Most high throughput genomic data analysis pipelines currently rely on over-representation or gene set enrichment analysis (ORA/GSEA) approaches for functional analysis. In contrast, topology-based pathway analysis methods, which offer a more biologically informed perspective by incorporating interaction and topology information, have remained underutilized and inaccessible due to various limiting factors. These methods heavily rely on the quality of pathway topologies and often utilize predefined topologies from databases without assessing their correctness. To address these issues and make topology-aware pathway analysis more accessible and flexible, we introduce the PSF  toolkit R package. Our toolkit integrates pathway curation and topology-based analysis, providing interactive and command-line tools that facilitate pathway importation, correction, and modification from diverse sources. This enables users to perform topology-based pathway signal flow analysis in both interactive and command-line modes. To showcase the toolkit’s usability, we curated 36 KEGG signaling pathways and conducted several use-case studies, comparing our method with ORA and the topology-based signaling pathway impact analysis (SPIA) method. The results demonstrate that the algorithm can effectively identify ORA enriched pathways while providing more detailed branch-level information. Moreover, in contrast to the SPIA method, it offers the advantage of being cut-off free and less susceptible to the variability caused by selection thresholds. By combining pathway curation and topology-based analysis, the PSF toolkit enhances the quality, flexibility, and accessibility of topology-aware pathway analysis. Researchers can now easily import pathways from various sources, correct and modify them as needed, and perform detailed topology-based pathway signal flow analysis. In summary, our PSF toolkit offers an integrated solution that addresses the limitations of current topology-based pathway analysis methods. By providing interactive and command-line tools for pathway curation and topology-based analysis, we empower researchers to conduct comprehensive pathway analyses across a wide range of applications. Biological signaling pathways are spatially and temporally distributed series of biomolecular interactions that transduce information in a directional manner and elicit changes in cellular physiology . Within Pathway analysis tools can be classified into two main categories. The first category includes overrepresentation and gene set enrichment analysis tools (ORA/GSEA) and their variations, which treat pathways solely as gene sets (or lists). The advantage of these approaches is the ability to handle any pathway or interaction network in a simple and straightforward way. However, they disregard information about pathway component interactions and functional outcomes. The second category comprises tools that leverage pathway topology and protein interaction information which allow for the analysis of information flow within a pathway . These mHigh-quality curated pathways serve as a crucial resource for the accurate performance of topology-aware algorithms. Correcting pathway topology ensures that the pathway accurately represents the known interactions and relationships among its components . It alsoTo overcome these limitations of existing topology-aware tools, we developed a pathway curation and activity analysis package for R. With the PSF  toolkit, users can easily modify and improve existing pathway topologies, build new pathways from scratch, and analyze them with the branch and node selective pathway signal flow algorithm to study the activity changes along individual pathway branches. The package offers a comprehensive set of curation and editing tools that allow for the seamless integration of pathways from diverse data sources, and facilitate their transformation into a biologically and computationally useable format for topology-aware analysis.In several use-cases, we demonstrate the utility of our pathway analysis package.The PSF toolkit consists of two interconnected but independent modules for 1) pathway curation , and 2) pathway activity  analysis.The toolkit offers both programmatic access in R and a graphical user interface (GUI) as an R Shiny app  using the GitHub Actions automated build and testing system.The PSF toolkit comprises an interactive Shiny application designed for the curation and editing of pathways from diverse sources. Pathways can be imported automatically from the KEGG database via the API or as data frames of nodes and interactions from other databases. Alternatively, users can build pathways from scratch using the integrated “visNetwork” editor.https://github.com/hakobyansiras/psf).The Shiny app has two visualization modes: overlay of the pathway graph object on the KEGG pathway image with R “magick” and interactive visualization with the “visNetwork” R packages  search engine  Figure . FurtherDedicated tools are provided within the application for parsing and editing KEGG pathways. These tools include a parser for KEGG pathways, which automatically corrects “protein-compound-protein” interactions by adding missing interactions, grouping nodes, and establishing binding directions. The parsing tool is inherited from KEGGParser (for Matlab/Octave) and CyKEGGParser (for Cytoscape), which were previously developed by the authors . AdditioThe detailed description of the pathway curation process is presented as one of the use cases in the Results section.After curation users can save edited pathways as a collection of graph objects , use them for further pathway analysis in R, or continue analysis in the interactive mode. The toolkit also allows pathway graph modifications programmatically using the “graph” package.The PSF algorithm computes the activity state of each node (gene or gene groups) in a pathway based on the relative gene expression values and interactions with upstream nodes . It has j represents the node signal, FCj is the fold change value of a node, Si is the signal of the parent/upstream node, wij is the weight of interaction between the jth and ith node , and Kij is the impact . The proportion of the incoming node signal is calculated using the following formula:The algorithm computes the activity values of each node  through the branches of the pathway, starting from input nodes (ligands/receptors) and progressing toward the end of the pathway branches . Node signal values are calculated using the following formula:The significance of PSF value change at branch or pathway levels can be assessed with bootstrapping by reshuffling either sample labels or gene expression values in the pathway.PSF analysis of the pathway can be performed both in the Shiny app and in the R environment. With a programmatic approach, users can analyze all the pathways in the collection simultaneously, while the GUI allows for real-time visual analysis of a single pathway.In the Shiny app, the GUI elements enable to select a pathway, upload expression fold change values, run the analysis, and visualize the results with heatmaps, volcano plots, and interactive pathway plots .. The reMost topology-aware pathway analysis tools utilize the KEGG Pathway database as their primary source of topological networks . SeveralHowever, the KEGG Markup Language (KGML) representation of KEGG pathways suffers from several topology-related flaws, including non-connected nodes, missing interactions, and incorrectly annotated interaction types and directions . For insUsing the curation module of our package, we improved 36 signaling pathways from the KEGG database and conducted several use-case studies to demonstrate the applicability of our approach. The manual curation process in the pathway editor was performed with two steps. It has been observed that KGML files often contain inconsistencies compared to pathway images, which serve as the “ground truth” in the KEGG database. In the first step, networks were recovered and curated based on pathway image information. Image-based visualization enables quick detection and resolution of inconsistencies between the image and KGML using the editor’s tools. Fixes made during this step include recovering missing interactions in KGML files based on pathway images, correcting wrong interaction directions, removing extra parts of pathways that are outside the main network, and adding nodes for processes and events present only in the pathway images. The second step of manual curation involved addressing more complex cases that cannot be resolved solely using the pathway image. The second part of curation was performed with the help of a literature review and the use of an integrated PPI database.We demonstrate here the improvements in Hippo signaling pathway topology. After automatic error correction during parsing (18 fixes), additional curations  were performed interactively in the toolkit’s GUI . OverallWe compared the performance of PSF and one of the most popular topology-based pathway analysis methods - SPIA . SPIA isp adjusted < 0.05, for SPIA and cutoff-free fold change values for PSF). To calculate PSF significance we used bootstrapping (n = 2000) to check the probability of getting the same or more extreme PSF values by chance. The output of SPIA was a ranked list of pathways and their states based on the combined significance of ORA and topology analysis. The output of PSF included the activity of all pathway branches with calculated significance. To compare the results of both methods, we considered the list of significant pathways from SPIA and the list of pathways that had at least one significantly deregulated branch according to PSF.We conducted a pathway analysis to identify differences between primary melanoma and benign