Artemisiae Scopariae Herba (ASH) has been widely used as plant medicine in East Asia with remarkable antitumor activity. However, the underlying mechanisms have not been fully elucidated.in silico docking coupled with multiple complex networks was applied to interpret the molecular mechanisms.This study aimed to construct a multi-disciplinary approach to screen topoisomerase I (topo I) inhibitors from ASH extract, and explore the antitumor mechanisms. Bioaffinity ultrafiltration-UFLC-ESI-Q/TOF-MS/MS was used to identify chemical constitution of ASH extract as well as the topo I inhibitors, and Crude ASH extract exhibited toxicogenetic and antiproliferative activities on A549 cells. A series of 34 ingredients were identified from the extract, and 6 compounds were screened as potential topo I inhibitors. Docking results showed that the formation of hydrogen bond and π-π stacking contributed most to their binding with topo I. Interrelationships among the 6 compounds, related targets and pathways were analyzed by multiple complex networks model. These networks displayed power-law degree distribution and small-world property. Statistical analysis indicated that isorhamnetin and quercetin were main active ingredients, and that chemical carcinogenesis-reactive oxygen species was the critical pathway. Electrophoretic results showed a therapeutic effect of ASH extract on the conversion of supercoiled DNA to relaxed forms, as well as potential synergistic effect of isorhamnetin and quercetin.Artemisiae Scopariae Herba on the treatment of tumor. Moreover, the combination of multi-disciplinary methods provided a new strategy for the study of bioactive constituents in medicinal plants.The results improved current understanding of The online version contains supplementary material available at 10.1186/s12906-023-04146-x. Artemisiae Scopariae Herba (ASH) is the dried aerial part of Artemisia scoparia Waldst.et Kit [t.et Kit , which it.et Kit . Chemicat.et Kit . This plt.et Kit . Pharmact.et Kit . Its extt.et Kit , as wellt.et Kit , which wt.et Kit . Severalt.et Kit . In thisNothapodytes nimmoniana (J. Graham) Mabberly exhibited significant antitumor activity in vitro as a topo I inhibitor [Topoisomerase I (topo I), an enzyme involved in the relaxation of supercoiled DNA, is an important biological target in the treatment of tumor , 11. Inhnhibitor . Some ofnhibitor . Additionhibitor . Structunhibitor . Curcuminhibitor . HoweverAstragalus membranaceous and Angelica sinensis in the treatment of diabetic nephropathy (DN), which might treat DN by acting on vascular endothelial growth factor A (VEGFA), tumor Protein P53 (TP53), interleukin-6 (IL-6), tumor necrosis factor (TNF), microtubule affinity regulating kinase 1 (MARK1), etc., and regulate apoptosis, oxidative stress, inflammation, glucose, and lipid metabolism processes [The underlying mechanisms of compounds from medicinal plants against tumor are obscured as complex chemical composition and diverse pathways . Recentlrocesses . These rin silico docking. Multiple complex networks were constructed based on the selected topo I inhibitors, related target proteins and pathways. Characteristics of the networks were further analyzed, designed to clarify the antitumor mechanisms. This study would provide a new strategy for the research of topo I inhibitors originated from medicinal plants.In the present study, an interdisciplinary analysis was performed to screen topo I inhibitors from ASH extract, and explore the underlying mechanisms. The topo I inhibitors were identified by bioaffinity ultrafiltration-UFLC-ESI-Q/TOF-MS/MS. Combination mechanisms of hydroxygenkwanin, chlorogenic acid, and topo I were revealed using Artemisiae Scopariae Herba was obtained from Baoji Chenguang Biotechnology Co., Ltd . Samples were collected at the seedling stage. Plant species was authenticated by Prof. Shifeng Ni from Northwest University, Key Laboratory of Resource Biology and Biotechnology in Western China, Northwest University, Xi’an, China. A voucher specimen (BJWLXY-CC-SKLP220301) was deposited at Shaanxi Key Laboratory of Phytochemistry, Baoji University of Art and Sciences, Baoji, China. Dried ASH powders (100 g), consisted of leaf and stem of the plant, were treated with ultrasonic-assisted extraction method using 50% ethanol for 30 min, and repeated for another two times. Then, the 50% ethanol solution was leached with ethyl acetate (saturated by water). Solvents were subsequently removed by a vacuum centrifugal concentrator. The residues were stored at -80℃ for 2 weeks before use.The above-ground part of crude DNA topoisomerase I  and sulphorhodamine B (SRB) were acquired from Sigma-Aldrich . Amicon Ultra-0.5 centrifugal filters (3 kDa) and ultrapure water were obtained from Millipore Co. Ltd. . Methanol and Acetonitrile (HPLC grade) were purchased from Merck . Other chemicals were analytical grade and supplied by Aladdin industrial corporation . Fetal bovine serum (FBS), plasmid pBR322, 4 S Green Plus nucleic acid dye and TAE buffer were obtained from Sangon Biotech Co., Ltd. . A549 cells were supplied by Beyotime Institute of Biotechnology . DNA topoisomerase I  was acquired from Sigma-Aldrich .v/v), glutamine (2 mM), and antibiotics  in a 5% CO2 atmosphere at 37 °C. Cells were seeded overnight in a 96-well plate (6 × 103 cells/well), and treated with ASH extract, quercetin (positive control) and dimethyl sulfoxide  for 48 h. SRB assay was then performed to measure cell viability according to previous report [50 was acquired by modified Karber’s method [The A549 cells  were maintained in Dulbecco’s modified eagle medium (DMEM) supplemented with 10% fetal bovine serum  were incubated with topo I  and 460 µL buffer (pH 7.9) at 37 °C for 30 min. Then the samples were transferred into ultrafiltration tubes (3 kDa) and centrifuged at 14,000 g for 20 min. The tubes were flushed using 500 µL buffer for two times to remove the compounds not bound to the enzyme. Subsequently, methanol  was added to the samples and incubated for 10 min, then centrifuged at 14,000 g for 20 min, which also repeated twice. The filtrate were combined and processed with 0.22 μm filter membrane.XR UFLC-4600 Q/TOF system , equipped with a Shim-pack XR-ODS column . Detection parameters were set according to our previous report with minor modifications [m/z. Data were collected by Analyst , and processed with PeakView  and MasterView . Compounds were identified by Natural Products HR-MSMS Spectral Library . Mass tolerance of the accurate molecular weight was set as ± 5 ppm. Furthermore, MS/MS fragment patterns were analyzed to verify the results.The samples were analyzed by a LC-20ADications . The injStructures of potential topo I inhibitors from ASH were saved in Mol format. PDB code of Human DNA Topoisomerase I was 1T8I. Docking was performed by Discovery Studio (BIOVIA). Original ligands and water were removed from the complex, and that hydrogen atoms were added. Proteins were refined with CHARMm force field. Active binding sites were Arg 488, Lys 532, Arg 590, His 632, and Tyr 723 according to the literature . Pose clhttps://sea.bkslab.org/) [https://david.ncifcrf.gov/, P < 0.05). Multiple complex networks were then built, consisted of large amounts of nodes and edges. Nodes represent the topo I inhibitors, related target proteins or involved pathways, and that edges exhibit interactions between them. The networks were visualized using Pajek .To explore the underlying mechanisms, multiple complex networks were build based on the selected topo I inhibitors from ASH, their target proteins and involved pathways. Targets were predicted by Similarity ensemble approach (SEA) (ab.org/) , on basii were involved in pathway node j, there is a connection between i and j. Besides, if two inhibitors nodes i1, i2 had common targets, or a target is both involved in pathway nodes j1, j2, a connection emerges between them. Then, three subnetworks were extracted from IPI network for different interpretations, including the ingredient-ingredient interaction (TTI) network, ingredient-pathway bimodal (IPB) network, and pathway-pathway interaction (PPI) network. TTI network describes interactions between inhibitors connected with identical targets. IPB network exhibits connections between the inhibitors and pathways possessing common targets. Besides, the PPI network characterizes relationships between pathways involved identical targets.First, an ingredient-pathway interaction (IPI) network was constructed. If targets of an inhibitor node k), the average degree < k>, degree distribution P(k), diameter (D), average path length (L), clustering coefficient (C), and three centrality indicators, degree centrality (Cd), betweenness centrality (Cb), and closeness centrality (Cc). Part of these parameters and their formulas were listed in Table The network characteristics were calculated to screen critical nodes, using MATLAB 2016a (The MathWorks Inc.). Most parameters have been described in our previous work , includiInhibition of topo I for ASH extract and the selected ingredients was tested by agarose gel electrophoresis. The reaction mixtures contained plasmid pBR322 (0.175 µg), the samples (dissolved in DMSO), topo I (0.5 U), diluted Topo I buffer , BSA . Camptothecin was applied as positive control. The mixtures were incubated at 37 °C for half an hour. Electrophoresis was conducted on 1% agarose gel . The gels were dyed in 4 S Green Plus nucleic acid stain  for 30 min and discolored for another 30 min, then visualized using a G:BOX Chemi XRQ gel imaging system .50 = 113.64 µg/mL). Morphological assay of cell death was performed using Hoechst 33,342 staining for fluorescence microscopy. A549 cells were exposed to gradient concentrations of ASH extract for 24 h. The nuclear was slightly condensed at 125 µg/mL ASH extract, whereas untreated controls showed evenly dispersed blue fluorescence. Moreover, lots of chromatin condensation or dense stained debris was found with increasing concentration of the sample , generating a product ion of m/z 329.0233. Furthermore, the ion m/z 329.0233 produces fragment ions at m/z 121.0124, 287.0167 and 312.0234. The product ion m/z 287.0167 derives from loss of C2H2O (42 Da). It also loses a molecule of H2O (18 Da) to produces a fragment at m/z 269.0097. Subsequently, one or two neutral loss of CO (28 Da) from m/z 269.0097 produces ions at m/z 241.0176 and m/z 212.0175. In addition, the production ion at m/z 183.0288 is attributed to 1,3 A+, and that substituent groups of two OH and an OCH3 