Investigating the protective mechanism of Astragalus membranaceous (Fisch.) against cerebral ischemia-reperfusion injury in rats: A metabolomics and network pharmacology approach

Investigating the protective mechanism of Astragalus membranaceous (Fisch.) against cerebral ischemia-reperfusion injury in rats: A metabolomics and network pharmacology approach | 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 Investigating the protective mechanism of Astragalus membranaceous (Fisch.) against cerebral ischemia-reperfusion injury in rats: A metabolomics and network pharmacology approach Ruimin Liang, Yue Chen, Ruizhen Zhang, Kai Wang, Yan Shu, Yi Qiao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5733320/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Astragalus membranaceous (Fisch.) has a rich history as a traditional medicine in various Asian countries, showcasing a notable neuroprotective effect. However, the underlying therapeutic mechanisms warrant further investigation. This study employs metabolomics and network pharmacological analysis to elucidate the protective effects of Radix Astragali (Huangqi, HQ) against cerebral ischemia-reperfusion injury (CI/RI) in rats. The investigation aims to reveal the potential protective mechanisms of HQ in CI/RI rats. Plasma metabolomics analysis, utilizing multivariate statistical methods, highlights biomarkers and associated metabolic pathways. The integrated approach of network pharmacology comprehensively analyzes HQ’s effective components, therapeutic targets, and amino acid metabolites. Pharmacodynamic experiments demonstrate a significant cerebral protective effect in the HQ group compared to the model group (p < 0.05). Metabolomics results indicate significant differences (P < 0.05) in L-glutamic acid, L-arginine, L-ornithine hydrochloride, L-valine, and L-phenylalanine in the model group compared to the sham operation group, indicating plasma metabolic disorders in CI/RI rats. Network pharmacology analysis identifies quercetin, kaempferol, and astragaloside IV components within HQ that may act on IL6, TNF, and IL-1B targets, influencing five different amino acids to exert brain protection. This study provides valuable insights into the neuroprotective mechanisms of HQ in the context of CI/RI. Metabolomics Network pharmacology Cerebral ischemia-reperfusion injury Molecular docking Amino acid metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Stroke is the leading cause of global mortality and disability, with ischemic stroke constituting 87% of all stroke cases [ 1 ] . It originates from the interruption of cerebral blood supply due to vascular rupture or thrombosis; ischemic stroke results in the deprivation of oxygen and nutrients, inflicting damage to the brain tissue [ 2 ] . Presently, thrombolysis stands out as the preferred treatment for ischemic stroke, capable of dissolving thrombus, restoring blood flow, and protecting the surrounding brain tissue [ 3 , 4 ] . However, the therapeutic window for recanalization is limited, especially in acute ischemic stroke cases, potentially leading to secondary ischemia-reperfusion (I/R) injury [ 5 ] , thereby impacting its clinical efficacy. Traditional Chinese medicine is distinguished by its utilization of multiple ingredients, targeting a multitude of pathways and mechanisms, rendering it particularly effective in addressing the complex pathogenesis of ischemic stroke. Radix Astragali (Huangqi), derived from the dried root of Astragalus membranaceus (Fisch.) It is a renowned qi tonic in Chinese herbal medicine. Characterized by its warm nature and sweet taste, Huangqi (HQ) boasts effects such as replenishing qi, promoting yang, strengthening the surface to stop sweating, and invigorating qi while promoting blood circulation [ 6 ] . Modern studies have demonstrated that HQ can improve neurological impairment and restore neurological function in ischemic stroke patients, exhibiting a regulatory effect on cerebral vascular insufficiency, thereby holding significant promise in stroke prevention [ 7 ] . However, despite its recognized benefits, there is a scarcity of studies investigating the protective mechanisms, especially through the study of metabolomics. Metabolomics serves as a tool to scrutinize changes in metabolic pathways within the whole metabolic network of organisms [ 8 ] . This methodology allows for the characterization of the dynamic properties of metabolites across the biological system, offering a powerful platform for discovering new biomarkers, unraveling metabolic pathways, and enhancing predictive, diagnostic, and treatment capabilities for complex systems [ 9 ] . On the other hand, network pharmacology, rooted in systems biology and multi-directional pharmacology, emerges as a novel drug design method capable of comprehensively analyzing the effective ingredients, targets, and mechanisms of drugs [ 10 ] . The integration of network pharmacology and metabolomics offers a potent approach to elucidate the potential combination strategies within traditional Chinese medicine (TCM) [ 11 , 12 ] . This combined approach sheds light on the mechanism and targets of TCM from both a system biology and molecular perspective. In this study, liquid chromatography-mass spectrometry (LC-MS) metabolomics methods combined with network pharmacology were used to meticulously monitor changes in plasma metabolism and identify potential therapeutic targets. This innovative methodology unequivocally substantiates the prophylactic efficacy of HQ in the context of cerebral ischemia-reperfusion injury (CI/RI), marking a groundbreaking revelation (Fig. 1 ). This strategy not only addresses the limitations inherent in the absence of network pharmacology experiments but also bridged the gap in metabolomics by fusing upstream molecular mechanism with drug targets. Through the establishment of a rat middle cerebral artery occlusion (MCAO) model, coupled with HQ lavage, five metabolite biomarkers were identified and studied, along with their associated metabolic pathways. Furthermore, ten potential therapeutic targets for CI/RI were recognized. This study provides a novel perspective on the neuroprotective effect of HQ in the treatment of CI/RI. 2. Material and methods 2.1. Reagents and materials HQ extract (lot no. TL1703018-2) was produced and obtained from Guangdong Yifang Pharmaceutical Co. Ltd. (Guangdong, China). Nao Xin Tong capsule (batch no. 2006149340, Shaanxi Buchang Pharmaceutical Co. Ltd.) in the form of 0.4 g * 36 granules was utilized. HPLC-grade methanol and formic acid were procured from TCI Chemical Industry Development Co. Ltd. (Shanghai, China). Ultrapure water was generated using a Milli-Q purification system (Billerica, MA, USA). 2.2. Animals and treatments Clean Sprague Dawley (SD) rats, purchased from the Animal Experiment Center of Air Force Military Medical University (production license no. SCXK (Shaan) 2019-001), possessed a male gender, weighed 250 ± 30 g, and were maintained at 45 ± 5% relative humidity and 22–24°C. The rats underwent a one-week temperature adaptation feeding period before the initiation of the experiment. The research ethics committee approved all experimental procedures for the care of laboratory animals at Fourth Military Medical University, and the experiments were conducted in accordance with its guidelines for experimental animals. Modern studies have shown the Buyang Huanwu decoction, historically employed by Qingren Wang in the Qing Dynasty for treating qi deficiency and blood stasis, has demonstrated significant efficacy in ischemic stroke treatment [ 13 ] . Consequently, the experimental design is based on the Buyang Huanwu decoction, where HQ serves as the monarch drug with a dosage of 120 g. In the experiment, 1 g of HQ extract was used, equivalent to 2.86 g HQ. The daily dose for rats was calculated at 4.37 g/kg, reflecting the daily requirement of HQ extract for humans at 41.96 g. Thus, the selected dose for this study is 4.37 g/kg of HQ. SD rats were assigned randomly into four groups using a random number table method: Sham group, model group, HQ group (4.37 g/kg), and Nao Xin Tong capsule group (NXT, as a positive control group). All rats received intragastric administration at a volume of 10 mL/kg. Rats in the sham and model groups were administered the same volume of saline. After 72 h of cerebral ischemia-reperfusion, samples were collected, and the rats were humanely sacrificed. Prior to surgery, all rats underwent abdominal anesthesia using 3% sodium pentobarbital (Merck Company, USA). Following anesthesia, the rats were fixed on a board in a supine position, and the orientation of head and tail fixation was determined based on individual operation habits. After routine disinfection, a vertical opening was made in the middle of the neck to expose and separate the right common carotid artery (CCA), external carotid artery (ECA), and internal carotid artery (ICA). A small incision approximately 5 mm from the CCA bifurcation was made for the insertion of the bolt thread, reaching a depth of about 18 mm. Resistance indicated the stop point, and the bolt thread was ligated, followed by cleaning of the surgical area and skin suturing. The thrombus was extracted 2 h later to complete the post-ischemia reperfusion. In the sham group, surgery involved the separation of CCA, ECA, and ICA without ligation. All surgeries were conducted in an environment maintained at 25–28°C, with the centrifuge set to 3000 g, 4°C, and 20 min. Rat blood supernatant was obtained by centrifuging the blood, and both plasma and brain samples were promptly frozen at − 80°C until LC-MS analysis. 2.3. Infarct volume measurement The rat brain was meticulously isolated and sliced into coronal sections with a thickness of 2 mm. These sections were then stained using 2,3,5-triphenyl tetrazolium chloride (TTC) at a concentration of 2% in phosphate-buffered saline. The staining process involved immersing the sections in the dye solution and incubating them at 37°C for 0.5 h under shaded conditions. To ensure thorough coloring, the glass dish was periodically shaken every 10 min. Following staining, the sections were fixed with 4% paraformaldehyde. The Image Plus Pro software was employed as the measurement tool for determining the infarct area. The ratio of cerebral infarct volume in rats was calculated according to the formula. Ratio of cerebral infarct volume = (section thickness of infarct area)/ (section thickness of total cerebral area) ×100%. In the resulting images, normal brain tissue was represented by bright red areas, whereas infarcted brain tissue was depicted in white. This method allowed for accurate quantification and visualization of cerebral infarct volume in the rats. 2.4. Hematoxylin-eosin (HE) staining Rat brain tissue was carefully harvested, and any residual blood and cerebrospinal fluid on the surface were removed using filter paper. The tissue was then sectioned into three pieces, with the middle piece being preserved and fixed in a 4% tissue fixative. Standard histological procedures that included paraffin embedding and routine HE staining were conducted. This involved slicing, dewaxing, staining, dehydration, sealing, microscopic examination, and subsequent image acquisition and analysis. 