Animal models are crucial for advancing our knowledge about the molecular pathways involved in human diseases. However, it remains unclear to what extent tissue expression of pathways in healthy individuals is conserved between species. In addition, organism-specific information on pathways in animal models is often lacking. Within these limitations, we explore the possibilities that arise from publicly available data for the animal models mouse, rat, and pig. We approximate the animal pathways activity by integrating the human counterparts of curated pathways with tissue expression data from the models. Specifically, we compare whether the animal orthologs of the human genes are expressed in the same tissue. This is complicated by the lower coverage and worse quality of data in rat and pig as compared to mouse. Despite that, from 203 human KEGG pathways and the seven tissues with best experimental coverage, we identify 95 distinct pathways, for which the tissue expression in one animal model agrees better with human than the others. Our systematic pathway-tissue comparison between human and three animal modes points to specific similarities with human and to distinct differences among the animal models, thereby suggesting the most suitable organism for modeling a human pathway or tissue.  Animal models play important roles in understanding human diseases. A main concern in using animal models for studying human diseases is the fundamental, but not clearly proven, assumption that the genes, pathways and diseases in model organisms are comparable to those of human. No systematic studies have tested this assumption at the tissue expression or pathway levels, although specific tissues or pathways have been compared ,2. Even Model animals are extensively used to dissect underlying mechanisms of human diseases and to develop new treatments , but thiEven though many genes between human and model animals are highly similar in both sequence and function, their regulation and interplay can differ. While many databases and resources characterize genes in numerous species , most paWe present the first systematic comparison of human and animal pathway activity for three specific model organisms , and aim to facilitate researchers in prioritizing animal models for human disease modeling. Key limitations in establishing disease-specific animal models include incomplete pathway annotation in animals and lack of knowledge of organism-specific pathway regulation. However, we show that it is possible to derive the animal pathways by orthology-based transfer of their human counterparts and study their regulation using organism-specific data such as gene expression. We thus map tissue expression data from healthy individuals onto the established derived animal pathways. Tissue expression data has already been compiled for human, mouse, rat, and pig through the TISSUES database . HoweverGene numbers for the genomes of human, mouse, rat and pig were extracted from the Ensembl release 95 websites  for eachat least one protein for any organism, (b) OGs that contain at least one protein for each organism, (c) OGs with exactly one protein for any organism, (d) OGs with exactly one protein for each organism.To identify the orthology relationships between the genes of the studied organisms, we made use of the public resource eggNOG v4.5  . It provAt the mammalian eggNOG level, there are 8665 mOGs, which contain exactly one protein for each of the four considered organisms , and 11 500 mOGs, which contain more than one protein for each of the four organisms. When we only require pair-wise relationships, the number of mOGs is larger , which contains a list of PubMed identifiers for each gene of this organism. In these files, a publication is assigned to a gene in an organism if both the gene and the organism were mentioned in the same publication according to tagger. In Table Our in-house text mining software tool called from PMC . In ordelab.org/ . For eacExperimental: supported directly by an experiment, including high-throughput methodologies ; (ii) Author statement: based on statements by the authors in the cited reference ; (iii) Inferred: derived from phylogenetic  and computational  analysis as well as inferred by curators (IC); (iv) Electronic: automatically generated and not reviewed electronic evidence (IEA). For each organism and group, we counted the number of unique pairs of GO terms assigned to a protein  annotations were retrieved on 25 May 2020 from the GO FTP server . Each GOtransferred interactions (by orthology). To compare the available, high-confidence, not predicted interactions, we considered the experimental and experimental transferred interactions for each organism. For each interaction type, we counted the number of interactions that have a confidence score ≥0.7  and all genes with any expression information in this pathway , Spliceosome, Ribosome, Proteasome, Oxidative phosphorylation and Propanoate metabolism) such that these pathways were expressed in most tissues  for each tissue–pathway combination (203 pathways and seven tissues). We defined the JI for a given pathway and tissue between two organisms as the intersection of expressed pathway genes of the two organisms divided by the union of expressed pathway genes in any of the two organisms. A pathway gene is considered expressed if it has a TISSUES confidence score above the chosen organism- and tissue-specific cutoff.scikit-learn Python package .The principal component analysis (PCA) on the JIs for the comparison of human–mouse, human–rat and human–pig was computed using the  package  for all Several limitations and possibilities about modelling human pathways in animal models arise from publicly available data for the two well-established animal models mouse and rat as well as for the emerging one, pig. The quality of genome assemblies and orthology mapping between organisms have significantly improved in the last years, and increasingly more tissue expression data is becoming available. In contrast, annotations in terms of functions, pathways, and protein interactions are still lacking high-quality experimental data to allow detection of differences between animal models and human. Therefore, we derive animal pathways from curated human pathways using reliable orthology relationships and further integrate these pathways with tissue gene expression data from the animal models. Despite the better coverage and quality of data in mouse and human as compared to rat and pig, we can identify several pathways in specific tissues that agree better with human in one animal model compared to the other two.For the systematic comparison of animal models to human, we need to answer the following important questions: Which data is publicly available and what is the quality of this data? Here, we analyze and compare the following resources: Ensembl for quality of genome assembly and gene annotation , eggNOG et al.  at different taxonomic levels. We chose the OGs at mammalian level (mOGs) as they are the most fine-grained OGs that contain all four organisms of interest. The number of genes of each organism that are assigned to mOGs is given in Table tagger text-mining software  using the software , which asoftware . We consThe Gene Ontology is one the most used resources for functional annotation of genes and proteins. GO annotations can be supported by different types of evidence, including experimental, author statements, computationally inferred such as based on phylogeny, as well as non-curated electronic annotation. For each of these categories, we listed the number of annotations for each organism. As for other resources, there is an imbalance between the different types of evidence and the different organisms, human having most annotations with experimental 107 301) or author statement support (48 894). By contrast, even mouse has a huge proportion of annotations inferred computationally (170 033), in addition to many experimentally supported ones (89 360). For rat, and especially for pig, most annotations are supported only by computationally inferred evidence or electronic annotations  due to the good quality annotation of protein-coding genes.To assess the availability of molecular interaction data for each organism, we counted the high-confidence (confidence score ≥ 0.7) experimental protein–protein interaction data in STRING v10.5. The lack of such data in most considered organisms is evident with only ∼900 interactions for rat and ∼1300 for mouse and pig, which is surprising considering how well studied mouse and rat are (e.g. as indicated by their mentions in the literature). In all four organisms, however, many protein–protein interactions can be transferred by orthology from the other organisms in STRING . Using the mammalian-level orthology assignments to compare between organisms, we further analyzed GO annotations based on experimental and author statement evidence type. We observe by far the highest overlap between human and mouse, reflecting that these are the two most studied organisms. The high similarity of inferred GO terms between mouse and rat can be attributed to database curators annotating GO terms based on sequence similarity to the same experimentally characterized human genes , TGF-beta (67 genes) and Adipocytokine signaling pathways (61 genes), for rat they belong to the Glycan biosynthesis and metabolism KEGG subcategory (15 pathway genes on average), and for pig they include RNA polymerase (29 genes), Mismatch repair (22 genes) and Steroid biosynthesis (19 genes). The least transferable pathways (50–60% of genes transferred) relate to the nervous system (Long-term potentiation and Dopaminergic synapse), certain signaling pathways , and the Circadian rhythm pathway. The latter is consistent with rat and mouse being nocturnal animals.The pathways that can be transferred best to all organisms are mainly pathways in the KEGG categories In the last couple of years, more and more healthy tissue expression data for animal has been produced and made available in public repositories . Howeverexpressed in a tissue if it has a confidence score above the chosen cutoff (see Methods for details). In this analysis, we considered the confidence scores based on experimental evidence for seven tissues with good coverage, i.e. at least two experimental datasets available for each organism. We also performed the analysis using the scores that integrate all types of evidence in the TISSUES database as well as different cutoffs for the tissue confidence scores  is an example of a