are located in A-ring. Therefore, compound 11 was finally identified as eupatilin, which was also supported by previous report [Chemical composition of ASH extract was investigated using UFLC-ESI-Q/TOF-MS/MS. A total of 34 compounds were identified. These ingredients contained 3 coumarins, 10 flavones, 4 organic acids, 5 alkaloids, 4 terpenes, and other compounds . It proved that all the 6 inhibitors from ASH extract were tightly connected with topo I. Their specific binding mechanisms were further analyzed in detail. Hydroxygenkwanin and chlorogenic acid were selected as examples. As shown in Fig. Differences in functional groups of natural products and stereochemical conformation have significant influence on topo I inhibition . In thisNatural products originated from natural products work through regulation of many key target proteins and metabolic pathways . MultiplC = 0.91), indicating a strong tendency to aggregate. Additionally, an ingredient or pathway node was more likely to connect with the same kind of nodes. A relatively small average path length of the IPI network (L = 1.21) suggested that influences on nodes would spread rapidly to rest of the network, and that regulation of critical nodes could affect the whole network. Figure k. It was found that degrees of most nodes were close to 80, and that average degree of the network was 74.85. The nodes were more likely to be involved in small clusters. These results displayed that a few topo I inhibitors and certain pathways contribute most to the antitumor effect of ASH extract.Statistical properties of IPI network were analyzed in detail Table . The resThree subnetworks were extracted from IPI network for further interpretation, including ingredient-ingredient interaction (TTI) network, ingredient-pathway bimodal (IPB) network, and pathway-pathway interaction (PPI) network. As Fig. k = 90), that linked to all the 90 pathway nodes. As changes in critical positions of the network are always more important than those in marginal or relatively isolated positions [The ingredient-pathway bimodal (IPB) network was extracted to investigate relationships between topo I inhibitors from ASH extract and pathways related to the same target proteins. This network exhibited as a bipartite graph Fig. , consistositions , the twoositions . It mighositions . Quercetositions . Variousositions . These sk). Clustering coefficient of this network was relatively high (C = 0.92), coupled with a small average path length (L = 1.24). It showed a small-world character of the network. This property suggested that many small and loosely linked, hierarchical clusters existed in the PPI network, and formed to be large, less cohesive units. In other words, a few pathway nodes were keys to the overall network. Degree centrality (Cd), betweenness centrality (Cb), and closeness centrality (Cc) were then analyzed to evaluate significances of all the nodes . It was supposed to be critical pathway of the antitumor effect of topo I inhibitors from ASH extract.Interrelationships among pathway nodes were also extracted from IPI network, shown as a pathway-pathway interaction (PPI) network Fig. , constitdes Fig. b. The paThis pathway displays that a group of carcinogens induce cancer via nongenotoxic mechanisms. The biochemical modes of action for nongenotoxic carcinogens are diverse, one example of which is induction of oxidative stress. Reactive oxygen species (ROS) are also generated due to induction of various cytochrome P450 isoenzymes during detoxification of chemical carcinogens. Increased ROS generation often has been linked to DNA damage that can lead to mutations and may, therefore, play an important role in the initiation and progression of multistage carcinogenesis. Hsa05208 has been considered as key to many kinds of tumors , such asw/w) according to the LC-MS results, both showed a similar behavior to camptothecin. Besides, individual addition of quercetin had remarkable inhibitory effect, whereas that of isorhamnetin was relatively weak. These results confirmed the inhibitory effect of ASH extract against topo I, and suggested a potential synergistic effect of isorhamnetin and quercetin, which might be on account of coordination and balance of natural products on topo I-DNA complex [A topo I inhibitory binding assay was further performed to validate the results. The inhibition of ASH extract, the main active ingredients isorhamnetin and quercetin, and their mixtures against topo I were evaluated using agarose gel electrophoresis. As shown in Fig.  complex .Artemisiae Scopariae Herba. They often focused on of specific constituents or pathways. For example, Prasad et al. reported that cirsiliol suppressed epithelial to mesenchymal transition in B16F10 malignant melanoma cells through alteration of the PI3K/Akt/NF-κB signaling pathway [in silico docking and multiple complex networks. First, the application of ultrafiltration LC-MS could facilitate the separation of ligands binding to target proteins from natural products [in silico docking, which determine the topo I inhibitory activity [Some previous researches have explored antitumor effect of  pathway . Hyperos pathway . A comprproducts . On the activity . Finallyactivity . CombinaCurrently, natural topo I inhibitors used in clinical treatment of tumors are mainly originated from camptothecin and its derivatives, including topotecan against ovarian and cervical cancers, irinotecan against colorectal and pancreatic cancers, as well as belotecan, widely used against small cell-lung cancer . The preArtemisiae Scopariae Herba extract exhibited toxicogenetic and antiproliferative activities on A549 cells. A series of 34 ingredients were identified, and 6 of them were screened as potential topo I inhibitors. Their fragment patterns in ESI source were analyzed in detail. Molecular docking showed that the formation of hydrogen bond and π-π stacking contributed most to the binding of topo I and the inhibitors. The inhibitors, related targets and pathways were analyzed in multiple complex networks model, which showed a power-law degree distribution and small-world property. Network analysis revealed that isorhamnetin and quercetin were main active ingredients, and that chemical carcinogenesis-reactive oxygen species (hsa05208) was the critical pathway. In addition, the ASH extract exhibited therapeutic effect on the conversion of supercoiled DNA to relaxed forms, and a potential synergistic effect of isorhamnetin and quercetin was also observed, which would be studied in our next study. The results improved current understanding of Artemisiae Scopariae Herba on the treatment of tumor. The combination of bioaffinity ultrafiltration-UFLC-ESI-Q/TOF-MS/MS, in silico docking and multiple complex networks provided a new strategy for the study of bioactive constituents in medicinal plants.This research presents one of the first multi-disciplinary surveys of topoisomerase I inhibitors from medicinal plants. The Additional file 1. Results of molecular docking for topo I and potential inhibitors from Artemisiae Scopariae Herba extractAdditional file 2. Collection of target proteins and pathways for topoisomerase I Inhibitors from ASH extract


Glioblastoma multiforme (GBM) is one of the most common primary malignant brain tumors. Yi Qi Qu Yu Jie Du Fang (YYQQJDF) is a traditional Chinese medicine (TCM) prescription for GBM. The present study aimed to use a network pharmacology method to analyze the underlying mechanism of YQQYJDF in treating GBM.GBM sample data, active ingredients and potential targets of YQQYJDF were obtained from databases. R language was used to screen differentially expressed genes (DEGs) between GBM tissues and normal tissues, and to perform enrichment analysis and weighted gene coexpression network analysis (WGCNA). The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was used to perform a protein‒protein interaction (PPI) analysis. A Venn diagram was used to obtain the core target genes of YQQYJDF for GBM treatment. Molecular docking was used to verify the binding between the active ingredient molecules and the proteins corresponding to the core target genes. Cell proliferation assays and invasion assays were used to verify the effect of active ingredients on the proliferation and invasion of glioma cells.P < 0.01).A total of 73 potential targets of YQQYJDF in the treatment of GBM were obtained. Enrichment analyses showed that the biological processes and molecular functions involved in these target genes were related to the activation of the G protein-coupled receptor (GPCR) signaling pathway and the regulation of hypoxia. The neuroactive ligand‒receptor pathway, the cellular senescence pathway, the calcium signaling pathway, the cell cycle pathway and the p53 signaling pathway might play important roles. Combining the results of WGCNA and PPI analysis, five core target genes and their corresponding four core active ingredients were screened. Molecular docking indicated that the core active ingredient molecules and the proteins corresponding to the core target genes had strong binding affinities. Cell proliferation and invasion assays showed that the core active ingredients of YQQYJDF significantly inhibited the proliferation and invasion of glioma cells (The present study predicted the possible active ingredients and targets of YQQYJDF in treating GBM, and analyzed its possible mechanism. These results may provide a basis and ideas for further research.The online version contains supplementary material available at 10.1186/s12906-023-04174-7. Glioblastoma multiforme (GBM) is one of the most common human primary malignant brain tumors . At presTraditional Chinese medicine (TCM) has been used to treat brain tumors for thousands of years. The TCM classic “Zhongzang Jing”, which was written during the Han Dynasty in ancient China around 200 AD), records the treatment of brain tumors with TCM. Many ingredients of TCM have been proven to have therapeutic effects on GBM 00 AD, re. Yi Qi QNetwork pharmacology is a method of understanding drug actions and interactions with multiple targets . It usesTranscriptome sequencing data and corresponding clinical information of The Cancer Genome Atlas (TCGA)-GBM cohort were obtained from the TCGA database  using thP < 0.01 and | log2 (fold change) |> 2. The “ggplot” package was used to plot the volcano plot.Using the “limma” and “DESeq2” packages in R software, the differentially expressed mRNAs were identified between five normal and 169 tumor tissues in the TCGA-GBM