2.5. Neurological function assessment Neurological functions were assessed 72 h post-ischemia-reperfusion using a 5-grade, 4-point scoring method based on the approach by Bederson et al [ 14 ] . The scoring criteria were as follows: zero points - normal behavior, no neurological dysfunction; one point - incomplete extension of the left forepaw, indicating mild injury; two points - turning left, suggestive of moderate damage; three points - falling to the left, indicating of severe damage; four points - inability to walk spontaneously, accompanied by loss of consciousness. The degree of neurological impairment was positively correlated with the assigned score; higher scores indicated more severe animal behavior disorders. This standardized scoring system facilitated a comprehensive evaluation of the rats’ neurological status following ischemia-reperfusion. 2.6. Sample preparation The plasma samples were carefully measured and transferred into 2 mL EP tubes. Subsequently, 400 µL of a 10% formic acid-methanol solution-H 2 O (1:1) mixture was accurately added along with 50 mg glass beads. The sample was placed in a high-throughput tissue grinding machine and vigorously shaken at 55 Hz for 1 min, with the process repeated twice. Following this, centrifugation was performed at 12000 rpm 4°C for 5 min. From the original supernatant, 10 µL was extracted and combined with 190 µL of a 10% formic acid methanol-H 2 O (1:1) solution. After vortexing for 30 s, 100 µL of the diluted sample was taken, and 100 µL of a dual isotope internal standard (100 ppb concentration) was added. Another round of vortex oscillation for 30 s ensued, and the supernatant was then filtered through a 0.22 µm membrane before being transferred into the detection bottle. Plasma samples, all with a volume of 10 µL, were collected from each rat, and quality control (QC) samples were prepared using the same procedure. Additionally, random plasma samples were divided into eight parts, and each underwent identical processing to verify the repeatability of the sample preparation methods. Throughout the analysis, strict temperature control was maintained at 4°C. 2.7 Liquid chromatography-mass spectrometry (LC-MS) analysis Chromatographic conditions were as follows: The analysis utilized an ACQUITY UPLC BEH C18 column (2.1 × 100 mm,1.7 µm) from Waters, USA, with an injection volume of 5 µL. The column temperature was maintained at 40°C. The mobile phase consisted of A (10% methanol-water containing 0.1% formic acid) and B (50% methanol-water containing 0.1% formic acid). The gradient elution proceeded through the following phases: 0–6.5 min, 10–30% B; 6.5–7 min, 30–100% B; 7–8 min, 100% B; 8–8.5 min, 100–10% B; 8.5–12.5 min, 10% B. The flow rate was set at 0–8.5 min, 0.3 mL/min, and 8.5–12.5 min, 0.3–0.4 mL/min. The MS conditions were as follows: The analysis employed electrospray ionization (ESI) as the ionization source, operating in positive ion ionization mode. The ion source temperature was set at 500°C, and the ion source voltage to 5500 V. Collision gas was maintained at 6 psi, curtain gas at 30 psi, and both atomized gas and auxiliary gas were set at 50 psi, enabling scanning through multiple reaction monitoring (MRM). 2.8. Data analysis and biomarker selection To explore differences in plasma metabolites between the sham group and the model group, a partial least squares discriminant analysis (PLS-DA) was conducted. The model’s quality was assessed based on R2Y and Q2 (cum) parameters. Initially, group comparisons were made, and metabolites with a projected variable importance in projection (VIP) value VIP > 1 were identified as potential differences. The Student’s t-test, implemented in GraphPad Prism 8.0.1 software, was employed to assess significant differences between groups, considering p < 0.05 as statistically significant. This criterion was then applied to screen for potential biomarkers. By observing the changes in the intensity of these biomarkers between the HQ group and the model group, the impact of HQ on the CI/RI in rats was shown. Additionally, MetaboAnalyst5.0, an online software, was utilized for biomarker enrichment and metabolic pathway analysis. 2.9. Network pharmacology analysis Utilizing network pharmacology analysis, the network relationship between the main active ingredients of HQ and their associated targets was elucidated. Active ingredient-related targets were extracted from the TCMSP ( http://tcmspw.com/tcmsp.php ) and TCMID ( http://www.megabionet.org/tcmid/ ) databases. Disease-related targets were obtained from Genecards ( https://www.genecards.org/ ) and OMIM ( https://omim.org/ ) databases. Protein-protein interaction (PPI) networks were established for HQ’s effective constituent-related targets and disease targets using STRING ( https://string-db.org/cgi/input.pl ). Common targets between HQ and ischemic stroke were identified using Venny. Cytoscape3.7.2 ( http://www.cytoscape.org/ ) was employed to construct the ingredient-target-disease (I-T-D) and metabolite-target (M-T) network maps. For the amino acid metabolic pathway map, MetaboAnalyst 5.0 ( https://www.metaboanalyst.ca/ ) was utilized, while the DAVID database ( https://david.ncifcrf.gov/summary.jsp ) was employed for GO and KEGG signaling pathways analyses of key targets. Ultimately, targets directly linked to the disease, effective compounds, and differential metabolites were selected as potential therapeutic targets. 2.10. Key target analysis The key targets identified through a stepwise screening process underwent validation through ELISA, immunohistochemistry, and immunofluorescence techniques. Subsequently, molecular docking was performed, involving the relevant components and associated amino acids. 2.11. Statistical analysis For comparison across multiple groups, a one-way analysis of variance (ANOVA) was employed. The data were presented as the mean ± standard error of the mean (SEM). A significance threshold of P<0.05 was set for statistical significance. All analyses and graphical representations were performed using GraphPad Prism 8.0 software (San Diego, CA, USA). 3. Results 3.1. The influence on CI/RI in rats of Huangqi (HQ) The effects of HQ on CI/RI in rats were evaluated through the examination of neurological function scores and measurement of cerebral infarction volume measurement. As depicted in Fig. 2 A, rats in the sham group exhibited normal behavior and intact neurological function, reflected by a score of zero. Conversely, rats in the model group displayed higher scores, indicating neurological impairment following ischemia/reperfusion (I/R). Administration of HQ resulted in a significant improvement in neurological damage compared to the model group. Figures 2 B and 2 C illustrate the cerebral infarction volumes for each group, with no observed infarct volume in the Sham group. Post-middle cerebral artery occlusion (MCAO), a substantial infarct size emerged in the coronal brain region, corresponding to the highest neurological coloboma score across all groups. Following HQ treatment, there was a noticeable recovery in the infarct area, as evidenced by the analysis of TTC-stained brain sections. The therapeutic effect of HQ alone was significantly pronounced compared to the model group (P < 0.01). These results signify the neuroprotective efficacy of HQ in rats with CI/RI. HE results (Fig. 2 D) revealed notable differences in the number and arrangement of neurons in the cerebral cortex among the experimental groups. In the sham group, neurons were abundant, displaying an organized arrangement with normal cell morphology. Conversely, the model group exhibited pathological changes in neuronal morphology on the infarcted side, characterized by abnormal structural features. Notably, neurons were randomly distributed, their numbers reduced, intercellular spaces increased, and nuclei appeared condensed and stained. Comparative analysis among the model, HQ, and NXT groups demonstrated that both HQ and NXT interventions led to improvements in the pathological morphology of cerebral cortex neurons in CI/RI rats. Specifically, nuclear pyknosis and deeply stained cells were reduced, intercellular spaces became smaller, cell morphology became more apparent and identifiable, and the number of dead neurons decreased. This observation underscores the therapeutic potential of HQ and NXT in alleviating the detrimental effects on cerebral cortex neurons in CI/RI rats. 3.2. Multivariate statistical analysis of plasma metabolites In Fig. 3 A, a discernible separation of data gathering and dispersion regions between the model and sham groups indicates the successful establishment of the modeling process. The loading plot in Fig. 3 B illustrates the metabolites contributing to differences between sample groups. Metabolites farther from the origin signify a higher separation contribution rate, suggesting their potential as metabolic markers. The PLS-DA score plot in Figs. 3 C and 3 D reveal a significant divergence between the model and sham groups, indicating substantial changes in serum metabolites due to MCAO-induced metabolic disorders in rats. The HQ and NXT groups, positioned closer to the Sham group, suggest that intervention with HQ and NXT induced changes in metabolites and exerted a specific recovery effect on MCAO injury. Figures 3 E and 3 G further demonstrate the successful establishment of the model by the clear separation of samples between the model and sham groups. The validity of the OPLS-DA model is confirmed in Figs. 3 F and 3 H through pairwise comparison permutation tests (n = 200) between the sham and model groups and between the model and HQ groups. The results of the permutation test, indicative of good stability and reliability, confirm the absence of over-fitting phenomena in all models. 