P < 0.01). The underlying mechanisms of Plantaginis Herba may be related to insulin resistance, PI3K/AKT, TNF, VEGF, AMPK, and glucagon signaling pathways. Thus, the present study provided potential and promising strategies of Plantaginis Herba for hyperuricemia treatment.This study used a network pharmacology approach to investigate the potential active ingredients of Plantaginis Herba and its underlying mechanisms in hyperuricemia treatment. The potential active ingredients of Plantaginis Herba were obtained from TCMSP and ETCM databases, and the potential targets of the active ingredients were predicted using the Swiss TargetPrediction database. The potential therapeutic targets of hyperuricemia were retrieved from the GeneCards, DisGeNET, and Online Mendelian Inheritance in Man (OMIM) databases. Then, the integrative bioinformatics analyses of candidates were performed by GO analysis, KEGG analysis, and PPI network construction. There were 15 predicted active ingredients in Plantaginis Herba and 41 common targets that may be involved in the treatment of hyperuricemia. A total of 61 GO annotations and 35 signaling pathways were identified by enrichment analysis ( Hyperuricemia is a syndrome involving metabolic abnormalities (increases in uric acid production and/or decreased excretion of uric acid) caused by purine metabolism disorders . ExcessiThe pathogenesis of hyperuricemia is related to a variety of factors, such as genetics, intense exercise, alcoholism, radiotherapy and chemotherapy, renal insufficiency, drugs, and metabolic syndrome . In receCurrently, to treat hyperuricemia, Western medicine therapeutic strategies mainly involve the inhibition of uric acid synthesis, the promotion of uric acid excretion, and the alkalinization of urine. The commonly used Western medicines are allopurinol and benzbromarone, which can cause adverse reactions in the liver, gastrointestinal tract, and on the skin .Plantago asiatica L. or Plantago depressa Willd, which belongs to Plantaginaceae. It can help reduce fever and diuresis, dispel phlegm, and detoxify [Plantaginis Herba is the dried whole grass of detoxify . Previoudetoxify . Howeverhttp://tcmspw.com/) and the Encyclopedia of Traditional Chinese Medicine (ETCM) database (http://www.tcmip.cn/ETCM/), and the retrieved results were screened based on the following parameters: oral bioavailability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18. Then, the existing literature was searched for studies involving the ingredients of Plantaginis Herba for hyperuricemia treatment. The molecular structures (SDF format) of the obtained ingredients were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/), and potential targets were predicted using the Swiss TargetPrediction database (http://www.swisstargetprediction.ch/). The potential targets were subjected to gene normalization using the UniProt database (http://www.UniProt.org/). The active ingredients and corresponding targets of Plantaginis Herba were imported into Cytoscape 3.8.0 software to construct an active ingredient-target network for Plantaginis Herba.The keyword “Plantaginis Herba” was searched in the Chinese medicine systems pharmacology database and analysis platform (TCMSP) (https://www.drugbank.ca/), DisGeNET (https://www.disgenet.org/), GeneCards (https://www.genecards.org/), Online Mendelian Inheritance in Man (OMIM) (https://www.omim.org/), and Therapeutic Target Database (TTD) (http://db.idrblab.net/ttd/). Additionally, using the data retrieved from DrugBank, targets of first-line Western drugs used for the clinical treatment of hyperuricemia were also included. The obtained target genes were imported into the UniProt database for gene standardization.“Hyperuricemia” and “hyperuricaemia” were used as keywords to search the following disease gene databases: DrugBank (https://string-db.org/), and the species “Homo sapiens” was selected to obtain a PPI network. The data were then imported into Cytoscape 3.8.0 software for visualization, and the core targets in the PPI network were obtained.The targets of the active ingredients of Plantaginis Herba and hyperuricemia-related targets were imported into Venny 2.1 software to generate a Venn diagram to obtain the common targets of Plantaginis Herba for the treatment of hyperuricemia. The common targets were imported into the STRING database (https://david.ncifcrf.gov/) for Gene Ontology (GO) analysis and Kyoto Genomics and Genomics Encyclopedia (KEGG) analysis; the screening parameter was set as the false discovery rate (FDR) < 0.05. The results were visualized using Image GP, and the analyzed results were displayed using bubble diagrams.The overlapped targets were imported into DAVID 6.8 : enzyme binding, protein binding, steroid receptor activity, transcription factor binding, ATP binding, and RNA polymerase II transcription factor activity. Enrichment was observed in the following biological processes: the positive regulation of RNA polymerase II promoter transcription, smooth muscle cell proliferation, positive regulation of drug reactions, nitric oxide biosynthesis, cell proliferation, negative regulation of gene expression, lipopolysaccharide-mediated signaling pathways, and steroid-mediated signaling pathways. A total of 35 KEGG pathways were enriched  and the expression of its target genes, interleukin-1β (IL-1β), prostaglandin E2 (PGE2), and IL-8, thereby playing an important role in the prevention and treatment of hyperuricemia and gout [β-sitosterol palmitate, eupatilin, jaceosidin, and eupatorin in the treatment of hyperuricemia are still unclear. Therefore, the active ingredients of Plantaginis Herba identified in this study may play important roles in the prevention and treatment of hyperuricemia.Plantaginis Herba clears heat, disinhibits dampness, increases diuresis, frees stranguries, eliminates phlegm, cools blood, and detoxifies , 14. Modand gout . Howeverand gout . In addiand gout –23. In tWe constructed an “active ingredient-target-pathway” network to further investigate the mechanism of action underlying Plantaginis Herba and its active ingredients in the prevention and treatment of hyperuricemia. AKT1, MAPK3 (ERK1), MAPK1 (ERK2), TNF, PTGS2 (COX-2), KDR, PYGL, and IGF1R were key targets based on a topological data analysis of the network.α through the ROS/MAPK (p38)/NF-κB signaling axis, thereby promoting the inflammation and necrosis of vascular smooth muscle cells [β (PDGFR-β) through the p38 MAPK and ERK1/2 pathways, thereby inducing and aggravating cardiovascular diseases [β, and TNF-α and play an important role in the development of complications related to hyperuricemia [Accumulating previous studies have shown that AKT plays an important role in the occurrence and development of hyperuricemia and gout. Activation of the AMP-activated protein kinase (AMPK)/AKT/cAMP-response element-binding protein (CREB) signaling axis promotes the binding of CREB to the ATP-binding cassette superfamily G member 2 (ABCG2) promoter and upregulates the expression of ABCG2, thereby promoting the excretion of uric acid . Uric acle cells . Uric acdiseases . In a hyuricemia .