broadly expressed KEGG pathway with >92% of pathway genes expressed in each organism in all seven tissues (except for lung in mouse) as shown in Axon guidance KEGG pathway (Citrate cycle (TCA cycle), Spliceosome, Ribosome, Proteasome, Oxidative phosphorylation, Protein processing in endoplasmic reticulum, Propanoate metabolism, Pyruvate metabolism, 2-Oxocarboxylic acid metabolism and Valine, leucine and isoleucine degradation.Based on the number of tissues, in which a pathway is expressed , we can  pathway  is – not pathway . Of thesTo evaluate which of the pathways expressed in human tissues agree with those in mouse, rat and pig, we assessed how many genes from a pathway are expressed in the same tissue for each pair of organisms . For each tissue and pathway, we calculated the Jaccard index (JI) as the overlap of expressed pathway genes divided by the union of all expressed pathway genes. A pathway gene is considered expressed if it has a TISSUES confidence score above the chosen cutoff. As a result, for each pathway–tissue combination, we have three JIs of how well this pathway agrees between human and one of the model organisms in the given tissue .The average JI over all tissues between human and mouse is 0.63, followed by 0.62 for human–pig, and 0.60 for human–rat . When weCitrate cycle and Proteasome. Of those, the highest number of pathways is associated with liver tissue and none of them with the lung. The 43 left-most pathway–tissue pairs according to PC1 (PC1 < –0.5) are mostly small pathways (average size of 8.6 human genes) with low JI (average JI of 0.24 for rat and 0.3 for mouse and pig) and they are distributed across all tissues.To further analyze the similarities and differences between human and the three model organisms on pathway–tissue level, we performed a principal component analysis (PCA) on the JIs for all pathway–tissue pairs, where at least 5 pathway genes are expressed in the given tissue  show better agreement between human and mouse. We thus used the PC2 & PC3 plot to further identify combinations of pathways and tissues, for which the agreement between human and one of the model organisms is better than with the others. Pathway–tissue pairs that are close to the center of the PC2 & PC3 plot show consistent agreement between human and any of the model organisms; this agreement can be consistently good, if all the JIs are high, or consistently bad, if the JIs are low. In contrast, any point that is very far from the center of the PC2 & PC3 plot represents a specific pathway–tissue combination, for which one of the model organisms has better agreement with human than the others  and those shared by mouse & pig (36 pathways), i.e. not seen in rat. For the latter, lung stands out by the highest number of 12 pathways. Finally, the liver stands out as the tissue with the most expressed pathways and the one for which all three animal models agree equally well with human.Overall, the pathway–tissue pairs that show distinct consistency between human and certain model organisms distribute as follows. The two biggest groups are the 41 pathway–tissue pairs unique to pig and the 44 shared by mouse & rat, i.e. not seen in pig. Of these, 19 pathways relate to muscle. For pig, these pathways are in the KEGG category Although there are limitations due to varying data availability for each organism, our findings indicate that we can successfully approximate the tissue-specific pathway activity and identify similarities and differences between the three considered model organisms and human.To summarize our observations, there is an abundance of both experimental and inferred information with good quality for human, including genome quality, orthology relationships, biomedical literature, tissue expression data, gene annotations, and protein associations. Unfortunately, the same is not the case for mouse, rat or pig. While mouse is very often mentioned in the literature and is well covered by tissue expression data and GO annotations, there are very few experimentally determined protein associations reported for it. Meanwhile there is a shortage of most types of annotations and data for both rat and pig. Thus, one of the biggest limitations of the current analysis is the availability of public data for model organisms. This can be improved in the future by encouraging researchers to make publicly available more experimental, curated, high-quality findings generated for organisms other than human, especially when these organisms are popular model animals such as mouse and rat.Pathway transferability, both in our study and in pathway databases in general, is limited by the data availability and agreement of the individual resources, in particular, the amount of pathway annotations and the quality of orthology relationships. Nevertheless, the pathway transfer from human works very well for mouse (95% on average) and fairly well for rat or pig . The pathway transferability also highlights the extent to which the animal models agree with human at a pathway level given the available data. Due to the lack of organism-specific information on pathways, we are not able to detect more pronounced differences between the organisms by using only this type of data. Ideally, we would like to have one single resource with pathways that are curated separately for each organism. Using it would allow us to identify the specific parts of the pathways that are only present in the animal model but not in human. Unfortunately, this is not possible with the current pathway and interaction databases, even though individual resources such as the Mouse and Rat Genome Database ,16 try tThe availability of organism-specific tissue expression datasets is considerably better, although still far from ideal. The deposited datasets often come from one individual and tissue and only sometimes cover several tissues in the same individual(s). This gap has been decreasing lately as more and more high-throughput sequencing data is being generated and deposited in public repositories for human  and farmAnother important aspect and possible limitation of our analysis is the comparability between organisms. Most importantly, we need well defined orthology relationships between the compared organisms. Identifying orthology between species has improved over the years  and alloThe comparison of individual types of data between the selected four organisms indicates that the observed agreement is more driven by the availability of data than by evolutionary relationships. This is likely due to the lack of organism-specific data at various levels. However, we also showed that through integration of pathway data with tissue expression, we can identify both similarities between human and the model organisms and differences with respect to how well the animal models agree with human at a pathway–tissue level. The resulting pathway–tissue–organism associations revealed both expected and unexpected findings as mentioned previously. For example, so-called house-keeping pathways consist primarily of genes that we see expressed in most tissues and organisms, while other pathways were found to be much more tissue-specific. In terms of pathway–tissue differences between the organisms, all tissues except for the liver were associated with more pathways, for which only one or two, but not all three model organisms were consistent with human. With respect to the question, which of these animal models is best suited for modelling a human disease, we can conclude that there is no universal answer and that it depends on the specific tissue and sometimes even the specific pathways involved in the disease.To make sure that this specific approach of integrating orthology-derived pathways with tissue expression data from human and animal models is robust with respect to the chosen algorithms and cutoffs, we performed a robustness analysis. In order to use the confidence scores for gene–tissue associations from the TISSUES database, we needed to set a cutoff for whether a gene is expressed or not in a given tissue, which is not a straightforward choice. In addition, when identifying which and how many pathways are expressed in a given tissue, we chose a cutoff for the number of expressed pathway genes. The robustness analysis confirmed that, although the absolute numbers change, the trends remain the same, and thus, our findings are consistent and reproducible irrespective of the specific cutoffs chosen.Our systematic data integration of pathways with tissue expression enables the investigation of mammalian pathway activity in several different healthy tissues of mouse, rat and pig as well as the comparison with the corresponding human tissues. We highlight tissue-specific features of the pathways and point out similarities and differences between human and the model organisms. Ultimately, we identify distinct pathway–tissue combinations, which are specifically more consistent with human for either of the three studied animal models. These findings can support researchers in the decision of which model organism to choose for a human disease of interest.In the current analysis, we focused only on the three animal models mouse, rat and pig and on the seven tissues, which are well covered by experimental datasets in the TISSUES database. However, if the used resources become more elaborate in the future, it should be possible to conduct the same type of analysis on more tissues and for more model organisms. This would of course require enough tissue expression data that can be calibrated and made comparable, for example, as done for the TISSUES database. Furthermore, although we based our study on the KEGG pathways database, our workflow for data integration and comparison is applicable to other pathway databases or gene–phenotype and gene–disease associations. The presented framework can also be extended to study the similarity of pathways upon activation or perturbation or to take into account the effect of specific genes, drugs or even diseases on the pathways in the same tissue for different model organisms, given that such a comprehensive collection of data exists and is made publicly available. Thus, future analysis would require the systematic assembly of associations between pathways, tissues and diseases to further aid researchers in choosing the best model organism for studying human diseases.This study includes no data deposited in external repositories. All results generated in this study are included as supplementary files.gkab012_Supplemental_FilesClick here for additional data file.


