Oral ulcer diseases are complex inflammatory diseases caused by multi-factors, which severely impact patient quality of life. Kouyanqing Granule (KYQG) has been used to treat inflammatory diseases of the mouth and throat, including recurrent aphthous stomatitis (RAS), traumatic ulcers, oral leukoplakia and so on. However, the underlying molecular mechanisms of KYQG in treating these diseases are still unclear. We aimed to explore the possible mechanisms in KYQG for the treatment of oral ulcers.An innovative network pharmacology method was established by incorporating targets searching and fishing, network analysis, and silico validation to discover the pharmacological mechanisms of action of KYQG for the treatment of oral ulcers. Then, we verified the reliability of this method by an animal experiment.Our data indicated that a total of 47 key targets were screened, which mainly involved in three function modules including the inhibition of inflammation, the regulation of immunological response, and the suppression of oxidative stress. The implementation of these functions relies on the complex multi-pathways network, especially TNF signaling pathway and HIF-1 signaling pathway. The results of the experimental verification indicated that KYQG significantly inhibited the serum levels of cyclooxygenase-2 (COX2), matrix metalloproteinase 9 (MMP9) and tumor necrosis factor-alpha (TNF-α) in rats with oral ulcer.KYQG exhibited the therapeutic effects on oral ulcers probably by inhibiting inflammation, regulating immunological response, and suppressing oxidative stress through a complex multi-pathways network. Particularly, TNF signaling pathway and HIF-1 signaling pathway may play crucial roles in the protection of KYQG against oral ulcers. This work not only offers a method for understanding the functional mechanisms of KYQG for treating oral ulcer diseases from a multi-scale perspective but also may provide an efficient way for research and development of complex composition formula. Oral ulcers are secondary lesions characterized by loss of mucosal tissue in the oral cavity. Usually, oral ulcers include conditions such as local trauma, recurrent aphthous stomatitis (RAS), viral and bacterial infections, allergic reactions, adverse drug reactions, or systemic disease , 2. OralOphiopogon japonicus (Thunb.) Ker-Gawl., root of Asparagus cochinchinensis (Lour.) Merr., flower bud of Lonicera macranthoides Hand.-Mazz., root of Scrophularia ningpoensis Hemsl., and root of Glycyrrhiza uralensis Fisch.. KYQG is recorded in the Chinese Pharmacopoeia 2015 edition to treat mouth and throat inflammatory diseases, such as RAS, oral leukoplakia, and oral lichen planus [Kouyanqing Granule (KYQG) is made from the following five herbs: the root of n planus . Miao etn planus . HoweverIn the present study, an innovative network platform was established to discover the potential pharmacological mechanisms of KYQG acting on oral ulcer through the following steps as shown in Fig. http://tcmspw.com/tcmsp.php) [http://ctdbase.org/) [http://www.swisstargetprediction.ch/) [In our previous study, the ingredients of KYQG were identified by using high resolution mass spectrometry technology, and we found that sixteen compounds play an important role in KYQG’s anti-inflammatory effects . Therefomsp.php) , Toxicogse.org/) , and Swiion.ch/) . To idenion.ch/) . We drewhttps://omim.org/), a database of gene-disease associations , Integrity (https://integrity.thomson-pharma.com/integrity/xmlxsl/), and Human Gene Database .To collect potential targets in oral ulcer, “oral ulcer/mouth ulcer” as the keywords were searched on the following resources: Online Mendelian Inheritance in Man  file for further network analysis by using Cytoscape software. The component target-oral ulcer target (CT-OT) network was constructed based on their PPI data and visualized by Cytoscape 3.7.0 software. The component targets were mapped to the oral ulcer targets for obtaining the common targets of both. Then the common targets and their directly connected neighbors were extracted from the CT-OT network to conduct a component-key target network. In the component-key target network, nodes represent components and targets, and edges represent the interactions between nodes. The targets without interaction were removed from the network. Afterward, the topological parameters of each node in the net were analyzed by using Network Analyzer tool in Cytoscape. The topological parameters including degree, betweenness centrality, and closeness centrality were used to evaluate the significance of the nodes in networks. The nodes with “Degree” values 2-fold greater than all the network nodes’ median value, “Closeness centrality” and “Betweenness centrality” values above all the network nodes’ median value were identified as the key targets [Protein-protein interaction (PPI) data were constructed by inputting official gene symbol to the “Multiple Proteins” search on String website , with or targets , 15.https://david.ncifcrf.gov/) [P < 0.05 was set. The specific operation steps were as following: inputting the official gene symbols of the key targets and setting the species to “Homo sapiens,” then selecting the functional annotation tool to perform GO enrichment and KEGG analysis. The top 15 pathways were mapped to the oral ulcer disease related pathways in the CTD database to get common pathways.The DAVID database (rf.gov/)  was usedThe 3D structures of the sixteen bioactive compounds were downloaded from PubChem website and saved as (.sdf) file. The structures were optimized with Discovery studio 2016. The molecular docking performance was operated by site-features directed docking (LibDock) for the study of the interactions between receptor and ligand according to previously published studies , 18. In Male Sprague-Dawley rats (Eight-week-old) were purchased from Guangdong Medical Laboratory Animal Center, and housed under a 12/12-h light-dark cycle with free supply of water and food. The rats were randomly divided into five groups of seven rats each: Control group, Model group and other three groups with different doses of KYQG treated. KYQG groups were administered orally at doses of 0.522, 1.57, and 4.70 g/kg/d, respectively, from day 1 to day 9, and other groups treated with distilled water. On day 4, buccal mucosa of rats in the Model and KYQG groups were chemically damaged with phenol to create oral ulcers as described in our previous study [After the end of sleep deprivation, the rats were anesthetized under isoflurane  delivered through airstream at a flow rate of 500 μL/min using Matrx anesthesia machine (Midmark Corp.). Then, blood was collected from all anesthetized rats via the abdominal aortic, which sacrificed the rats due to bleed to death.P < 0.05.The data represent the mean values ± standard deviation (SD) for each group. The graphics presented and statistical analyses were performed using GraphPad Prism 6 program (Version 6.01). Statistical analysis was performed using the one-way ANOVA followed by the Tukey test for multiple comparisons. Results were considered significant at A total of 274 potential targets were obtained for sixteen active components. Detailed information is described in Supplementary Table In the present study, to estimate the significance of the nodes in a network, degree and betweenness centrality were calculated. Degree represents the number of edges connected to the node. Betweenness centrality reflects the times of a node served as a bridge providing the shortest path between two other nodes . TherefoP < 0.05. The top 10 significantly enriched terms in the biological process, cell component, and molecular function categories are shown in Fig. P-value < 0.05  and Toll-like receptor signaling pathway . Eight targets are enriched in NF-kappa B signaling pathway  and Jak-STAT signaling pathway , and seven targets enriched in B cell receptor signaling pathway . The T cell receptor signaling pathway has also been implicated in the stimulation of an abnormal Th1 immune response in RAS patients [Presently, 14 targets including BCL2, EGFR, EGF, HMOX1, IFNG, NOS2, NOS3, TLR4, VEGFA, RELA, EDN1, IL6, MAPK1, and MAPK3 are involved in HIF-1 signaling pathway which is an important regulator of the immune system. NOS2 and NOS3 are two isoforms of NOS, which produce high-level sustained NO synthesis and strongly affect adaptive immune responses . NO regupatients . TherefoAmong the top 15 pathways, the PI3K-Akt signaling pathway is considered to regulate fundamental cellular functions such as cell growth, proliferation, and cell cycle . It has The targets with the protein original ligand inhibitor in both HIF-1 signaling pathway and TNF signaling pathway were selected to perform molecular docking. Herein, the highest LibDockScore value was