Result. There were 139 predicted active components and 248 related biotargets of Sini Powder and Tong xie yao fang decoction which were involved in IBS treatment, and 522 annotations and 101 related pathways are obtained by enrichment analysis . The underlying mechanisms of Sini Powder and Tong xie yao fang decoction may be related to neuroactive ligand-receptor interaction, calcium, cAMP, and HIF-1 signaling pathways. In conclusion, our results showed that the effect and mechanism of Sini Powder and Tong xie yao fang decoction in IBS treatment were in multi-ingredient, multitargets and multipathways, which would provide several potential and promising strategies for the further research and development of Sini Powder and Tong xie yao fang decoction on IBS treatment.This study used a network pharmacology approach to investigate the potential active ingredients of Sini Powder and Tong xie yao fang decoction and the underlying mechanisms in irritable bowel syndrome (IBS) treatment. The potential active ingredients of Sini Powder and Tong xie yao fang decoction were obtained from TCMSP databases, and the potential targets of the active ingredients were predicted and analyzed by using the Swiss Target Prediction database. T Genecard, DisGeNET, and OMIM databases were processed to screen the potential therapeutic targets in IBS. The interaction of overlapped candidates between the potential biotarget of herb extracts and the potential therapeutic target of IBS were analyzed by STRING website and visualized by the Cytoscape V3.8.0 software. Gene ontology (GO) analysis and Kyoto Genomics and Genomics Encyclopedia (KEGG) pathway were processed to categorize and map the potential biofunctions and effects of these candidates by using David database.  Irritable bowel syndrome (IBS) is a dysfunctional gastrointestinal disease, which is characterized by recurrent abdominal pain, and associated with abnormal fecal form and/or abnormal frequency of defecation , 2. The Glycyrrhiza uralensis). Tong xie yao fang decoction is derived from Danxi's Mastery of Medicine and is composed of Rhizoma Atractylodis, Radix Paeoniae Alba, Citri Reticulatae Pericarpium (Chenpi), and Saposhnikovia divaricate root. Both Sini powder and Tong xie yao fang decoction are based on the principle of reconciling the function of liver and spleen. The combination of two formulas can disperse the stagnated main and collateral channels in liver and spleen, which in turn to restore their normal function to ameliorate diarrhea, abdominal pain, and abdominal distension symptoms in IBS patients [Traditional Chinese medicine has showed promising advantages in IBS treatment , 14. Trapatients , 17. HowIn the present study, we explored the effective components, and then screened the hub targets and the relevant signaling pathways of Sini powder and Tong xie yao fang decoction based on network pharmacology analysis. Thus, our results would provide a theoretical reference for the study of its pharmacological mechanism and its clinical application.https://tcmspw.com/) was used to retrieve Chinese medicines , and Chinese licorice , Rhizoma Atractylodis, Citri Reticulatae Pericarpium (Chenpi), and Saposhnikovia divaricata root, which are components of Sini powder and Tong xie yao fang decoction. The active components of these Chinese medicines were collected and screened based on oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18. The SDFs (structure data files) of the molecular structure of potential active components were obtained through the PubChem database (https://pubchem.ncbi.nlm.nih.gov/), and their potential targets were predicted using the Swiss Target Prediction database (https://www.swisstargetprediction.ch/). Their potential targets were finally subjected to gene standardization through the UniProt database (https://www.UniProt.org/).The traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) (https://www.drugbank.ca/), DisGeNET (https://www.disgenet.org/), Genecards (https://www.genecards.org), OMIM (https://www.omim.org/), and TTD (https://db.idrblab.net/ttd/). The obtained target genes were introduced into the UniProt database for gene standardization.Using “irritable bowel syndrome” as the keyword, searches, and screening were conducted using disease gene databases such as DrugBank , the species “homo sapiens” was selected, and the threshold was set to >0.7. Free targets were hidden to obtain PPI networks. The data were imported into Cytoscape 3.8.0 software for visualization, and the core targets in the PPI network were obtained. The Molecular Complex Detection (MCODE) plug-in in Cytoscape 3.8.0 software was used to perform a modular analysis of the core targets of the PPI network. Degree cutoff = 2, degree cutoff > 2, node score cutoff = 0.2, K-Core = 2, and Max.Depth = 100 were set, and the features of the targets were analyzed to select core target modules.The common targets obtained with the procedure described in “1.3” were imported into the STRING database (https://david.ncifcrf.gov/) for GO and KEGG analysis. The screening conditions were set as P < 0.01 and FDR < 0.05. The top 20 enriched results were displayed, the results were visualized using the Image GP tool, and the analysis results were displayed in the form of bubble graphs.The common targets obtained in step “1.3” were imported into the DAVID 6.8 database (Chinese licorice (including 6 duplicate compounds), 3 Rhizoma Atractylodis, 14 Divaricate Saposhnikovia root (including 3 duplicate compounds), and 4 Citri Reticulatae Pericarpium (including 3 duplicate compounds). A total of 139 active ingredients were finally obtained , 11 Radix Paeoniae Alba (including 4 duplicate compounds), 20 Fructus aurantii Immaturus (including 4 duplicate compounds), 87 obtained . ChineseFive disease databases—DrugBank, DisGeNET, Genecards, OMIM, and TTD—were searched, and 57, 429, 768, 623, and 27 of target sites were obtained, respectively. After summarizing the abovementioned targets and removing duplicate targets, 1,690 disease targets were finally obtained. A total of 248 common targets were obtained by taking the intersection of the targets obtained in the steps described in sections “2.1” and “2.2” . CytoscaThe 248 common targets obtained in the process described in “2.3” were imported into the STRING database to construct the PPI network. After the TSV file was obtained, it was imported into Cytoscape 3.8.0 software for visualization and network topology analysis. The PPI network contained a total of 232 nodes (16 target proteins are not involved in the interaction), and there were 1884 edges. The larger the degree value is, the larger the size of the hexagonal area and the darker the color . The medP < 0.01 and FDR < 0.05, a total of 522 GO functions were enriched in common targets . In terms of molecular functions, these functions were mainly enriched in protein binding, ATP binding, drug binding, enzyme binding, 5-hydroxytryptamine binding, protein kinase activity, protein serine/threonine kinase activity, protein tyrosine kinase activity, transcription factor binding, etc  P65 in the lipopolysaccharides (LPS) signaling pathway [α through the protein kinase B/glycogen synthase kinase β (AKT/GSK3β) signaling pathway to increase interleukin 10 (IL-10) production [In the present study, the topological properties of the active ingredients-potential target network of Sini powder and Tong xie yao fang decoction were analyzed. We screened and picked up the ingredients with the highest degree value, such as obacunone, praeruptorin B, 7-acetoxy-2-methyl isoflavone, alfalfa toxin, poncimarin, licochalcone B, wogonin, and quercetin for the subsequent analysis. Obacunone is a natural compound that is widely present in plants in the Rutaceae family. It has anti-inflammatory, antitumor, antioxidative, and antipulmonary fibrosis effects. It has been confirmed in animal models that obacunone can inhibit the production of proinflammatory mediators . It alsocades to . Licocha pathway . Wogonin pathway . Previouoduction . Synergioduction . As a cooduction , 25. Howγ/PKC signaling pathways, and it plays an active role in regulating intestinal permeability and promoting the integrity of the intestinal barrier [We next investigate the underlying mechanism of the valuable ingredients in the Sini powder and Tong xie yao fang decoction. Top ten core targets, such as AKT1, PIK3CA, PIK3R1, STAT3, VEGF, MAPK1, HRAS, SRC, EGFR, and CXCL8 were selected and further analysis. Previous studies have showed that overexpression of microRNA495 downregulated STAT3 to improve intestinal mucosal barrier function in ulcerative colitis . Cell ad barrier . VEGF is barrier . Camelli barrier . The ERK barrier . Taken tα is a sensitive indicator of intestinal hypoxia and is related to the maintenance of the intestinal barrier. Studies have confirmed that intestinal epithelial HIF-1α is an important protective factor against colitis and can effectively alleviate inflammatory colonic injury [Our results further showed that the top ten core genes mainly involved cell proliferation, inflammatory response, protein phosphorylation, ERK1/2 cascade, MAPK cascade, neurons, 5-hydroxytryptamine (5-HT) binding, and protein binding by GO functional enrichment analysis. 5-HT is a neurotransmitter in the intestinal tract that is considered an important signaling molecule. It mainly acts on various receptors on smooth muscle, enteric neurons, intestinal cells, and immune cells to regulate various intestinal functions. The symptoms of IBS  are related to the level of 5-HT in the intestine. Previous studies have showed that an increase in 5-HT levels is correlated with diarrhea-dominant IBS. A decrease in the 5-HT level is correlated with constipation-dominant IBS . The KEGc injury . Taken tIn summary, this study investigated the potential material basis and mechanism of action of the Sini powder and Tong xie yao fang decoction in the treatment of IBS through a network pharmacology method. The main mechanism of Sini powder and Tong xie yao fang decoction in the treatment of IBS may be the regulation of intestinal 5-HT levels to improve intestinal nerve function, act on calcium channels to improve visceral sensitivity, maintain intestinal barrier function to provide mucosal protection, and inhibit inflammatory responses. In general, the drug has multi-ingredient, multitarget, and multipathway characteristics and plays a therapeutic role through the synergistic effect of multipathway system regulation. The present study had provided an alternative potential strategy for IBS treatment. However, more evidence from the IBS animal model and IBS patients should be evaluated in the future studies.