3.3. Differential metabolite identification and pathway analysis By utilizing the variable importance projection (VIP) value and Student’s t-test, the metabolites were identified to be the most significant contributions to the distinction between the sham and model groups. Specifically, metabolites with VIP > 1 and P < 0.05 were deemed potential biomarkers. This analysis revealed a total of ten variant amino acid metabolites present in the plasma of both the sham and model groups, as detailed in Table 1 and illustrated in Fig. 4 A. To unravel the metabolic pathways influenced by HQ in CI/RI rats, the differentially regulated amino acids were subjected to metabolic pathway analysis and visualization using MetaboAnalyst 5.0 (Figs. 4 B-C). Seven metabolic pathways, identified based on a pathway impact > 0.1, were highlighted in Fig. 4 C, shedding light on the potential pathways affected by HQ in CI/RI rats. Table 1 Ten differential amino acid metabolites. Differential amino acid Abbreviations VIP P-value L-histidine His 1.672024 0.00044 L-methionine Met 1.586397 6.33E-06 L-glutamic acid Glu 1.48271 0.000477 L-lysine Lys 1.402341 0.012977 L-arginine Arg 1.336722 0.002926 L-ornithine hydrochloride Orn 1.157049 2.22E-05 L-phenylalanine Phe 1.115118 0.00422 L-leucine Leu 1.101549 0.007876 L-alanine Ala 1.055945 0.025897 L-valine Val 1.053893 0.019297 3.4. Network pharmacology analysis Figure 5 A presents a Venn diagram illustrating the potential targets of HQ in disease treatment. In Fig. 5 B, a step-by-step screening process for identifying key targets of HQ in disease treatment is outlined. To evaluate the therapeutic effect of HQ on CI/RI, an I-T-D network was constructed by combining the ingredient-target (I-T) network of HQ with the target-disease (T-D) network. This network unveils the intricate relationships between HQ’s active ingredients, targets, and associated diseases. In Fig. 5 C(a), eight active ingredients and 59 key targets of HQ related to disease treatment are highlighted. The integration of differential amino acids into Cytoscape 3.7.2 resulted in the generation of a metabolite-target (M-T) network, depicted in Fig. 5 C(b). The comprehensive ingredient-target-metabolite-disease (I-T-M-D) network, comprising 77 nodes (8 active ingredients, 59 targets, one disease, eight metabolites) and 274 edges, is presented in Fig. 5 C. By focusing on nodes with a degree of connectivity exceeding six, Fig. 5 D identifies six active ingredients, five differential amino acids, and 16 targets. Figure 6 showcases the results of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses performed on the 16 key targets. Notably, the TNF signaling pathway emerges as highly enriched. 3.5. Key targets for experimental verification Figure 7 A presents the immunohistochemical results for IL-6. Neurons in the sham group exhibited a clear and intact structure, while those in the model group appeared disordered mostly stained dark brown. In the HQ and NXT groups, some neurons displayed varying degrees of dark or light brown staining, accompanied by a minor degree of neuronal damage and necrosis. Figure 7 B showcases the immunofluorescence results for TNF-α. Blue indicates DAPI nuclear staining, red represents TNF-α labeled positive cell staining, and pink depicts TNF-α nuclear staining. Notably, compared to the model group, the HQ and NXT groups showed a reduction in the number of TNF-α positive cells. In Fig. 7 C, immunohistochemical results for IL-1β demonstrated clear and complete neuron structures in the sham group. In the model group, nuclear pyknosis and deep staining were observed, while the HQ and NXT groups exhibited a small number of neurons with dark brown staining. In Fig. 7 D, the ELISA results reveal a noteworthy elevation in plasma levels of TNF-α, IL6, and IL-1β in the model group (p < 0.01), demonstrating statistical significance when compared with the sham group (p < 0.05). Conversely, the HQ group exhibited a substantial reduction in TNF-α, IL6, and IL-1β levels following CI/RI (p < 0.01), establishing statistical differentiation from the model group (p < 0.05). 3.6. Key target docking verification In Fig. 8 A, a network connection analysis reveals that IL-6, TNF-α, and IL-1β were predominantly associated with five amino acids and five components of HQ. This network diagram illustrates the intricate relationships between these inflammatory markers and the constituents of HQ. Figure 8 B and 8 C displays heat maps showcasing the docking results of IL-6, TNF-α, and IL-1β with their respective components and amino acids. The lower the docking score, the more favorable the docking effect. Notably, TNF-α and IL6 exhibited optimal docking with astragaloside IV, while IL-1β demonstrated superior docking with quercetin and isorhamnetin. Additionally, L-phenylalanine emerges as the optimal docking partner for TNF-α and L-Alanine for IL-6. In Fig. 8 , the docking results of TNF-α, IL6, and IL-1β are presented in descending order of their top three docking scores, providing a comprehensive view of their molecular interactions. 4. Discussion In this study, the LC-MS metabolomics method was employed to uncover variations in plasma metabolites among different groups. Five plasma amino acid biomarkers were identified, including L-glutamate, L-arginine, L-ornithine, L-valine, and L-phenylalanine, to distinguish the model group from the sham group. Metabolism pathway analysis highlighted the involvement of these amino acids in pathways such as phenylalanine, tyrosine, and tryptophan biosynthesis, D-glutamine and D-glutamate metabolism, phenylalanine metabolism, and arginine and proline metabolism. Integrating these findings with network pharmacology analysis revealed six main active ingredients in HQ, namely quercetin, kaempferol, isorhamnetin, formononetin, astragaloside IV, and calycosin, acting on 16 potential targets. These interactions were found to modulate brain protection through pathways like TNF, FoxO, toll-like receptor signaling, and the HIF-1 signaling pathway. Experimental verification further demonstrated that five key components of HQ acted on IL6, TNF-α, and IL-1β, contributing to their therapeutic role. Notably, the intersection of these components with the six components of HQ identified in network pharmacological analysis highlighted four consistently crucial components - quercetin, kaempferol, isorhamnetin, and astragaloside IV. This suggests a potentially pivotal role of HQ in brain protection, where quercetin, kaempferol, isorhamnetin, and astragaloside IV influence IL6, TNF-α, and IL-1β, ultimately affecting the levels of L-glutamate, L-arginine, L-ornithine, L-valine, and L-phenylalanine and exerting neuroprotective effects. In the pathophysiological process of CI/RI, amino acid metabolism emerges as a prominent and distinct feature [ 15 ] . Notably, the disruption of metabolic pathways, particularly phenylalanine, tyrosine, and tryptophan biosynthesis, signifies an imbalance in central neurotransmitters during CI/RI [ 16 , 17 ] . Elevated levels of phenylalanine in the bloodstream have been linked to an increased susceptibility to cerebral ischemia [ 18 , 19 ] . Furthermore, the pathological release of excessive glutamate in D-glutamine and D-glutamic acid metabolism contributes to excitatory neurotoxicity, a crucial factor in stroke pathogenesis [ 20 , 21 ] . Arginine, serving as a precursor for proline, nitric oxide (NO), and glutamic acid synthesis, undergoes complex metabolic transformations [ 22 ] . This includes arginine lysing to ornithine and subsequent proline synthesis under enzymatic action of arginine enzyme. Then, L-ornithine synthesizes proline under the action of ornithine aminotransferase [ 23 ] . Changes in the metabolic pathways involving arginine, proline, alanine, aspartic acid, and glutamate in stroke patients imply a disordered energy supply under hypoxic-ischemic conditions [ 24 ] . Consequently, alteration in amino acid profiles within these metabolic pathways may serve as potential markers for CI/RI. Upon investigation, three out of the four ingredients identified in HQ were closely related to CI/RI. Quercetin, with its antioxidant, anti-inflammatory, and antiviral activities, has been shown to reduce CI/RI [ 25 ] . Kaempferol exerts protective effects on ischemic brain injury by combating oxidative stress, inflammation, and apoptosis [ 26 ] . Astragaloside IV demonstrates neuroprotective effects by inhibiting oxidative stress and inflammatory responses, improving brain energy metabolism post-reperfusion, and inhibiting neuronal apoptosis [ 27 , 28 ] . Notably, HQ’s neuroprotective role primarily hinges on quercetin, kaempferol, and astragaloside IV. It is plausible that HQ regulates the changes of Orn, Arg, Glu, Val, and Phe in rats by acting on IL1B, TNF, and IL6—key inflammatory response element—through astragaloside IV, kaempferol, and quercetin. A noteworthy observation is the decrease in IL1B levels, a prominent feature in patients with acute stroke [ 29 ] . KEGG pathway analysis showed the significant role of the TNF signaling pathway, indicating its pivotal involvement in the release of inflammatory factors such as IL-6 and TNF-α from ischemic neurons post-cerebral ischemia. These factors, in turn, induce additional inflammatory responses, cytotoxic substance release, blood-brain barrier damage, and extracellular matrix disruption, thereby accelerating the occurrence of CI/RI [ 30 ] . In summary, the identified effective ingredients in HQ and the metabolomics-screened differential amino acids are intricately linked to CI/RI targets through network pharmacology. This comprehensive approach provides systematic insights into the relationship between HQ’s effective components and amino acids, laying a foundational understanding for further elucidating HQ’s protective effects on CI/RI. 5. Conclusion In this study, a novel approach was employed by integrating metabolomics and network pharmacology to comprehensively investigate the neuroprotective effect of HQ on CI/RI. Through this method, five significantly altered amino acid biomarkers in rat plasma were identified, intricately linked to the biosynthesis of phenylalanine, tyrosine, and tryptophan, as well as D-glutamine and D-glutamic acid metabolism, and phenylalanine metabolism (P < 0.01). The observed changes in amino acids not only serve as potential biomarkers for CI/RI but also offer valuable insights for disease diagnosis and treatment. The network pharmacology analysis revealed that HQ primarily targets IL6, TNF-α, and IL-1β, along with the TNF signaling pathway, key players in the inflammatory response. This modulation is attributed to the effective ingredients of quercetin, kaempferol, and astragaloside IV. These findings underscore HW’s multifaceted neuroprotective effects, acting through a combination of ingredients and targeting multiple pathways. The potential clinical relevance of these results lies in their ability to deepen our understanding of HQ’s protective mechanisms against CI/RI in rats. Experimental validation and molecular docking further demonstrated the potential mechanism by which Astragalus, a key component of HQ, contributes to brain protection. However, despite these promising insights, further research is warranted to delve deeper into the underlying mechanisms, paving the way for the future development of HQ as a potential complementary therapeutic for CI/RI. Declarations Consent for publication By submitting my article I agree to pay the APC in full if my article is accepted for publication (unless it is covered by an institutional agreement or journal partner, or a full waiver has been granted). Ethics approval and consent to participate Human subjects or samples were not used in this study. All animal experiments were approved by the Animal Experiment Center of Air Force Military Medical University (production license no. SCXK (Shaan) 2019-001). Competing interests The authors declare that they have no conflict of interest. Funding Fund project: Supported by the National Natural Science Foundation of China (81503280, 81573549, 82001862); Shaanxi Province key research and development plan project (S2022-YF-ZDCXL-ZDLSF-0069). Author Contribution Data curation, Yue Chen and Ruizhen Zhang ; Funding acquisition, Fahuan Song and Zhifu Yang; Methodology, Yue Chen; Supervision, Yi Qiao; Validation, Kai Wang; Visualization, Ruimin Liang and Yan Shu; Writing–original draft, Ruimin Liang; Writing–review & editing, Jinyi Cao and Zhifu Yang. Acknowledgments Data curation, Yue Chen and Ruizhen Zhang ; Funding acquisition, Fahuan Song and Zhifu Yang; Methodology, Yue Chen; Supervision, Yi Qiao; Validation, Kai Wang; Visualization, Ruimin Liang and Yan Shu; Writing–original draft, Ruimin Liang; Writing–review & editing, Jinyi Cao and Zhifu Yang. Availability of Data and Materials Not applicable (this manuscript does not report data generation or analysis). References Rita VK, Takayoshi I, Valery LF, Global. Regional and Country-Specific Burden of Ischaemic Stroke, Intracerebral Haemorrhage and Subarachnoid Haemorrhage: A Systematic Analysis of the Global Burden of Disease Study 2017.