{"paper_id":"41e57c56-d3d1-467a-ac49-e57cb767c2a8","body_text":"Unraveling the Pharmacological Mechanisms of Scutellaria barbata in Parkinson's Disease: An Integrated Bioinformatics and Experimental Validation Study | 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 Research Article Unraveling the Pharmacological Mechanisms of Scutellaria barbata in Parkinson's Disease: An Integrated Bioinformatics and Experimental Validation Study Ao Sun, Yufei Li, Miao Yang, Hongxia Wang, Linlin Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9189068/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Parkinson's disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons and neuroinflammation. Scutellaria barbata D.Don (SB), a traditional Chinese medicine, exhibits significant anti-inflammatory and neuroprotective properties; however, its molecular mechanisms underlying PD treatment remain poorly understood. This study aims to elucidate the potential therapeutic targets of SB in treating PD through an integrated approach of bioinformatics and experimental validation. Active ingredients and targets of SB were retrieved from TCMSP and SwissTargetPrediction, while PD-related targets were identified from TTD, GeneCards, and GEO datasets (GSE106608 and GSE160299). Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning algorithms (Random Forest, SVM, and LASSO) were employed to screen for core genes. We identified 26 active ingredients and 230 targets, which were primarily enriched in oxidative stress, inflammatory pathways, and apoptosis. Machine learning pinpointed core brain-related targets ( SLC6A3, CDK2 ) and peripheral inflammation-related targets ( BAX, HMOX1 ). Molecular docking confirmed high binding affinities between these core targets and key ingredients, such as wogonin and stigmasterol. Finally, in vitro experiments demonstrated that these active ingredients significantly improved the viability of MPP⁺-induced SH-SY5Y cells and reduced TNF-α and IL-6 secretion in LPS-stimulated THP-1 cells. In conclusion, SB exerts anti-PD effects through a multi-component and multi-target mechanism involving the synergistic regulation of neuroinflammation and apoptosis, providing a robust theoretical basis for its clinical application. Scutellaria barbata Parkinson's disease network pharmacology WGCNA machine learning neuroinflammation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Parkinson's disease (PD) is the second most common neurodegenerative disorder globally. It is pathologically characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the intracellular accumulation of Lewy bodies, which clinically manifest as motor symptoms including resting tremor, muscle rigidity, and bradykinesia [ 1 ]. However, mounting evidence suggests that PD is essentially a multisystemic disorder rather than being confined solely to the central nervous system [ 2 ]. Peripheral immune dysregulation, metabolic abnormalities, and a surge of inflammatory cytokines in the bloodstream often precede or accompany central symptoms [ 3 ], indicating that the \"brain-periphery axis\" plays a crucial driving and reflecting role in disease progression [ 4 ]. Driven by the global aging population, the incidence of PD is rising significantly, imposing a heavy economic burden on society and families [ 5 ]. Currently, mainstream therapeutic strategies, such as levodopa, primarily focus on replenishing central neurotransmitters. These treatments merely alleviate symptoms without reversing the disease course, and they largely neglect the modulation of the peripheral systemic environment [ 6 ]. Therefore, there is an urgent need to discover novel therapeutic agents capable of delaying neuronal degeneration and exerting comprehensive neuroprotective effects. Accumulating evidence highlights that neuroinflammation and oxidative stress occupy central roles in the pathogenesis of PD[ 7 ]. The overactivation of microglia leads to the excessive release of pro-inflammatory cytokines, such as TNF-α and IL-6, which subsequently induce neuronal apoptosis [ 8 ]. Consequently, targeting neuroinflammatory signaling pathways has emerged as a highly promising strategy for PD intervention. Scutellaria barbata D.Don (SB) is a widely used traditional Chinese medicine (TCM) recognized for its efficacy in clearing heat, detoxifying, dispersing blood stasis, and relieving pain. Modern pharmacological studies have demonstrated its significant anti-tumor, anti-inflammatory, and antioxidant activities [ 9 ]. Notably, its active flavonoid constituents (e.g., wogonin and apigenin) have exhibited strong neuroprotective potential in neurological disease models by suppressing neuroinflammation and attenuating oxidative damage [ 10 ]. However, owing to the \"multi-component, multi-target, and multi-pathway\" paradigm characteristic of TCM, the precise pharmacodynamic material basis and molecular regulatory networks of SB in treating PD remain incompletely understood. Specifically, whether SB exerts its anti-PD efficacy through a \"central-peripheral synergy\" mechanism across tissues requires systematic elucidation. To bridge this gap, the present study abandons the traditional approach of focusing solely on single-brain-region data and innovatively integrates \"central-peripheral\" dual-source transcriptomic datasets. We selected the subthalamic nucleus (STN) dataset (GSE106608) to represent the critical node in the central pathological circuit of PD, alongside the plasma transcriptomic dataset (GSE160299) to represent the peripheral systemic state. By combining network pharmacology, weighted gene co-expression network analysis (WGCNA), and multiple machine learning algorithms (e.g., Support Vector Machine [SVM], Random Forest, and LASSO), we aimed to systematically screen for potential active compounds in SB that can cross the blood-brain barrier (BBB) to act directly on neural circuits while simultaneously modulating peripheral immune signaling. Network pharmacology emphasizes evaluating the complex interactions between drugs and diseases from a holistic perspective, making it highly suitable for deciphering the synergistic mechanisms of TCM [ 11 ]. Furthermore, with the rapid advancement of bioinformatics, WGCNA and machine learning algorithms have been widely utilized for the precise identification of key biomarkers highly correlated with disease phenotypes from high-dimensional gene expression data [ 12 , 13 ]. In summary, this study aims to systematically screen the potential core targets and signaling pathways of SB against PD by integrating network pharmacology, transcriptomic data analysis (GEO), WGCNA, and machine learning. Furthermore, we employ molecular docking technology and in vitro cellular experiments—specifically, an MPP⁺-induced SH-SY5Y cell injury model and an LPS-induced THP-1 cellular inflammation model—to validate the reliability of our computational predictions. Ultimately, this research is expected to provide novel scientific evidence and mechanistic insights for the clinical development of SB as a therapeutic agent for PD. 2. Materials and Methods 2.1. Acquisition of Active Compounds and Potential Targets of SB The active ingredients of SB and their corresponding targets were retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Furthermore, target prediction databases, including SwissTargetPrediction and PharmaMapper, were utilized to predict the potential targets of these active compounds. Together, these constituted the preliminary drug-target dataset. 2.2. Construction of the Disease-Related Target Database Using \"Parkinson’s disease\" as the search keyword, disease-associated targets were queried and screened across databases such as the Therapeutic Target Database (TTD) and GeneCards. After removing duplicate entries and integrating the results, a comprehensive PD-related target dataset was constructed. This dataset served as the foundation for identifying the potential therapeutic targets of SB's active compounds against PD. 2.3. Acquisition of Spatial Transcriptomic Data and \"Central-Peripheral\" Differential Expression Analysis To construct a cross-tissue \"brain-blood\" map that comprehensively reflects the pathological features of PD, we strategically selected two independent datasets from the Gene Expression Omnibus (GEO) database, representing the central pathological microenvironment and the peripheral circulatory system. First, to represent the \"central pathological microenvironment,\" we acquired the GSE106608 dataset (based on the GPL16791 platform, Illumina HiSeq 2500). This dataset contains gene expression profiles of the post-mortem STN from PD patients and normal controls. Given that the STN is a critical downstream nucleus in the basal ganglia motor circuit most severely affected by dopamine depletion, its transcriptomic characteristics accurately reflect the neural circuit dysfunction in PD. Second, to represent the \"peripheral systemic microenvironment,\" we obtained the GSE160299 dataset (based on the GPL20301 platform, Illumina HiSeq 4000), which encompasses RNA sequencing data of plasma from PD patients and healthy controls. The plasma transcriptome harbors abundant immune factors and metabolic signals, effectively characterizing the systemic inflammatory status and peripheral alterations in PD patients. During the data processing phase, raw sequencing data were standardized and quality-controlled using the GEO2R analysis tool and R packages (e.g., Seurat 4.3.0). By setting strict thresholds (|log2FC| > 1.0, P-value < 0.05), we identified \"central nervous system injury-related genes\" in the STN tissue and \"peripheral system disorder-related genes\" in the plasma, laying the groundwork for deciphering the cross-tissue synergistic mechanisms of SB. 2.4. Enrichment Analysis of Potential Therapeutic Targets of SB against PD The intersection of the three aforementioned datasets (active compound targets, PD disease targets, and DEGs from GSE106608/GSE160299 analyzed separately) yielded the list of potential targets for SB in treating PD. These overlapping genes were subsequently uploaded to the DAVID database for Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The target gene lists were also imported into the STRING database ( https://www.string-db.org ) to perform protein-protein interaction (PPI) analysis, which was visualized using Cytoscape (version 3.7.1). Key target genes within the PPI network were screened using Cytoscape network analysis plugins, based on critical topological parameters including \"degree,\" \"closeness,\" and \"betweenness.\" 2.5. Weighted Gene Co-expression Network Analysis (WGCNA) Using the GSE106608 and GSE160299 datasets as target matrices, WGCNA was conducted via the OECloud platform ( https://cloud.oebiotech.com/ ) to screen for modules highly correlated with PD, thereby identifying PD-associated module genes. The final candidate gene sets were obtained by taking the intersection of the active compound targets of SB, the database-derived PD disease targets, the DEGs, and the WGCNA-identified PD module genes (analyzed separately for GSE106608 and GSE160299). 2.6. Screening of Core Genes via Machine Learning Three machine learning algorithms—Random Forest, SVM, and Least Absolute Shrinkage and Selection Operator (LASSO)—were employed to further refine the intersected genes. The intersection of the genes selected by these three algorithms was extracted. By cross-referencing the results from the PPI network, WGCNA, machine learning algorithms, and existing literature, the final core genes were determined. 2.7. Molecular Docking of Core Genes and Active Compounds Cytoscape (version 3.9.1) was utilized to visualize the correlation between the active compounds and the core targets, which was subsequently validated via molecular docking. The 3D structure files of the core genes were downloaded from the PDB database ( https://www.rcsb.org/ ) and converted to PDB format using OpenBabel 2.4.1. Sequential docking analyses were performed using the CB-DOCK2 online server ( https://cadd.labshare.cn/cb-dock2/php/index.php ) to obtain blind docking scores between the core genes and key active compounds. Finally, PyMOL software was used for visualization. 