Bronchiolitis obliterans (BO) is a chronic airway disease that was often indicated by the pathological presentation of narrowed and irreversible airways. However, the molecular mechanisms of BO pathogenesis remain unknown. Although neutrophil extracellular traps (NETs) can contribute to inflammatory disorders, their involvement in BO is unclear. This study aims to identify potential signaling pathways in BO by exploring the correlations between NETs and BO. GSE52761 and GSE137169 datasets were downloaded from gene expression omnibus (GEO) database. A series of bioinformatics analyses such as differential expression analysis, gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and gene set enrichment analysis (GSEA) were performed on GSE52761 and GSE137169 datasets to identify BO potential signaling pathways. Two different types of BO mouse models were constructed to verify NETs involvements in BO. Additional experiments and bioinformatics analysis using human small airway epithelial cells (SAECs) were also performed to further elucidate differential genes enrichment with their respective signaling pathways in BO. Our study identified 115 differentially expressed genes (DEGs) that were found up-regulated in BO. Pathway enrichment analysis revealed that these genes were primarily involved in inflammatory signaling processes. Besides, we found that neutrophil extracellular traps (NETs) were formed and activated during BO. Our western blot analysis on lung tissue from BO mice further confirmed NETs activation in BO, where neutrophil elastase (NE) and myeloperoxidase (MPO) expression were found significantly elevated. Transcriptomic and bioinformatics analysis of NETs treated-SAECs also revealed that NETs-DEGs were primarily associated through inflammatory and epithelial-to-mesenchymal transition (EMT) -related pathways. Our study provides novel clues towards the understanding of BO pathogenesis, in which NETs contribute to BO pathogenesis through the activation of inflammatory and EMT associated pathways. The completion of our study will provide the basis for potential novel therapeutic targets in BO treatment. BO is the cause for concerns primarily due to the patients’ reduced long-term survival after lung transplantation3. Besides, BO can present itself as complication following adenoviral pneumonia in children, hence there is an urgent need to further understand BO pathogenesis and progression4. However, despite the efforts put into BO research, the exact underlying mechanisms that modulated BO pathogenesis remain obscure. Consequently, the effective treatments available for BO are scarce and severely lacking. Therefore, in order to formulate effective therapeutic strategies, a further in-depth understanding of BO pathogenesis is deemed critical.Bronchiolitis obliterans (BO) is a chronic airway disease, often represented through pathological changes in the narrowing and obstructive airways that were found rare and irreversible. The BO patient usually has symptoms such as recurrent cough, persistent wheezing and sputum production, decreased exercise tolerance, shortness of breath, or other types of breathing difficulties6. Moreover, by analyzing and comparing existing BO-related transcriptomic data from various studies, researchers may gain opportunities to discover novel target, that was previously neglected in the study of BO. Hence, two BO-related transcriptomic datasets were selected and downloaded from the gene expression omnibus (GEO) database for this study. Specifically, these datasets were used to examine the potential involvements of the neutrophil extracellular traps (NETs) formation signaling pathways in BO pathogenesis. We further confirmed the involvement of NETs in BO pathogenesis by evaluating data obtained from BO mouse models that were constructed using two publicly well-recognized approaches, which are 2,3 butanedione treatment and nitric acid treatment. Consistent with our bioinformatics analysis, crucial indicators of NETs molecules such as neutrophil elastase (NE) and myeloperoxidase (MPO) were found elevated in the lung tissues of BO mice. Altogether, our efforts had revealed the potential links between NETs regulation and the activation of inflammatory and epithelial-mesenchymal transition (EMT)-related pathways, that are vital for pathogenesis and progression of BO.Our approach focuses on the use of bioinformatics analyses and were coupled with the use of cellular and animal models. Specifically, bioinformatics analyses derived from next-generation sequencing technologies can offer researchers a more comprehensive understanding of the identification of molecular mechanisms crucial for diseases pathogenesis and progression. For example, research in BO recently had suggested the involvement of innate immune reactions in BO pathogenesis, in which the combinational analysis of transcriptomic sequencing and bioinformatics had provided the means to better identify the gene and its respective signaling pathways in BO pathogenesisMale SPF C57 mice (6–8 weeks old and weighing 18–22 g) were obtained from the Southern Medical University Animal Center  and housed in appropriate cages with water and routine diet. The protocols were approved by the institutional ethics committee (ethics number: 20230018). This animal study procedures were performed according to ARRIVE guideline. All the methods described in this study were performed in accordance with the relevant guidelines and regulations of the Institution.7. Briefly, mice were anaesthetized with isoflurane  and immobilized on a mouse tracheal intubation table, and a 24G intravenous needle was inserted through the mouth into the trachea to drip 1 μL/g of 0.5% nitric acid. After a single drip of nitric acid, BO-affected mice were established 21 days after instillation. To obtain lung samples, mice were euthanized by an overdose of 5% isoflurane and cervical dislocation. To remove blood from the lungs, they were flushed with 3 mL of PBS via the right ventricle. Lung samples were collected and snap frozen in liquid nitrogen until further processing. Adequate amounts of lung samples from each mouse were used for protein quantification. In addition, fresh lung samples were soaked in 4% paraformaldehyde for biochemical analyses such as H&E and Masson’s staining.Mouse models of nitric acid-induced bronchiolitis obliterans were established according to established procedures in the field8. Anesthesia, tracheal dropping and sampling were performed as above. 2,3-butanedione was titrated to a concentration of 30% for titration of 0.333 μL/g. Animals were euthanized by cervical dislocation after isoflurane overdose 14 days after 2,3-butanedione instillation.The application of 2,3-butanedione to generate mouse models of bronchiolitis obliterans was conducted in accordance with established methodologies and protocols detailed in earlier scientific publications within this field of studyIn our study, we utilized the percentage of luminal occlusion area as a quantitative measure to assess the degree of bronchiolar obstruction. The specific method involved selecting three non-overlapping fields of view at 4 × magnification on HE-stained lung tissue slides. Within each field of view, we calculated the percentage of occluded area in relation to the total luminal area of the bronchioles. The average occlusion percentage from these three fields of view was then taken as the final quantification of the extent of obstruction. This approach enables us to evaluate and quantify the observed bronchial occlusion.https://www.ncbi.nlm.nih.gov/geo/) for gene expression profile data of BO and screened the identified datasets according to the inclusion and exclusion criteria. The inclusion criteria were as follows: (1) containing three or more pairs of samples from the BO group and the normal group, (2) the dataset type is expression profiling by array or RNA profiling by array, (3) the sample type is lung or bronchial. Finally, two datasets related to BO in lung or bronchial tissue transcriptome data were retrieved from the GEO database. The information on two datasets is shown in Table We searched the GEO database  |≥ 1.5 were considered as differentially expressed genes (DEGs) in the respective databases. The overlapping DEGs between the two datasets were considered the final DEGs.The volcano maps and Venn diagrams were generated using Sangerbox 3.0 (http://sangerbox.com/)10.Raw microarray data for the BO datasets were downloaded from the GEO database. We normalised the data using the R package limma11. The Kyoto encyclopedia of genes and genomes (KEGG) database provides a comprehensive collection of data on protein interaction networks and biointerpretation of genomic sequences12. In our study, GO and KEGG analysis of DEGs were completed by the ‘clusterProfiler’ package13. For all cases, we recorded the top 5 significant items in BP, CC and MF and the top 20 significant pathways in KEGG analysis.Gene ontology (GO) analysis is an effective method for annotating genes and identifying characteristic biological attributes, including biological processes (BP), molecular functions (MF) and cellular components (CC)14. GSEA was performed using GSEABase, clusterProfiler and org.Mm.eg.db packages. The pathways with P value < 0.05 and enrichment scores > 0.45 were defined as the predictive up-regulated pathway from GSEA.Gene Set Enrichment Analysis (GSEA) is a gene set-based functional pathway enrichment analysis method that calculates the enrichment fraction of gene sets in each functional pathwayt test (P < 0.05) using GraphPad Prism software.Lung tissue samples were lysed in a buffer containing Tris–HCl, SDS, glycerol, and other reagents. The resulting lysate was then centrifuged and the protein concentration was determined using the BCA method. Equal amounts of protein were separated using SDS-PAGE and transferred to PVDF membranes. The membranes were blocked and incubated with primary antibodies against myeloperoxidase (MPO)  and neutrophil elastase (NE) , followed by a secondary antibody, and protein bands were detected using chemiluminescence. ImageJ software was used to calculate the ratio of MPO and NE to β-actin, and significance was determined by Student’s g for 5 min, a cell-free supernatant enriched in NETs was obtained.To quantify the amount of NETs, we employed the PicoGreen dsDNA analysis method  to measure the levels of free DNA. This part of the experiment has been approved by the ethical review of the First Affiliated Hospital of Guangzhou Medical University (ES-2023-011-01).We utilized a commercially available human neutrophil isolation kit  to isolate neutrophils from freshly collected whole blood obtained from healthy blood donors. The isolation procedure was performed according to the manufacturer’s instructions. Subsequently, we employed BeyoMag™ Protein A + G Magnetic Beads  to isolate IgG from the plasma of bronchiolitis obliterans (BO) patients who were confirmed as ANCA-positive using ELISA detection method.The isolated neutrophils were then resuspended in RPMI 1640 medium at a density of 1.8 × 106 cells per milliliter and stimulated with ANCA-positive IgG at a concentration of 5 μg/mL. The stimulation was carried out at a temperature of 37 °C for a duration of 24 h.To separate and quantify neutrophil extracellular traps (NETs), the supernatant was removed, and the NETs layer at the bottom of the plate was collected using phosphate-buffered saline (PBS). The collected NETs were subsequently transferred to a centrifuge tube. Following centrifugation at a speed of 20×l-glutamine  at 37 °C with 5% CO2. SAECs were seeded in 24-well plates to 90% confluency, washed once with PBS, and 20 μg/mL NETs or DMEM were added. The total volume in each well was kept equal by adding DMEM medium. After 24 h treatment, SAECs were harvested for RNA sequencing.Human small airway epithelial cells (SAECs) were purchased from Procell Life Science & Technology Company  and cultured in DMEM high glucose supplemented with 10% FBS, 1% penicillin–streptomycin solution, and 1% https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE232805).Total RNA was extracted from NETs-treated SAECs and untreated SAECs. 