[J]. Neuroepidemiology, 2020,54(2). Fawad Ali S, Faisal A, Abdullah A et al. Quercetin attenuated ischemic stroke induced neurodegeneration by modulating glutamatergic and synaptic signaling pathways.[J]. Heliyon, 2024,10(7). Francesco A, Simone F, Richard L et al. 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Integrated strategy for accurately screening biomarkers based on metabolomics coupled with network pharmacology.[J]. Talanta, 2020,211(0). Fahrul N, Elvan W, Nurpudji Astuti T et al. Unraveling diabetes complexity through natural products, miRNAs modulation, and future paradigms in precision medicine and global health.[J]. Clin Nutr ESPEN, 2024,63(0). Jun-Ling R, Hui D, Ying H et al. Network pharmacology combined with metabolomics approach to investigate the protective role and detoxification mechanism of Yunnan Baiyao formulation.[J]. Phytomedicine, 2020,77(0). Wu SS, Xu XX, Shi YY, et al. System pharmacology analysis to decipher the effect and mechanism of active ingredients combination from herb couple on rheumatoid arthritis in rats.[J]. J Ethnopharmacol. 2022;288:114969. Chen J, Hao W, Zhang C et al. Explore the Therapeutic Composition and Mechanism of Schisandra chinensis-Acorus tatarinowii Schott on Alzheimer's Disease by Using an Integrated Approach on Chemical Profile, Network Pharmacology, and UPLC-QTOF/MS-Based Metabolomics Analysis.[J]. Oxidative medicine and cellular longevity, 2022,2022:6362617. Yanmeng Z, Xiujuan M, Wentao Y et al. Protective Effect of Buyang Huanwu Decoction on Cerebral Ischemia Reperfusion Injury by Alleviating Autophagy in the Ischemic Penumbra.[J]. Evid Based Complement Alternat Med, 2021,2021(0). L H P JBB. M T, Rat middle cerebral artery occlusion: evaluation of the model and development of a neurologic examination.[J]. Stroke, 1986,17(3). Yu D, Chang L, Shouchao X et al. LC-MS/MS combined with blood-brain dual channel microdialysis for simultaneous determination of active components of astragali radix-safflower combination and neurotransmitters in rats with cerebral ischemia reperfusion injury: Application in pharmacokinetic and pharmacodynamic study.[J]. Phytomedicine, 2022,106(0). Liu D, Hong Y, Chen Z et al. The Tryptophan Index Is Associated with Risk of Ischemic Stroke: A Community-Based Nested Case-Control Study.[J]. Nutrients, 2024,16(11). Petersson JN, Bykowski EA, Ekstrand C et al. Unraveling Metabolic Changes following Stroke: Insights from a Urinary Metabolomics Analysis.[J]. Metabolites, 2024,14(3). Baranovicova E, Kalenska D, Grendar M et al. Metabolomic Recovery as a Result of Ischemic Preconditioning Was More Pronounced in Hippocampus than in Cortex That Appeared More Sensitive to Metabolomic Blood Components.[J]. Metabolites, 2021,11(8). Ormstad H, Verkerk R, Sandvik L. Serum Phenylalanine, Tyrosine, and their Ratio in Acute Ischemic Stroke: on the Trail of a Biomarker?[J]. J Mol neuroscience: MN. 2016;58(1):102–8. Rao GN, Jupudi S, Justin A. A Review on Neuroinflammatory Pathway Mediating Through Ang-II/AT1 Receptors and a Novel Approach for the Treatment of Cerebral Ischemia in Combination with ARB's and Ceftriaxone.[J]. Annals neurosciences. 2024;31(1):53–62. Nakajima Y, Iguchi H, Kamisuki S, et al. Low doses of the mycotoxin citrinin protect cortical neurons against glutamate-induced excitotoxicity.[J]. J Toxicol Sci. 2016;41(2):311–9. Nitya B, Claudia RM. The role of the arginine metabolome in pain: implications for sickle cell disease.[J]. J Pain Res, 2016,9(0). William R, Paulo C, Haroldo CR. A T, Arginase: A Multifaceted Enzyme Important in Health and Disease.[J]. Physiol Rev, 2018,98(2). Bakshi N, Morris CR. The role of the arginine metabolome in pain: implications for sickle cell disease.[J]. J pain Res. 2016;9:167–75. Li L, Jiang W, Yu B, et al. Quercetin improves cerebral ischemia/reperfusion injury by promoting microglia/macrophages M2 polarization via regulating PI3K/Akt/NF-κB signaling pathway.[J]. Volume 168. Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie; 2023. p. 115653. Wang J, Mao J, Wang R, et al. Kaempferol Protects Against Cerebral Ischemia Reperfusion Injury Through Intervening Oxidative and Inflammatory Stress Induced Apoptosis.[J]. Front Pharmacol. 2020;11:424. Yin F, Zhou H, Fang Y, et al. Astragaloside IV alleviates ischemia reperfusion-induced apoptosis by inhibiting the activation of key factors in death receptor pathway and mitochondrial pathway.[J]. J Ethnopharmacol. 2020;248:112319. An H, Wei D, Qian Y, et al. SQYZ granules, a traditional Chinese herbal, attenuate cognitive deficits in AD transgenic mice by modulating on multiple pathogenesis processes.[J]. Am J translational Res. 2018;10(11):3857–75. Hovhannesyan RA, Hovhannisyan IG. Platelet Aggregation and Interleukins Indicators Impacting the Outcomes of Ischemic Stroke.[J]. J stroke Cerebrovasc diseases: official J Natl Stroke Association. 2019;28(7):2038–44. Chen AQ, Fang Z, Chen XL, et al. Microglia-derived TNF-α mediates endothelial necroptosis aggravating blood brain-barrier disruption after ischemic stroke.[J]. Volume 10. Cell death & disease; 2019. p. 487. 7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5733320","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":401951562,"identity":"989d3135-54ea-4e2d-89f6-b8e380f05d8a","order_by":0,"name":"Ruimin Liang","email":"","orcid":"","institution":"Xijing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ruimin","middleName":"","lastName":"Liang","suffix":""},{"id":401951563,"identity":"95e562a3-7b97-486f-a4e7-5a8116b2bb7c","order_by":1,"name":"Yue Chen","email":"","orcid":"","institution":"Department of Pharmacy, School of Stomatology, the Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Chen","suffix":""},{"id":401951564,"identity":"600e6b69-0cb6-4d0f-bf56-230e45b6f474","order_by":2,"name":"Ruizhen Zhang","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruizhen","middleName":"","lastName":"Zhang","suffix":""},{"id":401951565,"identity":"0232c855-153d-4c23-a5cd-0d13d399887b","order_by":3,"name":"Kai Wang","email":"","orcid":"","institution":"Xi ’an Daxing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Wang","suffix":""},{"id":401951567,"identity":"a045e0ec-d278-456d-a930-307b9edcf0e4","order_by":4,"name":"Yan Shu","email":"","orcid":"","institution":"Xi ’an Daxing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Shu","suffix":""},{"id":401951568,"identity":"e38fad97-9fea-493f-a403-b112b05a4d8e","order_by":5,"name":"Yi Qiao","email":"","orcid":"","institution":"Xijing Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Qiao","suffix":""},{"id":401951570,"identity":"ed8bb81b-f08c-4f73-a275-bce6516b59f2","order_by":6,"name":"Fahuan Song","email":"","orcid":"","institution":"Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Fahuan","middleName":"","lastName":"Song","suffix":""},{"id":401951571,"identity":"312e95c7-222a-47e8-8ef2-909efa58ebca","order_by":7,"name":"Jinyi Cao","email":"","orcid":"","institution":"Fourth Military Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinyi","middleName":"","lastName":"Cao","suffix":""},{"id":401951572,"identity":"437cb184-c39c-4f33-9d38-c95741200d86","order_by":8,"name":"Zhifu Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIie3PsWrDMBCA4TNX5MVG65lA8gpnBCFT+yo2hUzGpGQxtDSCQJ6i5BkyZRYYkiXZOzbt2AxaCx1qu2SVMxaqHyQ4uA8hAJ/vD8amu5uDuP+yFQ1HVxHTklBkAzpMVKp7SNIudC/JiCleVfnv5EiGx/rTzm5LwIg5WVMWaDy9vzqIiMrpxPD9HFDMsoctlSEIpQoXgWLMhjHXiBuTbGke6EgMnESeW7JoCKQ6fqFcmz5ChXozXDfkRkGsryLnMRx43xAxRdqRSpd9f5GFstX3Y65lvQvs0/NwFC5PHy7SPdTed+Yyo3u9W7H9Oz6fz/ev+wEUOUXptA0wBwAAAABJRU5ErkJggg==","orcid":"","institution":"Xijing Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zhifu","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-12-30 07:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5733320/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5733320/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73966259,"identity":"70559010-d6cd-4396-9e6e-16fb4ff4b914","added_by":"auto","created_at":"2025-01-16 12:44:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":982811,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental flow chart of Huangqi (HQ) for CI/RI.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5733320/v1/e0d0856d759ea47ac5835681.png"},{"id":73967657,"identity":"7025ac83-ffa3-4da2-95fd-62ae46129531","added_by":"auto","created_at":"2025-01-16 13:00:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":915571,"visible":true,"origin":"","legend":"\u003cp\u003eThe influence of HQ on CI/RI in rats. (A) Neurobehavioral score. (B) 2,3,5-Triphenyl tetrazolium chloride (TTC) staining of the brain. (C) Infarct volume. (D) Hematoxylin-eosin (HE) staining. ##p \u0026lt; 0.01, the model group versus sham group; *p \u0026lt; 0.05, HQ group versus model group; **p \u0026lt; 0.01, the NXT group or HQ group versus model group.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5733320/v1/d7cd8837759a7213191cba30.png"},{"id":73966262,"identity":"0dceb752-fa2c-4d04-a818-6e6b440bb176","added_by":"auto","created_at":"2025-01-16 12:44:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":290417,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariate statistical analysis from LC-MS. (A and C) PLS-DA score plots. (B) PLS-DA model validation diagram. (D) PLS-DA loading plot. (E and G) OPLS-DA score plots. (F and H) OPLS-DA model validation diagram.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5733320/v1/d934ce3631097a56c7138879.png"},{"id":73966263,"identity":"175cfde8-86ce-4dd5-bb7b-9736a2358e94","added_by":"auto","created_at":"2025-01-16 12:44:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":159879,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis and visualization of differential amino acid metabolic pathways. (A) Bubble diagram illustrating the differential abundance of amino acid. (B-C) Metabolic pathway analysis highlighting key pathways influenced by differentially regulated amino acids: 1. Phenylalanine, tyrosine, and tryptophan biosynthesis; 2. D-glutamine and D-glutamate metabolism; 3. phenylalanine metabolism; 4. arginine and proline metabolism; 5. alanine, aspartate, and glutamate metabolism; 6. arginine biosynthesis; 7. tyrosine metabolism.