To investigate the pharmacological mechanism of HuangQiXiXin decoction (HQXXD) on cough variant asthma (CVA) and validate the clinical curative effect.  The active compounds and target genes of HQXXD were searched using TCMSP. CVA-related target genes were obtained using the GeneCards database. The active target genes of HQXXD were compared with the CVA-related target genes to identify candidate target genes of HQXXD acting on CVA. A medicine-compound-target network was constructed using Cytoscape 3.6.0 software, and a protein-protein interaction (PPI) network was constructed using the STRING database. Gene ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using RGUI3.6.1 and Cytoscape 3.6.0. We searched the main database for randomized controlled trials of HQXXD for CVA. We assessed the quality of the included studies using the Cochrane Reviewers' Handbook. A meta-analysis of the clinical curative effect of HQXXD for CVA was conducted using the Cochrane Collaboration's RevMan 5.3 software.  We screened out 48 active compounds and 217 active target genes of HQXXD from TCMSP. The 217 active target genes of HQXXD were compared with the 1481 CVA-related target genes, and 132 candidate target genes for HQXXD acting on CVA were identified. The medicine-compound-target network and PPI network were constructed, and the key compounds and key targets were selected. GO function enrichment and KEGG pathway enrichment analysis were performed. Meta-analysis showed that the total effective rate of the clinical curative effect was significantly higher in the experimental group than the control group.  The pharmacological mechanism of HQXXD acting on CVA has been further determined, and the clinical curative effect of HQXXD on CVA is remarkable. Cough variant asthma (CVA) has the same pathogenesis as asthma and is characterized by persistent chronic airway inflammation and airway hyperresponsiveness . The cliβ2-agonists is recommended for the treatment of CVA [Inhalation of corticosteroids and long-acting t of CVA , 6. Howet of CVA , 8. Huant of CVA .In this study, we used network pharmacology and an evidence-based medicine approach to determine the pharmacological mechanism of HQXXD on CVA and validate the clinical curative effect. First, we identified the active compounds and target genes of the monarch and minister herbs in HQXXD. Next, we used the CVA-related target genes and the active target genes of HQXXD to construct the medicine-compound-target network and protein-protein interaction (PPI) network. Subsequently, gene ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed. Finally, we screened randomized controlled trials (RCTs) that investigated the clinical curative effect of HQXXD on CVA and performed a meta-analysis to validate the clinical curative effect.http://tcmspw.com/tcmsp.php) [Huangqi, Xixin, Jingjie, and Fangfeng are monarch and minister herbs, which are regarded as the main active herbs in HQXXD, and have the Latin names Radix Astragali (RA), Herba cum Radix Asari (HRA), Herba Schizonepetae (HS), and Radix Saposhnikoviae (RS), respectively. We have determined that these four herbs are the main components in HQXXD. The compounds in these four herbs were obtained using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (msp.php) . We usedhttp://www.uniprot.org/) and setting the search format as “Homo sapiens,” the human official gene symbols were identified, and the active target genes of HQXXD were screened out.We retrieved the corresponding target genes of active compounds from TCMSP. After importing the target genes into the UniProt knowledgebase (https://www.genecards.org/), we searched and acquired CVA-related target genes. Then, the active target genes of HQXXD were compared with the CVA-related target genes, and the intersecting genes were defined as the candidate target genes for HQXXD acting on CVA.Using “cough variant asthma” as the keyword in the GeneCards database  to construct a PPI network of HQXXD acting on CVA. The research species was defined as “Homo sapiens,” the lowest interaction score was set to 0.4, and the rest of the parameters were set to the default settings to obtain the PPI network. Using Cytoscape 3.6.0 software and its Network Analyzer tool, the PPI network was subjected to topology analysis, and the key targets of HQXXD acting on CVA were then selected according to the degree values of each target gene in the PPI network.The candidate target genes were introduced into the STRING database , molecular function (MF), and cellular component (CC) analysis, and KEGG pathway enrichment analysis . AdjusteWe searched using the terms “HuangQiXiXin decoction” and “cough variant asthma” in PubMed, the Cochrane Central Register of Controlled Trials, the Chinese Biomedical Literature database, Chinese National Knowledge Infrastructure (CNKI), the Chongqing VIP database (CQVIP), and Wanfang Data from the establishment of each database to January 15, 2020. When the terms were not included in the titles and abstracts, if the terms were present in the full text, the full-text paper was also screened.The inclusion criteria included that the study was designed as a RCT, the participants had symptoms that were in accordance with a diagnosis of CVA, HQXXD was used in the experimental group, the control group used conventional therapy without TCM therapy, and the clinical curative effect was the main outcome measure. Exclusion criteria included studies where there were incomplete data on the clinical curative effect. For reports of the same study, the earliest published report was included and the rest were excluded.Two reviewers independently extracted the information from the included studies. The main information included the first author, year of publication, number of patients in each group, methods of intervention in the experimental and control groups, and outcome data of the clinical curative effect.We used the Cochrane Reviewers' Handbook for guidelines to assess the risk of bias. Seven criteria were used: random sequence generation; allocation concealment; patient blinding; assessor blinding; incomplete outcome data; selective outcome reporting; and other risks of bias .These main data were input into the Cochrane Collaboration's RevMan 5.3 software for meta-analysis to analyze the clinical efficacy of HQXXD on CVA.P < 0.05 was considered as statistically significant. When RevMan 5.3 software was used for meta-analysis, the total efficiency rates were expressed as the odds ratio (OR) with 95% confidence interval (CI). Heterogeneity was assessed by the Q test , and P < 0.10 indicated heterogeneity across studies. Studies with I2 < 50% were considered to have no heterogeneity and those with I2 ≥ 50% were considered to have heterogeneity. If no heterogeneity was detected, the fixed effects model was used as the pooling method; otherwise, the random effect model was considered to be the appropriate choice [P < 0.05 was considered as statistically significant.Some of the statistical analyses were performed automatically using the bioinformatic tools of the platforms and software mentioned above. In GO function enrichment and KEGG pathway enrichment, adjusted e choice , 15. P <We obtained 87 compounds from RA, 192 compounds from HRA, 159 compounds from HS, and 173 compounds from RS in TCMSP . Setting the conditions as OB ≥ 30% and DL ≥ 0.18, 20 active compounds from RA, 8 active compounds from HRA, 11 active compounds from HS, and 18 active compounds from RS were selected. Finally, with exclusion of duplications, 53 active compounds from HQXXD were identified. The basic information on the active compounds from HQXXD is shown in The corresponding target genes of the 53 active compounds were also obtained in TCMSP (Supplementary file 2A). Using the UniProt knowledgebase, the corresponding gene symbols were screened by setting the format as “Homo sapiens” (Supplementary file 2B ). Four cUsing “cough variant asthma” as the keyword in the GeneCards database, 1481 CVA-related target genes were acquired .The 217 active target genes of HQXXD were compared with the 1481 CVA-related target genes, and 132 candidate target genes of HQXXD acting on CVA were identified , Table 2The 132 overlapping candidate target genes corresponded to 47 active compounds (MOL000433 had correspondence to an overlapping candidate target gene), including 15 compounds from RA, 8 compounds from HRA, 10 compounds from HS, and 16 compounds from RS (Supplementary file 4).Then, a network of medicine-compound-target was constructed using the Cytoscape software and analyzed by the Network Analyzer tool. There were 184 nodes  and 653 edges in the network . The topTo further study the mechanism of HQXXD acting on CVA, 132 overlapping target genes were mapped into the STRING database, and the PPI network was obtained. When we set the lowest interaction score to 0.40, 132 target proteins in the network had interactions, and 2373 edges represented the interactions between the proteins . The top 20 GO BP enrichments ranked by the adjusted G protein-coupled amine receptor activity, nuclear receptor activity, transcription factor activity direct ligand-regulated sequence-specific DNA binding, oxidoreductase activity acting on paired donors with incorporation or reduction of molecular oxygen, heme binding, tetrapyrrole binding, cytokine activity, ubiquitin-like protein ligase binding, and so on (Supplementary file 6B). The top 20 GO MF enrichments ranked by the adjusted P value are shown in GO MF function enrichment analysis showed that the candidate target genes of HQXXD acting on CVA that were significantly enriched in protein heterodimerization activity, cytokine receptor binding, P value are shown in GO CC function enrichment analysis showed that candidate target genes of HQXXD acting on CVA were significantly enriched in membrane raft, membrane microdomain, membrane region, cyclin-dependent protein kinase holoenzyme complex, mitochondrial outer membrane, serine/threonine protein kinase complex, integral component of presynaptic membrane, organelle outer membrane, outer membrane, protein kinase complex, and so on (Supplementary file 6C). The top 20 GO CC enrichments ranked by the adjusted P value are shown in KEGG pathway enrichment analysis was further conducted for determining candidate target genes of HQXXD acting on CVA. Candidate target genes were significantly enriched in advanced glycation end products- (AGE-) receptor for the AGE (RAGE) signaling pathway in diabetic complications, fluid shear stress and atherosclerosis, hepatitis B, interleukin- (IL-) 17 signaling pathway, tumor necrosis factor (TNF) signaling pathway, Kaposi sarcoma-associated herpesvirus infection, bladder cancer, prostate cancer, endocrine resistance, pancreatic cancer, and so on (Supplementary file 6D). The top 20 KEGG pathway enrichments ranked by the adjusted Using the ClueGO plugin app and CluePedia plugin app of the Cytoscape software, the GO function enrichment and KEGG pathway enrichment from ClueGO can be displayed more intuitively as shown in A total of 21 studies were retrieved through database searching. After removing duplication, nine studies were retained. A total of four irrelevant studies were excluded after reading the title, abstract, and full text. Five RCTs –20 were Five eligible RCTs were identified. The five RCTs were all conducted in China and included 342 patients. The five studies were all single-center studies. The basic features of the included studies are outlined in Two RCTs , 19 empl2 = 1.35, P=0.85, I2 = 0%). When the fixed effect model was used to merge OR values, the pooled OR was 2.86 . This indicated that the total effective rate of the clinical curative effect was significantly higher in the experimental group compared with the control group (The five studies that compared the total effective rate of the clinical curative effect included a total of 342 participants, 171 in the experimental groups and 171 in the control groups. The five studies had homogeneity (heterozygosity test, Chiol group .CVA is characterized by wheezing cough, wind cough, and stubborn cough in TCM, which attributes qi deficiency and vigorous wind as the main causes of this disease and emphasizes that the treatment of patients should be based on this aspect .HQXXD is added or reduced from the TCM prescription Yupingfeng powder and Zhisou powder . RA, HRAChinese herbal medicines are complex and contain many compounds. At present, most studies of TCM are limited to explaining the mechanism of a certain compound and lack a holistic view. Currently, network pharmacology is widely used in the study of TCM and advocates whole and group analysis, which is consistent with the multicomponent, multitarget, and multipathway characteristics of TCM –24.O-methylisomucronulatol, beta-sitosterol, formononetin, 3,9-di-O-methylnissolin, stigmasterol, and isorhamnetin. Quercetin has a potential protective and preventive effect on the frequency of attacks and in controlling the most common symptoms of asthma [We identified the active compounds and target genes of HQXXD from TCMSP. Then, 132 candidate target genes of HQXXD acting on CVA were identified, and the key compounds of HQXXD acting on CVA were