recorded, which represents molecular docking with the best combining conformation  levels in serum. These results suggest that the therapeutic effects of KYQG were related to the decreases of COX2, MMP9, and TNF-α, which are consistent with our network pharmacology study. Thus, these combined results suggest our network pharmacology method was considered to be reliable.We preliminarily inferred the active mechanisms of KYQG by using present network pharmacology method, but such a method needs for further experimental verification. In order to demonstrate the reliability of our method, we conducted the animal experiment on an oral ulcer model in rats. In the component-key target network, PTGS2, referred to as cyclooxygenase 2 (COX2), MMP9, and TNF-α are the top three degree-ranked targets, which were considered as the important roles in oral ulcers treatment of KYQG. Therefore, we detected the COX2, MMP9, and TNF-α levels in serum. As shown in Fig. Oral ulcers are complex inflammation diseases caused by multi-factors and impact patients’ quality of life severely. The etiology of oral ulcerations is not entirely clear, and it has been reported that several local, systemic, iatrogenic, immunologic, genetic, allergic, nutritional, and microbial factors are responsible . Many stPresently, a comprehensive network pharmacology method was established to study the therapeutic targets and possible mechanisms of KYQG for treating oral ulcers.The mechanisms of KYQG for treating oral ulcers are fulfilled at least through its three function modules, i.e., the inhibition of inflammation, the regulation of immunological response, and the suppression of oxidative stress. And the implementation of these functions relies on a smooth run of the complex multi-pathways network, especially TNF signaling pathway and HIF-1 signaling pathway. Moreover, we adopted in silico validation for studying the interactions between the bioactive compounds and key targets in the predicted pathways. In silico validation of key targets for bioactive components offers an alternative path for the exploration of ligand-target interactions and action mechanisms. We could conclude that the different components display diverse interactions with these targets in the predicted pathways. The results demonstrated a synergistic effect mode of the KYQG in its oral ulcer treatment effects. For example, isochlorogenic acid B could interact with MMP-9 by active site residues TYR179, VAL101, and PHE110; cryptochlorogenic acid interacts with PTGS2 by active site residue SER530, and Isochlorogenic acid A interacts with HMOX-1 by active site residue ASP140. Consequently, the strong interactions between these active components and targets are the basis of these molecules’ biological activities. Usually, TCM formula tends to play a role in a holistic manner for the treatment of complex disease. The molecular docking study helps us to better understand the synthetic functions of multi-components in KYQG for oral ulcer treatment. To demonstrate the reliability of our method, we conducted the animal experiment on an oral ulcer model in rats and detected the serum levels of COX2, MMP9, and TNF-α. MMP9 has a significant association with RAS , and COXIn summary, KYQG is advantageous as complementary medicine in the multimodal treatment of oral ulcer disease due to its multi-component and multi-target characteristics. KYQG exhibited the therapeutic effects on oral ulcers probably by inhibiting inflammation, regulating immunological response, and suppressing oxidative stress based on 47 key targets, such as MMP9, COX2, and TNF-α. Particularly, TNF signaling pathway and HIF-1 signaling pathway may play crucial roles in the protection of KYQG against oral ulcers. Besides, this work is expected to be useful for carrying out a systematic study of multi-component formula as well as developing novel bioactive ingredients.Additional file 1: Supplementary Table 1. The table of compound targets.Additional file 2: Supplementary Table 2. The table of oral ulcer related targets.Additional file 3: Supplementary Table 3. The results of GO analysis.Additional file 4: Supplementary Table 4. The results of pathway enrichment.Additional file 5: Supplementary Table 5. LibDockScore results of molecular docking.Additional file 6: Supplementary Figure 1. The time-line diagram of animal experiment.Additional file 7: Supplementary Figure 2. The component target-oral ulcer target (CT-OT) network.Additional file 8: Supplementary Figure 3. The protein-protein interaction network.


Atherosclerosis is a common metabolic disease characterized by lipid metabolic disorder. The processes of atherosclerosis include endothelial dysfunction, new endothelial layer formation, lipid sediment, foam cell formation, plaque formation, and plaque burst. Owing to the adverse effects of first-line medications, it is urgent to discover new medications to deal with atherosclerosis. Berberine is one of the most promising natural products derived from traditional Chinese medicine. However, the panoramic mechanism of berberine against atherosclerosis has not been discovered clearly. In this study, we used network pharmacology to investigate the interaction between berberine and atherosclerosis. We identified potential targets related to berberine and atherosclerosis from several databases. A total of 31 and 331 putative targets for berberine and atherosclerosis were identified, respectively. Then, we constructed berberine and atherosclerosis targets with PPI data. Berberine targets network with PPI data had 3204 nodes and 79437 edges. Atherosclerosis targets network with PPI data had 5451 nodes and 130891 edges. Furthermore, we merged the two PPI networks and obtained the core PPI network from the merged PPI network. The core PPI network had 132 nodes and 3339 edges. At last, we performed functional enrichment analyses including GO and KEGG pathway analysis in David database. GO analysis indicated that the biological processes were correlated with G1/S transition of mitotic cells cycle. KEGG pathway analysis found that the pathways directly associated with berberine against atherosclerosis were cell cycle, ubiquitin mediated proteolysis, MAPK signaling pathway, and PI3K-Akt signaling pathway. After combining the results in context with the available treatments for atherosclerosis, we considered that berberine inhibited inflammation and cell proliferation in the treatment of atherosclerosis. Our study provided a valid theoretical foundation for future research. Atherosclerosis is a common chronic disorder that plaque builds up in the arteries . Plaque Berberine is a bioactive ingredient discovered in many plants such as Chinese goldthread, goldenseal, European barberry, tree turmeric, and Phellodendron .  FigureCoptidis rhizome, Scutellariae radix, Puerariae lobatae radix, Glycyrrhizae radix) and Huanglian-Jie-Du decoction  contain berberine [β signaling axis [Traditional Chinese medicine takes into account that berberine has excellent effects including resolving dampness, clearing away heat, detoxification, and purging fire. In addition, berberine is the main ingredient in the many famous decoctions of Chinese medicine. The famous decoctions Gegen-Huangqin-Huanglian decoction  networks of berberine targets and atherosclerosis targets. Later, we merged the two PPI networks and analyzed the network topological features to obtain the core PPI network. At last, we performed the functional enrichment analyses including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis to characterize the biological processes and pathways. The results of this study provided a comprehensive and meaningful analysis of berberine in treating atherosclerosis. This study also provided a novel mechanism of berberine against atherosclerosis.http://lsp.nwu.edu.cn/tcmsp.php) is a unique system pharmacological database of traditional Chinese medicines describing the relationships between drugs, targets, and diseases. STITCH database (http://stitch.embl.de/) is a useful tool to discover the interaction networks of chemicals and proteins.Two databases were conducted to identify potential targets related to berberine. The two databases included traditional Chinese medicine systems pharmacology (TCMSP) database and STITCH database. TCMSP database (https://db.idrblab.org/ttd/) is a useful database providing data about exploring therapeutic protein and nucleic acid targets, the targeted disease. The DrugBank database (https://www.drugbank.ca/) is a powerful and comprehensive bioinformatics database containing information on drugs and disease. GAD database (https://geneticassociationdb.nih.gov/) is a useful database of genetic and protein association data from complex disorders and diseases. OMIM (https://www.omim.org/) is an authoritative, timely, and