In the present study, a subject of atopic dermatitis (AD) is exposed progressively to allergic rhinitis (AR) and asthma (AS), which is defined as atopic march (AM). However, both the targets and compounds against AM are still largely unknown. Hence, we investigated the overlapping targets related directly to the occurrence and development of AD, AR, and AS through public databases . The final overlapping targets were considered as key targets of AM, which were visualized by a Venn diagram. The protein–protein interaction (PPI) network was constructed using R package software. We retrieved the association between targets and ligands via scientific journals, and the ligands were filtered by physicochemical properties. Lastly, we performed a molecular docking test (MDT) to identify the significant ligand on each target. A total of 229 overlapping targets were considered as AM causal elements, and 210 out of them were interconnected with each other. We adopted 65 targets representing the top 30% highest in degree centrality among 210 targets. Then, we obtained 20 targets representing the top 30% greatest in betweenness centrality among 65 targets. The network analysis unveiled key targets against AM, and the MDT confirmed the affinity between significant compounds and targets. In this study, we described the significance of the eight uppermost targets  and eight ligands  against AM, providing a scientific basis for further experiments. Atopic lesions, including atopic dermatitis (AD), allergic rhinitis (AR), and allergic asthma (AA), have seen elevated incidence rates for some decades, impacting around 30% of the world population . The atoThe first line drug of AD is topical corticosteroids such as Pimecrolimus and Tacrolimus, the mechanism of which is to block the T cell’s activation to dampen immune responses ,7.Secondly, AR is an inflammatory disease of the nasal mucous membrane mediated by IgE . The firThe analysis of protein–protein interaction (PPI) networks for drug repositioning is a significant approach, providing clues about causal relationships between targets that carry out certain functions . TherefoThe targets on AD, AR, and AA were retrieved through DisGeNET, OMIM, and the literature, suggesting that the overlapping targets from the three diseases were considered as AM-related targets. We hypothesize that targets with the 30% greatest betweenness centrality (BC) from targets with the top 30% highest degree centrality (DC) values  in the ohttps://www.disgenet.org/) (accessed on 2 December 2021), and OMIM (https://www.omim.org/) (accessed on 3 December 2021), which are therapeutic relevant components. The overlapping targets from the three diseases  were considered as core targets of AM. The core targets were analyzed using the STRING database (https://string-db.org/) (accessed on 4 December 2021). We performed the topological analysis of AM on a PPI network. The workflow is as below.Step 1:Identifying of targets  from DisGeNET and OMIM.Step 2:Identifying of overlapping targets  via a Venn diagram.Step 3:The overlapping targets were screened on the top 30% by degree centrality (DC), and the PPI network was constructed on RPackage software.Step 4:After constructing the PPI network of the top 30% DC, the core targets were identified, and the PPI network of the top 30% by betweenness centrality (BC) in the top 30% DC were constructed on RPackage software.Step 5:Suggesting that targets with the highest DC in the top 30% BC targets were considered as the promising targets against AM.Step 6:https://www.selleckchem.com/) (accessed on 24 April 2022).The retrieval of ligands from Selleckchem.com  or the lowest binding energy  of each target.Step 9:https://www.rcsb.org/) (accessed on 7 December 2021). The AutoDockTools-1.5.6 software was utilized to evaluate the affinity of the promising targets and ligands. The ligands were docked with targets using autodock4 by arranging 4 energy ranges and 8 exhaustiveness as default to identify 10 different poses of ligands [The identified ligands were converted to .sdf format from PubChem into .pdb format utilizing Pymol, and the ligands were converted into .pdbqt format via AutoDock. Then, the PDB ID of targets were obtained via RCSB PDB , CTLA4 , CXCL8 , ICAM1 , IL10 , IL17A , IL1B , IL2 https://www.ebi.ac.uk/thornton-srv/software/LigPlus/) (accessed on 8 December 2021) was used to parameterize 2D binding interactions [The grid box size was set to 40 Å × 40 Å × 40 Å. The LigPlot+ v.2.2 (ractions . The adohttps://www.disgenet.org/) (accessed on 4 December 2021) and OMIM (accessed on 4 December 2021) (https://www.omim.org/), of which there were 229 overlapping targets (AM) , 517 (AR), and 2374 targets (AA) were retrieved by DisGeNET (ets (AM) . The tarets (AM) A (Suppleets (AM) , and theets (AM) B (Suppleets (AM) . After fets (AM) C (Suppleets (AM) .2 or Lipinski’s rule , 14 out of 20 targets had the same degree centrality (19) , which wi’s rule B. MDT wai’s rule C.2) by Lipinski’s rule. Hence, (5) AX-024 HCl (PubChem ID: 129909862), with −7.5 kcal/mol on IL10 (PDB ID: 1LK3), was ligand with the most potential on IL10 (PDB ID: 1LK3) (The MDT of eight ligands demonstrated that (1) Bindarit (PubChem ID: 71354) had the greatest affinity of -7.2 kcal/mol on CCL2 (PDB ID: 1DON) A, (2) CTD: 1LK3) E. (6) Y-D: 1LK3) F. (7) T-D: 1LK3) G. (8) ApD: 1LK3) H. The MDD: 1LK3) .Antrodia cinnamomea have a potent anti-inflammatory effects on AD, AR, and AA [As a matter of fact, targets with a high betweenness centrality (BC) (the measurement of the shortest route between other targets) are the most significant element to be associated with vital multiple therapeutic utilization and then are a noteworthy value to be repositioned . There i, and AA ,18. This, and AA . Study i, and AA ,21,22,23, and AA ,25,26. A, and AA .CCL2 (C-C motif chemokine ligand 2) is the initial chemokine stimulated by itching and pain symptoms on keratinocytes, fibroblasts, and endothelial cells’ infiltered monocytes and lymphocytes . The oveCollectively, this study indicates the potential ligands and critical targets to alleviate AM via orchestrating targets of AD, AR, and AA, providing theoretical clues for further experimental evaluation.In summary, eight promising compounds  and eight targets  have been uncovered to alleviate AM with synergistic effects. Besides, the topological analysis on AM was orchestrated with a network-based concept. We conclude that multi-target agents are a potential strategy to treat comorbid AM, and network-based analysis especially provides critical clues to select targets on AM. Given the limitations of network pharmacology perspectives, the potential therapeutic ligands of the treatment of AM are predicted by topological analysis on PPI networks, and its efficacy in vivo was not validated, which needs to be further evaluated through therapeutic and clinical tests.


Background: Advanced ovarian cancer (AOC) develops rapidly, adding to difficulties in treatment. Traditional Chinese medicine (TCM) plays a significant role in the treatment of AOC, and so to explore the efficacy and safety of TCM in the treatment of AOC and its effective targets, we performed the following review.Methods: The major databases were searched for randomized controlled trials of TCM for the treatment of AOC. A meta-analysis of the efficacy of Chinese herbs on AOC was conducted using RevMan 5.4 software. Active compounds and target genes were acquired using the TCMSP database. The main targets of AOC were obtained through the GenCards, OMIM, TTD, and DrugBank databases. A protein–protein interaction network carried out on the STRING platform was used to select core genes. The Metascape platform was applied to achieve GO and KEGG enrichment analysis.Results: A total of 24 studies were included. Meta-analysis shows the TCM group improved the overall response rate , overall survival , and progression-free survival  of AOC patients, as well as reducing many adverse events. There were 120 compounds, 246 herb target genes, and 1503 disease targets extracted. The 10 most important components were quercetin, kaempferol, 7-methoxy-2-methyl isoflavone, formononetin, isorhamnetin, hederagenin, stigmasterol, luteolin, 7-O-methylisomucronulatol, and calycosin. The 20 core targets were TP53, STAT3, JUN, AKT1, MAPK3, RELA, MAPK1, ESR1, IL6, FOS, MAPK14, TNF, CDKN1A, RB1, CCND1, EGFR, STAT1, MDM2, MAPK8, and CAV1. KEGG enrichment analysis showed that there are many pathways directly related to different types of tumors, such as in pathway cancer and prostate cancer.Conclusion: Our article reveals TCM is effective and safe against AOC and that Chinese herbs exert effects on the disease through multi-target, multi-component, and multi-pathway mechanisms.Systematic Review Registration: (www.crd.york.ac.uk/PROSPERO/), identifier (CRD42022369731). Ovarian cancer (OC) ranks fifth among cancer deaths in women, representing a larger number of deaths than any other cancer type of the female reproductive system, and is connected to the highest number of deaths among gynecological cancers in developed countries .Due to the complex anatomical structure and endocrine function of ovarian tissue, together with the lack of obvious clinical symptoms in the early stages , the onsIn recent years, considerable experience has been accumulated in the use of traditional Chinese medicine (TCM) in the treatment of OC, and with the characteristics of overall conditioning and multi-target intervention, TCM has achieved good results in assisting with chemotherapy . HoweverWe searched PubMed, Embase, the Cochrane Library, the Chinese Biomedical Literature (CBM) database, the China National Knowledge Infrastructure (CNKI) database, and the Wanfang database for updated articles published from the establishment of each database to 1 August 2022. The search keywords we used included:  AND  AND (“neoplasm metastasis” OR “IV stage” OR “advanced ovarian cancer” OR “metastatic ovarian cancer” OR “ovarian cancer” OR “ovarian neoplasms”).Detailed inclusion and exclusion criteria are shown in Two researchers (ZY and XW) independently searched the articles in the databases. The titles and abstracts were screened according to the inclusion and exclusion criteria, and then the full texts of the remaining articles were screened for a final decision. If there was a disagreement, the third researcher (RWY) would be consulted. The extraction information from the included studies entailed: first author, sample size, mean age or age range, clinical status, common treatment (regimen), TCM interventions, control interventions, duration time, and main outcomes.Cochrane Handbook for Systematic Reviews of Interventions  independently, based on the Cochrane risk bias tool in the ventions . The risp ＜ 0.1 or I2 > 50%) between the two groups, the random-effects model was adopted. Otherwise, the fixed-effects model was used. Subgroup analyses were performed based on the types of OS and AEs. Publication bias was evaluated visually using funnel plots in RevMan 5.4 software.RevMan 5.4 software was employed for analyzing the collected data. The odds ratio (OR) was used as the effect size index for the dichotomous variables, and the mean difference (MD) was used as the effect size index for the continuous variables, with 95% confidence intervals (CI) in forest plots. If heterogeneity existed . Active compounds of herbs were selected if their drug-likeness (DL) index ≥ 0.18 and oral bioavailability (OB) ≥ 30%. Then, using the UniProt database (https://www.uniprot.org) to annotate the related target genes, the herb–component–target network of herbs effective against AOC was established by Cytoscape3.7.2 software, with the 10 most important components selected according to their degree value in the network.The chemical compounds of effective herbs were obtained through the TCMSP database (https://www.drugbank.ca/), GeneCards (https://www.genecards.org/), OMIM (http://omim.org/), and TTD (http://db.idrblab.net/ttd/).High correlation AOC-related targets were extracted by searching the keyword term “advanced ovarian cancer” from the following public databases: DrugBank (https://bioinfogp.cnb.csic.es/tools/venny/index.html). The intersectional genes were then imported into the STRING platform (https://stringdb.org/). Homo was the species, a score > 0.9 was set, and the independent target protein nodes were hidden. The results were exported in TSV format and then imported into Cytoscape3.7.2. The CytoNCA plugin calculated the following four parameters: betweenness centrality (BC), closeness centrality (CC), degree centrality (DC), and eigenvector centrality (EC), and the first 20 core genes with higher than average values were extracted.Genes intersecting the target genes of effective herbs and the targets of AOC were extracted using a Venn diagram made on a website (https://metascape.org/gp/index.html). The GO