cohort based on adjusted standards of The DEG data and clinical data of the TCGA-GBM cohort were used to construct the weighted gene coexpression network using the “WGCNA” package in R software to identify DEGs related to the overall survival (OS) of patients with GBM. The soft threshold power was seven.Hedysarum Multijugum Maxim.), “Chuangxiong” (Chuanxiong Rhizoma), “Banxia” (Arum Ternatum Thunb.), “Baihuashecao” (Hedyotis Diffusae Herba), “Gancao” (Liquorice), “Shancigu” (Pseudobulbus Cremastrae Seu Pleiones), “Shichangpu” (Acoritataninowii Rhizoma) and “Taizishen” (Pseudostellariae Radix). The related ingredients were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) [The formulas of YQQYJDF were “Huangqi” ( (TCMSP)  and Trad (TCMSP) . IngrediThe targets of the active ingredients of YQQYJDF were retrieved from the TCMSP database. The gene symbols and Entrez ID of targets were obtained by the “org.Hs.eg.db” package in R software.The target genes of YQQYJDF active ingredients and DEGs of GBM were matched using a Venn diagram with Venny 2.1 , and thep value cutoff = 0.05 and q value cutoff = 0.05 were used to perform Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) [The “clusterProfiler”, “org.Hs.eg.db”, “enrichplot” and “pathview” packages in R software with s (KEGG)  signal pThe STRING database  was usedThe core target genes of YQQYJDF for the treatment of GBM were obtained by using the Venn diagram to intersect genes closely related to the patient’s prognosis obtained from WGCNA with the target genes of the active ingredients of YQQYJDF, the DEGs of GBM and the genes with a degree value greater than 10 in the PPI network.The core active ingredients of YQQYJDF were obtained by matching the core target genes of YQQYJDF for GBM treatment and the target genes of YQQYJDF active ingredients. The 2D structure of the active ingredient small molecule ligand was obtained from the Drugbank database . The 3D 2. Luteolin, quercetin and kaempferol were purchased from Sigma‒Aldrich . Stigmasterol was purchased from Avanti Polar Lipids, Inc. . All drugs were dissolved in dimethyl sulfoxide (DMSO) and stored at -20 °C.The U87MG human glioblastoma cell line was purchased from the Cell Resource Center of the Chinese Academy of Sciences . U87 cells were cultured in Dulbecco’s modified Eagle’s medium  with 10% fetal bovine serum (FBS)  at 37 °C in the presence of 5% CO3 cells/well in 24-well plates. The cells were incubated in medium containing 40 μmol/L luteolin, quercetin, kaempferol or stigmasterol. The control group was treated with an equal volume of the solvent (DMSO) in the culture medium. After 24 h, glioma cells were then analyzed by CCK-8 assay according to the manufacturer’s instructions. Briefly, 50 μL CCK-8 solution was added to each well. After incubating at 37 °C for 4 h, the absorbance value at 450 nm was measured.The Cell Counting Kit-8 (CCK-8)  assay was used to analyze the proliferation of glioma cells. U87 cells were seeded at a density of 5 × 104 cells/well in Transwell chambers  precoated with 50 μl Matrigel (BD Biosciences) diluted with culture medium. Serum-free culture medium containing 40 μmol/l luteolin, quercetin, kaempferol or stigmasterol was used to incubate the U87 cells and medium containing 20% FBS was used in the lower chamber as the chemoattractant. After 24 h, noninvasive cells were removed with cotton swabs, and the invasive cells were dyed with 0.1% crystal violet (Sigma‒Aldrich) and analyzed directly under an inverted fluorescent microscope . The average cell number in five random visual fields was considered to represent the number of invasive cells of each group.U87 cells were seeded at a density of 5 × 10P value < 0.05 indicated a statistically significant difference.Values are presented as the mean ± standard deviation. Data were analyzed using SPSS 25.0 software . One-way analysis of variance was used to compare the groups, and the least significant difference post hoc test was performed to further assess intergroup differences. A As shown in Fig. P < 0.05).Expression data for 3100 DEGs and the OS data of GBM patients were used to construct a scale-free network by WGCNA. As shown in Fig. After screening, a total of 119 active ingredients and 252 corresponding target genes of YQQYJDF were obtained  terms, 57 enriched cellular component (CC) terms and 94 enriched molecular function (MF) terms. Figure Sixty-eight pathways were obtained through KEGG pathway enrichment analysis. The top 20 pathways are shown in Fig. As shown in Fig. As shown in Fig. Molecular docking of proteins encoded by five core target genes with the corresponding core active ingredients showed that their binding energies were all less than -5.0 kcal/mol, indicating that there are good binding effects between the target proteins and active ingredients Table . The visF = 52.075, P < 0.01).After treatment with the core active ingredients of YYQQJDF, the proliferation ability of U87 glioma cells was detected by CCK-8 assay. As shown in Fig. F = 25.648, P < 0.01)  Fig. .Fig. 9ThAlthough TCM has been used to treat various diseases for thousands of years, it is difficult to gain recognition in the field of modern medicine due to theoretical differences and the lack of direct experimental evidence . For exaAccording to the results, five core target genes and four corresponding active ingredients were obtained. Among the five core target genes, MMP-1 is considered closely related to the malignant degree of glioma  and the In YQQYJDF, the core active ingredients corresponding to these five target genes are luteolin, quercetin, kaempferol and stigmasterol. Luteolin is a natural flavone compound that is considered to have antitumor effects , 38. SomIn addition to the five core target genes, 68 other potential target genes of YQQYJDF for GBM treatment were obtained in this study. According to the results of the GO analysis, many of the biological processes and molecular functions involving in these target genes are related to the activation of the G protein-coupled receptor (GPCR) signaling pathway and the regulation of hypoxia. GPCRs and their downstream signaling targets play a central role in the initiation and progression of tumors . They caThere are still some limitations to this study. First, the ingredients and target genes in this study are mainly from databases. Due to the limitations of data sources and differences in screening strategies, some ingredients or target genes may be omitted. Second, this is a study based on network pharmacology and molecular docking, and the results have been preliminarily verified in vitro. Further in vitro and in vivo experiments are needed to verify these possible mechanisms. These shortcomings will be addressed in further research.In conclusion, the present study involved the prediction of the possible active ingredients and targets of YQQYJDF in the treatment of GBM using network pharmacology and the analysis of its possible mechanism. These results may provide a basis and ideas for further research.Additional file 1: Supplementary Table 1. Genes in WGCNA color modules related to the prognosis of GBM patients.Additional file 2: Supplementary Table 2. Active ingredients and target genes of YQQYJDF.Additional file 3: Supplementary Table 3. YQQYJDF targets in the treatment of GBM.


Diabetic kidney disease (DKD) has been the leading cause of chronic kidney disease in developed countries. Evidence of the benefits of resveratrol (RES) for the treatment of DKD is accumulating. However, comprehensive therapeutic targets and underlying mechanisms through which RES exerts its effects against DKD are limited.Drug targets of RES were obtained from Drugbank and SwissTargetPrediction Databases. Disease targets of DKD were obtained from DisGeNET, Genecards, and Therapeutic Target Database. Therapeutic targets for RES against DKD were identified by intersecting the drug targets and disease targets. GO functional enrichment analysis, KEGG pathway analysis, and disease association analysis were performed using the DAVID database and visualized by Cytoscape software. Molecular docking validation of the binding capacity between RES and targets was performed by UCSF Chimera software and SwissDock webserver. The high glucose (HG)-induced podocyte injury model, RT-qPCR, and western blot were used to verify the reliability of the effects of RES on target proteins.After the intersection of the 86 drug targets and 566 disease targets, 25 therapeutic targets for RES against DKD were obtained. And the target proteins were classified into 6 functional categories. A total of 11 cellular components terms and 27 diseases, and the top 20 enriched biological processes, molecular functions, and KEGG pathways potentially involved in the RES action against DKD were recorded. Molecular docking studies showed that RES had a strong binding affinity toward PPARA, ESR1, SLC2A1, SHBG, AR, AKR1B1, PPARG, IGF1R, RELA, PIK3CA, MMP9, AKT1, INSR, MMP2, TTR, and CYP2C9 domains. The HG-induced podocyte injury model was successfully constructed and validated by RT-qPCR and western blot. RES treatment was able to reverse the abnormal gene expression of PPARA, SHBG, AKR1B1, PPARG, IGF1R, MMP9, AKT1, and INSR.RES may target PPARA, SHBG, AKR1B1, PPARG, IGF1R, MMP9, AKT1, and INSR domains to act as a therapeutic agent for DKD. These findings comprehensively reveal the potential therapeutic targets for RES against DKD and provide theoretical bases for the clinical application of RES in the treatment of DKD. The prevalence of diabetes has been increasing at an alarming rate worldwide. According to reports from the International Diabetes Federation, it is estimated that 537 million adults aged 20–79 years were living with diabetes in 2021 . TherefoSynthetic drugs used to treat DKD are loaded with many side effects , 8, therWith the rapid development of bioinformatics, network pharmacology based on big databases has emerged as a powerful tool to characterize the action mechanisms of complicated drug systems in detail, from the molecular level to the pathway level , 16. AndAs such, the purpose of this study was designed to use the network pharmacology and molecular docking approach to reveal potential therapeutic targets, biological functions, and the signaling pathways of RES against DKD. Multiple lines of evidence indicate that high glucose (HG)-induced podocyte injury and loss is a critical factor in the development of DKD , 20. DecThe flowchart of the present study is illustrated in Fig. DrugBank is a comprehensive, freely