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5733320/v1/57ec7329042bc4ac80a2bb51.png"},{"id":73966265,"identity":"fd9285f9-bfab-4a32-b5ab-443a0a5c69f3","added_by":"auto","created_at":"2025-01-16 12:44:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":778391,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Screening Venn diagram of HQ on disease targets. (a) Intersection of different names of the same disease; (b) potential targets of HQ in treating diseases. (B) Stepwise screening of key targets of Astragalus. (C) The “ingredient-target-disease-metabolite” network. (a) Network diagram of active ingredients of HQ acting on CI/RI targets; (b) network diagram of differential amino acids acting on CI/RI targets. (D) The connectivity degree distribution histogram of the active ingredients, metabolites, and targets with a connectivity degree higher than six.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5733320/v1/ccdeaa313b83e80df0a41dc4.png"},{"id":73966271,"identity":"369f29b2-092f-4e5d-8d38-e9d47559d5f9","added_by":"auto","created_at":"2025-01-16 12:44:58","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":222046,"visible":true,"origin":"","legend":"\u003cp\u003eBubble diagram of GO (A) and KEGG (B) pathway enrichment analyses.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5733320/v1/60706343a1580bfb6a0d5489.png"},{"id":73966270,"identity":"98729e8a-b287-48cc-b21f-eae77c6ed55e","added_by":"auto","created_at":"2025-01-16 12:44:58","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":686551,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Expression of IL-6 positive cells in the cerebral cortex of rats in each group. (B) Expression of TNF-α positive cells in the cerebral cortex of rats in each group. (C) Expression of IL-1β positive cells in the cerebral cortex of rats in each group. (D) Contents of IL-6, TNF-α, and IL-1β in rat plasma. (\u003csup\u003e#\u003c/sup\u003ep \u0026lt; 0.05, \u003csup\u003e##\u003c/sup\u003ep \u0026lt; 0.01 vs. sham; \u003csup\u003e*\u003c/sup\u003ep \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003ep \u0026lt; 0.01 vs. model).\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-5733320/v1/1f82e3a520fc3e74d7668cdf.png"},{"id":73966557,"identity":"eab75dda-0450-41f7-8b29-661d41f1ea26","added_by":"auto","created_at":"2025-01-16 12:52:58","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":245700,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking score heat map. (A) Network diagram related to IL6, TNF-α and IL-1β. (B) Heatmap of the docking of IL6, TNF-α, and IL-1βwith HQ components. (C) Heatmap of docking of IL6, TNF-α, and IL-1β with amino acids.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-5733320/v1/3cd60b744c31bc7b60c625d5.png"},{"id":73966560,"identity":"a47bb075-10f8-4996-855e-89070ce7c3c3","added_by":"auto","created_at":"2025-01-16 12:52:58","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":925723,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking scores higher docking results.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-5733320/v1/0d54442db454fa6e67470ab3.png"},{"id":75677229,"identity":"ccaf02a8-3e9d-4396-8222-e9a3711c9d37","added_by":"auto","created_at":"2025-02-07 04:17:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5992456,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5733320/v1/ab7f4116-ca51-4ddb-9af7-9018f45b8487.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating the protective mechanism of Astragalus membranaceous (Fisch.) against cerebral ischemia-reperfusion injury in rats: A metabolomics and network pharmacology approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eStroke is the leading cause of global mortality and disability, with ischemic stroke constituting 87% of all stroke cases\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. It originates from the interruption of cerebral blood supply due to vascular rupture or thrombosis; ischemic stroke results in the deprivation of oxygen and nutrients, inflicting damage to the brain tissue\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Presently, thrombolysis stands out as the preferred treatment for ischemic stroke, capable of dissolving thrombus, restoring blood flow, and protecting the surrounding brain tissue\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. However, the therapeutic window for recanalization is limited, especially in acute ischemic stroke cases, potentially leading to secondary ischemia-reperfusion (I/R) injury\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, thereby impacting its clinical efficacy.\u003c/p\u003e \u003cp\u003eTraditional Chinese medicine is distinguished by its utilization of multiple ingredients, targeting a multitude of pathways and mechanisms, rendering it particularly effective in addressing the complex pathogenesis of ischemic stroke. \u003cem\u003eRadix Astragali\u003c/em\u003e (Huangqi), derived from the dried root of Astragalus membranaceus (Fisch.) It is a renowned qi tonic in Chinese herbal medicine. Characterized by its warm nature and sweet taste, Huangqi (HQ) boasts effects such as replenishing qi, promoting yang, strengthening the surface to stop sweating, and invigorating qi while promoting blood circulation\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Modern studies have demonstrated that HQ can improve neurological impairment and restore neurological function in ischemic stroke patients, exhibiting a regulatory effect on cerebral vascular insufficiency, thereby holding significant promise in stroke prevention\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. However, despite its recognized benefits, there is a scarcity of studies investigating the protective mechanisms, especially through the study of metabolomics.\u003c/p\u003e \u003cp\u003eMetabolomics serves as a tool to scrutinize changes in metabolic pathways within the whole metabolic network of organisms\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. This methodology allows for the characterization of the dynamic properties of metabolites across the biological system, offering a powerful platform for discovering new biomarkers, unraveling metabolic pathways, and enhancing predictive, diagnostic, and treatment capabilities for complex systems\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. On the other hand, network pharmacology, rooted in systems biology and multi-directional pharmacology, emerges as a novel drug design method capable of comprehensively analyzing the effective ingredients, targets, and mechanisms of drugs\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The integration of network pharmacology and metabolomics offers a potent approach to elucidate the potential combination strategies within traditional Chinese medicine (TCM)\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. This combined approach sheds light on the mechanism and targets of TCM from both a system biology and molecular perspective.\u003c/p\u003e \u003cp\u003eIn this study, liquid chromatography-mass spectrometry (LC-MS) metabolomics methods combined with network pharmacology were used to meticulously monitor changes in plasma metabolism and identify potential therapeutic targets. This innovative methodology unequivocally substantiates the prophylactic efficacy of HQ in the context of cerebral ischemia-reperfusion injury (CI/RI), marking a groundbreaking revelation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This strategy not only addresses the limitations inherent in the absence of network pharmacology experiments but also bridged the gap in metabolomics by fusing upstream molecular mechanism with drug targets. Through the establishment of a rat middle cerebral artery occlusion (MCAO) model, coupled with HQ lavage, five metabolite biomarkers were identified and studied, along with their associated metabolic pathways. Furthermore, ten potential therapeutic targets for CI/RI were recognized. This study provides a novel perspective on the neuroprotective effect of HQ in the treatment of CI/RI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Reagents and materials\u003c/h2\u003e \u003cp\u003eHQ extract (lot no. TL1703018-2) was produced and obtained from Guangdong Yifang Pharmaceutical Co. Ltd. (Guangdong, China). Nao Xin Tong capsule (batch no. 2006149340, Shaanxi Buchang Pharmaceutical Co. Ltd.) in the form of 0.4 g * 36 granules was utilized. HPLC-grade methanol and formic acid were procured from TCI Chemical Industry Development Co. Ltd. (Shanghai, China). Ultrapure water was generated using a Milli-Q purification system (Billerica, MA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Animals and treatments\u003c/h2\u003e \u003cp\u003eClean Sprague Dawley (SD) rats, purchased from the Animal Experiment Center of Air Force Military Medical University (production license no. SCXK (Shaan) 2019-001), possessed a male gender, weighed 250\u0026thinsp;\u0026plusmn;\u0026thinsp;30 g, and were maintained at 45\u0026thinsp;\u0026plusmn;\u0026thinsp;5% relative humidity and 22\u0026ndash;24\u0026deg;C. The rats underwent a one-week temperature adaptation feeding period before the initiation of the experiment. The research ethics committee approved all experimental procedures for the care of laboratory animals at Fourth Military Medical University, and the experiments were conducted in accordance with its guidelines for experimental animals.\u003c/p\u003e \u003cp\u003eModern studies have shown the Buyang Huanwu decoction, historically employed by Qingren Wang in the Qing Dynasty for treating qi deficiency and blood stasis, has demonstrated significant efficacy in ischemic stroke treatment\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Consequently, the experimental design is based on the Buyang Huanwu decoction, where HQ serves as the monarch drug with a dosage of 120 g. In the experiment, 1 g of HQ extract was used, equivalent to 2.86 g HQ. The daily dose for rats was calculated at 4.37 g/kg, reflecting the daily requirement of HQ extract for humans at 41.96 g. Thus, the selected dose for this study is 4.37 g/kg of HQ.\u003c/p\u003e \u003cp\u003eSD rats were assigned randomly into four groups using a random number table method: Sham group, model group, HQ group (4.37 g/kg), and Nao Xin Tong capsule group (NXT, as a positive control group). All rats received intragastric administration at a volume of 10 mL/kg. Rats in the sham and model groups were administered the same volume of saline. After 72 h of cerebral ischemia-reperfusion, samples were collected, and the rats were humanely sacrificed.