enrichment analysis included the biological process (BP), molecular function (MF), and cellular component (CC) analysis. The top 10 records with q value < 0.05 in terms of GO enrichment analysis and the top 20 KEGG pathways were extracted, with the latter imported into Cytoscape3.7.2 for visualization of the target–pathway network of effective prescription herbs against AOC.The KEGG pathway enrichment analysis and GO enrichment analysis were obtained based on the Metascape database   .p < 0.00001).As shown in p < 0.00001). However, there were no significant differences in 5-year survival .There were significant differences in 3-year survival between the TCM group and the control group in the subgroup analysis  . This result indicated that the PFS in the TCM group was significantly higher than in the control group  .p < 0.00001). Three studies reported the occurrence of diarrhea in the two groups, while the TCM group also did better in reducing the incidence of nausea . Only two studies mentioned constipation, and in the TCM group, the incidence of constipation was significantly lower . The results showing gastrointestinal reaction are in Concerning adverse events, 17 of the included articles reported these. The most common type of adverse event reported was a gastrointestinal reaction. There were 16 studies reporting this, in which 5 reported gastrointestinal reactions generally, while others reported specific reactions, such as nausea, diarrhea, and constipation. The pooled results of 12 studies showed that the incidence of nausea in the TCM group was lower than that in WM group . Ten trials reported the adverse event of leukopenia between the two groups, and the results showed that the TCM group did better in decreasing the incidence of leukopenia . In addition, 9 studies reported the occurrence of anemia. The pooled results showed that there were significant differences in the occurrence of anemia between the two groups . Subgroup analysis of seven studies showed that TCM intervention can reduce the incidence of thrombocytopenia .The adverse event of myelosuppression was mentioned in 15 studies, 5 of them reporting myelosuppression generally, with the remainder reporting an instance of specific myelosuppression, such as leukopenia, anemia, or thrombocytopenia, as shown in p = 0.002) and kidney injury  were reduced in the TCM group , but no obvious effect on decreasing the incidence of muscle and joint pain , fatigue , neurotoxicity , cardiotoxicity , and hair loss   .Funnel plots of ORR were adopted to assess publication bias. It was apparent from the funnel plot , that thAtractylodes macrocephala Koidz. ; Astragalus mongholicus Bunge ; Smilax glabra Roxb. ; Scleromitrion diffusum (Willd.) R. J. Wang ; Curcuma aromatica Salisb. ; Codonopsis pilosula (Franch.) Nannf. ; and Glycyrrhiza glabra L. , as shown in We analyzed the frequency of herbs appearing in the twenty different TCM formulas mentioned in the included articles. Sorted according to their frequency, the most effective herbs included: Baizhu, Huangqi, Fuling, Bai Hua She She Cao, Ezhu, Dangshen, and Gancao. The compounds of seven herbs were as follows: Through the search of the prescribed database, the seven effective herbs were found to be comprised of 120 compounds, and 246 herb target genes. Cytoscape3.7.2 software was adopted to establish the network of the compound and target genes of the effective herbs .p < 1.0e-16)  . The PPI1.0e-16) .From the analysis of The top 20 KEGG pathways are shown in With changes in life pressure and dietary structure, the incidence of OC is increasing. Because of non-obvious early symptoms, OC has the characteristic of a low early diagnosis rate, and more than 70% of patients are in advanced stages when diagnosed . AOC devThese meta-analysis results reveal that, compared with chemotherapy alone, the treatment of TCM combined with chemotherapy improved the ORR of AOC patients. In solid tumor treatment, the ORR indicates the sum of patients with complete and partial remission after treatment in relation to the total number of evaluable cases, which is equal to the sum of cases in complete remission and partial remission divided by the total number of evaluable cases. TCM can also improve the 3-year survival rate and PFS, which illustrates its efficacy in increasing the sensitivity of chemotherapy, and avoiding the chemo-resistance and experience of relapses in the treatment of AOC. However, TCM had no significant effect on improving the 5-year survival rate in the analysis, although in the original study of After the meta-analysis was completed, we extracted high-frequency herbs from all the TCM prescriptions in the included studies of effective herbs. Their composition was Baizhu, Huangqi, Fuling, Bai Hua She She Cao, Ezhu, Dangshen, and Gancao. In TCM theory, OC belongs to “Zhengjia,” the root cause of which is the declining function of the spleen and stomach qi, leading to deficiency of qi and blood. Blood stasis in the ovarian area is another important pathogenesis. The effective herbs of Baizhu, Fuling, Dangshen, and Gancao are a Sijunzi decoction, which is good at replenishing qi and invigorating the spleen function. Huangqi is also a qi-invigorating herb that can enhance the efficacy of a Sijunzi decoction, and the above 5 herbs can tonify qi and help the body expel pathogens. The functions of Ezhu are breaking blood and activating qi, removing stagnation, and relieving pain, which is suitable for the pathogenesis of blood stasis. Bai Hua She She Cao is good at clearing away heat and toxic materials as well as reducing swelling and removing stasis, and has been confirmed as a key antitumor herb in many studies. These seven effective herbs are mutually compatible, achieving the effect of strengthening the healthy qi and anti-tumor effect.We also performed network pharmacology to explore the specific effects of effective herbs on AOC. We screened 120 components and 246 targets of effective herbs and constructed the PPI network by integrating 121 intersecting targets of these seven herbs associated with AOC. Among the herb–component–target network, the 10 most important components were selected according to the degree value, including MOL000098, MOL000422, MOL003896, MOL000392, MOL000354, MOL000296, MOL000449, MOL000006, MOL000378, and MOL000417, including: quercetin, kaempferol, 7-methoxy-2-methyl isoflavone, formononetin, isorhamnetin, hederagenin, stigmasterol, luteolin, 7-O-methylisomucronulatol, and calycosin. Considerable evidence supports the anti-tumor function of the aforementioned components of kaempferol, quercetin, formononetin, and isorhamnetin .TP53, STAT3, JUN, AKT1, MAPK3, RELA, MAPK1, ESR1, IL6, FOS, MAPK14, TNF, CDKN1A, RB1, CCND1, EGFR, STAT1, MDM2, MAPK8, and CAV1. Clinical studies have shown that the above core genes are related to the occurrence and development of a variety of tumors. For example, TP53 gets activated in response to a variety of stress signals, such as DNA damage, and hyperproliferative signals, and is involved in the orchestration of basic events that must be overcome for cancer initiation and progression (STAT3 cooperates with other targets in promoting glycolysis or lipid catabolism, which have potential roles in different aspects of the metabolism switches in cancer cells that support tumor progression (AKT1 is a direct target of miR-153 in ovarian cancer cells (In addition, the 20 core genes in the PPI networks were gression . STAT3 cgression . AKT1 iser cells . Next, aer cells . KEGG ener cells . Hence, This review has several limitations. First, the quality evaluation of many articles in terms of allocation concealment and blinding was unclear, and the lack of large, multicenter RCTs may lead to the potential risk of bias and affect the reliability of the results. Second, the differences in application of chemotherapy and duration of treatment among the included trials may lead to a certain degree of heterogeneity. Third, screening of herb components based on DL and OB values may miss some effective components.Despite these limitations, this study is the first to integrate meta-analysis and network pharmacology to explore the efficacy and potential pharmacological mechanisms of TCM on AOC. We hope it will provide evidence for clinicians to treat AOC patients with a better strategy, as well as provide scientific clues for researchers in this field, which can be further validated experimentally.in vivo and in vitro are needed to validate the therapeutic mechanism of these findings.In conclusion, this article reveals that combined with chemotherapy, TCM is more effective and safer than chemotherapy alone in treating AOC. In addition, TCM treats AOC patients through a multi-target, multi-component, and multi-pathway mechanism. To make these results more reliable, more rigorously designed RCTs are required in the future and further pharmacological experiments


Single sample pathway analysis (ssPA) transforms molecular level omics data to the pathway level, enabling the discovery of patient-specific pathway signatures. Compared to conventional pathway analysis, ssPA overcomes the limitations by enabling multi-group comparisons, alongside facilitating numerous downstream analyses such as pathway-based machine learning. While in transcriptomics ssPA is a widely used technique, there is little literature evaluating its suitability for metabolomics. Here we provide a benchmark of established ssPA methods , and z-score) alongside the evaluation of two novel methods we propose: ssClustPA and kPCA, using semi-synthetic metabolomics data. We then demonstrate how ssPA can facilitate pathway-based interpretation of metabolomics data by performing a case-study on inflammatory bowel disease mass spectrometry data, using clustering to determine subtype-specific pathway signatures.https://pypi.org/project/sspa/), providing implementations of all the methods benchmarked in this study.While GSEA-based and z-score methods outperformed the others in terms of recall, clustering/dimensionality reduction-based methods provided higher precision at moderate-to-high effect sizes. A case study applying ssPA to inflammatory bowel disease data demonstrates how these methods yield a much richer depth of interpretation than conventional approaches, for example by clustering pathway scores to visualise a pathway-based patient subtype-specific correlation network. We also developed the sspa python package (freely available at This work underscores the value ssPA methods can add to metabolomic studies and provides a useful reference for those wishing to apply ssPA methods to metabolomics data.The online version contains supplementary material available at 10.1186/s12859-022-05005-1. As metabolomics continues to emerge as a powerful platform for profiling small molecules in various areas of research –3, interConventional PA approaches widely used across omics data types  commonly focus on a two-group analysis, seeking to identify significantly impacted pathways between the study groups. These methods can be broadly classified into three main categories: over-representation analysis (ORA) , function rows representing samples and m columns representing metabolites, could be transformed to the matrix Single-sample PA (ssPA) refers to methods used to compute a score representing the enrichment level of each pathway for each individual sample in a study . Anothermentclass2pt{minimCurrent ssPA methods can be broadly categorised into three main groups: dimensionality reduction (DR)-based , 23, GSEBy generating sample-wise pathway scores, ssPA enables numerous downstream analyses to be performed that cannot be achieved using conventional PA approaches. At the most basic level, multi-group comparisons can be made using the pathway scores, for example using statistical association testing to determine pathways which differ significantly between groups . This caAlthough most ssPA methods are applicable to most omics datatypes, this will not necessarily equate to their consistent performance across omics. Indeed, the most well-known ssPA methods have all been developed for transcriptomic data analysis. Other omics datatypes such as metabolomics differ greatly in composition and statistical properties from transcriptomic data. The most obvious difference lies in the number of metabolites versus the number of genes/transcripts profiled in metabolomics and transcriptomics respectively, which has a direct effect on pathway coverage (the proportion of entities in a given pathway that have been assayed). Another key difference is the uncertainty in metabolite identification in metabolomics which remains one of the field’s greatest challenges , an issuThe purpose of the present research, therefore, is twofold: we begin by performing a benchmark of widely used and established ssPA methods using semi-synthetic metabolomics datasets, after which we demonstrate how ssPA methods can aid in the interpretation of metabolomics data by performing an application case study using publicly available inflammatory bowel disease (IBD) data. Specifically, in the benchmarking portion of this manuscript, we compare the performance of four well-known ssPA methods, namely SVD (PLAGE) , ssGSEA In the benchmarking section of this work, the Su et al.  