available web-based bioinformatics and cheminformatics database containing detailed drug data and comprehensive information on drug targets, drug actions, and drug interactions . The mosDisGeNET covers the most exhaustive and publicly available catalogues of genes and genomic variants associated with diseases . Its advhttp://bioinformatics.psb.ugent.be/webtools/Venn/) was used to identify potential therapeutic targets for RES against DKD. The functional classification of therapeutic targets was performed by Panther database (accessed in September 2022) [The online tool Draw Venn Diagram (er 2022) .The therapeutic targets for RES against DKD were uploaded to the online tool String database  with a cP < 0.05 in top 20 Enrichment Score were selected for further analysis). The KEGG_PATHWAY analysis was utilized for pathway enrichment analysis. The GO functional enrichment analysis was performed using GOTERM_BP_DIRECT, GOTERM_CC_DIRECT, and GOTERM_MF_DIRECT. The top 20 enriched KEGG and GO terms with P < 0.05 were selected for further analysis. Finally, the diseases-targets-functional annotations-signaling pathways network was visualized using Cytoscape software.Gene ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes Genomics (KEGG) pathway analysis, and disease association analysis were performed using the DAVID database (accessed in October 2022) . The theThe 3-dimensional structure of RES was downloaded from PubChem database  and tran2 incubator . The culture medium was changed every 2 days. When the cells were in good condition and fused to about 80%, they were divided into the normal control group , high glucose group  [The mouse podocyte clone-5 (MPC-5) cell lines were purchased from the American type culture collection. MPC-5 cells were cultured in Dulbecco’s modified Eagle’s medium  containing 10% fetal bovine serum , 100 U/ml penicillin, 100 μg/ml streptomycin, and 0.25 μg/ml amphotericin B  in a 37℃, 5% COglucose) , 21, andglucose)  to conti® Green Premix Pro Taq HS qPCR Kit . PCR amplification was performed with an initial denaturation at 95 ℃ for 30 s, followed by 40 cycles of 95℃ for 5 s and annealing at 60 ℃ for 30 s. Expression of the β-actin gene was assayed simultaneously with samples as an internal control. Relative gene expression was determined by the 2−ΔΔCT method. All samples were performed three times in parallel. The specificity of each primer pair was verified both by in silico analysis (NCBI Primer-BLAST) and melting curve analysis after qPCR amplification.The total RNA was extracted from the MPC-5 cells in NC, HG, and RES groups by using SteadyPure Universal RNA Extraction Kit II  according to the manufacturer’s instructions. RNA concentrations were measured on a Nano-300 Micro-Spectrophotometer . If D(λ)260/D(λ)280 was 1.8–2.0, RNA was stored in a − 80 ℃ refrigerator for later use. A total of 1 μg RNA was taken to synthesize complementary DNA by using Evo M-MLV RT Premix for qPCR AG . The reverse transcription was performed at 37 ℃ for 15 min, 85 ℃ for 5 s, and 4 ℃ on a Bio-Rad S1000TM Thermal Cycler . All primers for PCR amplification were designed using NCBI Primer-BLAST and purchased from Shanghai Sangon Biotechnology. The primer sequences are shown in Table g and 4℃ for 15 min. Then the total protein level of the supernatant was quantified by using the bicinchoninic acid protein assay kit . Supernatants were then mixed with 5 × protein sample loading buffer , boiled for 10 min, and stored at – 20 ℃ for later use. Equal amounts of protein (40 μg/lane) from each sample were loaded on 10% SDS-PAGE gel and then transferred to the 0.45 μm polyvinylidene difluoride membrane. The gel blocking was performed with 5% nonfat dry milk  in Tris-buffered saline containing 0.1% Tween-20 (TBST)  at room temperature for 2 h, followed by incubation with primary antibodies overnight at 4 ℃. Primary antibodies β-actin , Nephrin  were purchased from Affinity Biosciences  and WT-1  was purchased from Zen-Bioscience . After washing with 1 × TBST solution for three times (10 min each), membranes were incubated with HRP-conjugated goat anti-rabbit secondary antibody  for 1.5 h at room temperature. After washing three times with 1 × TBST, chemiluminescence detection reagents  were used to visualize the bands via the Amersham Imager 600 . Densitometric analysis of the bands was performed with Image J software and relative protein expression was determined by normalizing to β-actin.The protein in each group was extracted by using RIPA lysis buffer  containing 1 mM phosphatase inhibitors , and 1 mM protease inhibitors . Cell lysates were centrifuged at 10,625 × P > 0.05 was considered as normal distribution. Homogeneity of variance was tested using Levene’s test with P > 0.05 considered homogenous. For data that met normal distribution assumptions and equal variances, statistical analyses were performed through one-way analysis of variance (ANOVA) and least significant difference (LSD) post hoc test for multiple comparisons. Otherwise, the multiple sample rank-sum tests were performed using the Kruskal–Wallis test and P-values for multiple comparisons were adjusted using Bonferroni correction. A value of P < 0.05 was considered statistically significant.Data from western blot and RT-qPCR experiments were plotted and analyzed by using GraphPad Prism 9.0 and Statistical software SPSS 26.0 software. The normality of the data was tested by the Shapiro–Wilk test and There were 26 and 69 RES drug targets in Drugbank and SwissTargetPrediction databases respectively. We combined these drug targets and removed 9 duplicates. Finally, 86 drug targets were selected for further analysis.In the DisGeNET, Genecards, and TTD databases, 224, 437, and 22 targets were associated with DKD, respectively. After removing redundant targets, 566 DKD-related targets were obtained.The above RES drug targets were intersected with DKD targets to obtain potential therapeutic targets for RES against DKD. Finally, a total of 25 targets were identified  were recorded. Docking sites of RES with PTGS2, SIRT1, CYP1A1, HDAC2, CYP19A1, CYP3A4, CYP1A2, and MTNR1B were not obtained from the SwissDock database. The other molecular docking results of RES with target proteins are shown in Table Murine podocytes were treated with NC, HG, HG plus 5 μM RES for 72 hours. The mRNA and protein expression levels of podocyte markers, including nephrin and WT1, were analyzed by RT-qPCR and western blot. The relative mRNA and protein levels of nephrin and WT1 were significantly downregulated in the HG group compared with those in the NC group Fig. . It indiThe mRNA expression levels of target proteins in the NC, HG, and RES-treated groups were measured by RT-qPCR. The PCR results Fig.  showed tWith the rapid development of the economy and society, the prevalence of DKD is gradually increasing worldwide which imposes a heavy economic and social burden. Our previous bioinformatics analysis found that RES can reverse gene expression abnormalities in DKD patients’ glomerulus . MoreoveTo further explore the specific binding sites where RES plays the above roles, we performed molecular docking validation. Generally, results of molecular docking are mainly evaluated by minimum binding free energy (Delta G) . The lowMany studies have proved the benefits of PPARA activators. A previous study has indicated that RES treatment can increase the expression level of PPARA and restore normoglycemia and insulin secretion in diabetic rats . A bioafSHBG is a plasma glycoprotein that regulates the action of steroid hormones including androgens and estradiol . PreviouAKR1B1 is one of a subfamily members of aldo‐keto reductase family 1 (AKR1) which plays an important role in glucose metabolism , 53. TheMMP9 and MMP2 are members of the matrix metalloproteinase family that provide homeostasis between the synthesis and degradation of the extracellular matrix to maintain the structural and functional integrity of the glomerulus . It has AKT1, also known as protein kinase B, is a key regulatory kinase that transduces signals through the phosphoinositide 3-kinase (PI3K)-AKT signaling cascade to control cell growth and survival . In the Accordingly, RES may be a promising pharmacological agent which comprehensively regulates the above therapeutic targets to counteract the development of diabetic complications including DKD. And RES may be recommended as a dietary supplement for delaying the progression of DKD at an early stage. At the same time, RES intervention may also contribute to delaying the development of other diabetic complications.The strengths of the current study are that we identified comprehensive therapeutic targets for RES against DKD through network pharmacology analysis which integrated various databases, and validated the identified targets by molecular docking simulations and in vitro experiments. To the best of our knowledge, there have been no published studies available conducting such a comprehensive screening and validation of the therapeutic targets for RES against DKD. These findings comprehensively reveal the potential therapeutic targets for RES against DKD and provide theoretical bases for the application of RES in the treatment of DKD.However, several limitations also existed in the study. First, we only verified the identified targets through computer simulations and in vitro experiments, without conducting in vivo experiments. Second, we did not compare RES with other hypoglycemic drugs, nor did we conduct a combined intervention to further assess the effect of RES on DKD. Third, the results of the present study didn’t provide a reference for the optimal intervention dose of RES in clinical application. Therefore, further in vitro and in vivo experiments are needed subsequently to confirm the mechanisms and effects of RES against DKD in the presence of other hypoglycemic agents. And additional pharmacological studies are needed to establish the optimum dose and bioavailability of RES to maximize long-term cost-effectiveness.Aside from the above limitations, we conclude that therapeutic targets for RES against DKD are screened comprehensively via network pharmacology, bioinformatics analysis, molecular docking simulation, and in vitro cell evaluations. RES treatment can reverse the abnormal gene expression of PPARA, SHBG, AKR1B1, PPARG, IGF1R, MMP9, AKT1, and INSR. These findings comprehensively reveal the potential therapeutic targets for RES against DKD and provide theoretical bases for the application of RES in the treatment of DKD.


