT1), Tumor Protein P53 (TP53) and interleukin-1β (IL-1b). Notably, MAO\u003csub\u003eA\u003c/sub\u003e, MOA\u003csub\u003eB\u003c/sub\u003e and SLC6A4 which are also strongly implicated in serotonergic and dopaminergic signaling and significant contributors to the monoamine hypothesis of depression were identified as pivotal genes modifiable by stigmasterol based on the degree analysis (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe pathogenesis of depression is complex and involves multiple signal transduction pathways and molecular changes (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). AKT is a serine/threonine protein kinase that plays a central role in the signaling network involving MAPK and mTOR, and which regulates multiple cellular processes including glucose metabolism, apoptosis, cell proliferation, transcription and cell migration (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The AKT pathway is an essential signaling pathway in neurons, which is widely expressed in emotion-related regions of the brain, playing a key role in the survival, proliferation, and apoptosis of neurons (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The involvement of AKT in neuropsychiatric disorders has been recently established showing that the depletion of AKT evokes depressant-like behavior (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). AKT mainly consists of AKT1, AKT2, and AKT3 subtypes. AKT1 is the most widely expressed (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) and its activation increases neuronal survival against hypoxic stress and nitric oxide toxicity especially in the hippocampal regions, a region highly implicated in major depressive disorder (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Thus the finding that stigmasterol modifies the gene provides novel mechanistic insights that are required for novel antidepressants to be superior therapeutics.\u003c/p\u003e\u003cp\u003eAdditionally, accumulating evidence suggests the involvement of neuroinflammatory processes in the pathophysiology of major depressive disorder and resistance to antidepressant treatment (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Indeed, stigmasterol has been shown to possess anti-inflammatory effects (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This finding coupled with the fact that we found significant potential gene modulation of interleukin-1β, a key pro-inflammatory cytokine, suggest that stigmasterol will possess additional mechanisms that would enhance the therapeutic efficacy in managing the gaps in current depression treatment. IL-1b serves as one of the main mediators in the cross-talk between the central nervous system and the immune (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) and its link with MDD is underscored by meta-analyses confirming higher concentrations of IL1B in individuals with depression (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Moreover, IL-1β levels have been associated not only with the presence of depressive symptoms but also with their severity and the degree of resistance to conventional antidepressant treatments especially in elderly patients (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Therefore, antidepressants with multimodal action including modulation of IL-1b would be significant in the search of novel antidepressants that address current treatment gaps.\u003c/p\u003e\u003cp\u003eTo highlight the multimodal potential of stigmasterol, we observed its potential modulatory effect on SLC6A4 (Solute Carrier Family 6 Member 4), which encodes the serotonin transporter protein which regulates serotonin levels by reuptaking serotonin from the synaptic cleft (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The serotonin transporter has received a lot of attention in MDD research, in part since it is the primary mechanism to decrease extracellular 5-HT, but also because it is the target of the most commonly used antidepressant drugs (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). The serotonin transporter is one of the most investigated parameters of the serotonergic system in depression research. The interest in this transporter stems down from antidepressants such as selective serotonin reuptake inhibitors (SSRIs) directly binding to serotonin transporter inhibiting serotonin reuptake serving as the most efficacious pharmacological treatment target for depressive disorders (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Thus, while we find stigmasterol to be interacting with unconventional targets described above, it also interacts with known antidepressant targets like the serotonin transporter. This points to the multimodal utility of this small molecule as a great scaffold.