2.8. Chemicals and Reagents Wogonin (CAS: 632-85-9, B20489), quercetin (CAS: 117-39-5, B20527), stigmasterol (CAS: 83-48-7, B20314), baicalein (CAS: 491-67-8, B20571), beta-sitosterol (CAS: 83-46-5, B21972), and eriodictyol (CAS: 552-58-9, B21160) were purchased from Yuanye Biotechnology (Shanghai, China). Moslosooflavone (CAS: 3570-62-5, HY-N2035) was purchased from MedChemExpress (Monmouth Junction, NJ, USA). N-Methyl-4-phenylpyridinium iodide (MPP⁺) (N137206) was purchased from Aladdin (Shanghai, China). 2.9. Cell Culture and Model Construction The SH-SY5Y and THP-1 cell lines were obtained from the Yancheng Medical Research Center, Affiliated Hospital of Nanjing University Medical School. Human neuroblastoma SH-SY5Y cells were cultured in DMEM/F-12 supplemented with 10% FBS, 2 mM L-glutamine, and 1% penicillin/streptomycin in a humidified incubator at 37°C with 5% CO₂. To simulate damaged neurons in PD in vitro, SH-SY5Y cells were treated with 3 mM MPP⁺ for 24 h. Human monocytic THP-1 cells were cultured in RPMI-1640 medium. For macrophage differentiation, THP-1 cells were stimulated with 100 ng/mL Phorbol 12-myristate 13-acetate (PMA) for 3 days to induce adherent differentiation, followed by treatment with 1 µg/mL LPS for 24 h to establish an in vitro peripheral inflammation model. 2.10. Cell Viability Assay Cell viability was assessed using the Cell Counting Kit-8 (CCK-8) assay. SH-SY5Y and THP-1 cells were seeded in 96-well plates and incubated overnight. To evaluate the cytotoxicity of the active compounds of SB, cells were treated with varying concentrations of the compounds for 48 h. To assess the neuroprotective effects, SH-SY5Y cells were pre-treated with the active compounds for 24 h, followed by stimulation with 3 mM MPP⁺ for an additional 24 h. Subsequently, the CCK-8 solution was added to each well, and the plates were incubated at 37°C for another 2 h. The optical density (OD) at 450 nm was measured using a microplate reader (Thermo Fisher Scientific, Waltham, MA, USA). 2.11. Measurement of Inflammatory Markers To evaluate the effects of the active compounds on peripheral inflammation, THP-1 cells (differentiated with PMA and adherent) were pre-treated with various concentrations of the active compounds of SB for 24 h, followed by stimulation with 1 µg/mL LPS for another 24 h. The culture supernatants were collected to measure the levels of the inflammatory cytokines IL-6 and TNF-α using ELISA. The Human IL-6 Uncoated ELISA Kit (Cat. number: 88-7066-77) and Human TNF alpha Uncoated ELISA Kit (Cat. number: 88-7346-88) were obtained from Thermo Fisher Scientific (Waltham, MA, USA) and used according to the manufacturer’s instructions. 2.12. Mathematical and Statistical Analyses All data are expressed as the mean ± standard deviation (SD) from three independent experiments. Statistical analyses were performed using one-way analysis of variance (ANOVA) for multiple group comparisons via GraphPad Prism 8.0 (GraphPad Software, Inc., La Jolla, CA, USA). Differences were considered statistically significant when P ≤ 0.05 (*), P ≤ 0.01 (**), and P ≤ 0.001 (***). 3. Results 3.1. Screening of Active Compounds and Potential Targets of SB To identify the active ingredients of SB, we searched the TCMSP database and applied stringent pharmacokinetic filtering criteria: oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18. Consequently, 26 active compounds were obtained (Fig. 1 ). Subsequently, SwissTargetPrediction and PharmaMapper were utilized to predict the corresponding targets of these active components, yielding a comprehensive drug-target dataset comprising 230 unique target genes after the removal of duplicates. 3.2. Identification of Potential Targets for PD Using \"Parkinson’s disease\" as the query keyword, we comprehensively searched the TTD and GeneCards databases. After eliminating redundant entries and integrating the data, a robust dataset of PD-related targets was established. This disease target profile served as the foundation for the subsequent screening of potential therapeutic targets of SB against PD. 3.3. Identification and Enrichment Analysis of DEGs from GEO Datasets To capture the transcriptomic signatures of PD, the GEO2R tool and the Seurat R package were employed to analyze the GSE106608 and GSE160299 datasets, respectively (Figs. 2 A, 3 A). Based on predefined thresholds, differentially expressed genes (DEGs) were identified. As depicted in the volcano plots (Figs. 2 B, 3 B), red dots represent significantly upregulated genes, whereas blue dots indicate significantly downregulated genes. Next, we intersected the SB drug targets, the PD disease targets, and the DEGs from the two GEO datasets separately (Figs. 2 C, 3 C). This resulted in 46 overlapping genes for GSE106608 and 48 overlapping genes for GSE160299. To elucidate the biological functions of these intersection genes, GO and KEGG pathway enrichment analyses were performed using the DAVID database. The results revealed that genes from the GSE106608 dataset were predominantly enriched in biological processes and signaling pathways such as \"Response to oxygen-containing compound,\" \"Pathways in cancer,\" and \"Fluid shear stress and atherosclerosis\" (Figs. 2 D-E). Conversely, genes from the GSE160299 dataset were primarily enriched in functions such as the \"Positive regulation of intracellular signal transduction\" and the \"PI3K-Akt signaling pathway\" (Figs. 3 D-E). Furthermore, we constructed PPI networks for the 46 and 48 potential target genes (Fig. 4 ) and identified key hub genes using Cytoscape topological analysis plugins. The color intensity of the nodes reflects their degree values, while the node size correlates with interaction connectivity. Based on comprehensive scores calculated from \"degree,\" \"closeness,\" and \"betweenness,\" the top 12 genes from the GSE106608 dataset were selected as key central targets (Fig. 4 A), among which metabolism- and apoptosis-regulating genes like PPARG and BCL2 exhibited strong relevance (detailed parameters in Fig. 4 B). For the GSE160299 dataset, the top 7 genes were identified as key peripheral targets (Fig. 4 C), highlighting inflammation-regulatory genes such as IL-6 and TGFB1 (detailed parameters in Fig. 4 D). 3.4. WGCNA of the GEO Datasets To further mine gene modules intimately associated with PD, WGCNA was performed on the GSE106608 and GSE160299 datasets. First, we calculated scale independence and mean connectivity to determine the optimal soft-thresholding power for constructing a scale-free network (Figs. 5 A, 6 A). Based on the Topological Overlap Matrix (TOM), genes were clustered into distinct modules (Figs. 5 B-C, 6 B-C). Heatmaps of module-trait relationships revealed significant correlations between specific modules and PD pathogenesis. In the GSE106608 analysis (Fig. 5 D), the \"darkred\" module exhibited a significant positive correlation with PD (r = 0.73). In the GSE160299 analysis (Fig. 6 D), the \"green\" module (r = 0.97) and \"coral2\" module (r = -0.93) showed exceedingly high correlations with PD. Finally, we intersected the drug targets, disease targets, DEGs, and key WGCNA module genes (Figs. 5 E, 6 E) to preliminarily define the candidate key genes for subsequent analysis. 3.5. Screening of Core Targets via Machine Learning To precisely pinpoint core biomarkers from the candidate genes, three machine learning algorithms—Random Forest, SVM, and LASSO—were introduced for feature selection. Figures 7 and 8 display the cross-validation accuracy and error curves for each algorithm across both datasets, verifying the robustness of the models. By intersecting the genes selected by the three algorithms (Venn diagrams, Figs. 7 E, 8 E) and integrating them with the previous PPI results, we finalized the core target genes. For the central nervous system dataset (GSE106608), the core targets included CDK2, SLC6A3, and MGAM (Fig. 9 A). For the peripheral systemic dataset (GSE160299), the core targets comprised BAX, MYC, GABRA2, SPP1, and HMOX1 (Fig. 10 A). 3.6. Molecular Docking of Core Genes and Active Compounds of SB To validate the binding likelihood and stability between the active compounds of SB and the identified core targets, molecular docking simulations were conducted. The visualization results (Figs. 9 B-E, 10 B-E) demonstrate that the active small molecules seamlessly anchor into the active pockets of the target proteins. Binding energy analysis revealed strong affinities. For the brain-related central targets (GSE106608), the binding energy was − 9.9 kcal/mol for Stigmasterol-SLC6A3, -8.9 kcal/mol for Wogonin-CDK2, -8.8 kcal/mol for Moslossooflavone-CDK2, and − 8.7 kcal/mol for Quercetin-MGAM. For the peripheral inflammation-related targets (GSE160299), the binding energy was − 8.8 kcal/mol for Beta-sitosterol-BAX, -7.8 kcal/mol for Eriodictyol-HMOX1, -7.3 kcal/mol for Baicalein-BAX, -7.1 kcal/mol for Beta-sitosterol-GABRA2, -7.0 kcal/mol for Quercetin-SPP1, and − 6.9 kcal/mol for Wogonin-BAX. These low binding energies (large absolute values indicate higher stability) strongly suggest that the active compounds of SB may exert anti-PD efficacy by directly interacting with these core targets to modulate apoptosis, neural signaling, inflammatory responses, and energy metabolism. 3.7. Experimental Validation In Vitro To simulate damaged neurons in PD patients, SH-SY5Y cells were exposed to 3 mM MPP⁺ for 24 h. Based on the docking results, four compounds (Wogonin, Moslossooflavone, Quercetin, and Stigmasterol) were selected as potential CNS-acting candidates, and their protective effects were evaluated. Concurrently, a peripheral inflammation model was established by stimulating THP-1 cells with 1 µg/mL LPS for 24 h. Similarly, five compounds (Baicalein, Wogonin, Quercetin, Beta-sitosterol, and Eriodictyol) were selected as potential peripherally acting candidates, and their anti-inflammatory effects were assessed. The CCK-8 cell viability assay (Figs. 11 A, C) demonstrated that treatment with these compounds for 48 h did not significantly alter the viability of normal SH-SY5Y or THP-1 cells compared to the control group (viability maintained > 90%), indicating no obvious cytotoxicity within the tested concentration ranges. In the MPP⁺-induced PD neuronal injury model (Fig. 11 B), cell viability drastically decreased after 24 h of 3 mM MPP⁺ treatment, confirming successful model establishment. Pre-treatment with higher concentrations of Wogonin, Quercetin, and Stigmasterol significantly rescued cell viability compared to the MPP⁺-only group. This finding suggests that these SB compounds markedly attenuate MPP⁺-induced neuronal damage, exerting a neuroprotective effect. In the LPS-induced peripheral inflammation model (Figs. 11 D, E), the secretion levels of IL-6 and TNF-α surged significantly after 24 h of LPS treatment. Pre-treatment with Baicalein, Wogonin, Quercetin, and Eriodictyol significantly reduced the secretion of IL-6 and TNF-α compared to the LPS group in a concentration-dependent manner. This implies that these active compounds effectively inhibit LPS-induced pro-inflammatory cytokine release, thereby exerting peripheral anti-inflammatory effects. In conclusion, within safe concentration ranges, Wogonin and Quercetin from SB not only protect MPP⁺-injured SH-SY5Y neurons and improve cell survival but also effectively suppress the secretion of inflammatory cytokines (IL-6, TNF-α) in LPS-stimulated THP-1 cells. Multiple active ingredients in SB function synergistically to provide central neuroprotection and peripheral anti-inflammation, offering robust in vitro evidence for the application of SB in treating PD and inflammation-related diseases. 