COVID19 For the application section, we used inflammatory bowel disease (IBD) MS metabolomics data from Lloyd-Price et al. . This dahttps://www.metaboanalyst.ca/MetaboAnalyst/upload/ConvertView.xhtml).Both datasets are publicly available  and are derived from untargeted mass spectrometry, see Table oAnalyst  identifihttps://reactome.org/download-data. The ChEBI2Reactome_All_Levels.txt file was used and filtered for Homo sapiens pathways only. The metabolite coverage of the Reactome human pathways using the COVID dataset ranged from 2 to 39 metabolites per pathway. KEGG human pathways (release 101) were downloaded using the KEGG REST API (https://www.kegg.jp/kegg/rest/keggapi.html).Reactome pathways (release 76) were downloaded from L. As part of the benchmarking procedure a non-redundant set of Reactome pathways We describe a pathway by the set of metabolites annotated to it: The simulations in this work are based on semi-synthetic datasets created using the COVID dataset. The use of a “permutation and spike" procedure allows the original signal in the dataset to be erased and replaced with known pathway signals, while preserving the underlying distributions  and heterogeneity of the experimental data. This is advantageous compared to generating fully synthetic data such as by random sampling from a Gaussian distribution, as it preserves the complex biological relationships occurring in omics data, which will influence method performance. A limitation of our approach is that the underlying distribution of a particular dataset may differ from that of other datasets. Despite this limitation, we believe this approach reflects more accurately the level of complexity found in real data compared to fully-synthetic simulation approaches.The semi-synthetic metabolomics data generation process is illustrated schematically in Fig. Let entclass1pt{minimaY can thus be expressed as:A pathway collection is required, consisting of entclass1pt{minimahttps://pypi.org/project/sspa/, and the source code is freely available at https://github.com/cwieder/py-ssPA.Details of the Python/R packages used for each of the methods benchmarked in this work are given in Additional file Mk metabolites in pathway k.For each of the On each pathway matrix m-dimensional space defined by Obtain the coordinates of the cluster centroids Mk.For each pathway obtain the unit column vector between the two cluster centroids k) onto k × 1). The projected values can be used as pathway scores Project the pathway matrix }Ak (Eq. ).3\documRepeat steps 4–5 for each pathway matrix Additional file The projection of each pathway matrix For each of the For each pathway matrix rnel PCA  with a rThe scores of the first principal component (PC1) can directly be used as scores Repeat step 2 for each pathway matrix The kPCA method is outlined as follows:Overlap between pathway metabolites was calculated using the Szymkiewicz-Simpson Overlap Coefficient (OC) Eq. . An OC oWe used the OC as an alternative to the Jaccard Index (JI) as it is more sensitive to overlapping metabolites, i.e., the OC will return a value of 1 when the metabolites in a pathway are all present in a larger pathway (irrespective of the size of the larger pathway), whereas the JI will only equal 1 if two sets are identical.3 randomly selected pathways As outlined in the section ‘entclass1pt{minimaE) were considered positives . All simulations were repeated 200 times, with randomly selected enriched pathways and semi-synthetic data randomly permuted at each realisation.Runtime profiling was repeated 10 times for each method and average results are reported. The Python libraries cProfiler and line-profiler were used to determine the wall time of each method.The kPCA method was used to demonstrate ssPA applied to the IBD metabolomics dataset.kPCA pathway scores were standardised prior to clustering  and the top 50 pathways were used to produce a hierarchical clustering as detailed above. Pathway IDs from the resulting clusters were extracted and used to build the network. A Spearman rank correlation matrix was produced from the pathway scores. The NetworkX Python package was used to create a pathway-pathway network, with nodes representing pathways and edges representing correlation between the pathway scores. Cytoscape was used to visualise the network using an edge-weighted spring embedded layout.kPCA pathway scores derived from the IBD dataset were ranked by t-test p-values) for visual clarity. Each node is coloured by the average pathway score of samples in each COVID WHO severity level .The COVID pathway correlation network was created in the same way as the IBD network, using the top 30 pathways , it is essential to know the identity of truly perturbed “enriched" pathways. Here we use the terms “positively enriched" or “negatively enriched" to refer to pathways that are significantly perturbed with respect to the control group, depending on the directionality of the effect. In experimental datasets it remains challenging to distinguish between true and false positive enriched pathways, so instead we used semi-synthetic data to accomplish this task. As detailed in the methods, we removed the original signal from an experimental untargeted metabolomics dataset of COVID patients and replaced it with artificial known pathway signals (enriched pathways) in one of two study groups. Using this simulation procedure, we can vary the strength of the enrichment of a pathway, the number of differentially abundant metabolites in the pathway, and the coverage level.We propose two novel ssPA methods based on unsupervised machine learning concepts for generating pathway scores (see Methods for further details). ssClustPA makes use of k-means clustering and for each pathway projects the original datapoints onto the unit vector between two cluster centroids to yield pathway scores. ssClustPA is best suited to two-group study designs due to the two-cluster centroid constraint; for designs with two or more study groups, we developed the kPCA ssPA method. The kPCA ssPA method uses kernel PCA  with a rBiological pathways represented as lists of molecules can be seen as an arbitrary way of partitioning a network and defining precise pathway boundaries remains a challenge in pathway curation. It is therefore expected that all pathway databases will contain some degree of redundancy, meaning that not all molecules will be unique to a single pathway. If a pathway p-values are compared to the overlap coefficient (OC) of each pathway with pathway R-HSA-1483206 in Fig. p-value, with pathways with higher overlap tending to have lower p-values. Importantly, if an arbitrary p-value threshold e.g. pA simple example of the effect of pathway overlap is demonstrated in Fig. Due to this overlap effect, and to simplify the procedure, we first performed benchmarking on a small subset of non-redundant pathways from the Reactome pathway database , ssGSEA, GSVA, z-score, ssClustPA, and kPCA methods using semi-synthetic data. These are categorised as GSEA-based (ssGSEA and GSVA) and dimensionality reduction (DR)/clustering-based . We also included comparison to non-ssPA methods ORA and GSEA where possible.Beginning with the non-redundant pathway set, in which each of the pathways contains unique metabolites, three random pathways were artificially enriched, and performance metrics were computed and averaged over 200 iterations Fig.  across ap < 0.05). In terms of precision, regardless of the overlap threshold, conventional pathway analysis methods ORA and GSEA outperform ssPA methods at moderate-high effect sizes (0.6–1.0) (p < 0.05). Following this, DR/clustering-based methods  provide the highest precision values amongst the ssPA methods, with GSEA-based methods ssGSEA and GSVA as well as the z-score yielding the lowest precision values . Similar observations can be made in terms of AUC at an overlap threshold of ≥ 0.5 . At an overlap threshold ≥ 0.25  with the same simulation setup to evaluate performance , the ranks were normalised by the total number of pathways tested using each method.The ability of the ssPA methods to rank enriched pathways highly was investigated, again using the semi-synthetic COVID data. Briefly, t-tests were performed on the ssPA scores for each pathway (testing for a difference between mean scores in disease and control groups) and the resulting In concordance with the performance metrics calculated in the previous section, at lower effect sizes, the GSEA/z-score-based methods rank the truly enriched pathways the highest 7), and ap-values, but do not have a high enough OC to be considered true positives, hence resulting in a rise in false positives and a decreased AUC. Importantly, we found that higher coverage generally improves method performance, but does not have profound effects on the rankings of method performance, which recapitulate those observed in the previous simulations investigating effect size and signal strength.We calculated performance metrics as in the previous simulations, keeping the minimum overlap coefficient threshold to declare true positive pathways fixed at OC = 0.5, and demonstrating two effect size scenarios (low effect at α = 0.25 and moderate effect at α = 0.75). For both pathways  and 335 metabolites (columns), as well as Reactome pathways with sufficient coverage in the dataset  by Lloyd-Price et al. . It is iTo obtain an exploratory perspective on the strength of the biological signals in the dataset, PCA was used to visualise the data at both the metabolite and pathway level. We transformed the metabolite-level data to pathway scores using the kPCA method described earlier and compared the PCA results obtained. No strong separation between the sample classes was observed using either approach, with most samples appearing amalgamated together in a central cluster  vs. pathway scores, we ranked each of the metabolites/pathways using BH-FDR corrected t-test q-values (testing for differences between the IBD and non-IBD groups) and performed clustering after progressively adding each entity significant at q .05 Fig. . Hierarcmentclass2pt{minimThe networks shown in Fig. IBD encompasses a group of diseases broadly characterised by chronic inflammation of the gut, and is widely attributed to a complex interplay of immune dysregulation, dysbiosis of the gut microbiome, and genetic and environmental factors. A sub-cluster of immune-related pathways can be seen in cluster 1 Fig. a, highliIn the second cluster, which consists of pathways whose metabolites are depleted in IBD, a prominent sub-cluster containing pathways related to platelet homeostasis, VEGF, and nitric oxide (NO) signalling is visible Fig. e. It is Another large sub-cluster within cluster 2 focuses on DNA repair processes, including base-excision repair Fig. f. This sWe also trained a random forest (RF) classifier on the IBD dataset, which based on fivefold repeated stratified cross-validation achieved comparable AUC using kPCA scores  based on 3 randomly enriched pathways, derived using semi-synthetic data based on the IBD dataset. Fig. S3. Performance of ssPA methods using the full KEGG human pathway set (including redundancy) based on 3 randomly enriched pathways, derived using semi-synthetic data based on the COVID dataset. Fig. S4. ssPA method ability to rank highly the 3 randomly enriched pathways using the full set of Reactome pathways. Fig. S5. ssPA method performance in response to varying levels of pathway coverage. Fig. S6. ssPA method performance in response to varying levels of pathway coverage. Fig. S7. PCA scatter plots and density plots of PC1 scores obtained using the IBD data at the metabolite (upper panels) and pathway level using kPCA (lower panels). Fig. S8. Clustered heatmap of IBD data transformed to pathway scores using the kPCA method. Fig. S9. Pathway clusters derived using hierarchical clustering on COVID dataset transformed to pathway scores using kPCA (top 30 pathways). Table S2. Runtimes of ssPA methods, alongside GSEA* for comparison to conventional PA methods (average across 10 iterations). Table S3. Over-representation analysis results from IBD dataset. Table S4. Top 50 features in random forest model based on IBD data.Additional file 2. Excel spreadsheet containing results of statistical testing of benchmarking results. Mann Whitney U tests were performed for each pairwise combination of methods tested in the effect size simulation (corresponds to Fig. P-values were adjusted using Bonferroni FWER correction.