1–2 ug of total RNA was selected from each sample to construct RNA sequencing libraries. The constructed libraries were quality controlled by Agilent 2100, and the libraries were quantified by the qPCR method and sequenced on an Illumina NovaSeq 6000 sequencer. The raw sequencing files were imported into StringTie to calculate transcript abundance by comparing the results to known transcriptomes, and then the data were converted to fragments per million mapped reads per thousand bases of transcription (PFKM) by Ballgown calculation, and the genes with a PFKM mean > 0.5 were selected for further analysis. The sequencing data were uploaded to GEO database  |≥ 1 were considered NETs-DEGs.GO and KEGG were used to analyse the pathways enriched by NETs-DEGs and sorted by P value to show the ranked top 5 results.https://string-db.org/)15. Then, Cytoscape 3.8.0 (14) was used to visualize the PPI network. The screening condition for constructing the PPI network was a combined score > 0.4.Protein–protein interaction (PPI) network analysis of NETs-DEGs was performed using the online STRING website  is a plugin for Cytoscape to build important functional modules in PPI networksKEGG was used to analyze the pathways enriched by the key module genes and sorted by P value to show the top 6 results.17. We performed enrichment analysis on the key module genes as mentioned above based on DO terms and sorted by P value to show the top 15 DO terms.Like GO, disease ontology (DO) is a formal ontology of diseaseshttp://www.nc3rs.org.uk/arrive-guidelines).The study was approved by the Institutional Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (protocol code: 20230018 and ES-2023-011-01). Mouse husbandry followed ARRIVE guidelines  and nitric acid exposure (n = 4) Fig. B–EcomparThe small airway epithelial cells treated with NETs (NETs-SAECs) were separated from untreated SAECs the in the principal component analysis (PCA) and the clustering heat mapping Fig. A,C. ExprWe subjected the 164 NETs-DEGs to KEGG and GO analyses. KEGG pathway analysis revealed that the pathways enriched by NETs-DEGs include ‘TNF signaling pathway,’ ‘NOD-like receptor signaling pathway,’ ‘IL-17 signaling pathway,’ ‘Rheumatoid arthritis,’ and ‘NF-kappa B signaling pathway’ Fig. A,B. AccoWe uploaded NETs-DEGs to the STRING online database to form the PPI network. We used the Cytoscape software to generate a PPI network. we built a PPI network with 107 NETs-DEGs after hiding disconnected nodes Fig. A. We ideKEGG pathway analysis revealed that the top6 KEGG pathways enriched by key module genes include ‘TNF signaling pathway’, ‘IL-17 signaling pathway’, ‘Rheumatoid arthritis’, ‘Viral protein interaction with cytokine and cytokine receptor’, ‘NOD-like receptor signaling pathway’ and ‘NF-kappa B signaling pathway’ Fig. A.Figure DO analysis revealed that the disease enriched by key module genes include ‘bacterial infectious disease’, ‘primary bacterial infectious disease’, ‘bronchial disease’, ‘tuberculosis’, ‘mouth disease’, ‘asthma’, ‘Coronavirus infectious disease’, ‘pneumonia’, ‘periodontal disease’, ‘pancreatitis’, ‘pancreas disease’, ‘chronic obstructive pulmonary disease’, ‘obstructive lung disease’, ‘periodontitis’ and ‘COVID − 19’ Fig. B.1. Limited understanding of the basic underlying mechanisms modulated through BO pathogenesis had restricted current effective treatment options for BO patients. In order to gain further insights into BO pathogenesis, our study utilized combinatorial analytical approaches such as transcriptome sequencing and bioinformatics analysis to screen and explore differential gene expression changes and their respective underlying molecular mechanisms that were associated with the disease onset and progression. Our findings will provide novel therapeutic avenues into understanding the complex BO pathophysiology, ultimately contributing to the development of new therapeutic strategies for treating BO.BO is a rare chronic respiratory disease that can result in respiratory insufficiency1. Pathological changes that were first observed in BO initiation are the infiltration of white blood cells such as lymphocytes, neutrophils, and eosinophils, into the peripheral airways, bronchial walls, and bronchioles18. Studies have revealed that the elevated sputum production in children with BO had a significantly increased neutrophil count with enhanced neutrophil chemokines levels such as IL-1β, IL-6, and IL-819. Besides, clinical studies also found that the percentage of neutrophils and IL-8 in bronchoalveolar lavage fluid were significantly increased in post-measles BO patients, at the levels that were similarly observed in lung transplanted BO patients21. Bioinformatics analysis from our study further reinforces the importance of inflammation regulatory networks, and at the same time, pointed out the the dominant roles of neutrophils in BO pathogenesis. By inhibiting neutrophils activation and subsequent inflammation activation, novel treatments to achieve complete recovery for BO patients may be possible.Our study managed to identify 115 up-regulated DEGs that are distinctive for BO pathogenesis, while additional GO analysis revealed that they were found involved in multiple inflammatory-related biological processes, such as leukocyte migration, neutrophil chemotaxis, regulation of tumor necrosis factor production, chemokine signaling pathway, and leukocyte transendothelial migration. The findings inevitably had provided the basis for further research into BO-associated inflammation regulatory signaling pathways. It is undeniable that inflammation plays pivotal roles for BO development and progression22. While NETs help to trap and eliminate microbes in the lungs, persistent activation of NETs could lead to adverse health effects23. A study by Keir et al. had shown that the excessive formation and accumulation of NETs in lung tissue can lead to serious tissue damage due to constant inflammatory processes, which could promote the onset and progression of lung diseases24. Interestingly, no correlations have been established between NETs activation and BO pathogenesis. Our results further showed that core components of NETs, such as NE and MPO, were significantly up-regulated in the lungs of BO mice compared to healthy controls, suggesting that the NETs formation pathway may be involved in BO pathogenesis. Therefore, further studies are warranted to explore and dissect the specific role of NETs and their respective molecular mechanisms in BO. Completion of this study may provide the basis for new insights into the diagnosis and treatment of BO.Not surprisingly, our study found that NETs were activated in BO based on GSEA and KEGG analysis results. Briefly, the components of NETs are DNA, histones, NE, MPO, and various types of granule proteins25. Tumor necrosis factor, better known as TNF-α, is an important cytokine that was found involved in a series of inflammatory networks, and its activation can promote downstream activation of several biological effects that include the chemotaxis of inflammatory cells, stimulation of inflammatory factor production, oxidative and nitrosative stress response, and epithelial-mesenchymal transition28. Some studies had also demonstrated that TNF is elevated in sputum, blood, and bronchoalveolar lavage fluid from BO patients29. Although considerable effort had been put into TNF, no substantial association with the occurrance and BO progression currently were found.In our study, we also investigated the role of NETs in BO via the use of small airway epithelial cells, in order to confirm roles of NETs in BO by conducting transcriptome sequencing. Results from the transcriptome sequencing data had suggested that NETs may dysregulate multiple signaling pathways, including the TNF signaling pathway, NOD-like receptor signaling pathway, IL-17 signaling pathway, NF-kappaB signaling pathway, and the responses to molecule of bacterial origin. Consistent with Hudock’s study, we found that NETs could induce and promote TNF-α overexpression in SAEC30. Additionally, Park et al. also found that NLRP3 release by human bronchial epithelial cells can induce inflammation and EMT in asthma patients31. The research further highlighted the importance to understand the correlations between the NLR signaling pathway and pulmonary diseases. In addition, previous studies also had shown that NETs can activate macrophages and non-small cell lung cancer cells through the NLR pathway, leading to inflammation and EMT33. However, the exact mechanisms of how NETs regulate the NLR signaling pathway in small airway epithelial cells were still lacking. Based on the observations from our results and the aforementioned studies, we hypothesize that NETs may activate the NLR signaling pathway in small airway epithelial cells, leading to inflammation and EMT activation.Besides, the NOD-like receptor (NLR) signaling pathway also plays an important role in the innate immune response against pathogens. A study of BO showed that the inhibition of the NLRP3 inflammasome can reduce airway inflammation and fibrosis34, which eventually leads to increased neutrophils recruitment and exacerbated inflammation. Accumulated studies have also revealed that IL-17 might be a part of NETs-associated molecules36, in which the activation of bronchial epithelial cells by IL-17 activation had been reported to promote IL-1 and IL-8 releases, and consequently inducing neutrophilic inflammation37. In line with these findings, our results also demonstrated that NETs-treated airway epithelial cells can exhibit significant IL-1, IL-2, and IL-8 up-regulation, which may be attributed to NF-κB pathway and IL-17 signaling activation.Aside from that, a study from Wan et al. had demonstrated that NETs can stimulate IL-1, IL-2, and IL-8 overexpression in airway epithelial cells by activating the TLR4/NF-κB pathway39. Specifically, previous studies have demonstrated NETs were found to be enriched in the peripheral blood and synovial fluid of RA patients, in which the NETs overexpression significantly enhances the inflammatory reaction of RA synovial fibroblasts by inducing the production of IL-6, IL-8, chemokines, and adhesion molecules that leads to substantial joint damages41. However, due to the rarity of RA-associated BO cases, the research to dissect the relationship between RA, BO, and NETs are substantially lacking. Hence, further experimental validations are needed to investigate the relationship between these diseases. We speculate that NETs may be a common mechanism that was activated, linking between RA and BO, in which RA induces BO occurrence by inducing NETs overexpressions in the lungs. Our study also provides a basis to explore the molecular mechanisms and target therapy of BO in RA patients.It is interesting to note that patients with rheumatoid arthritis (RA) history may also develop BO43. The production of various cytokines, such as TNF, IL-1, IL-6, and other inflammatory factors stemmed from the interaction between LPS and host cells may inevitably leads to prolonged inflammation and cell damage45. Hence, we infer that NETs not only function to eliminate LPS invasion, but may also play a role in protecting the host from pathogenic bacterial invasion through activating and amplifying the response of human airway cells to LPS. However, considering that LPS is a strong inducer for NETs activation, there are instances that LPS may indirectly cause substantial damages to the airway due to the excessive NETs production by neutrophils upon stimulation. The idea to manipulate this cellular response into desired beneficial cell responses may, in turns, provide novel ideas to combat the mechanisms of infectious diseases involving NETs and LPS.Apart from chemokine activation and medical background, our study also revealed that NETs may amplify the response of airway epithelial cells to bacterial-derived molecules, such as lipopolysaccharide (LPS). LPS is a type of endotoxin-like biomolecule that exists in the outer membrane of many bacteria, which had been found to induce plethora of immune and inflammatory reactions52. This suggests that the negative impact of NETs on airway epithelial cells may be a common mechanism underlying the pathogenesis of these diseases including BO. However, additional research is still needed to fully understand the roles of NETs in these diseases and to identify potential therapeutic targets.Lastly, our study had also constructed vital subnetworks of NETs associated DEGs, which were comprised of a set of 26 genes that were primarily involved in inflammatory regulatory networks and cellular chemotaxis factors. The pathways enriched by these genes were found to overlap with the pathways of NETs associated DEGs, indicating that they may play an essential role in the regulation of NETs associated DEGs. Furthermore, disease enrichment analysis also highlighted that these key subnetwork genes were highly associated with several airway or infectious diseases, including asthma, tuberculosis, pneumonia, coronavirus, bronchial diseases and chronic obstructive pulmonary disease, in which respective studies have shown a correlations between them and NETsIn summary, our study had demonstrated significant correlation between NETs and BO, thereby suggesting a potential role for NETs in BO pathogenesis. Moreover, our findings also suggest that NETs may contribute to small airway epithelial cells dysregulation through the activation of inflammation and EMT related pathways. Although further investigations are required to validate and expand upon our observations, our report on the correlations between NETs and BO may provide novel insights into how the correlations may be exploited for future research and to develop better clinical management of BO.Supplementary Figure 1.Supplementary Figure 2.Supplementary Table 1.Supplementary Table 2.Supplementary Information.