4. Discussion PD is a highly complex neurodegenerative disorder driven by multifaceted pathogenic mechanisms, including oxidative stress, neuroinflammation, mitochondrial dysfunction, and abnormal protein aggregation. Currently, mainstream clinical medications primarily offer symptomatic relief, leaving a critical unmet need for effective disease-modifying therapies capable of halting or reversing progressive neuronal loss [ 14 ]. SB, a traditional Chinese herbal medicine renowned for its robust anti-inflammatory and antioxidant properties, has exhibited profound translational potential in the field of neuroprotection. To address the elusive pharmacological basis of SB, this study systematically decoded the core targets and molecular mechanisms underlying its anti-PD efficacy by comprehensively integrating network pharmacology, WGCNA, machine learning algorithms, and in vitro experimental validation. Innovatively, this study integrated transcriptomic profiles from both the STN and peripheral plasma, successfully breaking the conventional limitation of focusing exclusively on central nervous system targets. Our findings compellingly propose that SB and its active compounds exert a \"two-pronged\" therapeutic mechanism against PD, functioning synergistically across both the central neural circuits and the peripheral immune system. First, through the joint screening of transcriptomic targets from the STN tissue, we identified CDK2, MGAM, and SLC6A3 as the core central targets for the active compounds of SB against PD. Previous studies have suggested that both CDK2 and MGAM may be involved in the pathogenesis and progression of PD, albeit through distinct mechanisms. CDK2, a canonical cyclin-dependent kinase, primarily regulates the G1-to-S phase transition and DNA replication, thereby maintaining normal proliferation in dividing cells [ 15 ]. In the context of neurodegenerative diseases, animal experiments have demonstrated that CDK2 activity is upregulated in dopaminergic neurons of the substantia nigra pars compacta in MPTP-induced PD mouse models. Furthermore, viral-mediated expression of a dominant-negative CDK2 conferred approximately 25% protection against MPTP-induced loss of dopaminergic neurons, suggesting that aberrant CDK2 activation contributes to PD-related erroneous cell cycle re-entry and neuronal death [ 16 ]. In contrast, MGAM encodes maltase-glucoamylase, a brush-border digestive enzyme primarily involved in the terminal hydrolysis of starch and energy absorption in the small intestine [ 17 ]. Multi-omics analyses and large-scale population data have revealed signals of co-occurrence or differential expression of MGAM in PD-related gene expression profiles and text-mining databases, particularly identifying it as a potential sex-specific gene in female PD patients in stratified analyses [ 18 ]. These findings suggest that MGAM may indirectly modulate PD susceptibility and progression by influencing energy metabolism, gut-immune responses, and other pathways. Although no direct molecular interaction between CDK2 and MGAM has been reported to date, they respectively represent two critical pathological dimensions associated with PD: \"intraneuronal dysregulation of cell cycle/stress pathways\" and \"peripheral metabolic-immune environmental alterations.\" This highlights that the molecular pathology of PD may involve synergistic imbalances across multiple pathways and levels, both within and outside central neurons. These insights provide a theoretical basis for future dual-target interventions in PD focusing on cell cycle regulation and the metabolism-inflammation axis. Additionally, the SLC6A3 gene, which encodes the dopamine transporter, has been identified as a PD susceptibility gene. Meta-analyses of previous genetic studies have indicated that its polymorphisms are moderately but statistically significantly associated with the risk of developing PD [ 19 ]. Taken together, these three core genes operate across distinct dimensions—blocking aberrant apoptosis (CDK2), ameliorating brain energy metabolism (MGAM), and maintaining synaptic neurotransmitter homeostasis (SLC6A3). This multi-dimensional regulation not only perfectly explains why the active compounds of SB significantly reversed MPP⁺-induced SH-SY5Y neuronal death and improved cell viability in our in vitro experiments, but also robustly substantiates the underlying \"multi-target synergistic\" neuroprotective mechanisms of SB in the central nervous system. Our molecular docking results revealed exceptionally low binding energies for the complexes of Stigmasterol with SLC6A3, Wogonin and Moslossooflavone with CDK2, and Quercetin with MGAM, indicating highly stable binding affinities between these active ingredients and their respective target proteins. Highly consistent with these computational predictions, our in vitro cellular assays demonstrated that these compounds significantly enhanced the viability of SH-SY5Y neurons under MPP⁺-induced toxicity. Based on the biological functions of these specific targets, we reasonably hypothesize that these active components synergistically exert profound neuroprotective effects in the central nervous system by precisely modulating these core proteins, thereby ameliorating the pathological progression of PD. Specifically, Stigmasterol may directly bind to and regulate the function or expression of SLC6A3 (dopamine transporter), thereby improving dopaminergic neurotransmission and maintaining synaptic transmitter homeostasis. Wogonin and Moslossooflavone likely target and inhibit the aberrant hyperactivation of CDK2, effectively blocking neurotoxin-induced abnormal cell cycle re-entry and the subsequent apoptotic cascades in dopaminergic neurons. Concurrently, Quercetin holds the potential to modulate MGAM activity, thereby correcting the imbalance in brain energy metabolism and restoring mitochondrial function in damaged neurons. Such a multi-target, multi-dimensional molecular intervention mechanism provides a compelling theoretical basis for the robust efficacy of SB active constituents against MPP⁺-induced neurotoxicity. Secondly, neuroinflammation serves as a critical driver of PD progression, with a well-established link between peripheral immune system activation and central neurodegeneration. Recent reviews highlight significant crosstalk between central immunity (characterized by microglial and astrocyte activation) and peripheral immunity (marked by alterations in monocyte/lymphocyte populations and cytokine profiles) in PD. Consequently, the peripheral–central immune axis plays a pivotal role in both the initiation and progression of the disease [ 3 ]. Through joint screening of peripheral blood transcriptomic targets, we identified BAX, HMOX1, GABRA2, and SPP1 as core peripheral targets mediating the anti-PD effects of active compounds derived from SB. HMOX1 (heme oxygenase-1) is a pivotal enzyme in cellular defense against oxidative stress. Previous studies indicate that HMOX1 regulates iron homeostasis and oxidative stress responses; its aberrant expression is closely linked to α-synuclein pathology, influencing the misfolding and toxic progression of α-synuclein in PD [ 20 ]; see also recent reviews on the HMOX1–α-synuclein axis and iron-lipid peroxidation crosstalk [ 21 ]. Furthermore, BAX is a canonical pro-apoptotic protein. Mitochondria-mediated apoptosis is recognized as one of the primary mechanisms driving dopaminergic neuron death in PD. Extensive research, including early studies and recent reviews, demonstrates that BAX upregulation and the resulting imbalance in the Bcl-2/BAX ratio drive neuronal apoptosis in both toxin-induced and genetic PD models [ 22 ].GABRA2 encodes the α2 subunit of the γ-aminobutyric acid type A (GABA_A) receptor, a critical component of central inhibitory neurotransmission. In disease models and transcriptomic studies, GABRA2 exhibits aberrant expression patterns in PD-related contexts. For instance, in brain organoid models derived from PD patients, GABRA2 is significantly upregulated in PD organoids compared to its marked downregulation in non-PD controls, suggesting its involvement in PD-associated remodeling of GABAergic synaptic function [ 23 ]. Conversely, in toxin-induced PD animal models (e.g., paraquat exposure), Gabra2 expression is downregulated in the striatum [ 24 ]. Similarly, in MAO-B knockout mice, GABRA2 is significantly reduced as part of a PD-related gene signature, co-occurring with alterations in various inflammation and injury-related genes [ 25 ]. Collectively, these findings support the role of GABRA2 in PD pathology through the modulation of GABAergic inhibitory pathways and neuroinflammation. In contrast, SPP1 (secreted phosphoprotein 1), also known as osteopontin (OPN), functions primarily as an extracellular matrix protein and an immunomodulatory molecule. In PD, it is recognized as a key mediator of neuroinflammation and a potential biomarker. Previous studies have reported elevated levels of SPP1/OPN in the serum and cerebrospinal fluid of PD patients, which correlate with the severity of motor phenotypes and the risk of dementia [ 26 ]. In MPTP-induced PD mouse models, Spp1 transcription is altered in regions such as the substantia nigra; notably, SPP1 deficiency attenuates dopaminergic neuron loss and microglial activation [ 27 ]. Furthermore, integrated single-cell and spatial transcriptomic analyses of human PD brain tissue reveal that SPP1 is markedly upregulated in activated microglia within lesioned areas, including the substantia nigra and cingulate cortex, identifying it as a core marker of the neuroinflammatory microenvironment in PD [ 28 ]. Our molecular docking results revealed exceptionally low binding energies for the complexes of Beta-sitosterol with BAX, Eriodictyol with HMOX1, Baicalein with BAX, Beta-sitosterol with GABRA2, and Quercetin with SPP1, indicating highly stable binding conformations between these peripheral targets and the active compounds. Consistent with these docking affinities, our in vitro THP-1 inflammatory model demonstrated that these active components of SB significantly suppressed the excessive secretion of pro-inflammatory cytokines, specifically TNF-α and IL-6. It is well established that elevated peripheral TNF-α not only directly triggers neuronal apoptosis but also compromises brain microvascular endothelial cells via the TNF-α/NF-κB signaling pathway. This process disrupts the integrity of the BBB, thereby facilitating the infiltration of peripheral inflammatory factors and immune cells into the CNS. This infiltration drastically exacerbates the central neuroinflammatory microenvironment—a vicious pathological cycle that has been robustly validated in α-synuclein-associated BBB damage models [ 29 ]. Our in vitro findings strongly suggest that Eriodictyol, Quercetin, Baicalein, and Beta-sitosterol effectively mitigate the peripheral inflammatory cascade in PD patients by precisely modulating these specific target proteins, thereby interrupting the disease progression. Specifically, Eriodictyol may interact with HMOX1 to enhance cellular resistance against systemic oxidative stress and shift macrophages away from a hyperactive pro-inflammatory state. Baicalein and Beta-sitosterol likely target the pro-apoptotic protein BAX, potentially protecting crucial structural cells (such as BBB endothelial cells) from inflammation-induced apoptosis, thereby maintaining barrier integrity. Furthermore, Beta-sitosterol's binding to GABRA2 hints at a novel mechanism of dampening excessive macrophage activation through peripheral GABAergic immunomodulation. Concurrently, Quercetin may inhibit the signaling of SPP1 (osteopontin), thereby restricting the chemotaxis and profound inflammatory activation of monocytes. By synergistically suppressing the peripheral inflammatory storm and safeguarding the BBB, these multi-target compounds effectively sever the pathological cross-talk between the periphery and the brain, ultimately delaying the progression of PD. Among the active constituents identified in our study, flavonoids—particularly Wogonin and Quercetin—occupy a predominantly dominant position. Extensive previous research has demonstrated the inherent ability of these flavonoid compounds to cross the BBB and exert potent neuroprotective effects. For instance, wogonin has been firmly established to inhibit the activation of the NF-κB signaling pathway, thereby attenuating the inflammatory responses and subsequent cell death associated with central neurodegeneration. Its comprehensive anti-inflammatory, antioxidant, and neuroprotective properties in central nervous system disorders have been systematically reviewed [ 10 ]. Similarly, multiple studies have corroborated that quercetin can activate the Nrf2-dependent antioxidant pathway and upregulate downstream antioxidant enzymes, including heme oxygenase-1 (HO-1/HMOX1). This mechanism significantly alleviates oxidative stress