Moreover, 43 T2DM-related targets acted on by these chemicals were identified, and the function of phlorotannin to prevent and treat T2DM was elucidated in a holistic way based on the established compound-target-disease network, and GO function and KEGG pathway enrichment analysis.Type 2 diabetes mellitus (T2DM) has developed into an important health concern worldwide. The discovery of phlorotannins and their efficacy in the treatment of T2DM has become a hotspot for research in various fields. In this study, the potential phlorotannins and mechanism of six brown algae against T2DM were in-depth investigated using biological activity assays, LC-MS, and network pharmacology. First, the ethyl acetate fraction (EA frac.) showed high polyphenolic content and possessed significantly antioxidant and enzyme inhibitory abilities. Further, a total of fifty-nine peaks were obtained from six EA fracs. via UPLC-QE-MS/MS analysis, and fifteen of them were identified as phlorotannins and their isomers or derivatives. In detail, the chemical structures of six phlorotannins were inferred as dibenzodioxine-1,3,6,8-tetraol, bifuhalol, dioxinodehydroeckol, eckol, fucofurodiphlorethol, and fucotriphlorethol; three phlorotannin isomers were deduced to be fucophlorethol, trifucol, triphlorethol A, or triphlorethol B; and the phlorotannin derivative of  As the most common form of diabetes, type 2 diabetes mellitus (T2DM) has become an increasingly serious global health problem . T2DM, aAt present, the treatment of T2DM mainly includes insulin and various oral antidiabetic drugs, such as α-glucosidase inhibitors, rosiglitazone, sulfonylurea, metformin, and thiazolidinedione ,9. HowevPhenolic compounds, the most studied phytochemicals, are ubiquitous and abundant in all plant-based diets. It is well known that marine algae contain rich bioactive compounds, which have great potential for pharmaceutical foods and biomedicine . As polyTherefore, we aimed at exploring the in vitro effect of phlorotannin-targeted extract from six brown algae on the metabolic changes of diabetes, that is, its regulation is involved in carbohydrate hydrolase (α-glucosidase and α-amylase) and antioxidant activity; mass spectrometry was used to identify the phlorotannins and determine the correlation between the polyphenol components and the anti-T2DM activity, predicting the mechanism of phlorotannins against T2DM using a network pharmacology approach. The above research provides a strong basis for the application of phlorotannins .3), starch, and sodium hydroxide were purchased from Damao Chemical Reagent Factory . Phloroglucinol (≥99.0%), sodium carbonate, diammonium salt (ABTS), α-glucosidase, α-amylase, 3,5-dinitro salicylic acid, and potassium sodium tartrate were purchased from Shanghai Aladdin Biochemical Technology Co., Ltd. . 4-nitrophenyl-α-D-glucopyranoside (PNPG), acarbose, 2,2-diphenyl-1-picrylhydrazyl radical (DPPH), and Folin-Ciocalteu reagent were purchased from Beijing Solarbio Technology Co., Ltd. (Beijing China). Potassium ferricyanide and potassium persulfate were purchased from Xilong Science Co., Ltd. . Methanol was chromatographic grade and purchased from Concord Technology .Ethanol, n-hexane, and ethyl acetate were purchased from Guangdong Guanghua Sci-Tech Co., Ltd. . Potassium dihydrogen phosphate, dipotassium hydrogenphosphate, ascorbic acid (≥99.0%), trichloroacetic acid, Iron(III) chloride (FeClhttps://www.swisstargetprediction.ch (accessed on 12 April 2023)), the human gene integrator ), Metascape database (https://metascape.org/ (accessed on 16 April 2023)), Cytoscape version 3.8.2, online interactive tool for Venn (http://jvenn.toulouse.inra.fr/ (accessed on 20 April 2023)), ChemOffice version 2019.The following databases and software were used: SwissTargetPrediction database (Undaria pinnatifida (U. pinnatifida), Sargassum pallidum , Scytosiphon lomentarius (S. lomentarius), Sargassum hemiphyllum (S. hemiphyllum), Sargassum fusiforme (S. fusiforme), and Laminaria japonica (L. japonica) were purchased from Dalian local supermarkets . After removing impurities, the algae samples were dried at 50 °C and ground into powder (particle size < 0.6 mm). The obtained powder samples were vacuum-packed and stored below −30 °C until further experiments.v/v) and extracted for 90 min. The ethanol extract (EE) was filtered through a suction filter to remove impurities and concentrated to a volume of about 50 mL in a rotary evaporator. The concentrated extract was extracted several times using n-hexane  until n-hexane appeared colorless. The aqueous phase was then subjected to further liquid–liquid extraction using ethyl acetate . Solvents in ethanol extract, n-hexane (HEX), ethyl acetate (EA), and aqueous (AQ) fractions were evaporated and stored at −20 °C until analysis − at m/z 265), dioxinodehydroeckol ([M-H]− at m/z 369), eckol ([M-H]− at m/z 371), fucofurodiphlorethol ([M-H]− at m/z 479), and fucotriphlorethol ([M-H]− at m/z 621) −, [M-H-44]−, and [M-H-44-18-18]− groups, respectively [− at m/z 265) has fragment ions at m/z 247.09 ([M-H-18]−) and m/z 221.12 ([M-H-18-18]−). In addition, fragment ions at m/z 123.04 are considered to be caused by the loss of a pyrogallol unit and water molecules − at m/z 711 is believed to have removed a hydroxyl bond on the basis of dieckol (m/z 742); and [M-H]− at m/z 403 and 421 is considered to have added two and three hydroxyl bonds on the basis of triphlorethol (m/z 247), respectively [The formation of phlorotannin derivatives may be caused by an increase or decrease in hydroxyl groups or water molecules on phlorotannins or by the conversion of fuhalol-type to eckol-type. Based on the information of ion fragments of derivatives of phlorotannin in six brown algae, we deduced the structures of the obtained compounds. The compounds with a mass to nucleus ratio of 263 are thought to have been formed in two ways: the polymerization of a ring between two benzene rings in bifuhalol and the addition of a hydroxyl bond to the benzene ring in dioxinodehydroeckol . The derectively .2 spectra. This is mainly because its derivatives show a fragmentation pattern similar to that of phlorotannins, or produce ions indicating one or more phloroglucinol units and characteristic products, as well as common water loss or cross ring breakage [Notably, even though the structure of all phlorotannins has not been clarified, the structure of six derivatives of phlorotannin has been further deduced based on their MS breakage ,48.The structures of the identified phlorotannin were drawn by ChemBioDraw software and uploaded to the SwissTargetPrediction database to predict a total of 48 relevant targets (Probability > 0). Using the keyword “Type 2 diabetes mellitus”, 13594 T2DM-related targets were retrieved from the GeneCards database. Using the Venn online tool to map the active ingredients to T2DM-related targets, a total of 43 intersecting targets were obtained , which aUsing the Metascape database, the GO function and KEGG pathway were analyzed. A total of 600 GO items were associated with the effect of phlorotannins against T2DM, including 490 biological process (BP) items, 48 cellular component (CC) items, and 62 molecular function (MF) items. Each item was ranked according to its q-value and the top ranked ones were presented visually, as shown in A total of 98 signaling pathways were obtained from the KEGG pathway enrichment analysis, and the top 20 pathways were selected for presentation based on q-value ranking, as shown in Inhibition of epidermal growth factor receptor (EGFR) activity protects diabetic patients from DN . PI3K/AKIn the C-T-D network, yellow squares represent targets, red circles represent diseases, and green hexagons represent phlorotannins . The C-TS. hemiphyllum-EA. The S. pallidum-EA showed the highest DPPH scavenging activity, similar results were shown for other biological activity. A total of six phlorotannins, three phlorotannin isomers, and six phlorotannin derivatives were inferred. A total of six phlorotannins were deduced as dibenzodioxine-1,3,6,8-tetraol, bifuhalol, dioxinodehydroeckol, eckol, fucofurodiphlorethol, and fucotriphlorethol. Dibenzodioxine-1,3,6,8-tetraol was first identified from S. pallidum, L. japonica, U. pinnatifida, and S. fusiforme. The phlorotannin isomers correspond to fucophlorethol, trifucol, triphlorethol A, or triphlorethol B. The chemical structure of the phlorotannin derivative of m/z 263 was deduced and the two forms present were named dibenzodioxine-1,2,3,6,8-pentanol or dibenzodioxine-1,2,4,5,7-pentanol. A total of 43 T2DM-related targets were obtained via network pharmacological prediction, mainly focusing on EGFR tyrosine kinase inhibitor resistance, endocrine resistance, the PI3K-Akt signaling pathway, and the Rap1 signaling pathway. Phlorotannins were unique among brown algae and showed great physiological activities. Considering the synergistic effects of other active substances, further studies combining multiple analytical methods are needed to reveal the active substances in brown algae more comprehensively and accurately. The in vivo and vitro studies supplemented the targets and mechanisms of the effects of phlorotannins on T2DM to promote the development of related new drugs, which provided a certain theoretical basis for the exploitation of the active value of phlorotannins and maximized the potential of brown alga. This highlights the potential use of brown algae for future commercial applications and their suitability as a model species for further studies of brown algal polyphenols.The present study was designed to provide a scientific basis for the analysis of potentially active phlorotannins against T2DM via component isolation, bioactivity determination, UPLC-QE-MS/MS, and network pharmacology. Antioxidant activity and inhibition of carbohydrate hydrolases were assessed in 24 groups of brown algae samples, and the structures of phlorotannins and their derivatives were deduced via UPLC-QE-MS/MS. Predicting the mechanism of phlorotannins against T2DM using a network pharmacology approach. Polyphenolic compounds and bioactivities differed significantly among the brown algal species evaluated. In addition, the biological activity of polyphenols was closely related to the total phenolic content. In the samples analyzed, phlorotannins were predominantly present in the EA frac., with the highest TPC in the


Although coronavirus disease 2019 (COVID-19) pandemic is still rage worldwide, there are still very limited treatments for human coronaviruses (HCoVs) infections. Xiaochahu decoction (XCHD), which is one of the traditional Chinese medicine (TCM) prescriptions in Qingfeipaidu decoction (QFPDD), is widely used for COVID-19 treatment in China and able to relieve the symptoms of fever, fatigue, anorexia, and sore throat. To explore the role and mechanisms of XCHD against HCoVs, we presented an integrated systems pharmacology framework in this study.We constructed a global herb-compound-target (H-C-T) network of XCHD against HCoVs. Multi-level systems pharmacology analyses were conducted to highlight the key XCHD-regulated proteins, and reveal multiple HCoVs relevant biological functions affected by XCHD. We further utilized network-based prediction, drug-likeness analysis, combining with literature investigations to uncover the key ani-HCoV constituents in XCHD, whose effects on anit-HCoV-229E virus were validated using cytopathic effect (CPE) assay. Finally, we proposed potential molecular mechanisms of these compounds against HCoVs via subnetwork analysis.Based on the systems pharmacology framework, we identified 161 XCHD-derived compounds interacting with 37 HCoV-associated proteins. An integrated pathway analysis revealed that the mechanism of XCHD against HCoVs is related to TLR signaling pathway, RIG-I-like receptor signaling pathway, cytoplasmic DNA sensing pathway, and IL-6/STAT3 pro-inflammatory signaling pathway. Five compounds from XCHD, including betulinic acid, chrysin, isoliquiritigenin, schisandrin B, and (20R)-Ginsenoside Rh1 exerted inhibitory activity against HCoV-229E virus in Huh7 cells using in vitro CPE assay.Our work presented a comprehensive systems pharmacology approach to identify the effective molecules and explore the molecular mechanism of XCHD against HCoVs.The online version contains supplementary material available at 10.1186/s12906-023-04024-6. Thus, it is of great urgency to develop the new effective anti-HCoVs drugs for patient therapy.Coronavirus (CoV) is a kind of single-stranded RNA virus that can infect many animal species which can lead to different degrees of lesions in the respiratory tract, liver, intestines and nervous system . CoV conCOVID-19 diagnosis and treatment scheme in China. From a retrospective multicenter study on 782 COVID-19 patients from 54 hospitals in nine provinces of China, compared with treatment with QFPDD initiated after 3 weeks of infection, treatment started less than 1 week, 1–2 weeks, or 2–3 weeks had a significantly shorter recovery time, with adjusted hazard ratio of 3.81 (2.65–5.48), 2.63 (1.86–3.73), and 1.92 (1.34–2.75), respectively [Bupleurum chinense DC. (Chaihu), Scutellaria baicalensis Georgi. (Huangqin), Pinellia ternata (Thunb.) Breit.