Gastrodin can reduce neuronal damage through multiple targets and pathways, and can be useful in preventing and treating degenerative lesions of the central nervous system, but the specific mechanism has not been elucidated.The aging‐related genes in the hippocampus and the frontal cortex were detected in adult and aged mice treated with gastrodin or not. In addition, we collected the target genes of gastrodin and aging from a network database, and a Venn diagram was created to obtain the intersection target genes of gastrodin and aging. Then, the String database was used to analyze the protein–protein interactions (PPIs) between aging‐related genes and the target genes of gastrodin and aging. The “drug–disease–target–pathway” network was constructed using Cytoscape 3.7.2 software, and the main mechanism and pathway of key genes were analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). Finally, the reliability of these key genes was further verified by molecular docking technology.The results showed that 6 out of 10 genes related to brain aging were differentially expressed after gastrodin intervention. Moreover, there were 11 key genes between gastrodin and differentially expressed genes related to brain aging. GO and KEGG results suggested that material metabolism and carbohydrate digestion and absorption were associated with the pathological mechanism of gastrodin antiaging. Molecular docking results also confirmed the good binding activity of gastrodin to the key genes.Gastrodin plays a potential role in antiaging by regulating substance metabolism and carbohydrate digestion and absorption. In this study, we first explored whether gastrodin affected the expression of aging‐related genes through animal experiments. Multiple databases were used to screen potential drug targets of gastrodin and disease targets of aging. A protein–protein interaction network is constructed to analyze the connection among these targets and identify the hub genes on the basis of the topological interaction between these targets. Afterward, an enrichment analysis of the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology was performed to elucidate the biological functions of hub genes. Finally, molecular docking techniques were applied to confirm the reliability of these crucial targets. Its active ingredients have physiological and health‐promoting features, including antitumor, memory improvement, and neuroprotective activities.Aging has remained an unresolved issue in the medical field. It is a multifactorial process that leads to a gradual decline in the function of cells, tissues, and organisms. Although aging cannot be stopped, it is possible to slow down the rate of aging through various methods. Traditional Chinese medicine (TCM) is receiving increasing attention in the prevention and treatment of various neurodegenerative diseases (NDs) caused by aging.With the development of interdisciplinary fields such as bioinformatics, system biology, and comprehensive pharmacology, the research strategy for exploring the interaction between drugs and diseases has gradually shifted from independent research to systematic overall analysis.Consequently, the use of new analysis methods to explore the key genes and mechanisms of antiaging of TCM will provide a new avenue for subsequent research and application of TCM. In this study, we first explored whether gastrodin affected the expression of aging‐related genes through animal experiments and then further explored the antiaging targets and possible mechanisms of gastrodin in combination with network pharmacology. At the same time, we also constructed a potential “drug–disease–target–pathway” network of gastrodin and identified the key genes by molecular docking technology to provide a theoretical and experimental basis for subsequent research Figure .22.1n = 5/group): adult control group, adult gastrodin group, aged control group, and aged gastrodin group. The gastrodin injection  was diluted with medical 0.9% normal saline at 5 mg/ml, and mice in the adult gastrodin group and the aged gastrodin group were administered an intraperitoneal injection at a dose of 1 g/kg, once a day, for 14 consecutive days, with the left and right abdominal cavity alternated every other day. Neither the adult control group nor the aged control group received any treatment.The animal experimental design in this study was approved by the Animal Experimental Ethics Review Committee of Kunming Medical University . SAM‐P/8 mice were purchased from the Experimental Animal Center of Tianjin University of TCM. The adult mice were 7 months old and the elderly mice were 13 months old, with a weight of (25 ± 2) g. The mice were kept in a room with the temperature maintained at 22 ± 2.0°C and humidity maintained at 50 ± 5%, with light exposure for about 12 h a day and free access to food and water. Twenty SAM‐P/8 mice were randomly divided into four groups  after left ventricular catheterization. The mice were intraperitoneally injected with 1% pentobarbital sodium at 50 mg/kg. After the tail pinch reflex disappeared, the anesthesia took effect, and then the perfusion was performed. Hippocampus and frontal cortex tissues were rapidly separated and rinsed with precooled diethyl pyrocarbonate‐treated water, placed into a 1.5 ml Eppendorf tube containing 1 ml of TRIzol lysis RNAase‐free, and homogenized in an ice bath.2.3β‐actin and 10 aging‐related genes were amplified, and the primer sequences are shown in Table β‐actin gray value.RNA was isolated according to the instructions of the kit. The concentration and purity of RNA were detected by agarose gel electrophoresis and an ND‐1000 spectrophotometer (UV‐250IPC Shimadzu). Total RNA was isolated by TRIzol reagent (Takara Bio Inc.) and complementary DNA (cDNA) was synthesized according to the RevertAidTM First Strand cDNA Synthesis Kit (Thermo Scientific). The first strand of synthesized cDNA can be directly used for PCR amplification. According to the instructions of the 2× PCR Master Mix Kit , 2.42.4.1https://pubchem.ncbi.nlm.nih.gov/). Swiss Target Prediction (http://swisstargetprediction.ch/) is a website that can predict possible targets according to the chemical structure of drug components. The canonical SMILES sequence of gastrodin was imported into the website for target prediction. In addition, the collected targets were converted into target names and gene names in the Uniport database (https://www.uniprot.org/). With “aging” as the keyword, all relevant disease targets were found in the GeneCards database (https://www.genecards.org/) and downloaded for analysis. After taking two medians of correlation, targets with correlations >1.43 were identified.The chemical structure of gastrodin was obtained from the chemical information database PubChem using “Gastrodin” as the keyword and its “Canonical SMILES” sequence was found (2.4.2http://www.bioinformatics.com.cn/static/others/jvenn/example.html), and the Venny diagram was plotted to find the common targets of drugs and diseases. These common targets were the required core targets. String (https://string-db.org/) is a website that can analyze the relationship between proteins online. The String database was used to input 11 cross‐linked targets of gastrodin and aging together with six changed targets in the experimental results into the String to search and continue to produce protein interaction maps. The high‐definition interaction diagram and the interaction table were exported and downloaded. The interaction table was imported into Cytoscape 3.7.2. According to the degree value and combined_score, nodes with different sizes and connection lines with different thicknesses were generated to draw a PPI network diagram.The screened gastrodin and aging‐related targets were uploaded to the plotting website Jvenn (2.4.3http://david.abcc.ncifcrf.gov/), and the p value and fold enrichment value were determined.In this study, the common targets of gastrodin and aging were analyzed by bioconcentration, and the genes and gene products were annotated by GO analysis according to the biological process (BP), the cellular component (CC), and the molecular function (MF). KEGG is a useful resource for the systematic analysis of gene function and related high‐level genomic function information. Therefore, to further clarify the function of the selected intersection genes and their roles in the signal transduction pathway, GO function analysis and KEGG pathway enrichment analysis were performed using the DAVID database (2.4.4http://www.rcsb.org/). The crystal structure of the protein was initialized using AutoDockTools software (version: 1.5.6), including removal of solvent molecules and ligands, addition of polar hydrogen, energy initialization, and so forth, and converted into the pdbqt format file required for docking. The three‐dimensional structure of gastrodin was optimized by the MM2 force field using Chem3D Pro 14.0 software (version: 14.0.0.117), and the number of rotatable keys was set by AutoDockTools software and converted into the pdbqt format file required for docking. Molecular docking scoring was performed using the molecular docking software AutoDock Vina (version: 1.1.2).Molecular docking has become an important technology for computer‐aided drug research in recent years. It can be used to evaluate the interaction between active ingredients and core targets and predict their binding modes and affinity. The three‐dimensional structure file of the core protein was obtained from the RCSB protein database (2.5x¯ ± SD). The ratio of the average optical density of each gene β‐actin between the two groups was tested using an independent‐sample t‐test, and p < 0.05 indicated that the difference was statistically significant.All data in this study were processed using SPSS 21.0 statistical software, and all measurement data were expressed as mean ± standard deviation . Compared with the control group, the expression of MAP2K4, Sortilin‐1, and Rab6A in the hippocampus and the frontal cortex of aged mice decreased .The expression of β‐actin and aging‐related genes was detected in the hippocampus and the frontal cortex of all groups, and the target gene fragment was obtained, which was consistent with the expected fragment length Figures  and 3. G3.23.2.1Through database retrieval, 16 target genes of gastrodin