and neuronal damage across various neurological injury models [ 30 , 31 ]. Crucially, in the present study, our computational docking and in vitro validation reveal that these multiple active components exhibit exceptionally high binding affinities for both central and peripheral core targets. This robustly substantiates the material basis of SB in intervening against PD through a synergistic, multi-component paradigm. Notably, Wogonin and Quercetin—two flagship bioactive components of SB—not only demonstrate the capacity for direct neuronal protection in our models but also show profound efficacy in downregulating key systemic inflammatory mediators such as TNF-α. By rectifying the dysregulation of the peripheral-central immune-inflammatory axis, these compounds synergistically exert comprehensive anti-PD effects, thereby emerging as highly promising therapeutic candidates for the clinical management of PD. 5. Conclusions In summary, this study employed a comprehensive integrative strategy—combining network pharmacology, WGCNA, machine learning algorithms, and in vitro experimental validation—to systematically elucidate the active constituent profile and the underlying pharmacological mechanisms of SB against PD. Our findings robustly demonstrate that the flagship active compounds of SB, particularly wogonin and quercetin, exert potent direct and indirect neuroprotective effects. These therapeutic benefits are driven by the precise targeting of core genes, including SLC6A3, HMOX1, and BAX, which coordinately alleviate oxidative stress, inhibit neuronal apoptosis, and attenuate both peripheral and central neuroinflammation. Ultimately, this study not only establishes a compelling scientific rationale for the clinical application of SB in PD management, but also highlights the exceptional robustness of integrating machine learning with multi-omics data mining to decipher the complex, \"multi-target\" paradigms of traditional Chinese medicine. These novel insights offer highly valuable candidate targets and theoretical foundations for future anti-PD targeted drug discovery and development. Declarations Funding: This research was funded by the Yancheng Science and Technology Project, grant number YCBK2024068, and the Medical Research Project of Yancheng Health Commission, grant number YK2024034. Conflicts of Interest: The authors declare that they have no conflict of interest. Ethics approval: Not applicable. Consent to participate : Not applicable. Consent for publication : Not applicable. Availability of data and material: Publicly available datasets were analyzed in this study. This data can be found at GEO (GSE106608 and GSE160299). Code availability : Not applicable. Author Contributions: Conceptualization, Y.L.; methodology, A.S.; investigation, Y.L.; resources, H.W. and L.Z.; writing—original draft preparation, Y.L. and A.S.; writing—review and editing, A.S.; supervision, Y.M.; project administration, Y.M. and H.W.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript. 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Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 29 Apr, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviewers invited by journal 27 Apr, 2026 Editor invited by journal 24 Apr, 2026 Editor assigned by journal 23 Mar, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 22 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9189068\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":633800694,\"identity\":\"a30f92de-d8bf-4ad6-a611-ccc568f7fb05\",\"order_by\":0,\"name\":\"Ao Sun\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First People's Hospital of Yancheng\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ao\",\"middleName\":\"\",\"lastName\":\"Sun\",\"suffix\":\"\"},{\"id\":633800695,\"identity\":\"a7bbe067-105f-47ec-8eba-bd1485843761\",\"order_by\":1,\"name\":\"Yufei 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GSE160299.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9189068/v1/f3b3c80d500cd353037766ef.png\"},{\"id\":108531645,\"identity\":\"935b98c5-1984-46c8-bd89-3c74a60907fd\",\"added_by\":\"auto\",\"created_at\":\"2026-05-05 16:04:05\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":443814,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eWeighted gene co-expression network analysis (WGCNA) and module screening for the GSE106608 dataset. \\u003cstrong\\u003e(A) \\u003c/strong\\u003eAnalysis of network topology for various soft-thresholding powers. The left panel shows the scale-free fit index as a function of the soft-thresholding power, and the right panel displays the mean connectivity. \\u003cstrong\\u003e(B)\\u003c/strong\\u003eHierarchical clustering dendrogram of genes based on topological overlap, together with assigned module colors. \\u003cstrong\\u003e(C)\\u003c/strong\\u003eHeatmap depicting the topological overlap matrix (TOM) among all genes. \\u003cstrong\\u003e(D) \\u003c/strong\\u003eHeatmap of module-trait relationships. Rows represent distinct gene modules, and columns represent clinical traits (NC and PD). \\u003cstrong\\u003e(E)\\u003c/strong\\u003e Venn diagram showing the intersection of drug targets, disease targets, DEGs, and key WGCNA module genes for GSE106608.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9189068/v1/6da2c12b7561f47aaa138548.png\"},{\"id\":108805173,\"identity\":\"57508627-992c-44ad-9c8c-d53f79c18fe4\",\"added_by\":\"auto\",\"created_at\":\"2026-05-08 15:25:04\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":507828,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eWeighted gene co-expression network analysis (WGCNA) and module screening for the GSE160299 dataset. \\u003cstrong\\u003e(A)\\u003c/strong\\u003eAnalysis of network topology for various soft-thresholding powers. \\u003cstrong\\u003e(B) \\u003c/strong\\u003eHierarchical clustering dendrogram of genes based on topological overlap, together with assigned module colors.\\u003cstrong\\u003e (C)\\u003c/strong\\u003e Heatmap depicting the TOM among all genes. \\u003cstrong\\u003e(D)\\u003c/strong\\u003e Heatmap of module-trait relationships. \\u003cstrong\\u003e(E) \\u003c/strong\\u003eVenn diagram showing the intersection of drug targets, disease targets, DEGs, and key WGCNA module genes for GSE160299.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9189068/v1/3ae30c5e20926b4ddfc5e396.png\"},{\"id\":108531646,\"identity\":\"a12e1ad6-1dfb-4aec-90ae-5d268bb031c0\",\"added_by\":\"auto\",\"created_at\":\"2026-05-05 16:04:05\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":280357,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMachine learning algorithms for screening core genes of GSE106608. \\u003cstrong\\u003e(A) \\u003c/strong\\u003eAccuracy of SVM cross-validation. \\u003cstrong\\u003e(B)\\u003c/strong\\u003e Cross-validation error of SVM. \\u003cstrong\\u003e(C) \\u003c/strong\\u003eCross-validation accuracy of random forest. \\u003cstrong\\u003e(D) \\u003c/strong\\u003eLasso cross-validation curve. \\u003cstrong\\u003e(E) \\u003c/strong\\u003eGene intersection Venn diagram of three methods.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9189068/v1/3635a9b33fa9fa25f262891a.png\"},{\"id\":108804257,\"identity\":\"ba37a6c9-30d3-46a9-bfc2-0dae518d3746\",\"added_by\":\"auto\",\"created_at\":\"2026-05-08 15:18:31\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":280448,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMachine learning algorithms for screening core genes of GSE160299.\\u003cstrong\\u003e(A) \\u003c/strong\\u003eAccuracy of SVM cross-validation. \\u003cstrong\\u003e(B) \\u003c/strong\\u003eCross-validation error of SVM. \\u003cstrong\\u003e(C)\\u003c/strong\\u003e Cross-validation accuracy of random forest. \\u003cstrong\\u003e(D)\\u003c/strong\\u003e Lasso cross-validation curve. \\u003cstrong\\u003e(E)\\u003c/strong\\u003e Gene intersection Venn diagram of three methods.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9189068/v1/c8afd2a32d8f3388518aaee5.png\"},{\"id\":108531648,\"identity\":\"62c33f73-306e-4062-ac08-dd7787f7365f\",\"added_by\":\"auto\",\"created_at\":\"2026-05-05 16:04:05\",\"extension\":\"png\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":546434,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMolecular docking of active components from SBwith core targets from GSE106608. \\u003cstrong\\u003e(A)\\u003c/strong\\u003eDrug-Active Compound-Brain Target-Disease Network Diagram. \\u003cstrong\\u003e(B-E)\\u003c/strong\\u003eMolecular docking of four compounds with target proteins.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9189068/v1/394f1acfa2da691b98374182.png\"},{\"id\":108531651,\"identity\":\"65876b0f-4445-4716-a5c1-3c260ae98365\",\"added_by\":\"auto\",\"created_at\":\"2026-05-05 16:04:05\",\"extension\":\"png\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":644452,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eNetwork construction and molecular docking of active compounds from SB with peripheral core targets (GSE160299). \\u003cstrong\\u003e(A) \\u003c/strong\\u003eThe \\\"Drug - Active Compound - Peripheral Target - Disease\\\" interaction network. \\u003cstrong\\u003e(B-G) \\u003c/strong\\u003e3D and 2D molecular docking models illustrating the binding interactions between key active compounds and their respective target proteins.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9189068/v1/3e52db1d20c271fd09796e0a.png\"},{\"id\":108531652,\"identity\":\"e824f3de-d452-41fb-b2d2-9cc4fd43f3a8\",\"added_by\":\"auto\",\"created_at\":\"2026-05-05 16:04:05\",\"extension\":\"png\",\"order_by\":11,\"title\":\"Figure 11\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":397562,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eIn vitro experimental validation of the neuroprotective and anti-inflammatory effects of active compounds from SB. (A) Cell viability of normal SH-SY5Y cells treated with four active compounds for 48 h. (B) Effects of the four active compounds on the viability of SH-SY5Y cells exposed to 3 mM MPP⁺. (C) Cell viability of normal THP-1 cells treated with five active compounds for 48 h. (D-E) Effects of the active compounds on the secretion levels of inflammatory cytokines IL-6 (D) and TNF-α (E) in THP-1 cells stimulated with 1μg/ml LPS. Data are presented as mean ± SD from three independent experiments.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage11.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9189068/v1/2f36ddaecdd19d4b778b5bd8.png\"},{\"id\":108809568,\"identity\":\"eebb589d-e244-4af6-91a6-d55043694eb1\",\"added_by\":\"auto\",\"created_at\":\"2026-05-08 15:53:54\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":4938260,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9189068/v1/f17b0c23-e717-4c23-9ee2-a84dd3f218ed.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Unraveling the Pharmacological Mechanisms of Scutellaria barbata in Parkinson's Disease: An Integrated Bioinformatics and Experimental Validation Study\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eParkinson's disease (PD) is the second most common neurodegenerative disorder globally. It is pathologically characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and the intracellular accumulation of Lewy bodies, which clinically manifest as motor symptoms including resting tremor, muscle rigidity, and bradykinesia [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. However, mounting evidence suggests that PD is essentially a multisystemic disorder rather than being confined solely to the central nervous system [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Peripheral immune dysregulation, metabolic abnormalities, and a surge of inflammatory cytokines in the bloodstream often precede or accompany central symptoms [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e], indicating that the \\\"brain-periphery axis\\\" plays a crucial driving and reflecting role in disease progression [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. Driven by the global aging population, the incidence of PD is rising significantly, imposing a heavy economic burden on society and families [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Currently, mainstream therapeutic strategies, such as levodopa, primarily focus on replenishing central neurotransmitters. These treatments merely alleviate symptoms without reversing the disease course, and they largely neglect the modulation of the peripheral systemic environment [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. Therefore, there is an urgent need to discover novel therapeutic agents capable of delaying neuronal degeneration and exerting comprehensive neuroprotective effects.