\u003c/p\u003e\u003cp\u003eGene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was employed to further analyze the 40 intersecting genes, revealing numerous pathways as well as highlighting associations with other diseases. The GO enrichment analysis revealed that cellular response to stimuli, signal transduction, nitric oxide synthase regulation, monoamine transmembrane transporter activity and tyrosine kinase activity pathway are associated with antidepressant activity of stigmasterol. Furthermore, the KEGG pathway analysis highlighted the critical role played by the serotonergic synapse within the designated network. This corroborates the network pharmacology assessment that shows significant modulation of serotonergic transporters. In summary, our findings from the network pharmacology indicated that stigmasterol may exhibit antidepressant activity through increasing neuronal survival, attenuating neuroinflammation and modulation of monoamines, especially serotonin and confirms indicates a promising, multifaceted approach leveraging stigmasterol as potential multitarget therapeutic agent.\u003c/p\u003e\u003cp\u003eArmed by the network pharmacology results, we conducted further studies via molecular docking to explore the binding affinity and pose of stigmasterol to core intersecting targets of major depressive disorders. Stigmasterol was docked against SLC6A4, MAO\u003csub\u003eA\u003c/sub\u003e, HTR2A, COMT, DRD3 and TPH2. MAO\u003csub\u003eA\u003c/sub\u003e (monoamine oxidase A), SLC6A4 (serotonin transporter), HTR2A (serotonin receptor 2A) and TPH2 (Tryptophan hydroxylase 2) had high docking scores above − 7.0; -7.767, -7.237, -7.685 and − 8.073 kcal/mol respectively. These were relatively good affinities compared to their respective co-crystalized structures (Co-crystalized ligands; MAO\u003csub\u003eA\u003c/sub\u003e: -9.108, SLC6A4: -9.726, HTR2A: -8.593 and TPH2: -7.921) further buttressing the possible involvement of the serotonergic pathway in the antidepressant-like activity of stigmasterol.\u003c/p\u003e\u003cp\u003eResults obtained from prior in-silico study pointed to the involvement of monoamine modulation in the antidepressant activity of stigmasterol. To confirm this in in vivo study, synthesis or storage of specific neurotransmitters were inhibited prior to drug treatment to diminish antidepressant action of drug acting via their modulation. But first, two murine behavioral despair models of depression were used to experimentally validate the antidepressant-like activity of depression as well as evaluate the dose-response of stigmasterol. The forced swim test and tail suspension test have long been used to evaluate the antidepressant-like activity of novel compounds and crude extracts due to their good predictive validity (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Stigmasterol exhibited an antidepressant-like effect similar to fluoxetine by reducing the level of despair presented by the reduction in duration of immobility compared to saline treated group. Nevertheless, fluoxetine showed to be more potent and efficacious than stigmasterol.\u003c/p\u003e\u003cp\u003eTo confirm monoamine involvement in this observed antidepressant-like activity, pre-treatment with Reserpine, a vesicular mononine transportor (VMAT) inhibtor marginally reversed the activity of stigmasterol. Depletion of norepinephrine stores by AMPT seem to have potentiated the antidepressant-like activity of stigmasterol while depletion of serotonin stores by pCPA significantly reversed the observed antidepressant-like activity of stigmasterol in the forced swim test. Serotoninergic role in stigmasterol's antidepressant-like effects was further confirmed when ondansetron and pizotifen were used to selectively block 5HT\u003csub\u003e3\u003c/sub\u003e and 5HT\u003csub\u003e2\u003c/sub\u003e receptors respectively. Reversal of antidepressant-like activity was observed in ondansetron pretreated groups but not in pizotifen pretreated group suggesting more involvement of 5-HT receptors but Propranolol, a non-selective beta adrenoceptor antagonist, marginally reversed the observed antidepressant-like activity of stigmasterol. Similar observation was reported by Ghosh et al., (2022) using prazosin, a α1 -adrenoceptor antagonist, to assess the role of the adrenergic system in the antidepressant activity of stigmasterol.\u003c/p\u003e\u003cp\u003eMulti-targets therapeutics are promising approaches for management of depressive disorders as it helps in overcoming significant limitations of monotherapies through modulation of multiple cellular pathways (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Stigmasterol, from our study, is presented as a promising multitarget compound for treatment of depression, targeting neuronal survival, neuroinflammation and serotonergic modulation.