β, in turn has an important protective effect against cardiac hypertrophy [Other studies have shown that plantamajoside, an extract of Plantaginis Herba, can inhibit the activation of histone deacetylase 2 (HDAC2), and the transduction of downstream signals, AKT/GSK-3ertrophy . Ethanolertrophy . The regα and inhibiting the activation of ERK1/2 and the abnormal proliferation of glomerular mesangial cells. These active ingredients may alleviate hyperuricemia, slow the occurrence and development of gout, and inhibit the damage caused by high uric acid levels in the kidney and cardiovascular system through the integration of multiple signals. Thus, the present study suggests a potential mechanism and potential clinical application values of Plantaginis Herba for the treatment of hyperuricemia and its complications.In summary, this study selected a group of active ingredients of Plantaginis Herba with potential application prospects. They may promote ABCG2 expression and uric acid excretion through the activation of AKT-related pathways. Additionally, these active ingredients may also downregulate the body's inflammatory response by inhibiting the expression of the inflammatory factors COX-2 and TNF-


Additionally, PDE3A is mainly distributed in the LU65 cell line that originated from Asian population. In summary, our study results showed that DBD can treat NSCLC through the synergistic correlation between multiple ingredients, multiple targets, and multiple pathways, thus effectively improving NSCLC prognosis. This study not only reflected the medicinal value of DBD but also provided a solid structural basis for future new drug developments and targeted therapies.While lung cancer poses a serious threat to human health, non-small-cell lung cancer (NSCLC) is the most common type of lung cancer. Danggui Buxue Decoction (DBD) is a classical traditional antitumor medicine commonly used in China. However, the potential mechanism of DBD against NSCLC has not yet been expounded. Therefore, this study clarified the potential molecular mechanism and key targets of DBD in NSCLC treatment through several technological advances, such as network pharmacology, molecular docking, and bioinformatics. Firstly, the relative active ingredients and key DBD targets were analyzed, and subsequently, a drug-ingredient-target-disease network diagram was constructed for NSCLC treatment with DBD, resulting in the identification of five main active ingredients and ten core targets according to the enrichment degree. The enrichment analysis revealed that DBD can achieve the purpose of treating NSCLC through the AGE-RAGE signaling pathway in diabetic complications. Secondly, the molecular docking approach predicted that quercetin and hederagenin have the best working mechanisms with PDE3A and PTGS1, while the survival analysis results depicted that high PDE3A gene expression has a relatively poor prognosis for NSCLC patients ( According to the estimates from World Health Organization's “Global Cancer Report 2020,” lung cancer (2.21 million cases) remains the second most commonly diagnosed cancer in the world . Among aAs described in Li Gao's “Differentiation of Endogenous and Exogenous Disorders,” DBD is a fixed-ratio (5 : 1) coformulation consisting of Chinese medicines Danggui and Huangqi, which is also considered as a classic formula for invigorating qi and producing blood, thus increasing hematopoietic function . DangguiAs a novel research approach, network pharmacology is based on high-throughput omics data analysis, computer virtual computing, and network database retrieval, which acts as an effective means for discovering active drug substances and revealing their pharmacological mechanisms of action from a crucial perspective of molecular biological networks . The mulOwing to the fact that there is no network pharmacology study in the literature to date on NSCLC treatment with DBD, this study explored the functional molecular mechanism of DBD in NSCLC treatment through network pharmacology and molecular docking as well as analyzed the survival of important targets through The Cancer Genome Atlas (TCGA) database to duly assess the target's impact on NSCLC prognosis. In addition, this study also conducted an in-depth analysis of the expression of important targets in NSCLC tumor cell lines through the Cancer Cell Line Encyclopedia (CCLE) database. Our research plan is shown in https://tcmsp-e.com/) was used to collect the active ingredients of DBD. For identifying the active ingredients in DSD, “Danggui” was heavily explored in the database, followed by scheduling screening conditions through oral bioavailability (OB) and druglikeness (DL) parameters to acquire the needed active ingredients and downloading them. Since OB represents the percentage of traditional Chinese medicine's absorption in the human circulatory system, while DL is the similarity between the ingredients and known drugs [Traditional Chinese medicine systems pharmacology (TCMSP) database  files were also obtained for further utilization in the molecular docking procedure. Due to the nonavailability of information about the main active ingredients, MOL2 files of all active ingredients were downloaded for further screening processes. However, in the case of active ingredients lacking MOL2 files, ChemBioDraw Ultra and ChemBio3D Ultra software were used to draw them and later saved them in MOL2 format for further usage.https://www.genecards.org/) and Online Mendelian Inheritance in Man (OMIM database) (https://omim.org/) to search the NSCLC-related targets. For instance, after inserting “Non-Small Cell Lung Cancer” as a keyword in two databases, the relevant targets downloaded in databases were merged and then deduplicated to acquire the NSCLC relevant targets. After the utilization of R software to draw a Venn diagram of DBD and NSCLC targets, finally, our core targets were achieved.We used the GeneCards database (https://string-db.org/) website. Firstly, the “Multiple Proteins” option was selected, followed by mentioning the core targets in the List of Names and inserting “homo sapiens” in the Organism inquiry. Lastly, after inserting the default values for other parameters, the PPI network map was obtained for our perusal.The construction of the protein-protein interaction (PPI) network was completed on the string  functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis were conducted. GO functional enrichment analysis was performed by using Cytoscape software and R software while the results consisted of an amalgamation of biological process, cellular component, and molecular function and were presented in a bubble chart and a histogram with a https://www.rcsb.org/) and the chemical structure of Tripos molecule structure format (MOL2) file of active ingredients from TCMSP platform were obtained. Additionally, AutoDock tools were also utilized to process the above-mentioned protein receptors and ligands, whereas