\\u003c/p\\u003e \\u003cp\\u003eAccumulating evidence highlights that neuroinflammation and oxidative stress occupy central roles in the pathogenesis of PD[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. The overactivation of microglia leads to the excessive release of pro-inflammatory cytokines, such as TNF-α and IL-6, which subsequently induce neuronal apoptosis [\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Consequently, targeting neuroinflammatory signaling pathways has emerged as a highly promising strategy for PD intervention.\\u003c/p\\u003e \\u003cp\\u003eScutellaria barbata D.Don (SB) is a widely used traditional Chinese medicine (TCM) recognized for its efficacy in clearing heat, detoxifying, dispersing blood stasis, and relieving pain. Modern pharmacological studies have demonstrated its significant anti-tumor, anti-inflammatory, and antioxidant activities [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Notably, its active flavonoid constituents (e.g., wogonin and apigenin) have exhibited strong neuroprotective potential in neurological disease models by suppressing neuroinflammation and attenuating oxidative damage [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. However, owing to the \\\"multi-component, multi-target, and multi-pathway\\\" paradigm characteristic of TCM, the precise pharmacodynamic material basis and molecular regulatory networks of SB in treating PD remain incompletely understood. Specifically, whether SB exerts its anti-PD efficacy through a \\\"central-peripheral synergy\\\" mechanism across tissues requires systematic elucidation. To bridge this gap, the present study abandons the traditional approach of focusing solely on single-brain-region data and innovatively integrates \\\"central-peripheral\\\" dual-source transcriptomic datasets. We selected the subthalamic nucleus (STN) dataset (GSE106608) to represent the critical node in the central pathological circuit of PD, alongside the plasma transcriptomic dataset (GSE160299) to represent the peripheral systemic state. By combining network pharmacology, weighted gene co-expression network analysis (WGCNA), and multiple machine learning algorithms (e.g., Support Vector Machine [SVM], Random Forest, and LASSO), we aimed to systematically screen for potential active compounds in SB that can cross the blood-brain barrier (BBB) to act directly on neural circuits while simultaneously modulating peripheral immune signaling.\\u003c/p\\u003e \\u003cp\\u003eNetwork pharmacology emphasizes evaluating the complex interactions between drugs and diseases from a holistic perspective, making it highly suitable for deciphering the synergistic mechanisms of TCM [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Furthermore, with the rapid advancement of bioinformatics, WGCNA and machine learning algorithms have been widely utilized for the precise identification of key biomarkers highly correlated with disease phenotypes from high-dimensional gene expression data [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eIn summary, this study aims to systematically screen the potential core targets and signaling pathways of SB against PD by integrating network pharmacology, transcriptomic data analysis (GEO), WGCNA, and machine learning. Furthermore, we employ molecular docking technology and in vitro cellular experiments\\u0026mdash;specifically, an MPP⁺-induced SH-SY5Y cell injury model and an LPS-induced THP-1 cellular inflammation model\\u0026mdash;to validate the reliability of our computational predictions. Ultimately, this research is expected to provide novel scientific evidence and mechanistic insights for the clinical development of SB as a therapeutic agent for PD.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e\"},{\"header\":\"2. Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1. Acquisition of Active Compounds and Potential Targets of SB\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eThe active ingredients of SB and their corresponding targets were retrieved from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Furthermore, target prediction databases, including SwissTargetPrediction and PharmaMapper, were utilized to predict the potential targets of these active compounds. Together, these constituted the preliminary drug-target dataset.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2. Construction of the Disease-Related Target Database\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eUsing \\\"Parkinson\\u0026rsquo;s disease\\\" as the search keyword, disease-associated targets were queried and screened across databases such as the Therapeutic Target Database (TTD) and GeneCards. After removing duplicate entries and integrating the results, a comprehensive PD-related target dataset was constructed. This dataset served as the foundation for identifying the potential therapeutic targets of SB's active compounds against PD.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3. Acquisition of Spatial Transcriptomic Data and \\\"Central-Peripheral\\\" Differential Expression Analysis\\u003c/h2\\u003e \\u003cp\\u003e\\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\u003cp\\u003eTo construct a cross-tissue \\\"brain-blood\\\" map that comprehensively reflects the pathological features of PD, we strategically selected two independent datasets from the Gene Expression Omnibus (GEO) database, representing the central pathological microenvironment and the peripheral circulatory system.\\u003c/p\\u003e\\u003cp\\u003eFirst, to represent the \\\"central pathological microenvironment,\\\" we acquired the GSE106608 dataset (based on the GPL16791 platform, Illumina HiSeq 2500). This dataset contains gene expression profiles of the post-mortem STN from PD patients and normal controls. Given that the STN is a critical downstream nucleus in the basal ganglia motor circuit most severely affected by dopamine depletion, its transcriptomic characteristics accurately reflect the neural circuit dysfunction in PD.\\u003c/p\\u003e\\u003cp\\u003e Second, to represent the \\\"peripheral systemic microenvironment,\\\" we obtained the GSE160299 dataset (based on the GPL20301 platform, Illumina HiSeq 4000), which encompasses RNA sequencing data of plasma from PD patients and healthy controls. The plasma transcriptome harbors abundant immune factors and metabolic signals, effectively characterizing the systemic inflammatory status and peripheral alterations in PD patients.\\u003c/p\\u003e\\u003cp\\u003eDuring the data processing phase, raw sequencing data were standardized and quality-controlled using the GEO2R analysis tool and R packages (e.g., Seurat 4.3.0). By setting strict thresholds (|log2FC| \\u0026gt; 1.0, P-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05), we identified \\\"central nervous system injury-related genes\\\" in the STN tissue and \\\"peripheral system disorder-related genes\\\" in the plasma, laying the groundwork for deciphering the cross-tissue synergistic mechanisms of SB.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4. Enrichment Analysis of Potential Therapeutic Targets of SB against PD\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eThe intersection of the three aforementioned datasets (active compound targets, PD disease targets, and DEGs from GSE106608/GSE160299 analyzed separately) yielded the list of potential targets for SB in treating PD. These overlapping genes were subsequently uploaded to the DAVID database for Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The target gene lists were also imported into the STRING database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.string-db.org\\u003c/span\\u003e\\u003cspan address=\\\"https://www.string-db.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) to perform protein-protein interaction (PPI) analysis, which was visualized using Cytoscape (version 3.7.1). Key target genes within the PPI network were screened using Cytoscape network analysis plugins, based on critical topological parameters including \\\"degree,\\\" \\\"closeness,\\\" and \\\"betweenness.\\\"\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5. Weighted Gene Co-expression Network Analysis (WGCNA)\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eUsing the GSE106608 and GSE160299 datasets as target matrices, WGCNA was conducted via the OECloud platform (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://cloud.oebiotech.com/\\u003c/span\\u003e\\u003cspan address=\\\"https://cloud.oebiotech.com/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) to screen for modules highly correlated with PD, thereby identifying PD-associated module genes. The final candidate gene sets were obtained by taking the intersection of the active compound targets of SB, the database-derived PD disease targets, the DEGs, and the WGCNA-identified PD module genes (analyzed separately for GSE106608 and GSE160299).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.6. Screening of Core Genes via Machine Learning\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eThree machine learning algorithms\\u0026mdash;Random Forest, SVM, and Least Absolute Shrinkage and Selection Operator (LASSO)\\u0026mdash;were employed to further refine the intersected genes. The intersection of the genes selected by these three algorithms was extracted. By cross-referencing the results from the PPI network, WGCNA, machine learning algorithms, and existing literature, the final core genes were determined.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.7. Molecular Docking of Core Genes and Active Compounds\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eCytoscape (version 3.9.1) was utilized to visualize the correlation between the active compounds and the core targets, which was subsequently validated via molecular docking. The 3D structure files of the core genes were downloaded from the PDB database (\\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) and converted to PDB format using OpenBabel 2.4.1. Sequential docking analyses were performed using the CB-DOCK2 online server (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://cadd.labshare.cn/cb-dock2/php/index.php\\u003c/span\\u003e\\u003cspan address=\\\"https://cadd.labshare.cn/cb-dock2/php/index.php\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) to obtain blind docking scores between the core genes and key active compounds. Finally, PyMOL software was used for visualization.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.8. Chemicals and Reagents\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eWogonin (CAS: 632-85-9, B20489), quercetin (CAS: 117-39-5, B20527), stigmasterol (CAS: 83-48-7, B20314), baicalein (CAS: 491-67-8, B20571), beta-sitosterol (CAS: 83-46-5, B21972), and eriodictyol (CAS: 552-58-9, B21160) were purchased from Yuanye Biotechnology (Shanghai, China). Moslosooflavone (CAS: 3570-62-5, HY-N2035) was purchased from MedChemExpress (Monmouth Junction, NJ, USA). N-Methyl-4-phenylpyridinium iodide (MPP⁺) (N137206) was purchased from Aladdin (Shanghai, China).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.9. Cell Culture and Model Construction\\u003c/h2\\u003e \\u003cp\\u003e\\u003cdiv class=\\\"BlockQuote\\\"\\u003e\\u003cp\\u003e The SH-SY5Y and THP-1 cell lines were obtained from the Yancheng Medical Research Center, Affiliated Hospital of Nanjing University Medical School. Human neuroblastoma SH-SY5Y cells were cultured in DMEM/F-12 supplemented with 10% FBS, 2 mM L-glutamine, and 1% penicillin/streptomycin in a humidified incubator at 37\\u0026deg;C with 5% CO₂. To simulate damaged neurons in PD in vitro, SH-SY5Y cells were treated with 3 mM MPP⁺ for 24 h. Human monocytic THP-1 cells were cultured in RPMI-1640 medium. For macrophage differentiation, THP-1 cells were stimulated with 100 ng/mL Phorbol 12-myristate 13-acetate (PMA) for 3 days to induce adherent differentiation, followed by treatment with 1 \\u0026micro;g/mL LPS for 24 h to establish an in vitro peripheral inflammation model.