were screened Figure . There w3.2.2The PPI network can be used to discover multiple interactions among multiple proteins in diseases and determine potential therapeutic targets.3.2.3To explore the biological function of core targets, GO function and KEGG pathway enrichment analyses were performed. Using the DAVID database, GO enrichment analysis of 17 target genes involved in the PPI network was carried out, and 42 enrichment results were obtained, including 19 BP items, 17 CC items, and 6 MF items Figure . Similar3.3In this study, the possible interactions between 17 key target proteins and gastrodin in the experimental and network pharmacology analysis results were verified by molecular docking. AutoDock Vina software was used to perform molecular docking between gastrodin and 17 proteins such as SCN2B Figure . The doc4In this paper, the molecular mechanism of gastrodin antiaging was explored by combining experimental verification using mice and network pharmacology analysis. The experimental results obtained from mice showed that gastrodin could regulate brain aging‐related genes, which might act on target proteins SCN2B, Sortilin‐1, MAP1B, RAP2A, MAP2K4, and Rab6A. To further explore the molecular mechanism by which gastrodin improves aging, the potential targets and mechanism of gastrodin antiaging were analyzed by screening, target prediction, and bioinformatics analysis using network pharmacology and a public database. From the results of network pharmacology analysis, 11 gastrodin and aging intersection genes were constructed with six genes changed in the experiment to construct PPI, and GO and KEGG analyses were performed to explore the three most relevant pathways. The “drug–disease–target–pathway” diagram was drawn and verified by molecular docking technology. The results showed that 10 of the 17 proteins had high binding activity to gastrodin.SCN2B can affect the formation and transmission of neuronal action potential by regulating the sodium ion channels, which plays an important role in the maintenance of neuronal physiological functions, and is related to aging and memory decline.Given the current knowledge of these core targets, it can be inferred that gastrodin may play an antiaging role by promoting neurological development, regulating neuronal activity and function, altering the expression of neurotransmitter transporters, and participating in the proliferation of astrocytes to promote the repair of neural injury. Meanwhile, the results of GO enrichment analysis of key targets indicated that the response to substances, regulation of the sleep cycle, glucose transport and absorption, and regulation of the adenosine receptor signaling pathway play an important role in the antiaging of gastrodin. KEGG pathways enriched by these key genes mainly include galactose metabolism, metabolic pathways, and carbohydrate digestion and absorption, suggesting that gastrodin may play an antiaging role by regulating substance metabolism and carbohydrate digestion and absorption. Metabolic dysfunction may play an important role in delaying aging. Numerous studies have shown that most age‐related diseases, such as hypertension, Type 2 diabetes, cardiovascular disease, and osteoporosis, are closely associated with metabolic dysfunction.5In summary, in this study, aging‐related genes were detected in the hippocampus and the frontal lobe of aging mice by RT‐PCR, and the data were analyzed in combination with network pharmacology. According to the results of analyses, it is speculated that the antiaging effect of gastrodin may be related to the regulation of material metabolism and carbohydrate digestion and absorption, which provides an avenue for further verification of its mechanism in future experimental studies.Nan Zhao and Rui‐Jiang conducted animal experiments and data collection and analysis. Jun‐Jie Cheng analyzed the network pharmacology data and created pictures. Nan Zhao wrote the manuscript. Qiu‐Xia Xiao drafted and reviewed the manuscript. All authors had read and approved the final manuscript. [Correction added on 2 June 2023 after first online publication: This section was revised at the request of author.]The authors declare no conflict of interest.The animal experimental design in this study was approved by the Animal Experimental Ethics Review Committee of Kunming Medical University . All experimental processes complied with the guidelines for the care and use of laboratory animals published by the National Institutes of Health.


Elucidating compound mechanism of action (MoA) is beneficial to drug discovery, but in practice often represents a significant challenge. Causal Reasoning approaches aim to address this situation by inferring dysregulated signalling proteins using transcriptomics data and biological networks; however, a comprehensive benchmarking of such approaches has not yet been reported. Here we benchmarked four causal reasoning algorithms  with four networks , using LINCS L1000 and CMap microarray data, and assessed to what extent each factor dictated the successful recovery of direct targets and compound-associated signalling pathways in a benchmark dataset comprising 269 compounds. We additionally examined impact on performance in terms of the functions and roles of protein targets and their connectivity bias in the prior knowledge networks.direct targets. With respect to the recovery of signalling pathways, CARNIVAL with the Omnipath network was able to recover the most informative pathways containing compound targets, based on the Reactome pathway hierarchy. Additionally, CARNIVAL, SigNet and CausalR ScanR all outperformed baseline gene expression pathway enrichment results. We found no significant difference in performance between L1000 data or microarray data, even when limited to just 978 ‘landmark’ genes. Notably, all causal reasoning algorithms also outperformed pathway recovery based on input DEGs, despite these often being used for pathway enrichment. Causal reasoning methods performance was somewhat correlated with connectivity and biological role of the targets.According to statistical analysis , the combination of algorithm and network most significantly dictated the performance of causal reasoning algorithms, with the SigNet recovering the greatest number of Overall, we conclude that causal reasoning performs well at recovering signalling proteins related to compound MoA upstream from gene expression changes by leveraging prior knowledge networks, and that the choice of network and algorithm has a profound impact on the performance of causal reasoning algorithms. Based on the analyses presented here this is true for both microarray-based gene expression data as well as those based on the L1000 platform.The online version contains supplementary material available at 10.1186/s12859-023-05277-1. The mod targets —not only targets , identif targets , and to  targets . The com targets .starting point for the understanding of drug mechanisms, a key question is how such data should be exploited and interpreted in a meaningful way to shortlist targets or pathways for experimental validation. Differentially expressed genes (DEGs) can be analysed with pathway enrichment methods which calculate the statistical significance of the association of their protein products with annotated biological pathways and processes [Recently, large -omics databases have become available in the public domain, such as the LINCS L1000 database   of signed and directed  protein–protein interactions (PPIs) to trace upstream of mRNA regulation to the targets and signalling proteins modulated by a compound. Case studies of mechanism of action elucidation using causal reasoning include the elucidation of key processes involved in a DGAT1 inhibitor for obesity [Methods known collectively as “causal reasoning” have been developed with the aim of identifying causal molecules (of an observed response) by treating differential gene expression as a  obesity , and an  obesity , with thOne previous benchmarking study  comparedand pathway-level) from gene expression data, in particular with respect to the following parameters which we anticipated would present the greatest influence on the results, and which had not yet been investigated in previous benchmarking studies , CausalR (ScanR), SigNet, CARNIVAL).Source of input gene expression data .Input gene subset .Prior knowledge network .Properties and biological functions of protein nodes .This study aims to benchmark casual reasoning algorithms for their ability to recover compound mechanism of action . Such mechanisms are out of scope of validation in this benchmarking study, which is focused on small molecules with direct protein target(s). It is also important to note that annotated targets may not directly relate to compound efficacy, for example in the case of off-targets, and in vitro activities in particular may not be relevant for in vivo mechanism of action , however for the purpose of this study we use target annotations as the best proxy for mechanism of action which is currently available in the public domain for a large-scale benchmark study.To quantify the performance of the algorithms, we assessed the ability of each algorithm, in combination with different networks and parameter settings, to recover known targets directly, as well as compound-associated pathways (those annotated with the known targets) using a two-step enrichment approached described in the CARNIVAL study . We obtaFollowing the computation of both evaluation metrics, we used a negative-binomial model to understand the contribution of each parameter  to the recovery of direct targets. Additionally, we investigated the ability of the algorithms to recover informative pathways, and evaluated the applicability domain of the methods by investigating any potential association between the successful recovery of a particular target class and its connectivity in the prior knowledge networks, or its biological role.Hence, the overall aim of this study was to systematically investigate the ability of causal reasoning algorithms to infer compound mechanism of action on both the target- and pathway-levels, with respect to both the key factors which influence their quantitative performance as well as for which targets/signalling proteins the methods are likely to be more or less successful.In terms of causal reasoning algorithms, we aimed to see how they handled bias in biological networks, and if this behaviour differed across the different methods. Different causal reasoning algorithms have been implemented in open source and software packages and each requires a specific type of input data  and PKN Table . Nodes oThe input gene expression data used in this study were from the publicly available Connectivity Map (CMap ) and LINquantity of input genes  from the effects arising from the quality of poorly-predicted non-landmark genes, as well as any differences in technologies between the two platforms. Hence, for each compound measured under particular conditions , we obtained three signatures forming three different input gene sets—consisting of landmark genes only (n = 987), “best-inferred” genes , and all genes . Landmark genes were selected in a data-driven way rather than for biological discovery[By comparing L1000 with its predecessor, CMap, we aimed to disentangle any effects arising from the We also evaluated whether different algorithms handled increasing network size and density in a different manner, owing to the differences in how they prioritise causal proteins, and to understand whether performance scales with network size; or rather if added noise is detrimental. There are many sources of publicly available protein–protein interaction data all containing different information of varying levels of completeness and confidence , so in tFor pathway annotations , we used the Reactome knowledgebase because it is generally considered to be high-quality , coversThe overall workflow for the benchmarking study can be found in Fig. All compound data in CMap and LINCS derived at a time point of 6 h after compound treatment and using a compound concentration of 10µM