In the kernel, a nonparametric approach that accounts for the gene-gene correlation structure is used to test whether the gene set is differentially coexpressed between two comparative conditions, from which a permutation test p-statistic is computed for each individual study. A meta-analysis is then performed to combine individual study results with one of two options: a random-intercept logistic regression model or the inverse variance method. We demonstrated MetaGSCA in case studies investigating two human diseases and identified pathways highly relevant to each disease across studies. We further applied MetaGSCA in a pan-cancer analysis with hundreds of major cellular pathways in 11 cancer types. The results indicated that a majority of the pathways identified were dysregulated in the pan-cancer scenario, many of which have been previously reported in the cancer literature. Our analysis with randomly generated gene sets showed excellent specificity, indicating that the significant pathways/gene sets identified by MetaGSCA are unlikely false positives. MetaGSCA is a user-friendly tool implemented in both forms of a Web-based application and an R package “MetaGSCA”. It enables comprehensive meta-analyses of gene set differential coexpression data, with an optional module of post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles.Analyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to provide definitive results. Researchers can greatly benefit from an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects. We developed Meta Gene Set Coexpression Analysis (MetaGSCA), an analytical tool to systematically assess differential coexpression of an  post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles. We established the usefulness of MetaGSCA via case studies of chronic kidney disease and non-small cell lung cancer, and applied it to a pan-cancer analysis of 11 cancer types. We further demonstrated the tool with 100 randomly generated gene sets and showed excellent specificity, indicating low false positive rates.Analyses of gene set differential coexpression often shed light on molecular mechanisms underlying phenotypes and diseases. However, results from conceptually similar individual studies are often inconsistent and underpowered to reach definitive conclusions. We provide an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects, with an optional module of  While differential coexpression analyses have become increasingly popular for their potential of uncovering dysregulation mechanisms underlying human diseases, there are often barriers between informatics and biological researchers, which need to be bridged to translate omics data into valuable biological or medical discoveries. A key benefit of meta-analysis is the aggregation of information leading to a higher statistical power and a more robust point estimate than is possible from individual studies. Here, we propose a tool for aggregating data across studies to empower differential coexpression analysis in various contexts and facilitate meta-analysis of complex large gene expression datasets.Compared to conventional differential expression approaches where genes are evaluated individually assuming gene independence, differential coexpression analysis interrogates gene-gene co-transcription relations and represents a complementary perspective into diseased transcriptomes. Although transcriptome data are typically analyzed to find differentially regulated individual genes, an alternative analysis strategy exists that aims to identify sets of potentially correlated genes that, together, explain a significant proportion of phenotypic variance –3. The ka priori gene set across studies. Within each study (or dataset), a kernel algorithm measures the strength of differential coexpression of the gene set between two conditions. MetaGSCA wraps a meta-analysis framework around the kernel algorithm to estimate an overall effect over individual studies and constructs nonparametric confidence intervals via bootstrapping to provide a measure of uncertainty in the point estimate. MetaGSCA encloses 379 cellular pathways  to systematically assess the differential coexpression pattern of an pathways  and provMetaGSCA consists of three main modules: differential coexpression analysis for individual datasets, meta-analysis over multiple datasets, and pathway crosstalk network analysis. The overall structure of MetaGSCA is illustrated in p<0.05)  and one in non-small cell lung cancer (NSCLC). The results of the CKD and NSCLC case studies are visualized in side-by-side color bars (significance threshold p<0.05) . The CKDRegulation of retinoblastoma protein, PDGFR-alpha signaling pathway, and CD4 T cell receptor signaling) were missed because they had a p-value slightly higher than the significance threshold in one of the individual datasets. In the current MetaGSCA, the estimated overall effects showed that these pathways are significant across studies and worth further investigation. Notably, Regulation of nuclear SMAD2/3 signaling, a pathway with direct implication in chronic kidney disease [In our previous work with the three datasets in the CKD case study, we attempted to integrate the GSNCA results from individual datasets with ad hoc criteria . Among t disease ,11, was p-statistic<0.05), three are related to integrins, namely amb2 Integrin signaling, a6b1 and a6b4 Integrin signaling, and Integrin signaling pathway  are also related to the survival and drug resistance of NSCLC [In the NSCLC case study of four datasets, among the top 10 pathways identified by MetaGSCA  data, each consisting of 10 to 20 randomly selected genes. The study design of both paired and unpaired analyses is the same as the pan-cancer application described in the next section. Among the 100 random gene sets analyzed by MetaGSCA, none were found with significant coexpression in the unpaired analysis; and only 4 and 3 were found significant in the paired analysis by the generalized linear mixed model (GLMM) and inverse variance method, respectively. These results showed a very high specificity (96–100%), suggesting that the significant differential coexpression patterns identified in the case studies are most likely true positives.IL12 signaling mediated by STAT4 pathway. IL12 is the major instructive cytokine signal-boosting the ability of CD8(+) T cells to express CD40L and has been used extensively in T-cell immunotherapy to treat cancers [p-statistics<0.05). The meta-analysis indicates strong overall pathway coexpression across cancer types   Fig . These rvaline, leucine, and isoleucine degradation pathway for both methods. Valine, leucine, and isoleucine are essential amino acids used in the biosynthesis of proteins; all three have been linked to cancer. For example, valine can be reprogramed by targeting HIBCH to treat colorectal cancer [p-statistics<0.05). The meta-analysis indicates a strong overall pathway coexpression across cancer types   Fig . These rWe further summarized the analysis results between the GLMM and the inverse variance method with the default logit transformation. Regardless of pairing, both models generated very similar results, though the GLMM seemed to be slightly more sensitive as it found all significant pathways identified by the inverse variance method Fig . Both mop-statistic<0.01). Pathway similarity was quantified using Pearson’s phi to describe connectivity between pathway pairs. Two pathways were connected if Pearson’s phi p-value<0.01. The crosstalk network analysis returned 8 connections with 8 pathways from the paired data and 10 connections with 14 pathways from the unpaired data. These two networks shared only one pathway vertex  and therefore had no common connections. As an alternative approach, we performed the pathway crosstalk analysis using the Jaccard similarity coefficient, which generated a bigger network that contains the network created by Pearson’s phi. Overall, our pathway crosstalk analysis protocol showed satisfactory robustness against varying parameters and variations in the data. In the current analysis, we chose a relatively stringent cutoff (p-statistic<0.01) to demonstrate this feature of MetaGSCA. A relaxed threshold would lower the sensitivity in detecting coexpressed pathways, therefore include more pathways in the crosstalk analysis. On the other hand, a more stringent threshold may be used to minimize the rate of false positives. Of course, the results will also depend on the sample size and study design of individual studies in the meta-analysis.MetaGSCA provides an optional module to facilitate pathway crosstalk analysis. This function was demonstrated using the results from the TCGA pan-cancer analysis . PathwayDifferential coexpression network analysis has been used increasingly to explore the systemic functionality of genes. As the number of available genomic datasets grows exponentially, there is also a rising demand for aggregating genomic data from multiple datasets or studies to provide more robust statistical estimates with a measure of uncertainty for gene set differential coexpression analysis. In this paper, we combined the strengths of differential coexpression network analysis and meta-analysis to develop MetaGSCA, which allows for a systematic meta-evaluation of the differential coexpression of a gene set between two conditions. Testing of MetaGSCA on tumor RNAseq expression data from 11 cancer types demonstrated high sensitivity, and negative trials using randomly generated gene sets demonstrated excellent specificity.metaprop”: the inverse variance method and the GLMM with a random intercept logistic regression model. Recommendations in the literature regarding the choice of transformation are summarized in the R documentation [A weakness of meta-analysis is that sources of bias are not controlled by the method but depend on the design and availability of covariate data in the original studies. In the paired analysis, since each patient served as their own control, no confounding issues would arise from factors such as age and sex. In the unpaired analysis, the overall effect in the meta-analysis may be computed from effect estimates adjusted for covariates in individual studies. In the meta-analysis, two different approaches are available for estimating the pooled overall effect provided by the R function “entation . Overallangiogenesis pathway was identified as significant for 10 out of 11 cancer types for both the GLMM and inverse variance methods. Angiogenesis is the physiological process through which new blood vessels form from pre-existing vessels, and is important because cancer cell proliferation and metastatic spread require sufficient oxygen and nutrients delivered through the vascular system [thromboxane A2 receptor signaling pathway and the TCR-signaling pathway. Thromboxane A2 receptor signaling pathway has been hailed as an emerging paradigm in cancer progression and metastasis [TCR-signaling pathway is a T-cell immunotherapy related pathway which has direct implications in chimeric antigen receptor t-cell (CAR-T) cancer treatment [There is a plethora of cancer-related pathways identified in the pan-cancer analysis. For example, the r system . Multiplr system . Other ntastasis . The TCRreatment . A potenreatment . The autp-statistic. The meta-analysis framework of MetaGSCA, however, is compatible with any algorithm designed for estimating gene set differential coexpression in a single dataset. Future work will focus on the investigation of alternative test statistics and algorithms to provide more methodological flexibility for users.Currently, MetaGSCA implements GSNCA  as the cMetaGSCA is developed in the R environment as a tool to empower meta-analysis of gene set differential coexpression . TheoretK individual studies containing a gene set with expression profiles of q genes in two comparative conditions. Focusing on one of the K studies, for each condition l , let Rl with elements rij denote a q×q gene correlation matrix for each condition and Nl denote the coexpression network connecting the two conditions, with q nodes (genes) and q(1−q)/2 edges. The weight of an edge between two nodes (genes) i and j is given by 1−|rij| and can be considered to be the correlation distance, where the rij′s are pairwise correlations.An abstract depiction of the meta-analysis wrapping around gene set coexpression is given in wi: wi = ∑j≠iwjrij, 1≤i≤q. In a nutshell, an eigenvector is derived from the full-connection correlation matrix, whose elements are assigned as weights of the gene vertices in the network. Each gene, therefore, obtains a weight value that characterizes its cross-correlation strength with all other genes in the coexpression network Nl. Let the weight vector wl be the first eigenvector  of the q×q gene correlation matrix Rl. Thus, two weight vectors, w1 and w2, can be constructed for condition 1 and condition 2, respectively. The test statistic capturing differences in gene set coexpression between the two conditions is defined as the L1 norm between the two scaled weight vectors: d statistic does not follow a known distribution, a p-value cannot be directly computed from the d statistic. Permutations are generated to construct an exact test for which the distribution of the d statistic under the null hypothesis is obtained by randomly labeling samples across conditions . The p-value of the permutation test is calculated by the proportion of the d test statistics falling into the rejection region in the permutations; that is, the proportion of d test statistic values of the permutations that are at least as extreme as the test statistic calculated from the original data. Let nperm be the number of permutations, dobs be the observed value of the test statistic and d test statistics falling into the rejection region as