Background: Polycystic ovarian syndrome (PCOS) is the most common endocrine disorder affecting women. Chinese herbs have been considered as an alternative treatment for PCOS, and Yi-mu-cao (Leonuri Herba) is one of the most commonly used herbs to treat PCOS, which can relieve symptoms of PCOS patients. But the mechanism of its treatment remains unclear.Method: The main active ingredients and potential targets of Leonuri Herba were obtained by TCMSP and Swiss Target Forecast, and the related targets of PCOS were obtained by searching DrugBank, GeneCard and DisGeNet databases. The Protein-Protein Interaction (PPI) network was constructed using STRING database. GO and KEGG were used to detect the enrichment pathways of key targets. Cytoscape software was used to construct the component-target-pathway network, analyze the PPI network core, and verify the reliability of target binding by molecular docking technology.Result: 8 components and 116 targets of Leonuri Herba on PCOS were screened. Common targets mainly involve the Lipid and atherosclerosis, Endocrine resistance, AGE-RAGE signaling in diabetic complications and other signaling pathways. It is suggested that it can form multi-target and multi-pathway regulatory network through quercetin, kaempferol and other active substances to regulate endocrine disorders and reduce inflammatory response, so as to systematically improve PCOS. Molecular docking experiments showed that the active constituents of Leonurus had good binding activity with potential targets of PCOS.Conclusion: In summary, this study elucidates the potential effect of Leonuri Herba on PCOS, which is helpful to provide reference for clinical practice. This is also conducive to the secondary development of motherwort and its monomer components, and precision medicine for PCOS. Polycystic ovary syndrome (PCOS) can affect 5–18% of women . PCOS inLeonuri Herba) is naturally found in plants and has traditionally been used in China for thousands of years for uterine contractions, postpartum congestion, breast tenderness, and other gynecological disorders , which were screened according to the two ADME attribute values of oral availability  ≥30% and drug-like drugs  ≥ 0.18 (https://pubchem.ncbi.nlm.nih.gov/), get PubChem ID, smiles number, and 2D chemical structures. Then, SwissTargetPrediction (http://swisstargetprediction.ch/) was used to predict protein targets, the feasibility of value as the >0.5, get the target of 189. All target genes are converted into gene symbols using the UniProt knowledge base (http://www.UniProt.org).The active components were found by TCMSP  ≥ 0.18 . A totalhttps://www. genecards.org) and Map database of OMIM (http://www.omim.org), DisGeNet database (http://bidd.nus.edu.sg/group/cjttd) the potential targets of PCOS, Enter DrugBank database (https://www.drugbank.ca) to search for first-line clinical western drug targets for the treatment of PCOS. In the GeneCards database, a higher score indicates that the target is closely related to the disease. According to experience, if there are too many targets, the target whose Score is greater than the median is set as the potential target of PCOS twice. After merging the four disease database targets together, there are 2,440 disease target intersection genes in the four databases in With “PCOS” and “Polycystic ovary syndrome” as key words, Mine Gene in GeneCard database  was used to draw the Venn diagram, get 116 intersection genes in To explore active ingredients of https://string-db.org) was used to construct the PPI network diagram of 116 related targets: Set the biological species as “Homo sapiens,” and the minimum interaction threshold Highest confidence >0.7 as the screening condition. Each node represents a protein and its structure, and each edge represents the association between different proteins.The STRING database  were applied to the STRING online database (https://string-db.org/) to annotate and classify common targets  and the Kyoto Encyclopedia of Genes and Genomes  to download the molecular structures and transform them into mol2 formats. The structure of the receptor can be downloaded from the PDB Protein Database (http://www.rcsb.org). The docking simulation was performed with selected key proteins such as AKT1, IL6, EGFR, and MMP9 by AutoDock Vina 1.5.6. The binding affinity between molecules and proteins is predicted based on the docking minimum free energy. The lower the free energy, the higher the affinity. The results are saved in the pdbqt file. Finally, the results were analyzed and demonstrated by PyMOL.According to the enrichment results of compounds KEGG and GO and the comprehensive analysis of the current research status, we selected the two most critical molecules of this drug: quercetin (MOL000098) and kaempferol (MOL000422). TCMSP database  and 726 edges (interactions) were identified, representing the major genes corresponding to the active ingredient of The PPI results are exported as TSV files and then imported into Cytoscape3.7.2 for further network analysis using the CytoNCA plug-in. Through 6 parameters in CytoNCA “Betweenness/Closeness/Degree/Eigenvector/LAC/Network” to filter 116 genes. First filter condition, “Betweenness: 49.38088469/Closeness: 0.436213992/Degree: 24/Eigenvector: 0.063172609/LAC: 11.38461538/Network: 12.8”. Getting 35 nodes, 598 edges; Second filter condition, “Betweenness: 11.15689699/Closeness: 0.653846154/Degree: 32/Eigenvector: 0.156082243/LAC: 20.66666667/Network: 23.41193154”. Get 14 nodes (core genes), 152 edges, specific genes such as JUN, AKT1, HSP90AA1, CASP3, FOS, MYC, EGFR, HIF1A, TP53, TNF, IL6, IL1B, MMP9, MAPK14 in Leonuri Herba was obtained through KEGG enrichment analysis. 166 signaling pathways were discovered, and the top 20 were shown in Subsequently, GO enrichment analysis was performed. The top 8 enrichment results for BP, MFs and CCs are listed in Leonuri Herba , kaempferol (MOL000422) and key targets AKT1, IL6, EGFR and MMP9 were shown in 8, kaempfPCOS is one of the most common endocrine disorders among women of reproductive age and is heterogeneous in that women may develop reproductive, endocrine, and/or metabolic symptoms that vary throughout their lives , 18. DueIn recent years, more and more Chinese medicines and their preparations have come into the world’s attention. Of course, these TCMs can also be effective in treating PCOS, and many studies on the treatment of PCOS with TCMs have been reported . For exaLeonuri Herba in this study is one of the traditional Chinese herbs, which has been widely used to treat gynecological and obstetric diseases for thousands of years. Its main ingredients are reported to include leonurine, 4′,5-dihydroxy-7-methoxyflavone, rutin, hyperoside, apigenin, quercetin, kaempferol and salicylic acid , estradiol (E2), luteinizing Hormone (LH), Bax, IL-1β, IL-6 and TNF-α, increase the expression of FSH and Bcl-2, and inhibit the expression of androgen receptor (AR), thus restoring the maturation and ovulation of oocytes . Among them, High levels of AKT1 is associated with Granulosa cells (GC) dysfunction . MMP9 hari Herba –44. LeonLeonuri Herba and their pharmacological effects on PCOS. This helps researchers and pharmacologists understand the mechanisms of motherwort. However, further in vitro experiments are needed to verify the predicted process.To the best of our knowledge, this is the first time to reveal the active ingredients of Leonuri Herba in PCOS. Our current research is only from the simple prediction of drug components on the target nuclear pathway of network pharmacology. However, further experiments are needed to confirm the specific therapeutic mechanism of Leonuri Herba in the treatment of PCOS. Look forward to the accurate treatment of PCOS with active ingredients of TCM in the future.Through the analysis of network pharmacology, the step-by-step mining of data and the analysis of multi-target and multi-way methods, we can more clearly understand the important role of


C. elegans WDFY-3 protein is important for cargo selection during selective autophagy and for regulating axon termination. The C-terminal region of WDFY-3 contains BEACH, WD repeats, and FYVE-like domains, all of which are required for selective autophagy. WDFY-3 also contains a large N-terminal region that is relatively uncharacterized. Currently,wdfy-3(ok912)is the only mutant allele that has been characterized for this gene. This allele features a small deletion that is predicted to disrupt the C-terminal region of the protein. Here, we used CRISPR Cas9 to produce a newwdfy-3(cue30) allelethat is a near complete deletion of the coding region. We report that, unlike the existingwdfy-3(ok912)allele, this newwdfy-3(cue30)null allele causes a weak overextension phenotype in the PLM axon. Like the existingwdfy-3(ok912)allele, the newwdfy-3(cue30)null allele can suppress PLM axon termination defects caused by anfsn-1null allele. Creating and characterizing newwdfy-3 alleles will increase our understanding of this gene and could help elucidate more of the gene’s conserved functions.The WDFY3gene  encodes a scaffold protein that is required for selective autophagy and has been associated with autism . WDFY3 functions as an adaptor that links ubiquitinated cargo to the core autophagy components, thereby recruiting ubiquitinated targets into autophagosomes for eventual destruction in autophagolysosomes. Large scale genome sequencing studies have revealed a significant association betweenWDFY3and autism . Consistent with this observation,WDFY3is required for the normal development of axon tracts in mice . Moreover, genetic analysis inC. eleganshas indicated thatwdfy-3regulates axon targeting by functioning in a genetic pathway withegl-19, an ortholog of theCACNA1Cautism-associated gene . However, the mechanism through which WDFY-3 affects axon targeting remains mostly unknown.The humanwdfy-3(cue30)allele. This newwdfy-3(cue30)allele is a near complete deletion of the coding region ofwdfy-3, except for the last three exons that make up the FYVE-type domain at the C-terminus of the protein . This is unlike the previously studied loss-of-functionwdfy-3(ok912)allele, which contains a small deletion that removes part of the BEACH domain in the C-terminal region . The removal of the BEACH domain in thewdfy-3(ok912) allele also causes a frameshift mutation, likely resulting in the disruption of the WD repeats and FYVE-type domains . However, the large N-terminal region of WDFY-3 may not be affected by this mutation.As a first step in further exploring the role of WDFY-3 in axon targeting, we used CRISPR Cas9 to create the