selected. The main key compounds were quercetin, luteolin, kaempferol, wogonin, 7-f asthma ; quercetf asthma . Luteolif asthma . Kaempfef asthma ; kaempfef asthma . Wogoninf asthma . Beta-sif asthma . Stigmasc-Jun amino terminal kinase pathway in asthma [PPI network analysis suggested that the action of HQXXD on CVA is related to multiple targets. The key targets were AKT1, IL6, VEGFA, JUN, EGFR, CXCL8, CASP3, MMP9, PTGS2, and MAPK8. Previous research has indicated that AKT1 is related to pathway mechanisms in children with severe asthma ; IL6 poln asthma ; EGFR acn asthma ; the EGRn asthma ; neutropn asthma . CASP3 mn asthma ; the PTGn asthma . The relG protein-coupled amine receptor activity, nuclear receptor activity, transcription factor activity, direct ligand-regulated sequence-specific DNA binding, oxidoreductase activity acting on paired donors with incorporation or reduction of molecular oxygen, heme binding, tetrapyrrole binding, cytokine activity, and ubiquitin-like protein ligase binding. GO CC analysis indicated that HQXXD in the treatment of CVA were enriched in the membrane raft, membrane microdomain, membrane region, cyclin-dependent protein kinase holoenzyme complex, mitochondrial outer membrane, serine/threonine protein kinase complex, integral component of presynaptic membrane, organelle outer membrane, outer membrane, and protein kinase complex. These regions are closely related to airway inflammation, airway epithelial cell injury, airway hyperresponsiveness, and airway remodeling, which are closely related to the pathological mechanisms of CVA [GO functional enrichment analysis suggested that GO BP for HQXXD in the treatment of CVA were enriched in response to toxic substances, molecules of bacterial origin, lipopolysaccharides, extracellular stimuli, nutrient levels, metal ions, reactive oxygen species, oxidative stress, antibiotics, and cellular responses to oxidative stress. GO MF analysis indicated that HQXXD in the treatment of CVA involved in protein heterodimerization activity, cytokine receptor binding, s of CVA –45.KEGG enrichment pathway analysis showed that many pathways were closely related to the pathogenesis of CVA. The main pathways included the AGE-RAGE signaling pathway in diabetic complications, fluid shear stress and atherosclerosis, the IL-17 signaling pathway, TNF signaling pathway, and pathways involved in hepatitis B, Kaposi sarcoma-associated herpes virus infection, bladder cancer, prostate cancer, endocrine resistance, and pancreatic cancer. A relationship between these pathways and CVA has also been found or preliminarily indicated. These results indicated that HQXXD acts on CVA through multiple pathways. The main pathways and main target genes in the pathways are worthy of further study.The pharmacological mechanisms of HQXXD acting on CVA have been investigated through a network pharmacology approach, but there are some limitations with this approach. We identified active compounds and target genes of HQXXD from the TCMSP database and acquired CVA-related target genes from the GeneCards database. Although these databases are the most comprehensive databases currently available, the data collection is still not necessarily comprehensive, and the establishment of the screening criteria for active compounds cannot be completely accurate. In addition, we constructed a PPI network and performed GO function enrichment and the KEGG pathway enrichment analysis to investigate the pharmacological mechanisms of HQXXD acting on CVA. Further investigation of the key target genes and pathways through experimental analysis is required, which will be the focus of future research in our laboratory.In addition, we used an evidence-based medicine approach to perform a meta-analysis of the clinical curative effect of HQXXD on CVA. Our previous meta-analysis showed that the total effective rate of HQXXD in the treatment of CVA was not significant compared with the control group . We searIn conclusion, based on a network pharmacology approach, this study investigated the relationships between the active compounds, target genes, and pathways, and further studied the multicomponent, multitarget, and multipathway characteristics of HQXXD acting on CVA. HQXXD acts on CVA via the effect of multiple compounds related to multiple targets and through multiple pathways. Finally, we investigated the clinical curative effect of HQXXD on CVA using an evidence-based medicine approach, which indicated that the total effective rate of the clinical curative effect was significantly higher in the experimental group compared with the control group. In brief, the pharmacological mechanism of HQXXD acting on CVA has been investigated, and the clinical effect of HQXXD on CVA is remarkable.


Banxia Houpu decoction (BXHPD) has been used to treat depression in clinical practice for centuries. However, the pharmacological mechanisms of BXHPD still remain unclear. Network Pharmacology (NP) approach was used to explore the potential molecular mechanisms of BXHPD in treating depression. Potential active compounds of BXHPD were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform Database. STRING database was used to build a interaction network between the active compounds and target genes associated with depression. The topological features of nodes were visualized and calculated. Significant pathways and biological functions were identified using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. A total of 44 active compounds were obtained from BXHPD, and 121 potential target genes were considered to be therapeutically relevant. Pathway analysis indicated that MAPK signaling pathway, ErbB signaling pathway, HIF-1 signaling pathway and PI3K-Akt pathway were significant pathways in depression. They were mainly involved in promoting nerve growth and nutrition and alleviating neuroinflammatory conditions. The result provided some potential ways for modern medicine in the treatment of depression. Depression is a widespread chronic mental illness characterized by low mood, sadness, and insomnia. Depression affects physical health . The worCurrent therapies, including selective 5-serotonin reuptake inhibitors, serotonin-noradrenergic reuptake inhibitors and tricyclic antidepressants , can allBanxia Houpu decoction (BXHPD) is a famous Chinese medicine prescription, which firstly recorded in Jingui Yaolue (Synopsis of Prescriptions of the Golden Chamber) in Eastern Han Dynasty of Chinese history (25AD–220AD). BXHPD was 5 herbs, which included Pinellia rhizome , Magnolia officinalis cortex , Poria , ginger rhizome , and Perilla folium . In the theory of TCM, BXHPD could promote chi, eliminate stagnation, calm the adverse chi and dissolve phlegm. BXHPD was widely used in the treatment of depression and achieved effective results , 10. ZheSome previous studies predicted the molecular mechanism on depression of BXHPD, which mainly included activation of the inflammatory response system , monoamiIn order to explore details of the mechanism and relevant pathways, it is essential to elucidate the molecular and biological basis of TCM preparations and NP approaches have been proven to be a powerful approach , 22. Thehttp://tcmspw.com/tcmsp.php/) [The main chemical constituents of BXHPD were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform Database (TCMSP) (sp.php/) . TCMSP dsp.php/) , such assp.php/) .http:www.talete.mi.it/products/dragon_description.htm). "B" represents the average descriptor of all drugs based on The Drug Bank database [OB and DL were important indicators to evaluate ADME  charactedatabase . The Drahttps://www.uniprot.org/) is a protein database with abundant information and extensive resources [https://www.genecards.org/) is a searchable and comprehensive database which automatically integrates data from about 125 web-based sources of genes, including genomes, transcriptomes and proteomics [An important step after screening active compounds was to identify their molecular targets for triggering biological effects . UniProtesources . The prooteomics . Gene Cahttps://string-db.org/cgi/input.pl) was used for expanding protein-protein interaction (PPI) network data [STRING  is a database that functionally marks genes and proteins as three main terms: biological process (BP), cellular component (CC) and molecular function (MF) . The funThe active compounds, core targets and signal pathways were utilized to construct a compound-target-pathway (C-T-P) network. In this C-T-P network, "nodes" of different colors represented the protein targets, compounds, signal pathways or diseases. "Edge" indicated the interaction of compound targets, compound pathways, or diseases. The networks were constructed by using Cytoscape v3.7.1.In the TCMSP database, 13 kinds of compounds from PR, 15 from PO, 5 from GR and 14 from MOC were included. After removed 5 duplicates from 49 compounds, 44 compounds were further studied .In the 44 compounds, 12 compounds had no protein targets. A total of 159 protein targets for 32 compounds were obtained from TCMSP. After inputting the 159 protein targets into the Uniprot, we found that 11 protein targets could not found the corresponding genes. The 11 protein targets above were removed. Adding up to 148 gene targets and 32 compounds were preserved for further researches.In order to identify the relationship between TCM and the corresponding targets , a compoFrom Gene Cards database, 8,959 genes were related to depression. After intersecting 148 potential active targets with 8,959 depression related genes, 121 potential drug target genes were obtained. The PPI network was built by STRING . The PPIGO enrichment analysis and KEGG pathways analysis were performed on the 23 target genes by David . PathwayGO enrichment analysis contains three broad categories: BP, CC and MF. BP terms mainly contained response to estradiol, response to drug, positive regulation of transcription, DNA-templated, positive regulation of smooth muscle cell proliferation and positive regulation of transcription from RNA polymerase II promoter. CC terms mainly contained cytosol, membrane raft, protein complex, and extracellular space and nucleus. MF terms mainly contained identical protein binding, enzyme binding, transcription factor binding and protein binding. KEGG pathways mainly contained MAPK signaling pathway, PI3K-Akt signaling pathway, Oxytocin signaling pathway, ErbB signaling pathway and HIF-1 signaling pathway.KEGG Enrichment Analysis reveals the possible biological processes of pathway by analyzing biological processes, which unlocks the molecular roles of targets in the treatment of depression. There were more than 20 pathways associated with depression. In these pathways, MAPK signaling pathway  and PI3KOne of the MAPK signaling pathway: ERK, part of the MAPK pathway in the prefrontal cortex and hippocampal played an important role in antidepressant processing . ERK actCompound-Target-Pathway networks of BXHPD were shown in Through the screening of active compounds, Luteolin (53 targets), Baicalein (34 targets), Beta-sitosterol (34 targets), Stigmasterol (29 targets) and Cavidine (26 targets) might have primary effects for antidepression in BXHPD. (1) As an active flavonoid derived from astragalus root, baicalein could alleviate the development of depressive symptoms by up-regulating the level of dopamine in the hippocampus and the level of BDNF . AdditioOur results showed that the important pathways of BXHPD mainly contained PI3k-Akt signaling pathway, MAPK signaling pathway, Oxytocin signaling pathway, ErbB signaling pathway and HIF-1 signaling pathway. These pathways are linked to the core genes chiefly including FOS, IL6, TNF-α, Bcl-2, c-Jun, ERK, and EGF gene in our research in the treatment of depression. Those results were supported by lots of studies. (1) PI3k-Akt pathway phosphorylates the downstream factor Forkhead box O3a and inhibits neurotoxic apoptosis conducted by Corticosterone (CORT), which is the effective antidepressant process , 52. In Our research had some limitations. First, it was uncertain whether Chinese medicine was absorbed in the intestinal tract as the active compounds in our research. Second, core genes of specific pathways related to downstream products were not objectively presented in our results. Third, whether the role of pathways in our study was up-regulated or down-regulated was unclear. Fourth, our study used the network construction method to speculate the potential anti-depression targets of BXHPD, which still needs to be further verified by biological experiments.BXHPD can effectively alleviate the symptoms of depression through the molecular mechanisms predicted by NP. NP analysis demonstrates that there were multi-scale curative activity in regulating depression related biological processes. Pathway enrichment analysis indicated that MAPK signaling pathway, ErbB pathway, HIF-1 pathway and PI3K-Akt pathway were significant pathways in depression. They mainly were involved in promoting nerve growth and nutrition and alleviating neuroinflammatory conditions. This study provided some potential ways for modern medicine in the treatment of depression.S1 File(RAR)Click here for additional data file.