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.10. Cell Viability Assay\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eCell viability was assessed using the Cell Counting Kit-8 (CCK-8) assay. SH-SY5Y and THP-1 cells were seeded in 96-well plates and incubated overnight. To evaluate the cytotoxicity of the active compounds of SB, cells were treated with varying concentrations of the compounds for 48 h. To assess the neuroprotective effects, SH-SY5Y cells were pre-treated with the active compounds for 24 h, followed by stimulation with 3 mM MPP⁺ for an additional 24 h. Subsequently, the CCK-8 solution was added to each well, and the plates were incubated at 37\\u0026deg;C for another 2 h. The optical density (OD) at 450 nm was measured using a microplate reader (Thermo Fisher Scientific, Waltham, MA, USA).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.11. Measurement of Inflammatory Markers\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eTo evaluate the effects of the active compounds on peripheral inflammation, THP-1 cells (differentiated with PMA and adherent) were pre-treated with various concentrations of the active compounds of SB for 24 h, followed by stimulation with 1 \\u0026micro;g/mL LPS for another 24 h. The culture supernatants were collected to measure the levels of the inflammatory cytokines IL-6 and TNF-α using ELISA. The Human IL-6 Uncoated ELISA Kit (Cat. number: 88-7066-77) and Human TNF alpha Uncoated ELISA Kit (Cat. number: 88-7346-88) were obtained from Thermo Fisher Scientific (Waltham, MA, USA) and used according to the manufacturer\\u0026rsquo;s instructions.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.12. Mathematical and Statistical Analyses\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eAll data are expressed as the mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation (SD) from three independent experiments. Statistical analyses were performed using one-way analysis of variance (ANOVA) for multiple group comparisons via GraphPad Prism 8.0 (GraphPad Software, Inc., La Jolla, CA, USA). Differences were considered statistically significant when P\\u0026thinsp;\\u0026le;\\u0026thinsp;0.05 (*), P\\u0026thinsp;\\u0026le;\\u0026thinsp;0.01 (**), and P\\u0026thinsp;\\u0026le;\\u0026thinsp;0.001 (***).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1. Screening of Active Compounds and Potential Targets of SB\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eTo identify the active ingredients of SB, we searched the TCMSP database and applied stringent pharmacokinetic filtering criteria: oral bioavailability (OB)\\u0026thinsp;\\u0026ge;\\u0026thinsp;30% and drug-likeness (DL)\\u0026thinsp;\\u0026ge;\\u0026thinsp;0.18. Consequently, 26 active compounds were obtained (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Subsequently, SwissTargetPrediction and PharmaMapper were utilized to predict the corresponding targets of these active components, yielding a comprehensive drug-target dataset comprising 230 unique target genes after the removal of duplicates.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2. Identification of Potential Targets for PD\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eUsing \\\"Parkinson\\u0026rsquo;s disease\\\" as the query keyword, we comprehensively searched the TTD and GeneCards databases. After eliminating redundant entries and integrating the data, a robust dataset of PD-related targets was established. This disease target profile served as the foundation for the subsequent screening of potential therapeutic targets of SB against PD.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3. Identification and Enrichment Analysis of DEGs from GEO Datasets\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eTo capture the transcriptomic signatures of PD, the GEO2R tool and the Seurat R package were employed to analyze the GSE106608 and GSE160299 datasets, respectively (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA, \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA). Based on predefined thresholds, differentially expressed genes (DEGs) were identified. As depicted in the volcano plots (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB, \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eB), red dots represent significantly upregulated genes, whereas blue dots indicate significantly downregulated genes.\\u003c/p\\u003e \\u003cp\\u003eNext, we intersected the SB drug targets, the PD disease targets, and the DEGs from the two GEO datasets separately (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eC, \\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eC). This resulted in 46 overlapping genes for GSE106608 and 48 overlapping genes for GSE160299. To elucidate the biological functions of these intersection genes, GO and KEGG pathway enrichment analyses were performed using the DAVID database. The results revealed that genes from the GSE106608 dataset were predominantly enriched in biological processes and signaling pathways such as \\\"Response to oxygen-containing compound,\\\" \\\"Pathways in cancer,\\\" and \\\"Fluid shear stress and atherosclerosis\\\" (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eD-E). Conversely, genes from the GSE160299 dataset were primarily enriched in functions such as the \\\"Positive regulation of intracellular signal transduction\\\" and the \\\"PI3K-Akt signaling pathway\\\" (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eD-E).\\u003c/p\\u003e \\u003cp\\u003eFurthermore, we constructed PPI networks for the 46 and 48 potential target genes (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e) and identified key hub genes using Cytoscape topological analysis plugins. The color intensity of the nodes reflects their degree values, while the node size correlates with interaction connectivity. Based on comprehensive scores calculated from \\\"degree,\\\" \\\"closeness,\\\" and \\\"betweenness,\\\" the top 12 genes from the GSE106608 dataset were selected as key central targets (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eA), among which metabolism- and apoptosis-regulating genes like PPARG and BCL2 exhibited strong relevance (detailed parameters in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eB). For the GSE160299 dataset, the top 7 genes were identified as key peripheral targets (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eC), highlighting inflammation-regulatory genes such as IL-6 and TGFB1 (detailed parameters in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eD).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4. WGCNA of the GEO Datasets\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eTo further mine gene modules intimately associated with PD, WGCNA was performed on the GSE106608 and GSE160299 datasets. First, we calculated scale independence and mean connectivity to determine the optimal soft-thresholding power for constructing a scale-free network (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eA, \\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eA). Based on the Topological Overlap Matrix (TOM), genes were clustered into distinct modules (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eB-C, \\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eB-C).\\u003c/p\\u003e \\u003cp\\u003eHeatmaps of module-trait relationships revealed significant correlations between specific modules and PD pathogenesis. In the GSE106608 analysis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eD), the \\\"darkred\\\" module exhibited a significant positive correlation with PD (r\\u0026thinsp;=\\u0026thinsp;0.73). In the GSE160299 analysis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eD), the \\\"green\\\" module (r\\u0026thinsp;=\\u0026thinsp;0.97) and \\\"coral2\\\" module (r = -0.93) showed exceedingly high correlations with PD. Finally, we intersected the drug targets, disease targets, DEGs, and key WGCNA module genes (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eE, \\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003eE) to preliminarily define the candidate key genes for subsequent analysis.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.5. Screening of Core Targets via Machine Learning\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eTo precisely pinpoint core biomarkers from the candidate genes, three machine learning algorithms\\u0026mdash;Random Forest, SVM, and LASSO\\u0026mdash;were introduced for feature selection. Figures\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e and \\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e display the cross-validation accuracy and error curves for each algorithm across both datasets, verifying the robustness of the models.\\u003c/p\\u003e \\u003cp\\u003eBy intersecting the genes selected by the three algorithms (Venn diagrams, Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eE, \\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eE) and integrating them with the previous PPI results, we finalized the core target genes. For the central nervous system dataset (GSE106608), the core targets included CDK2, SLC6A3, and MGAM (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003eA). For the peripheral systemic dataset (GSE160299), the core targets comprised BAX, MYC, GABRA2, SPP1, and HMOX1 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003eA).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.6. Molecular Docking of Core Genes and Active Compounds of SB\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eTo validate the binding likelihood and stability between the active compounds of SB and the identified core targets, molecular docking simulations were conducted. The visualization results (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003eB-E, \\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003eB-E) demonstrate that the active small molecules seamlessly anchor into the active pockets of the target proteins.\\u003c/p\\u003e \\u003cp\\u003eBinding energy analysis revealed strong affinities. For the brain-related central targets (GSE106608), the binding energy was \\u0026minus;\\u0026thinsp;9.9 kcal/mol for Stigmasterol-SLC6A3, -8.9 kcal/mol for Wogonin-CDK2, -8.8 kcal/mol for Moslossooflavone-CDK2, and \\u0026minus;\\u0026thinsp;8.7 kcal/mol for Quercetin-MGAM. For the peripheral inflammation-related targets (GSE160299), the binding energy was \\u0026minus;\\u0026thinsp;8.8 kcal/mol for Beta-sitosterol-BAX, -7.8 kcal/mol for Eriodictyol-HMOX1, -7.3 kcal/mol for Baicalein-BAX, -7.1 kcal/mol for Beta-sitosterol-GABRA2, -7.0 kcal/mol for Quercetin-SPP1, and \\u0026minus;\\u0026thinsp;6.9 kcal/mol for Wogonin-BAX. These low binding energies (large absolute values indicate higher stability) strongly suggest that the active compounds of SB may exert anti-PD efficacy by directly interacting with these core targets to modulate apoptosis, neural signaling, inflammatory responses, and energy metabolism.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.7. Experimental Validation In Vitro\\u003c/h2\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eTo simulate damaged neurons in PD patients, SH-SY5Y cells were exposed to 3 mM MPP⁺ for 24 h. Based on the docking results, four compounds (Wogonin, Moslossooflavone, Quercetin, and Stigmasterol) were selected as potential CNS-acting candidates, and their protective effects were evaluated. Concurrently, a peripheral inflammation model was established by stimulating THP-1 cells with 1 \\u0026micro;g/mL LPS for 24 h. Similarly, five compounds (Baicalein, Wogonin, Quercetin, Beta-sitosterol, and Eriodictyol) were selected as potential peripherally acting candidates, and their anti-inflammatory effects were assessed.\\u003c/p\\u003e \\u003cp\\u003eThe CCK-8 cell viability assay (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e11\\u003c/span\\u003eA, C) demonstrated that treatment with these compounds for 48 h did not significantly alter the viability of normal SH-SY5Y or THP-1 cells compared to the control group (viability maintained\\u0026thinsp;\\u0026gt;\\u0026thinsp;90%), indicating no obvious cytotoxicity within the tested concentration ranges.