newwdfy-3(cue30)allele affects axon targeting, we examined the PLM axon. The PLM cell body resides in the tail and extends an axon anteriorly along the lateral body wall. In wildtype worms, the PLM axon terminates at the midbody, prior to reaching the ALM cell body . We found that our newwdfy-3deletion allele causes PLM axon termination defects that are significantly greater than those observed in wildtype . Interestingly, thewdfy-3(ok912)allele does not cause PLM axon termination defects. Together, these observations suggest the possibility that thewdfy-3(ok912) allele might not be a null allele and that the uncharacterized N-terminal region of WDFY-3 could have functions important for axon guidance. Future investigations could test this hypothesis through the analysis of additional targeted deletions inwdfy-3.To determine how our newwdfy-3(ok912)allele can suppress axon termination defects caused by anfsn-1null allele . To determine if our newwdfy-3(cue30)allele also has this property, we analyzed PLM axon termination inwdfy-3(cue30);fsn-1(null)double mutants. We found thatwdfy-3(cue30)also suppresses overextension caused byfsn-1 . However, this suppression did not bring the overextension penetrance back to wildtype levels like thewdfy-3(ok912) allele . These observations are consistent with our previous report of a genetic interaction betweenwdfy-3andfsn-1 .Our prior work demonstrated that thewdfy-3(cue-30)mutation. Unique guide RNAs were designed to cut in two places to excise most of the coding region of thewdfy-3 gene without perturbing the function of nearby genes or the non-coding UTR region ofwdfy-3. Guide RNAs used were upstream: GCGATTATTGGATTATCTCG & downstream: AGTTTTGAACACGTGCGACG.A CRISPR editing technique was used to create theC. elegansstrains were cultured and maintained on nematode growth medium (NGM)-agar plates using standard methods at 20°C . Axons were labeled and observed as previously described . Briefly, animals were mounted on a 5% agarose pad and observed with a 40x objective. PLM neurons were visualized with themuIs32transgene which encodesPmec-7::gfp+lin-15(+)and is expressed in all mechanosensory neurons .muIs32[mec-7p::gfp+ lin-15(+)]II;fsn-1(gk429) IIIAGC52:muIs32[mec-7p::gfp+ lin-15(+)]II;wdfy-3(ok912) IIAGC135:muIs32[mec-7p::gfp+lin-15(+)]II;wdfy-3(cue30) IVAGC209:muIs32 [mec-7p::gfp+ lin-15(+)]II;fsn-1(gk429) III; wdfy-3(ok912) IVAGC259:muIs32 [mec-7p::gfp+ lin-15(+)]II;fsn-1(gk429) III; wdfy-3(cue30) IVAGC260:


Background: Interstitial lung disease (ILD) is the major cause of morbidity and mortality in patients with various rheumatic diseases. However, more interventions need to be sought. Tripterine, an extract of Tripterygium wilfordii Hook. F, has been widely studied for its powerful anti-inflammatory effect. However, its mechanism of action in treating connective tissue disease-related (CTD)-ILD remains unclear.Purpose: To investigate the mechanism of tripterine in CTD-ILD treatment by combining network pharmacology and an in vivo experiment.Methods: The related targets of tripterine were obtained after searching the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform, Comparative Toxicogenomics Database, GeneCards, Search Tool for Interacting Chemicals database, and SymMap database. Following this, Online Mendelian Inheritance in Man, GeneCards, Genebank, and DrugBank were used to screen the targets of CTD-ILD. A target-signalling pathway network was constructed using Cytoscape. Additionally, topological analysis was performed. Protein interaction analysis was performed using the STRING online analysis platform. Following this, Gene Ontology (GO) and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) signalling pathway enrichment analyses were performed. Subsequently, the molecular docking between tripterine and the core targets was verified. Finally, experimental verification was performed in bleomycin-induced model mice.Results: A total of 134 common targets and 10 core targets of tripterine, including signal transducer and activator of transcription 3, tumour necrosis factor (TNF), v-rel avian reticuloendotheliosis viral oncogene homolog A, protein kinase B (Akt) α (Akt1), mitogen-activated protein kinase (MAPK) 1, Jun transcription factor family, tumour protein 53, MAPK3, nuclear factor kappa B subunit 1, and caspase 8, were obtained. GO enrichment analysis revealed that, while treating CTD-ILD, tripterine was mainly involved in cytokine receptor binding, receptor-ligand activity, signal receptor activation, cytokine activity, protein ubiquitination, deoxyribonucleic acid transcriptase activity, etc. The KEGG pathway enrichment analysis revealed that the most significant signalling pathways were multiple viral infections and the phosphatidylinositol-3-kinase (PI3K)/Akt, TNF, and apoptosis signalling pathways. Molecular docking results revealed that tripterine had good docking activity with the core targets. Experimental studies also demonstrated that tripterine could inhibit the activation of PI3K/Akt, apoptosis, and TNF-α signalling pathways in lung tissue and significantly improve lung pathology and collagen deposition in the model mice.Conclusions: This study preliminarily revealed the potential molecular biological mechanism of tripterine while treating CTD-ILD might be related to inhibiting the PI3K/Akt, apoptosis, and TNF-α signalling pathways. Tripterygium wilfordii Hook. F. and its extract could be used clinically for treating CTD-ILD. Connective tissue disease (CTD), a common clinical autoimmune disease involving multiple organs and systems, usually affects the respiratory system. Interstitial lung disease (ILD) is one of its severe pulmonary complications . StudiesRecently, it has been reported that transforming growth factor (TGF)-β1 , plateleTripterygium wilfordii Hook. F. polyglycoside tablets satisfactorily treated CTD-ILD; however, its mechanism of action remains unclear , Comparative Toxicogenomics Database, (http://ctdbase.org/), GeneCards database platform (https://www.genecards.org), Search Tool for Interacting Chemicals (STITCH) database (http://stitch.embl.de/), SymMap database (https://www.symmap.org/), Genebank database (https://www.ncbi.nlm.nih.gov/genebank), Online Mendelian Inheritance in Man (OMIM) (http://www.omim.org), and DrugBank database (https://www.drugbank.ca). ‘Tripterine’ was used as the keyword to retrieve key targets from the TCMSP, Comparative Toxicogenomics Database, GeneCards, STITCH, and SymMap databases. “Connective Tissue Disease-related Interstitial Lung Diseases” was then used as the retrieval term in OMIM, GeneCards, Genebank, and DrugBank. The potential targets were obtained after eliminating the duplicated targets. Organism equal to Homo sapiens was limited.The eight databases exploited to obtain the related targets were as follows: the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP) (https://string-db.org), the species was selected “Homo sapiens,” and the confidence was set at > 0.9. A PPI network was constructed by importing the results into Cytoscape 3.8.0.R software (R 4.0.2) was used to map tripterine potential targets to CTD-ILD-related targets, and a Venn diagram was drawn to obtain an intersection of the two targets. The targets common to tripterine and CTD-ILD were uploaded to the STRING database (R software (R 4.0.2) was used to analyse the molecular function (MF), biological process (BP), and cellular components (CC) to identify the interaction characteristics of the common targets in the gene function and signalling pathway. The KEGG pathway enrichment analysis was performed to analyse the possible tripterine interventional pathway in CTD-ILD.https://pubchem.ncbi.nlm.nih.gov/) and imported into AutoDock Tools 1.5.6 saved in the Protein Data Bank, Partial Charge (Q), and Atom Type (T) (PDBQT) format. The 3D structure of the target protein was obtained from the Protein Data Bank database (https://www.rcsb.org), and the water molecule and the original ligand were eliminated using PyMOL. Following this, the target protein was imported into AutoDock Tools 1.5.6 for hydrogenation, charge calculation, and non-polar hydrogen bonding, the results of which were saved in the PDBQT format. The size of the grid box was 40 × 40 × 40. Finally, molecular docking was performed using the CMD command of AutoDock Vina.Molecular docking was performed between the screened target protein and tripterine. The three-dimensional (3D) structure of tripterine was obtained from PubChem ; bleomycin (BLM) hydrochloride ; anti-collagens I and III, anti-vimentin, and anti-α-smooth muscle actin (SMA) ; anti-phosphatidylinositol-3-kinase (PI3K), anti-phospho-PI3K, anti-protein kinase B (Akt), and anti-phospho-Akt  antibodies; anti-β-actin ; mouse tumour necrosis factor-α kit and mouse hydroxyproline kit ; terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick end labelling (TUNEL) apoptosis assay kit .Specific pathogen-free C57BL/6J male mice  were procured from Cytointelligen . All the mice were kept at room temperature and acclimatised for a week before initiating the experiment. All animal experiments involved in this study were approved by the Ethics Committee of the Nanjing University of Chinese Medicine. Thirty mice were divided into the following five groups: the normal, model, low-dose tripterine, medium-dose tripterine, and high-dose tripterine groups. The mice in the high-, moderate-, and low-dose groups were intraperitoneally injected with 2 mg/kg, 1 mg/kg, and 0.5 mg/kg tripterine, respectively, every other day. The same volume of normal saline was injected intraperitoneally in the mice belonging to the normal and model groups.BLM intratracheal instillation was employed to simulate the CTD-ILD model. After the mice were anaesthetised with isoflurane in the supine position, the lumbar puncture needle was inserted into the trachea under light source irradiation. Following this, 50 μL of BLM solution was quickly injected into the trachea using a syringe. Finally, the mice were kept in an erect position and shaken to facilitate the even distribution of the solution in the lungs.Lung tissue was fixed in 4% paraformaldehyde and then dehydrated with alcohol at different concentration gradients. Subsequently, the sample was embedded in paraffin and sectioned for H&E and Masson staining. The degree of alveolitis and PF was scored according to the method reported by Ashcroft et al. .On the 28th day, the mice underwent micro-CT under isoflurane anaesthesia. Mouse lung imaging was performed using a Quantum GX Micro-CT scanner . Specific parameter settings are performed according to previous studies .The hydroxyproline level in the lung tissue samples was determined using a hydroxyproline assay kit. Twenty milligrams of lung tissue samples were homogenised using radioimmunoprecipitation assay (RIPA) buffer and proteinase inhibitors, and the supernatant was aspirated for use. The assay kit was removed from the refrigerator and kept at room temperature for at least 30 min. The concentrated wash buffer was diluted with double distilled water. The blank control, sample, and standard wells were set up on the plate according to the manufacturer’s instructions, and the corresponding reagents were added. A standard curve was plotted based on the measured optical density values, and the measured concentrations of the samples in each well were calculated based on the curve equation. These concentration values were then multiplied by the dilution factors to obtain the final concentrations.The samples were removed from the refrigerator (−80°C), and dissolved at room temperature. After the standard substances were diluted, the samples were loaded and washed, the enzyme was added, and the solution was incubated following the instructions of the TNF-α enzyme-linked immunosorbent assay kit. Following this, the linear regression equation was derived based on the absorbance value of the standard hole. The concentrations of the samples were calculated based on the absorbance values of the samples and the regression equation. Finally, the final concentration was obtained based on the product of the measured concentration and the dilution factor.Paraffin blocks were sectioned for dewaxing and hydration. To block the activity of the endogenous peroxidases for 10 min, 3% hydrogen peroxide was added. After antigenic repair, the paraffin blocks were incubated with serum blocking solution for 30 min to seal the non-specific binding site. Following this, the serum was removed, and the sections were washed. Subsequently, the sections were incubated overnight with the primary antibodies at 4°C. The next day, the slides were washed, treated with secondary antibodies, and stained with diaminobenzidine and haematoxylin. Brownish-yellow areas observed under light microscopy (×400 magnification) indicated a positive result.® 20 Detergent (TBST) for 10 min at room temperature. The following day, the corresponding secondary antibody (1:2000) was added, and the mixture was incubated at room temperature for 1 h. The membrane was washed thrice with TBST three times for 10 min and then exposed to a colour-developing reagent under dark conditions. ImageJ 1.49 software was used to analyse the grey values.The lung tissues were lysed using the RIPA buffer comprising proteinase inhibitors. The protein content was measured, separated using 10% sodium dodecyl sulphate-polyacrylamide gel electrophoresis, and then electroblotted onto a nitrocellulose membrane. Non-specific antigens were incubated in 5% bovine serum albumin with the membrane for 2 h. Following this, the membrane was incubated overnight with primary antibodies at 4°C. The membrane was washed thrice in 0.1% Tris-buffered saline with 0.1% TweenThe paraffin section was dewaxed twice for 5–10 min. This was achieved by treating the section with anhydrous ethanol for 5 min, followed by 90% ethanol for 2 min, 70% ethanol for 2 min, and distilled water for 2 min. A drop of 20 μg/ml DNase-free protease K was added, and the section was kept at room temperature for 15–30 min. The section was then washed thrice with phosphate-buffered saline. Following this, the reaction solution was added separately. Finally, the sections were sealed using an antifade mounting medium with 4′,6-diamidino-2-phenylindole.p < 0.05.Data analysis was performed using GraphPad Prism 8.0 software. Between-group differences were tested using one-way analysis of variance. Values are expressed as the mean ± standard deviation (* represents the difference between the BLM-treated and the other groups). Statistical significance was set as A total of 247 tripterine targets were obtained by searching TCMSP, Comparative Toxicogenomics Database, GeneCards, STITCH, and SymMap databases. A total of 2,860 disease-related targets were obtained after eliminating duplicates by searching the OMIM, GeneCards, Genebank, and DrugBank databases. A total of 134 intersection targets were obtained by mapping disease-related targets to potential drug targets .α (Akt1), mitogen-activated protein kinase (MAPK) 1, Jun transcription factor family (JUN), tumour protein 53 (TP53), MAPK3, nuclear factor kappa B subunit 1 (NF-κB1), and caspase 8 (CASP8)  . The detp-values < 0.05 in the MF, BP, and CC categories were selected. The bubble size represented the number of targets during the GO analysis, and the bubble colour represented the enrichment significance. It mainly involved cytokine receptor binding, receptor-ligand activity, signal receptor activation, cytokine activity, protein ubiquitination, deoxyribonucleic acid transcriptase activity, ribonucleic acid transcriptase Ⅱ specificity, etc. (A total of 2,719 items were obtained by the GO enrichment analysis. The first 10 items with ty, etc. . A totalty, etc. .Molecular docking was performed between the first five core targets and tripterine. Furthermore, we docked tripterine with four main targets of nintedanib in the treatment of CTD-ILD. The docking results suggested that tripterine could exhibit good docking activities with target proteins such as STAT3, TNF, RELA, Akt1, MAPK1, TGF, PDGF, FGF, and VEGF . All binAfter intratracheal instillation of BLM or normal saline, the mice lost weight during the first week. However, the mice belonging to the control group gained weight gradually after the second week. In contrast, the weight of the mice treated with tripterine belonging to the model group was relatively stable . After 2One of the main pathological manifestations as ILD progresses is the thickening of the alveolar septum caused by collagen deposition, which further affects lung diffusion. CT is widely used in the clinical diagnosis and management of ILD, and micro-CT is a mature tool for evaluating lung imaging changes in small animals . Therefoα-SMA, vimentin, and hydroxyproline expression levels significantly increased in the model group and significantly decreased after 28 days of treatment with tripterine, particularly in the medium- and high-dose groups (α-SMA is a phenotypic marker of myofibroblasts in PF. Vimentin anchors and supports organelles in the mesenchymal cell cytoplasm. Both of these are involved in EMT . EMT-dere groups .Our network pharmacology study reported that the PI3K/Akt and TNF-α signalling pathways might be involved in the anti-CTD-ILD effect of tripterine. Therefore, we measured the key proteins of the PI3K/Akt signalling pathway and the TNF-α content in the serum and lung tissues to identify the effects of tripterine on these pathways. Our results revealed that the phosphorylated PI3K and Akt levels significantly increased in the model group but decreased to varying degrees after tripterine administration . SimilarApoptosis is a biological process closely associated with ILD . The apoα-SMA and vimentin (in vivo experiments, we reported that tripterine could decrease lung pathology and collagen deposition in the lung tissues of the model mice. Increased levels of fibrosis markers, namely α-SMA and vimentin, were observed in the model group. The levels of these proteins in the lung tissue decreased to varying degrees after treatment with tripterine. These results suggest that tripterine could reduce BLM-induced fibrosis in model mice. Therefore, we used network pharmacology to investigate the related targets and pathways of tripterine in the treatment of CTD-ILD.CTD-ILD is a heterogeneous disease characterised by diffuse alveolar inflammation and fibrosis , with prvimentin . ThroughA total of 134 potential targets of tripterine were obtained for treating CTD-ILD, of which STAT3, TNF, RELA, Akt1, MAPK1, JUN, TP53, MAPK3, NF-κB1, and CASP8 were strongly correlated with CTD-ILD. Additionally, the above-mentioned core targets were closely associated with PF. KEGG pathway analysis revealed that the potential mechanisms of triptolide in the treatment of CTD-ILD might be attributed to multiple viral infections and the TNF, PI3K/Akt, and apoptosis signalling pathways. These pathways are involved in the pathogenesis of various inflammatory diseases and are crucial for anti-pulmonary fibrosis progression .Firstly, viruses have been considered pathogens or propagators of ILD-related inflammation , and mulin vivo experiments. The TNF signalling pathway is a pro-inflammatory signalling pathway that plays a crucial role in joint and lung inflammatory diseases. Previous studies (Secondly, according to the KEGG enrichment analysis results, apoptosis, TNF-α, and PI3K/Akt signalling pathways might be related to the mechanism of tripterine in CTD-ILD treatment, as verified by  studies  have rep studies . Apoptos studies , which i studies . We repoIn addition, the PI3K/Akt signalling pathway has been confirmed to play a pathogenic role in ILD. For example, TGF-β can promote EMT, induce fibrosis through the PI3K/Akt signalling pathway, and modulate fibroblast differentiation into myofibroblasts that regulate ECM accumulation . PI3K isThese results were consistent with our predictions based on network pharmacology analysis. Our findings suggested that tripterine attenuates collagen accumulation and fibrosis in lung tissue, resulting in the inhibition of the PI3K/Akt, apoptosis, and TNF-α signalling pathways. This may partly explain why a lesser degree of inflammation and fibrosis were observed in the BLM-induced lungs. Indubitably, this study has some limitations, which need to be resolved by further research. First, various cells play an important role in the pathological development of ILD; however, specific cellular experiments were not performed to verify the above findings in this study. Secondly, whether the PI3K/Akt signalling pathway plays a role through the apoptotic pathway and the concrete downstream regulatory mechanism involved needs to be elucidated. Lastly, the BLM-induced ILD model undoubtedly does not completely mimic immune-mediated lung interstitial lesions, and whether tripterine has the same effect in ILD models induced by other factors likewise needs to be explored.Tripterygium wilfordii Hook. F. and its extract can be employed clinically for treating CTD-ILD.Tripterine can lower the degree of pathology and fibrosis in BLM-induced ILD in mice. The specific mechanism may be related to inhibiting the PI3K/Akt, apoptosis, and TNF-α signalling pathways.