Diabetic polyneuropathy (DPN) is the most common neuropathy manifested in diabetes. Symptoms include allodynia, pain, paralysis, and ulcer formation. There is currently no established radical treatment, although new mechanisms of DPN are being vigorously explored. A pathophysiological feature of DPN is abnormal glucose metabolism induced by chronic hyperglycemia in the peripheral nerves. Particularly, activation of collateral glucose-utilizing pathways such as the polyol pathway, protein kinase C, advanced glycation end-product formation, hexosamine biosynthetic pathway, pentose phosphate pathway, and anaerobic glycolytic pathway are reported to contribute to the onset and progression of DPN. Inhibitors of aldose reductase, a rate-limiting enzyme involved in the polyol pathway, are the only compounds clinically permitted for DPN treatment in Japan, although their efficacies are limited. This may indicate that multiple pathways can contribute to the pathophysiology of DPN. Comprehensive metabolic analysis may help to elucidate global changes in the collateral glucose-utilizing pathways during the development of DPN, and highlight therapeutic targets in these pathways. Diabetes leads to various neuropathies, such as diabetic polyneuropathy (DPN) ,2. DPN cThe current understanding of the mechanisms involved in DPN is based on aberrant glucose metabolism ,5. The iThe pathogenesis of diabetes involves insulin resistance in the peripheral tissues, such as adipose tissue, muscle, and liver, and a lack of insulin secretion from pancreatic β-cells, leading to abnormal glucose metabolism. Similarly, abnormal glucose metabolism is elicited by insulin resistance in the peripheral nerves in DPN, and is common between type 1 and type 2 diabetes . NeverthGlucose is transported into the cytosol by the glucose transporters, Glut-1 and Glut-3, in the peripheral nerves  19,20].,20.19,20Glucose transportation via Glut-1 and Glut-3 is independent on the effects of insulin, and differs from the effects of Glut-4 expressed in adipose tissue. Transported glucose is metabolized via glycolysis. Glycolysis is a fundamental glucose metabolic pathway in which glucose is step-wisely phosphorylated and metabolized in cells. Almost all the reactions in glycolysis are reversible, although the reactions catalyzed by hexokinase, phosphofructokinase, and pyruvic acid kinase are irreversible. Pyruvic acid (pyruvate), which is the final metabolite of glycolysis, is transported into the TCA cycle in the mitochondria under the aerobic conditions. Pyruvate is metabolized into lactic acid and released from the cells in anaerobic conditions. Glut-4 dysfunction is evident in the adipose tissue of patients with diabetes, resulting in reduced incorporation of glucose. In contrast, dysfunction of Glut-1 and Glut-3 is not apparent in the peripheral nerves in DPN. Our previous comprehensive metabolome analysis revealed that sequential metabolites involved in glycolysis are presrved in the sciatic nerve tissues of streptozotocin (STZ)-induced type 1 diabetic mice . TherefoThere are several collateral pathways associated with glycolysis, such as the polyol pathway, hexosamine biosynthetic pathway including glucosamine metabolism, PKC pathway, AGEs pathway, pentose phosphate pathway, and anaerobic glycolytic pathway . In the In the polyol pathway, glucose is metabolized to sorbitol in a reaction catalyzed by aldose reductase, which is a rate-limiting enzyme  21,22].,22.21,22+. Activation of aldose reductase consumes NADPH, resulting in a reduced ability to defend against oxidative stress. Subsequently, sorbitol dehydrogenase metabolizes sorbitol into fructose, leading to the conversion of NAD to NADH. This reaction leads to an imbalance of the osmotic pressure in the cytosol, osmotic stress, and subsequent efflux of myoinositole and taurine. The shortage of myoinositole is directly linked with low activation of Na+/K+ ATPase and a decline in cell function. Myoinositol insufficiency can also elicit the production of diacylglycerol (DAG), which is an activator of PKC. Aldose reductase inhibitors (ARIs) are the only clinically approved compound used as a therapeutic agent for DPN in Japan. Hotta et al. revealed that the application of ARIs significantly ameliorated the delay of nerve conduction velocity in Japanese DPN patients, although its effects were shown to be limited in patients with poor glycemic control [In the polyol pathway, nicotinamide adenine diphosphate (NADPH) is simultaneously converted to NADP control . This fi control , implyinPKC is a serine/threonine kinase involved in various cell signaling pathways . Activat+/K+ ATPase activity, expression of vascular endothelial growth factor and TGF-β, and ischemia and hypoxia in tissues. In an experimental model of DPN, inhibition of PKC-β ameliorated hyperalgesia in DPN [DAG accumulation can activate PKC. Interestingly, PKC activation in DPN is different depending on the organ involved. In endothelial cells and smooth muscle cells in blood vessels, PKC can be activated, while its activity is decreased in neuronal cells and Schwann cells ,26. Actia in DPN . HoweverDuring chronic hyperglycemia, the aldehyde base in reducing sugars such as glucose reacts with the amino group in proteins in a non-enzymatic manner, resulting in the generation of nitrogen glucoside, Schiff base, Amadori compound, and AGEs. Intermediate products of AGEs are also generated from glycolysis .Binding of AGEs to their receptor in macrophages leads to nuclear translocation of NF-kB and an inflammatory reaction, and tissue injury . AGEs maThe hexosamine biosynthetic pathway is responsible for 2% to 3% of glucose metabolism in glycolysis . Since gN-acetylglucosamine (UDP-GlcNAc), which can bind to serine or threonine residues in proteins via O-linked-glycosylation, resulting in post-translational modifications by monomeric O-linked N-acetyl-d-glucosamine (O-GlcNAc). The hexosamine biosynthetic pathway has been suggested to function together with O-GlcNAc modification of proteins as a nutrient sensor in cells [The final metabolite of this pathway is uridine diphosphate (UDP)-in cells ,34. Thisin cells . The hexin cells . In compin cells . This isin cells . Similarin cells . AlthougGlucosamine is a precursor of UDP-GlcNAc, the main product of the hexosamine biosynthetic pathway, and is often used to mimic its activation . In addiWe recently reported the detailed roles of glucosamine in experimental DPN models of type 1 diabetes . We evalIn the pentose phosphate pathway, glucose 6-phosphate is metabolized to a series of pentoses, such as ribose 5-phosphate .As final metabolites in pentose phosphate pathway, fructose 6-phosphate and glyceraldehyde 3-phosphate are produced, generating NADPH, which is an antioxidant substance necessary for lipid synthesis. Ribose 5-phosphate is a source of nucleic acid. One cycle of the pentose phosphate pathway generates one molecule of carbon dioxide and two molecules of NADPH from one molecule of glucose 6-phophate. Transketolase depends on thiamine 2-phosphate, which can catalyze several important steps in the pentose phosphate pathway. In a physiological state, transketolase is activated depending on the concentration of the substrate . When glPyruvate is the final metabolite in the aerobic glycolytic pathway .Pyruvate is transferred to the TCA cycle and electron transport chain reaction in mitochondria, resulting in the generation of 32 ATP molecules as a final product. Conversely, pyruvate can be metabolized to lactic acid by lactate dehydrogenase in anaerobic conditions. Anaerobic glycolytic pathway generates two ATP molecules and one lactate molecule from one glucose molecule. In DPN, peripheral nerves are under chronic ischemia because of the thickness and narrowing of microvessels due to diabetic microangiopathy, which lowers the intranervous blood flow . Thus, aThe pathophysiology of DPN changes during its course ,12,13. BVarious collateral glucose-utilizing pathways of glycolysis can be differently or synergistically activated in response to increases in glucose flux in the peripheral nerves of DPN, which can contribute to the pathophysiology of DPN. It is crucial to identify the timings of activation and selections of those pathways for the therapeutic application. In order to establish the radical treatment of DPN including the application of collateral glucose-utilizing pathways, we should also consider the whole changes of metabolism including other factors than glucose in the peripheral nerves of DPN. Therefore, it is expected to identify compounds that have multiple and heterochronous targets and comprehensively treat DPN patients.