\\u003c/p\\u003e \\u003cp\\u003eIn the MPP⁺-induced PD neuronal injury model (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e11\\u003c/span\\u003eB), cell viability drastically decreased after 24 h of 3 mM MPP⁺ treatment, confirming successful model establishment. Pre-treatment with higher concentrations of Wogonin, Quercetin, and Stigmasterol significantly rescued cell viability compared to the MPP⁺-only group. This finding suggests that these SB compounds markedly attenuate MPP⁺-induced neuronal damage, exerting a neuroprotective effect.\\u003c/p\\u003e \\u003cp\\u003eIn the LPS-induced peripheral inflammation model (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e11\\u003c/span\\u003eD, E), the secretion levels of IL-6 and TNF-α surged significantly after 24 h of LPS treatment. Pre-treatment with Baicalein, Wogonin, Quercetin, and Eriodictyol significantly reduced the secretion of IL-6 and TNF-α compared to the LPS group in a concentration-dependent manner. This implies that these active compounds effectively inhibit LPS-induced pro-inflammatory cytokine release, thereby exerting peripheral anti-inflammatory effects.\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, within safe concentration ranges, Wogonin and Quercetin from SB not only protect MPP⁺-injured SH-SY5Y neurons and improve cell survival but also effectively suppress the secretion of inflammatory cytokines (IL-6, TNF-α) in LPS-stimulated THP-1 cells. Multiple active ingredients in SB function synergistically to provide central neuroprotection and peripheral anti-inflammation, offering robust in vitro evidence for the application of SB in treating PD and inflammation-related diseases.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003ePD is a highly complex neurodegenerative disorder driven by multifaceted pathogenic mechanisms, including oxidative stress, neuroinflammation, mitochondrial dysfunction, and abnormal protein aggregation. Currently, mainstream clinical medications primarily offer symptomatic relief, leaving a critical unmet need for effective disease-modifying therapies capable of halting or reversing progressive neuronal loss [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. SB, a traditional Chinese herbal medicine renowned for its robust anti-inflammatory and antioxidant properties, has exhibited profound translational potential in the field of neuroprotection. To address the elusive pharmacological basis of SB, this study systematically decoded the core targets and molecular mechanisms underlying its anti-PD efficacy by comprehensively integrating network pharmacology, WGCNA, machine learning algorithms, and in vitro experimental validation.\\u003c/p\\u003e \\u003cp\\u003eInnovatively, this study integrated transcriptomic profiles from both the STN and peripheral plasma, successfully breaking the conventional limitation of focusing exclusively on central nervous system targets. Our findings compellingly propose that SB and its active compounds exert a \\\"two-pronged\\\" therapeutic mechanism against PD, functioning synergistically across both the central neural circuits and the peripheral immune system.\\u003c/p\\u003e \\u003cp\\u003eFirst, through the joint screening of transcriptomic targets from the STN tissue, we identified CDK2, MGAM, and SLC6A3 as the core central targets for the active compounds of SB against PD. Previous studies have suggested that both CDK2 and MGAM may be involved in the pathogenesis and progression of PD, albeit through distinct mechanisms. CDK2, a canonical cyclin-dependent kinase, primarily regulates the G1-to-S phase transition and DNA replication, thereby maintaining normal proliferation in dividing cells [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. In the context of neurodegenerative diseases, animal experiments have demonstrated that CDK2 activity is upregulated in dopaminergic neurons of the substantia nigra pars compacta in MPTP-induced PD mouse models. Furthermore, viral-mediated expression of a dominant-negative CDK2 conferred approximately 25% protection against MPTP-induced loss of dopaminergic neurons, suggesting that aberrant CDK2 activation contributes to PD-related erroneous cell cycle re-entry and neuronal death [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. In contrast, MGAM encodes maltase-glucoamylase, a brush-border digestive enzyme primarily involved in the terminal hydrolysis of starch and energy absorption in the small intestine [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Multi-omics analyses and large-scale population data have revealed signals of co-occurrence or differential expression of MGAM in PD-related gene expression profiles and text-mining databases, particularly identifying it as a potential sex-specific gene in female PD patients in stratified analyses [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. These findings suggest that MGAM may indirectly modulate PD susceptibility and progression by influencing energy metabolism, gut-immune responses, and other pathways. Although no direct molecular interaction between CDK2 and MGAM has been reported to date, they respectively represent two critical pathological dimensions associated with PD: \\\"intraneuronal dysregulation of cell cycle/stress pathways\\\" and \\\"peripheral metabolic-immune environmental alterations.\\\" This highlights that the molecular pathology of PD may involve synergistic imbalances across multiple pathways and levels, both within and outside central neurons. These insights provide a theoretical basis for future dual-target interventions in PD focusing on cell cycle regulation and the metabolism-inflammation axis. Additionally, the SLC6A3 gene, which encodes the dopamine transporter, has been identified as a PD susceptibility gene. Meta-analyses of previous genetic studies have indicated that its polymorphisms are moderately but statistically significantly associated with the risk of developing PD [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. Taken together, these three core genes operate across distinct dimensions\\u0026mdash;blocking aberrant apoptosis (CDK2), ameliorating brain energy metabolism (MGAM), and maintaining synaptic neurotransmitter homeostasis (SLC6A3). This multi-dimensional regulation not only perfectly explains why the active compounds of SB significantly reversed MPP⁺-induced SH-SY5Y neuronal death and improved cell viability in our in vitro experiments, but also robustly substantiates the underlying \\\"multi-target synergistic\\\" neuroprotective mechanisms of SB in the central nervous system.\\u003c/p\\u003e \\u003cp\\u003eOur molecular docking results revealed exceptionally low binding energies for the complexes of Stigmasterol with SLC6A3, Wogonin and Moslossooflavone with CDK2, and Quercetin with MGAM, indicating highly stable binding affinities between these active ingredients and their respective target proteins. Highly consistent with these computational predictions, our in vitro cellular assays demonstrated that these compounds significantly enhanced the viability of SH-SY5Y neurons under MPP⁺-induced toxicity. Based on the biological functions of these specific targets, we reasonably hypothesize that these active components synergistically exert profound neuroprotective effects in the central nervous system by precisely modulating these core proteins, thereby ameliorating the pathological progression of PD. Specifically, Stigmasterol may directly bind to and regulate the function or expression of SLC6A3 (dopamine transporter), thereby improving dopaminergic neurotransmission and maintaining synaptic transmitter homeostasis. Wogonin and Moslossooflavone likely target and inhibit the aberrant hyperactivation of CDK2, effectively blocking neurotoxin-induced abnormal cell cycle re-entry and the subsequent apoptotic cascades in dopaminergic neurons. Concurrently, Quercetin holds the potential to modulate MGAM activity, thereby correcting the imbalance in brain energy metabolism and restoring mitochondrial function in damaged neurons. Such a multi-target, multi-dimensional molecular intervention mechanism provides a compelling theoretical basis for the robust efficacy of SB active constituents against MPP⁺-induced neurotoxicity.\\u003c/p\\u003e \\u003cp\\u003eSecondly, neuroinflammation serves as a critical driver of PD progression, with a well-established link between peripheral immune system activation and central neurodegeneration. Recent reviews highlight significant crosstalk between central immunity (characterized by microglial and astrocyte activation) and peripheral immunity (marked by alterations in monocyte/lymphocyte populations and cytokine profiles) in PD. Consequently, the peripheral\\u0026ndash;central immune axis plays a pivotal role in both the initiation and progression of the disease [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThrough joint screening of peripheral blood transcriptomic targets, we identified BAX, HMOX1, GABRA2, and SPP1 as core peripheral targets mediating the anti-PD effects of active compounds derived from SB. HMOX1 (heme oxygenase-1) is a pivotal enzyme in cellular defense against oxidative stress. Previous studies indicate that HMOX1 regulates iron homeostasis and oxidative stress responses; its aberrant expression is closely linked to α-synuclein pathology, influencing the misfolding and toxic progression of α-synuclein in PD [\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]; see also recent reviews on the HMOX1\\u0026ndash;α-synuclein axis and iron-lipid peroxidation crosstalk [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. Furthermore, BAX is a canonical pro-apoptotic protein. Mitochondria-mediated apoptosis is recognized as one of the primary mechanisms driving dopaminergic neuron death in PD. Extensive research, including early studies and recent reviews, demonstrates that BAX upregulation and the resulting imbalance in the Bcl-2/BAX ratio drive neuronal apoptosis in both toxin-induced and genetic PD models [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e].GABRA2 encodes the α2 subunit of the γ-aminobutyric acid type A (GABA_A) receptor, a critical component of central inhibitory neurotransmission. In disease models and transcriptomic studies, GABRA2 exhibits aberrant expression patterns in PD-related contexts. For instance, in brain organoid models derived from PD patients, GABRA2 is significantly upregulated in PD organoids compared to its marked downregulation in non-PD controls, suggesting its involvement in PD-associated remodeling of GABAergic synaptic function [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. Conversely, in toxin-induced PD animal models (e.g., paraquat exposure), Gabra2 expression is downregulated in the striatum [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Similarly, in MAO-B knockout mice, GABRA2 is significantly reduced as part of a PD-related gene signature, co-occurring with alterations in various inflammation and injury-related genes [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. Collectively, these findings support the role of GABRA2 in PD pathology through the modulation of GABAergic inhibitory pathways and neuroinflammation. In contrast, SPP1 (secreted phosphoprotein 1), also known as osteopontin (OPN), functions primarily as an extracellular matrix protein and an immunomodulatory molecule. In PD, it is recognized as a key mediator of neuroinflammation and a potential biomarker. Previous studies have reported elevated levels of SPP1/OPN in the serum and cerebrospinal fluid of PD patients, which correlate with the severity of motor phenotypes and the risk of dementia [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]. In MPTP-induced PD mouse models, Spp1 transcription is altered in regions such as the substantia nigra; notably, SPP1 deficiency attenuates dopaminergic neuron loss and microglial activation [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]. Furthermore, integrated single-cell and spatial transcriptomic analyses of human PD brain tissue reveal that SPP1 is markedly upregulated in activated microglia within lesioned areas, including the substantia nigra and cingulate cortex, identifying it as a core