Untargeted metabolomics (UM) allows for the simultaneous measurement of hundreds of metabolites in a single analytical run. The sheer amount of data generated in UM hampers its use in patient diagnostics because manual interpretation of all features is not feasible. Here, we describe the application of a pathway‐based metabolite set enrichment analysis method to prioritise relevant biological pathways in UM data. We validate our method on a set of 55 patients with a diagnosed inherited metabolic disorder (IMD) and show that it complements feature‐based prioritisation of biomarkers by placing the features in a biological context. In addition, we find that by taking enriched pathways shared across different IMDs, we can identify common drugs and compounds that could otherwise obscure genuine disease biomarkers in an enrichment method. Finally, we demonstrate the potential of this method to identify novel candidate biomarkers for known IMDs. Our results show the added value of pathway‐based interpretation of UM data in IMD diagnostics context.  These approaches are generally very specific, testing the activity of a single enzyme or a preselected set of metabolites. The high heterogeneity, immediate postnatal presentation, and nonspecific symptomology of IMDs make the selection of an adequate panel of targeted metabolic tests challenging. Important diagnostic biomarkers can be absent from the panel if they were identified recently, and there is no opportunity for the detection of novel biomarkers.Recent advances in metabolomic methods have enabled the use of untargeted metabolomics (UM) as a viable alternative to targeted approaches.The biggest challenge in the identification of novel biomarkers in NGMS data is the prioritisation of relevant metabolites from the hundreds or thousands that are identified. Prioritisation based on peak intensity, fold change, and case–control statistics is useful, but disease relevant metabolites can sometimes be concealed by metabolite aberrations caused by confounding factors such as diet or medication.To this end, we implemented a metabolite set enrichment analysis (MSEA) method,22.1The data used in this study were described previously by Coene et al.2.2MSConvert was used to convert data to mzML format (ProteoWizard version 3.0.19161).2.3α < 0.05) between the patient and controls.An in‐house diagnostic pipeline as described by Coene et al.2.4+, mNa+, mH− and mCl− adducts in their respective ion modes. Features were then assigned putative metabolite annotations by searching the Human Metabolome Database Neutral masses were estimated by correcting the feature m/z for mH2.5github.com/Genome-Bioinformatics-RadboudUMC/MetaboliteSetEnrichmentAnalysis. Statistical enrichment of pathways was computed using a one‐sided Fisher's exact test comparing aberrant pathway‐associated and pathway‐unassociated features with their non‐aberrant counterparts. Only pathways associated with >1 aberrant features were considered. Fisher's p‐values were Bonferroni–Holm corrected for multiple testing by the total number of tested pathways, and those that remained significant at p < .05 were retained.MSEA was built using Java version jre1.8.0_121. The code and README will be publicly available on GitHub: 2.6p‐value; (3) when available, give preference to pathways categorised as “Metabolic” in SMPDB and “Metabolism” in KEGG.Many of the pathways available in SMPDB and KEGG contain partially overlapping processes or share many metabolites. This overlap can cause redundant enriched pathways in MSEA output, for example, due to a single set of features enriching several pathways with a high degree of similarity to each other. To group pathways enriched by the same set of metabolites, a clustering approach was applied. Clustering was performed based on shared aberrant metabolites, and enriched pathways with 100% overlap (in either direction) were clustered together. Metabolite IDs between HMDB and KEGG are not well connected; therefore, pathways from either database were clustered separately. But pathway clusters with similar aberrant features are mentioned in the output file. This clustering step serves to group pathways with the same root metabolite set driving enrichment. For each cluster of pathways, we selected the most descriptive pathway through following steps: (1) select pathways that are associated to the highest number of aberrant features; (2) choose the pathway with lowest 2.7For each of the 29 IMDs included in this study one or more metabolite biomarkers are known Tables , S3B.22.8p‐value and then by feature intensity, with aberrant features in the most enriched pathways or pathway clusters prioritised. Across all three sets, the highest ranked feature associated with a biomarker metabolite was selected. Feature intensity was chosen for ranking as receiver operating characteristic curves showed that it was a better binary classifier of biomarker status in our data than fold change and p‐value . (2) The feature's presence was checked in the raw data, to determine potential alignment errors. (3) The laboratory technician's expertise in biochemistry and clinical chemistry was used to assess the likelihood of the metabolite annotation.The cystathionine ß‐synthase (CBS)‐deficiency patient was manually analysed to demonstrate the results given by MSEA in more detail. Incorrect metabolite annotations were determined for the CBS‐deficiency patient as following: (1) When the retention time (RT) for a metabolite was known on our LC system , and 2 Agilent instrument types . Sample counts per IMD are summarised in Table To calculate enrichment in biological pathways, we used the primary pathways from the Small Molecule Pathway Database  and the human pathways from Kyoto Encyclopedia of Genes and Genomes . The method, process, and output of MSEA described here is summarised in Figure p‐value followed by feature intensity gave a median feature biomarker rank of 4 out of a median 519 aberrant pathway‐associated feature‐metabolite pairs per sample . The distributions of biomarker ranks are shown in Figure p = 0.032; Figure p = 0.13, Figure p = 0.00075, Figure S4). Amongst enriched pathways and pathway clusters, the median biomarker rank was 1  and l‐methionine sulphoxide , both were present in pathways  and third (ESIneg_743) positions of the feature list  position of the feature list. Pathway feature rank and cluster feature rank  and 3‐sulfinoalanine (KEGG ID: C00606), were incorrect results as they were either misannotated or misaligned (Table Looking at aberrant metabolites for the CBS‐deficiency sample in the cysteine and methionine metabolism pathway (KEGG ID: hsa00270) Figure , we founed Table D–F. The We determined whether such putative novel biomarkers were also present in other samples of our cohort by systematically detecting all aberrant non‐biomarker metabolites present in an enriched biomarker pathway Table . We repop = 0.016). By binning enriched pathways based on the number of IMDs that share them, we were also able to detect 40 enriched pathways shared across 9 IMDs  other pathways enriched. To better understand these pathways, we explored the distribution of all enriched pathways across the 29 IMDs in our dataset. In addition, we showed that biomarker pathways are significantly more IMD‐specific than non‐biomarker pathways, and were shared across a maximum of 4 IMDs versus 13 for non‐biomarker pathways  registered their informed consent for the possible use of their leftover body fluid samples from clinical diagnostics for laboratory method validation purposes in their electronic patient record, in agreement with institutional and national legislation.FIGURE S1 ROC curve, intensity outperforms absolute fold change and Bonferroni–Holm feature p‐value for the ranking of known biomarkersFIGURE S2. Boxplot of aberrant features versus annotated features versus pathway featuresFIGURE S3. Boxplot biomarkers have a lower row index in MSEA output than in raw feature output sorted by intensityFIGURE S4. Boxplot clustering of enriched pathways promotes easy interpretation and reduces pathways enriched by the same aberrant features.FIGURE S5. Histogram of pathway and clustered pathway biomarker ranksClick here for additional data file.TABLE S1 SamplesClick here for additional data file.TABLE S2 Method parametersClick here for additional data file.TABLE S3 BiomarkersClick here for additional data file.TABLE S4 Cystathionine ß‐synthase deficiency (CBSD)Click here for additional data file.TABLE S5 Pathways enriched for nine different IEMs, mostly nonsteroidal anti‐inflammatory drugs (NSAIDs) tableClick here for additional data file.TABLE S6 3‐Hydroxy‐3‐methylglutaryl‐CoA lyase deficiency (HMCLD) pathway co‐enzymes and metabolitesClick here for additional data file.


Xanthii Fructus (XF), a famous traditional Chinese medicine (TCM), has been widely used in the treatment of rhinitis and other diseases. However, the targets of the main XF components found in the blood after oral administration of XF extract are still unclear. In the current study, a feasible systems pharmacology method was developed to predict these targets. In accordance with our previous research, XF components were selected including cleomiscosin A, myristic acid, succinic acid, xanthosine, sitostenone, emodin, apigenin, and chrysophanol. Three components, namely emodin, apigenin, and chrysophanol, failed to be detected with target proteins, thus the other five components, namely cleomiscosin A, myristic acid, succinic acid, xanthosine and sitostenone, were eventually chosen for further systematic analysis. Ninety-nine target proteins and fifty-two pathways were found after a series of analyses. The frequency of some target proteins was much higher than that of others; high frequencies were obtained for P15086, P07360, P07195, MAOM_HUMAN (P23368), P35558, P35520, ACE_HUMAN (P12821), C1S_HUMAN (P09871), PH4H_HUMAN (P00439), FPPS_HUMAN (P14324), P50613, P12724, IMPA1_HUMAN (P29218), HXK1_HUMAN (P19367), P14061, and MCR_HUMAN (P08235). The frequency of eight pathways was also high, including Generic Transcription Pathway, RNA Polymerase II Transcription, Metabolism, Metabolism of steroids, Gene expression (Transcription), Cellular responses to stress, Platelet activation, signaling and aggregation, Signaling by Receptor Tyrosine Kinases, and Cellular Senescence. This study identified a common pathway – the Metabolism pathway – for all five XF components. We successfully developed a network pharmacology method to predict the potential targets of the main XF components absorbed in serum after oral administration of XF extract. This paper developed a network pharmacology method to predict the potential pathways targeted by oral administration of XF extract. TCM has exerted synergistic effects in the treatment of complex diseases with its multi-component properties and multi-target functioning, creating a difficult challenge for its modernization. Recently, network pharmacology has risen rapidly in the research field. It explores drug targets by finding the overall correlation between drugs and diseases when combined with systems biology, multidirectional pharmacology and multidisciplinary technology, such as in network analysis, computational biology and disease-gene–drug network construction. It could therefore provide a new approach for overcoming barricades in the way of TCM modernization.1–7Network pharmacology, based on the network of “disease-gene-target-drug” interactions, is a way of revealing the synergistic effects of complex drugs on the human system and finding efficient and low toxicity multi-target new drugs at the network level by observing the intervention of drugs and their impact on disease. With information databases such as gene network libraries, protein network libraries, disease network