its plugin AutoGrid was used to detect the docking active sites, and a molecular docking process was conducted to obtain the affinity. In our study, the binding energy ≤ −5.0 kJ · mol−1 was used as the key molecule possessing a better binding affinity with the key targets [AutoDock Vina software was used to verify the molecular docking mechanism between the top ten key targets in the PPI network and the five main active DBD ingredients. The three-dimensional structure of the ten target proteins from the RCSB PDB protein structure database  was carried out. The target gene expression was ranked from high to low and used high (50%) and low (50%) cutoff values as expression thresholds to divide the high expression group and the low expression group. The resultant worst prognosis of the high expression group relatively reflected that this specific gene might promote tumor development, and vice versa was considered as a protective factor. Data analysis was conducted on R software, and log-rank test and Kaplan-Meier method were utilized to analyze 1014 samples to obtain survival maps; p < 0.05 was considered as a statistically significant difference. Moreover, R software was also used to draw the Sankey diagram in which the width of the extended branch in the figure corresponded to the data flow size, which could be further used to depict the relationship between a certain gene's clinical characteristics, such as smoking, radiation, and racial predilection in NSCLC tumor samples and patient survival, as well as the distribution trend of high and low expressions.In order to better understand the influence of core targets on NSCLC prognosis, a precise analysis of the influence of the ten main targets on the prognosis of all squamous cell carcinomas and adenocarcinomas of NSCLC from TCGA database (https://sites.broadinstitute. org/ccle) to obtain the cell line gene expression matrix of NSCLC tumors and analyzed the expression of core target genes in different NSCLC tumor cell lines by R software, to select suitable cell lines for further verification.Since NSCLC has many cell lines, the core genes are distributed differently in various cell lines. For understanding the expression of core genes in the same tumor, we used the Cancer Cell Line Encyclopedia (CCLE) database  due to the formation of hydrogen bond interactions with the PTGS1 active site, which was the main force promoting its binding capability. Our results also disclosed that these five main active ingredients had better binding affinities towards these protein targets for proper intermolecular interactions.The top five target proteins, as well as the top ten active DBD ingredients in the drug-ingredient-target-disease network, were verified by molecular docking to explore the intertwined interactions between the receptors and ligands. Moreover, if the binding energy was less than zero, it indicated that the ligand molecules could bind to the receptor protein spontaneously while the affinity less than −5.0 kJ·molp = 0.0384), while the overall survival analysis of other nine core genes with high and low expression did not have any statistical significance (p < 0.05) which is shown in p = 0.0384 was utilized to draw the Sankey diagram  is the most common type of lung cancer, accounting for a large proportion of all lung cancer cases , 35. Theμg/mL can induce apoptosis in A549 cells, upregulate the expression of apoptotic genes Bax, Caspase-3, and p53, and downregulate the expression of Bcl-2, cyclin D1, and PCNA proteins. Isorhamnetin's mechanism of action may involve apoptosis initiation induced by downregulation of oncogenes and upregulation of apoptotic genes, thus proving that it can significantly inhibit the growth of A549 cells by inducing cell apoptosis [According to the drug-ingredient-target-disease network, it was observed that DBD has five main active ingredients: quercetin, kaempferol, formononetin, isorhamnetin, and hederagenin. Since quercetin, a bioflavonoid, can induce Hsp70 inhibition involved in growth inhibition of lung cancer cells, it has a great potential as a chemosensitizer in lung cancer treatment as well as the incorporation of dietary quercetin can also be a promising option for cancer prevention , 39. Kuopoptosis . Hederagpoptosis . A studypoptosis . TherefoAfter exploring 5773 candidate NSCLC targets from GeneCards and OMIM databases, 140 common targets were obtained between the NSCLC and DBD, which were subsequently considered as potential targets for the NSCLC treatment. Through the visual analysis of the drug-ingredient-target-disease network and degree ranking, ten core genes were identified. The degree of association between these genes is shown in −1), while the binding energy of hederagenin and PDE3A was also good (affinity = −8.9 kJ · mol−1). These results observed that, although PTGS1 and PDE3A might be the key targets in DBD for treating NSCLC, quercetin and hederagenin may also be effective ingredients for the potential treatment of NSCLC.GO annotation is an important means to examine the function of gene products . ThroughHowever, it should be noted that our study was based on public databases, which had finite information and need to be continuously improved. Additionally, our study also overlooked the influence of concentration, picking time, processing method, and medication time of DBD on NSCLC. But overall, our study is worthy of further exploration and verification to extract the intricate molecular mechanisms governing therapeutic targets for NSCLC treatment.Through several bioinformatics methods, our study results predicted the key active ingredients and related targets of DBD as well as explored the potential targets of NSCLC to devise effective drug therapies for the same. Through the enrichment analysis, it was revealed that the potential working mechanism of DBD in NSCLC treatment mainly focused on three crucial interactions: DNA-binding transcription activator activity, RNA polymerase II-specific activity, and chemical carcinogenesis-receptor activation activity. Additionally, few important observations were also discovered. Namely, the AGE-RAGE signaling pathway in diabetic complications, IL-17 signaling pathway, and TNF signaling pathway are the three main DBD signaling pathways, and PDE3A and PTGS1 are the potential key genes, while quercetin and hederagenin are the two main active ingredients crucial for NSCLC treatment. Our study results also depicted that DBD can achieve the goal of treating NSCLC while improving the prognosis of NSCLC patients through a synergistic interplay between multiple ingredients, multiple targets, and multiple signaling pathways. Nevertheless, further experimental validation is also required to ascertain effective therapeutic approaches for NSCLC patients. At a later stage, our team might conduct a following pharmacological study on important signaling pathways and targets of DBD against NSCLC and try to provide a functional basis and rational references toward a new future for drug development and targeted therapy.