marker of the neuroinflammatory microenvironment in PD [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eOur molecular docking results revealed exceptionally low binding energies for the complexes of Beta-sitosterol with BAX, Eriodictyol with HMOX1, Baicalein with BAX, Beta-sitosterol with GABRA2, and Quercetin with SPP1, indicating highly stable binding conformations between these peripheral targets and the active compounds. Consistent with these docking affinities, our in vitro THP-1 inflammatory model demonstrated that these active components of SB significantly suppressed the excessive secretion of pro-inflammatory cytokines, specifically TNF-α and IL-6. It is well established that elevated peripheral TNF-α not only directly triggers neuronal apoptosis but also compromises brain microvascular endothelial cells via the TNF-α/NF-κB signaling pathway. This process disrupts the integrity of the BBB, thereby facilitating the infiltration of peripheral inflammatory factors and immune cells into the CNS. This infiltration drastically exacerbates the central neuroinflammatory microenvironment\\u0026mdash;a vicious pathological cycle that has been robustly validated in α-synuclein-associated BBB damage models [\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. Our in vitro findings strongly suggest that Eriodictyol, Quercetin, Baicalein, and Beta-sitosterol effectively mitigate the peripheral inflammatory cascade in PD patients by precisely modulating these specific target proteins, thereby interrupting the disease progression. Specifically, Eriodictyol may interact with HMOX1 to enhance cellular resistance against systemic oxidative stress and shift macrophages away from a hyperactive pro-inflammatory state. Baicalein and Beta-sitosterol likely target the pro-apoptotic protein BAX, potentially protecting crucial structural cells (such as BBB endothelial cells) from inflammation-induced apoptosis, thereby maintaining barrier integrity. Furthermore, Beta-sitosterol's binding to GABRA2 hints at a novel mechanism of dampening excessive macrophage activation through peripheral GABAergic immunomodulation. Concurrently, Quercetin may inhibit the signaling of SPP1 (osteopontin), thereby restricting the chemotaxis and profound inflammatory activation of monocytes. By synergistically suppressing the peripheral inflammatory storm and safeguarding the BBB, these multi-target compounds effectively sever the pathological cross-talk between the periphery and the brain, ultimately delaying the progression of PD.\\u003c/p\\u003e \\u003cp\\u003eAmong the active constituents identified in our study, flavonoids\\u0026mdash;particularly Wogonin and Quercetin\\u0026mdash;occupy a predominantly dominant position. Extensive previous research has demonstrated the inherent ability of these flavonoid compounds to cross the BBB and exert potent neuroprotective effects. For instance, wogonin has been firmly established to inhibit the activation of the NF-κB signaling pathway, thereby attenuating the inflammatory responses and subsequent cell death associated with central neurodegeneration. Its comprehensive anti-inflammatory, antioxidant, and neuroprotective properties in central nervous system disorders have been systematically reviewed [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Similarly, multiple studies have corroborated that quercetin can activate the Nrf2-dependent antioxidant pathway and upregulate downstream antioxidant enzymes, including heme oxygenase-1 (HO-1/HMOX1). This mechanism significantly alleviates oxidative stress and neuronal damage across various neurological injury models [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eCrucially, in the present study, our computational docking and in vitro validation reveal that these multiple active components exhibit exceptionally high binding affinities for both central and peripheral core targets. This robustly substantiates the material basis of SB in intervening against PD through a synergistic, multi-component paradigm. Notably, Wogonin and Quercetin\\u0026mdash;two flagship bioactive components of SB\\u0026mdash;not only demonstrate the capacity for direct neuronal protection in our models but also show profound efficacy in downregulating key systemic inflammatory mediators such as TNF-α. By rectifying the dysregulation of the peripheral-central immune-inflammatory axis, these compounds synergistically exert comprehensive anti-PD effects, thereby emerging as highly promising therapeutic candidates for the clinical management of PD.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e\"},{\"header\":\"5. Conclusions\",\"content\":\"\\u003cp\\u003e \\u003cdiv class=\\\"BlockQuote\\\"\\u003e \\u003cp\\u003eIn summary, this study employed a comprehensive integrative strategy\\u0026mdash;combining network pharmacology, WGCNA, machine learning algorithms, and in vitro experimental validation\\u0026mdash;to systematically elucidate the active constituent profile and the underlying pharmacological mechanisms of SB against PD. Our findings robustly demonstrate that the flagship active compounds of SB, particularly wogonin and quercetin, exert potent direct and indirect neuroprotective effects. These therapeutic benefits are driven by the precise targeting of core genes, including SLC6A3, HMOX1, and BAX, which coordinately alleviate oxidative stress, inhibit neuronal apoptosis, and attenuate both peripheral and central neuroinflammation. Ultimately, this study not only establishes a compelling scientific rationale for the clinical application of SB in PD management, but also highlights the exceptional robustness of integrating machine learning with multi-omics data mining to decipher the complex, \\\"multi-target\\\" paradigms of traditional Chinese medicine. These novel insights offer highly valuable candidate targets and theoretical foundations for future anti-PD targeted drug discovery and development.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u003c/strong\\u003e This research was funded by the Yancheng Science and Technology Project, grant number YCBK2024068, and the Medical Research Project of Yancheng Health Commission, grant number YK2024034.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflicts of Interest:\\u0026nbsp;\\u003c/strong\\u003eThe authors declare that they have no conflict of interest.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval:\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to participate\\u003c/strong\\u003e\\u003cstrong\\u003e:\\u003c/strong\\u003e Not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003cstrong\\u003e:\\u003c/strong\\u003e Not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and material:\\u0026nbsp;\\u003c/strong\\u003ePublicly available datasets were analyzed in this study. This data can be found at GEO (GSE106608 and GSE160299).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCode availability\\u003c/strong\\u003e\\u003cstrong\\u003e:\\u003c/strong\\u003e Not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions:\\u003c/strong\\u003e Conceptualization, Y.L.; methodology, A.S.; investigation, Y.L.; resources, H.W. and L.Z.; writing\\u0026mdash;original draft preparation, Y.L. and A.S.; writing\\u0026mdash;review and editing, A.S.; supervision, Y.M.; project administration, Y.M. and H.W.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eZhu J, Cui Y, Zhang J, Yan R, Su D, Zhao D, Wang A, Feng T. Temporal trends in the prevalence of Parkinson's disease from 1980 to 2023: a systematic review and meta-analysis. 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J Biochem Mol Toxicol. 2026;40:e70656. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1002/jbt.70656\\u003c/span\\u003e\\u003cspan address=\\\"10.1002/jbt.70656\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e.\\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\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-complementary-medicine-and-therapies\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bcam\",\"sideBox\":\"Learn more about [BMC Complementary Medicine and Therapies](https://bmccomplementmedtherapies.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"BMC Complementary Medicine and Therapies\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Scutellaria barbata, Parkinson's disease, network pharmacology, WGCNA, machine learning, neuroinflammation\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9189068/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9189068/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eParkinson's disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons and neuroinflammation. \\u003cem\\u003eScutellaria barbata\\u003c/em\\u003e D.Don (SB), a traditional Chinese medicine, exhibits significant anti-inflammatory and neuroprotective properties; however, its molecular mechanisms underlying PD treatment remain poorly understood. This study aims to elucidate the potential therapeutic targets of SB in treating PD through an integrated approach of bioinformatics and experimental validation. Active ingredients and targets of SB were retrieved from TCMSP and SwissTargetPrediction, while PD-related targets were identified from TTD, GeneCards, and GEO datasets (GSE106608 and GSE160299). Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning algorithms (Random Forest, SVM, and LASSO) were employed to screen for core genes. We identified 26 active ingredients and 230 targets, which were primarily enriched in oxidative stress, inflammatory pathways, and apoptosis. Machine learning pinpointed core brain-related targets (\\u003cem\\u003eSLC6A3, CDK2\\u003c/em\\u003e) and peripheral inflammation-related targets (\\u003cem\\u003eBAX, HMOX1\\u003c/em\\u003e). Molecular docking confirmed high binding affinities between these core targets and key ingredients, such as wogonin and stigmasterol. Finally, in vitro experiments demonstrated that these active ingredients significantly improved the viability of MPP⁺-induced SH-SY5Y cells and reduced TNF-α and IL-6 secretion in LPS-stimulated THP-1 cells. In conclusion, SB exerts anti-PD effects through a multi-component and multi-target mechanism involving the synergistic regulation of neuroinflammation and apoptosis, providing a robust theoretical basis for its clinical application.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Unraveling the Pharmacological Mechanisms of Scutellaria barbata in Parkinson's Disease: An Integrated Bioinformatics and Experimental Validation Study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-05-05 16:03:58\",\"doi\":\"10.21203/rs.3.rs-9189068/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"reviewerAgreed\",\"content\":\"261628902581152990170108050942041171417\",\"date\":\"2026-04-29T09:56:37+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"79785270880688247054664686261878465789\",\"date\":\"2026-04-28T12:22:38+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-04-27T09:05:16+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2026-04-24T13:07:36+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-03-23T06:27:28+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-03-23T06:27:12+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Complementary Medicine and Therapies\",\"date\":\"2026-03-22T04:39:23+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-complementary-medicine-and-therapies\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"bcam\",\"sideBox\":\"Learn more about [BMC Complementary Medicine and Therapies](https://bmccomplementmedtherapies.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"BMC Complementary Medicine and Therapies\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"7328dbaf-ae66-4dc2-9dd4-1cb897dcdf5f\",\"owner\":[],\"postedDate\":\"May 5th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"reviewerAgreed\",\"content\":\"261628902581152990170108050942041171417\",\"date\":\"2026-04-29T09:56:37+00:00\",\"index\":32,\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-05T16:03:58+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-05-05 16:03:58\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9189068\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9189068\",\"identity\":\"rs-9189068\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}