comprehensive database about human genes and genetic diseases.Four databases were applied to identify potential targets related to atherosclerosis. The four databases included Therapeutic Target Database (TTD), DrugBank database, Genetic Association Database (GAD), and Online Mendelian Inheritance in Man (OMIM). TTD , the Biological General Repository for Interaction Datasets (BioGRID), the Human Protein Reference Database (HPRD), the IntAct Molecular Interaction Database (IntAct), the Molecular INTeraction Database (MINT), and the Biomolecular Interaction Network Database (BIND). PPI network predicting berberine targets was visualized and constructed by Cytoscape software . In addition, the PPI network predicting atherosclerosis targets was visualized and constructed by Cytoscape software, either. After obtaining two PPI networks, we merged these two networks to achieve the core PPI network. Later, topological features of the core PPI network were analyzed.Six parameters were used to analyze the topological properties of every node in the interaction network. The six parameters included degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), local average connectivity (LAC), and network centrality (NC). The plugin cytoNCA in the Cytoscape software was conducted to perform the analysis. The computation equations and definitions of these parameters indicated the topological importance of nodes in the core PPI network.https://www.omicshare.com/) was used to visualize the bubble chart.The Database for Annotation, Visualization, and Integrated Discovery (David) was used to perform GO and KEGG pathway analyses in this study. GO analysis is a useful bioinformatics tool to characterize molecular function (MF), cellular components (CC), and biological process (BP) of genes. KEGG pathway analysis is a collection of databases describing biological pathways, genomes, drugs, and diseases. The OmicShare platform , estrogen receptor 1 (ESR1), tumor protein p53 (TP53), cullin 3 (CUL3), heat shock protein 90 (HSP90), amyloid beta precursor protein (APP), minichromosome maintenance complex component 2 (MCM2), fibronectin 1 (FN1), ubiquitin C (UBC), and so on.David was used to perform GO and KEGG pathway analysis in this study. GO analysis of candidate targets for berberine against atherosclerosis was conducted to discover the biological process in David database. The results showed that the biological processes were correlated with the regulation of transcription from RNA polymerase II promoter, negative regulation of the apoptotic process, regulation of DNA-templated transcription, G1/S transition of the mitotic cell cycle, DNA damage response, protein ubiquitination, transcription-coupled nucleotide-excision repair, nucleotide-excision repair . The tarBesides, we conducted a KEGG pathway analysis to investigate biological pathways of berberine against atherosclerosis. The top 10 signaling pathways were listed as follows: cell cycle, ubiquitin mediated proteolysis, MAPK signaling pathway, PI3K-Akt signaling pathway, Estrogen signaling pathway, adherens junction, spliceosome, HIF-1 signaling pathway, NOD-like receptor signaling pathway, and Hippo signaling pathway . The tarAtherosclerosis is a common metabolic disease characterized by lipid metabolic disorder. It is a multifactorial pathological disorder changing the morphology and function of arterial walls , 20. Stain vivo. Berberine administration significantly decreased the atherosclerotic plaque area in ApoE−/− mice [−/− mice, berberine promoted the stability of plaque in the artery by downregulating the oxidative stress [As a member of traditional medicine, berberine is widely used in Asian countries such as China, Korea, and Japan owing to the clinical and safety profiles . Previou−/− mice . In homoe stress . HoweverCurrent treatments of atherosclerosis aim to control risk factors and to maintain perfusion in affected arteries. The available drugs to deal with atherosclerosis include cholesterol medication, antiplatelet medication, and other medications . Cholestα, IL-6, and IFN-γ produced by SMCs and macrophages could be downregulated by statins [α, MCP-1, and NF-κB activity were inhibited by aspirin treatment. The endothelial function in the presence of inflammation could be protected by aspirin. In our study, we revealed that the beneficial effects of berberine in atherosclerosis are attributed to their anti-inflammatory properties. KEGG analysis in our study indicated that signaling pathways of berberine against atherosclerosis were related to ubiquitin mediated proteolysis, MAPK signaling pathway, and PI3K-Akt signaling pathway. These inflammatory processes take part in the different stages of atherosclerosis. Ubiquitin-proteasome system could regulate many biological cellular processes such as inflammation and oxidative stress [α MAPK. PI3K-Akt signaling pathway could regulate inflammatory processes of atherosclerosis including cellular composition and final fibrous cap establishment [Firstly, available treatment statins and aspirin alleviate the inflammatory status of atherosclerosis. Atherosclerosis is a chronic inflammatory disease, and inflammation plays a critical role in the pathogenesis of atherosclerosis . In the  statins . Apart f statins . Vasculae stress . Dysfunce stress . Foam celishment . InhibitExcept for inhibiting inflammation, available treatments such as statins inhibit cell proliferation in the treatment of atherosclerosis . The proTaken together, the available treatments such as aspirin and statins deal with atherosclerosis by inhibiting inflammation and cell proliferation. After combining the results in context with the available treatments for atherosclerosis, we considered that berberine inhibits inflammation and cell proliferation in the treatment of atherosclerosis. The inflammatory pathways including ubiquitin mediated proteolysis, MAPK signaling pathway, and PI3K-Akt signaling pathway could be suppressed by berberine. Aberrant cell proliferation could be attenuated by barbering by regulating the cell cycle. G1/S transition of mitotic cells cycle might be involved in the mechanism of berberine in regulating cell cycle.In conclusion, the multitargets and multicomponents of berberine effects against atherosclerosis were clearly clarified with network pharmacology. We identified potential targets related to berberine and atherosclerosis with several databases. Then, we merged the two PPI networks and obtained the core PPI network from the merged PPI network. At last, we performed functional enrichment analyses including GO and KEGG pathway analysis to clarify the multiple mechanisms of berberine against atherosclerosis. The pathways directly associated with berberine against atherosclerosis were cell cycle, ubiquitin mediated proteolysis, MAPK signaling pathway, and PI3K-Akt signaling pathway. After combining the results in context with the available treatments for atherosclerosis, we considered that berberine could inhibit inflammation and cell proliferation in the treatment of atherosclerosis.


Firstly, 324 active compounds have been identified in the GDL formula. Meanwhile, we identified 1496 human genes which are related to WD or liver cirrhosis. Functional and pathway enrichment analysis indicated that NOD-like receptor signaling pathway, bile secretion, calcium signaling pathway, steroid hormone biosynthesis, T cell receptor signaling pathway, apoptosis, MAPK signaling pathway, and so forth can be obviously regulated by GDL. Further, in a mouse model of WD, in vivo experiments showed that GDL treatment can not only reduce the pathological symptoms of the liver but also reduce the apoptosis of hepatocytes.  In this study, systemic pharmacological methods were proposed and the mechanism of GDL combined therapy for WD was explored. This method can be used as a reference for the study of other mechanisms of traditional Chinese medicine. Wilson's disease (WD) is a hereditary disease which was first proposed by Kinnear Wilson in 1912 and caused by mutations in the ATP7B copper transporter gene that causes copper to accumulate in the liver and brain . There iPenicillium has therapeutic effects on liver diseases [Gynochthodes officinalis (F.C.How) Razafim. & B.Bremer , Schisandra chinensis (Turcz.) Baill , Rheum officinale Baill , Curcuma zedoaria (Christm.) Roscoe , Coptis chinensis Franch , Curcuma longa L. , Lysimachia christinae Hance , and Rehmannia glutinosa (Gaertn.) DC.  [According to its pathogenesis, WD is treated by blocking intestinal copper uptake by chelating agents, such as triene amine, tetrathiomolybdate, or zinc salts. However, their high cost or side effects may hinder clinical use . At presdiseases , 13, espdiseases , 13. A ldiseases , 15, whidiseases –19. Specng, SDH) , 21.Network pharmacology is an emerging discipline, which is based on the characteristics of biomolecules and a number of authoritative databases showing us a preliminary understanding of mechanisms of medicine and disease. The previous research study used the method of network pharmacology to provide a promising method for understanding the pharmacological mechanism of TCM. In the article, we developed a network pharmacology analysis to determine the anti-WD mechanism of GDL and the experiments in vitro were then performed given the results of network pharmacology analysis.http://lsp.nwu.edu.cn/index.php) [GanDouLing is a TCM, and as an in-hospital preparation, it was prepared by the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine and the quality control of chemical fingerprint has been performed , 15, whidex.php)  with thedex.php) , drug-lidex.php) , and so dex.php) ; next, tdex.php)  and DisGdex.php) , 27 weredex.php)  and KEGGdex.php)  pathway dex.php) . Last, tdex.php)  and visudex.php) , and thedex.php) .Twenty female TX mice (20 ± 2 g) and ten female DL mice were at the ages of 8–10 weeks . OriginaThe twenty female TX mice were divided into two groups, 10 mice in each group: the Wilson group and GanDouLing group  . The tenThe mice liver isolated tissues were fixed with 4% paraformaldehyde for 3 h and then dehydrated with ethanol and xylene. According to the previous literature, staining was carried out for liver samples . The resAccording to the previous literature, HE staining was carried out for liver samples . The detCaspase-3 activity assay kit  and Caspase-8 activity assay kit  were used to detect the activity of caspase-3 and caspase-8 in three groups, according to the manufacturer's protocol.t-test was used for analyzing the comparison. When P < 0.05, it was considered statistically significant.GraphPad Prism 7 was employed for comparing the expression of Bcl-2 and Bax and the activity of caspase-3 and caspase-8. And the method of Student's Following a system pharmacology model, the pharmacology mechanisms of WD by GDL were encoded. Firstly, all compounds of GDL were gathered from a database. Next, the active components were filtered out by the ADME method. Then, the targets, pathway, and PPI network were identified from integrated predictive models. Finally, we employed the TX-j mouse model for proving that GDL could improve WD liver injury by regulating apoptosis.In total, 802 compounds were obtained for GDL, including 174 compounds for Bajitian, 130 compounds for Beiwuweizi, 92 compounds for Dahuang, 202 compounds for Danshen, 81 compounds for Ezhu, 48 compounds for Huanglian, 52 compounds for Jianghuang, 61 compounds for Jinqiancao, and 76 compounds for Shudihuang . Up to 6To explain the molecular biology of the therapy mechanism of GDL for WD, all target proteins were used to perform the enrichment of GO and KEGG pathways by clusterProfiler.P < 0.05 were significantly enriched with 287 targets, 1915 of which were BP, 108 of which were CC, and 192 of which were MF. The top 10 enriched GO biological processes were response to organic cyclic compound (GO:0014070), response to hormone (GO:0009725), response to organic substance (GO:0010033), response to lipid (GO:0033993), response to organonitrogen compound (GO:0010243), regulation of cell proliferation (GO:0042127), response to steroid hormone (GO:0048545), response to drug (GO:0042493), cell proliferation (GO:0008283), and cellular response to organic substance (GO:0071310). In particular, the results show that targets are involved in the regulation of cell death , apoptotic process , inflammatory response , and so forth. Extracellular space (GO:0005615), cell surface (GO:0009986), vesicle (GO:0031982), membrane-bounded vesicle (GO:0031988), extracellular region (GO:0005576), cytoplasmic vesicle (GO:0031410), cytoplasmic membrane-bounded vesicle (GO:0016023), secretory granule (GO:0030141), cytoplasmic membrane-bounded vesicle lumen (GO:0060205), and cytosol (GO:0005829) were the top 10 enriched cellular components. The enriched GO molecular functions were receptor binding (GO:0005102), oxidoreductase activity (GO:0016491), identical protein binding (GO:0042802), enzyme binding (GO:0019899), steroid binding (GO:0005496), transcription factor binding (GO:0008134), carboxylic acid binding (GO:0031406), organic acid binding (GO:0043177), antioxidant activity (GO:0016209), and iron ion binding (GO:0005506). The top 30 GO-terms with the highest enrichment factor are shown in Enrichment analysis showed that a total of 2216 GO-terms with P < 0.05. The top 10 enriched pathways were Chagas disease (American trypanosomiasis) (hsa05142), prostate cancer (hsa05215), leishmaniasis (hsa05140), osteoclast differentiation (hsa04380), malaria (hsa05144), African trypanosomiasis (hsa05143), pathways in cancer (hsa05200), adipocytokine signaling pathway (hsa04920), rheumatoid arthritis (hsa05323), and NOD-like receptor signaling pathway (hsa04621). In particular, we found that targets are involved in the bile secretion , calcium signaling pathway , T cell receptor signaling pathway , fatty acid degradation , apoptosis , MAPK signaling pathway , cytokine-cytokine receptor interaction , PPAR signaling pathway , Toll-like receptor signaling pathway , ErbB signaling pathway , B cell receptor signaling pathway , Jak-STAT signaling pathway , and so forth. The top 30 pathway terms with the highest enrichment factors are shown in For 287 targets, a total of 176 pathway terms were enriched by KEGG enrichment analysis, and 43 KEGG pathway terms were significant with The PPI network for the target genes was constructed based on the STRING database. As shown in The plug-in of MCODE was used to identify the modules with the PPI, and a total of 13 modules were gained. By sorting with MCODE score, two modules with MCODE score ≥3 and nodes ≥10 were screened out and named module 1 and module 2. And then, the subnetwork visualization was performed for module 1 and module 2 .In order to observe the protection effects of GDL with WD, TX mice were treated by intragastric administration with GanDouLing in 0.486 g/kg every day for 8 weeks. The results indicated that WD induced liver injury in the mouse model. HE staining results shown in Collagen was stained blue in liver sections with Masson staining. The TX mice showed obvious collagen accumulation compared with the control group , and admIn the analysis of network pharmacology, we found that there are more than 102 targets for anti-WD with GDL enriched to cell death, and up to 10 targets for anti-WD with GDL were enriched to the apoptosis signaling pathway, including NFKB1, PPP3CA, APAF1, RELA, AKT1, PPP3CB, IL1B, NFKBIA, TNF, and BCL2.In order to explore the possible mechanism for WD, effects of the GDL formula on apoptosis relative proteins including Bcl-2 and Bax were observed using the western blotting method. In comparison with the control group, Bax was obviously overexpressed and Bcl-2 was suppressed in the Wilson group, and GanDouLing could reverse the expression of Bax and Bcl-2 Figures . As showβ 1/Smad signal pathway is relatively important. They also demonstrated through animal experiments that SCG can significantly improve liver fibrosis by inhibiting TGF-β 1/Smad pathway [At present, systemic pharmacology provides a powerful tool for encoding the compatibility and mechanism of traditional Chinese medicine –37; ther pathway . In orde pathway . At the  pathway –43.In this study, we performed the system pharmacology to construct a strategy for decoding the TCM pharmacologic molecular mechanism of GanDouLing which has proved that it can be an effective improvement of liver and neuropathy in clinical, animal, and cell models –19 With The TX-j mouse model was employed for proving that GDL could improve WD liver injury, by regulating apoptosis. Firstly, HE and Masson staining showed that GanDouLing could improve the necrosis and fibrosis of the TX-j liver tissue. Furthermore, targeting the apoptosis signaling pathway identified by network pharmacology, we found that GanDouLing significantly inhibited the expression of proapoptosis Bax protein and promoted the expression of antiapoptosis BCL2. In addition, the activity of caspase-3 and caspase-8 was tested and found to be significantly increased in TX-j mice, which was consistent with the previous research results: the activity of caspase-3 increased in WD patients or copper-induced hepatocytes , 50. MeaIn conclusion, the mechanism of GDL in the treatment of WD involves a variety of active components, targets, and pathways. In this study, 151 active components, 287 potential targets, and 43 related signaling pathways were predicted. Animal validation tests showed that GDL could effectively improve WD liver lesions, especially by inhibiting the apoptosis of liver tissues.