melanocytic nevi, using RNA-seq data (GEO accession number GSE112509) . Both alFour pathways were labeled as differentially deregulated both by SPIA and PSF algorithms. Another four pathways were additionally detected only by SPIA, and 16 pathways were detected only by the PSF algorithm. Examination of PSF-detected significant pathways showed that the majority of deregulated pathways had few or only one deregulated branch. One such example is the AMPK signaling pathway where the “Growth arrest” branch showed a 10-fold activity drop due to the activation of the AMPK gene through the Leptin Receptor .p-value. A detailed view however shows that, while a large portion of the pathway is indeed activated, three terminal nodes, in contrast, were significantly downregulated because of the inhibitory effect from the activated AKT3 gene  had only marginal fold change not impacting the overall activity of pathways. After filtering out DEGs with low fold change (logFC>|1|) two out of the four deregulated pathways were removed from the SPIA list. Hence, SPIA is sensitive to threshold settings with consequences for the robustness of the results . The RNA-seq raw FASTQ files were processed with Kallisto , which pCPC2 tool was used to detect non-coding isoforms from their sequences .To filter out the non-coding isoform expression we multiplied the total fraction of coding isoforms with corresponding gene expression values. Next, we performed differential expression analysis between melanoma type 1 and nevi type 1 groups with the DESeq2 R package for full and coding gene expression data. Then, the calculated fold change values were used to perform PSF analysis on 36 curated KEGG signaling pathways. The significance of PSF values was calculated with bootstrap (2000 steps) to check the probability of getting the same or more extreme PSF values by chance. Finally, we compared lists of significantly deregulated pathway sinks between gene and coding isoform expression level results .The comparison of full and coding gene expression PSF values revealed a general correlation between pathway activities with few obvious outliers . The patHere we were interested in whether our PSF algorithm has utility in single-cell data analysis. The single-cell level transcriptomic analyses can provide, for example, important insights into tumor heterogeneity , microenWe used the Seurat R package  to compaBoth gene-level and PSF-level clustering showed a clear separation of tissues . DespiteTo compare gene-level and pathway-level results, we performed an overrepresentation analysis for 36 curated signaling pathways to identify enriched pathways for cluster-specific genes. Then, we compared the list of overrepresented pathways on the gene level with the deregulated pathways detected by PSF for each cluster . We notiWe demonstrated that utilizing PSF analysis in conjunction with a pseudo-bulking approach effectively reduces the number of analyzed features by leveraging pathway analysis. This reduction in features ensures consistent clustering while offering valuable pathway-branch level information on cluster-specific features.In this paper, we present the “PSF toolkit,” a novel R tool for topology-aware pathway analysis that integrates pathway curation and analysis modules. The curation platform offers a comprehensive set of tools to curate and enhance pathways from any source. Platform also provides specific tools to enhance existing KEGG pathways by aligning them with image templates and accurately representing signal flows from upstream to downstream nodes. Users can also create pathways from scratch using the interactive Visnetwork editor or import them through node and interaction data frames. In addition to incorporating features from previous curation and pathway analysis packages, the PSF toolkit includes improved functions for report generation, pathway graph manipulation, and enhanced performance compared to Matlab- and Cytoscape-based packages . AnotherPathway analysis has become a widely used method for the functional analysis of high-throughput genomic data . In geneThe first class of analysis methods, known as enrichment methods, offers several advantages. Firstly, they leverage a vast number of gene sets that potentially cover almost all human genes. Secondly, they offer flexibility in terms of combining genes in various gene sets depending on the biological context. Lastly, these methods employ computationally efficient approaches, making them easily applicable to large datasets. However, enrichment methods neglect the network properties of biological processes as they intentionally disregard the relationships between genes within the gene sets.The second class of methods explicitly takes into account the graph-structure of biological pathways and considers interactions and their types. These methods enable analysis of signal flow along the pathway from upstream receptors to downstream effectors. However, there are several limitations that hinder the widespread use of these methods. The value of pathway analyses critically depends on the correctness of the pathway-graph. Hence, the primary limitation is the reliance on high-quality, manually curated pathways. Currently, available curated pathway databases with rich topology coverage only encompass approximately 10,000 human genes, which is less than half of the complete set. Additionally, these methods can involve complex analysis approaches that are time-consuming and computationally expensive. Lastly, interpreting the results and visualizing the detailed information can pose challenges with these approaches.To address some of the limitations associated with topology-aware analysis methods and advance this direction, we have developed the PSF toolkit. The toolkit includes an interactive curation module that tackles data availability issues by allowing quick improvement and curation of pathways from any source. Additionally, the visualization module enables result visualization and facilitates the exploration of disrupted pathways in an interactive manner.Since the development of the PSF algorithm, it has been applied in several studies to assess pathway activity in malignant and chronic lung diseases , autoinfFurthermore, we compared the performance of the PSF algorithm with the widely used and cited SPIA algorithm . The finIn conclusion, our presented R package, the ‘PSF toolkit,’ offers a comprehensive solution with its pathway signal flow algorithm and interactive curation and visualization platform. The package caters to diverse user preferences by providing both programmatic and GUI access options. Its versatility extends to various applications, including bulk and single-cell expression data analysis, and other gene-level -omics data, ultimately broadening the scope of topology-aware pathway analysis.


Intracranial aneurysm (IA) is a common cerebrovascular disease. The immune mechanism of IA is more complicated, and it is unclear so far. Therefore, it is necessary to continue to explore the immune related molecular mechanism of IA.All data were downloaded from the public database. Limma package and ssGSEA algorithm was used to identify differentially expressed mRNAs (DEmRNAs) and analyze immune cell infiltration, respectively. Machine learning and cytoscape-cytohubba plug-in was used to identify key immune types and multicentric DEmRNAs of IA, respectively. Multicentric DEmRNAs related to key immune cells were screened out as key DEmRNAs by Spearman correlation analysis. Diagnostic models, competing endogenous RNA (ceRNA) regulatory network and transcription factor regulatory network were constructed based on key DEmRNAs. Meanwhile, drugs related to key DEmRNAs were screened out based on DGIdb database. The expression of key DEmRNAs was also verified by real time-PCR.In this study, 7 key DEmRNAs  associated with key differential immune cell infiltration  were identified. Functional enrichment analysis showed that VEGFA and IL6 may be involved in the regulation of the PI3K-Akt signaling pathway. Moreover, IL6 was also found to be enriched in cytokine-cytokine receptor interaction signaling pathway. In the ceRNA regulatory network, a large number of miRNAs and lncRNAs were found. In the transcription factor regulatory network, the transcription factor SP1 was correlated with VEGFA, SYP and IL6. It is also predicted that drugs related to key DEmRNAs such as CARBOPLATIN, FENTANYL and CILOSTAZOL may contribute to the treatment of IA. In addition, it was also found that SVM and RF models based on key DEmRNAs may be potential markers for diagnosing IA and unruptured intracranial aneurysm (UIA), respectively. The expression trend of key DEmRNAs verified by real-time PCR was consistent with the bioinformatics analysis results.The identification of molecules and pathways in this study provides a theoretical basis for understanding the immune related molecular mechanism of IA. Meanwhile, the drug prediction and diagnosis model construction may also be helpful for clinical diagnosis and management.The online version contains supplementary material available at 10.1186/s12863-023-01121-w. Intracranial aneurysm (IA) is a common cerebrovascular disease. Most of unruptured intracranial aneurysms (UIAs) are incidentally found, asymptomatic and typically benign . The risPrevious studies have found that the formation, development and rupture of IA are closely related to immune inflammatory response –6. Loss Machine learning is an emerging field of medicine, and a large number of machine learning algorithms are often used to process medical data and perform feature selection on variables to play an important role in disease detection, diagnosis and treatment , 12. CytFirstly, “intracranial aneurysm” was used as the key word to search in gene expression