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003cdiv id=\"Sec15\" class=\"Section4\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"METHOD","content":"\u003ch2\u003eNETWORK PHARMACOLOGY\u003c/h2\u003e\u003ch2\u003eScreening for Stigmasterol-related Targets and Depression-associated Targets\u003c/h2\u003e\u003cp\u003eWe screened for all possible biological targets of stigmasterol using online the databases GeneCard (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, SwissTarget (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swisstargetprediction.ch/\u003c/span\u003e\u003cspan address=\"http://www.swisstargetprediction.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e and PharmMapper (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.lilab-ecust.cn/pharmmapper/\u003c/span\u003e\u003cspan address=\"https://www.lilab-ecust.cn/pharmmapper/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The SMILES and SDF of stigmasterol were acquired from the PubChem database and submitted to SwissTarget and PharmMapper respectively for identification of potential targets.\u003c/p\u003e\u003cp\u003eDepression-associated genes were obtained from the OMIM (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.omim.org\u003c/span\u003e\u003cspan address=\"http://www.omim.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, PharmGKB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pharmgkb.org\u003c/span\u003e\u003cspan address=\"https://www.pharmgkb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, DisGeNET (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.disgenet.org/\u003c/span\u003e\u003cspan address=\"http://www.disgenet.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e and GeneCards (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e databases using the term s ‘Major Depressive Disorder’ as search keyword. The UniProt database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.UniProt.org/UniProt/\u003c/span\u003e\u003cspan address=\"https://www.UniProt.org/UniProt/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e was employed to unify gene abbreviations.\u003c/p\u003e\u003cp\u003eThe intersection targets between stigmasterol-related genes and depression-associated genes were computed and the resultant venn diagram constructed using the bioinformatics tool Venn 2.1(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfogp.cnb.csic.es/tools/venny/\u003c/span\u003e\u003cspan address=\"https://bioinfogp.cnb.csic.es/tools/venny/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e\u003ch2\u003eProtein-protein interaction network construction and core target screening\u003c/h2\u003e\u003cp\u003eAnalysis of protein–protein interactions (PPIs) is a crucial tool for understanding the complex involvement of proteins in various biochemical cascades. This approach aids in obtaining a comprehensive understanding of cellular architecture, biological processes, and functional modalities (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Intersection targets for stigmasterol and depression identified via network pharmacology were uploaded into the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org\u003c/span\u003e\u003cspan address=\"https://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e to obtain the PPI network. The species type was limited to “homo sapiens,” with the interaction confidence score threshold set to 0.4.\u003c/p\u003e\u003cp\u003eThe resulting PPI network was visualized and a network analysis was performed using Cytoscape 3.10.0 software. The topological properties of targets were analyzed using the CytoHubba tool (a plug-in of Cytoscape), in order to identify key target genes with high connectivity based on their degree of centrality (DC). Targets with higher degree indicate they might be the primary targets in the molecular mechanism.\u003c/p\u003e\u003ch2\u003eFunction enrichment analysis\u003c/h2\u003e\u003cp\u003eWe further examined the intersecting gene targets for Gene ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Gene Ontology (GO) analysis is an important method for annotating genes and determining biological properties such as cellular component (CC), molecular function (MF), and biological process (BP) (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). The Kyoto Encyclopedia of Genes and Genomes (KEGG) anaylses explores the functions of various pathways of the intersecting genes that have been identified (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The gene ontology tool, Shiny GO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.sdstate.edu/go/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.sdstate.edu/go/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e was used for CC, MF, BP and KEGG pathway analysis of hub genes (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eMOLECULAR DOCKING\u003c/h2\u003e\u003cp\u003eMolecular docking was conducted using Maestro V13.5 of Schrödinger Suite 2023-1 to confirm the connection and underlying mechanism of action between candidate