(Banxia), Zingiber officinale Roscoe (Shengjiang), Panax ginseng C.A.Mey. (Renshen), Glycyrrhiza uralensis Fisch. (Zhigancao) and Ziziphus jujuba Mill. (Dazao). Previous pharmacological studies showed the effects of XCHD on resisting various RNA and DNA viruses (including coronaviruses) infection, improving immune function and restoring body temperature by hypothalamus regulating [Preliminary clinical practice evidences showed that traditional Chinese medicine (TCM) prescriptions, especially those consisting of a combination of herbs, have achieved beneficial effect for COVID-19 patients by shortening of hospitalization duration and reducing the chance of getting complications as well as the mortality rate . Qingfeiectively . As one ectively . Clinicaectively . XCHD isgulating , 16. Chagulating , 18. Huagulating . AlthougTCM is characterized by complex components, multiple targets and synergistic actions. Systems pharmacology had been recentely developed in TMC studies, it provided a network perspective to explore the relationship between components and targets . It has In this study, we presented a systems pharmacology-based framework to identify the effective components of XCHD and explore the underlying mechanism of XCHD against HCoVs Fig. . BrieflyXCHD is composed of seven medicinal herbs, including Chaihu, Huangqin, Banxia, Shengjiang, Renshen, Zhigancao and Dazao. The scientific species names of each herb are shown in Table Total 30 compounds from these herbs for in vitro assays were commercially obtained from Topscience Biochemical Technology Co., Ltd. The quality control assessment was performed by NMR or HPLC–MS to assure the purities are all greater than 95%.Huh7 cells and HCoV-229E were kindly provided from CAMS Key Laboratory of Antiviral Drug Research, Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College.The chemical structures of all compounds in XCHD were collected from the following databases: Traditional Chinese medicine integrative database (TCMID) , TCM DatBoth known and predicated targets of XCHD were included in the current study. The known targets were extracted from the previous integrated database , which cWe comprehensively retrieved the literatures to obtain the human genes associated with multiple types of HCoVs, including HCoV-OC43, HCoV-229E, HCoV-NL63, HCoV-HKU1, SARS-CoV, MERS-CoV and SARS-CoV-2. The names of the collected genes were standardized into gene symbol according to GeneCards  (retrievhttps://admetmesh.scbdd.com/) and completed in April 2021. Specifically, the RF model was trained by 6,731 positive samples from DrugBank and 6,769 negative samples from ChEMBL with IC50 or Ki values < 10 μm. The obtained model has good ability to generalize the new chemical entity with classification accuracy of 0.800 and AUC score of 0.867 on external test set. More detailed information can be found in previous study [In this study, drug-likeness analysis was conducted using a classification model based on random forest (RF) method, the analysis was available at ADMETlab platform (us study .https://gephi.org/) and Cytoscape . The compounds, pathways and genes (targets) were represented as nodes. The interactions were denoted as edges. The degree of each node was defined by the number of edges linked to it, which represents the hierachy of the node in the network. For the PPI network, the functional relationships among interacting proteins were generated through STRING database [Homo Sapiens", while the reliability score of the PPI edge required interaction score was set as greater than 0.4.In our study, we generated three types of networks to explore the molecular mechanism of XCHD against HCoVs, including compound-target (C-T) networks, target-pathway network and protein–protein interaction (PPI) network. These networks were constructed by Gephi  [https://david.ncifcrf.gov/) [p < 0.05 were considered as statistically significantly enriched.To explore how do the targets of XCHD exert their anti-HCoVs effect through pathway regulation, we annotated the biological functions of the HCoVs targets of XCHD to find out the potential signaling pathways and biological processes. Gene ontology (GO) term and KEGG pathway enrichment analysis (g1.html)  were perrf.gov/)  . The inhibition rates of each drug on HCoV-229E in Huh7 cells were calculated by normalizing the CPE of each group to the CPE of virus-only well.For CPE assay, the cells were either infected with 100 TCID50) and the half inhibitory concentration (IC50) of each test sample were calculated using the Reed-Muench method.The half toxic concentration (TChttps://www.rcsb.org) [Molecular docking is a process that identifies the complementary molecules for a target spatially and electrically. The target protein structures were downloaded through the PDB protein database (csb.org) , and thePrevious preclinical studies and clinical trials demonstrated that the multi-component synergy of TCM are related to the interaction between components , 43. To To understand the potential synergistic effect mechanism of the herbs against HCoV, we used UpSet Wayne diagram to analyze the distribution of HCoV-associated genes regulated by XCHD Fig. . There aD) larger than 20 were displayed. Most compounds were connected to multiple shared targets. Of note, the H-C-T network also indicated that several important HCoVs associated genes with high degrees (D > 20), including DPP4 (D = 59), BCL2 (D = 32), IL6 (D = 30), JUN (D = 25) and MCL1 (D = 23). Overall, it is likely that the ingredients in XCHD work to prevent HCoVs infection by regulating multiple HCoV-associated targets.In the study, to identify the effective components of XCHD against HCoV, we performed systems pharmacology-based framework analysis and obtained 344 components in XCHD connecting to 2,656 known targets and 561 predicated targets , retinoic acid-inducible gene (RIG)-I-like receptor signaling pathway , cytosolic DNA-sensing pathway , focal adhesion , and apoptosis  were all significantly enriched (p < 0.05 was considered as significant). Moreover, we constructed a target-pathway network to illustrate the relationships between these HCoVs-associated targets and the enriched pathways. The network is composed of 19 target-pathway interactions between 9 targets and 5 potential signaling pathways -Ginsenoside Rh1 are the most promising anti-HCoVs components of XCHD. To investigate whether they can perform synergistic effects against HCoVs, we established a C-T subnetwork for the five compounds through extracting the corresponding CTIs from the global H-C-T network. As shown in Fig. The X-ray crystal structures of IKBKB, IL6, STAT3 and TGFB1 were downloaded using PDB IDs for 3BRT, 1ALU, 6NJS and 4KV5, respectively.The Libdock results of chrysin, isoliquiritigenin, betulinic acid, schisandrin B, and (20R)-Ginsenoside Rh1 with their respective key targets are shown in the Table COVID-19 diagnosis and treatment scheme in China. This gave an inspairation that TCM-derived natural products have strong prospect to be developed into the novel drugs. However, how to identify the active components and elucidate the underlying mechanism of their actions remain challenging, which hinders the further clinical translation and application.Currently, the pandemic of COVID-19 still prevails around the world. The shortage of medicine and mutation of HCoV prompt more candidates from a broader scope to be discovered. In this tough campaign against COVID-19, TCM has been widely applied for treating over 85% infected patients and displays a promising effect in China . Some TCIn this study, we provided a systems pharmacology-based framework to identify the potential active components in XCHD and explore the potential anti-HCoVs mechanism. This framework was based on a global H-C-T network, which incorporated 2,454 herb-compound pairs, 90 high-quality HCoVs-associated targets, 45,624 known and predicted compound-target interactions obtained from multiple authoritative databases, predictive network model, and published literatures.According to the mechanisms of marketed agents against HCoVs (mainly SARS-CoV-2), there are several therapeutic adoption pathways for HCoVs, including the inhibition of viral proteins (3C-like protease and RNA-dependent RNA polymerase), the blockage the spike-ACE2 interaction, and the antagonization of Janus kinase and cytokines . In thisWe further proposed an integrated pathway to comprehensively understand the anti-HCoVs effects of XCHD in different directions and signaling pathways. Meanwhile, with the combination of in silico prediction and in vitro validation, we for the first time discovered 16 compounds in XCHD exerting inhibitory activity against HCoV-229E virus. Among them, betulinic acid, chrysin, isoliquiritigenin, schisandrin B, and (20R)-Ginsenoside Rh1 showed comparable activity as anti-virus drug ribavirin. It was further confirmed that 5 key compounds showed good affinities for the four HCoV-associated targets  by LibDock. Betulinic acid, chrysin and isoliquiritigenin were detected from Dazao, Huangqin, and Zhigancao, with the content of 1602.008 μg/g, 36.23 ± 8.48 mg/g, and 0.281 ± 0.008 mg/g, respectively –58. SchiThe systems pharmacology-based infrastructure has several progresses: (i) constructing a global H-C-T network by integrating high quality HCoVs associated proteins and comprehensive validated CTIs. The completeness of the H-C-T network was further improved via adding computationally predicted targets inferred by bSDTNBI method; (ii) the systems pharmacology approach was independent from high-quality negative samples or 3D structures of targets, which are critical for algorithms such as molecular docking and machine learning ; (iii) cHowever, several potential limitations should be addressed in the study. First, although the C-T network was assembled by the large-scale CTIs from accessible databases and computational data via bSDTNBI method, there are still incompleteness to some extend. The drug-induced transcriptome and proteome data should be considered to update into the current C-T network. Second, due to the complexity of the intrinsic interactions and the diversity of components in the mixture of multiple herbs, the current system pharmacology analysis could not accurately reflect the actual biological effects of XCHD on HCoV inhibition in patients . Last, aOverall, the systems pharmacology is a booming research field, which can be used to find the potential acting target for traditional medicine. Our study proposed several anti-HCoVs drug candidates from XCHD, which are betulinic acid, chrysin, isoliquiritigenin, schisandrin B, and (20R)-Ginsenoside Rh1. In addition, our analysis show the implicated mechanism of XCHD against HcoVs, including TLR signaling pathway, RIG-I-like receptor signaling pathway, cytoplasmic DNA sensing pathway, and IL-6/STAT3 pro-inflammatory signal transduction axis. All these results shed light on that system pharmacology is a powerful tool to dissect the mechanisms of TCM to cure disease.Additional file 1: Supplementary table 1.Additional file 2: Supplementary table 2. Additional file 3: Supplementary table 3. Additional file 4: Supplementary table 4. Additional file 5: Supplementary table 5. Additional file 6: Supplementary table 6. Additional file 7: Supplementary table 7.