\u003c/p\u003e \u003cp\u003ePrior to surgery, all rats underwent abdominal anesthesia using 3% sodium pentobarbital (Merck Company, USA). Following anesthesia, the rats were fixed on a board in a supine position, and the orientation of head and tail fixation was determined based on individual operation habits. After routine disinfection, a vertical opening was made in the middle of the neck to expose and separate the right common carotid artery (CCA), external carotid artery (ECA), and internal carotid artery (ICA). A small incision approximately 5 mm from the CCA bifurcation was made for the insertion of the bolt thread, reaching a depth of about 18 mm. Resistance indicated the stop point, and the bolt thread was ligated, followed by cleaning of the surgical area and skin suturing. The thrombus was extracted 2 h later to complete the post-ischemia reperfusion. In the sham group, surgery involved the separation of CCA, ECA, and ICA without ligation. All surgeries were conducted in an environment maintained at 25\u0026ndash;28\u0026deg;C, with the centrifuge set to 3000 g, 4\u0026deg;C, and 20 min. Rat blood supernatant was obtained by centrifuging the blood, and both plasma and brain samples were promptly frozen at \u0026minus;\u0026thinsp;80\u0026deg;C until LC-MS analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Infarct volume measurement\u003c/h2\u003e \u003cp\u003eThe rat brain was meticulously isolated and sliced into coronal sections with a thickness of 2 mm. These sections were then stained using 2,3,5-triphenyl tetrazolium chloride (TTC) at a concentration of 2% in phosphate-buffered saline. The staining process involved immersing the sections in the dye solution and incubating them at 37\u0026deg;C for 0.5 h under shaded conditions. To ensure thorough coloring, the glass dish was periodically shaken every 10 min. Following staining, the sections were fixed with 4% paraformaldehyde. The Image Plus Pro software was employed as the measurement tool for determining the infarct area. The ratio of cerebral infarct volume in rats was calculated according to the formula.\u003c/p\u003e \u003cp\u003eRatio of cerebral infarct volume = (section thickness of infarct area)/ (section thickness of total cerebral area) \u0026times;100%.\u003c/p\u003e \u003cp\u003eIn the resulting images, normal brain tissue was represented by bright red areas, whereas infarcted brain tissue was depicted in white. This method allowed for accurate quantification and visualization of cerebral infarct volume in the rats.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Hematoxylin-eosin (HE) staining\u003c/h2\u003e \u003cp\u003eRat brain tissue was carefully harvested, and any residual blood and cerebrospinal fluid on the surface were removed using filter paper. The tissue was then sectioned into three pieces, with the middle piece being preserved and fixed in a 4% tissue fixative. Standard histological procedures that included paraffin embedding and routine HE staining were conducted. This involved slicing, dewaxing, staining, dehydration, sealing, microscopic examination, and subsequent image acquisition and analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Neurological function assessment\u003c/h2\u003e \u003cp\u003eNeurological functions were assessed 72 h post-ischemia-reperfusion using a 5-grade, 4-point scoring method based on the approach by Bederson et al\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The scoring criteria were as follows: zero points - normal behavior, no neurological dysfunction; one point - incomplete extension of the left forepaw, indicating mild injury; two points - turning left, suggestive of moderate damage; three points - falling to the left, indicating of severe damage; four points - inability to walk spontaneously, accompanied by loss of consciousness.\u003c/p\u003e \u003cp\u003eThe degree of neurological impairment was positively correlated with the assigned score; higher scores indicated more severe animal behavior disorders. This standardized scoring system facilitated a comprehensive evaluation of the rats\u0026rsquo; neurological status following ischemia-reperfusion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Sample preparation\u003c/h2\u003e \u003cp\u003e The plasma samples were carefully measured and transferred into 2 mL EP tubes. Subsequently, 400 \u0026micro;L of a 10% formic acid-methanol solution-H\u003csub\u003e2\u003c/sub\u003eO (1:1) mixture was accurately added along with 50 mg glass beads. The sample was placed in a high-throughput tissue grinding machine and vigorously shaken at 55 Hz for 1 min, with the process repeated twice. Following this, centrifugation was performed at 12000 rpm 4\u0026deg;C for 5 min. From the original supernatant, 10 \u0026micro;L was extracted and combined with 190 \u0026micro;L of a 10% formic acid methanol-H\u003csub\u003e2\u003c/sub\u003eO (1:1) solution. After vortexing for 30 s, 100 \u0026micro;L of the diluted sample was taken, and 100 \u0026micro;L of a dual isotope internal standard (100 ppb concentration) was added. Another round of vortex oscillation for 30 s ensued, and the supernatant was then filtered through a 0.22 \u0026micro;m membrane before being transferred into the detection bottle.\u003c/p\u003e \u003cp\u003ePlasma samples, all with a volume of 10 \u0026micro;L, were collected from each rat, and quality control (QC) samples were prepared using the same procedure. Additionally, random plasma samples were divided into eight parts, and each underwent identical processing to verify the repeatability of the sample preparation methods. Throughout the analysis, strict temperature control was maintained at 4\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Liquid chromatography-mass spectrometry (LC-MS) analysis\u003c/h2\u003e \u003cp\u003eChromatographic conditions were as follows: The analysis utilized an ACQUITY UPLC BEH C18 column (2.1 \u0026times; 100 mm,1.7 \u0026micro;m) from Waters, USA, with an injection volume of 5 \u0026micro;L. The column temperature was maintained at 40\u0026deg;C. The mobile phase consisted of A (10% methanol-water containing 0.1% formic acid) and B (50% methanol-water containing 0.1% formic acid). The gradient elution proceeded through the following phases: 0\u0026ndash;6.5 min, 10\u0026ndash;30% B; 6.5\u0026ndash;7 min, 30\u0026ndash;100% B; 7\u0026ndash;8 min, 100% B; 8\u0026ndash;8.5 min, 100\u0026ndash;10% B; 8.5\u0026ndash;12.5 min, 10% B. The flow rate was set at 0\u0026ndash;8.5 min, 0.3 mL/min, and 8.5\u0026ndash;12.5 min, 0.3\u0026ndash;0.4 mL/min.\u003c/p\u003e \u003cp\u003eThe MS conditions were as follows: The analysis employed electrospray ionization (ESI) as the ionization source, operating in positive ion ionization mode. The ion source temperature was set at 500\u0026deg;C, and the ion source voltage to 5500 V. Collision gas was maintained at 6 psi, curtain gas at 30 psi, and both atomized gas and auxiliary gas were set at 50 psi, enabling scanning through multiple reaction monitoring (MRM).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Data analysis and biomarker selection\u003c/h2\u003e \u003cp\u003eTo explore differences in plasma metabolites between the sham group and the model group, a partial least squares discriminant analysis (PLS-DA) was conducted. The model\u0026rsquo;s quality was assessed based on R2Y and Q2 (cum) parameters. Initially, group comparisons were made, and metabolites with a projected variable importance in projection (VIP) value VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 were identified as potential differences. The Student\u0026rsquo;s t-test, implemented in GraphPad Prism 8.0.1 software, was employed to assess significant differences between groups, considering p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as statistically significant. This criterion was then applied to screen for potential biomarkers. By observing the changes in the intensity of these biomarkers between the HQ group and the model group, the impact of HQ on the CI/RI in rats was shown. Additionally, MetaboAnalyst5.0, an online software, was utilized for biomarker enrichment and metabolic pathway analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Network pharmacology analysis\u003c/h2\u003e \u003cp\u003eUtilizing network pharmacology analysis, the network relationship between the main active ingredients of HQ and their associated targets was elucidated. Active ingredient-related targets were extracted from the TCMSP (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tcmspw.com/tcmsp.php\u003c/span\u003e\u003cspan address=\"http://tcmspw.com/tcmsp.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and TCMID (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.megabionet.org/tcmid/\u003c/span\u003e\u003cspan address=\"http://www.megabionet.org/tcmid/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases. Disease-related targets were obtained from Genecards (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and OMIM (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://omim.org/\u003c/span\u003e\u003cspan address=\"https://omim.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases. Protein-protein interaction (PPI) networks were established for HQ\u0026rsquo;s effective constituent-related targets and disease targets using STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/cgi/input.pl\u003c/span\u003e\u003cspan address=\"https://string-db.org/cgi/input.pl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Common targets between HQ and ischemic stroke were identified using Venny. Cytoscape3.7.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cytoscape.org/\u003c/span\u003e\u003cspan address=\"http://www.cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed to construct the ingredient-target-disease (I-T-D) and metabolite-target (M-T) network maps. For the amino acid metabolic pathway map, MetaboAnalyst 5.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized, while the DAVID database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/summary.jsp\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/summary.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed for GO and KEGG signaling pathways analyses of key targets. Ultimately, targets directly linked to the disease, effective compounds, and differential metabolites were selected as potential therapeutic targets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Key target analysis\u003c/h2\u003e \u003cp\u003eThe key targets identified through a stepwise screening process underwent validation through ELISA, immunohistochemistry, and immunofluorescence techniques. Subsequently, molecular docking was performed, involving the relevant components and associated amino acids.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11. Statistical analysis\u003c/h2\u003e \u003cp\u003eFor comparison across multiple groups, a one-way analysis of variance (ANOVA) was employed. The data were presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM). A significance threshold of P\u0026lt;0.05 was set for statistical significance. All analyses and graphical representations were performed using GraphPad Prism 8.0 software (San Diego, CA, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1. The influence on CI/RI in rats of Huangqi (HQ)\u003c/h2\u003e \u003cp\u003eThe effects of HQ on CI/RI in rats were evaluated through the examination of neurological function scores and measurement of cerebral infarction volume measurement. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, rats in the sham group exhibited normal behavior and intact neurological function, reflected by a score of zero. Conversely, rats in the model group displayed higher scores, indicating neurological impairment following ischemia/reperfusion (I/R). Administration of HQ resulted in a significant improvement in neurological damage compared to the model group.