were extracted for this study, measured in the two cell lines common to both datasets (MCF7 and PC3), see Additional file Each of the 2016 compounds were queried in the ChEMBL , and cluFor this study, we benchmarked signed and directed networks from Omnipath and MetaBase™ Fig. C. The OmTo convert the data from gene- to protein-level to be used with protein–protein interaction networks , we first used the DoRothEA regulon with the VIPER statistical method to infer upstream TFs from the expression levels of their gene targets  was then applied to the processed data .glmmTMB(Overlap ~ (Network + Algorithm + Platform + Gene_Set + Cell_Line)^2+ (1 | Compound), REML = T, family="nbinom2”) where the (1|Compound) term accounts for repeated observations . A final model was built which included the single effect terms and only the significant (p < = 0.05) interaction effect terms from the first model: glmmTMB(Overlap ~ Network + Algorithm + Platform + Gene_Set + Cell_Line +Network*Algorithm + Platform*Gene_Set + (1 | Compound), REML = T, family="nbinom2”) The final model had a dispersion ratio of 1.010 (p = 0.055) indicating no overdispersion. Post-hoc comparisons were carried out using estimated marginal means implemented in the emmeans R package using the following function:emmeans where “term” is either a significant single effect not appearing in any interaction effects  or a significant interaction effect (Network + Algorithm). If factors interact in a statistical model, it is generally considered to be not useful to perform post-hoc analysis on the individual factors themselves. This is because in the presence of a significant interaction, any effort to interpret the main effects of the factors involved will be based on the false premise that differences on one factor exist across all levels of the other factor(s) [cldTo understand which factors were most influential in recovering compound targets, the number of targets recovered for each compound for every combination of factor levels  . The estTo understand which factors are most important when employing causal reasoning algorithms to recover compound targets, we modelled the target overlap evaluation metric . To aid in the comparisons, p-values were transformed to their − log10 and the mean found for each combination of network and algorithm. The results are plotted as a heatmap in Fig. As can be seen from Fig. The best enrichment results using differentially expressed genes had a mean of 2.39  which contains several sub-pathways which are more specific processes , which themselves have sub-pathways , and so on. To quantify how informative recovered pathways were, we assumed that pathways which are lower down in the Reactome pathway hierarchy, and that contain fewer genes/proteins, are more specific and informative for MoA understanding than higher-level, larger, general pathways.As well as considering the enrichment p-value as a performance measure, we next aimed to understand how informative, and hence practically useful for understanding compound mechanism, the recovered pathways were. Reactome pathways are laid out in several hierarchies, consisting of the high-level pathway term , and hierarchy (number of superpathways), can be seen in Fig. effect of protein signalling, while causal nodes represent the signalling proteins themselves.Other than with respect to average pathway size for SigNet with Omnipath, all combinations of network and algorithm recovered more informative pathways compared to average baseline gene expression results , indicating that pathways recovered from DEGs generally capture higher-level processes compared to causally inferred proteins. This was also found in an application of causal reasoning to understand the mechanisms of a DGAT1 inhibitor, where the authors found that enrichment with gene expression data generally pointed to higher-level processes compared to the knowledge captured by the causally inferred proteins . We hypoOverall, these results show that performing pathway enrichment with causal reasoning-derived proteins captures more pathways which contain the compound’s direct target, as well as being more informative , compared to using differentially expressed genes.We next investigated performance of the algorithms as a function of the connectivity of a protein target in the network, due to the known connectivity bias present in biological networks , as wellFor each combination of network and algorithm we calculated how many times each target was recovered. We then normalised this value to account for annotation prevalence by dividing it by the number of times the target was annotated in the compound set. Finally, we calculated the Spearman rank correlation of the normalised target recovery with their degree in the corresponding PPI network Fig. . The lowWe next analysed the best-performing algorithm  in more detail with respect to its ability to recover targets across different protein classes  showing less of an association between target degree and its successful recovery. The biological role of the considered targets was also reflected in the results, with protein kinases recovered most successfully both in terms of direct target recovery, and the recovery of relevant pathways. A potential way to mitigate the connectivity bias is for random simulation studies be carried out to identify which network nodes may be recovered by chance, an approach which has been used previously . We noteTo contextualise our findings, we present a case study investigating the factors affecting target and pathway recovery of the drug Diethylstilbestrol Fig.  which waTable In terms of canonical targets only, across all 48 potential combinations of platform, gene set, cell line and network, SigNet was able to recover ESR1 every time, and ESR2 in all cases except when using the Omnipath network (as ESR2 was not included in this network). Despite CARNIVAL recovering the lowest average number of targets, a canonical target was recovered across 20 combinations of the other parameters, or in around 42% of cases. On the other hand, the CausalR results table, which recovered a higher average number of all targets, only recovered a canonical target in 10 cases (~ 21%). Finally, CausalR ScanR network recovered a canonical target in 31 cases (~ 65%) . These are summarised in Table Considering all possible parameter combinations using the CARNIVAL algorithm, 38 out of 48 or 79.2% of cases recovered estrogen-related pathways, while when just considering the Omnipath network results, 11 out of 12 or 91.7% of cases recovered estrogen-related pathways. This agrees with the overall trends shown in Fig. While we aimed for a comprehensive parameter exploration and benchmarking of causal reasoning algorithms in this work, it still has some limitations as well. Firstly, we were limited by the annotations available for the compounds used, and chemical space (and mode of action) coverage in this set in the first place. This has profound implications for our work : Different areas of chemical and mode of action space behave differently, and assuming unavailable data as inactive punishes ‘false positives’, which may very well be novel true positives which are just not annotated as such.The mechanisms of action under investigation in this study were specifically small molecules with human protein target-mediated mechanisms, but not all compounds achieve their desired pharmacological effects in this way. For example, some anti-bacterial agents act by inhibiting cell wall synthesis in bacterial cells. Such mechanisms of action would not be identified using the approach undertaken in this study.We were additionally not able to benchmark all available causal reasoning algorithms due to the high computational cost of the algorithms which scale with network size. Further benchmarking should be carried out for other causal reasoning algorithms. Furthermore, the causal reasoning algorithms additionally infer node directionality , which we did not consider when benchmarking the results as it was not possible to obtain consistent and complete functional pharmacology information about the compound-target interactions .Furthermore, the cell lines used in the gene expression experiments considered MCF7 and PC3) are quite similar in terms of baseline gene expression , cell line MCF7 or PC3), and gene set  were either not found to be statistically significant or did not result in any practically relevant changes in performance. These results indicate that the LINCS L1000 data is suitable for use with causal reasoning algorithms, at least based on the other factors used in this work, presenting an opportunity for novel research to be carried out using this large dataset. They also indicate that the 978 landmark genes are truly informative enough to gain insight into compound mechanism of action, as hypothesised in the original publication [ or PC3, and how informative the pathways were. Based on these results we recommend that performing causal reasoning using SigNet, CARNIVAL or ScanR could be a better strategy for understanding modulated signalling pathways compared to traditional pathway enrichment on the transcript-level. Our case study with the compound Diethylstilbestrol along with the overall results also indicate that in general, the algorithms are better utilised for recovering modulated pathways rather than the direct target(s) of compounds.We also examined the average performance for each combination of network and algorithm when recovering compound-associated pathways, and quantified how specific (and therefore informative for MoA understanding) the recovered pathways were, in terms of their position in the Reactome hierarchy and their gene set size. CARNIVAL, though less appropriate for discovering direct targets, was able to recapitulate networks representative of specific and informative pathways encompassing actual compound targets. The CausalR ranked table showed the worst performance, while SigNet again showed the best performance with the Omnipath network. Notably, we found that the causal reasoning-derived nodes generally outperformed differentially expressed genes (DEGs) as input for pathway enrichment both in terms of the statistical significance of enrichment in compound-derived pathways Furthermore, we sought to understand the applicability domain of the methods with regards to target function and network biases. We found that target recovery heavily depends on target connectivity (number of edges) in a given network, and that the CausalR ranked table (which corrects for degree bias) had the lowest concordance between connectivity and recovery. This has two effects: On the one hand, this penalises hub nodes, which often represent disease targets (52) . On the other hand, for atypical (or incompletely annotated targets) this type of behaviour may be rather beneficial. Furthermore, we found that the biological role of the proteins influenced their successful recovery: transcription factors and nuclear receptors were most often recovered as direct targets, and protein kinases and mediators of cellular signalling on the pathway level. We also found that CNS-related pathways were not often recovered, corresponding to their low baseline expression in the MCF7 and PC3 cell lines used to derive transcriptomics data. It is hence recommended to choose an in vitro cell line that is best able to recapitulate in vivo disease/phenotypes—for example, breast cancer cell lines which have been found to have genomic similarity to breast cancer patient samples . To corrOverall, we were in this work hence able to explore performance, and for the first time other factors that influence performance , when using gene expression data in combination with causal reasoning algorithms for mechanism of action analysis, which provides guidelines for their use by researchers in this field in the future.Additional file 1: Supplementary figures and tablesAdditional file 2: Supplementary methods.Additional file 3: List of compounds considered in the benchmarking study with extracted bioactivity information.Additional file 4: Results for the case study Diethylstilbestrol.