omnibus (GEO) database . Then, c2 fold change|> 1 (|log2 FC|> 1). To understand the function of DEmRNAs, GO and KEGG [https://david.ncifcrf.gov/). FDR < 0.05 was considered significant.The “limma” package was used for differential expression analysis to obtain DEmRNAs of IA. The screening criterion for DEmRNAs was set as false discovery rate (FDR) < 0.05, |logand KEGG –17 functThe ssGSEA algorithm was used to quantify the relative abundance of each immune cell infiltration in the immune microenvironment (IME) of the IA and control samples. Sets of genes that mark each immune cell type were obtained from Charoentong’s study . The Wilhttps://cn.string-db.org/) to study the regulatory relationship between DEmRNAs. The interactions considered by the PPI network include known interactions , predicted interactions  and others . Cytoscape software was used to visualize the PPI network. Subsequently, multicentric DEmRNAs were screened using betweenness, degree, edge percolated component (EPC), maximal clique centrality (MCC), maximum neighborhood component (MNC) and stress in the cytoscape-cytoHubba plug-in. DEmRNAs in each algorithm were sorted according to “Score”. The intersection DEmRNAs of the top 30 node DEmRNAs of each algorithm were screened by the “UpSet” package. The Spearman correlation was used to analyze the correlation between multicentric intersection DEmRNAs and key immune cells to explore the synergistic effect of immune cells and DEmRNAs in the occurrence and development of IA. DEmRNAs associated with immune cells were regarded as key DEmRNAs. The screening criterion was P < 0.05.A protein–protein interaction (PPI) network was constructed based on string database (http://starbase.sysu.edu.cn/index.php) database, miRNAs that regulate key DEmRNAs were searched. Then, differentially expressed miRNAs (DEmiRNAs) of IA were identified based on the GSE66239 dataset (10 control tissue samples and 7 IA tissue samples). The screening criterion was P < 0.05 and |log2FC|> 1. Subsequently, DEmiRNAs negatively regulated with key DEmRNAs were selected and their corresponding lncRNAs were searched based on the ENCORI database. Finally, a ceRNA regulation network was constructed.Based on the ENCORI (https://www.grnpedia.org/trrust/) to explore the relationship between transcription factors and key DEmRNAs. In order to provide a new perspective for the diagnosis, treatment and research of IA, drugs related to key DEmRNAs were screened out based on DGIdb database (https://dgidb.org/).Transcription factors related to key DEmRNAs were screened out based on TRRUST database , decision tree (DT) and support vector machine (SVM) models based on key DEmRNAs, respectively. Receiver operating characteristic (ROC) analysis was performed using the “pROC (version 1.15.3)” package. The accuracy of model was evaluated by area under curve (AUC). Higher value of the AUC indicates the higher diagnostic accuracy . AUC > 0Inclusion criteria for patients with IA were confirmed by digital subtraction angiography (DSA) and the patients were Chinese over 18 years of age. Patients with other cerebral hemangiomas, malignancies, severe complications, ongoing pregnancy or lactation, and incomplete clinical information were excluded. The individuals in the control group were gender and age matched with the IA group and had no disease. Those individuals with a family history of IA were ongoing pregnancy or lactation was excluded.−ΔΔCt method was used for relative quantitative analysis of data [Blood samples from 10 healthy individuals and 8 IA patients were included in this study according to the above screening criteria. Detailed clinical information is shown in Table S of data . The preAll statistical analyses were performed in R software (version 3.5.3). The Wilcoxon test was used to statistically analyze the difference of immune cell infiltration between IA and control groups, as well as the difference of key DEmRNAs expression between UIA, RIA and normal control groups. Spearman correlation was used to analyze the correlation between multicentric intersection DEmRNAs and key immune cells. In real time-PCR, t-test was used to evaluate the statistical significance.After pretreatment of the original data, a total of 13,607 mRNAs were identified in the discovery cohort   Fig. I.Fig. 3IA total of 20 negatively regulated DEmRNAs-DEmiRNAs relationship pairs (including 5 DEmRNAs and 19 DEmiRNAs) were obtained based on ENCORI database and GSE66239 dataset analysis Fig. A. SubseqRF, SVM and DT models all had high diagnostic accuracy in the discovery cohort (AUC > 0.7), and SVM model had the highest diagnostic accuracy (AUC = 0.835) Fig. A-C. SimiIn addition, IA group was divided into UIA group and RIA group for further study. The Wilcoxon test was used to statistically analyze the difference of key DEmRNAs expression between UIA, RIA and normal control groups. Compared with the normal control group, the expressions of NRXN1, GRIA2, SLC1A2, SLC17A7 and SYP were significantly down-regulated in the RIA group, while the expressions of IL6 and VEGFA were significantly up-regulated Fig. . SubsequExpression validation of NRXN1, GRIA2, SLC1A2, SLC17A7, IL6, VEGFA and SYP was performed by real time-PCR. Primers used for real time-PCR are shown in Table IA is a serious clinical disease. Microarray data analysis is a common method to identify abnormal expression molecules of IA. Previous researchers identified a large number of important molecules related to IA based on weighted gene co-expression network analysis (WGCNA), ferroptosis-related ceRNA network analysis and ClusterONE clustering algorithm analysis –23. AlthTo understand the molecular mechanism associated with IA immunity, 7 key DEmRNAs  associated with key differential immune cell infiltration  were identified. Some researchers found that neurexin 1 (NRXN1) was closely related to neuropsychiatric diseases –26. NRXNPlasma interleukin 6 (IL6) is an independent prognostic biomarker that can be used to help identify patients at high risk for poor neurological outcomes following RIA . IL-6 isTo further understand the molecular regulatory mechanisms of key DEmRNAs, ceRNA and transcription factor regulatory networks were constructed. In the ceRNA regulatory network, 19 DEmiRNAs were included. Hsa-miR-409-3p can regulate the proliferation and apoptosis of human brain vascular smooth muscle cells . Hsa-miRIn this study, RF, SVM and DT models were constructed based on machine learning. For the normal control and IA groups, the SVM model had the highest diagnostic accuracy in the discovery and validation cohorts. Furthermore, the diagnostic accuracy of the SVM model was higher than that of all the individual key DEmRNAs. Therefore, it is speculated that SVM model is of great significance in the clinical diagnosis of control and IA. UIA is mostly asymptomatic and difficult to diagnose. RF, SVM and DT models were constructed based on key DEmRNAs to distinguish normal control and UIA groups. The result showed that the RF model had the highest diagnostic accuracy (AUC = 0.824). This implies that the RF model may have an important role in clinically distinguishing the normal control and UIA groups. Both the SVM model and the RF model considered as potential diagnostic tools are based on public datasets, and a large number of clinical samples are needed to collect for further research if they can be used clinically.This study has some limitations. Firstly, the sample size in the RT-PCR is small, and a large number of samples are needed to collect for further research. Secondly, the diagnostic model obtained in this experiment needs to be verified in a large number of clinical samples. Finally, the specific mechanisms of the identified molecules require extensive in vitro studies. In a word, the identification of molecules and pathways in this study provides a theoretical basis for understanding the immune related molecular mechanism of IA.Additional file 1:Table S1. Clinicalinformation of IA patients and normal controls in the real time-PCRAdditional file 2:Table S2. screening ofmulticentric DEmRNAsAdditional file 3:Supplementary Figure 1. Diagnostic accuracy analysis of NRXN1 (A), GRIA2 (B), SLC1A2 (C), SLC17A7 (D),IL6 (E), VEGFA (F) and SYP (G) based on control group and IA group.ROC, receiver operatingcharacteristic; AUC, area under curveAdditional file 4:Supplementary Figure 2. Diagnostic accuracy analysis of NRXN1 (A), GRIA2 (B), SLC1A2 (C), SLC17A7 (D),IL6 (E), VEGFA (F) and SYP (G) based on control group and UIA group.ROC, receiver operatingcharacteristic; AUC, area under curve