\u003c/p\u003e \u003cp\u003eFigures\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC illustrate the cerebral infarction volumes for each group, with no observed infarct volume in the Sham group. Post-middle cerebral artery occlusion (MCAO), a substantial infarct size emerged in the coronal brain region, corresponding to the highest neurological coloboma score across all groups. Following HQ treatment, there was a noticeable recovery in the infarct area, as evidenced by the analysis of TTC-stained brain sections. The therapeutic effect of HQ alone was significantly pronounced compared to the model group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These results signify the neuroprotective efficacy of HQ in rats with CI/RI.\u003c/p\u003e \u003cp\u003eHE results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) revealed notable differences in the number and arrangement of neurons in the cerebral cortex among the experimental groups. In the sham group, neurons were abundant, displaying an organized arrangement with normal cell morphology. Conversely, the model group exhibited pathological changes in neuronal morphology on the infarcted side, characterized by abnormal structural features. Notably, neurons were randomly distributed, their numbers reduced, intercellular spaces increased, and nuclei appeared condensed and stained. Comparative analysis among the model, HQ, and NXT groups demonstrated that both HQ and NXT interventions led to improvements in the pathological morphology of cerebral cortex neurons in CI/RI rats. Specifically, nuclear pyknosis and deeply stained cells were reduced, intercellular spaces became smaller, cell morphology became more apparent and identifiable, and the number of dead neurons decreased. This observation underscores the therapeutic potential of HQ and NXT in alleviating the detrimental effects on cerebral cortex neurons in CI/RI rats.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Multivariate statistical analysis of plasma metabolites\u003c/h2\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, a discernible separation of data gathering and dispersion regions between the model and sham groups indicates the successful establishment of the modeling process. The loading plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB illustrates the metabolites contributing to differences between sample groups. Metabolites farther from the origin signify a higher separation contribution rate, suggesting their potential as metabolic markers.\u003c/p\u003e \u003cp\u003eThe PLS-DA score plot in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD reveal a significant divergence between the model and sham groups, indicating substantial changes in serum metabolites due to MCAO-induced metabolic disorders in rats. The HQ and NXT groups, positioned closer to the Sham group, suggest that intervention with HQ and NXT induced changes in metabolites and exerted a specific recovery effect on MCAO injury. Figures\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG further demonstrate the successful establishment of the model by the clear separation of samples between the model and sham groups. The validity of the OPLS-DA model is confirmed in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH through pairwise comparison permutation tests (n\u0026thinsp;=\u0026thinsp;200) between the sham and model groups and between the model and HQ groups. The results of the permutation test, indicative of good stability and reliability, confirm the absence of over-fitting phenomena in all models.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Differential metabolite identification and pathway analysis\u003c/h2\u003e \u003cp\u003eBy utilizing the variable importance projection (VIP) value and Student\u0026rsquo;s t-test, the metabolites were identified to be the most significant contributions to the distinction between the sham and model groups. Specifically, metabolites with VIP\u0026thinsp;\u0026gt;\u0026thinsp;1 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were deemed potential biomarkers. This analysis revealed a total of ten variant amino acid metabolites present in the plasma of both the sham and model groups, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. To unravel the metabolic pathways influenced by HQ in CI/RI rats, the differentially regulated amino acids were subjected to metabolic pathway analysis and visualization using MetaboAnalyst 5.0 (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C). Seven metabolic pathways, identified based on a pathway impact\u0026thinsp;\u0026gt;\u0026thinsp;0.1, were highlighted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, shedding light on the potential pathways affected by HQ in CI/RI rats.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTen differential amino acid metabolites.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifferential amino acid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbbreviations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-histidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.672024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-methionine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.586397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.33E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-glutamic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.48271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-lysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.402341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012977\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-arginine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.336722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002926\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-ornithine hydrochloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.157049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.22E-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-phenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.115118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-leucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.101549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-alanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.055945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-valine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.053893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Network pharmacology analysis\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA presents a Venn diagram illustrating the potential targets of HQ in disease treatment. In Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, a step-by-step screening process for identifying key targets of HQ in disease treatment is outlined. To evaluate the therapeutic effect of HQ on CI/RI, an I-T-D network was constructed by combining the ingredient-target (I-T) network of HQ with the target-disease (T-D) network. This network unveils the intricate relationships between HQ\u0026rsquo;s active ingredients, targets, and associated diseases. In Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC(a), eight active ingredients and 59 key targets of HQ related to disease treatment are highlighted. The integration of differential amino acids into Cytoscape 3.7.2 resulted in the generation of a metabolite-target (M-T) network, depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC(b). The comprehensive ingredient-target-metabolite-disease (I-T-M-D) network, comprising 77 nodes (8 active ingredients, 59 targets, one disease, eight metabolites) and 274 edges, is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC. By focusing on nodes with a degree of connectivity exceeding six, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD identifies six active ingredients, five differential amino acids, and 16 targets. Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e showcases the results of gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses performed on the 16 key targets. Notably, the TNF signaling pathway emerges as highly enriched.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Key targets for experimental verification\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA presents the immunohistochemical results for IL-6. Neurons in the sham group exhibited a clear and intact structure, while those in the model group appeared disordered mostly stained dark brown. In the HQ and NXT groups, some neurons displayed varying degrees of dark or light brown staining, accompanied by a minor degree of neuronal damage and necrosis. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB showcases the immunofluorescence results for TNF-α. Blue indicates DAPI nuclear staining, red represents TNF-α labeled positive cell staining, and pink depicts TNF-α nuclear staining. Notably, compared to the model group, the HQ and NXT groups showed a reduction in the number of TNF-α positive cells. In Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, immunohistochemical results for IL-1β demonstrated clear and complete neuron structures in the sham group. In the model group, nuclear pyknosis and deep staining were observed, while the HQ and NXT groups exhibited a small number of neurons with dark brown staining. In Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD, the ELISA results reveal a noteworthy elevation in plasma levels of TNF-α, IL6, and IL-1β in the model group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), demonstrating statistical significance when compared with the sham group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, the HQ group exhibited a substantial reduction in TNF-α, IL6, and IL-1β levels following CI/RI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), establishing statistical differentiation from the model group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Key target docking verification\u003c/h2\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA, a network connection analysis reveals that IL-6, TNF-α, and IL-1β were predominantly associated with five amino acids and five components of HQ. This network diagram illustrates the intricate relationships between these inflammatory markers and the constituents of HQ. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC displays heat maps showcasing the docking results of IL-6, TNF-α, and IL-1β with their respective components and amino acids. The lower the docking score, the more favorable the docking effect. Notably, TNF-α and IL6 exhibited optimal docking with astragaloside IV, while IL-1β demonstrated superior docking with quercetin and isorhamnetin. Additionally, L-phenylalanine emerges as the optimal docking partner for TNF-α and L-Alanine for IL-6. In Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the docking results of TNF-α, IL6, and IL-1β are presented in descending order of their top three docking scores, providing a comprehensive view of their molecular interactions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, the LC-MS metabolomics method was employed to uncover variations in plasma metabolites among different groups. Five plasma amino acid biomarkers were identified, including L-glutamate, L-arginine, L-ornithine, L-valine, and L-phenylalanine, to distinguish the model group from the sham group. Metabolism pathway analysis highlighted the involvement of these amino acids in pathways such as phenylalanine, tyrosine, and tryptophan biosynthesis, D-glutamine and D-glutamate metabolism, phenylalanine metabolism, and arginine and proline metabolism. Integrating these findings with network pharmacology analysis revealed six main active ingredients in HQ, namely quercetin, kaempferol, isorhamnetin, formononetin, astragaloside IV, and calycosin, acting on 16 potential targets. These interactions were found to modulate brain protection through pathways like TNF, FoxO, toll-like receptor signaling, and the HIF-1 signaling pathway.