The PI3K/AKT and cAMP signaling pathways were also implicated. Ten of the identified compounds, quercetin, pseudoephedrine, ephedrine, β-asarone, methylephedrine, α-linolenic acid, cathine, ferulic acid, nardosinone, and higenamine, seemed to account for most of the beneficial effects of MFXD in AR. This study showed that LC-MS/MS followed by network pharmacology analysis is useful to elucidate the complex mechanisms of action of TCM formulas.The decoction is an important dosage form of traditional Chinese medicine (TCM) administration. The Mahuang Fuzi Xixin decoction (MFXD) is widely used to treat allergic rhinitis (AR) in China. However, its active compounds and therapeutic mechanisms are unclear. The aim of this study was to establish an integrative method to identify the bioactive compounds and reveal the mechanisms of action of MFXD. LC-MS/MS was used to identify the compounds in MFXD, followed by screening for oral bioavailability. TCMSP, BindingDB, STRING, DAVID, and KEGG databases and algorithms were used to gather information. Cytoscape was used to visualize the networks. Twenty-four bioactive compounds were identified, and thirty-seven predicted targets of these compounds were associated with AR. DAVID analysis suggested that these compounds exert their therapeutic effects by modulating the Fc epsilon RI, B-cell receptor, Toll-like receptor, TNF, NF- Allergic rhinitis (AR), an immunoglobulin E- (IgE-) mediated inflammatory disease, seriously impairs the quality of life of patients . EpidemiHerba Ephedrae), Fuzi , and Xixin (Radix et Rhizoma Asari), which are boiled in water at specific proportions before administration. Pharmacological studies have shown that MFXD exerts anti-inflammatory and antiallergic effects by preventing the release of mediators from macrophages and mast cells and inhibiting the production of interferon gamma and interleukin (IL)-4 [in vitro [The decoction is the main form of TCM administration. The identification of the chemical components in a decoction underpins research on the mechanism of action of TCM. Mahuang Fuzi Xixin decoction (MFXD), a classical Chinese herbal formula, is widely used to treat AR. MFXD consists of Mahuang (n (IL)-4 , 6. MFXDn (IL)-4 . Using cin vitro . HoweverNetwork pharmacology is a new field that integrates pharmacological information, omics, and systems biology . It is bTherefore, the aim of this study was to identify the active compounds in the decoction prepared from MFXD to investigate their potential synergistic effects on cellular signaling pathways that are associated with AR. In this study, the combinatorial approach of LC-MS/MS followed by network pharmacology analysis was established to identify the active chemical compounds in the decoction prepared from MFXD and their potential pharmacological mechanism in AR. This research considers bioactive compounds, protein targets, protein-protein interactions, genes, and signaling pathways to elucidate the mechanisms responsible for the curative effects of MFXD in AR.HPLC-grade acetonitrile and methanol were procured from Merck . Formic acid was procured from Sigma-Aldrich . Other chemicals were of analytical grade.The decocting pieces of Mahuang, Fuzi, and Xixin were purchased from Kangmei Pharmaceutical Co., Ltd.  and met the standards of the Chinese Pharmacopeia. Voucher specimens (no. 20191117) were deposited at the authors' laboratory at Southern Medical University.MFXD consisted of Mahuang, Fuzi, and Xixin at a weight ratio of 2 : 3 1. Mahuang was immersed in distilled water  for 30 min and boiled for 20 min. Fuzi and Xixin were then added to the suspension, which was simmered for another 90 min. The liquid extract obtained was concentrated to 1.52 g/mL under reduced pressure.μm). The mobile phases consisted of acetonitrile (A) and 0.1% aqueous formic acid (v/v) (B) using a gradient elution of 0% A at 0−1 min, 0−20% A at 1−2 min, 20−50% A at 2−12 min, 50−95% A at 12−15 min, and 95−100% A at 15−20 min. The flow rate was set at 0.4 mL/min, and the column temperature was maintained at 30°C. The injection volume was 5 μL. The electrospray ionization source was set to positive and negative modes. The mass range scanned was 100−1000 m/z. MS data were collected with Thermo Xcalibur software (version 4.0).LC-MS/MS was carried out using a UPLC-Orbitrap-HRMS platform (Thermo Fisher) with a Waters ACQUITY UPLC HSS T3 Column , which provided a list of compounds with their formulas and MS/MS fragment modes. Based on errors less than 5 ppm and MS/MS fragment matching, we identified compounds in decoction prepared from MFXD.OB is an important pharmacokinetic property used to assess the rate and percentage of an orally administered drug that has been absorbed into the blood circulation to produce pharmacological effects. Important parameters of the identified compounds, such as Caco-2 cell permeability, human intestinal absorption (HIA), and OB limits F , were screened using ADMETlab, a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database .http://lsp.nwu.edu.cn/tcmsp.php) and BindingDB (https://www.bindingdb.org/bind/index.jsp) databases. After the deletion of redundant hits, the remaining protein targets were standardized by their ID and gene symbols in the UniProtKB database (http://www.uniprot.org/) [Predicting whether a compound interacts with intended targets is a critical phase of drug discovery . The tarot.org/) .http://db.idrblab.net/ttd/), DrugBank (https://www.drugbank.ca), and DisGeNET (https://www.disgenet.org/) databases and standardized using the UniProtKB database.Information on AR-associated targets was retrieved from the therapeutic target disease  using the minimum required interaction score of 0.400. Subsequently, the topological property of the PPI network was analyzed using plugins in Cytoscape 3.7.2. Genes were ranked to define hub genes using cytoHubba with the maximal clique centrality (MCC) algorithm. The module was extracted by MCODE using a node score cutoff of 0.2 and K-core of 2.The common target proteins were used to construct the PPI network on the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) 11.0 platform  functional annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment of genes and their roles in signal transduction, the Database for Annotation, Visualization, and Integrated Discovery (DAVID 6.8) was employed. DAVID is an online analytical program that provides a comprehensive set of functional annotation tools to explore the biological functions of genes from gene lists . DAVID cTo understand the underlying mechanism of MFXD in AR, a large-scale combinatorial network was established by integrating data obtained on drug information, drug-target interaction, and target-related pathway interactions. Through the hierarchical network, we characterized the relationships and pathways targeted by MFXD in AR.The total ion chromatogram of MFXD is shown in in vitro [If a compound is poorly absorbed following oral administration, the pharmacological effects may not be realized, even if the compound has potent effects on the pharmacological target in vitro . In thisThe TCMSP database contains 499 Chinese herbs with 19,384 compounds, 3,311 targets, and 837 associated diseases . BindingTo visualize the relationship between compounds and their targets, we constructed a compound-target interaction network . This nen = 36) connected by edges (n = 135) with an average node degree of 7.5 and average local clustering coefficient of 0.548. The PPI enrichment p value was less than 1.0 e−16; thus, proteins have more interactions among themselves than would be expected for a random set of proteins of similar size drawn from the genome. Such a significant enrichment indicated that the proteins are at least partially biologically connected as a group.Through the comparative analysis of 198 component targets and AR-related disease targets, we identified 37 common potential targets for MFXD in AR . The STRTNF, PPARG, ALB, PTGS2, IL-10, ACTB, NR3C1, ACE, SERPINE1, and AHR, which were considered crucial targets of MFXD against AR.The genes ranked by the MCC algorithm were selected using the cytoHubba plugin. The predicted top 10 contributing hub genes were κB (NF-κB) signaling pathway and tumor necrosis factor (TNF) signaling pathway. Cluster 2 genes were significantly enriched in the T-cell receptor signaling pathway. These data suggested that MFXD likely acts on AR through all three signaling pathways.Two significant modules were selected from the PPI network by MCODE Figures . For modTo further investigate the multiple mechanisms of MFXD at a systematic level, 37 common genes were uploaded into DAVID 6.8. Functional association clustering analysis discovered 14 annotation clusters, and the highest enrichment score was 4.07. The top 20 terms in molecular function, cellular component, and biological process are presented in Figures GO enrichment analysis showed that the target genes were expressed in the plasma membrane, cell surface, cell membrane, and other cell compartments. At the molecular level, the target genes were involved in drug binding, enzyme binding, G-protein acetylcholine receptor activity, and steroid binging. At the cellular level, they were related to cell apoptosis, proliferation, and migration. Moreover, biological processes were also enriched with inflammation-related terms. AR is a chronic inflammatory disease that involves the release of various inflammatory mediators . TherefoκB signaling pathways are involved in inflammation. We also identified the PI3K/AKT, cAMP, and AMPK signaling pathways in this analysis. The PI3K/AKT signaling pathway is important for cell growth, differentiation, metabolism, survival, and apoptosis [KEGG pathway annotation showed that 31 of the 37 (86.1%) potential target genes were enriched and involved in 153 pathways associated with the immune system, cardiovascular system, cancer, inflammation, and diabetes. As AR is an allergic disease, pathways that are associated with the immune system and inflammation were selected . For exapoptosis . Recent poptosis , 25. Morpoptosis . KEGG papoptosis .PIK3CG, TNF, PTGS2, CHRM2, CHRM1, GSK3B, ADRB1, CHRM5, CHRM3, and HSP90AB1.Comparative analysis of these KEGG pathways revealed that, among the 37 potential target genes identified, genes that were repeatedly associated with these pathways were β-asarone, methylephedrine, α-linolenic