the p-statistic in the rest of the paper, which will be used in the meta-analysis as the summary statistic to describe the overall effect size.In GSNCA, genes are assigned with weight factors that are computed proportionally to cross-correlation  . Denote p-statistic provides a point estimate of the strength of differential coexpression. The precision of its estimate can be addressed with a confidence interval [p-statistic. Suppose the same bootstrap procedure is repeated nboot times, we then create a list of p-statistics,th and 97.5th percentiles of the bootstrap sampling distribution approximate the 95% confidence interval of the estimated permutation test p-statistic under the null in nboot repetitions. While the theory of permutation tests is based on the idea of looking at every possible permutation of the test statistic, in practice, it is often not feasible to make the permutations exhaustive to compute an exact p-value. And because of that, approximate permutation tests are often conducted with a large number of resamples. The bootstrap interval for the p-statistic, therefore, provides a measure of uncertainty about the permutation test. If the confidence interval contains the significance level threshold based on Type I error, it suggests the number of permutations (nperm) needs to be larger. In other words, if the number of permutations is sufficiently large, the confidence interval will be narrow enough, and a decision to reject the null hypothesis or not to reject it can be confidently made. The bound of the confidence interval that is closer to the significance level threshold can be used as a conservative estimate.The interval ,25. We uinterval  to provimeta [metafor [p-statistics is only due to sampling error; if we choose the random-effects model, we assume that the observed difference between the proportions cannot be entirely attributed to sampling error and may be caused by other factors such as differences in study population, study designs, etc. Cochran’s Q test  of heterogeneity can be performed in the meta-analysis to examine the finding’s consistency across studies. If heterogeneity is a concern, the random-effects model is recommended. In this case, each study provides a p-statistic, and the overall estimate describes their mean across studies. The variance parameter describes the heterogeneity among the studies . Sources of variability can be divided into within-study variance ε2 and between-study variance τ2. Under the fixed-effects model, the observed effect size is measured by p-statistic can be estimated using two approaches. (1) The inverse variance method uses the transformed proportions and corresponding standard errors. In the R function “metapro”, options available are log transformation, Freeman-Tukey double arcsine transformation, arcsine transformation, and untransformed. (2) The GLMM with a random intercept logistic regression model that implicitly uses the logit transformation. With the logit transformation, the binary outcome logit function maps the probabilities of the binary outcome to the full range of real numbers : The main aims of meta-analyses are to obtain an overall estimate of an effect and determine whether the effect exists across studies or datasets. To summarize the strength of gene set differential co-expression over multiple studies, we propose a meta-analysis approach. The R packages meta  and meta[metafor  allow usResearch interest in delineating pathway crosstalk networks dates back to as early as 2008 . The evop-statistics of all pathways into a binary data matrix, with 1’s indicating the presence of differential coexpression, and 0’s the absence; 2) quantify pairwise pathway similarity by one of the two alternative measures—the asymmetric binary similarity that is equivalent to the Jaccard similarity coefficient and the Pearson’s phi index ; 3) apply a threshold on the pathway similarity; 4) reduce the pathway network by keeping connections incident to pathways that have a significant p-statistic. This pathway crosstalk analysis protocol uses three threshold parameters, separately applied to the p-statistics of individual datasets, the overall meta-analysis p-statistic, and the similarity measure between two pathways. The R package iGraph [In MetaGSCA, we provide a pathway crosstalk network module, which is relatively independent of the main components of MetaGSCA and allow users to execute it optionally. This module relies on the results of differential coexpression meta-analyses on many pathways  and consists of the following empirical steps : 1) conve iGraph  is used PID [Pathways were obtained from three primary pathway sources: PID , PANTHERPID , and INOPID . We enclWe demonstrated MetaGSCA in two case studies of human diseases. The CKD study includes three independent gene expression datasets: one from our group  and two Additionally, we applied MetaGSCA to conduct a pan-cancer analysis. Gene expression data were downloaded from the TCGA Data Portal. In ~10% of TCGA samples, molecular profiling data of normal tissue adjacent to the tumor (NAT) were also generated. These NAT samples present a unique intermediate state between healthy and tumor and are commonly used as controls in cancer studies . Therefod statistic takes the following steps in both paired and unpaired analyses. (1) Patient samples were separated into two groups by condition . The two groups can be of different sizes in the case of unpaired analysis. (2) Within each group , a gene-gene correlation matrix Rl was constructed. The elements rij of the correlation matrix represents the strength of correlation between gene i and gene j. The (first) eigenvector was subsequently derived from the correlation matrix Rl. (3) The test statistic d was then calculated as the L2 norm between the two groups .Without making assumptions about homogeneity, we used a random-effects model. The calculation of the We generated 100 random gene sets to conduct the specificity analysis, each consists of 10–20 genes randomly selected from the TCGA gene expression datasets . MetaGSCA was applied to these 100 random gene sets following the same steps as in the pan-cancer application.S1 Fig(TIFF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Nedozralova et al. performed cryo-electron tomography on axon branches from mouse primary neurons and discovered concentrations of cellular activities at branching sites: ER spreading and ER comigration with microtubules, mitochondria fission, and protein synthesis in free and ER-attached polysomes. Neurons are highly polarized cells forming an intricate network of dendrites and axons. They are shaped by the dynamic reorganization of cytoskeleton components and cellular organelles. Axon branching allows the formation of new paths and increases circuit complexity. However, our understanding of branch formation is sparse due to the lack of direct in-depth observations. Using in situ cellular cryo-electron tomography on primary mouse neurons, we directly visualized the remodeling of organelles and cytoskeleton structures at axon branches. Strikingly, branched areas functioned as hotspots concentrating organelles to support dynamic activities. Unaligned actin filaments assembled at the base of premature branches accompanied by filopodia-like protrusions. Microtubules and ER comigrated into preformed branches to support outgrowth together with accumulating compact, ∼500-nm mitochondria and locally clustered ribosomes. We obtained a roadmap of events supporting the hypothesis of local protein synthesis selectively taking place at axon branches, allowing them to serve as unique control hubs for axon development and downstream neural network formation. The development of neurons with their extremely polarized structure and function is unique. Several long protrusions or neurites extend from the main cell body, the soma, where the nucleus is located. One of the protrusions develops into the axon, while the remaining protrusions develop into dendrites . Axons aThe long distance from the soma to the tip of an axon requires a regulatory system controlling local molecules . This re2+ concentration for signal transduction and branch morphogenesis . We show the localization of small, ∼500-nm mitochondria and short actin fragments at the branches. An intricate network of ER membranes was often found between MT bundles and mitochondria, occasionally wrapping around the other components. ER was generally found comigrating with MTs to the mature axon branch, accompanying the maturation process of the axon branch. We further demonstrate the first direct observation of the accumulation of ribosomes at axon branches. In some cases, clustered ribosome attached to meshed-planar ER membranes as ER tubes widened, spreading into the space made for the branching activity. Subtomogram averaging and distance analysis indicated that the clustered ribosomes formed active polysomes. Isolated ribosomes, possibly monosomes synthesizing a small number of proteins , were onn = 325, n = 148, n = 414, To visualize the molecular organization of axons and axon branches, we prepared primary neuronal cell cultures from hippocampus and thalamus explants of mouse embryos at embryonic day 15.5 (E15.5). Various regions including axonal branches, axonal shafts, and growth cones were acquired at different magnifications to target areas for tomographic data collection . We obseBesides the unique organization of the cytoskeleton, branching sites also showed a particular localization of mitochondria. Of the surveyed areas of 66 branches (43 mature and 23 premature) and 53 unbranched areas (43 axon shafts and 10 growth cones), we found 34 mitochondria among 22 mature and premature branches and 10 along axon shafts, indicating a strong enrichment of mitochondria in axon branches . This ten = 63) at branches and 470 nm (n = 119) in shafts, overall agreeing with cryo-EM observations. In contrast, mitochondria found in dendrites showed longer entities , while those in axon shafts were measured to be 589 nm n = 10; . Whole-n release , which e release . In our  release , either  release  or more  release . The wra release . However release . ER at b release , while i release  to wide  release . ER memb release  or occas release . We foun release . Interes release  occurred release .It is critical that neurons control their local environment due to their polarized and compartmentalized morphologies, with long distances between the soma and the tips of axons. To react rapidly to local requirements, specific areas of neurons may locally translate proteins ; howeverTo understand the molecular topology of ribosomes in axon branches, we computationally extracted 1,614 ribosomes and performed subtomogram averaging to a resolution of 38.4 Å with Fourier shell correlation (FSC) at 0.143 or 49.3 Å with FSC at 0.5 criteria . The parAlthough most of the observed ER in axons had no ribosomes bound regardless of their morphology, we found ribosomes attached to the surface of planar areas of the ER mesh network , which are critical for axon development and outgrowth. Axons are thin structures, filled with MT bundles; therefore, the space for cellular events is limited. That could be attributed to the fact that a mature axon is stable, with a tightly packed robust cytoskeleton. Axon branch points form cellular hubs, at which MT bundles bifurcate, providing space for additional cellular components and dynamic cellular events. In addition, the bifurcated cytoskeleton at branch points likely provides static reinforcement to hold cellular machineries in place. We found actual evidence of ongoing cellular activities, including active mitochondria fission, active ribosomes, spreading of ER, and short actin filaments. Future studies will address the mechanisms by which cellular machineries are recruited to and regulated at axon branches. The process of axon branching is critical for neural network formation during the development of the nervous system. Moreover, it plays a crucial role in neural homeostasis throughout the life cycle of the brain, including axon pruning during brain maturation and axon regeneration after brain injury. Elucidating the mechanisms governing the formation of axon branches will not only provide insights into fundamental neuronal processes but also build the basis for the understanding of neuronal circuit formation and function.The stabilization of MTs within axons requires neuronal MT-associated proteins (MAPs) such as Tau, MAP7, and DCX , but at The mechanisms that drive the rearrangement of cytoskeleton elements, MTs, and actin filaments inside axons and at axon branches are poorly understood. Actins are found sparsely along the axon shaft outside of MT bundles. At axon branching points, short, unaligned actins accumulated, accompanied by aligned actin filaments forming filopodia-based membrane protrusions. Where do these actins originate from? While it is possible that shorter actins are the depolymerization products of existing actin filaments at the shaft, actins are indicated to be locally synthesized during axon branch development . Our datQuantifoil  gold EM grids were plasma cleaned for 40 s and sterilized by UV light for 30 min. Grids were then coated with 1 mg/ml poly-L-lysine in 0.1 M borate buffer (Sigma-Aldrich) overnight, then washed three times in PBS and coated with laminin (Sigma-Aldrich) for 4 h (5 µg/ml for suspension culture and 20 µg/ml for explants). Grids were washed three times with PBS, covered with neurobasal/B27 medium, and incubated at 37°C.2. Explants were placed onto EM grids covered in neurobasal/B27 medium: NeuroBasal (Life Technologies) supplemented with 1× Glutamax, 1× B27 serum, and 1× penicillin and streptomycin. Half the culture medium was changed at 1 day in vitro (DIV1). Cell cultures were grown for 6–10 d. Then, EM grids with neuron