To explore the material basis of action of the Shuang Bai Su Qing recipe in the treatment of periodontitis using network pharmacology.Using TCMSP, we screened the chemical components of 5 drugs. The components were input into the UniProt and PubChem databases to obtain target proteins; Genecards, Online Mendelian Inheritance in Man (OMIM), and CEO databases were used to screen target proteins for periodontal disease. The targets were imported into the Cytoscape software to obtain intersecting targets, and conduct visual analysis to build the PPI network. The intersecting targets were then input into the Matescape database and subjected to biological process (BP) analysis, molecular function (MF) analysis, cell component (CC) analysis, and KEGG enrichment analysis.Twenty-seven TCM chemical components were obtained, with 198 target proteins associated with drugs and 2587 target proteins for periodontitis. Ten core targets were identified. Gene Ontology (GO) functional enrichment analysis yielded results for 20 BP, 11 MF, and 10 CC. KEGG analysis revealed that the main mechanisms of action were related to MAPK signaling pathway. Molecular docking results showed that luteolin strongly bind to TNF, IL6, and IL1B target proteins.The mechanism underlying the treatment of periodontitis with the recipe formula may be closely related to multiple targets in the MAPK signaling pathway. Common clinical manifestations include redness and swelling, gum bleeding, toothache, difficulty chewing, and loose teeth, with may lead to tooth loss in some severe cases. Periodontitis has been closely associated with many major diseases, such as hypertension, Parkinson disease, and Alzheimer disease. The Fourth National Epidemiological Survey of Oral Health in China reported that the rate of healthy teeth in people older than 65 years old was only 9.3%, and tooth loss caused by eating difficulties has plagued a large number of elderly Chinese people, with periodontitis accompanied by diabetes severely affecting the daily activities of the elderly. In addition, the survey results showed that a low frequency of brushing, shorter brushing time, smoking, and other bad habits increased the incidence of the disease.With the aging of the population, chronic periodontitis has become an important factor endangering dental health and affecting people daily activities.Periodontitis is a sustained infectious disease, and treatment mainly focuses on preventing bacterial invasion and eliminating persistent infections of the periodontal tissue. The main therapies include taking antibiotics, subgingival scaling, phototherapy, periodontal tissue regeneration, and orthodontic treatment. Yet, antibiotics and other medications have been associated with some side effects, such as nausea, vomiting, diarrhea, and gastrointestinal problems. On the other hand, instrumental treatments cannot effectively clear pathogenic bacteria in important areas such as root bifurcations and deep periodontal tissues.Therefore, seeking safer and more effective treatment methods for preventing and treating periodontitis has become increasingly important in the medical field. In recent years, the application of network pharmacology in studying TCM has gained widespread attention, as it provides a convenient, innovative, comprehensive, and effective new approach for researching diseases and drugs’ chemical components, targets, and interaction networks. In this study, network pharmacology was used to explore the material basis and mechanism of action of the Shuang Bai Su Qing recipe in the treatment of periodontitis, providing a basis for further clinical and pharmacological research on traditional Chinese medicine for treating periodontitis Fig. . analysis platform, the main chemical components of 5 drugs, including Bletilla Striata, Imperatae Rhizoma, Ecliptae Herba, Galla Chinensis, and Houttuyniae Herba were searched. Oral bioavailability (OB) is one of the most important pharmacokinetic parameters. A compound with OB ≥ 30% is considered to have high OB. Druglikeness (DL) index is used to evaluate the drug-likeness of compounds and is employed for rapid screening of active substances. In the DrugBank database, the average DL index is 0.18. Compounds with a DL index ≥0.18 are considered to have higher drug-likeness. Screening criteria were selected as having OB ≥ 30% and drug-likeness (DL) ≥ 0.18. However, literature research identified compounds with a DL index ≥0.18 but OB < 30%.Through the Traditional Chinese Medicine Systems Pharmacology (TCMSP) The obtained targets were then input into the Uniprot and Pubchem databases, with official gene symbols replacing the corresponding targets, and any targets without gene symbols were removed.Targets related to periodontitis were obtained using “periodontitis” as a keyword. Periodontitis targets were obtained using the Genecard and Online Mendelian Inheritance in Man (OMIM) databases, and further supplemented by analyzing the GEO database (GES59939) using R language.The selected chemical component targets of the Chinese medicine and periodontitis disease targets were separately imported into the Cytoscape 3.7.2 software to obtain the intersection of drug and disease targets. Then, an online Venn diagram was created using the Venn diagram tool.The obtained intersection genes were imported into the String data analysis platform to construct a protein-protein interaction (PPI) network, which was then subjected to visualization analysis. The exported TSV-formatted data were imported into Cytoscape for screening and construction of the PPI network. The core target genes were selected using the MCC algorithm of the Cytoscape software cytoHubba plugin.The core targets obtained through the Cytoscape 3.7.2 analysis software were used to construct a drug component-target-periodontitis-gene interaction network.Using the Metascape database, the obtained genes related to drug treatment of periodontitis were imported for Gene Ontology (GO) and KEGG pathway enrichment analysis to obtain relevant data. In addition, the obtained data were imported into a bioinformatics website for obtaining GO analysis charts and KEGG enrichment pathway maps.Using the TCMSP database, chemical structures of Chinese medicine components were searched and saved in mol2 format. The 3D structure images of target proteins TNF, IL6, VEGFA, AKT1, MMP9, PTGS2, CASP3, CXCL8, EGF, and IL1B were searched using the PDB database for “Homo sapiens” species with a small Resolution value and were then downloaded. AutoDock software was used to remove water, hydrogenate, and ligands from the large protein, followed by docking with small molecules to output results. The obtained docking results were imported into Pymol for visualization analysis.- and Na+ ions. Following the steepest descent method, energy optimization of 5.0 × 104 steps was performed to minimize the energy consumption of the entire system, and finally to reduce the unreasonable contact or atom overlap in the entire system. After energy minimization, first-phase equilibration was performed with the NVT ensemble at 300 K for 100 ps to stabilize the temperature of the system. Second-phase equilibration was simulated with the NPT ensemble at 1 bar and 100 ps. The primary objective of the simulation is to optimize the interaction between the target protein and the solvent and ions so that the simulation system is fully pre-equilibrated. All MD simulations were performed for 50 ns under an isothermal and isostatic ensemble with a temperature of 300 K and a pressure of 1 atmosphere. The temperature and pressure were controlled by the V-rescale and Parrinello-Rahman methods, respectively, and the temperature and pressure coupling constants were 0.1 and 0.5 ps, respectively. Lennard-Jones function was used to calculate the Van der Waals force, and the nonbond truncation distance was set to 1.4 nm. The bond length of all atoms was constrained by the LINCS algorithm. The long-range electrostatic interaction was calculated by the Particle Mesh-Ewald method with the Fourier spacing 0.16 nm.The molecular dynamics (MD) simulations were carried out by GROMACS 2020.3 software. The amber99sb-ildn force field and the general Amber force field (GAFF) were used to generate the parameter and topology of proteins and ligands, respectively. The simulation box size was optimized with the distance between each atom of the protein and the box >1.0 nm. Then, fill the box with water molecules based on a density of 1. To make the simulation system electrically neutral, the water molecules were replaced with ClB striata, Imperata Rhizoma, Euphorbia Herba, G Chinensis, and Houttuynia Herba, yielded 10 effective components for B striata, 5 effective components for Imperata Rhizoma, 11 effective components for Euphorbia Herba, 1 effective component for G Chinensis, and 6 effective components for Houttuynia cordata. After removing duplicate and non-targeted components, as well as those without corresponding official names of targets, 27 key chemical components (Table Through the TCM Systems Pharmacology Database (TCMSP), a search for 5 traditional Chinese medicines, namely Using the Genecards, OMIM and CEO databases with the keyword “periodontitis,” 2587 unique targets were obtained after removing duplicates.The related targets obtained were imported into the Cytoscape software, and 115 common targets between drugs and diseases were obtained. A Venn diagram was constructed using an online Venn diagram tool Fig. . The comAfter importing the 10 core intersecting targets into Cytoscape software, the network diagram of drug chemical composition-target-periodontitis disease-target interaction Fig.  was obtaThe 115 intersecting targets were input into the Matescape database to obtain relevant data, and the common targets were subjected to GO biological process analysis, GO molecular function analysis, GO cellular component analysis, and KEGG enrichment analysis. After 3 different GO analyses, the top 10 biological processes (BP), top 11 molecular functions (MF), and top 10 cellular components (CC) were selected for analysis Fig. . The BP The molecular docking of the chemical components of quercetin, luteolin, and kaempferol with key targets TNF, IL6, VEGFA, AKT1, MMP9, PTGS2, CASP3, CXCL8, EGF, and IL1B resulted in docking scores that indicate the strength of the binding energy between the molecule and the target protein. Lower docking scores indicate a stronger binding capacity between the molecule and the target  measures the coordinate deviation of a specific atom relative to a reference structure and is often used to assess whether a simulation system has reached stability. A stable RMSD indicates that the corresponding atom has become stable, while a fluctuating RMSD indicates fluctuations. As shown in Figure Solvent accessible surface area (SASA) is calculated by considering the interaction between solute molecules and solvent molecules through van der Waals forces. The SASA of a protein