The goal of metabolomics is to measure as many metabolites as possible in order to capture biomarkers that may indicate disease mechanisms. Variable selection in chemometric methods can be divided into the following two groups: (1) sparse methods that find the minimal set of variables to discriminate between groups and (2) methods that find all variables important for discrimination. Such important variables can be summarized into metabolic pathways using pathway analysis tools like Mummichog. As a test case, we studied the metabolic effects of treatment with nicotinamide riboside, a form of vitamin B3, in a cohort of patients with ataxia–telangiectasia. Vitamin B3 is an important co-factor for many enzymatic reactions in the human body. Thus, the variable selection method was expected to find vitamin B3 metabolites and also other secondary metabolic changes during treatment. However, sparse methods did not select any vitamin B3 metabolites despite the fact that these metabolites showed a large difference when comparing intensity before and during treatment. Univariate analysis or significance multivariate correlation (sMC) in combination with pathway analysis using Mummichog were able to select vitamin B3 metabolites. Moreover, sMC analysis found additional metabolites. Therefore, in our comparative study, sMC displayed the best performance for selection of relevant variables.  Metabolites are small molecules that are the substrates, intermediates, or end products of metabolism. Alterations in metabolite concentration may be related to a disease or may be caused by xenobiotics . Untarget-tests and also multivariate analysis such as principal component analysis (PCA), partial least squares (PLS), and orthogonal projections to latent structures discriminant analysis (OPLS-DA)  adduct at a retention time of 2.86 minutes and M+CH3COO[−] adduct at 2.97 minutes for metabolite 3 or 4. Mummichog probably assigns many false positives at 5 ppm and misses some  at 1 ppm. Most features and metabolites in p-value is low and the sMC F-value is extremely high. A high sMC F-value means the feature explained much variance of the response to treatment compared to other structures in the data. Nicotinic acid (m/z 87.0089 (C3H4O3 M − H[−]) was increased more than 47-fold upon treatment. Mummichog assigned this feature to eight metabolites and, of the total fifteen features that CARS-PLS-DA found, only two others were assigned to known metabolites. The same features were also found with sPLS-DA, but additionally, eleven more features could be annotated including the M + Cl[−] of metabolite 3 or 4. All of the 48 selected features had a high positive (>0.63) or high negative (<-.55) correlation with the features of p-values were randomly permutated 100 times, each time the significance level was calculated for the pathway, and the part of permutations in which the pathway had a lower significance level than the actual data was calculated. The permutation test results of the vitamin B3 pathway can be seen in The feature table used for analysis contained a total of 22,684 features. p-values derived from the Wilcoxon sign test only found the vitamin B3 pathway to be significantly altered, whereas sMC identified additional significant pathways. The metabolic features associated with metabolites of the arachidonic acid pathway were decreased during nicotinamide riboside treatment. Another pathway found with sMC at α = 0.01 involved methionine and cysteine metabolism. Adenosine and another metabolite were significantly increased, one metabolite was significantly decreased, and five metabolites showed a significant multivariate correlation with response to the treatment, including cysteine. The additional pathways identified by Mummichog are listed in Finally, pathway analysis does not show any relationship between the vitamin B3 metabolites and other metabolites. For this reason, the correlation matrix was calculated between all significant features annotated with primary ions. An absolute correlation of 0.8 was used as cut-off to find structures in the data. Most features were uncorrelated except for one highly connected network containing 21 features, including the vitamin B3 metabolites. The following assigned metabolites in the network were significantly increased upon treatment with nicotinamide riboside: nicotinamide, nicotinic acid, N1-methyl-4-pyridone-5-carboxamide, N1-methyl-2-pyridone-5-carboxamide, hypoxanthine, guanosine, inosine, adenosine, and two more. Four assigned metabolites were significantly decreased. The purine metabolism pathway was not significant in the pathway analysis because many non-significant features were also assigned to metabolites from this pathway. Untargeted metabolomics with LC/MS identifies thousands of features in patient body fluids. Adequate variable selection is therefore essential to find clinically relevant features. In our study, a cohort of patients with A–T was sampled before and during treatment with nicotinamide riboside. The goal of this study was to compare multiple variable selection methods on their ability to find the nicotinamide riboside-related metabolites and how treatment with vitamin B3, as an important co-factor in human metabolism, would influence other metabolic pathways. All the compared variable selection methods could easily distinguish between before and during treatment patient samples, indicating that the individual variability is much smaller than the difference between before and during treatment. The sparse methods CARS-PLS-DA and sPLS-DA were used to find the minimal set needed for optimal discrimination between before and during treatment. Wilcoxon signed test and partial least squares in combination with VIP and sMC were used to find all discriminatory features. Although no metabolic feature was assigned to nicotinamide riboside, features were assigned to vitamin B3 metabolites. The reason that we do not observe nicotinamide riboside itself as increased may be found in matrix-related ion suppression, as we were able to detect the pure compound when dissolved in water. Another reason could be that nicotinamide riboside itself is metabolized rapidly to the associated metabolites that we do assign.p-value, high fold change, high VIP, and high sMC. However, sparse methods only need a subset of those features and thus may only find features that correlate to the feature of interest. For example, CARS-PLS-DA did not find any metabolic feature related to the vitamin B3 pathway and sPLS-DA only found one metabolic feature. The found features did have a high correlation with the vitamin B3 metabolic features. Moreover, not all found features had the highest multivariate correlation significance. These sparse methods probably use some high discriminatory features to discriminate and some less discriminatory features to stabilize the multivariate space. Variable selection in combination with random forest or support vector machines was briefly explored (results not shown), but we did not find any deterministic results and neither did variable selection based on genetic algorithms, because there are multiple subsets possible of the hundreds of discriminatory features. The vitamin B3-related features were significantly increased and had a low Wilcoxon signed test m/z value. Mummichog thus considers the ppm value and the number of adducts that were also assigned to the same metabolite [Another challenge in untargeted metabolomics is the annotation of features to metabolites. Mummichog can automatically assign features to metabolites that are in known metabolic pathways. At a molecular weight tolerance of 1 ppm, Mummichog only assigned 4.55% of all features. Using a higher molecular weight tolerance leads to more assignments, but also increases the rate of false-positive assignments. Mummichog automatically assigns all features to metabolites based on their tabolite . For thetabolite . For all−7 found the same number of metabolites as the Wilcoxon signed rank test for the arachidonic acid pathway, but because the total number of significant metabolites was lower, arachidonic acid was a significant pathway when using sMC compared to the Wilcoxon signed rank test. Thus, finding more important features is of less value if these features are not biochemically related and sMC may help to find many correlated features. Pathway analysis of the effects of nicotinamide riboside treatment using sMC as input for the Mummichog algorithm pointed to vitamin B3, arachidonic acid, and methionine and cysteine metabolism to be affected. Metabolites of arachidonic acid were decreased and vitamin B3 is known to inhibit the breakdown of lipids . In anotPlasma samples from 14 patients with A–T before treatment and during treatment with vitamin B3 (nicotinamide riboside) were measured using UHPLC-QTOF-MS in both positive and negative ionization mode according to a previously described procedure . Data fim/z value, a retention time, and an intensity (relative abundance) for every measured sample. Features with more than 20% zeroes  in the samples or with any zero values in the QC samples were removed. Features with an m/z value lower than 70 or higher than 700 were removed. Features with a retention time lower than 0.4 minutes or higher than 16 minutes were removed. Intra-batch correction was performed using the internal QC samples and support vector regression (QC-SVR) [The preprocessed output encompasses a large feature intensity table where each feature contains an (QC-SVR) . Other p(QC-SVR) , KNN imp(QC-SVR) , and mea(QC-SVR) . The ratFrom each duplicate measurement, the mean was calculated prior to univariate analysis with a Wilcoxon signed rank test. Multiple testing correction was performed using the false discovery rate (FDR) approach of Benjamini–Hochberg . For mulhttps://www.metaboanalyst.ca) [m/z values, retention time values, p-values, and information on whether the feature is acquired from negative or positive ionization mode. The sMC F values were converted to p-values and were used, as well as the p-values of the