cultures were manually vitrified in liquid ethane using a homemade plunger or vitrobot (Thermo Fisher Scientific).Primary embryonic mouse neurons were prepared as either dissociated hippocampal cultures or tissue explants from thalamus. Neurons were prepared from E15.5 mice. Dissected hippocampi were placed into cold HBSS  medium treated with trypsin and incubate at 37°C for 16 min, followed by washing with HBSS with FBS and then neurobasal/B27 medium followed by trituration. Thalamus tissue was placed into neurobasal/B27 medium, and explants were prepared by cutting thalamus into small pieces, which were incubated at 37°C for 30 min. 11 of the observed cryo-EM images of hippocampus neurons were from the effort of transducing MT-binding protein SSNA1. The transduction rate was low, and MT organization was not assessed in this study. The other components did not show any notable differences to the extent of our experimental evaluation. The dissociated cells were plated on coated EM grids at a concentration of 150,000 cells/ml and incubated at 37°C in 5% CO−/Å2, and the nominal magnification was 26,0000×, corresponding to a final pixel size of 5.46 Å. Images were taken in superresolution mode as 10-frame videos, and the video frames were aligned, combined, and dose-filtered using in-house frame alignment software implementing MotionCor2 (Cryo-ET data were collected on Titan Krios (Thermo Fisher Scientific) with a Gatan Quantum 967 LS and K2 Summit direct detector, an option of phase-plate, and an acceleration voltage of 300 kV. 66 tilt-series were collected using the phase-plate option with 0 defocus, and 53 tilt-series were collected without phase-plate at a defocus range of −3.5 to −5 µm. Tilt series were collected at −60° to 60° with a 2° angular increment in a dose-symmetric scheme using Serial-EM software . The tottionCor2 . A totalIndividual images of the collected tilt-series were assessed manually, and low-quality images at the high tilt angle were removed from the dataset. Tilt-series were filtered according to the cumulative radiation dose  and aligTomograms were manually segmented using Amira software (Thermo Fisher Scientific). When applicable, membranes were segmented automatically using deconvolution filtering  and a toU test was used to test the significance.For actin analysis, we analyzed 148 actins from axon shafts, 325 actins from mature branches, and 414 actins from premature branches. Actin was segmented manually using IMOD software. The IMOD model file generated was then converted into coordinate files. Each actin filament was then resampled into 10-nm spaced segments for further analysis. For each actin filament, two parameters were calculated: (a) angle of all 10-nm segments in the given actin filament and (b) length of each actin filament. For determination of angle, a vector pointing toward the longitudinal axis of a neuron was taken as a reference vector, and the angle between this vector and each actin segment was calculated using the Python3 NumPy library. Similarly, the length of each actin filament was calculated by adding the distance of all segments present in each filament using Python NumPy and SciPy libraries. The data was shown as scatter plots with median and interquartile range. Mann–Whitney 1,614 ribosome particles from 11 unbinned tomograms that were collected with phase plate were picked using IMOD 3dmod software. The picked coordinates were then transferred into the subtomogram averaging package DYNAMO , and rib2. Mitochondria movements in the axons were performed in DIV4 neurons (2. The microscope was controlled using Zeiss software. The videos were recorded with a 60× oil objective at a time interval of 10 s. The longest distance of individual mitochondria was defined as length, and it was measured using the ImageJ 2.3.0/1.53f particle analyzer plugin. Briefly, the first frame of the video was converted into binary and segmented using watershed algorithm, and then the particles were analyzed. The total numbers of mitochondria analyzed in different regions was 63 in axon branches, 119 in axon shafts, and 104 in dendrites. Mann–Whitney U test was used to test the significance of differences for mitochondria sizes and density.Primary hippocampal neurons were cultured on glass-bottom dishes for 4 d in vitro at 37°C in 5% CO neurons . DIV4 neU test. P values obtained for the comparisons were given directly on the graphs.All numerical data corresponding to the cryo-ET are provided in Table S1is the numerical summary of analyzed tomograms. Analyzed parameters were sorted according to the brain region from which the neurons originated.Click here for additional data file.


With the advent of ageing population, osteoporosis (OP) has already become a global challenge. Jintiange capsule is extensively applied to treat OP in China. Although recent studies demonstrate that it generates significant effects on strengthening bone, the exact mechanism of the jintiange capsule for treating OP remains unknown.  To understand the main ingredients of the jintiange capsule, predict the possible targets and the relevant signal transduction pathways, and explore the mechanism of the jintiange capsule for the treatment of OP.  Main ingredients of the jintiange capsule, drug targets, and potential disease targets for OP were obtained from public databases. Molecular biological processes and signaling pathways were determined via bioinformatic analysis, containing protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, the disease-drug-ingredient-targets-pathways networks were constructed using Cytoscape. According to CytoNCA, core targets were acquired. Finally, the present study conducted molecular docking for better testing the abovementioned results.  In the current work, we found that 4 main ingredients of the jintiange capsule, 33 drug targets, 4745 potential disease targets for OP, and 12 overlapping targets were identified. PPI network containing 12 nodes and 25 edges proved that there existed a complex relationship. As revealed by GO functional annotation, the intersected targets were mostly associated with BP, CC, and MF. The targets were enriched to 368 items in BP, 27 items in CC, and 42 items in MF. They mainly included calcium ion homeostasis, calcium channel complex, and calcium channel regulator activity. According to KEGG pathway analysis, the intersected targets were mostly associated with Rap 1, cGMP-PKG, Ras, cAMP, calcium pathways, and so on. Based on the analysis with CytoNCA, we acquired 4 core targets, respectively—CALR, SPARC, CALM1, and CALM2. Besides, 2 core targets, CALR and CALM1, were selected for molecular docking experiments. Molecular docking revealed that the main ingredient, calcium phosphate, had good binding with the CALR protein and CALM1 protein.  To conclude, the main ingredient of the jintiange capsule, particularly calcium phosphate, may interact with 2 targets, CALR and CALM1, and regulate multiple signaling pathways to treat OP. Additionally, this also benefits us in further understanding the mechanism of the jintiange capsule for treating OP. Osteoporosis (OP) refers to the systemic progressive skeletal disorder, which is featured by bone tissue microarchitectural deterioration and decreased bone density, causing fracture tendency and bone fragility . The USAAs the valuable treasure in China, Traditional Chinese Medicine (TCM) exhibits particular effects on preventing and treating diseases . JintianNetwork pharmacology (NP) is a new discipline based on the theory of system biology, which explores the network of the biological system and selects specific signal nodes to design multitarget drug molecules. First proposed by Hopkins in 2007, NP is a promising approach that can combine information science with systems medicine . NP provhttp://www.megabionet.org/tcmid/) was employed to obtain the main ingredients of tiger bone. TCMID is a TCM database with 842 disorders and novel connections, 18203 herbal components, 85 targets, 1356 drugs, and 15 prescriptions [The Traditional Chinese Medicine Integrated Database  was adopted to retrieve drug targets for main ingredients. Those selected ingredients were uploaded into DrugBank database, and targets were collected.DrugBank database (https://www.omim.org/), GeneCards (https://www.genecards.org/), and DisGeNET (https://www.disgenet.org/). Then, we combined all targets obtained in the three databases.OP-associated targets were searched in three human gene databases, including Online Mendelian Inheritance in Man , a database that predicted known protein interactions, was utilized for constructing and visualizing the PPI network of the overlapping targets. At the same time, the custom value of STRING was set to 0.2.STRING database . D-D-I-T-P networks were established to comprehend the mechanism of the jintiange capsule for treating OP. The networks were constructed and visualized by Cytoscape 3.8.2 software. CytoNCA, a Cytoscape plugin, was used for performing topological analysis of the PPI network. Thus, the core targets can be obtained.https://pubchem.ncbi.nlm.nih.gov/). Thereafter, we converted the 2D structures to the MOL2 format by adopting Chem3D software and employed them as the ligands. Additionally, we obtained 3D structures for receptor proteins in RCSB PDB database (https://www.rcsb.org/). Subsequently, the SYBYL-X software was used to achieve MD between the receptors and ligands.This study obtained 2D structures for major ingredients in PubChem database , cellular component (CC), and molecular function (MF). We selected the top 10 according to the A total of 45 pathways  were scrA total of 4 main ingredients were gained from tiger bone. Among 4 ingredients, 3 ingredients could not successfully predict targets. As a result, only 1 ingredient was retained. Furthermore, we chose 1 ingredient, 33 drug targets, and 10 pathways involving most targets for the construction of the D-D-I-T-P networks  by adoptIn our study, CALR, SPARC, CALM1, and CALM2 presented a higher degree. Compared with KEGG enrichment analysis results and UniProt database, we chose 2 target genes, CALR and CALM1, to perform MD experiments. The red part represents the receptor protein. The blue part denotes calcium phosphate. The result indicated that the ingredient, calcium phosphate, enters and binds to active pockets in CALM1 and CALR proteins Figures . The totAlthough several western medicines, such as bisphosphonates, calcitonin, and estrogen, generate a good effect on OP, the important role of TCM could not be ignored. It is well-known that TCM has been utilizing in China for over 2500 years . JintianThen, we predicted disease targets from GeneCards, DisGeNET, and OMIM databases, finally obtaining 4745 disease targets. The drug targets in tiger bone were intersected with disease targets, and 12 overlapping targets were obtained, including CASR, CALR, SPARC, FBN2, RGN, S100A6, TPT1, NUCB2, CALM1, FBN3, CALM2, and NRXN1. Then, we constructed a PPI network of overlapping targets and screened 12 nodes and 25 edges via STRING database, revealing that there existed a complex relationship between these targets.Subsequently, GO and KEGG pathway analysis were conducted on the overlapping targets. As a result, these overlapping targets were enriched in BP, CC, and MF. According to the results, the jintiange capsule treats OP through many BP, mainly including calcium ion homeostasis, divalent inorganic cation homeostasis, response to metal ion, and others. CC mainly included collagen-containing extracellular matrix, nuclear envelope, spindle, and calcium channel complex. Simultaneously, MF mainly contained calcium channel regulator activity, ion channel binding, cytokine activity, and phosphatase activator activity.In addition, we observed that 45 pathways were related to these overlapping targets. As revealed by the KEGG enrichment analysis, those identified key targets were mostly associated with Rap1, Ras, cAMP, cGMP-PKG, calcium signaling pathways, and so on. In recent studies, the cGMP-PKG pathway is suggested to modulate the growth and differentiation of osteoblasts by forming the mechanosome that contains Src . As a reAs an important research method, D-D-I-T-P networks could better demonstrate the relationship between disease, drug, ingredients, targets, and pathways. In our study, we constructed the current networks. According to the analysis with CytoNCA, we acquired 4 core targets including CALR, SPARC, CALM1, and CALM2.As the calcium-binding chaperone, CALR targets the calreticulin/calnexin cycle to enhance endoplasmic reticulum (ER) folding, quality control, and oligomeric assembly. Such lectin exhibits transient interaction with nearly every monoglucosylated glycoprotein produced within ER . AdditioTo conclude, this study found that 4 main ingredients of the jintiange capsule, particularly calcium phosphate, could act on 33 targets. Totally, 4745 potential disease targets for OP and 12 overlapping targets were identified by the approach of NP. Then, we constructed a PPI network of these targets, conducted the enrichment analysis of GO and KEGG, constructed D-D-I-T-P networks, obtained 4 core targets, and performed MD experiments to investigate the potential mechanism of the jintiange capsule for treating OP. In summary, the main ingredient of jintiange capsule, particularly calcium phosphate, may interact with 2 targets, CALR and CALM1, and regulate multiple signaling pathways to treat OP. Although these findings based on NP have great significance for understanding the potential mechanism of the jintiange capsule for treating OP and discovering new effective ingredients, there remain some limitations in our study. The obtained findings are only theoretical predictions. In the future, a large number of experiments in vivo or in vitro are still needed to verify these findings.