Acat1, Acadvl, Acaa2, Hadhb, Acss1, Oxct1, Hadha, Hadh, Acaca, and Cpt1b. Thus, we screened the key genes and pathways that may be involved in muscle atrophy after SCI and provided support for finding valuable markers for this disease.Spinal cord injury (SCI) is often accompanied by muscle atrophy; however, its underlying mechanisms remain unclear. Here, the molecular mechanisms of muscle atrophy following SCI were investigated. The GSE45550 gene expression profile of control (before SCI) and experimental (14 days following SCI) groups, consisting of Sprague–Dawley rat soleus muscle (n = 6 per group), was downloaded from the Gene Expression Omnibus database, and then differentially expressed gene (DEG) identification and Gene Ontology, pathway, pathway network, and gene signal network analyses were performed. A total of 925 differentially expressed genes, 149 biological processes, and 55 pathways were screened. In the pathway network analysis, the 10 most important pathways were citrate cycle (TCA cycle), pyruvate metabolism, MAPK signalling pathway, fatty acid degradation, propanoate metabolism, apoptosis, focal adhesion, synthesis and degradation of ketone bodies, Wnt signalling, and cancer pathways. In the gene signal network analysis, the 10 most important genes were  Muscle atrophy not only affects the care management and activities of daily living for patients with SCI, but also has significant effects on their health by increasing the risk of secondary complications, such as osteoporosis, diabetes, and cardiovascular disease6. Thus, prevention of muscle atrophy is essential for maintaining metabolic health and normal life activities after SCI7.Spinal cord injury (SCI) is a major cause of disability in humans which can lead to muscle atrophy. Approximately 20–55% of muscle atrophy cases appear within 6 months to 1 year after complete SCI10 have shown that the changes in pathways associated with protein ubiquitination and energy generation are shared features in the process of muscle atrophy. Growth factors such as insulin-like growth factor11, myogenic regulatory factors12, transforming growth factors13, and bone morphogenetic proteins14 may play important roles in muscle atrophy. However, the aetiology and precise underlying mechanisms of muscle atrophy following SCI are still poorly understood. To date, only one global gene profiling study on the changes in gene expression in muscle tissue in the first few days (3 and 5 days) after SCI has been conducted; studies at later timepoints are lacking. The GSE45550 microarray dataset containing gene chip data of Sprague–Dawley rat soleus muscle tissue before SCI and 14 days after SCI was generated in a study by Baligand et al.7; however, an in-depth bioinformatics analysis was not conducted.Previous studiesIn the present study, we downloaded the GSE45550 gene profile dataset and conducted an in-depth bioinformatic analysis of gene profile data before and 14 days after SCI to obtain relevant genes, biological processes, and pathways that may play an important role in the development of muscle atrophy after SCI. We aimed to expand our systematic understanding of the mechanisms of muscle atrophy after SCI.Kcnj11, Chmp4c, Opn4, Ppp3cb, Tasp1, Gsr, Stk40, Alpk3, Pla2g12a, and Atf5 , 925 genes in the experimental group  were significantly differentially expressed in skeletal muscle samples. Of these, 205 genes were upregulated and 720 genes were downregulated. The results are shown as a dendrogram  < 0.05). The top 10 GO terms with significant enrichment were ‘response to drug’, ‘fatty acid beta-oxidation’, ‘response to hypoxia’, ‘biological_process’, ‘heart development’, ‘positive regulation of transcription, DNA-dependent’, ‘response to glucose stimulus’, ‘response to nutrient’, ‘positive regulation of transcription from RNA polymerase II promoter’, and ‘positive regulation of apoptotic process’  with statistically significant enrichment were obtained (s’ Table .Table 2Tp < 0.05). The top 10 KEGG pathways with significant enrichment were ‘metabolic pathways’, ‘PPAR signalling pathway’, ‘peroxisome’, ‘fatty acid degradation’, ‘fatty acid elongation’, ‘glutathione metabolism’, ‘propanoate metabolism’, ‘valine, leucine and isoleucine degradation’, ‘adipocytokine signalling pathway’, and ‘glyoxylate and dicarboxylate metabolism’ , and the results were consistent with the microarray data. These 10 genes were selected because they showed the greatest significance in the microarray results. The difference in mRNA expression of these genes between the before SCI group and the after SCI group was significant (P < 0.05) . It is believed that the expression levels of the first six genes  can be used as potential marker genes for different SCI subtypes. Niu et al.'s study23 used bioinformatics analysis to explore differentially expressed genes associated with the acute and chronic stages of SCI, starting from 1 day to 6 months after SCI, including data sets . Similarly, Zhang et al.'s research24 conducted bioinformatics analysis on the data set GSE45550  to identify key genes or important signal pathways related to SCI. This study focuses on a part of the data set GSE45550 (two groups: before SCI and 14 days following SCI), and compares the differentially expressed genes of the two groups. The results show that the same disease may have very different results and potential differences at different stages. This reminds us that the same patient needs different treatment adjustments at different stages of the disease.Chen et al.'s study25 was used to describe new gene expression involved in signalling pathways that regulate the loss of muscle proteins or increased response to atrophy stimuli in the first few days after SCI. Microarray analysis revealed that metallothionein activity  and protease inhibition activity (secretory leukocyte protease inhibitor) increased significantly at 2 and 5 days after SCI25. Our microarray analysis revealed that the 10 genes with the most significant differential expression at 14 days after SCI were Kcnj11, Chmp4c, Opn4, Kcnj11, Tasp1, Gsr, Stk40, Alpk3, Pla2g12a, and Atf5. This indicated that the changes in mRNA expression of muscle atrophy at different times after SCI were different. Shen et al.26 first proposed that innervation atrophy can be divided into four different transcription stages: oxidative stress stage (0–12 h), inflammation stage (24 h), atrophy stage (3–7 days), and atrophic fibrosis stage (14–28 days). Whether muscle atrophy after SCI involves similar stages remains to be further studied.A previous microarray analysis by Urso et al.27. Studies29 have shown that atrophy and death of skeletal muscle cells lead to sarcopenia, which is a disorder associated with normal ageing. It is estimated that 30–40% of skeletal muscle fibres are usually lost by the age of 80 years30. Although the mechanisms of this loss are not well understood, it is possible that apoptosis is involved. Mitochondrial dysfunction and sarcoplasmic reticulum stress that occur with increasing age may be possible factors that induce apoptosis. Therefore, the mitochondria and sarcoplasmic reticulum may be important organelles in skeletal muscle cells responsible for signal transduction associated with apoptosis. The activation of apoptosis may be a part of the reason for the initiation of muscle protein degradation, loss of muscle nuclei associated with local atrophy, and death of muscle cells. Exercise training and calorie restriction are two interventions known to enhance skeletal muscle function30.The GO analysis showed that the primary biological processes of differential genes were ‘response