libraries, and drug network libraries, and systematic spectrogram data analysis, network pharmacology is able to reveal mysterious disease–disease, disease phenotype-target protein, target protein–drug and drug–drug linkages.via the research ideas of network pharmacology.8–11Uncovering the material basis of TCM is the key and precondition for TCM quality control, which puts it at the core of TCM modernization. In a network pharmacology study, drug–drug networks can be constructed based on the similarities in the structures and efficacies of different drugs. In the process of TCM modernization, some researchers have achieved good initial results in exploring the essential properties of TCMs and revealing their comprehensive overall effects on multi-pathways, multi-targets and multi-components Xanthii Fructus (XF) is the ripe fruit of Xanthium sibiricum Patr. XF is used for the treatment of cramping, numbness of the limbs, ulcers, sinusitis, catarrhs, and pruritus, for its function in smoothing nasal orifices and eliminating wind-dampness.12 In modern clinic application, XF is commonly used for the treatment of rhinitis. Particularly when combined with Magnoliae flos, mint and other Chinese medicines, XF has enhanced effects in curing chronic rhinitis, allergic rhinitis and other rhinitis.132.2.1In our previous study (unpublished), components such as myristic acid, succinic acid, xanthosine, emodin, apigenin, and chrysophanol were identified from serum samples after oral administration of XF extracts. Components such as cleomiscosin A and sitostenone were filtered using the traditional Chinese medicine systems pharmacology (TcmSP™) database, and the parameters were set as follows: oral bioavailability (OB) ≥ 30%, drug-likeness (DL) ≥ 0.18. The structures of the components mentioned above are shown in 2.2Firstly, the MDL SD (*.sdf) type files of the above active ingredients were searched using the PubMed database. Secondly, targets, including information like the target name, matching value, target protein abbreviation, function, disease and applicable results related to the modified compound, could be predicted by importing each component file in *sdf format into the PharmMapper database. The top 20 high-matching targets, by value, were used as the TCM target proteins related to the components. The targets were then searched for in the UniProt database to identify human-related target codes.2.3P value was less than 0.01.The retrieved target protein information was analyzed using the Reactome database to obtain the result of the related pathway “pathwayIdexByPathway_kegg”. A pathway was selected as reliable when its 2.4The predicted targets of five chemical components of XF, namely cleomiscosin A, myristic acid, succinic acid, xanthosine and sitostenone, were recorded in excel tables titled as ‘component-protein’ and ‘protein-pathway’. The tables were imported into Cytoscape software to construct the main effect components of the XF-target-pathway network. The network was mainly composed of three types of nodes: effect component, protein and pathway. The effect components and their related target proteins, and the proteins and their related pathways were all side-linked. When the target protein of the effect component was the same as the target protein of the pathway, the effect component was side-linked to the pathway. A complete network diagram was built by the establishment of connections including effect component-protein-pathway, effect component-protein-effect component, pathway-protein-pathway, protein-effect component-protein and other four kinds of connection. The whole framework, based on the active component strategy of system pharmacology, is shown in 3.3.1Eight components in XF were initially selected to uncover potential target proteins. Of these, five components, namely cleomiscosin A, myristic acid, succinic acid, xanthosine, and sitostenone, were successfully analyzed. A total of 99 target proteins were related to these five XF components as shown in 3.2The potential pathway information for the five effect components in XF is shown in 3.3An effect component-target-pathway network model was established using Cytoscape software, and the relationship between the 5 components, 99 targets and 52 pathways is shown in via TRKA from the plasma membrane (Pw23), Reversible hydration of carbon dioxide (Pw24), O2/CO2 exchange in erythrocytes (Pw25), erythrocytes take up carbon dioxide and release oxygen (Pw26), cellular responses to stress (Pw27), MyD88 cascade initiated on plasma membrane (Pw28), Toll Like Receptor 10 (TLR10) Cascade (Pw29), Toll Like Receptor 5 (TLR5) Cascade (Pw30), TRAF6 mediated induction of NFkB and MAP kinases upon TLR7/8 or 9 activation (Pw31), platelet activation, signaling and aggregation (Pw32), oxidative stress induced senescence (Pw33), MyD88 dependent cascade initiated on endosome (Pw34), Toll Like Receptor 7/8 (TLR7/8) Cascade (Pw35), MyD88:Mal cascade initiated on plasma membrane (Pw36), Toll Like Receptor TLR6:TLR2 Cascade (Pw37), Netrin-1 signaling (Pw39), Toll Like Receptor 9 (TLR9) Cascade (Pw40), Toll Like Receptor 3 (TLR3) Cascade (Pw41), Toll Like Receptor TLR1:TLR2 Cascade (Pw42), Toll Like Receptor 2 (TLR2) Cascade (Pw43), TRIF(TICAM1)-mediated TLR4 signaling (Pw44), MyD88-independent TLR4 cascade (Pw45), Regulation of TP53 Activity (Pw48), Signaling by Receptor Tyrosine Kinases (Pw49), Cellular Senescence (Pw50) and Interleukin-12 family signaling (Pw52).Cleomiscosin A was related to the following pathways: nuclear receptor transcription pathway (Pw1), activation of the AP-1 family of transcription factors (Pw2), MAPK targets/nuclear events mediated by MAP kinases (Pw3), p38MAPK events (Pw4), Generic Transcription Pathway (Pw5), Transcriptional regulation by RUNX2 (Pw6), Signalling to RAS (Pw7), RNA Polymerase II Transcription (Pw8), Regulation of TP53 Activity through Phosphorylation (Pw9), Metabolism (Pw10), Nuclear Events (kinase and transcription factor activation) (Pw11), MAP kinase activation in TLR cascade (Pw14), erythrocytes take up oxygen and release carbon dioxide (Pw15), Signalling to ERKs (Pw17), Gene expression (Transcription) (Pw18), Interleukin-17 signaling (Pw19), Gene and protein expression by JAK-STAT signaling after Interleukin-12 stimulation (Pw21), DSCAM interactions (Pw22), NGF signalling via TRKA from the plasma membrane (Pw23), Cellular responses to stress (Pw27), MyD88 cascade initiated on plasma membrane (Pw28), Toll Like Receptor 10 (TLR10) Cascade (Pw29), Toll Like Receptor 5 (TLR5) Cascade (Pw30), TRAF6 mediated induction of NFkB and MAP kinases upon TLR7/8 or 9 activation (Pw31), Platelet activation, signaling and aggregation (Pw32), Oxidative Stress Induced Senescence (Pw33), MyD88 dependent cascade initiated on endosome (Pw34), Toll Like Receptor 7/8 (TLR7/8) Cascade (Pw35), MyD88:Mal cascade initiated on plasma membrane (Pw36), Toll Like Receptor TLR6:TLR2 Cascade (Pw37), Spry regulation of FGF signaling (Pw38), Toll Like Receptor 9 (TLR9) Cascade (Pw40), Toll Like Receptor 3 (TLR3) Cascade (Pw41), Toll Like Receptor TLR1:TLR2 Cascade (Pw42), Toll Like Receptor 2 (TLR2) Cascade (Pw43), TRIF(TICAM1)-mediated TLR4 signaling (Pw44), MyD88-independent TLR4 cascade (Pw45), Signaling by Receptor Tyrosine Kinases (Pw49) and Cellular Senescence (Pw50).Myristic acid was related to the following pathways: nuclear receptor transcription pathway (Pw1), Activation of the AP-1 family of transcription factors (Pw2), MAPK targets/nuclear events mediated by MAP kinases (Pw3), p38MAPK events (Pw4), Generic Transcription Pathway (Pw5), Transcriptional regulation by RUNX2 (Pw6), Signalling to RAS (Pw7), RNA Polymerase II Transcription (Pw8), Metabolism (Pw10), Nuclear Events (kinase and transcription factor activation) (Pw11), RUNX2 regulates osteoblast differentiation (Pw12), Metabolism of steroids (Pw13), MAP kinase activation in TLR cascade (Pw14), RUNX2 regulates bone development (Pw16), Signalling to ERKs (Pw17), Gene expression (Transcription) (Pw18), Interleukin-17 signaling (Pw19), NGF signalling via TRKA from the plasma membrane (Pw23), Platelet activation, signaling and aggregation (Pw32), Spry regulation of FGF signaling (Pw38), Netrin-1 signaling (Pw39), Metabolism of Angiotensinogen to Angiotensins (Pw47) and Signaling by Receptor Tyrosine Kinases (Pw49).Succinic acid was related to the following pathways: p38MAPK events (Pw4), Generic Transcription Pathway (Pw5), Transcriptional regulation by RUNX2 (Pw6), Signalling to RAS (Pw7), RNA Polymerase II Transcription (Pw8), Metabolism (Pw10), RUNX2 regulates osteoblast differentiation (Pw12), Metabolism of steroids (Pw13), RUNX2 regulates bone development (Pw16), Signalling to ERKs (Pw17), Gene expression (Transcription) (Pw18), NGF signalling Xanthosine was related to the following pathways: Metabolism (Pw10), Gene and protein expression by JAK-STAT signaling after Interleukin-12 stimulation (Pw21), Defective HK1 causes hexokinase deficiency (HK deficiency) (Pw46) and Interleukin-12 family signaling (Pw52).Sitostenone was related to the following pathways: nuclear receptor transcription pathway (Pw1), Generic Transcription Pathway (Pw5), transcriptional regulation by RUNX2 (Pw6), Signalling to RAS (Pw7), RNA Polymerase II Transcription (Pw8), Metabolism (Pw10), Nuclear Events (kinase and transcription factor activation) (Pw11), RUNX2 regulates osteoblast differentiation (Pw12), Metabolism of steroids (Pw13), RUNX2 regulates bone development (Pw16), Gene expression (Transcription) (Pw18), Interleukin-17 signaling (Pw19), Cellular responses to stress (Pw27), Signaling by Receptor Tyrosine Kinases (Pw49) and HSP90 chaperone cycle for steroid hormone receptors (SHR) (Pw51).We were surprised to find that the five components have one common pathway – the Metabolism pathway (Pw10). Nine other pathways occurred frequently including Generic Transcription Pathway (Pw5), RNA Polymerase II Transcription (Pw8), Metabolism (Pw10), Metabolism of steroids (Pw13), Gene expression (Transcription) (Pw18), Cellular responses to stress (Pw27), Platelet activation, signaling and aggregation (Pw32), Signaling by Receptor Tyrosine Kinases (Pw49) and Cellular Senescence (Pw50).4.The PharmMapper database can be used to search for potential targets based on small active molecules. This database uses a pharmacophore matching method to obtain drug point information by rapidly searching four major databases. This database is based on 7000 pharmacophore models and can cover most clinical indications.via the same target, and also can be connected with the same pathways with different targets. Different components can produce the same effect through different ways, and also can offer multi-target synergy.According to the network pharmacological prediction of the five components in XF, all five components can be connected with the same pathway Interestingly, this predicted common pathway is consistent with the result we got from the metabolic pathway analysis experiment (unpublished), which indicates that this result is reliable although it still requires further verification.5.In this paper, a network pharmacology method has been successfully developed to predict the potential targets of the main components absorbed in serum after oral administration of XF extract. When considered alongside our previous anti-allergic rhinitis metabolomics study, the predicted potential targets and the role of the pathways were considered to have a certain degree of accuracy. This article has established a “multi component-multi target-multi pathway” network model for TCM research, and started to unravel the multidimensional regulatory action of XF, which may provide a reference and basis for studying the molecular mechanism of XF.The authors have declared no conflicts of interest.RA-008-C8RA00186C-s001