Xuebijing Injection have been found to improve the clinical symptoms of COVID-19 and alleviate disease severity, but the mechanisms are currently unclear. This study aimed to investigate the potential molecular targets and mechanisms of the Xuebijing injection in treating COVID-19 via network pharmacology and molecular docking analysis. The main active ingredients and therapeutic targets of the Xuebijing injection, and the pathogenic targets of COVID-19 were screened using the TCMSP, UniProt, and GeneCard databases. According to the ‘Drug-Ingredients-Targets-Disease’ network built by STRING and Cytoscape, AKT1 was identified as the core target, and baicalein, luteolin, and quercetin were identified as the active ingredients of the Xuebijing injection in connection with AKT1. R language was used for enrichment analysis that predict the mechanisms by which the Xuebijing injection may inhibit lipopolysaccharide-mediated inflammatory response, modulate NOS activity, and regulate the TNF signal pathway by affecting the role of AKT1. Based on the results of network pharmacology, a molecular docking was performed with AKT1 and the three active ingredients, the results indicated that all three active ingredients could stably bind with AKT1. These findings identify potential molecular mechanisms by which Xuebijing Injection inhibit COVID-19 by acting on AKT1.  It was first identified in December 2019 in Wuhan, Hubei, China, and has since spread around the world. The WHO has declared that the COVID-19 outbreak constitutes a Public Health Emergency of International Concern (PHEIC). This disease can be clinically classified as mild, severe, or critical. Fever, dry cough, and fatigue are the main manifestations, and patients classified as severe can rapidly progress to acute respiratory distress syndrome (ARDS) and multiple organ failure (MOF), amongst other conditions. Unfortunately, at present, there is no cure officially approved for this disease, creating a formidable challenge in its treatment, prognosis, and control. Traditional Chinese medicines (TCM), that are characterized as being anti-viral and affecting multiple pathways and targets, have been proven to be significantly effective in treating COVID-19.National Drug Catalog for Basic Medical Insurance, Work-related Injury Insurance and Maternity Insurance stated that XBJ injection should be used in the first aid treatment of critically ill patients. Furthermore, the Diagnosis and Treatment Protocol for COVID-19  states that it should be used to treat severe and critically ill patients with COVID-19. However, its mechanisms in the treatment of COVID-19 have not yet been elucidated.The Xuebijing (XBJ) injection is a traditional Chinese medicine that is based on the XueFuZhuYu Decoction (XFZYD). Its main herbal components are Carthami Flos, Paeoniae Radix Rubra, Chuanxiong Rhizoma, Salviae Miltiorrhizae Radix et Rhizoma, and Angelicae Sinensis Radix, and it can have several effects, such as anti-inflammation, anti-oxidation, immune regulation, improving blood stasis, anti-endotoxin, and anti-shock. Previously, it has been used to treat patients with diseases like severe pneumonia and sepsis. In 2019, the Highly pathogenic viruses should be handled under strict laboratory conditions and in compliance with specific biosafety procedures, this study has attempted to investigate the possible molecular mechanisms of XBJ injection when treating COVID-19 via computer simulation based on network pharmacology and molecular docking analyses; thereby avoiding potential laboratory biosafety hazards and improving the efficiency of research into infectious disease outbreaks and modern Chinese medicines. Recently, network pharmacology-based drug repositioning has become increasingly important for the research and development of drugs. Network pharmacology is an application tool based on systems biology theory and network analysis statistics, that conducts network analysis for specific biological systems, and designs multi-target drug molecules by selecting specific signal nodes and comparing signal pathways. Zhou et al. ,3.This study has improved upon the network pharmacology and molecular docking anlaysis methods, and determined how to identify the core target in the gene network using the maximum Degree value, which indicates the role of the nodes in network pharmacology. The cluster Profiler package in R language was then used to screen the latest Gene Ontology and KEGG pathways for the core target. The network pharmacology results were verified using protein-small molecule docking to simulate the binding between active ingredients and the core target. These analyses were used to predict the active ingredients, targets, and signaling pathways, provide a molecular basis to investigate the mechanisms by which the COVID-19 inflammatory stress response is inhibited by the XBJ injection, as well as enhance the available theoretical support for the wider application of this drug in the treatment of COVID-19.2.2.12.1.1http://tcmspw.com/tcmsp.php) according to oral bioavailability (OB) ≥ 30% and drug likeness (DL) ≥ 0.18. The drug targets of the main active ingredients were then screened through the Targets information of TCMSP. Finally, gene symbol conversion was performed for the targets screened from the TCMSP according to UniProt [https://www.uniprot.org/). Additional information was added according to the literature, and 206 targets were obtained.The main active ingredients were screened from the 5 main herbal components of the XBJ injection, including Carthami Flos, Paeoniae Radix Rubra, Chuanxiong Rhizoma, Salviae Miltiorrhizae Radix et Rhizoma and Angelicae Sinensis Radix using the Traditional Chinese Medicine Systems Pharmacology Database  [In Genecards (ds.org/) , coronav2.32.3.1https://string-db.org/cgi/input.pl) [Basic Settings: ① Network type: full network ; ② meaning of network edges: evidence (line color indicates the type of interaction evidence); ③ active interaction sources: Textmining, Experiments, Databases, Co-expression, Neighborhood, Gene Fusion, and Co-occurrence; ④ minimum required interaction score: medium confidence (0.400); ⑤ max number of interactors to show: 1st shell: none/query proteins only, 2nd shell: none.Advanced Settings: ① network display mode: interactive svg; ② network display options: hide disconnected nodes in the network.Junction targets for the XBJ injection and COVID-19 were imported into the STRING database (nput.pl) , and theThe PPI network for the junction targets for the XBJ injection and COVID-19 was constructed using the STRING database, and the results were exported to a TSV file for further analysis using Cytoscape 3.7.2  to optimize the PPI network and screen for the core target with the maximum Degree value.2.3.2 GO enrichment analysis and KEGG pathway analysis for the core target for the XBJ injection and COVID-19Gene Ontology (GO) includes three aspects: biological process (BP), cellular component (CC) and molecular function (MF). GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted for the core target for the XBJ injection and COVID-19 using R (version 3.6.1)  package,2.4According to the network pharmacology results, AKT1 was identified as the core target of the XBJ injection when treating COVID-19, and the active ingredients of the XBJ injection, baicalein, luteolin, and quercetin were all connected with the core target AKT1. Consequently, further molecular docking verification was carried out based on these results.