to drug’, ‘fatty acid beta-oxidation’, ‘response to hypoxia’, ‘biological_process’, ‘heart development’, ‘positive regulation of transcription, DNA-dependent’, ‘response to glucose stimulus’, ‘response to nutrient’, ‘positive regulation of transcription from RNA polymerase II promoter’, and ‘positive regulation of apoptotic process’. These results showed that the pathogenesis of muscle atrophy after SCI is the result of mutual gene and network regulation of multiple factors and genes that directly or indirectly lead to the development and progression of muscle atrophy. Among them, ‘positive regulation of apoptotic process’ warrants further study because skeletal muscle atrophy is closely associated with apoptosis, but the specific mechanisms of action are not well understood30. These pathways are believed to be the key players in fatty acid metabolism (Lpl and Fabp3) and have proved to be the major sensors of SCI-induced inactivity and reloading with training. Pathway network analysis showed that ‘citrate cycle (TCA cycle)’, ‘pyruvate metabolism’, ‘MAPK signalling pathway’, ‘fatty acid degradation’, ‘propanoate metabolism’, ‘apoptosis’, ‘focal adhesion’, ‘synthesis and degradation of ketone bodies’, ‘Wnt signalling pathway’, and ‘pathways in cancer’ were the 10 pathways with the highest degree, indicating that these signalling pathways may play the most important roles in muscle atrophy after SCI. Among them, the focal adhesion pathway warrants further attention because some studies31 have reported that it may play an important role in amyotrophic lateral sclerosis. In addition, studies32 have reported that the mechanisms and strategies to counter muscle atrophy also include the focal adhesion pathway. The primary sensors for the changes in muscle activity (and load) are the integrin complex and the dystrophin-associated protein complex. Neither of these complexes has inherent signalling function, but they appear to mediate focal adhesion through focal adhesion kinase (FAK)34, a targeted kinase that indirectly provides nutrients through many different pathways, thereby leading to the activation of growth pathways and preventing apoptosis. Total FAK and its activity were reduced in atrophic soleus muscle, and other proteins associated with focal adhesion complexes were also downregulated35.In the present study, DEGs were primarily enriched in the pathways ‘metabolic pathways’, ‘PPAR signalling pathway’, ‘peroxisome’, ‘fatty acid degradation’, ‘fatty acid elongation’, ‘glutathione metabolism’, ‘propanoate metabolism’, ‘valine, leucine and isoleucine degradation’, ‘adipocytokine signalling pathway’, and ‘glyoxylate and dicarboxylate metabolism’, of which the ‘fatty acid degradation’ and ‘fatty acid elongation’ pathways are consistent with the major pathways (fatty acid metabolism) analysed in the study of muscle atrophy after SCI by Dirks et alAcat1, Acadvl, Acaa2, Hadhb, Acss1, Oxct1, Hadha, Hadh, Acaca, and Cpt1b may play key roles in the development and progression of muscle atrophy after SCI because they had the highest betweenness values . The results also showed the relationship between these 10 genes, providing new avenues for the prevention and treatment of muscle atrophy after SCI. Among them, the betweenness value of Acadvl was the highest. Acadvl is involved in the fatty acid degradation pathway36 and the fatty acid beta-oxidation biological process37. However, the fatty acid degradation pathway was screened from the pathway analysis and pathway network analysis, and fatty acid beta-oxidation was also screened from the GO analysis. These showed the interdependency of the results; they also suggest that Acadvl may be a key regulatory gene in the development of muscle atrophy after SCI, warranting further study. ACADVL is responsible for the production of very long-chain acyl-CoA dehydrogenase, an enzyme that plays a key role in the mitochondria. It is essential for fatty acid oxidation, a multi-stage process that metabolises fat into energy38. There is a close relationship between abnormalities associated with neurodegenerative diseases such as Alzheimer’s disease and brain fatty acid metabolism. The pathology associated with Alzheimer’s disease inhibits the homeostasis and regeneration of neural stem cells by interfering with fatty acid metabolism40.In the gene signal network analysis, The sample size of the data in the present study was small and the samples were selected from a platform, which may lead to some degree of false negatives. Further experimental studies and a larger sample size are needed to confirm the results of the present study. A key limitation of the original study design is that, while the SCI group underwent a laminectomy and injury, the control group did not undergo ‘sham’ surgery. Thus, the study was not controlled for surgery effects.In summary, the genetic profile data of atrophic muscle tissue before SCI and at 14 days after moderate SCI was downloaded from the Gene Expression Omnibus to identify genes differentially expressed between the two conditions. In addition, bioinformatic analyses were performed to gain new insights into the molecular mechanisms of muscle atrophy in this model. It is still not clear whether muscle atrophy 14 days after SCI in this model properly represents muscle atrophy in the atrophic fibrosis stage. Nevertheless, our current understanding of the mechanisms underlying muscle atrophy after SCI is undoubtedly expanded, and several potential targets for the prevention and treatment of this condition were also revealed.http://www.ncbi.nlm.nih.gov/geo/) and the GSE45550 gene profile dataset was obtained. These data represented a control group  and an experimental group . The sample tissue was soleus muscle tissue of Sprague–Dawley rats. The platform was GPL1355 [Rat230_2] Affymetrix Rat Genome 230 2.0 Array. The SCI model was established by anaesthetising the rat and dropping a 10 g cylinder from a height of 25 mm at the level of T7–T9 after a dorsal laminectomy, affecting the spinal cord at T8 to cause injury7. All procedures were performed in accordance with the US Government Principle for the Utilization and Care of Vertebrate Animals and were approved by the Institutional Animal Care and Use Committee at the University of Florida. The study was carried out in compliance with the ARRIVE guidelines (https://arriveguidelines.org).The keywords ‘SCI’ and ‘muscle atrophy’ were used to search the Gene Expression Omnibus in the National Centre for Biotechnology Information (NCBI) database (https://www.gcbi.com.cn/gclib/html/index) to perform all the bioinformatics analyses in this study. The details of this process are shown as a flowchart  analysis platform  for each gene to measure the degree of correlation between the gene expression level under the corresponding grouping and the designated group, and then simulated the distribution of these statistics randomly (randomly replaced samples) through a series of 1,000 replacements. The statistic value (d Score) was calculated for each gene di = ri/(si + s0), where ri reflects the difference in the average level among different groups and si reflects the variation in sample population. Considering the influence of the variance of low-abundance genes and the correlation between genes, the statistical calculations, and the methods for evaluating statistical significance were adjusted using appropriate methods to obtain the significance level for each gene. Finally, to adjust for the p values of multiple experiments, a q value was calculated by constructing the discretised rejection region to control the FDR43. Differences with q < 0.05 with