decreases with increasing protein compactness, so changes in SASA can predict changes in protein structure. As shown in Figure Gyration radius (Rg) is used to demonstrate the compactness of protein structures during the simulation process, and it represents the distance between the center of mass of all atoms within a specific time interval and their ends. Figure Root mean square fluctuation (RMSF) calculates the fluctuations of each atom relative to its average position, representing the average change of the structure over time and providing a characterization of the flexibility of different regions of the protein. From Figure To investigate the interaction between the protein and ligands, we first conducted hydrogen bond analysis of the protein-ligand complexes. As shown in Figure https://jerkwin.github.io/gmxtool/) script to determine the binding energies of all protein-ligand complexes at the equilibrium stage. In the application of the MMPBSA method, the total binding energy is decomposed into 4 independent components , with the nonpolar solvation term commonly referred to as SASA. The binding energies of the protein-ligand complexes are shown in Table To better explain the interaction energy between the protein and ligands, we used the gmx_mmpbsa  and 3 types based on its pathogenesis . The Zhi Zhi Fang states, “teeth are the endpoint of bones and the source of marrow, which are governed by the kidney. Therefore, tooth decay occurs when the kidney weakens; teeth become strong when the essence is abundant, while tooth movement occurs due to deficiency heat.” Modern research shows that replenishing the kidney can reduce the number of inflammatory cytokines associated with diabetes complicated by periodontitis. Similarly, boosting Qi and yang can reduce the expression of inflammatory cytokines associated with periodontitis.The study of periodontitis in TCM has a long history and is associated with disease names such as “yaxuan” and “chichuang,” which are mentioned in numerous classical texts, including the Yellow Emperor Inner Canon. E.g., the Tang Dynasty Bei Ji Qian Jin Yao Fang records symptoms such as “swollen and painful gums” and “bleeding between teeth” related to periodontitis.B Striata (Baiji), I Rhizoma (Baimaogen), E Herba (Mohanlian), G Chinensis (Wubeizi), and H Herba (Yuxingcao) recipe have been used for the treatment of periodontitis.–18. This herbal recipe has the advantages of quick action, convenient administration, good taste, and low cost. However, the mechanism of action is still unclear.Traditional Chinese medicine (TCM) has been applied for more than 4000 years in treating various diseases by nourishing yin and clearing heat, strengthening teeth, and promoting vitality. For example, G Chinensis have protein precipitation and coagulation effects, and they exhibit antibacterial and antiviral activities by inhibiting the formation of bacterial biofilms.,20 The chemical components present in G Chinensis do not harm beneficial bacteria that adhere to teeth but inhibit harmful bacterial populations in the oral cavity. Extracts of H cordata, especially the volatile oil fraction, demonstrate the best inhibitory effects against Porphyromonas gingivalis in vitro. Extracts of Imperata cylindrica can inhibit the activation of TLR4 and NF-κB signaling pathways and downregulate the release of inflammatory factors. Polysaccharide compound B striata polysaccharide, formed by the polymerization of extracts from Paeonia lactiflora, possesses characteristics such as anti-inflammatory, antioxidant, and strong hydrophilicity, making it suitable for use as a raw material in the formulation of drugs for the treatment and prevention of periodontitis. Research conducted by Wu Tian has shown that extracts of Dryopteris crassirhizoma alleviate tissue inflammation by reducing protein and gene expression of NF-κB and TNF-α,,26 and they also show therapeutic effects in the treatment of osteoporosis.,28Studies have shown that extracts from B Striata, I Rhizoma, E Herba, H Herba, and G Chinensis. H Herba can clear heat and toxins, eliminate abscesses, and drain pus. I Rhizoma and E Herba are assistant herbs that work together to clear lung and stomach fire while enhancing the detoxifying power of the main herb. E Herba is particularly effective in nourishing the kidney yin, which cleanses the oral cavity and strengthens teeth by nourishing the essence. B Striata and G Chinensis are used as adjuvant herbs, with the former having an astringent effect of assisting in clearing heat and stopping bleeding, while the latter augments the assistant herbs’ ability to nourish the kidney yin in addition to its astringent properties.–14 Combining these 5 herbs achieves a balance of clearing and nourishing, treating both symptoms and root causes, resulting in a good therapeutic effect.Based on years of clinical experience, the author has developed a new formula called Shuang Bai Su Qing Formula, which aims to nourish yin, clear heat, strengthen teeth, and nourish essence. The main herbs in the formula include  while the MAPK signaling pathway is an important signal pathway for regulating the body inflammatory response and is highly relevant to periodontitis. Among the 4 MAPK pathways, p38 is closely related to inflammation. After p38 is activated, it can promote the release of inflammatory factors and further activate the NF-κB pathway, leading to excessive expression of inflammatory factors. Activation of p38 MAPK can increase the expression of pro-inflammatory cytokines and participate in the immune response of cells, thereby promoting the development of diseases. P38 inhibitors are an effective treatment for inflammation. Scholars have demonstrated that many chemical components or drugs can treat periodontitis by inhibiting the p38MAPK pathway, such as Moringa oleifera leaf extract, 1,25-dihydroxyvitamin-D3, and Rho kinase inhibitor Y-27632. Additionally, p38MAPK can also upregulate the osteogenic ability of human periodontal ligament stem cells. The relationship between MAPK and periodontitis has received increasing attention in the medical field. Developing drugs that can block or promote the MAPK pathway is becoming a promising research direction for the effective treatment of periodontitis.This study employed network pharmacology and molecular docking methods to further investigate the mechanism of action of Shuang Bai Su Qing Formula in treating periodontitis. The results demonstrated that the main chemical constituents responsible for the anti-periodontitis effects of the 5 herbs mentioned above are quercetin, luteolin, kaempferol, and other chemical components. The topological analysis yielded 115 core targets that have a therapeutic role, including TNF, IL6, TP53, AKT1, VEGFA, IL1B, PTGS2, and CASP3, with TNF, IL6, VEGFA, AKT1, MMP9, PTGS2, CASP3, CXCL8, EGF, and IL1B being the key targets due to their higher degree values. By constructing a drug-disease protein interaction network and performing GO and KEGG enrichment analyses, the key pathways of the 5 TCM, besides cancer and hepatitis B pathways and cellular pathways, are mainly closely related to the PI3K-Akt signaling pathway and MAPK signaling pathway. Previous studies have shown that the PI3K-Akt signaling pathway inhibits the apoptosis of periodontal ligament fibroblasts induced by high glucose,This study demonstrated that the Shuang Bai Su Qing Formula conforms to this direction. Meanwhile, molecular docking studies show that the active ingredients of TCM can bind to key target proteins with negative docking scores. The docking scores for quercetin with IL1B protein and TNF protein and the docking score for luteolin with IL1B protein were all below-4. The smaller score was associated with the lower required reaction energy, which further validated the potential target proteins of this formula in treating periodontitis. Molecular dynamics simulation results showed that the RMSD reached equilibrium after a certain time, the protein SASA and Rg in quercetin-2 xyh composite system decreased, which indicates that the protein tightness increased in quercetin-2 xyh composite system, RMSF showed that 2 xyh kneading activity is >7z3w group, further proved that the protein coordination and stable binding, and the main interaction can be the van der Waals force interaction. It also further verifies the possible target role of this protein in the treatment of periodontitis.Overall, the Shuangbai Suqing Formula for treating periodontitis has the characteristics of multiple components, multiple genes, multiple targets, multiple pathways, and multiple approaches. The key targets and MAPK signaling pathway obtained in this study provide a solid foundation for further accurate and efficient basic research as well as clinical applications.Conceptualization: Yongzhi Chen.Data curation: Yongzhi Chen, Haomin Zhang.Formal analysis: Haomin Zhang.Investigation: Haomin Zhang, Ruirui Ning.Methodology: Haomin Zhang, Ruirui Ning.Project administration: Ruirui Ning.Resources: Haomin Zhang.Software: Ruirui Ning.Supervision: Haomin Zhang.Validation: Haomin Zhang, Ruirui Ning, Yongqin Zhao.Visualization: Haomin Zhang, Zhao Yan.Writing – original draft: Haomin Zhang.Writing – review & editing: Ruirui Ning.