−1 indicates that the protein and small molecule can bind, and ≤−7.0 kcal·mol−1 indicates a strong binding ability.RCSB PDB databases were used to download the 3D structure PDB file of the core target AKT1 (PDB ID: 1UNP), and it was then imported into the Discovery Studio 2019 Client (DS) to remove solvent molecules and ligands. Solvents were removed from the structure, while polar hydrogen atoms were added to the 1UNP by clicking ‘Add Polar’, and a forcefield was applied to the 1UNP by selecting ‘Apply Forcefield’. The PubChem database was used to obtain the SDF files of the active ingredients for the core target AKT1 as small molecules, which were imported into the DS. Apply Forcefield was used to give the force field to the small molecules. Finally, the CDOCKER (Dock Ligands) was used for protein-small molecule docking, by adjusting the Top Hits-Pose Cluster Radius to 0.5, and the other parameters were their default values. CDOKER  is a hig3.This study aimed to investigate the molecular targets and mechanisms of the Xuebijing injection when used to treat COVID-19 using network pharmacology and molecular docking analyses. According to the ‘Drug-Ingredients-Targets-Disease’ network, AKT1 was identified as the core target, and baicalein, luteolin, and quercetin were identified as the active ingredients of the Xuebijing injection that were associated with AKT1. The results of the GO enrichment analysis showed that response to lipopolysaccharide was the most significant biological process in which AKT1 was involved, the cellular component was spindle, and its molecular function involoved nitric-oxide synthase regulator activity. The results of the KEGG pathway analysis showed that AKT1 was involved in the treatment of COVID-19, mainly by regulating TNF and other signaling pathways. Based on the results of the network pharmacology, molecular docking analysis was performed with AKT1 and the three active ingredients, and the results indicated that all three active ingredients could bind stably with AKT1.3.13.1.1The 5 herbal components were retrieved using TCMSP. According to the screening requirements of an OB ≥ 30% and DL ≥ 0.18, we identified 44 active ingredients, including 9 from Carthami Flos, 8 from Paeoniae Radix Rubra, 6 from Chuanxiong Rhizoma, 19 from Salviae Miltiorrhizae Radix et Rhizoma, and 2 from Angelicae Sinensis Radix. MOL000449-Stigmasterol and MOL000358-beta-sitosterol were the common active ingredients mapping to Carthami Flos, Paeoniae Radix Rubra, and Angelicae Sinensis Radix. MOL002714- baicalein was the common active ingredient mapping by Carthami Flos and Paeoniae Radix Rubra, while MOL000006-luteolin was the common active ingredient mapping to Carthami Flos and Salviae Miltiorrhizae Radix et Rhizoma. Following the removal of the duplicates, a total of 38 non-repeated active ingredients were identified. In addition, 206 non-repeated drug targets were obtained from the TCMSP, according to the active ingredients mentioned above.3.1.2The ingredient-target network for the XBJ injection was constructed using Cytoscape 3.7.2 and included 38 non-repeated active ingredients and 206 non-repeated potential targets see . The cor3.2GeneCards was used to identify 277 targets of COVID-19, and STRING and Cytoscape were used to screen out the free targets. These targets, such as TNF, IL6, GAPDH, TP53, and IL10, were then arranged in order of degree see .Table 23.3−16, avg. local clustering coefficient was 0.675). The PPI network was then optimized using the Cytoscape software , luteolin , and quercetin (Carthami Flos), and they were all connected with AKT1, suggesting that these three active ingredients may be effective for XBJ injection in the prevention and treatment of COVID-19 see .Figure 3.5 Verification of molecular docking for the XBJ injection when treating COVID-19 by the Results of Network Pharmacology−1, −31.5575 kcal·mol−1, and −31.53 kcal·mol−1, respectively. The binding models are shown in The activity of the three active ingredients obtained through the network pharmacology analysis were validated using protein-small molecule docking.The results showed stable binding between the three active ingredients  and AKT1, with binding energies of −25.5854 kcal·mol4.Diagnosis and Treatment Protocol for COVID-19 , critically ill patients are more likely to develop dyspnea and/or hypoxemia within a week, and severe patients, like those with SARS and MERS, may suffer from a cytokine storm (CS), which can rapidly develop into acute respiratory distress syndrome, septic shock, and multiple organ failure. The XBJ injection has been shown in previous studies to be effective at improving end-point outcomes for severe community-acquired pneumonia (CAP) and reduced the 28-day mortality in septic patients [According to the patients .In this study, 38 non-repeated active ingredients and 206 non-repeated potential targets of the XBJ injection, 277 potential targets of COVID-19, and 46 junction targets were screened using network pharmacology analysis, and the core target from the junction targets was identified to be AKT1. AKT1 is a member of the AKT kinase family that regulates metabolism, proliferation, cell survival, growth, and angiogenesis through a series of downstream substrates. AKT regulates the metabolism of fat, amino acids, and other substances by activating AS160 and PFKFB2, and also induces the TSC1/TSC2 complex and mTORC signal transduction after the stimulation of inflammatory factors, so as to regulate the growth of the endothelium, restore the endothelial barrier function, and promote proliferation of the lung fibroblasts . A previAccording to the results of the GO enrichment analysis, the response to lipopolysaccharide (LPS) was the most significant biological process that AKT1, the core target of junction targets to the XBJ injection and COVID-19, was involved. LPS is a major component in the cell walls of Gram-negative bacteria. It has been reported that LPS can damage alveolar epithelial cells and capillary endothelium cells, resulting in changes to the intercellular space and permeability . AKT couAccording to the results of the KEGG pathway analysis, the TNF signaling pathway is the most significant pathway in which the core target AKT1, that is the core target of junction targets to XBJ injection and COVID-19, was involved. Tumor necrosis factors (TNF) include TNF-α and TNF-β which can promote the expression of proinflammatory factors and participate in systemic inflammatory responses, while it can also produce an anti-infection effect and prevent early viral proteins from being synthesized to inhibit viral replication and kill infected cells. Therefore, the XBJ injection can regulate the TNF signal pathway through AKT1, so that it has anti-inflammation and anti-virual effects, and thus the potential to reduce the inflammation caused by COVID-19.The protein-small molecule docking analysis revealed that all three active ingredients  had perfect CDOCKER Interaction Energy with AKT1 (1UNP), with the highest found in luteolin. This inferred that luteolin was the most important active ingredient contributing to the therapeutic effects of the XBJ injection on COVID-19. The analysis of the binding model showed that there were conventional hydrogen bonds between the luteolin and Gln104, Asp108, Lys111, and Gln59 of the AKT1 and van der Waals between luteolin and Cys60, Gln61, Cys77, and Leu78 of AKT1. It has been reported that luteolin can mitigate airway inflammation by regulating the body’s antioxidative stress response and the COX-2 signaling pathway. Luteolin is also shown to relieve myocardial ischemic injury by modulating the mitogen-activated protein kinase (MAPK)/NF-κB pathway and suppressing enterovirus type 71 infections by inhibiting cell apoptosis and inflammatory factor secretion, demonstrating its antiviral properties. Additionally, luteolin and quercetin are found to induce cell apoptosis by inhibiting the phosphorylation and activation of AKT . DespiteIn this study, potential targets were screened, a PPI network was constructed, and enrichment analysis was conducted to find potential pathways. The XBJ injection was shown to produce synergistic therapeutic effects on COVID-19 with ‘multi- active ingredients, multi-target and multi-pathway’, and molecular docking was performed to verify such results. However, the present study has some limitations: 1) The study results are based on existing data available in the given database, and differences in the results may arise if different databases were used. Therefore, future studies should focus on multidatabase analysis and databases that are updated regularly. 2) The biological network in this study was constructed using qualitative data, but TCM prescriptions usually have a complex composition, as well as intricate interactions, making it extremely difficult to perform an analysis based on the quantitative data. Consequently, the reliability of the conclusions from this study need to be further verified. Therefore, in vivo and in vitro experiments should be conducted to further examine the molecular mechanisms of the XBJ injection for the treatment of COVID-19.5.In conclusion, according to the network pharmacology and molecular docking analyses, the predicted active ingredients in the XBJ injection include baicalein, luteolin, and quercetin. The XBJ injection may inhibit the lipopolysaccharide-mediated inflammatory response, modulate NOS activity, and regulate the TNF signaling pathway by affecting the function of the AKT1, thereby ultimately suppressing the excessive inflammatory response associated with COVID-19. Due to the limitations of the computational methods for chemistry and biology, the results need to be verified with follow-up experiments to provide a basis for the treatment of COVID-19 with TCM.