fold change > 1.5 or <  − 1.5 were considered statistically significant.For DEG identification, significance analysis of microarrays (SAM)p value) and FDR in order to screen the significant functions of each differentially expressed gene. The enrichment results with FDR < 0.05 were considered statistically significant.The GO database is a cross-species, comprehensive, and descriptive database established by the Gene Ontology Consortium. In the GO analysis, the DEG between the groups was used to perform gene function annotation based on the GO database to obtain all the functions in which the gene is involved. Next, Fisher’s exact test and multiple comparison tests were used to calculate the significance level (The Kyoto Encyclopaedia of Genes and Genomes (KEGG) is a database that systematically analyses the relationship between genes (and their coded products), gene functions, and genomic information, and allows the study of genes and their expression as an entire network. Based on the KEGG database analysis, Fisher’s exact test was used for the differential genes to perform significance analysis of the pathways in which the target gene is involved in order to screen the significant pathways of each differential gene. Pathways with FDR < 0.05 were considered statistically significant.Pathway network analysis allows comprehensive, systematic analysis of the signal transduction relationships among significant pathways discovered from pathway analysis and intuitive discovery of the synergistic effects of major pathways when the sample changes, thereby providing a systematic understanding of the nature of the changes in sample traits. The pathway network analysis was used to construct an interaction network between pathways based on the interaction relationships in the KEGG database. The network was determined based only on the significant pathways from the pathway analysis. The calculation and description of network attribute: network N is denoted as The interaction between genes and gene products in the KEGG database and the interaction between each gene and other genes obtained from searching the database allow the interaction between the target gene clusters to be comprehensively discovered and positioned as upstream and downstream proteins. Next, an interaction network between genes was constructed. The network based on the DEGs. The betweenness of i:7 was established by anaesthetising the rat and dropping a 10 g cylinder from a height of 25 mm at the level of T7–T9 after a dorsal laminectomy, affecting the spinal cord at T8 to cause injury. The animals were deeply anaesthetised with a combination of ketamine (90 mg/kg body weight) and xylazine (8 mg/kg body weight). The sample tissue was soleus muscle tissue. Beyozol  was used to extract total RNA from the frozen tissues. The BeyoRT II cDNA Synthesis Kit  was used to reverse transcribe RNA into cDNA. Thereafter, 2 µg of cDNA was tested in each reaction using the BeyoFast SYBR Green One-Step qRT-PCR Kit  in the Applied Biosystems Real-Time PCR System . The 2−ΔΔCt method was adopted to calculate the expression of genes relative to the housekeeping gene GAPDH. Table Twelve adult female Spragdorley rats  were reared at a temperature of 22 ± 1 °C, humidity of 50% ± 10%, and light: dark cycle of 12:12, and provided a sufficient food and water. Animals were sacrificed: a control group  and an experimental group . The SCI modelSupplementary Information 1.Supplementary Information 2.Supplementary Information 3.


To explore the long-term influence of methamphetamine abuse on metabolomics character, with gas chromatography-mass spectrometry (GS-MS) technology, and the potential regulatory network using the bioinformatics method.Forty withdrawal methamphetamine abusers (WMA) were recruited from Shanghai Gaojing Forced Isolation Detoxification Institute. Forty healthy controls (HC) were recruited from society. GS-MS technology was used to detect metabolic products in serum. A bioinformatics method was used to build a regulatory network. Q-PCR was used to detect the candidate gene expressions, and ELISA was used to detect the regulatory enzyme expressions.p < 0.05, Impactor factor > 0.2). When focusing on the ‘Alanine, aspartate, and glutamate metabolism’ pathway, a regulatory network was established, and the expression of candidate regulatory genes and enzymes was verified. It was found that the expression of DLG2 (Discs large MAGUK scaffold protein 2), PLA2G4 (Phospholipase A2 group IVE), PDE4D (Phosphodiesterase 4D), PDE4B (Phosphodiesterase 4B), and EPHB2 (Ephrin type-B receptor 2) were significantly different between the two groups (p < 0.05), However, after adjusting for age and BMI, only DLG2, PLA2G4, and EPHB2 remained significant (p < 0.05). The expression of enzymes was not significantly different (p > 0.05).Four pathways were significantly changed in the MA compared to the HC: (1) the arginine synthesis pathway, (2) alanine, aspartic acid and glutamate metabolic pathway, (3) cysteine and methionine metabolic pathway, and (4) the ascorbate and aldarate pathway (enrichment analysis DLG2, PLA2G4, and EPHB2 may regulate the glutamate metabolism pathway.Methamphetamine abuse influences the metabolic process in the long term, and  Methamphetamine (METH) is a widely abused addictive psychostimulant, which accounts for a considerable share of global disease burden. METH abuse is increasing rapidly , especiaMetabolomics is a potentially powerful tool for understanding the global biological consequences of disease and drug administration. In recent years, with the improvement of metabolomics technology, metabolomics research in psychotic diseases has made great progress . HoweverInformed consent of the participants was obtained after the nature of the procedures used in the study had been fully explained. The study was carried out in accordance with the last version of the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Shanghai Mental Health Center (SMHC IRB 2016-11R). All participants were identified using numbers, instead of names.Forty male WMA were recruited from Shanghai Gaojing Forced Isolation Detoxification Institute in July 2017, and 40 male healthy controls were recruited from general society. The entire sample consisted of a Chinses Han population. The WMA accepted assessment from two senior psychiatric doctors and met the diagnosis criterion for methamphetamine abuse disorder according to the Diagnostic and Statistical Manual of mental disorders, fifth edition (DSM-5). Participants did not have the opportunity to use any illicit substance during the last 2 months. They completed a self-report form that included basic demographic information and accepted an interview of Addiction Severity Index (ASI). Exclusion criteria for both groups were: (1) history of craniocerebral trauma or epileptic seizure; (2) metal implants in the brain or an implanted pacemaker; (3) history of major physical diseases, organic mental disorders, and severe mental disorders. People in the control group did not have any history of illegal substance abuse. Venous blood was collected at 7:30–8:00 am before breakfast.g, for 15 min. The upper layer of the serum was collected. Sample preparation, GC-MS experimental process, and GC-MS data processing were carried out using previously published methods , AGT (Angiotensinogen), GAD1 (Glutamate decarboxylase 1), and GAD2 (Glutamate decarboxylase 2); 13 genes: DLG2 (Discs large MAGUK scaffold protein 2), NRG1 (Neuregulin 1), PLA2G4E (Phospholipase A2 group IVE), STX8 (Syntaxin 8), NEDD4L (NEDD4 like E3 ubiquitin protein ligase), PRKG1 (Protein kinase cGMP-dependent 1), PDE4D (Phosphodiesterase 4D), PDE4B (Phosphodiesterase 4B), SERPINA5 (Serpin family A member 5), PDE6C (Phosphodiesterase 6C), CD55 (CD55 molecule), EPHB2 (Ephrin type-B receptor 2), and RAPGEF5 (Rap guanine nucleotide exchange factor 5), and several intermediate proteins.The four differential expressed metabolites were treated as candidate metabolites. We collected 133 risk genes from previous genome-wide association studies . The mol® 96 real-time PCR machine . Primers are listed in GAPDH was used as a housekeeper gene. The relative gene expression was calculated using the 2−△CT method  according to the previous protocol , and stoT method . The CT AGT, GAD1, GAD2, and ASNS were assessed using an ELISA kit .The serum levels of All assays were performed according to the manufacturer’s directions and were performed in duplicate. The average value of the duplicates was used as the final value.t test, a was 0.05 and Cohen’s d was used to calculate the effect size of the t test. 2–△CT was used to calculate the relative expression of genes, and the expression ratio of the WMA group relative to the control group was calculated as previously described , and analyzed using SSPSS 22.0. Data was analyzed using the student escribed .t test was used after log transformation. Differential expressed metabolites were screened according to fold change (FC) and p value. FC > 1. 5 and p < 0. 05 showed that the metabolite was significantly differentially expressed. Based on the selected differential metabolites, a pathway analysis was performed using a hypergeometric test and KEGG pathways , and R was used to analyze the data. The NIST 2017 database was used for metabolite certification and Metaboanalyst 3.0 was used for the metabolic pathway analysis. Partial least squares discriminant analysis (PSL-DA) was performed, and a pathways  and the Hits is the hit number of differential metabolites in a certain metabolic pathway. TotalAll is the number of metabolites in all pathways of a certain species, such as Homo sapiens. PtwAll is the total number of metabolites in a certain pathway and TotalHits is the hit number of differential metabolites in all pathways. A significant p value means significantly changed metabolic pathway activities that need to be focused on. For multiple tests, a false discovery rate (FDR) was applied to correct the raw p values.Ptwp < 0.05).The demographic characteristic are shown in t test (p < 0.05), in which 21 metabolites could be found by searching metabolic pathways in the KEGG information database  , four pathways were found to be significantly changed; (1) arginine synthesis pathway, (2) alanine, aspartic acid and glutamate metabolic pathway, (3) cysteine and methionine metabolic pathways, and (4) ascorbate and aldarate pathway  .Previous research found that methamphetamine may result in glutamine system disturbance , either –△CT was used to calculate the relative expression of genes . However, after adjusting for age and BMI, only DLG2, PLA2G4, and EPHB2 remained significant (p < 0.05). STX8, RAPGEF5, and PDE6C were not detected because of their low expression.According to the regulatory network, we used Q-PCR to verify the 13 candidate genes expressed. 2of genes  .ELISA was used to detect the regulatory enzyme expression. As This study found that peripheral metabolites of WMA patients were mainly changed in amino acid metabolism. The most obvious pathways were; (1) the arginine synthesis pathway, (2) the alanine, aspartic acid and glutamate metabolic pathways, (3) the cysteine and methionine metabolic pathways, and (4) the purine metabolic pathway.Previous studies on methamphetamine addicted animals found that abnormal energy metabolism was the biochemical basis of toxicity and addiction . An incrIn addition, amphetamines cause serum/plasma neurotransmitter disturbances mainly in the glutamate-glutamine-GABA system . GlutamaGAD1, GAD2, ASNS, and AGT may directly regulate the glutamate pathway metabolites- pyruvate, L-aspartate, L-glutamine. The regulatory enzyme is regulated by DLG2, NRG1, PLA2G4E, STX8, NEDD4L, PRKG1, PDE4D, PDE4B, SERPINA5, PDE6C, CD55, EPHB2, and RAPGEF5 genes only through one intermediate protein. We verified the expression of the candidate genes and enzymes, and finally found five regulatory genes that were differentially expressed in the two groups, namely DLG2, PLA2G4, PDE4D, PDE4B, and EPHB2 genes. After adjusting for age and BMI, only DLG2, PLA2G4, and EPHB2 remained significant.We chose the glutamate metabolic pathway and used bioinformatics methods to establish a regulatory network model, which predicts that DLG2 plays a very important role in synaptic development and stability of plasticity  superfamily, and play an important role in the degradation of cAMP , and EPHB2 is a key regulator of synaptic localization of NMDA receptors (EPHB2 induces depression-like behavior and memory impairment in mice, which is mediated by NMDA receptor subtypes (The ormation . EPHB2 abehavior  and revebehavior . EPHB2 receptors . Knockousubtypes . This stThe expression of candidate regulatory enzymes was not differentially expressed between two groups. However, the activity of enzymes was not detected, which may influence the metabolites. Another complex regulatory mechanism could not be excluded.MA use has a long-term and extensive impact on the human body, which can be reflected by the peripheral metabolites. The glutamate system metabolic disturbance may be regulated by the genes that are associated with multiple pathophysiological processes such as synaptic plasticity, signal transduction, cAMP degradation, phosphorylation, and synaptic localization, which may be related to clinical symptoms of withdrawal MA, such as long-term cognitive impairment, anxiety and fear responses, and so on.There were some limitations in this study. First, only one time point was selected for the exploration of serum metabolomics in the addiction state of methamphetamine. If long-term follow-up can be performed during the acute phase and withdrawal period, the changes in metabolomics characteristics over time will be observed and the results will be more systematic. Second, the exploration of the mechanism is limited to biochemical and gene regulation mechanisms, and if it can be combined with the analysis of central neurotransmitter changes, it will be more in-depth. Third, the enzyme activity was not detected. Fourth, the sample size was limited, and all the enrolled participants were from the Shanghai area only. The conclusion can therefore only be extended to Shanghai Han nationality. A multi-center collaboration may therefore facilitate further discoveries.The raw data supporting the conclusions of this article will be made available by the authors.The studies involving human participants were reviewed and approved by Ethics Committee of Shanghai Mental Health Center. The patients/participants provided their written informed consent to participate in this study.SP: conceptualization, data curation, formal analysis, methodology, writing – original draft, review, and editing. HS: investigation, project administration, and supervision. TC: investigation and project administration. XL: investigation and project administration. JD: conceptualization, methodology, project administration, resources, and supervision. HJ: conceptualization, methodology, project administration, resources, and supervision. MZ: conceptualization, funding acquisition, methodology, resources, supervision, and writing – review and editing. All authors contributed to the article and approved the submitted version.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.