The target of kaempferol was obtained from the public database , and the target of non-small cell lung cancer was obtained from the disease database (Genecards and TTD). At the same time, we collected gene chips GSE32863 and GSE75037 in conjunction with GEO database to obtain differential genes. By drawing Venn diagram, we get the intersection target of kaempferol and NSCLC. Through enrichment analysis, PI3K/AKT is identified as the possible key signal pathway. PIK3R1, AKT1, EGFR and IGF1R were selected as key targets by topological analysis and molecular docking, and the four key genes were further verified by analyzing the gene and protein expression of key targets. These findings provide a direction for further research of kaempferol in the treatment of NSCLC.Non-small cell lung cancer (NSCLC) is a common pathological type of lung cancer, which has a serious impact on human life, health, psychology and life. At present, chemotherapy, targeted therapy and other methods commonly used in clinic are prone to drug resistance and toxic side effects. Natural extracts of traditional Chinese medicine (TCM) have attracted wide attention in cancer treatment because of their small toxic and side effects. Kaempferol is a flavonoid from natural plants, which has been proved to have anticancer properties in many cancers such as lung cancer, but the exact molecular mechanism is still unclear. Therefore, on the basis of  Non-small cell lung cancer (NSCLC) is the leading cause of cancer death worldwide . Non-smaAt first, CCK-8 kit was used to evaluate the effect of kaempferol on the viability of BEAS-2B cells. BEAS-2B cells were seeded in 96-well plates at 5 * 10 ^ 3 cells per well and cultured in 37°C and 5% CO2 incubator for 24 h. Complete culture medium (blank control), 0.1% DMSO (negative control),10 μg/mL cisplatin (DDP positive control) and KA  were given respectively, and the culture continued for 48 h. Then, the culture medium was removed, and 100 μL of fresh culture medium containing 10%CCK-8 was added to each well and cultured for 2 h. OD value at 450 nm was measured by microplate reader. Three independent experiments were conducted. Next, CCK-8 kit was used to evaluate the effect of kaempferol on the viability of A549 cells. Three independent experiments were conducted.Lung cancer A549 cells were inoculated into 24-well plates with 2 * 10 4 cells per well, and incubated overnight in 37°C and 5% CO2 incubator. Scribe vertically with 200 μL pipette tip, and then wash with PBS for 3 times. The cells were treated with complete culture medium (blank control), 0.1% DMSO (negative control), 10 μg/mL cisplatin (DDP positive control) and 50 μM kaempferol, and each group had three accessory holes, and continued to be cultured for 48 h. The migration distance of cells was measured and recorded by microscope. Three independent experiments were conducted.Serum-free medium was used to dilute the matrix glue at the ratio of 1:8, and 100 μL was added to the upper chamber, which was placed in an incubator at 37°C. When the matrix glue solidified, 200 μL of cell suspensions treated with different drugs  were added to the upper chamber, with 2 * 10^4 cells per well. Add 600 μL complete medium (containing 10% FBS) into the lower chamber. Lung cancer A549 cells were cultured in an incubator for 48 h. Then suck out the culture medium in the upper chamber, clean the remaining cells in the upper chamber with cotton swabs, and wash them twice with PBS. Soak in 4% paraformaldehyde for 20 min, air dry, dye with 1% crystal violet solution for 15 min, and wash with PBS for 3 times. The adherent cells were observed and counted by microscope. Three independent experiments were conducted.https://pubchem.ncbi.nlm.nih.gov/) the kaempferol Canonical SMILES obtained are imported into SwissTargetPrediction (https://www.swisstargetprediction.ch/), to obtain the target with p > 0; The target of kaempferol was obtained by Pharm Mapper (https://www.lilab-ecust.cn/pharmmapper/). The acquired target gene set was imported into UniprotKB (https://www.uniprot.gov/) database for standardization.Pubchem (https://www.genecards.org/) and TTD database (https://db.idrblab.net/ttd/). The target gene was integrated and de-duplicated. Selected and downloaded the data of GSE32863 and GSE75037 chips from the GEO database (https://www.ncbi.nlm.nih.gov/). Gene chip data came from the same annotation platform, and the sample species were Homo sapiens, including tumor group and control group. The differential genes were screened by Limma package in R software, with ∣Log2FC∣≥1 and correction p < 0.05; Using pheatmap and ggplot2 packages to draw the heat map and volcano map of different genes.Search target genes of non-small cell lung cancer from GeneCards database (https://bioinfogp.cnb.csic.es/tools/venny/) online website to obtain the intersection gene and draw the Wayne diagram. Introducing intersection targets into STRING online platform to construct protein interaction network. Set the species source as H. sapiens, and the confidence level as medium confidence level. The results were imported into Cytoscape3.9.0 software to build PPI network, and the core targets were screened out by CytoNCA plug-in. Select degree centrality, closeness centrality and betweenness centrality in CytoNCA plugin to screen core targets.In order to determine the interaction target of kaempferol in the treatment of NSCLC, we imported kaempferol prediction target gene and non-small cell lung cancer target gene into Venny2.1.0 (https://david.ncifcrf.gov/) for GO and KEGG enrichment analysis respectively, and some results were visualized by drawing bubble chart and chord chart with bioinformatics online tool (https://vip.sangerbox.com/login.html).The intersection targets were imported into DAVID database  database to analyze the differential expression of core targets between tumor tissues and normal tissues. The data comes from TCGA and GTEx databases.We used GEPIA (https://www.proteinatlas.org/) Database uses transcriptomics and protein omics techniques to study protein expression in different human tissues and organs at RNA and protein levels. According to the staining intensity and the percentage of stained cells in tissues, we compared the protein expression levels of core genes in NSCLC tissues and normal lung tissues, and selected representative immunohistochemical staining pictures from HPA database.HPA  is an online analysis database related to the prognosis of malignant tumors, which can be used to explore the relationship between target gene expression and the prognosis of related tumors. Therefore, we use this database to draw the survival curve of key genes, and analyze the influence of the expression of key genes in NSCLC on the overall survival of patients.Kaplan-Meier Plotter Database  for 48 h. The experimental results showed that , when thTo study the effect of kaempferol on the migration and invasion of A549 cells, scratch test and invasion test were carried out. The results showed that compared with the control group, kaempferol could significantly inhibit the migration of A549 cells, and the effect was slightly better than that of the positive control group. In the invasion experiment, compared with the control group, kaempferol can significantly inhibit the invasion of lung cancer A549 cells. .According to the screening conditions, we got 113 predicted targets of kaempferol. After de-duplication of two disease databases, we got 5818 targets of non-small cell lung cancer. 956 and 2856 differential genes were obtained from gene chips GSE32863 and GSE75037, respectively. There are 339 upregulated genes and 617 downregulated genes in GSE32863, and 1313 upregulated genes and 1543 downregulated genes in GSE75037. According to the two data sets, a volcano map is constructed . We seleBy drawing the Wayne diagram , we got p < 0.01, and the first 30 pathways were selected to draw chords for visualization .Survival analysis of four key genes, PIK3R1, AKT, EGFR and IGF1R , showed in vitro. First, we verified that kaempferol can effectively inhibit the proliferation, migration and invasion of lung cancer A549 cells through CCK-8 experiment, scratch experiment and invasion experiment. Then, we predicted and screened kaempferol targets with SwissTargetPrediction and PharmMapper database, obtained NSCLC targets with GeneCards, TTD and GEO database, and obtained cross targets with Wayne platform. Then, we build PPI network, visualize it through Cytoscape3.9.1, and use its plug-in SytoHbba to screen and identify the targets for further analysis AKT1, ESR1, SRC, EGFR, MMP9; Next, Go and KEGG enrichment analysis was performed on the cross targets to construct a “pathway-target” target network. The genes related to the most pathways were PIK3R1, AKT1, EGFR, IGF1R and SRC, and the signal pathway related to the most targets was PI3K/Akt. Seven targets AKT1, PIK3R1, ESR1, SRC, EGFR, MMP9 and IGF1R were verified by SYBYL software, and the targets AKT1, PIK3R1, EGFR and IGF1R with good docking effect were obtained. The gene differential expression analysis, protein expression analysis and survival analysis of these four core targets were carried out. The results showed that, The expression levels of genes and proteins of these four core targets may be related to the progress of NSCLC, and they are also involved in the whole survival process of NSCLC patients, and have reliable correlation, which may prove to be potential biomarkers for clinical detection and treatment of NSCLC. Therefore, we predict that kaempferol may regulate PI3K/AKT signaling pathway by targeting PIK3R1, AKT1, EGFR and IGF1R genes, and inhibit the proliferation, invasion and migration of NSCLC cells.According to the global cancer statistics in 2020, at present, lung cancer is the second most common malignant tumor among new malignant tumor cases in the world, accounting for 11.4% of all cases. It is also the “number one killer” of cancer deaths, accounting for 18.0% of the total cancer deaths 18.0% . NSCLC, PI3K/AKT signaling pathway is an important carcinogenesis pathway. The activation of this pathway can promote the growth, survival, migration and invasion of tumor cells by affecting tumor cell cycle, inhibiting tumor cell apoptosis, autophagy, promoting tumor angiogenesis and chemotherapy resistance, etc. The activation of this pathway is often found in NSCLC . KaempfeIn a word, EGFR, IGF1R, PIK3R1 and AKT are all expressed in different degrees in NSCLC. Kaempferol may regulate EGFR/PI3K/AKT and IGF1R/PI3K/AKT signaling pathways by targeting EGFR, IGF1R, PIK3R1 and Akt, and then play a role in the treatment of non-small cell lung cancer.in vitro experiments, and verified the pharmacological characteristics of kaempferol with multiple targets and multiple pathways based on network pharmacology, molecular docking and bioinformatics. It was predicted that Kaempferol may regulate EGFR/PI3K/AKT and IGF1R/PI3K/AKT signaling pathways by targeting EGFR, IGF1R, PIK3R1 and Akt1, which has certain guiding significance for the following experimental research and clinical research. Although there are some limitations in network pharmacology and molecular docking, it points out the direction for our subsequent experimental mechanism research.To sum up, this study verified that kaempferol can inhibit the proliferation, migration and invasion of lung cancer A549 cells based on