acid, cathine, ferulic acid, nardosinone, and higenamine. Quercetin can stimulate the immune system, inhibit the release of histamine, decrease the production of proinflammatory cytokines, increase the synthesis of leukotrienes, restrain the formation of IgE antibody, and improve the Th1/Th2 balance by participating in multiple signal pathways [α-Linolenic acid dampens AR through the eosinophilic production of 15-hydroxyeicosapentaenoic acid [Chinese herbal formulas can contain dozens, or even hundreds, of compounds, and each compound can act on one or multiple targets to exert synergistic therapeutic effects . To direpathways . All thepathways . Gallic pathways . α-Linoloic acid , and ephoic acid .Most of the compounds, such as kaempferitrin and pseudoephedrine, interact with PTGS2 (P35354). PTGS2 is responsible for the production of inflammatory prostaglandin E2, which inhibits T regulatory cell differentiation to induce AR-related inflammation . We alsoUnlike Western medicines, TCMs are commonly prescribed as herbal formulas that contain a mixture of herbs. Each herb may contain many active ingredients that have single or multiple targets; thus, it is difficult to pinpoint the mechanisms of action of TCM. Network pharmacology shares the core concepts of the holistic philosophy of TCM and meets the requirements to treat complex diseases systematically . HoweverκB, T-cell receptor, PI3K-AKT, cAMP, and AMPK signaling pathways. Ten compounds in the decoction prepared from MFXD were identified as candidates that could target AR.In this study, LC-MS/MS identification of compounds in the decoction followed by network pharmacology analyses provided insights into the mechanism of MFXD in the treatment of AR. In total, 24 bioactive compounds were identified in MFXD and 37 common targets were obtained and analyzed. The results indicated that MFXD was effective in the treatment of AR by regulating key pathways, including the Fc epsilon RI, B-cell receptor, Toll-like receptor, NF-These results reduce the prediction range, increase the accuracy of the prediction results, and provide important information for further pharmacological investigations on MFXD. This method, LC-MS/MS for bioactive compound identification followed by network pharmacology analyses, can increase the understanding of the mechanisms of Chinese herbal formulas and promote drug research and development.


We present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA is accessible through Reactome's web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data.We showcase ReactomeGSA's functionality by characterizing the role of B cells in anti-tumour immunity. Combining multi-omics data of five TCGA studies reveals marked opposing effects of B cells in different cancers. This showcases how ReactomeGSA can quickly derive novel biomedical insights by integrating large multi-omics datasets. HighlightsReactomeGSA is a novel tool for multi-species, multi-omics pathway analysis.Its quantitative pathway analysis methods offer high statistical power.Combining data of five TCGA studies shows B cells have opposing effects in cancers.ReactomeGSA reveals differences in key pathways between transcript- and protein-level. Pathway analyses are key methods to analyze 'omics experiments. Nevertheless, integrating data from different 'omics technologies and different species still requires considerable bioinformatics knowledge.Here we present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA can be used through Reactome's existing web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. Data from different species is automatically mapped to a common pathway space. Public data from ExpressionAtlas and Single Cell ExpressionAtlas can be directly integrated in the analysis. ReactomeGSA greatly reduces the technical barrier for multi-omics, cross-species, comparative pathway analyses.We used ReactomeGSA to characterize the role of B cells in anti-tumor immunity. We compared B cell rich and poor human cancer samples from five of the Cancer Genome Atlas (TCGA) transcriptomics and two of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteomics studies. B cell-rich lung adenocarcinoma samples lacked the otherwise present activation through NFkappaB. This may be linked to the presence of a specific subset of tumor associated IgG+ plasma cells that lack NFkappaB activation in scRNA-seq data from human melanoma. This showcases how ReactomeGSA can derive novel biomedical insights by integrating large multi-omics datasets. Increasingly available approaches such as transcriptome sequencing (RNA-seq), MS-based shotgun proteomics, and microarray studies enable us to characterize genome- and proteome-wide expression changes. This leads to the challenge of deriving relevant biological insights from lists of hundreds of regulated genes and proteins.Pathway analysis techniques have emerged as a solution to this problem. Resources like the Gene Ontology (GO) , the KyoThe increasing availability of public 'omics datasets has made it common practice to include these into analyses. These data integration is commonly complicated if datasets were created in different species or using different 'omics approaches. Pathway analysis approaches offer a solution to this problem because data can be mapped to the more general and comparable pathway space.Existing web-based pathway analysis resources, such as PANTHER , the DatThe recently developed iLINCS resource extends the concept of single-resource pathway analysis to a powerful multi-omics and multi-resource analysis . It testHere, we present the novel Reactome gene set analysis system “ReactomeGSA.” ReactomeGSA supports the comparative pathway analysis of multiple independent datasets. Datasets are submitted to a single pathway analysis and represented side-by-side on the pathway level. It uses gene set analysis methods that take the quantitative information into consideration and thereby performs the differential expression analysis directly on the pathway level. Data from different species is automatically mapped to a common pathway space through Reactome's internal mapping system. All supported gene set analysis methods are optimized for different types of 'omics approaches including single cell RNA-sequencing (scRNA-seq) data. Public datasets can be directly integrated from ExpressionAtlas and Single Cell ExpressionAtlas . We usedhttps://www.reactome.org) and the “ReactomeGSA” R Bioconductor package. Both access ReactomeGSA's web-based application programming interface (API) which is also publicly accessible at https://gsa.reactome.org.The ReactomeGSA analysis system is accessible through Reactome's web-based pathway browser application (https://kubernetes.io/) currently consisting of six deployments. Each deployment represents one Docker container . All data are stored in a Redis instance (https://redis.io/). The different components are linked through a message system provided by RabbitMQ . All components of the ReactomeGSA backend are developed in Python. The actual gene set analysis is performed using R Bioconductor (https://rpy2.github.io/) Python interface to the R language in the worker node (The backend is a Kubernetes application (onductor  packagesker node .https://hub.docker.com) the ReactomeGSA system can be deployed using the single “kubectl apply -f reactome_gsa.yaml” command. We created a single YAML-formatted configuration file to quickly adapt ReactomeGSA to different use cases (ie. the number of resources available to the different nodes). Detailed information on how to adapt ReactomeGSA can be found on the GitHub repository (https://github.com/reactome/ReactomeGSA). Thereby, users can set up their own version of the ReactomeGSA system within minutes and deploy it locally or in the cloud.A key advantage of this setup is that the complete ReactomeGSA application can be described in one so-called YAML file - a Kubernetes configuration file. Because all Docker containers are freely available on Docker Hub . Then, the selected pathway analysis is performed for each of the submitted datasets. The parameters for the pathway analysis  is automatically chosen based on the selected data type. Finally, the pathway analysis result is converted to Reactome's internal data format to render the result in the PathwayBrowser.Reactome's manual curation is based on human UniProt identifiers . Thus, aTo increase the coverage of Reactome pathways, pathways can be extended through medium and high confidence interactions derived from IntAct . This fuAt the time of writing, the ReactomeGSA system supports five types of quantitative 'omics data: Microarray intensities, transcriptomics raw and normalized read counts, and proteomics spectral counts and intensity-based quantitative data. Internally, these different types of data are processed using two different methods: statistics for discrete quantitative data  and statistics for continuous data. For Camera and PADOG, discrete values are normalized using edgeR's  calcNormhttps://github.com/reactome/gsa-backend for details). This approach to create pseudo-bulk RNA-seq data resembles previously described methods to calculate differentially expressed genes  , lung adThe abundance of plasmablast-like B cells (TIPB) was quantified using the single-sample Gene Set Enrichment Analysis (ssGSEA) method  as impleThe comparative pathway analysis was performed using the ReactomeGSA R Bioconductor package. In all studies, plasmablast “high” and “low” samples were compared with each other using PADOG .The complete R code of this analysis, including the detailed versions of all R packages used is available in the respective Jupyter notebook (see Data availability).https://cptac-data-portal.georgetown.edu/cptac/s/S015, ovarian cancer at https://cptac-data-portal.georgetown.edu/cptac/s/S020). For breast cancer  was downloaded from the CPTAC data portal  in plasmablast-like B cells. Finally, the ssGSEA analysis was performed using the ReactomeGSA R packages' analyze_sc_clusters function.B cells were extracted from the data set and re-processed, starting with the normalization step. Here, the top 11 components of the principal component analysis were used for the respective analysis steps. B cell clusters were subsequently classified following the strategy by Sanz et al. . PlasmabThe complete workflow including the detailed versions of all used R packages can be found in the respective Jupyter notebook (see Data availability).https://www.reactome.org/PathwayBrowser/#TOOL=AT) or through the novel “ReactomeGSA” R Bioconductor package  to perform the pathway analysis. The analysis system is a Kubernetes application based on the microservice paradigm that automatically scales to current demand (see Methods for details). This infrastructure enables us to offer computationally expensive pathway analysis methods through an open interface. ReactomeGSA currently supports three methods: PADOG . There was no significant difference in overall survival for lung adenocarcinoma, lung squamous cell, and breast cancer patients . Therefore, the effect of TIPB on anti-tumor immunity and patient survival differs across these types of cancers.We analyzed the difference between TIPB-high melanoma , lung admelanoma , lung sqmelanoma , ovarianmelanoma , and bremelanoma . Melanomsupplemental Data S1). 