protein targets and stigmasterol. The crystal structures of candidate proteins were obtained from RCSB Protein Data Bank (RCSB PDB, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e Candidate protein target was selected based on the intersecting genes from the four databases for major depressive disorder targets and results obtained from the PPI analysis. The ligand, stigmasterol, was obtained by drawing the structure in ChemDraw with the CAS number (83-48-7). The structure was copied as SMILES into maestro and prepared with the Ligprep module. Docking protocol described by Abebe et al., (2024) with some modification was employed for the molecular docking. Docking scores in kcal/mol were used to analyze the binding affinity. All the analyses and procedures were performed using the Schrodinger suite of programs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eIN VIVO\u003c/b\u003e \u003cb\u003eEXPERIMENTAL VALIDATION\u003c/b\u003e\u003c/p\u003e\u003ch2\u003eAnimals\u003c/h2\u003e\u003cp\u003eMale and female inbred Institute of Cancer Research (ICR) mice (20–30 g; 8–10 weeks old) purchased from the Noguchi Memorial Institute for Medical Research, University of Ghana, Legon, Ghana, were used for all behavioural assays. They were housed in groups of 7 in mouse housing cages with wood shaving as bedding. All experimental mice were housed in controlled conditions of 25 ± 2°C temperature and 60–80% humidity while light was alternated in 12 h light/dark cycle. Food was normal mouse pellet diet and tap water was given \u003cem\u003ead libitum\u003c/em\u003e. The National Institute of Health Guidelines for the Care and Use of Laboratory Animals was followed for all procedures and technique. Ethical clearance was obtained from the IRB of the Ghana Council for Scientific Research into Plant Medicine (CSIR) (RP001/CSIR-IACUC/2024). All experiments were also performed and reported in accordance with ARRIVE guidelines.\u003c/p\u003e\u003ch2\u003eAntidepressant-like activity\u003c/h2\u003e\u003cp\u003eTo confirm the antidepressant potential of stigmasterol, two acute mouse models of depression were employed. Mice were randomly assigned to groups (n = 7), and received either stigmasterol (1, 3, 10, 30, 100 mg kg\u003csup\u003e− 1\u003c/sup\u003e) i.p or fluoxetine (1, 3, 10, 30 mg kg\u003csup\u003e− 1\u003c/sup\u003e) p.o or saline 10 ml kg\u003csup\u003e− 1\u003c/sup\u003e. The mice were then subjected to the tail suspension test 30 minutes after i.p administration or 60 minutes after p.o administration. This was followed 30 minutes later by the forced swim test.\u003c/p\u003e\u003cp\u003eThe tail suspension test was done as described by Steru et al., (1985) with slight modifications. Mice were held by their tail (1 cm from the tip) on a horizontal bar raised 50 cm from the benchtop using an adhesive tape. Every mouse was individually recorded for 6 min for escape-oriented behaviour using a camera. Videos were later scored for the duration of immobility with JWatcher™.\u003c/p\u003e\u003cp\u003eThe forced swim test with slight modifications as described in Biney et al., (2016) was used. Each mouse was allowed to swim for 6 minutes in cylindrical tanks filled with water to a depth of 15 cm. The session was recorded and the duration of immobility during the last 4 minutes of the test quantified using JWatcher™.\u003c/p\u003e\u003ch2\u003eEvaluating possible mechanism(s) of antidepressant-like action of stigmasterol\u003c/h2\u003e\u003ch2\u003eInfluence of monoamines\u003c/h2\u003e\u003cp\u003eResults obtained from the network pharmacology and molecular docking implicated monoaminergic pathways as potential targets for stigmasterol’s biological effects. To experimentally validate the possible involvement of monoamines in the antidepressant-like effect of stigmasterol, storage and/or synthesis of monoamines was inhibited based on previous studies (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Mice were assigned to two groups; one group pre-treated with a single dose of reserpine (1 mg kg\u003csup\u003e− 1\u003c/sup\u003e) and the other with 10 ml kg\u003csup\u003e− 1\u003c/sup\u003e distilled water. Mice in both groups subsequently received either the vehicle or equipotent doses of stigmasterol (30 mg kg\u003csup\u003e− 1\u003c/sup\u003e), fluoxetine (30 mg kg\u003csup\u003e− 1\u003c/sup\u003e) or imipramine (30 mg kg\u003csup\u003e− 1\u003c/sup\u003e) 18 hours after reserpine pretreatment before repeating the forced swim test.