Renal fibrosis is the late stage of many chronic kidney diseases (CKD). Clinically, there is almost no effective treatment for renal fibrosis except dialysis. Renshen Guben oral liquid (RSGB) is a Chinese patent medicine approved by National Medical Products Administration (NMPA), which is suitable for clinical patients with chronic nephritis. Currently, the chemical constituents of RSGB remains unclear, and its efficacy and mechanism on renal fibrosis have not been reported.In our research, ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS/MS) was employed to describe the chemical profile of RSGB, unilateral ureteral obstruction (UUO) model in mice was established to evaluate the beneficial effect of RSGB on renal fibrosis by biochemical indexes, HE and Masson staining. RNA sequencing and “constituents-targets-pathways” multi-dimensional network was established to mine the mechanisms of RSGB. Key targets were verified by quantitative real-time PCR (qRT-PCR) and western bolt (WB).A total of 201 constituents were identified or tentatively characterized, 15 of which were confirmed with standards. The number of triterpenes was the highest with 49, followed by phenols with 46. RSGB ameliorated the blood urea nitrogen (BUN) and serum creatinine (Scr) levels in serum, normalizing pathological structure of kidney tissue. RNA sequencing revealed that RSGB regulates 226 differential genes, which were involved in kidney development. According to the “constituents-targets-pathways” network, 26 key active constituents may mainly regulate the inflammatory immune system through 88 corresponding targets. qRT-PCR and WB results showed that RSGB inhibited the activation of the Tgfβ1/Smad2/3 pathway, Wnt4/β-Catenin pathway and NGFR/NF-κB pathway.Overall, our study, for the first time, characterized 201 chemical constituents in RSGB, and 26 of them were screened out to alleviates renal fibrosis mainly through Tgfβ1/Smad2/3 pathway, Wnt4/β-catenin pathway and NGFR/NF-κB pathway, which may provide a new research strategy for research on the mechanism of traditional Chinese Medicine.The online version contains supplementary material available at 10.1186/s13020-023-00762-4. Renal fibrosis is a common irreversible pathway for the development of chronic kidney disease (CKD) to end stage, characterized by destruction of normal renal structure, fibroblast proliferation, and excessive deposition of extracellular matrix , 2. ClinPanax ginseng C.A.Mey.), Rehmanniae radix (Rehmannia glutinosa Libosch), Rehmanniae radix praeparata, Corni fructus , Dioscoreae rhizoma (Dioscorea opposita Thunb.), Moutan cortex (Paeonia suffruticosa Andr.), Alismatis rhizoma (Alisma plantago-aquatica Linn.), Poria (Poria cocos (Schw.) Wolf), Asparagi radix (Asparagus cochinchinensis (Lour.) Merr.) and Ophiopogonis radix (Ophiopogon japonicus (L. f) Ker-Gawl.). RSGB evolved from Liuwei Dihuang pill (LDP), and the effect of LDP on renal fibrosis has been reported. For example, Shu’s study found that LDP attenuates diabetic nephropathy by inhibiting renal fibrosis through TGF-β/Smad2/3 pathway− and m/z 845.4891[M + HCOO]−. As the glycosidic bond breaks, 162-fragments are gradually lost, forming [M-H-Glc]−  and [M-H-2Glc]−  . In the negative ion mode, its quasi-molecular ions were [M-H]− (m/z = 685.2159), and [M + HCOO]− (m/z = 731.2248). The glycosidic bond of [M-H]− breaks to form [M-H-Glc]− . Then lost a C6H11O5 to form [M-H-2Glc- C6H11O5]− , and lost a C9H8O3 to form [M-H-2Glc-C6H11O5-C9H8O3]− , or shed a H2O to form [M-H-2Glc-C6H11O5-H2O]−  . In the positive ion mode, paeonolide lost a C9H10O3 to form [M + H-C9H10O3]+ , meanwhile, it also can lost a C11H18O9 to form [M + H-C11H18O9]+  . In the negative ion mode, gallic acid lost a molecule of COOH to form [M-H-COOH]− , or lost a H2O to form [M-H-H2O]−   screening showed that, a total of 587 genes were regulated by RSGB, among which 226 genes (92 up-regulated and 134 down-regulated) were regulated by RSGB to a level close to that of the Sham group, which were called “direct effector genes” . As shown in the volcano map and heat map, some mRNA levels in UUO mice altered significantly after RSGB treatment . The 743 differential genes (FC > 10) of UUO/Sham were input into SoFDA for TCM syndrome enrichment, and the results showed that they were involved in Syndrome of Yin Depletion (P = 0.00277), Syndrome of Yin Deficiency (P = 0.012), and Qi-Yin Deficiency Syndrome (P = 0.0178)  (the main syndrome of RSGB treatment) was jointly analyzed. The relationship between UUO model and QYDS was explored by SoFDA (78) Fig. A, indicaPPI network analysis of disease, syndrome and formula genes was performed by STRING, and core genes were obtained by screening Degree, Closeness and Betweenness values, and KEGG pathway was enriched by DAVID. Finally, 23 pathways with the highest relevance to UUO model were retained. By analyzing the genes involved in the 23 pathways and RSGB constituents related to these genes, the “constituents-targets-pathways” network was constructed, including 26 key constituents of RSGB and 88 corresponding key genes, which were subdivided 26 disease genes, 27 syndrome genes, and 48 formula genes , 6 from Rehmanniae radix / Rehmanniae radix praeparata , 5 from Moutan cortex , 5 from Corni fructus , 2 from Poria , 1 from Alismatis rhizoma (raffinose). Some key components of RSGB have been reported to act on anti-inflammatory and renal protection. Ginsenoside  –18, paeoP < 0.05), indicating that RSGB inhibited the activation of these pathways , NFKB2, TGFB1, WNT4, nerve growth factor receptor (NGFR) involved in immune inflammation and cell cycle to perform quantitative real-time PCR (qRT-PCR). The results demonstrated that RSGB significantly altered these gene abnormalities induced by the UUO model Fig. A. In ordays Fig. B. Therefays Fig. C.In this study, we analyzed the chemical constituents of RSGB by UPLC-QTOF-MS/MS, and evaluated the beneficial effect of RSGB on renal fibrosis based on mice UUO model. BUN and Scr levels in serum, and HE and Masson staining of kidney tissue showed RSGB ameliorate renal injury. In order to study the mechanism of RSGB, transcriptome experiment was performed on renal tissues, and construct the “constituents-targets-pathways” multi-dimensional network. Finally, the Tgfβ1/Smad2/3 pathway, Wnt4/β-catenin pathway and NGFR/NF-κB pathway were verified by WB and qRT-PCR.UPLC-QTOF-MS/MS combined with UNIFI was used for comprehensive and efficient identification of RSGB constituents. The 15 key constituents in RSGB were verified by standards to prove the reliability of identification, and its analogues can be identified by the fragmentation pattern of the standard. In the “constituents-targets-pathways” network, it can be seen that the core components of RSGB in the treatment of renal fibrosis are mainly ginsenoside. Ginsenosides are the main active components of Ginseng radix et rhizoma. Studies have shown that various ginsenosides such as Rb1, Rb3, Rg3, Rh2, Rg1, Rg2 and Rh1 have significant protective effects on renal function, which can reduce renal injury, nephritis, renal fibrosis and renal injury in diabetic nephropathy models , 27. StuIn the UUO model, we demonstrated for the first time that RSGB has the activity of inhibiting renal fibrosis, and analyzed the mechanism of RSGB by RNA sequencing and network analysis. The biological process of GO directly shown the positive regulation of RSGB on the kidney, and the “constituents-targets-pathways” multi-dimensional network indicated that RSGB mainly regulated immune inflammation pathways, such as NF-κB signaling pathway, TGF-β signaling pathway, Wnt signaling pathway, MAPK signaling pathway and PPAR signaling pathway. The NF-κB pathway has been considered as a typical pro-inflammatory signaling pathway, mainly based on the role of NF-κB in the expression of pro-inflammatory genes, including cytokines, chemokines and adhesion molecules . TGF-β iIn conclusion, our study, for the first time, identified the chemical constituents in RSGB by UPLC-QTOF-MS/MS and demonstrate that the improvement of renal fibrosis by RSGB is related to the inhibition of Tgfβ1/Smad2/3 pathway, Wnt4/β-catenin pathway and NGFR/NF-κB pathway.Additional file 1: Table S1. The information of primer sequences. Table S2. Identification of chemical constituents of RSGB in positive ion mode. Table S3. Identification of chemical constituents of RSGB in negative ion mode. Figure S1. Fifteen standard mixtures and RSGB sample were detected by UPLC-QTOF-MS/MS. (A and B) BPI chromatogramsof 15 standard mixtures and RSGB detected in positive ion mode, respectively. (C and D) BPI chromatograms of 15 standard mixtures and RSGB detected innegative ion mode, respectively.