Morchella esculenta is an edible fungus with a uniquely delicious flavor and remarkable benefits for health. Herein, the molecular mechanism underlying the anti-inflammatory effects of Morchella esculenta was elucidated using molecular docking and network pharmacology. NPASS, Super-pred, SEA, Swiss Target Prediction, GeneCards, DisGeNET, Omim database, and STRING platform were used to select anti-inflammatory targets and construct target protein interaction networks using the active ingredients of Morchella esculenta. The OmicShare cloud platform was used to analyze GO functions and KEGG pathways related to the target, and the AutoDock Vina software was used to perform molecular docking and molecular dynamics (MD) simulation on the main target. Based on Cytoscape’s “Network Analysis”, the degree was used to identify potential key targets, and different inflammatory transcriptome data sets were used to evaluate core targets showing clinical significance. The active ingredient of Morchella esculenta identified from the NPASS database was EOYA, which had 43 anti-inflammatory targets, including NR1I2, PTGS1, PTGS2, CYP4F2, CYP3A4, TLR4, MAPK1, PLA2G4A, and PTPN11, and was mainly implicated in arachidonic acid metabolism, vascular endothelial growth factor signal pathway, and sphingomyelin signal transduction pathway, indicating that the anti-inflammatory effects of EOYA were mainly related to these biological processes. The degree was used to select 9 potential effective targets, namely NR1I2, PTGS1, PTGS2, CYP4F2, CYP3A4, TLR4, MAPK1, PLA2G4A, and PTPN11, among which NR1I2, PTGS1, PTGS2, PLA2G4A, MAPK1, CYP3A4, and TLR4 showed clinical significance. Molecular docking results showed that (E)-Octadec-11-En-9-Ynoic Acid (EOYA) could spontaneously bind to the 9 core targets, and the binding fractions of NR1I2, PTGS1, PTGS2, CYP4F2, and CYP3A4 were the highest. The MD simulation results showed that EYOA did indeed bind well NR1I2 to PTGS2, and the complex has high stability. Morchella esculenta can regulate the activity of prostaglandin endoperoxide synthetase, and affect the biosynthesis of prostaglandins, thereby impacting the metabolic pathway of arachidonic acid. It is characterized by fever, redness, pain, and swelling2. Inflammatory responses are essential for defense against external stimuli and critical to the healing process3. However, chronic inflammation is associated with several diseases, including cancer, arthritis, cardiovascular disease, atherosclerosis, nervous system disorders, and autoimmune diseases4. Identifying promising non-toxic anti-inflammatory drugs is a target area of biomedical research. Aspirin, diclofenac, and other commonly used drugs for treating inflammation have serious side effects. Thus, anti-inflammatory agents based on natural products have become popular in recent years5.Inflammation, mediated by inflammatory cytokines and immune cells, is a defense response to injury, infection, allergy, and necrosisMorchella esculenta is a delicious edible fungus with a unique flavor and remarkable health benefits6, and it contains various nutrients and exerts anti-oxidation, anti-inflammatory, and anti-tumor pharmacological activities7. Morchella esculenta produces several primary and secondary metabolites, including proteins, polysaccharides, sterols, polyphenols, alkaloids, fatty acids, flavonoids, and other small molecules6. A heteropolysaccharide isolated from the fruiting body of Morchella esculenta has an anti-inflammatory role as it blocks the activation of the NF-κB signaling pathway8. Simultaneously, total flavonoids of Morchella-fermented liquid show anti-inflammatory effects in RAW264.7 macrophages stimulated by lipopolysaccharide9. The hot water–ethanol extract of Morchella mycelia has an obvious inhibitory effect on acute and chronic inflammation10. EOYA is a natural octadecyl-conjugated enyne acid, wherein the special enyne-conjugated structure endows it with many biological activities, such as antibacterial11, antifungal12, larvicidal13 and anti-inflammatory15 properties.17. At present, most of the researches on network pharmacology are Chinese herbal medicines and prescriptions20, but there are few studies on edible and medicinal fungi.The pharmacological activity of cordycepin, the active component of Cordyceps militaris, was studied by means of network pharmacology and molecular docking, and the core targets for improving Alzheimer's disease were screened21. However, there is no related research on Morchella esculenta.Herein, we employed network pharmacology to assess the anti-inflammatory targets of the active ingredient of Morchella esculenta, EOYA, and conducted protein interaction analysis, GO function analysis, and KEGG pathway analysis of these targets. We also used the GEO database to evaluate the clinical significance of the core targets and performed molecular docking and molecular dynamics (MD) simulation verification for the core targets to comprehensively elucidate the molecular mechanism underlying the anti-inflammatory effects of the active ingredient of Morchella esculenta.In recent years, basic research on the active substances of traditional Chinese medicines has gained traction. To better clarify the pharmacological mechanisms underlying traditional Chinese medicine in the treatment of diseases, computational methods like molecular docking, network pharmacology, and dynamic simulation have emerged as effective toolshttp://bidd.group/NPASS/), “Morchella esculenta” was entered as the organism to search for the active ingredients of morel. In the Superpred (http://prediction.charite.de/), Swiss Target Prediction (http://swisstargetprediction.ch/), and SEA (http://sea.bkslab.org/) databases, the “SMILES” value of the active ingredient of Morchella esculenta was entered; the structure diagram of the substance was drawn, and the target of the active ingredient was assessed. The target of the active ingredient of Morchella esculenta was obtained by combining and removing the targets obtained from the above three databases.In the NPASS database (www.disgenet.org/), GeneCards (www.genecards.org/), and Omim databases (https://www.omim.org/) to search for related targets, which were scored according to the photographic significance. Subsequently, the anti-inflammation targets of the three databases were combined to obtain significant anti-inflammation targets. The disease targets and the active ingredient targets of Morchella esculenta were input into Venny2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/), and the intersection of the two yielded the anti-inflammation targets of the active ingredient of Morchella esculenta, which were together imported into the Cytoscape 3.9.1 software to build the active ingredient_anti-inflammation target network.The keyword “anti-inflammatory” was entered in DisGeNET (Morchella esculenta obtained as described above were imported into STRING (www.string-db.org/) to construct the protein–protein interaction networks (PPI). The organism was set as “Homo Sapiens”; the minimum interaction threshold value was set at “medium confidence = 0.4”, and default values were retained for other parameters. The PPI network was imported into the Cytoscape 3.9.1 software, and the “cytoHubba” function was used to calculate the degree, betweenness, closeness, and other topological parameters for each network node. The greater the betweenness, closeness, and degree of the node, the more important the position of the node in the network. The targets with a degree, betweenness, and closeness above the corresponding average values were selected as the main anti-inflammation target of the active ingredient of Morchella esculenta.The anti-inflammatory targets of the active ingredient of Morchella esculenta were analyzed by GO and KEEG analyses using the DAVID web tool (https://david.ncifcrf.gov/), and the obtained data were further analyzed by OmicShare (http://omicshare.com/) cloud platform for assessing dynamic GO enrichment. The UNIPROT website (www.uniprot.org/) was used to convert the anti-inflammatory targets of the active ingredient of Morchella esculenta into corresponding Gene Symbols, and the OmicShare cloud platform was used to conduct KEGG pathway (www.kegg.jp/kegg/kegg1.html) enrichment analysis for the anti-inflammatory targets of the active ingredient of Morchella esculenta.The anti-inflammatory targets of the active ingredient of https://www.ncbi.nlm.nih.gov/genome) to obtain the clinical significance of core targets. The differences in core target gene expression before and after inflammation were analyzed. Graphpad prism8 was used to draw a box chart. The Human eFP Browser (http://bar.utoronto.ca/efp_human/cgi-bin/efpWeb.cgi)22 was used to study the distribution of expression of the key targets, and the whole-body expression distribution map was obtained.The transcriptome data for inflammation were retrieved through the GEO database (Morchella esculenta was obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/), and the structure of ligand was sketched, hydrogens added, energy minimized and saved as mol2 files using the Chem3D software for storage. The target protein complex was searched in the PDB database (http://www.rcsb.org/), We manually curated the PDB to choose structures that described continuous, well resolved (resolution less than 2.5 Å), single-chain proteins and ligand binding. and the Discovery studio software was used to remove phosphate radicals, water molecules, and inactive ligands from