univariate test. The molecular weight tolerance was used at 1, 3, and 5 ppm together with the option to enforce primary ions. Only features with at least one assigned primary ion  were valid. The p-value cut-off was set to α = 0.01 or 3 × 10−7 for sMC and to 0.0012 (FDR-corrected α = 0.05) for univariate testing. The human reference pathways of the MetaFishNet database were used for pathway analysis in Mummichog [Mummichog v2.0 was used along with the MS Peaks-to-Pathway module available on Metaboanalyst v4.0 (lyst.ca) ,33. The ummichog .Through untargeted metabolomics, hundreds of altered metabolic features were identified in ataxia–telangiectasia (A–T) patients upon treatment with nicotinamide riboside, which included vitamin B3-associated metabolites. Chemometric methods can easily distinguish between samples obtained before and during treatment; however, assigning all the metabolic features to metabolites and connecting them to disease mechanisms is still a challenging task. Moreover, the definition of a limited set of key metabolites to monitor therapy response is preferred in light of future application in clinical diagnostics. p-values obtained from univariate testing only found the vitamin B3 pathway. Using Mummichog with significant multivariate correlation based on partial least squares discriminant analysis also found the vitamin B3 pathway and additionally found the arachidonic acid pathway. The metabolites related to the arachidonic acid pathway still need further confirmation of their identity, to draw definite conclusions. Additional correlation analysis of the significant annotated features found that the purine nucleosides, i.e., adenosine, inosine, and guanosine, correlate to the vitamin B3 metabolites. In conclusion, our data show that treatment with nicotinamide riboside in patients with A–T leads to an increase of vitamin B3 metabolites in plasma as expected, and also increases plasma purine nucleosides and may decrease metabolites in the arachidonic acid pathway. Even though we realize that many metabolic features that may provide additional relevant information still remain unannotated, we show that significant multivariate correlation in combination with pathway analysis and correlation analysis is able to render more leads for putatively affected biochemical processes as compared to sparse methods.For this reason, sparse methods were tested that focus on finding a minimum set of metabolic features to discriminate between patient samples before and during treatment. However, these sparse methods did not find vitamin B3 metabolites but only found features that correlate with the vitamin B3 metabolites. Another approach is to summarize the detected metabolites into their related pathways using pathway analysis. Mummichog in combination with


To reveal the molecular mechanisms of ulcerative colitis (UC) and provide potential biomarkers for UC gene therapy.  We downloaded the GSE87473 microarray dataset from the Gene Expression Omnibus (GEO) and identified the differentially expressed genes (DEGs) between UC samples and normal samples. Then, a module partition analysis was performed based on a weighted gene coexpression network analysis (WGCNA), followed by pathway and functional enrichment analyses. Furthermore, we investigated the hub genes. At last, data validation was performed to ensure the reliability of the hub genes.  Between the UC group and normal group, 988 DEGs were investigated. The DEGs were clustered into 5 modules using WGCNA. These DEGs were mainly enriched in functions such as the immune response, the inflammatory response, and chemotaxis, and they were mainly enriched in KEGG pathways such as the cytokine-cytokine receptor interaction, chemokine signaling pathway, and complement and coagulation cascades. The hub genes, including dual oxidase maturation factor 2 (DUOXA2), serum amyloid A (SAA) 1 and SAA2, TNFAIP3-interacting protein 3 (TNIP3), C-X-C motif chemokine (CXCL1), solute carrier family 6 member 14 (SLC6A14), and complement decay-accelerating factor (CD antigen CD55), were revealed as potential tissue biomarkers for UC diagnosis or treatment.  This study provides supportive evidence that DUOXA2, A-SAA, TNIP3, CXCL1, SLC6A14, and CD55 might be used as potential biomarkers for tissue biopsy of UC, especially SLC6A14 and DUOXA2, which may be new targets for UC gene therapy. Moreover, the DUOX2/DUOXA2 and CXCL1/CXCR2 pathways might play an important role in the progression of UC through the chemokine signaling pathway and inflammatory response. Ulcerative colitis (UC) is a chronic nonspecific inflammation of the rectum and colon whose etiology and pathogenesis are not yet well defined . UC has In this study, UC gene expression data uploaded by Li et al. were downloaded. We identified the DEGs between UC samples and normal samples. Then, a module partition analysis was performed based on a WGCNA, followed by pathway and functional enrichment analyses. Then, data validation was performed to ensure the reliability of the hub genes. This study forecasts the molecular mechanism of UC and the potential biomarkers for UC therapy.http://www.ncbi.nlm.nih.gov/geo/). A total of 127 mucosal biopsy samples were obtained from 106 UC patients and 21 control subjects for subsequent analysis. The UC samples consisted of adult UC samples (n = 87) and pediatric UC samples (n = 19). Adult UC patients of 44 male and 43 female were enrolled from all geographic regions of the USA and from both metropolitan and rural settings, with an average age of 41 (race of samples not available) [n = 21) were obtained from the Department of Gastroenterology, Perelman School of Medicine at the University of Pennsylvania  and the Department of Gastroenterology, University Hospital Gasthuisberg  [The gene expression profile of GSE87473 was obtained from the GEO database  (http://http://www.ncbi.nlm.nih.gov/geo/geo2r/) is based on R that comes with the GEO databases, which was used to identify DEGs between UC and control samples. |log‐fold change(LFC)| > 1 and P values <0.05 were selected as the thresholds for DEG screening.There were a total of 20741 probes in the present dataset. GEO2R  . WGCNA ihttps://david.ncifcrf.gov) software for the GO-biological function (GO-BP) and KEGG pathway analyses of the genes in main modules. We selected the P-false discovery rate (FDR) of <0.05 as the threshold for the identification of significant GO-BP terms and KEGG pathways.We used the DAVID 6.8  of a node with a connection degree of k is negatively correlated with the logarithm log(P(k)) of the probability of occurrence of the node, and the correlation coefficient is greater than 0.8. R software package WGCNA was used to build a weighted coexpression network. To ensure that the network was a scale-free network, we chose a soft threshold of β = 6 (P=3.0E − 18) was negatively correlated with the disease presence and the yellow , blue , and brown modules  were positively correlated with the disease presence; while the turquoise module  was negatively correlated with the disease extent, the yellow , blue , and brown modules  were positively correlated with the disease extent  and pathways such as the chemokine signaling pathway (P=0.004195). The DEGs in the turquoise module were mainly involved in functions such as the oxidation-reduction process (P=9.70E − 3) and pathways such as metabolic pathways (P=2.8E − 09).The top 3 GO-BP and KEGG terms enriched by DEGs are shown in The brown module was most relevant to the disease; therefore, we analyzed the correlation of gene expression in the brown module in the following study. To verify the robustness of the hub genes, the validation data GSE75214 were obtained from the GEO database. We performed ROC curve analysis using GraphPad Prism7.00. The results of the analysis showed that the hub genes related to UC, including DUOXA2, SAA1, SAA2, TNIP3, CXCL1, SLC6A14, and CD55, were identified as potential tissue biopsy molecules for UC diagnosis .UC is a kind of inflammatory bowel disease that is difficult to treat, easy to recur, and prone to cancerization , 12.Rece2O2 could lead to oxidative stress resulting in oxidative injuries and mucosal barrier impairment [SLC6A14 and DUOXA2 are involved in the development and carcinogenesis of UC. Multiple sequencing or microarray studies have shown that SLC6A14 was upregulated in UC patients , 10, 58 pairment . In addipairment –23. We sκB signal transduction in response to multiple stimuli [Our research also found that SAA, TNIP3, CD55, and CXCL1 were potential biomarkers for UC. SAA can reflect inflammation of UC at an early stage due to its higher sensitivity and specificity –26. CXCL stimuli . Ishani  stimuli , which wThe results of functional and pathway DEGs enrichment in this study show that the biological functions involved in the pathogenesis of UC include the inflammatory response, innate immune response, and chemotaxis, indicating that the pathogenesis of UC was multifactorial, involving epithelial barrier defects, genetic predisposition, environmental factors, and dysregulated immune responses. The 7 hub genes screened in this study are not only related to mucosal inflammation but they also accelerate the progression of colon cancer, so they should be given proper attention in the treatment of UC.Although we found 7 hub genes closely related to UC and confirmed the robustness of their diagnostic value, which may be useful for us to improve our understanding of the molecular mechanism of UC and as a potential prognostic and diagnostic biomarker, however, there were some limitations in this study such as small sample size and lack of verification test; thus, we still need large sample size with a wide verification analysis to confirm our hypothesis.In conclusion, DUOXA2, A-SAA, TNIP3, CXCL1, SLC6A14, and CD55 might be used as potential biomarkers for UC tissue biopsy, especially SLC6A14 and DUOXA2, which may be new targets for UC gene therapy. Furthermore, DUOXA2/DUOX2 and CXCL1/CXCR2 pathways may play important roles in UC progression via the inflammatory response.