64 of these pathways showed a differential regulation in one of the datasets compared with melanoma. We previously showed in vitro that NF-kappaB activation was significantly up-regulated in B cells after stimulation with melanoma conditioned medium . Next to the down-regulation of NF-kappaB related genes, there was an overall down-regulation of B cell receptor signaling, but also p53 related DNA damage response, cell cycle and apoptosis related pathways. This shows that lung adenocarcinoma samples with a high number of tumor induced plasmablast-like B cells have a distinct different signaling state compared with melanoma.We specifically assessed how the lung adenocarcinoma cohort differs from the melanoma cohort. In total, 18 pathways were significantly regulated in both the melanoma and the lung adenocarcinoma cohort B. Next tPathways related to B cell receptor signaling and apoptosis correlate with the survival benefit observed through higher numbers of TIPB. The melanoma and ovarian cancer cohort both showed the strongest survival benefit which was linked to the strongest up-regulation of apoptosis related pathways but also B cell receptor signaling. These results highlight that ReactomeGSA's comparative pathway analysis can quickly reveal clinically relevant conserved signaling events.In our recent characterization of melanoma associated B cells, key phenotypic changes in B cells were primarily observed on the protein but not the transcriptome level. We performed a comparative pathway analysis of the two TCGA cohorts that were also analyzed by CPTAC using a global proteomics approach.supplemental Data S2) in the proteomics and transcriptomics data from the breast and ovarian cancer study respectively. Out of these, 13 showed a different direction of regulation in the breast study, and one in the ovarian cancer study between proteomics and transcriptomics measurements. In breast cancer, these included VEGF signaling, EGFR signaling, and IGF1R signaling related pathways . In ovarian cancer, FGFR signaling was significantly up-regulated in the transcriptomics but down-regulated in proteomics data. All of these pathways are linked to proliferation and are relevant pathways to tumor biology. B cell receptor signaling associated pathways were significantly up-regulated in all datasets. This highlights how ReactomeGSA can quickly reveal biologically relevant differences and similarities between 'omics datasets.99 samples of the breast cancer CPTAC study  and 62 sSpecific subtypes of B cells seem to be primarily responsible for the B cell triggered anti-tumor response , 34–36. 34et al. (A). Consistent with their transitional phenotype between B cells and plasma cells, plasmablast-like B cells were the only to express SDC1 (CD138) and low levels of MS4A1 (CD20). This classification already highlights issues in classifying B cell subtypes as we had to classify seven clusters as memory B cells even though they showed marked differences in overall gene expression.The extracted B cells from the scRNA-seq data set by Jerby-Arnon et al.  formed 1et al.  we classet al. A. ConsisB). In the latter, this matches the previously described lack of functional B cell receptors in IgG positive plasma cells (C). Plasma cells and plasmablast-like B cells further differed in NTRK signaling which regulates cell survival, proliferation and motility   or the Phttps://github.com/reactome). All docker images of the ReactomeGSA analysis system are publically available on Docker Hub (https://hub.docker.com). Central links to all components of the ReactomeGSA system can be found at https://reactome.github.io/ReactomeGSA. The source code of the backend (ie. the Kubernetes application) can be found at https://github.com/reactome/gsa-backend. The source code of the R package is available at https://github.com/reactome/ReactomeGSA. Additionally, a detailed documentation on how to set up the ReactomeGSA analysis system on a local Kubernetes instance can be found on https://reactome.github.io/ReactomeGSA.The complete source code of the ReactomeGSA backend, the web-based pathway browser, and the ReactomeGSA Bioconductor R package are available under a permissive open source license on GitHub (https://gsa.reactome.org. Therefore, the complete analysis capabilities can easily be integrated into any other existing software platform.The detailed API specification of the ReactomeGSA system is available on https://github.com/Reactome/ReactomeGSA-tutorials.The code to analyze the example datasets presented in this manuscript can be found as Jupyter notebooks on


Osteoporosis is a common complex human disease. Until now, large-scale genome-wide association studies (GWAS) using single genetic variant have reported some novel osteoporosis susceptibility variants. However, these risk variants only explain a small proportion of osteoporosis genetic risk, and most genetic risk is largely unknown. Interestingly, the pathway analysis method has been used in investigation of osteoporosis mechanisms and reported some novel pathways. Until now, it remains unclear whether there are other risk pathways involved in BMD. Here, we selected a lip BMD GWAS with 301,019 SNPs in 5,858 Europeans, and conducted a gene-based analysis (SET SCREEN TEST) and a pathway-based analysis . On the gene level, BMD susceptibility genes reported by previous GWAS were identified to be the top 10 significant signals. On the pathway level, we identified 27 significant KEGG pathways. Three immune pathways including T cell receptor signaling pathway (hsa04660), complement and coagulation cascades (hsa04610), and intestinal immune network for IgA production (hsa04672) are ranked the top three significant signals. Evidence from the PubMed and Google Scholar databases further supports our findings. In summary, our findings provide complementary information to these nine risk pathways. Osteoporosis is a complex disease with reduced bone mineral density (BMD) and increased fracture risk . Until nHowever, these risk variants only explain a small proportion of osteoporosis genetic risk, and most genetic risk is largely unknown . ConsideInterestingly, the pathway analysis method has also been used in investigation of osteoporosis mechanisms and reported some novel pathways . In addiThis study included 5,861 Icelandic persons, and 5,858 of 5,861 persons had measurements of hip bone mineral density . For eacP values from all the genetic variants in each corresponding gene by an approximate Fisher’s test and could also adjust for the linkage disequilibrium (LD) (We selected a set screen test method implemented in PLINK to perform a gene-based test of the whole GWAS dataset . The metium (LD) .For all genetic variants in a specific gene, if all these genetic variants are independent, the Fisher’s statistic could be calculated byN is the number of selected genetic variants in a specific gene and ip  is the significance level about the association each genetic variant with BMD, x02 follows a chi-square distribution with the freedom degrees = 2N  method was used to adjust the original significance levels. Here, we define a pathway with adjusted P = 4.03E-07) (P = 6.72E-05)  (P = 1.58E-05), LALBA , and CPN2 . Detailed information about the 5% significant genes is described in Using the gene-based test, several BMD susceptibility genes reported by previous GWAS were among the top 10 significant signals, which included CKAP5  , LRP4 (t.72E-05) , and C6o.04E-04) . MeanwhiP < 0.04239) from the gene-based test for following pathway analysis. We identified 27 significant KEGG pathways (adjusted P < 0.01)  Table 1.We further verified these findings from pathway analysis using publicly available literatures in PubMed and Google Scholar databases. Interestingly, growing evidence supports the involvement of dilated cardiomyopathy and hypertrophic cardiomyopathy, T cell receptor signaling pathway, wnt signaling pathway, and regulation of actin cytoskeleton in MBD. More detailed information is described in Until now, pathway analysis of BMD GWAS datasets has identified several risk pathways. However, most genetic variants, risk genes, and genetic pathways influencing osteoporosis are unknown. In order to identify novel BMD risk pathways, we conducted a pathway analysis of hip BMD GWAS with 301,019 genetic variants in 5,858 Europeans using the meta-analysis method (SET SCREEN TEST) in PLINK  and a paCKAP5 , complement and coagulation cascades (hsa04610), and intestinal immune network for IgA production (hsa04672) are ranked the top three significant signals. In addition, we also identified other kinks of pathways associated with cellular processes, environmental information processing, infectious diseases, cardiovascular diseases, metabolism, and circulatory system, are also identified. In order to further evaluate the potential roles of these newly identified risk pathways, we conducted a comprehensive literature review. Interestingly, growing evidence from the PubMed and Google Scholar databases further supports the involvement of dilated cardiomyopathy, hypertrophic cardiomyopathy, T cell receptor signaling pathway, wnt signaling pathway, and regulation of actin cytoskeleton in MBD, as provided in th significant pathway, as provided in P = 3.71 × 10−13 . However, selection of different thresholds may have different findings, as described in recent studies .The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.