C4) confers genetic risk for schizophrenia. The variation includes numbers of the increased C4A copy number, which predicts increased C4A mRNA expression. C4-anaphylatoxin (C4-ana) is a C4 protein fragment released upon C4 protein activation that has the potential to change the blood–brain barrier (BBB). We hypothesized that elevated plasma levels of C4-ana occur in individuals with schizophrenia (iSCZ). Blood was collected from 15 iSCZ with illness duration < 5 years and from 14 healthy controls (HC). Plasma C4-ana was measured by radioimmunoassay. Other complement activation products C3-ana, C5-ana, and terminal complement complex (TCC) were also measured. Digital-droplet PCR was used to determine C4 gene structural variation state. Recombinant C4-ana was added to primary brain endothelial cells (BEC) and permeability was measured in vitro. C4-ana concentration was elevated in plasma from iSCZ compared to HC . The patients also carried more copies of the C4AL gene and demonstrated a positive correlation between plasma C4-ana concentrations and C4A gene copy number. Furthermore, C4-ana increased the permeability of a monolayer of BEC in vitro. Our findings are consistent with a specific role for C4A protein in schizophrenia and raise the possibility that its activation product, C4-ana, increases BBB permeability. Exploratory analyses suggest the novel hypothesis that the relationship between C4-ana levels and C4A gene copy number could also be altered in iSCZ, suggesting an interaction with unknown genetic and/or environmental risk factors.Structural variation in the complement 4 gene ( The complement system, a part of the innate immune system, contributes to the first line of defense against pathogens Fig. . In addiCSMD1 gene = 2.90, p-value = 0.09). C5-ana and TCC concentrations did not differ between groups.Results are shown in Fig. Only 5 patients and 6 controls completed lumbar puncture. Procedures are discussed and preliminary data are shown in Supplementary Fig. p = 0.03) in individuals with schizophrenia and a trend for all C4A (p = 0.06)  Fig. .Fig. 3Thρ = −0.37, p = 0.05). We then analyzed individuals with schizophrenia and controls separately. In individuals with schizophrenia, plasma C4-ana was positively correlated with C4A GCN ; in controls, there was no correlation . The correlations in individuals with schizophrenia and in controls were significantly different based on the modified Fisher’s Z-transformation test for rho  . The group-specific correlations between C4-ana and C4B GCN were in the opposite directions and were not significantly different from each other . A negative correlation between C4A and C4B GCNs is expected, based on population allele frequencies [Figure 18) Fig. . This diWe hypothesized that increased C4-ana could have a pathological effect by binding to PAR1 and PAR4  on brainCSMD1, is also associated with schizophrenia [Elevated C4 gene copy number has been implicated in schizophrenia etiology by GWAS  and by mophrenia , 11, 12.ophrenia . Based oophrenia .We found significantly higher plasma C4-ana levels, but not C3-ana or C5-ana levels, in individuals with schizophrenia compared to healthy controls Fig. . A previC4AL GNC and a trend towards increased total C4A GCN in individuals with schizophrenia compared to controls , including BEC . Indeed,We found that the addition of C4-ana increases the permeability of a BEC monolayer  the effect of medication and (ii) BMI on complement activation products. (i) One previous study reported an increase in C3-ana after medication treatment , howeverFurther work is needed to test hypotheses arising from the current findings: that a peripheral mechanism of C4 protein activation in patients with schizophrenia interacts with increased C4 GNC and structural variants, leading to increased C4-ana. Given the small sample size of our study, larger cohorts are needed in FEP and individuals with chronic schizophrenia.Supplementary Material


Eucommiae Cortex is a Chinese herbal medicine with bone protective effects and treats osteoporosis. This study aimed to explore the pharmacological mechanisms of this complex mixture.  The active compounds and disease targets involved in the study were obtained from publicly available websites and databases. Core target genes were identified by protein-protein interaction (PPI) network and topology analysis and mapped to critical components by a “component-target” regulatory network. Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway analysis and Gene Ontology (GO)analysis were performed to analyze the biological processes of target genes. Moreover, we carried out molecular docking, cell experiments, and quantitative real-time PCR to propel the research forward. −6 mol/L quercetin, 1∗10−5  mol/L kaempferol, and 1∗10−5  mol/L beta-carotene activated the ERK/JNK cascades and the heterodimer complex AP-1(Fos/Jun) in the MAPK pathway.  Eucommiae Cortex contained 28 active ingredients and 85 potential targets for antiosteoporosis. PPI networks and topology analysis screened 17 core target genes. The therapeutic mechanism involves a series of biological reactions, including host-virus response, chemical stress, oxidative stress, and cell cycle control. The KEGG enrichment illustrated that the MAPK signaling pathway might play a significant role. The final experiment detected ten genes  and three compounds  in the MAPK pathway. Firstly, the CCK-8 and ALP activity test results showed that three compounds could enhance the proliferation and differentiation of osteoblast-like MC3T3-E1 cells. Secondly, molecular docking confirmed the favorable binding potential. Subsequently, we observed that adding 1∗10 MAPK pathway might provide an essential mechanism for the antiosteoporosis effect of Eucommiae Cortex. Eucommiae Cortex and its active ingredients have the potential to treat osteoporosis. Osteoporosis is a common chronic metabolic bone disease with considerable social and health implications. The prevalence of osteoporosis increased with age, bringing safety hazards to the elderly, including signs of augmented fracture . AccordiCompendium of Materia Medica, it is recorded that Eucommia can nourish the liver and kidney and treat waist and knee pain. Eucommiae Cortex 60% ethanol extract can remarkably inhibit the osteopenia caused by tail suspension, protect the microstructure of femoral trabecular bone, and improve the biomechanical properties of rat femur [β-catenin, MAPK, and Rho A/ROCK signaling pathways [Eucommiae Cortex, a traditional Chinese medicine (TCM), is widely used to treat osteoporosis, lumbago, and hypertension in TCM clinics. In the pathways . EucommiBased on network pharmacology and cell experiments, this study explored the important mechanism and targets of Eucommiae Cortex against osteoporosis. After obtaining the targets of Eucommiae Cortex and osteoporosis in authoritative public databases, we constructed a “component-target” regulatory network. The primary pathways are analyzed with topology analysis and biological function analysis. Subsequently, in silico and in vitro experiments, we verified that relative components and core target genes of the primary pathway play significant roles in the proliferation and differentiation of osteoblasts.TCMSP platform is a platform for drug screening and evaluation of herbal medicine systems. It provides information on essential characteristics of ADME, such as half-life, oral bioavailability (OB), drug-like properties (DL), and blood-brain barrier. In previous studies, OB and DL were two crucial criteria for drug screening . ThroughThe keyword “Osteoporosis” was sought in the GeneCards database and online human Mendelian inheritance (OMIM) database. Further, obtained common target genes of drugs and diseases serve as potential therapeutic targets for antiosteoporosis.The active ingredients and potential antiosteoporosis targets were imported into Cytoscape 3.7.2 software to build a “component-target” regulatory network. This network can guide each compound and target to find the corresponding match.Potential therapeutic targets were retrieved from the STRING database and obtained the interaction relationships. The protein interaction network was drawn by Cytoscape software. The TSV files exported in the STRING database are imported into the Cytonca plugin. A total of six degrees are available for comprehensive screening of target genes, including betweenness (BC), clotonca, degree (DC), eigenvector (EC), local average connectivity- (LAC-) based method, and network (NC) . The sixP value <0.05 as the screening condition. Bar chart and bubble chart show the top 20 results.R software was used to study the core targets of Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway analysis and Gene Ontology (GO) analysis, with a X, Y, and Z) and the docking size on the X, Y, and Z axis. To display the docking results, open the receptor PDB file and the ligand PDBQT file by Pymol software simultaneously. AutoDock Vina was used to conduct molecular docking.The compounds' structures were obtained from PubChem's bioassay database. The compounds were executed to energy minimization using the software Chem3D (version 17.0) and saved in the PDB format. We set rotatable keys of the ligands and saved them as a PDBQT file for semiflexible docking using the software AutoDockTools-1.5.6. We downloaded the structures in the PDB format of the targets from the Uniport database. The receptor proteins were processed by Pymol software to delete the ligands bound initially to the receptors. We selected the docking region of the proteins, including the central site (Cell Counting Kit-8 (CCK-8) was purchased from Beijing Solar-bio Science & Technology Co., Ltd. . ALP kit was bought from Jiancheng Bioengineering Institute . MC3T3-E1 cells were purchased from the Cell Culture Collection Committee of the Chinese Academy of Sciences . DMEM and FBS were purchased from Life-iLab . TRIzol reagent was ordered from Invitrogen . SYBR Green Pro Tap was brought from AG . Primer was synthesized by Sangong Bioengineering . Quercetin, kaempferol, and beta-carotene were ordered from Sigma-Aldrich, Merck KGaA . The molecular formula is shown in MC3T3-E1 cells, established from C57BL/6 mouse calvaria, were outsourced from the Cell Culture Collection Committee of the Chinese Academy of Sciences . These cells were cultured in a DMEM medium , supplemented with 10% fetal bovine serum  at 37°C in a chamber with 5% CO2.3 cells/well, and then treated with quercetin at concentrations of 10−9, 10−8, 10−7, 10−6, 10−5, and 0 mol/L for 24, 48, and72 h in DMEM. Then, 100 μL fresh medium containing 10 μL CCK-8 solution was added to each well, and the cells were incubated at 37°C for one h. The study was using a microplate reader to measure the absorbance at 450 nm.As for CCK-8, MC3T3-E1 cells were cultured for 24 h in 96-well plates at a density of 5 × 104 cells/well and then treated with quercetin at concentrations of 10−9, 10−8, 10−7, 10−6, 10−5, and 0 mol/L for 24, 72, and 120 h in DMEM. Each hole cell was gently washed thrice with phosphate-buffered saline and then lysed with 500 μL 0.1% Triton X-100. The ALP activity assay kit was used to determine ALP activity in the lysate. The micromoles of p-nitrophenol liberated per-nanogram protein expressed the ALP activity. Kaempferol and beta-carotene adopt the same strategy.As for the ALP activity assay, MC3T3-E1 cells were cultured for 24 h in 24-well plates at a density of 2 × 10ΔΔCt method. Each sample was assayed at least five times.Total RNA was isolated from osteoblast cell cultures using TRIzol reagent. Then, RNA samples were reverse-transcribed into cDNA. cDNA is used as the template for qRT-PCR analysis of target genes using SYBR Green Pro Tap . Specific primers are listed in t-test or one-way ANOVA. Differences between groups were considered statistically significant if values of P < 0.05.Statistical analysis was processed with Prism 8 software and R4.1.0software. Data were expressed as the mean ± SD and analyzed using Student In the TCMSP database, Eucommiae Cortex contains 28 active ingredients in total, with OB ≥ 30% and DL ≥ 0.18 as the conditions . Then, tA total of 932 targets of osteoporosis were obtained in the Genecards database and OMIM database. After comparison with drug targets, 85 intersection targets were derived .The “component-target” regulatory network represents the correspondence between active compounds and antiosteoporosis targets . Among tPPI network showed the interaction between potential therapeutic targets, with 84 nodes (one of the targets is hidden because of no protein interaction) and 1516 edges . A subseGO analysis illustrates gene function on three levels: biological process (BP), cellular component (CC), and molecular function (MF) . BP mainMolecular docking mimics the interaction of small ligands with large receptor protein molecules. The affinity can be predicted by calculating the binding energy. Smaller binding energy indicates a more stable structure. Quercetin, kaempferol, and beta-carotene have good binding potential with MAPK pathway target genes .−6 mol/L, 1∗10−5 mol/L, and 1∗10−5 mol/L, respectively, the maximum proliferation capacity was obtained.As shown in Figures −6 reached the ALP activity peak, and differentiation was the highest. At 10−5, it was flat or slightly decreased. The optimal concentration of 1∗10−6 was selected. As for kaempferol and beta-carotene, the concentration gradient of 1∗10−5 is the optimum selection.ALP is a glycoprotein secreted by osteoblasts, indirectly reflecting differentiation and maturity. As shown in Figures −6 mol/L, kaempferol at 1∗10−5 mol/L, and beta-carotene at 1∗10−5 mol/L be selected to verify. As shown in The CCK-8 and ALP activity test results showed quercetin at 1∗10β signal transduction, RANKL rank system, and mechanical injury of bone cells [After entering old age, the body will slow down the production of new bone tissue. The etiology and pathogenesis of osteoporosis are frustratingly complex. Some of the leading causes may be the low level of sex hormones, the lack of differentiation of bone marrow stromal cells into osteoblasts, and the breaking of the dynamic balance between osteoclast bone resorption and osteoblast bone remodeling . A recenne cells . Our stuNetwork pharmacology combines molecular biology, genetics, and computer science to collect, analyze, mine, and deploy biological information. Behind diseases and natural drugs backgrounds, this technology can better predict and classify various therapeutical targets and interactions . In prevMitogen-activated protein kinase (MAPK) cascades are essential signaling pathways that regulate various cellular processes, including proliferation, differentiation, and apoptosis . Accordiβ and 1,25-dihydroxy vitamin D [Activator protein 1 (AP-1) transcription factor is a dimer complex composed of Jun and Fos proteins . It is titamin D . Rainer itamin D  reviewedQuercetin and kaempferol are natural flavonoids found in a variety of phytomedicine. They participate in bone remodeling through the methods of inhibiting adipogenesis, inflammation, oxidative stress, and osteoblast apoptosis . Beta-caIn summary, we found 17 core targets, and the MAPK signaling pathway contributed to Eucommiae Cortex in treating osteoporosis. Subsequently, cell experiments and qPCR detections confirmed that active components  promote the proliferation and differentiation of MC3T3-E1 cells. Molecular docking simulates the binding state at the computer level. Based on the above evidence, we speculate that Eucommiae Cortex can be used as a potential drug to treat osteoporosis. Our research experiment still has many shortcomings, but it can provide a basis for developing therapeutic drugs for low bone mass diseases and bone repair.