the target protein. The target protein was imported into AutoDock23 Tools software for hydrogenation and charging. Subsequently, the target protein and active components of Morchella esculenta were introduced into the molecular docking website, CB-DOCK2 (CB-Dock2: An accurate protein–ligand blind docking tool (labshare.cn)) for molecular docking, and the docking combination with the lowest binding free energy was selected. The receptor-ligand binding image was visualized using the Pymol (2.5.0) software (www.pymol.org/)24.The 2D structure of the active ingredient of 25 (version 2021.2). Protein topology file was generated using the AMBER99SB-ILDN force field, whereas ligand topology file was generated by ACPYPE script using the AMBER fore field. MD simulation carried out in a triclinic box filled with TIP3 water molecules and periodic bounding conditions were applied. System was neutralized with NaCl counter ions. Before MD simulation, the complex was minimized for 1000 steps and equilibrated by running NVT and NPT for 100 ps. Then MD simulation was performed for 100 ns for each system under periodic boundary conditions at 310 K temperature and 1.0 bar pressure.Molecular dynamics (MD) simulation of ligand-receptor docked complex EYOA-NR1I2 and EYOA-PTGS2 with the lowest Binding Free Energy were carried out using GROMACSMorchella esculenta” according to the NPASS database was (E)-Octadec-11-En-9-Yinoic Acid (EOYA) O. The targets obtained by searching the “EOYA Smile Value” in the Super-pred, SEA, Swiss Target Prediction databases were merged and de-weighed, and 107 targets of EOYA were obtained. A total of 1654 anti-inflammation-related targets were obtained from GeneCards, DisGeNET, and Omim databases. The EOYA and anti-inflammatory targets were imported into the Venny 2.1.0, and 43 EOYA–anti-inflammatory targets were obtained after de-duplication  were additionally considered for molecular dynamic simulations to validate the network pharmacology and molecular docking results Fig. . RMSD anMorchella esculenta is a fungus mushroom used both as medicine and food. It has the functions of invigorating the yang, resolving phlegm, tonifying the kidney, regulating the qi, invigorating the brain, pleen, and stomach, and aiding digestion. The role of Morchella esculenta in the arachidonic acid metabolic pathway has been studied. For instance, a specific LOX activity was determined from the crude extract of Morchella esculenta, and the affinity of LOX activity to the substrate, arachidonic acid, was strong (83%)27. The extracellular vesicles of Morchella esculenta contain some specific lipids, miRNAs, and proteins, which can inhibit LPS-induced inflammation by attenuating the production of ROS and reducing the phosphorylation levels in the p38 MAPK signaling pathway28. The active extract of Morchella esculenta can inhibit the expression of LPS-induced COX, and significantly inhibit the activity of NF-κB, indicating its preventive effect on inflammation29. To further analyze the anti-inflammatory mechanisms of action of Morchella esculenta, we then applied a network pharmacological method. However, the previous TCM network pharmacological research predicted drug targets through some databases only. This may be not fully consistent with the actual target of Morchella esculenta. To overcome these shortcomings, we use the 6 differents inflammation GEO database to provided more compelling evidence. Our results showed that we obtained one compounds EYOA derived from Morchella esculenta though the NPASS database, we searched multiple compound target databases and disease target databases and identified 43 anti-inflammatory targets of EOYA. According to the network pharmacology analysis, EOYA mainly played an anti-inflammatory role through arachidonic acid metabolism, vascular endothelial growth factor signaling pathway, and sheath phosphorus signal transduction pathway.9 potential targets were chosen as core targets based on the ‘Degree’, ‘Betweenness’ and ‘Closeness’ values in the PPI network, NR1I2, PTGS1, PTGS2, CYP4F2, CYP3A4, TLR4, MAPK1, PLA2G4A, and PTPN11. we expect to screen protein targets related to inflammation using 6 GEO databases related to inflammatory, Clinically relevant gene association networks obtained from these inflammation database. we obtained 5 tagerts significant differences with inflammation, including as NR1I2, PTGS1, PTGS2, CYP4F2 and CYP3A4. By using Autodock software EYOA is docked with 5 protein targets. The results show that all 5 proteins can bind well to EYOA. and the best characterized of these are NR1I2 and PTGS2. EYOA can form the strong interactions with the NR1I2 active site HIS 327 and GLN285, the PTGS2 active site ARG121. The studies of molecular dynamics simulations were carried out to understand more deeply the modes of interaction of the selected EYOA with the target proteins.The simulation result showed EYOA can form strong hydrogen bond interactions with the NR1I2 active site HIS 327 and GLN285, the PTGS2 active site ARG121 and TRY356, which was the main interaction force of EYOA bound with the active site of NR1I2 and PTGS2. The RMSD, RMSF, RG, hydrogen bond, and SASA define the complexes and show that they are more stable Fig. . The ana30. The continuously increasing NF-κB activity can accelerate the aging of certain immune cells, leading to an increase in the levels of related inflammatory factors31. Simultaneously, the increase in the levels of reactive oxygen species (ROS) from mitochondria and cytoplasm and defective antioxidant protection result in continually altered redox metabolism32, and this redox imbalance changes the signaling response to growth, nutrition factors, cytokines, and chemokines, eventually leading to cell aging and chronic inflammation33. To date, hormone drugs are commonly used to treat inflammation. Dexamethasone is a glucocorticoid used to treat inflammatory diseases; it has been used for more than 50 years but its various side effects have been confirmed, including hypertension, adrenal insufficiency, and hyperglycemia34. Therefore, it is necessary to identify drugs with significant anti-inflammatory effects and fewer side effects. For centuries, traditional Chinese medicine has been used to treat various diseases35.Chronic inflammation is caused by various stimuli from natural immune cells through different pathogen-related modes. Its reaction process is continuous and complex, and these are mainly transmitted through the NF-κB signaling pathway36. Many new compounds have been obtained from plant endophytic fungi, which can inhibit the excessive production of endotoxin-induced NO and pro-inflammatory cytokines IL-6, tumor necrosis factor-α and monocyte chemoattractant protein-1 at both gene and protein levels37. The compounds isolated from algae-producing fungi could inhibit the excessive production of NO and pro-inflammatory cytokines in lipopolysaccharide-treated RAW264.7 macrophages in a dose-dependent manner without any cytotoxicity38. New polyketones were isolated from marine fungus Eutyellahoraria. Nitric oxide was induced by lipopolysaccharide in RAW264.7 macrophages and showed strong anti-inflammatory activity. When the concentration was 50.0 μg/mL, the inhibition rate was 54.9%39. Edible and medicinal fungi are natural products with effective anti-inflammatory and antiviral properties. During the SARS-CoV-2 pandemic, people often ingested, reducing the impact of SARS-CoV-240. Agaricus blazei Murrill has the properties of anti-allergy, anti-infection, asthma and anti-tumor. In mouse model, it also has anti-inflammatory effect in patients with inflammatory bowel disease41.At present, the metabolites of fungi have become the main source of anti-inflammatory lead compounds because of their novel structure, low toxicity and significant inhibitory effect42 , lipoxygenases (LOXs), and cytochrome P450 (CYP) enzymes (ω-hydroxylase and cyclooxygenase)42 Fig. , which h44. PTGS1 can regulate peripheral vascular resistance, maintain renal blood flow, protect the gastric mucosa, and regulate platelet aggregation45. The expression of PTGS1 is related to several inflammatory pathways46. PTGS1 is distributed in microglia and plays a key role in regulating inflammation in the central nervous system47. Ursolic acid can treat autoimmune thyroiditis by inhibiting the mechanism and inflammatory pathways of the MALAT1/miR206/PTGS1 axis48. PTGS2 (or COX-2) is induced by hormones, growth factors, and inflammatory stimuli, and is considered an important source of prostaglandin formation in proliferative (such as cancer) and inflammatory diseases. CYP4F2 and CYP3A4 are enzymes of the cytochrome P450 family. The most prominent role of the CYP pathway is the metabolism of fat-soluble exogenous substances. The expression and activity of CYP are controlled by hormones, growth factors, and transcription factors49. The NR1I2-nuclear receptor subfamily, whose members are transcription factor coding proteins characterized by the presence of a DNA binding domain and a ligand binding domain. Its encoded protein is a transcriptional regulator of the cytochrome P450 gene CYP3A4.PTGS1 is an essential enzyme responsible for prostaglandin synthesisMorchella esculenta showed that MAPK1, PTPN11, and TLR4 were involved in inflammation; PTGS2, PLA2G4A, CYP3A4, CYP4F2, PTGS1, and NR1I2 were involved in arachidonic acid-related pathways. CYP3A4, PTGS2, NR1I2, and PTGS1 were involved in the synthesis of prostaglandins. NR1I2, CYP3A4, and CYP4F2 participated in the cytochrome P450 pathway (Fig. Morchella esculenta could participate in multiple metabolic pathways involving arachidonic acid, directly or indirectly regulate the activity of prostaglandin endoperoxide synthetase, and inhibit the release of prostaglandins, thereby reducing inflammation.The comparative and enrichment analyses of the effective components of way Fig. . This shMorchella esculenta has a multi-target pharmacological effect in the treatment of inflammation through the arachidonic acid metabolism pathway, which is expected to shed new insights into the treatment of inflammation. Herein, the network pharmacological strategy was used to study the multi-pathway and multi-target mechanisms of the active ingredient of Morchella esculenta, EOYA, in the prevention and treatment of inflammation. Morchella esculenta may be effective for the treatment of inflammation and provide a new direction for addressing the current shortcomings in treating inflammation.In summary,