We analyzed large-scale post-translational modification (PTM) data to outline cell signaling pathways affected by tyrosine kinase inhibitors (TKIs) in ten lung cancer cell lines. Tyrosine phosphorylated, lysine ubiquitinated, and lysine acetylated proteins were concomitantly identified using sequential enrichment of post translational modification (SEPTM) proteomics. Machine learning was used to identify PTM clusters that represent functional modules that respond to TKIs. To model lung cancer signaling at the protein level, PTM clusters were used to create a co-cluster correlation network (CCCN) and select protein-protein interactions (PPIs) from a large network of curated PPIs to create a cluster-filtered network (CFN). Next, we constructed a Pathway Crosstalk Network (PCN) by connecting pathways from NCATS BioPlanet whose member proteins have PTMs that co-cluster. Interrogating the CCCN, CFN, and PCN individually and in combination yields insights into the response of lung cancer cells to TKIs. We highlight examples where cell signaling pathways involving EGFR and ALK exhibit crosstalk with BioPlanet pathways: Transmembrane transport of small molecules; and Glycolysis and gluconeogenesis. These data identify known and previously unappreciated connections between receptor tyrosine kinase (RTK) signal transduction and oncogenic metabolic reprogramming in lung cancer. Comparison to a CFN generated from a previous multi-PTM analysis of lung cancer cell lines reveals a common core of PPIs involving heat shock/chaperone proteins, metabolic enzymes, cytoskeletal components, and RNA-binding proteins. Elucidation of points of crosstalk among signaling pathways employing different PTMs reveals new potential drug targets and candidates for synergistic attack through combination drug therapy. Protein post-translational modifications (PTMs), such as phosphorylation, ubiquitination, and acetylation, are extensively employed by cell signaling pathways that regulate cell division, differentiation, migration, and cancer. We used machine learning to identify PTM clusters that represent functional modules in cell signaling pathways. These clusters were used to identify protein-protein interactions, and interactions between cell signaling pathways, that were active in lung cancer cells that were treated with anti-cancer drugs. We model these interactions as networks at three levels of granularity at the pathway, protein-protein interaction, and PTM levels. Interrogation of these networks yielded insights into molecular interactions between cell signaling pathways activated by oncogenes, transmembrane transport of small molecules, and glycolysis and gluconeogenesis. These analyses identify previously unappreciated mechanisms of crosstalk among signaling pathways between oncogenic tyrosine kinase signaling and proteins that regulate metabolic reprogramming in lung cancer, revealing new potential drug targets for combination therapy. Protein post-translational modifications (PTMs) are intimately intertwined with cell signaling mechanisms that regulate cell differentiation, migration, and proliferation, and when mis-regulated become hallmarks and drivers of neurodegenerative diseases and cancer –8. WhileLung cancer is one of the most commonly diagnosed cancers and the cancer responsible for the most deaths worldwide . Non-smaOur approach to examining cell signaling pathways with high resolution is to integrate PTM proteomic data, monitoring changes in multiple PTMs in response to TKIs, with protein interaction networks –15 and bWe hypothesized that different TKIs would perturb cell signaling pathways, which would be reflected by PTM changes, to reveal mechanisms unique to the drug target and common core pathways that respond to all TKIs. To test this, lung cancer cell lines were treated with TKIs matched to inhibit the mutant oncogene. Phosphorylation, ubiquitination, and acetylation were chosen to examine crosstalk between signaling pathways involving these PTMs. We constructed a CCCN and CFN from measurements of phosphorylation, acetylation, and ubiquitination in 10 different lung cancer cell lines with driver mutations in EGFR, ALK, ERBB2/HER2, ROS1, and DDR2, treated with four different TKIs. In a new innovation, we used curated pathways from NCATS BioPlanet  to identSequential enrichment of post translational modification (SEPTM) proteomics  was used2 fold change in response to TKIs  to help identify groups of PTMs whose abundances changed in a coordinated manner across different drug and cell line combinations . For eac to TKIs  Figs.We then used the clusters to filter a network of physical protein-protein interactions obtained from the PPI databases STRING , GeneMANi.e., the mutated cancer-driver protein) in each cell line to the proteins whose PTMs changed in response to drug treatment. Our results suggest that despite the inherent noisiness of the underlying PTM proteomic data, the CFN can reproducibly capture signaling responses to TKIs  involved in various cellular processes . To modeWe compared the PTM cluster weights to two other measures of pathway-pathway similarity: i) Jaccard similarity, which captures the extent to which pathways have genes in common, and ii) Gene Ontology (GO) similarity, which measures the extent to which genes in the two pathways are annotated with common GO Biological Process terms see  28]. Th. Th28]. i.e., Jaccard similarity is 0). Considering the interacting pathways with no genes in common that have the highest PTM cluster weights, a few themes emerge. There were pathways that mediate many major steps of gene expression , pathways involved in glucose metabolism  and pathways that regulate transport of metabolic building blocks . We focused on two of these pathways—Glycolysis and gluconeogenesis and Transmembrane transport of small molecules  implicated in TKI efflux and multidrug resistance  had linkWe examined direct CFN connections between the two pathways  focusingA similar approach was used to analyze pathway crosstalk between the EGF/EGFR signaling and Glycolysis and gluconeogenesis pathways. The composite shortest paths connecting these two pathways was also very complex , CFN. ThUp-regulation of glycolysis in the presence of oxygen is a well-recognized phenomenon in cancer cells known as the Warburg effect . The PTMAlthough the specific proteins and sites affected differed, we saw a similar overall metabolic changes in (EML4-ALK fusion) cells treated with crizotinib . TPI, GAClues about the mechanism mediating the link between TKI treatment and unregulated glycolysis were found by examination of direct connections between RTKs, SFKs and the Glycolysis and gluconeogenesis pathway: the glycolysis enzyme PKM is a hub that integrates information from several RTKs and SFKs, and ENO1 and PGK1 are also key regulatory proteins .Recently, it was shown that accumulation of the glycolysis enzyme TPI1 in the nucleus is associated with poor prognosis and chemoresistance in lung cancer . Our anaIn our analyses, we repeatedly observed that SFKs were important intermediaries in the communication between EGFR signaling and transport and metabolism. To better understand the role of SFKs, we constructed a subnetwork of the CFN showing paths between EGFR and proteins whose PTMs were most affected by the SFK inhibitor dasatinib . FourteeWe hypothesize that the PTM clusters in the CCCN represent signaling modules—groups of related proteins that respond to perturbations in a coordinated fashion. Thus, we investigated several clusters in depth in order to characterize the biological processes that they might be controlling. Because we were particularly interested in how signaling modules respond to TKIs, we focused on clusters that were statistically enriched for drug-affected sites . Focal adhesions serve as an intermediary between the actin cytoskeleton and the extracellular matrix and play a key role in migration and metastasis of cancer cells . In totaFinally, cluster C had a very different PTM composition, consisting of 29 sites the overwhelming majority of which (twenty-five) were acetylation sites Figs  and 6B. We previously used similar methods to create a CFN from a dataset of phosphorylation, acetylation, and methylation PTMs in lung cancer cell lines (referred to here as the CST-CFN) . For a . While tWe applied k-core decomposition to the CFN generated from data in this study and the CST-CFN to identify the compositional differences between the two graphs in terms of the highest cores see . The 43 e.g., ubiquitin binding domains; bromodomains; SH2 and PTB domains) to regulate protein localization, degradation, and activity.Notably, there were a large number of negative correlations among different PTM types on these core proteins . Thirty-This integrated analysis of protein phosphorylation, acetylation, and ubiquitination in lung cancer cell lines in response to TKIs attempts to model cell signaling pathways using network-based approaches. The activity of cell signaling pathways can only be indirectly inferred by interrogating the transcriptome or the proteome. While analysis of PTMs does not comprehensively cover all signaling pathways, or directly assess the role of intracellular second messengers, the integrated analysis of several PTMs enables modeling cell signaling pathways that are likely to be upstream of transcription and translation with sufficient resolution to begin to describe the interactions among pathways that affect many cellular processes.i.e., some clusters were highly enriched for drug affected sites), supporting the hypothesis that the clusters define functional modules whose members respond in concert to drug treatment.Our network-based framework allows interrogation of multi-PTM proteomics data with three levels of granularity at the pathway (PCN), protein (CFN), and PTM (CCCN) levels. The highest-level network, introduced for the first time in this study, is the PCN, in which the highly-curated biological pathways from BioPlanet  were coni.e., the Warburg effect) was apparent in both EGFR-mutant cells treated with erlotinib and EML4-ALK fusion cells treated with crizotinib. Our data revealed detailed mechanisms mediated by coordinated changes in phosphorylation , acetylation , and ubiquitination   Figs –S10. AnoA2) Figs . We obseWe also observed potential erlotinib-induced effects on the localization of transporters and glycolysis enzymes that promote tumor growth. First, erlotinib treatment may result in increased localization of transporters ATP1A1 and SLC3A2 to the plasma membrane . Both ATOur results point to the importance of SFKs as key signaling intermediates in TKI-treated cells. First, multiple SFKs were “mutual friends” with direct connections to members of the EGFR signaling pathway and both the Transmembrane transport of small molecules pathway  and GlycWe