Cell , one of the largest families of signaling proteins. GPCRs are notoriously difficult to work with (due to their many transmembrane domains) and they operate in a complex way, interacting with several compounds and many internal metabolic pathways. What is more, GPCRs are present in most lifeforms and have an important role in many vital signaling processes. They therefore seem like a very unlikely choice for a biosensor. However, William Shaw and his colleagues proved otherwise in their recent article published in Saccharomyces cerevisiae to obtain a platform for their biosensors. A sensor can be roughly divided into three parts: the detector, the signal transduction/translation component and the reporter. The idea was to create a modular system where GPCRs act as plug-ins dedicated to a compound, while the signal transduction and reporting mechanism will remain the same. S. cerevisiae already contains a signaling pathway with its own self-regulation, the MAP kinase cascade, that can be used to translate a GPCR signal to a linear and graded translational response. Using CRISPR-mediated editing the researchers modified 18 genetic loci and removed the rest of the GPCR-related genes, generating a ‘clean’ environment without cross-pathway interactions. In theory, the derived strain can heterologously express a GPCR that recognizes any compound, and receptor activation will stimulate the same pathway and the same reporting event.The research group from Imperial College, UK, heavily modified the yeast Shaw and colleagues modified components of the GPCR receptor and measured the impact on its biosensor properties: the detection threshold, the saturation point and the linearity of response. The experiments took place using the yeast mating pheromone response pathway, where the presence of α-Factor pheromone stimulates a transcriptional response . By varyParacoccidioides brasiliensis was detected in a yes/no manner. The two cell types used here had different functions: one detected the fungus and released α-Factor pheromone, and the other picked up the pherormone and amplified the signal.To alter the linearity of response—whether the sensor operates in a linear manner or as an on-off switch—the researchers employed microbial consortia with differently tuned strains. This was displayed in two different scenarios. In the first instance, the presence of melatonin in the media was quantified. The researchers employed two strains with different sensitivities to melatonin, thus increasing the operational range. In the second instance, the presence of the pathogenic fungus This study displays the potential of using engineered cells as a solution to complex biosensing limitations. This work, an academic collaboration with AstraZeneca, will certainly set the stage for further developing the interplay of synthetic biology and biosensing.


Enrichment or over-representation analysis is a common method used in bioinformatics studies of transcriptomics, metabolomics, and microbiome datasets. The key idea behind enrichment analysis is: given a set of significantly expressed genes (or metabolites), use that set to infer a smaller set of perturbed biological pathways or processes, in which those genes (or metabolites) play a role. Enrichment computations rely on collections of defined biological pathways and/or processes, which are usually drawn from pathway databases. Although practitioners of enrichment analysis take great care to employ statistical corrections , they appear unaware that enrichment results are quite sensitive to the pathway definitions that the calculation uses.p-values by up to nine orders of magnitude, whereas statistical corrections typically alter enrichment p-values by only two orders of magnitude. We present multiple examples where the smaller pathway definitions used in the EcoCyc database produces stronger enrichment p-values than the much larger pathway definitions used in the KEGG database; we demonstrate that to attain a given enrichment p-value, KEGG-based enrichment analyses require 1.3–2.0 times as many significantly expressed genes as does EcoCyc-based enrichment analyses. The large pathways in KEGG are problematic for another reason: they blur together multiple (as many as 21) biological processes. When such a KEGG pathway receives a high enrichment p-value, which of its component processes is perturbed is unclear, and thus the biological conclusions drawn from enrichment of large pathways are also in question.We show that alternative pathway definitions can alter enrichment The choice of pathway database used in enrichment analyses can have a much stronger effect on the enrichment results than the statistical corrections used in these analyses.The online version contains supplementary material available at (10.1186/s12864-021-07502-8). Pathway analysis has become a popular way to analyze gene expression data. This family of analysis methods seeks to find which biological processes have changed their activity levels most significantly across two different biological states . PathwayEscherichia coli. Although this article uses example pathways from EcoCyc, the BioCyc databases for other organisms contain pathways of similar size and hence we would expect similar results to apply when comparing them to KEGG.Researchers have explored a number of mathematical methods for calculating pathway activity levels and pathway abundances, but all of these methods ultimately depend on a collection of pathways within pathway databases (DBs) such as BioCyc , KEGG 33, and ReIt has been noted previously that pathway DBs differ significantly in their content, both in how they conceptualize pathways and in the genes and reactions present in specific pathways , 8. The The second question we investigate is: If a given pathway has a high enrichment score , what does this result tell us biologically? That is, what have we learned about the biological system under study? For example, does the pathway clearly correspond to one biological process, or does the pathway integrate so many biological processes that the biological significance of identifying that pathway as enriched is of little meaning? We show that the answers to Questions 1 and 2 depend on the pathway database being used.p-value) for each. The degree to which a pathway P is considered to be enriched depends on two factors: (1) how many genes in P are present in the input list of significantly differentially regulated genes, and (2) how many total genes does P contain? After all, if 3 genes from P were present in the significant-genes set, we would consider that much more significant if P contained 4 total genes than if P contained 15 total genes. The enrichment p-value for a pathway indicates the probability that the set of significantly expressed genes of a given size would have occurred by chance. In over-representation analysis, unlike in the gene set enrichment analysis, the set by itself is irrelevant, only its size matters.For Question 1, we investigate the differences between EcoCyc and KEGG using one of the oldest and most popular pathway-analysis methods, enrichment analysis of gene-expression data using the hypergeometric distribution  [Several approaches are used to calculate enrichment scores , but allFor a given set of observed genes, assuming equivalent pathways in each database cover the same subset of the observed genes, the enrichment score will still depend on the number of genes in the pathway (the denominator of the first ratio) as well as the total number of genes that are assigned to pathways in the database (the denominator of the second ratio).Although methods based on binomial distributions or chi-squared tests have been used, tests based on the hypergeometric distribution are the most popular. As the hypergeometric is a discrete distribution, a one-tailed statistic is the sum of probability mass functions calculated at a set of values equal to or more extreme than the value of interest. The probability mass function is: K is the total number of significantly expressed genes, k is the number of significantly expressed genes in the pathway of interest, n is the total number of genes in the pathway, and N is the total number of (pathway associated) genes in the database.Where k or more significantly expressed genes in the pathway by chance. This is the sum of the mass functions for each gene count that is greater than or equal to the number of genes observed: For enrichment, the statistic of interest is the probability of observing p-values when other factors in the calculation are fixed. We show that the p-value computed by one over-representation analysis method can vary by up to nine orders of magnitude depending on the size of the corresponding EcoCyc and KEGG pathways — EcoCyc and KEGG pathways differ significantly in their average size. This finding is much larger than variations due to multiple-comparison corrections, which are viewed as essential refinements to the statistical methods.To address Question 1 we perform an enrichment analysis on corresponding EcoCyc and KEGG pathways using the same set of input genes and study how different the results are. We consider series of examples where we compute enrichment scores for the same sets of genes across corresponding EcoCyc and KEGG pathways. A second way we address the question is through a mathematical analysis in which we demonstrate the effect of pathway size on To address Question 2 we analyze several examples where one KEGG pathway contains multiple EcoCyc pathways.p-value (and thus more significant). Specifically, by expanding the expression for probability mass function, we found that the probability mass function will always have a smaller value for the smaller pathway. Because the final one-tailed p-value is a sum of probability mass values for a range of counts of genes, the relative advantage of the smaller pathway increases as the number of significantly expressed genes increases.As detailed in the Appendix in the Additional file We selected six pairings of EcoCyc and KEGG pathways, listed in Table N genes where N is the number of genes in the smaller pathway .Tables p-values.Tables E. coli genes assigned to pathways . In Tables p-values for the KEGG pathway: column 3 is computed using the actual KEGG total of 1686 genes, whereas to provide an apples-to-apples comparison, column 4 is computed for KEGG using the EcoCyc total of 1096 genes. Why does KEGG assign so many more genes to pathways than does EcoCyc? Most of the differences are due to the fact that one very large KEGG pathway (map02010) is not a pathway at all, it is an enumeration of 134 ABC transporters involving 179 E. coli genes. Further, KEGG assigns a number of enzymes to pathways that EcoCyc does not assign to any