Systemic lupus erythematosus (SLE) is characterized by poor regulation of the immune response leading to chronic inflammation and multiple organ dysfunction. Glucocorticoid (GC) is currently one of the main treatments. However, a high dose or prolonged use of GC may result in glucocorticoid-induced osteoporosis (GIOP). Jiedu Quyu Ziyin decoction (JP) is effective in treating SLE and previous clinical studies have proved that JP can prevent and treat SLE steroid osteoporosis (SLE-GIOP). We aim to examine JPs main mechanism on SLE-GIOP through network pharmacology and molecular docking.  TCMSP and TCMID databases were used to screen potential active compounds and targets of JP. The SLE-GIOP targets are collected from GeneCards, OMIM, PharmGkb, TTD, and DrugBank databases. R software was used to obtain the cross-targets of JP and SLE-GIOP and to perform GO and KEGG enrichment analysis. Cytoscape software was used to make the Chinese Medicines-Active Ingredient-Intersection Targets network diagram. STRING database construct protein–protein interaction network and obtain the core targets. Auto Dock Tools and Pymol software were used for docking.  Fifty eight targets overlapped between JP and SLE-GIOP were suggested as potential targets of JP in the treatment of SLE-GIOP. Network topology analysis identified five core targets. GO enrichment analysis was obtained 1,968 items, and the top 10 biological process, closeness centrality, and molecular function were displayed. A total of 154 signaling pathways were obtained by KEGG enrichment analysis, and the top 30 signaling pathways were displayed. JP was well bound by MAPK1, TP53, and MYC according to the molecular docking results.  We investigated the potential targets and signaling pathways of JP against SLE-GIOP in this study. It shows that JP is most likely to achieve the purpose of treating SLE-GIOP by promoting the proliferation and differentiation of osteoblasts. A solid theoretical foundation will be provided for the future study of clinical and experimental topics. Jingui Yaolue. JP is composed of 10 traditional Chinese medicines, including Radix Rehmanniae Glutinosae (Sheng Di Huang), Carapax Amydae Sinensis (Cu Bie Jia), Rhizomn Cimicifugae (Sheng Ma), Herba Oldenlandiae Diffusae (Bai Hua She She Cao), Herba Artemisiae Apiaceae (Qing Hao), Centellae Herba (Ji Xue Cao), Radix Paeoniae Rubra (Chi Shao), Semen Coicis Lachryma-jobi (Yi Yi Ren), Fructus Citri Sarcodactylis (Fo Shou), and Radix Glycyrrhizae Uralensis (Gan Cao). It is a clinical experience prescription for the treatment of SLE based on long-term clinical experience combined with Shengma Biejia decoction. The clinical study of 147 female SLE patients showed that JP combined with western medicine could prevent and treat of GIOP in SLE (SLE-GIOP). Compared with western medicine alone, JP could significantly increase the bone mineral density of patients and had synergistic and attenuated effects [Systemic lupus erythematosus (SLE) is a recurrent immune system disease. The cells and tissues of the patient's body are attacked by the body's own immune system, which causes facial butterfly erythema, joint pain, and damage to important organs and tissues such as kidneys. Women are more vulnerable than men, especially at childbearing age . Glucoco effects . Howeverhttp://lsp.nwu.edu.cn/tcmsp.php) [http://www.megabionet.org/tcmid/) [https://www.uniprot.org/) [First, the components of each JP herb were extracted from Traditional Chinese Medicine Database and Analysis Platform (TCMSP) (msp.php)  and Trad/tcmid/) . Then, a/tcmid/) . The amoot.org/) , 10.https://www.genecards.org/), OMIM (https://omim.org/), PharmGkb (https://www.pharmgkb.org/), TTD (http://db.idrblab.net/ttd/), and DrugBank (https://www.drugbank.ca/) using the “systemic lupus erythematosus,” “SLE,” “glucocorticoid-induced osteoporsis,” and “GIOP” as the keyword. Using R 4.1.3 software and installing Venn script to obtain the target of SLE-GIOP, SLE-GIOP is a cross gene of two diseases. In order to determine the prediction target of JP related to SLE-GIOP, an R 4.1.3 software intersects the potential target of JP with the related targets involved in SLE-GIOP to obtain a Venn diagram.The SLE- and GIOP-related targets were identified in the databases GeneCards  is a protein–protein interaction (PPI) database, and interactions include direct and indirect associations. In addition to known interactions, it can also be used to predict interactions between proteins [STRING , biological process (BP), and cellular component (CC), and the top 10 results were output. KEGG analysis output the top 30 pathways with the highest significant enrichment. Finally, consulting the literature, disease-related signaling pathways were mapped using the “Pathview” software package.Related R 4.1.3 software packages such as “BiocManager” were installed in advance. R 4.1.3 software was used to obtain Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis results and output visual images. https://pubchem.ncbi.nlm.nih.gov/) was used to download the 2D structure of small molecule ligands. Then, use ChemBio3D Ultra 14.0.0.117 software to convert the downloaded 2D structure into a 3D structure and optimize it. After retrieving the receptor protein through the Protein Data Bank database (https://www.rcsb.org/), PyMOL 2.4.0 software was used to dehydrate and remove ligand small molecules. The protein receptor molecule was hydrogenated using Autodock Tools 1.5.7 software. Based on the position of the active site of the protein molecule and the area where it may interact with the ligand small molecule, the center coordinates and size of the box were determined [The macromolecular proteins for molecular docking are the top three core targets in network topology analysis, and their corresponding compounds are small molecule ligands. Pub-Chem database  in lupus nephritis (LN) mice [β pathway in GIOP [We used network pharmacology analysis to screen the chemical components and corresponding target genes of a single herb in the formula, and integrated the target gene information of SLE-GIOP. The results showed that JP may act on MAPK1, TP53, RELA, MYC, and HSP90AA1 through various core active components such as quercetin, luteolin, kaempferol, baicalein, and ellagic acid. Quercetin is a kind of flavonoid, which is found widely in fruits and vegetables. It can inhibit osteoclastogenesis while promoting osteogenesis, reduce oxidative stress and inflammatory response, and enhance antioxidant expression and adipocyte apoptosis. It can regulate bone metabolism through the mitogen-activated protein kinase (MAPK) signaling pathway and produce the effect of stimulating and inhibiting bone . At the LN) mice . MoreoveLN) mice . Luteoli in GIOP . In a gl in GIOP . Kaempfe in GIOP . Kaempfe in GIOP . Baicale in GIOP  and has  in GIOP . In addi in GIOP . Ellagic in GIOP –33.β signaling pathway were highly correlated with the studied diseases. Studies have found that dexamethasone can induce osteoblast apoptosis through PI3K-Akt signaling pathway [β signaling pathway can be downregulated by 17-AAG to inhibit the function of T lymphocytes [β signaling pathway can affect the development of various bone types in the human body by regulating osteoblasts and osteoclasts [β signaling pathway [β1 (TGF-β1) can also indirectly promote bone resorption by directly regulating the proliferation, differentiation, and survival of osteoclasts [β1 by lymphocytes and its inability to regulate immunological functions [β and its ligands interact complexly, their complex interaction may represent a critical factor in regulating the tissue's response to immune injury. In most cases, the severity of proliferative glomerular lesions can be reflected by the expression of TGF-β [Through GO analysis of these intersectional genes, we found that JP affected SLE-GIOP through response to lipopolysaccharide, response to molecule of bacterial origin, and response to tumor necrosis factor. Combined with the literature review, KEGG enrichment analysis showed that PI3K-Akt signaling pathway and TGF- pathway . In vivo pathway , 36. In  pathway . HSP90 cphocytes . It has phocytes . TGF-β seoclasts . Studies pathway . Transfoeoclasts –44 or byeoclasts –47. Meanunctions . Despiteof TGF-β .The results of molecular docking showed that the predicted potential core targets bind strongly to the effective compounds corresponding to JP, with almost ≤−5 kcal/mol binding energy. In other words, it is predicted that the core targets and the corresponding active ingredients are capable of binding to them in a specific or even strong manner.This study clarified the main potential mechanism of JP in the treatment of SLE-GIOP by screening the potential active ingredients of JP, obtaining SLE-GIOP targets, and enrichment analysis of drug and disease cross targets. The effective active ingredients of JP (such as quercetin and luteolin) can affect multiple signaling pathways related to the disease by acting on core targets (such as MAPK1 and TP53) to treat SLE-GIOP. The lower binding energy values in molecular docking indicated the possibility of interaction between the main potential active ingredients and the core targets. A few limitations, however, are present in this study. First, we only analyzed the main compounds of various traditional Chinese medicines in JP, but the interaction between compounds and the specific dosage of traditional Chinese medicine were not taken into account, which restricted the results. Second, the target and pathway information based on online database retrieval is limited by the depth and breadth of current literature research, that is, there are still unknown ways of action. Finally, theoretical research has always been to solve practical clinical problems. This research results still need further clinical experimental verification, which is also our next research focus.In general, this study demonstrates preliminary evidence of JP's pharmacological effects against SLE-GIOP through network pharmacology and molecular docking. The pharmacological characteristics of JP multicomponent, multitarget, and multipathway in the treatment of SLE-GIOP were further verified by enrichment analysis. We can find a variety of effective active ingredients in JP, which can simultaneously play a role in the treatment of SLE and GIOP. The use of molecular docking preliminarily verified the interaction mode between JP active components and SLE-GIOP disease targets. This study provides a direction for further research on the mechanism of JP in the treatment of SLE-GIOP.