Aesculus hippocastanum. It has been demonstrated that escin has anti-inflammatory properties. This study combined the methods of network pharmacology, molecular docking, and molecular dynamics to explore the molecular mechanism of escin against neuropathic pain (NP).  Escin is the main active component in  The Swiss Target Prediction and the Pharm Mapper database were employed for predicting the targets of escin. Also, the candidate targets of NP were gathered via the databases including Therapeutic Targets, DisGeNet, GeneCards, DrugBank, and OMIM. Subsequently, the network of protein-protein interaction was screened for the key targets by the software Cytoscape 3.8.0. Then, the intersection of these targets was analysed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Additionally, we further investigated the ligand-target interactions by molecular docking and molecular dynamics simulations.  In total, 94 escin targets were predicted by network pharmacology. Among them, SRC, MMP9, PTGS2, and MAPK1 were the core candidate targets. Subsequently, the analysis of GO and KEGG enrichment revealed that escin affected NP by regulating protein kinase C, MAP kinase, TRP channels, the T-cell receptors signaling pathway, and the TNF signaling pathway. The results of molecular docking and molecular dynamics simulation confirmed that escin not only had a strong binding activity with the four core target proteins but also stably combined in 50 ns.  Our study revealed that escin acts on the core targets SRC, MMP9, PTGS2, MAPK1, and associated enrichment pathways to alleviate neuronal inflammation and regulate the immune response, thus exerting anti-NP efficacy. This study provided innovative ideas and methods for the promising treatment of escin in relieving NP. The International Association for the Study of Pain defines neuropathic pain (NP) as pain caused by a lesion or disease of the somatosensory nervous system . PrevaleAesculus hippocastanum seed, has attracted attention owing to its anti-inflammatory effects [α, IL-1β, and IL-6 in endotoxemia mice and LPS-induced macrophages [Lots of studies reported that natural compounds compared with synthetic compounds had the characteristics of being valid, nontoxic, safe, cheap, etc. –10. Thus effects . Escin a effects , antican effects , antioxi effects , etc. Th effects . Moreoverophages . NumerouNetwork pharmacology that integrates bioinformatics and multivariate pharmacology analyses the connection between natural compounds and the key targets protein, and it is a rapid and convenient new approach . In addiIn this article, combining network pharmacology and molecular docking with molecular dynamics simulation, we investigated the potential targets and signaling pathways of escin in treating NP. This study might provide a new combination of escin for the treatment of NP. The process drawing is shown in https://pubchem.ncbi.nlm.nih.gov/). The Swiss Target Prediction database (https://www.swisstargetprediction.ch/) was used for predicting the potential targets of bioactive compounds. The canonical SMILEs were imported into Swiss Target Prediction with the criteria of “Homo sapiens” and the probability of ≥0.6. The Pharm Mapper database (https://www.lilab-ecust.cn/pharmmapper/) was used for the method reverse pharmacophore matching to retrieve the targets of compounds, and the matching results were scored and ranked. The mol2 format file of escin was uploaded to Pharm Mapper for target prediction with the criteria of “Homo sapiens” and normal fit score ≥0.3 [https://www.uniprot.org/) to normalize the gene names.The mol2 format file and canonical SMILEs of escin were obtained from the PubChem database , DisGeNet (https://www.disgenet.org/), GeneCards (https://www.genecards.org), DrugBank (https://www.drugbank.ca), and OMIM (https://omim.org/). After discarding the duplicate targets, we obtained NP-related targets [NP-associated target genes were collected through the databases, including Therapeutic Targets Database ( targets .https://www.string-db.org/). Also, the results were exported as “TSV” format files. Then, the TSV file was imported into Cytoscape 3.8.2 software. After topology analysis by “Network Analyzer,” we mapped the network of common target interactions and calculated the values of node degree. According to the node degree values of the targets, we obtained the top 10 key targets for escin anti-NP [By comparing the intersection target genes of escin and NP, we need to obtain the potential targets of escin for alleviating NP by the PPI network . We set  anti-NP .https://david.ncifcrf.gov/). Also, biological process (BP), cellular component (CC), and molecular function (MF) are the three parts of GO enrichment. To determine the biological significance of the target genes, we set the criteria by choosing the probability score ≤0.05 as the cutoff values and obtained the top −log10 (P) terms in the results.GO and KEGG enrichment analyses of target genes were employed through DAVID (https://www.rcsb.org/) and saved in PDB format. After removing water molecules and residual ligands by PyMol-2.4.0 and adding hydrogen in AutoDock (version 1.5.6), we obtained the optimized structure of the top 10 targets. After molecular docking by AutoDock Vina, we obtained receptor-ligand docking energy. Finally, we visualized the escin-target complexes with the lowest binding energy by PyMol-2.4.0 [Molecular docking was used to predict the patterns of docking and binding affinity between escin and the top 10 targets in 3D structures. The crystal structures of the top 10 targets were downloaded from the RCSB Protein Data Bank database . Generally, lower binding energy represented better binding between escin and the targets. The binding energy smaller than −5.0 kcal/mol showed good binding activity, whereas the binding energy smaller than −7.0 kcal/mol showed strong binding activity , 29. OurEven though the excellent binding activity of escin to the targets has been confirmed by molecular docking, this approach could not reveal its stability in vivo. Molecular dynamics simulation provided insight into the stability of protein-ligand complexes. The RMSD is typically regarded as a key sign of a system's stability . As showNP is caused by damage or disease of the somatosensory system. In the last decade, the prevalence has increased with the development of the global population . The mecFrom the 94 intersection targets of PPI results, we further found out the top 10 targets: ALB, SRC, JUN, MMP9, IGF1, PTGS2, RHOA, MAPK1, PPARG, and IL2, which were identified as the key targets by their topological properties. Moreover, the results of molecular docking verified that their binding energy was less than −5 kcal/mol. In Figures α antagonists increased the paw withdrawal thresholds by influencing nerve growth factor [Our KEGG enrichment analysis showed that the anti-NP effect of escin may be closely related to inflammatory mediator regulation of TRP channels signaling pathway, TNF signaling pathway, and T-cell receptor signaling pathway. Also, the four core targets  of our PPI results play critical roles in the above three pathways . The firh factor , 44. In h factor . The thih factor . Also, Kh factor .In summary, this study demonstrated the anti-NP effect and mechanism of escin by combining network pharmacology with molecular docking and molecular dynamics validation. These results not only provided evidence for guiding the clinical application of escin but also explored the possible molecular mechanisms in the development of NP. Briefly, we found out that escin may act on targets such as PTGS2, SRC, MMP9, and MAPK1 and then regulate TNF, TRP channels, and T-cell receptor signaling pathway, thus alleviating neuroinflammation and against NP .Overall, our study provides new ideas that escin has “multitarget and multipathway” anti-NP activity. Escin may act on PTGS2, SRC, MMP9, and MAPK1 and then regulate TNF, TRP channels, and T-cell receptor signaling pathway. These results lay a foundation for investigating the anti-NP mechanism of escin. However, the interaction between drugs, targets, and diseases is complex. Our findings were mainly derived from online databases and network analysis; more cellular and animal experiments are needed to verify the predicted results of this study in the future. Researchers can first investigate drug-target interactions using computers and then validate the results through cell and animal experiments, which can more efficiently drive the clinical translation of drugs.