pathway, usually because EcoCyc considers these enzymes to define connections among pathways rather than defining their own pathway.Recall that the enrichment score depends in part on the total number of genes assigned to pathways within the database. These total numbers differ significantly for EcoCyc and KEGG: EcoCyc assigns 1096 genes to pathways, whereas KEGG reports 1686 p-value by a factor equal to the number of pathways considered, which is equivalent to the adjustment made for all p-values by the Bonferroni test. In some cases, a particular subset of genes will actually favor a different pathway with a lower p-value; in those instances, using the Bonferroni correction is conservative, but analytically more tractable than considering all possible subsets of genes in these examples.In the analysis in this section, we chose to adjust for multiple comparisons using Bonferroni corrections rather than the more common Benjamini-Hochberg method . This apE. coli in KEGG, and 354 as the number of pathways in EcoCyc. Note that this number is smaller than the number (1080) used in the Bonferroni and Benjamini-Hochberg corrections in the EcoCyc SmartTables and Dashboard. This later value includes all pathway types in the EcoCyc class hierarchy. Since no class hierarchy exists among the KEGG pathways , we adjusted using the simple count of pathways.We used a count of 119 for the number of pathways of E. coli pathways and is the multi-organism reference pathway database from which pathways are computationally projected when predicting pathways in other BioCyc databases. These six KEGG pathways contain the following number of MetaCyc pathways: 31, 17, 12, 12, 25, 26. We note that MetaCyc pathways whose names differ in the roman numerals they contain are usually pathway variants, meaning the pathways accomplish a similar biological function, and often share some reactions, but they will contain some different reactions.Additional file Imagine that a pathway-enrichment calculation or a pathway-abundance calculation has identified a EcoCyc pathway or a KEGG pathway as highly enriched or as highly abundant in a given biological situation. What does that result tell us biologically?For a EcoCyc pathway, the result is straightforward: we have learned that the expression of the biological process corresponding to that pathway is perturbed in the situation under study. For example, if the EcoCyc pathway for L-cysteine biosynthesis is highly enriched, then the cellular process of L-cysteine biosynthesis is highly perturbed. If the arginine biosynthesis pathway is highly enriched, then arginine biosynthesis is highly perturbed. The interpretation is so obvious because the vast majority of EcoCyc pathways correspond to a single biological process, one that is often regulated as a unit, and often evolved as a unit. Of course, due to post-transcriptional effects, changes in pathway expression may not yield corresponding changes in pathway activity.The result is much less straightforward to infer for KEGG pathways, in particular, for KEGG maps. For example, imagine that KEGG map00260, “glycine, serine, and threonine metabolism,” is highly enriched. Have we learned that glycine metabolism is highly perturbed in this biological situation, or that serine metabolism is highly perturbed, or that threonine metabolism is highly perturbed? Or should we conclude that some combination of these three processes are highly perturbed? But it is more complicated than this: the term “serine metabolism” could mean either serine degradation or serine biosynthesis (and in fact both processes are present in map00260) — so now there are six biological processes that might be perturbed: either the biosynthesis or degradation of glycine, serine, and threonine. Figure A similar pattern is present for the six KEGG pathways in Additional file Thus, because of the large mosaic nature of KEGG maps, it is not at all clear what to conclude if a KEGG map shows a high enrichment score. Any one, or any combination, of multiple biological processes might be perturbed. The mosaic quality of KEGG maps can be useful for some applications, such as for understanding the connectivity between multiple metabolic pathways. But for enrichment analysis, their mosaic-ism is a strong liability.module. Modules correspond much more closely to EcoCyc pathways and therefore to individual biological processes. Thus, KEGG modules do not suffer from the confusion just illustrated for KEGG maps  But KEGG modules suffer from a different limitation: they are quite incomplete. KEGG contains only 348 metabolic modules  compared to the 2801 pathways in MetaCyc version 24.0 . Thus, many biological processes are not covered by KEGG modules, so perturbations to those processes cannot be detected if KEGG modules are used for enrichment analysis.But KEGG contains a different type of pathway called a p-value threshold.Here we determined how differences in the number of significantly expressed genes affects the sensitivity of high-throughput analysis and representation of annotated pathways in high-throughput samples, which is an inverse problem to inferring over-representation on the basis of a p-values calculated from X genes drawn from a pathway of size Y, the more unlikely it is that at least X genes from a set of size Y could be drawn in a sample of size N by chance. However, the p-value, which is a random variable associated with the null distribution, is not a quantitative indicator of the enrichment itself, which is an alternative to the random draw hypothesis. To obtain a quantitative measure of enrichment which could be used in direct comparison between different yet significant outcomes, we fixed a p-value at a significant threshold and determined the minimum number of genes from a pathway that would have to be found in a biological sample in order for that pathway to be considered enriched. We called this quantity a critical subset size or a minimally required number of successes. We compared the effects of pathways sizes on the sizes of critical subsets. To that end we enumerated critical subsets for every pathway in EcoCyc and KEGG for sample sizes between 50–500 genes and a typical result is shown for a 100-gene sample . The largest differences occur in Tables p-values that would result from the presence of ten significantly expressed genes in EcoCyc pathway PWY-7184 versus KEGG pathway map00240: the p-values differ by a factor of over five orders of magnitude, even after correction for multiple comparisons: 1.5×10−19 for EcoCyc vs 3.1×10−14 for KEGG. The first row of the same table shows an uncorrected difference (columns 2 and 4) for 13 significantly expressed genes of nine orders of magnitude . Such large differences could easily influence the biological findings by results in a pathway being called as significantly enriched or not. The higher a fraction of the genes in a pathway are significantly expressed, the more this difference in p-values is amplified.We provide multiple examples of this situation Tables p-value, the KEGG pathway would need to have from 1.3–2.0 times as many significantly expressed genes observed as would the EcoCyc pathway  We showed that, when holding the enrichment p-value constant, 1.3–2.0 times as many significantly expressed genes would be needed to attain that p-value for KEGG as for EcoCyc. (3) We showed analytically how p-value depends on pathway size. Thus, smaller, more precise pathway definitions can lead to more sensitive enrichment analyses.We conclude that pathway size can have a significant impact on the results of enrichment-analysis calculations. We provided several types of evidence to support this conclusion: (1) We showed that direct comparisons of enrichment calculations for sets of significantly expressed genes where only the pathway database was allowed to vary demonstrated that the choice of EcoCyc (or BioCyc for other organisms) versus KEGG could affect the enrichment p-value, inferring which biological process is perturbed in the experimental condition of interest, or if any biological process is significantly perturbed, is very difficult — the strong p-value may result from the perturbation of genes from multiple biological processes where no single process is strongly perturbed.We further conclude that pathway size can have a large impact on the interpretability of enrichment calculations. When a large pathway is composed of many individual biological processes, and that pathway receives a strong p-value, it is commonly considered statistically conservative and adjustments such as a mid-range p-value [In a usual pathway enrichment analysis, several considerations must be made, such as how to adjust for multiple comparisons, and whether to consider just enrichment  or both enrichment and depletion (a two-tailed test). Although the hypergeometric test provides an “exact”  p-value  are usedE. coli pathways related to amino acid synthesis to serve as examples. The pathways, their corresponding EcoCyc and KEGG entries, and the sizes of each are listed in Table We selected four p-value cut-off and determined the minimum number of significantly expressed genes required to reach that level of significance. The p-value significance cutoff for over-representation was calculated from the cumulative hypergeometric distribution and adjusted for the multiplicity of pathways in each of the two annotations (EcoCyc and KEGG). The Benjamini-Hochberg method was used in this analysis to adjust for multiple comparisons. [We also conducted an analysis using a fixed arisons. . The criarisons. .p-value at a significant threshold and determined the minimum number of genes from a pathway that would have to be found in a biological sample in order for that pathway to be considered enriched. We called this quantity a critical subset size. We compared the effects of pathways sizes on critical subset sizes. To that end we enumerated critical subsets for every pathway in EcoCyc and KEGG.To obtain a quantitative measure of enrichment which could be used in direct comparison between different yet significant outcomes, we fixed a Additional file 1 Supplementary material as a pdf file.Additional file 2 R code to reproduce supplemental figures. Please set your R working directory  to the location of the following two files. You may also need to load package matrixStat ) if this is not in your R environment. This code has been tested in R 4.0.3. It will generate each of the supplemental figures in order.Additional file 3 Counts of EocCyc pathway genes used to generate supplement figures.Additional file 4 Set of 69 similar KEGG and EcoCyc pathways, used to generate Supplement Figure S2.Additional file 5 A listing of six KEGG maps and the multiple MetaCyc pathways those maps correspond to.