also explored how several of the PTM clusters in the CCCN defined functional modules that respond to drug treatment Figs –S13. Onehttps://www.proteinatlas.org/ENSG00000005022-SLC25A5/pathology). Similarity to related SLC25A4 (ANT1) suggests the possibility that SLC25A5 (ANT2) may play a role as a low-conductance pore-forming component of the mitochondrial permeability transition pore (mPTP) .In other words, Cluster Pathway Evidence  = ∑Weights for PTMs with ambiguous protein assignments  were divided by the number of ambiguous proteins, so that ambiguous PTMs were weighted proportionally to the number of possible matches.The result was a matrix with pathway names in columns and individual clusters as rows. This matrix was interpreted as a bipartite graph where PTM clusters form one projection and BioPlanet pathways form the second projection. An edge table was constructed consisting of pathway pairs that have non-zero Cluster Pathway Evidence from the same cluster(s). For each pathway pair, Cluster Pathway Evidences for both of the pathways were summed for all clusters where both pathways were represented.and on one or more proteins from the “EGF/EGFR Signaling Pathway”. The individual Cluster Pathway Evidences for the two pathways ranged from 0.0278 to 0.122 for these 68 clusters and summed to 1.659, which is strong evidence for interactions between these two pathways.To illustrate how this method works, consider the two BioPlanet pathways "Transmembrane transport of small molecules" and "EGF/EGFR signaling pathway." There were 68 clusters that contained PTMs on one or more proteins from the “Transmembrane transport of small molecules pathway” The raw weights were then normalized to fall into the range 0–1 to produce the final PTM Cluster Weights. Sub-networks of the PCN that were selected by thresholding the various edge weights were graphed in Cytoscape using RCy3 ,78.i.e., GOBP terms associated with the gene). Next, we calculated the similarity of the two genes using the Otsuka–Ochiai coefficient :We defined the similarity of a pathway pair in three steps as the average similarity of their genes as measured by their GO Biological Process (GOBP) annotations. First, we used the Gene Ontology to define a vector of a gene’s GOBP annotations  from every Bioplanet pathway pair . Note that each pathway had a different number of genes, and large pathways may create too many low gene similarity values. We carefully tailored our similarity computations to avoid bias against large pathways in the following way. Once we computed all gene similarities among two pathways, we considered the top-N similar gene pairs to compute the pathway similarity:geneniand genenj are the nth highest similarity gene pair from Pathwayi and Pathwayj. Note that we do not use shared genes in GOSim computations .We computed gene similarity for every gene pair . With this approach, we achieved the best results with N = 30  were considered drug-affected . Drug-affected PTMs were mapped to CCCN clusters. For each sample group, for each cluster, a 2x2 contingency matrix was constructed with the following values: the number of drug-affected sites in the cluster, the number of drug-affected sites not in the cluster , the number of remaining (not drug-affected) sites in the cluster, and the number of remaining (not drug-affected) sites not in the cluster, and a one-sided Fisher Test was performed in R to identify clusters with more drug-affected sites than expected by chance. p-values were corrected for multiple testing using the Benjamini-Hochberg method. To limit the number of tests, only clusters with at least three drug-affected sites were included in the analysis. Clusters with corrected p-values < 0.05 were considered significantly enriched (Clusters in the CCCN that are significantly enriched for drug-affected PTMs were identified for the following groups of samples: (i) h3122.criz: H3122 cells treated with crizotinb (5 samples); (ii) hcc78.criz: HCC78 cells treated with crizotinib (2 samples); (iii) pc9.erl: PC9 cells treated with erlotinib (5 samples); all.criz: H3122 (5 samples), HCC78 (2 samples), H2228 (1 sample), and STE1 (1 sample) cells treated with crizotinib; (iv) all.erl: PC9 (5 samples), HCC4006 (1 sample), and HCC827 (1 sample) cells treated with erlotinib; (v) all.dasat: H2286 (1 sample) and H366 (1 sample) cells treated with dasatinib; (vi) all.drug: all samples from all.criz, all.erl, and all.dasat as well as H1781 cells (1 sample) treated with afatinib. The drug-treated vs. control ratio was calculated for each PTM in each sample, and, for each sample group, PTMs with at least two observations and a median ratio of at least 2.25-fold up or down (ratio > 1.17 or < -1.17 on a logenriched .gplots package). They display sample groups  as columns and PTM sites from clusters of interest as rows. Cells were colored to indicate either: (i) the median of the log2 ratio of each PTM site in drug-treated vs. control cells or (ii) sites that met the threshold to be selected for the enrichment analysis; sites with median log2 ratios less than -1.17 (2.25-fold down) in each sample group are shown in blue, and sites with median log2 ratios greater than 1.17 (2.25-fold up) are shown in yellow (black indicates sites that did not meet the threshold threshold). Sites that met the down threshold were assigned a value of -1, sites that met the up threshold were assigned a value of +1 and sites (rows) in the heatmap were ordered according to the sum of these values across all sample groups.Heatmaps were constructed using the R function heatmap.2  with a degree less than k  A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. 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Please don't hesitate to contact us if you have any questions or comments.Sincerely,Feixiong Cheng, Ph.D.Guest EditorPLOS Computational BiologyJian MaSection EditorPLOS Computational Biology***********************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Systematic analysis of Protein post-translational modifications (PTMs) data in lung cancer can try to model cell signaling pathways. Uncovering crosstalk among signaling pathways employing different PTMs offer potential drug targets and candidates for synergistic attack through combination drug therapy. To model lung cancer signaling at the protein level, the authors applied SEPTM proteomics to identify PTMs, and constructed the Co-Cluster Correlation Network (CCCN) and Cluster Filtered Network (CFN). They then constructed a Pathway Crosstalk Network (PCN) to identify relationships among known biological pathways in human cells. Furthermore, they compared their result with previously published data, and supported their analysis result. Generally, this study have done a beautiful and integrative work. The results suggest unappreciated connections between RTK signal transduction and oncogenic metabolic reprogramming in lung cancer. However, several minor problems should be acknowledged in this study.1)The main text is very long in this manuscript, which makes the readers hard to follow. I suggest some of the main text should be removed as supplementary method ;2)The resolution of some figures is low. They should be replaced with high resolution ones;3)The authors compared networks (CFNs) from different lung cancer PTM data sets, and reveals a common core of PPIs. The more potential application of this finding in future should be discussed.Reviewer #2: The manuscript, entitled “Network models of protein phosphorylation, acetylation, and ubiquitination connect metabolic and cell signaling pathways in lung cancer”, has studied signaling pathways disturbed by different tyrosine kinase inhibitors (TKIs) in ten lung cancer cell lines. With the help of Network models in the manuscript, the author showed the changes of affected cell lines in cell signaling pathways activated by oncogenes, transmembrane transport of small molecules, and glycolysis and gluconeogenesis. In my opinion, this work is interesting and helpful for Multi-drug Therapy for Cancer in the future. Therefore, I support the publication of this manuscript after the following question are followed:1. In Introduction, the author introduced the concepts and importance of Protein post-translational modifications、lung cancer and their hypothesis, making it easier to understand the study’s background and targets. However, the motivation and innovation of this project is not very clearly in this part. I suggest that the motivation, innovation as well as the main contribution of this project should be summarized in the Introduction section.2. The authors try to use Figure 1 to illustrate the main strategy of this project. However, I don’t think this figure is suitable to show the pipeline of the methods (at least it not very obviously). I suggest the authors to reorganize the Figure 1, which would make the work more understandable.3. After the introduction the network model, the results focus on the analysis of EGFR. And it seems that the network models just verify the importance of the EGFR. How we can learn from the network model is not very clear. And more statistic analysis of the network models should be provided.4. There are many methods to identify the core of network, such as the mining of the dense subgraph. The authors used the k-core decomposition. And what about the result of the other methods?5. In page 8, the authors stated that “chose one target for each drug …”. Why only one target was selected? How to choose the target for more than one targets of the drug?6. The statement “Cluster Pathway Evidence  =…” in page 32. I suggest the authors can use the mathematical formula to describe the calculation. As well as the GeneSim, Jaccard Similarity.7. In the definition of the GoSim, the authors state that they achieved the best results with N=30. What’s the meaning of the best results? Are there any other parameters used in the network models? And what’s the influence?Reviewer #3: Review has been uploaded as an attachment.**********Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: NoneReviewer #3: Yes**********what does this mean?). 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All press must be co-ordinated with PLOS.Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. Best regards,Feixiong Cheng, Ph.D.Guest EditorPLOS Computational BiologyJian MaSection EditorPLOS Computational Biology***********************************************************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: It can be accepted for publication nowReviewer #2: All my comments are well addressed, and I recommend its publication on PLoS Computational Biology.Reviewer #3: I think that authors have addressed the comments I made last time.**********Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). 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