\u003c/p\u003e \u003cp\u003eExperimental verification further demonstrated that five key components of HQ acted on IL6, TNF-α, and IL-1β, contributing to their therapeutic role. Notably, the intersection of these components with the six components of HQ identified in network pharmacological analysis highlighted four consistently crucial components - quercetin, kaempferol, isorhamnetin, and astragaloside IV. This suggests a potentially pivotal role of HQ in brain protection, where quercetin, kaempferol, isorhamnetin, and astragaloside IV influence IL6, TNF-α, and IL-1β, ultimately affecting the levels of L-glutamate, L-arginine, L-ornithine, L-valine, and L-phenylalanine and exerting neuroprotective effects.\u003c/p\u003e \u003cp\u003eIn the pathophysiological process of CI/RI, amino acid metabolism emerges as a prominent and distinct feature\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Notably, the disruption of metabolic pathways, particularly phenylalanine, tyrosine, and tryptophan biosynthesis, signifies an imbalance in central neurotransmitters during CI/RI\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Elevated levels of phenylalanine in the bloodstream have been linked to an increased susceptibility to cerebral ischemia\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Furthermore, the pathological release of excessive glutamate in D-glutamine and D-glutamic acid metabolism contributes to excitatory neurotoxicity, a crucial factor in stroke pathogenesis\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Arginine, serving as a precursor for proline, nitric oxide (NO), and glutamic acid synthesis, undergoes complex metabolic transformations\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. This includes arginine lysing to ornithine and subsequent proline synthesis under enzymatic action of arginine enzyme. Then, L-ornithine synthesizes proline under the action of ornithine aminotransferase\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Changes in the metabolic pathways involving arginine, proline, alanine, aspartic acid, and glutamate in stroke patients imply a disordered energy supply under hypoxic-ischemic conditions\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Consequently, alteration in amino acid profiles within these metabolic pathways may serve as potential markers for CI/RI.\u003c/p\u003e \u003cp\u003eUpon investigation, three out of the four ingredients identified in HQ were closely related to CI/RI. Quercetin, with its antioxidant, anti-inflammatory, and antiviral activities, has been shown to reduce CI/RI\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Kaempferol exerts protective effects on ischemic brain injury by combating oxidative stress, inflammation, and apoptosis\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Astragaloside IV demonstrates neuroprotective effects by inhibiting oxidative stress and inflammatory responses, improving brain energy metabolism post-reperfusion, and inhibiting neuronal apoptosis\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Notably, HQ\u0026rsquo;s neuroprotective role primarily hinges on quercetin, kaempferol, and astragaloside IV. It is plausible that HQ regulates the changes of Orn, Arg, Glu, Val, and Phe in rats by acting on IL1B, TNF, and IL6\u0026mdash;key inflammatory response element\u0026mdash;through astragaloside IV, kaempferol, and quercetin. A noteworthy observation is the decrease in IL1B levels, a prominent feature in patients with acute stroke\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. KEGG pathway analysis showed the significant role of the TNF signaling pathway, indicating its pivotal involvement in the release of inflammatory factors such as IL-6 and TNF-α from ischemic neurons post-cerebral ischemia. These factors, in turn, induce additional inflammatory responses, cytotoxic substance release, blood-brain barrier damage, and extracellular matrix disruption, thereby accelerating the occurrence of CI/RI\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. In summary, the identified effective ingredients in HQ and the metabolomics-screened differential amino acids are intricately linked to CI/RI targets through network pharmacology. This comprehensive approach provides systematic insights into the relationship between HQ\u0026rsquo;s effective components and amino acids, laying a foundational understanding for further elucidating HQ\u0026rsquo;s protective effects on CI/RI.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, a novel approach was employed by integrating metabolomics and network pharmacology to comprehensively investigate the neuroprotective effect of HQ on CI/RI. Through this method, five significantly altered amino acid biomarkers in rat plasma were identified, intricately linked to the biosynthesis of phenylalanine, tyrosine, and tryptophan, as well as D-glutamine and D-glutamic acid metabolism, and phenylalanine metabolism (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The observed changes in amino acids not only serve as potential biomarkers for CI/RI but also offer valuable insights for disease diagnosis and treatment. The network pharmacology analysis revealed that HQ primarily targets IL6, TNF-α, and IL-1β, along with the TNF signaling pathway, key players in the inflammatory response. This modulation is attributed to the effective ingredients of quercetin, kaempferol, and astragaloside IV. These findings underscore HW\u0026rsquo;s multifaceted neuroprotective effects, acting through a combination of ingredients and targeting multiple pathways. The potential clinical relevance of these results lies in their ability to deepen our understanding of HQ\u0026rsquo;s protective mechanisms against CI/RI in rats. Experimental validation and molecular docking further demonstrated the potential mechanism by which Astragalus, a key component of HQ, contributes to brain protection. However, despite these promising insights, further research is warranted to delve deeper into the underlying mechanisms, paving the way for the future development of HQ as a potential complementary therapeutic for CI/RI.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eBy submitting my article I agree to pay the APC in full if my article is accepted for publication (unless it is covered by an institutional agreement or journal partner, or a full waiver has been granted).\u003c/p\u003e\n\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eHuman subjects or samples were not used in this study. All animal experiments were approved by the Animal Experiment Center of Air Force Military Medical University (production license no. SCXK (Shaan) 2019-001).\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eFund project: Supported by the National Natural Science Foundation of China (81503280, 81573549, 82001862); Shaanxi Province key research and development plan project (S2022-YF-ZDCXL-ZDLSF-0069).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eData curation, Yue Chen and Ruizhen Zhang ; Funding acquisition, Fahuan Song and Zhifu Yang; Methodology, Yue Chen; Supervision, Yi Qiao; Validation, Kai Wang; Visualization, Ruimin Liang and Yan Shu; Writing\u0026ndash;original draft, Ruimin Liang; Writing\u0026ndash;review \u0026amp; editing, Jinyi Cao and Zhifu Yang.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eData curation, Yue Chen and Ruizhen Zhang ; Funding acquisition, Fahuan Song and Zhifu Yang; Methodology, Yue Chen; Supervision, Yi Qiao; Validation, Kai Wang; Visualization, Ruimin Liang and Yan Shu; Writing\u0026ndash;original draft, Ruimin Liang; Writing\u0026ndash;review \u0026amp; editing, Jinyi Cao and Zhifu Yang.\u003c/p\u003e\n\u003ch2\u003eAvailability of Data and Materials\u003c/h2\u003e\n\u003cp\u003eNot applicable (this manuscript does not report data generation or analysis).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRita VK, Takayoshi I, Valery LF, Global. 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Cell death \u0026amp; disease; 2019. p. 487. 7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metabolomics, Network pharmacology, Cerebral ischemia-reperfusion injury, Molecular docking, Amino acid metabolism","lastPublishedDoi":"10.21203/rs.3.rs-5733320/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5733320/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eAstragalus membranaceous (Fisch.)\u003c/em\u003e has a rich history as a traditional medicine in various Asian countries, showcasing a notable neuroprotective effect. However, the underlying therapeutic mechanisms warrant further investigation. This study employs metabolomics and network pharmacological analysis to elucidate the protective effects of \u003cem\u003eRadix Astragali\u003c/em\u003e (Huangqi, HQ) against cerebral ischemia-reperfusion injury (CI/RI) in rats. The investigation aims to reveal the potential protective mechanisms of HQ in CI/RI rats. Plasma metabolomics analysis, utilizing multivariate statistical methods, highlights biomarkers and associated metabolic pathways. The integrated approach of network pharmacology comprehensively analyzes HQ\u0026rsquo;s effective components, therapeutic targets, and amino acid metabolites. Pharmacodynamic experiments demonstrate a significant cerebral protective effect in the HQ group compared to the model group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Metabolomics results indicate significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in L-glutamic acid, L-arginine, L-ornithine hydrochloride, L-valine, and L-phenylalanine in the model group compared to the sham operation group, indicating plasma metabolic disorders in CI/RI rats. Network pharmacology analysis identifies quercetin, kaempferol, and astragaloside IV components within HQ that may act on IL6, TNF, and IL-1B targets, influencing five different amino acids to exert brain protection. This study provides valuable insights into the neuroprotective mechanisms of HQ in the context of CI/RI.\u003c/p\u003e","manuscriptTitle":"Investigating the protective mechanism of Astragalus membranaceous (Fisch.) against cerebral ischemia-reperfusion injury in rats: A metabolomics and network pharmacology approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-16 12:44:53","doi":"10.21203/rs.3.rs-5733320/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"37b7a9c1-622e-4fcb-8948-9bdc2a38c544","owner":[],"postedDate":"January 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-11T03:53:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-16 12:44:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5733320","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5733320","identity":"rs-5733320","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}