\u003c/p\u003e\u003cp\u003eSelective inhibition of 5-HT synthesis was achieved by pretreatment with p-chlorophenylalanine (pCPA), a tryptophan hydroxylase inhibitor. This paradigm was used to assess the role of 5-HT in the observed antidepressant-like effect of stigmasterol. Mice were assigned to two groups: one group receiving pCPA (100 mg kg\u003csup\u003e− 1\u003c/sup\u003e i.p.) pre-treatment, daily for 4 consecutive days and the other, 10 ml kg\u003csup\u003e− 1\u003c/sup\u003e normal saline for the same period. Twenty-four hours after the last pCPA pretreatment, the groups received distilled water or equipotent doses of stigmasterol (30 mg kg\u003csup\u003e− 1\u003c/sup\u003e) i.p, fluoxetine (30 mg kg\u003csup\u003e− 1\u003c/sup\u003e) or imipramine (30 mg kg\u003csup\u003e− 1\u003c/sup\u003e) o.s. The forced swim test was repeated one hour after stigmasterol, fluoxetine or imipramine administration\u003c/p\u003e\u003cp\u003eLastly, a single dose of the tyrosine hydroxylase inhibitor, α-methyl-p-tyrosine (AMPT) (100 mg kg\u003csup\u003e− 1\u003c/sup\u003e i. p.), was used to deplete catecholamine stores to assess the influence of catecholamines on the antidepressant-like activity of stigmasterol. Mice were grouped into pre-treated and untreated groups. The pretreated group received a single dose of AMPT while the untreated group received 10 ml kg\u003csup\u003e− 1\u003c/sup\u003e distilled water. Four hours later both groups received equipotent doses of either stigmasterol (30 mg kg\u003csup\u003e− 1\u003c/sup\u003e) i.p, fluoxetine (30 mg kg\u003csup\u003e− 1\u003c/sup\u003e) or imipramine (30 mg kg\u003csup\u003e− 1\u003c/sup\u003e) p.o. Behavioral assessment was repeated using the FST one hour post drug treatment.\u003c/p\u003e\u003ch2\u003eInfluence on 5-HT receptors\u003c/h2\u003e\u003cp\u003eTo examine the implication of 5-HT receptors, mice were either pre-treated with ondansetron (3 mg kg\u003csup\u003e− 1\u003c/sup\u003e) or pizotifen (10 mg kg\u003csup\u003e− 1\u003c/sup\u003e) p.o. or distilled water before administration of equipotent doses of stigmasterol (30 mg kg\u003csup\u003e− 1\u003c/sup\u003e), or fluoxetine (30 mg kg\u003csup\u003e− 1\u003c/sup\u003e) after which the forced swim test was repeated.\u003c/p\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eBehavioural data collected were analysed using GraphPad Prism 9.0 (GraphPad Software, San Diego, CA, USA). Data is organized as bar graphs with individual data points showing bars that represent mean ± standard error of mean. Data was analysed for normality and when normally distributed, bar graphs are further analysed by one-way analysis of variance (ANOVA) following which \u003cem\u003epost hoc\u003c/em\u003e tests for multiple comparisons were conducted using the Tukey’s honest significant difference (HSD) test. In pre-treatment using pharmacological antagonism assays, results were analysed by 2-way ANOVA (mixed effects model) followed by Sidak multiple comparison test with antagonist pre-treatment and drug treatment as dependent variables.\u003c/p\u003e\u003cp\u003eFunctional enrichment analyses were computed with gene ontology tool, Shiny GO. Enriched terms were selected after a threshold of p ≤ 0.05, following the false discovery rate (FDR) determined via the Benjamini–Hochberg method. In molecular docking experiments, all the analyses were performed using the Schrodinger suite of programs.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study presents a novel perspective concerning the molecular mechanism of stigmasterol against depression. Further pharmacological experiments should be conducted to explore antidepressant-like activity in chronic models of depression including depression as a result of neuroinflammation as well as assaying of neurogenesis and neuronal survival.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eAuthor contributions statement\u003c/h2\u003e\u003cp\u003eRobert Peter Biney \u0026ndash; Project conceptualization, experimental design, data collection and analyses, manuscript writing\u003c/p\u003e\u003cp\u003eIsaac Blessed Mensah \u0026ndash; Experimental design, data collection and analyses, manuscript writing\u003c/p\u003e\u003cp\u003eAkua Afriyie Karikari- Project conceptualization, Experimental design, manuscript writing\u003c/p\u003e\u003cp\u003eAaron Opoku Antwi- Project conceptualization, Experimental design, manuscript writing\u003c/p\u003e\u003cp\u003eAbigail Wortsi \u0026ndash; Data collection and experimental analyses\u003c/p\u003e\u003cp\u003eAlbert Dwamena Nkansah- Data collection\u003c/p\u003e\u003cp\u003eGyan Nana Kyere Barnieh \u0026ndash; Data collection\u003c/p\u003e\u003cp\u003eIsaac Tabiri Henneh \u0026ndash; Experimental design and data analyses\u003c/p\u003e\u003cp\u003eAll authors reviewed and approved the drafted manuscript\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFUNDING\u003c/h2\u003e\u003cp\u003eWe received no direct fund for this project. This work was conducted with authors\u0026rsquo; funds\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.B.P, A.A.K and A.O.A conceptualized the project and designed the experiment with I.B.M and I.T.H. Data collection was done by R.B.P, I.B.M, A.W, A.D.N and G.N.K.B. Data collected was analysed by R.B.P, I.B.M and I.T.H. The main manuscript was written by R.B.P and I.B.M. All authors reviewed manuscript\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data is provided within the manuscript\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSantomauro, D. F. et al. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e398\u003c/b\u003e (10312), 1700\u0026ndash;1712 (2021).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu, Q. et al. 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(Berl)\u003c/em\u003e. \u003cb\u003e238\u003c/b\u003e (8), 2105\u0026ndash;2120 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mood disorders, Neuropsychiatry, Drug discovery, 5-hydroxytryptamine, Computational binding prediction","lastPublishedDoi":"10.21203/rs.3.rs-7271649/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7271649/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith a global prevalence of 3.8% and affecting approximately 300 million people, depression remains a global burden calling for renewed efforts including novel antidepressants to address treatment gaps. Stigmasterol, an unsaturated naturally abundant phytosterol with reported neuropsychiatric activity in preclinical studies, was studied for potential antidepressant-like activity and mechanism of action using computational and experimental approaches. Using network pharmacology, we identified potential key targets of major depressive disorder and stigmasterol (STG) by analysing intersection genes for protein-protein interaction, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. We subsequently carried out molecular docking of stigmasterol with key targets identified to confirm antidepressant activity and potential mechanism(s) while the forced swim test (FST) and tail suspension test (TST) were used for experimental validation. To experimentally validate the involvement of monoaminergic mechanism(s) in STG’s action, mice were pretreated with selective inhibitors of monoamine synthesis and storage after which the antidepressant-like effects of STG was re-evaluated in FST. Forty intersection target genes were obtained with AKT1, TP53 and IL1B as well as MAO\u003csub\u003eB\u003c/sub\u003e, MAO\u003csub\u003eA\u003c/sub\u003e and SLC6A4 being identified as key targets. GO and KEGG enrichment analysis further reinforced the involvement of monoamine regulation, especially serotonin. Molecular docking showed STG interacts with SLC6A4 (-7.237 kcal/mol), HTR2A (-7.685 kcal/mol) and TPH2 (-8.073 kcal/mol) further buttressing serotonergic involvement. STG demonstrated significant antidepressant-like effects similar to fluoxetine-treated mice in both FST and TST. This antidepressant action was significantly influenced by serotonin neurotransmission. Overall, we confirmed the antidepressant-like activity of STG and its possible multitarget potential in depression management.\u003c/p\u003e","manuscriptTitle":"Exploring the antidepressant-like effects of stigmasterol using network pharmacology with molecular docking and in vivo experimental validation: Involvement of serotonin modulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 12:55:38","doi":"10.21203/rs.3.rs-7271649/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-11T05:53:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-04T08:02:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-03T13:59:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44898882807640387368617748889450659553","date":"2025-08-19T11:17:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"152968789179307445735099347606592292043","date":"2025-08-16T09:07:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-13T18:42:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-13T18:37:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-11T13:16:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-06T17:15:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-06T17:12:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3d8de0c9-c04b-4de0-a9c4-a44d414d5ad7","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53381705,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":53381707,"name":"Health sciences/Diseases"},{"id":53381710,"name":"Biological sciences/Drug discovery"},{"id":53381711,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2025-12-15T16:04:35+00:00","versionOfRecord":{"articleIdentity":"rs-7271649","link":"https://doi.org/10.1038/s41598-025-27533-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-12-12 15:58:52","publishedOnDateReadable":"December 12th, 2025"},"versionCreatedAt":"2025-08-21 12:55:38","video":"","vorDoi":"10.1038/s41598-025-27533-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-27533-5","workflowStages":[]},"version":"v1","identity":"rs-7271649","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7271649","identity":"rs-7271649","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}