This study aimed to ascertain the immuno-inflammatory molecular targets of Xinfeng capsules (XFC) in the treatment of ankylosing spondylitis (AS) based on data mining, network pharmacology, and molecular docking.  The efficacy of XFC in the treatment of AS was assessed by clinical data mining. Network pharmacology was utilized to establish a network of the targets for XFC active ingredients in the treatment of AS. The binding mode and affinity of XFC active ingredients to the key targets for AS were predicted using molecular docking. κB signaling pathways. Quercetin, kaempferol, triptolide, and formononetin had free binding energies < -9 kcal/mol to inflammatory targets (TNF and PTGS2) in the molecular docking analysis of XFC-active ingredients, indicating that TNF and PTGS2 might be the targets of the action of XFC.  XFC significantly diminished immuno-inflammatory indicators of AS. In total, 208 targets of XFC were obtained from the TCMSP database and 629 disease targets of AS were screened from the GeneCards database, which were intersected to yield 57 targets of XFC in the treatment of AS. Protein-protein interaction, gene ontology, and Kyoto genome encyclopedia analyses showed that XFC might activate TNF and NF-κB signaling pathways. Collectively, XFC had a significant therapeutic effect on AS. Specifically, the active ingredients of XFC, including quercetin, kaempferol, triptolide, and formononetin, inhibited the inflammatory response in AS by downregulating TNF and PTGS2 in the TNF and NF- Ankylosing spondylitis (AS) is a kind of chronic inflammatory arthritis that mainly afflicts the axial and sacroiliac joints . It occuXinfeng capsules (XFC), as an in-hospital preparation of the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine (TCM), has the effects of invigorating spleens, replenishing Qi, removing dampness and numbness, and activating blood and collaterals . The preData mining technology is a technology that explores potentially valuable knowledge from a large amount of data, which has been widely used in the medical domain . RetrospThis piece of research sets out to screen the chemical active ingredients and disease-related targets of XFC in AS treatment on the basis of the methods of clinical data mining, network pharmacology, and molecular docking through data analysis under the guidance of bioinformatics theory to assess the impacts of the treatment process. The enrichment analysis of the highly correlated targets was conducted using molecular docking technology to fit the binding activity between the drug-active ingredients and target protein molecules to explore the potential effective ingredients and molecular mechanism of XFC in the treatment of AS and to further clinically verify the mechanism underlying the action of XFC.M (IgM), immunoglobulin G (IgG), complement component 3 (C3), and C4. This study was approved by the Medical Ethics Committee of Anhui Provincial Hospital of Traditional Chinese Medicine (2020AH-08). All study participants provided the written informed consent. The Apriori module in the IBM SPSS Modeler 18.0 software was adopted to analyze the correlation between XFC and immune-inflammatory indicators. The formula is as follows [The inpatient data were collected for 908 patients who were AS inpatients between July 2009 and December 2020 in the Department of Rheumatology and Immunology of the First Affiliated Hospital of Anhui University of Chinese Medicine. The data included the usage of XFC and the immuno-inflammatory indexes, such as the erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), immunoglobulin A (IgA), immunoglobulin  follows :(1)SuppoAstragalus, coix seed, Tripterygium wilfordii, and centipede in the XFC prescription were acquired from the traditional Chinese medicine system pharmacological analysis platform  [The chemical active ingredients of pw.com/) . The abopw.com/)  with thepw.com/) , 17. TheThe targets of the active ingredients of XFC were obtained using the predicted targets of the active ingredients in drugs from the TCMSP database. The predicted targets of the chemical active ingredients were harvested after the completion of the standardization conversion in the Universal Protein Resource (UniProt). Then, the network visualization software, Cytoscape , was apphttps://www.genecards.org/) and OMIM (http://www.omim.org/). The data of these two databases were intersected, and the unique values were retained to obtain known disease-related targets of AS.With “AS” utilized as a search term, the disease-related targets were screened in the databases of GeneCards (http:/string-db.org/) with the species defined as “Homo sapiens” and the confidence value set as ≥ 0.4, followed by the PPI network analysis of the overlapping targets. The acquired data were imported into the Cytoscape software to analyze the network topology parameter value, thus screening the top ten targets of the degree value  as the key targets in the network.The overlapping targets were imported into the STRING  protein p < 0.05. The top 10 pathways in the enrichment analysis were selected to draw a bubble chart.The overlapping targets obtained by the PPI network analysis were uploaded to the database for annotation, visualization, and integrated discovery (David) , followeThe key targets (top 10 target proteins) were selected in accordance with the median value of the PPI network. Thereafter, the corresponding chemical active ingredients related to these target proteins were identified in the protein interaction network to confirm the binding activity of these key target proteins to the chemical active ingredients.http://www.rcsb.org/) with the species restricted to “Homo sapiens.” We preferred the proteins with original ligand small molecules and high similarity between small molecular structure and the active ingredients of the docking chemical. The Autodock Vina was employed to perform modification operations, such as dehydration and hydrogenation, on the retrieved target protein. The protein was output in PDBQT format, and molecular docking was implemented to determine the global optimal binding conformation according to the value of docking binding energy. PyMOL was utilized to visualize the docking result.The TCMSP database was applied to acquire the chemical structure of the chemical active ingredients. The structure files of the target protein were identified in the RCSB PDB database  was implemented using the David database, demonstrating that the overall treatment process had multiple signaling pathways that participated in the orchestration of various biological processes. The results of the GO function enrichment analysis displayed that the overlapping targets of XFC in AS treatment were mainly enriched in biological processes, such as inflammatory response, cell response to lipopolysaccharide, positive regulation of NF- binding .κB signaling pathway, and cytokine-cytokine receptor interaction  and signaling pathways , thus exerting a multicomponent, multitarget, and multinetwork anti-inflammatory pharmacological effect.Our results elucidated that XFC could remarkably improve the immune-inflammatory indicators of AS and that the treatment of AS by XFC might act on IL-6, TNF, VEGFA, CXCL8, IL-1B, PTGS2, STAT3, CCL2, IL-10, and IL-4 through chemical active ingredients, such as quercetin, kaempferol, triptolide, α and IL-6) stimulated by lipopolysaccharide to exert an anti-inflammatory effect. The mechanism underlying the action of triptolide is to promote IκB upregulation and suppress IκBα phosphorylation, thereby blocking nuclear NF-κB p65 activation [β, IL-6, IL-8, and TNF-α levels, thus playing an anti-inflammatory role [The active ingredients with high values in the “drug-component-target” network included quercetin, kaempferol, triptolide, and formononetin. Quercetin, belonging to natural flavonols, has various effects, such as anti-inflammation, anti-platelet aggregation, and anti-oxidation. Quercetin inhibits inflammation by downregulating proinflammatory factors and upregulating anti-inflammatory factors –25. Triptivation . Kaempfeory role , 28. Forory role . Inflammory role . The majκB transcription factor activity, and immune response. Among them, the inflammatory response is strongly associated with the pathogenesis of AS.The results of the GO function enrichment analysis reflected that the treatment process was highly correlated with inflammation, cell response to lipopolysaccharide, positive regulation of NF-κB signaling pathway, and cytokine-cytokine receptor interaction. TNF in the TNF signaling pathway and NF-κB signaling pathway is one of the cytokines in the acute phase of inflammation, which is implicated in the systemic inflammatory response, and it accelerates the production of interleukins. TNF can repress chondrocyte apoptosis and cartilage bone destruction by inactivating TNF-binding receptors to reduce matrix metalloproteinase [κB signaling pathways. The official website of KEGG unraveled that PTGS2 in these two signaling pathways modulated inflammation. PTGS2 upregulation can lead to the proliferation of fibroblast-like synovial cells, which influences the levels of TNF-α, IL-1β, and IL-6 in the serum and aggravates the inflammatory response [α and diminishes TNF-α expression in the synovium of rats with adjuvant arthritis induced by collagen [In the results of the KEGG pathway enrichment analysis, the XFC formula exerted anti-inflammatory effects in AS by manipulating the TNF signaling pathway, NF-oteinase , 32. PTGresponse . Moreovecollagen , 35.α and of formononetin to PTGS2 molecule docking did not exceed -9 kcal/mol. This result illustrated that the chemical active ingredients of the XFC formulas had excellent binding activity to inflammatory targets.The results of molecular docking unveiled that the docking energy of the key targets in the PPI network and their corresponding chemical active ingredients were all less than −5 kcal/mol, indicating that the chemical active ingredients in the XFC formula and the disease-related targets of AS produced better protein-binding activity. In addition, the binding energies of quercetin, kaempferol, and triptolide to TNF-This article still has certain limitations. This article still lacks experimental verification although the mechanism behind the action of Chinese herbal compounds in the treatment of AS was preliminarily predicted and molecular docking verification was carried out.κB signaling pathways, thereby restricting the inflammatory response in AS.This research was conducted based on the methodologies of network pharmacology and molecular docking. Through the evaluation of the pharmacokinetic parameters of the XFC formula components and the enrichment analyses, it was noted that XFC could orchestrate cytokine activity, transcription activator activity, RNA polymerase II core promoter proximal region sequence-specific binding, and protein binding. The molecular mechanism of XFC in the treatment of AS was mainly achieved by the active ingredients, including quercetin, kaempferol, and triptolide. Formononetin reduced the expression of inflammatory factors (TNF and PTGS2) via TNF and NF-