Argyreia capitiformis stem was examined for anti-inflammatory activities following network pharmacology analysis and molecular docking study. Based on gas chromatography-mass spectrometry (GC-MS) analysis, 49 compounds were identified from the methanolic extract of A. capitiformis stem. A network pharmacology analysis was conducted against the identified compounds, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology analysis of biological processes and molecular functions were performed. Six proteins  were identified from the KEGG pathway analysis and subjected to molecular docking study. Additionally, six best ligand efficiency compounds and positive control (aspirin) from each protein were evaluated for their stability using the molecular dynamics simulation study. Our study suggested that IL1R1, IRAK4, MYD88, TIRAP, TLR4, and TRAF6 proteins may be targeted by compounds in the methanolic extract of A. capitiformis stem to provide anti-inflammatory effects.The methanolic extract of  Inflammation describes a biological process that occurs in tissues to protect the host against harmful stimuli, such as microorganisms and abnormal or damaged cells. Inflammation stimulates the immune system and regulates protective responses via immune cells, blood vessels, and molecular biological agents , 2. ManyArgyreia capitiformis (Poir.) Ooststr. is a member of the Convolvulaceae family of the Argyreia genus, which is not toxic and has medicinal and ornamental uses [A. capitiformis has been used as an effective treatment of bruising on the legs. A. capitiformis has also been used in traditional medicine as a purgative and to treat sexual debility and ear pain [Argyreia species, including antioxidant, anti-inflammatory, immunomodulatory, and CNS activities with several bioactive compounds [A. capitiformis except the recent study on the methanolic extract of A. capitiformis leaves that suppressed the nuclear factor-kappa B (NF-κB) pathway and inhibited the lipopolysaccharide-induced production of nitric oxide and inducible nitric oxide synthase in RAW 264.7 cells, demonstrating anti-inflammatory activities [A. capitiformis that are responsible for these anti-inflammatory effects and the underlying mechanisms remain unknown. Further research is necessary to identify the potential lead compounds responsible for these biological anti-inflammatory outcomes.tal uses , 10. Traear pain . Severalompounds –15. Howetivities . HoweverA. capitiformis [Network pharmacology has become a widely accessible analysis method following the increased availability of biomedical data sets during the postgenomic period, supporting the growth of the fields of systems biology and polypharmacology . Complextiformis –24.A. capitiformis stem using a network pharmacology approach. Bioactive compounds in the methanolic extract of A. capitiformis stem were identified for this study using gas chromatography-mass spectrometry (GC-MS) analysis, followed by a molecular docking assay to investigate potential ligand-receptor interactions, including the assessment of binding affinity and stability.The network pharmacology approach connects targeted genes with the effects of bioactive compounds; thus, the present study was designed to elucidate the anti-inflammatory effects of the methanolic extract of A. capitiformis were collected from the Sitakunda Eco-park, Chittagong, Bangladesh, in March 2020 and later identified by a taxonomist. The stems were subjected to air-drying and ground to a coarse powder. The powder (200 g) was soaked in methanol (1 L) for 7 days [The stems of r 7 days , 26. Subµm film thickness), using high-purity helium as the gas carrier at a flow rate of 1 mL per min. The injector temperature was 230°C, and the sample was injected using a splitless injector at 20 : 1. The temperature was set primarily to 40°C (held for 1 min), raised to 150°C at a rate of 5°C per min (held for 2 min), before being increased to 300°C at a rate of 5°C per min (held for 10 min). The temperature of the MS ion source was set to 150°C, and the temperature of the inlet line was set to 280°C. The scan range was set between 50 and 550 mass, with 70 eV electron energy and a 4-min solvent delay. Finally, by comparing the spectra against the NIST 2008 database , tentative compounds were identified. The total analysis time required for the sample was 65 min [An Agilent GC 7890A , combined with a triple-axis detector 5975 C single-quadrupole mass spectrometer, was used for GC-MS analysis. The chromatographic column was an Agilent HP 5MS column (30 m × 0.25 mm × 0.25 s 65 min .http://stitch.embl.de/) to identify putative associations between the identified compounds and target genes. Multiple compound targets were identified using the Homo sapiens genome [The network pharmacology analysis was performed using the STITCH platform (s genome –29. Multhttps://string-db.org), which includes predicted protein-protein interactions (PPIs), was used to predict functional protein interactions [The STRING  database (ractions ..pdb format from the Protein Data Bank (http://www.rcsb.org/pdb). The proteins were prepared using Schrödinger (Maestro v11.1), utilizing the force field OPLS3 [Six targeted proteins were selected from the KEGG analysis: interleukin 1 (IL-1) receptor type 1  , IL-1 reld OPLS3 .A. capitiformis, according to the qualitative GC-MS analysis, which we submitted to the molecular docking study. The selected compounds were retrieved in .sdf format from the PubChem database. In addition, aspirin (CID: 2244) was utilized in this study as a positive anti-inflammatory control. The three-dimensional (3D) structures of the selected compounds were constructed in Schrödinger using LigPrep (Maestro v11.1), utilizing the force field OPLS3.We selected 47 compounds identified from To create receptor grids and execute a molecular docking analysis, Glide  was used. The grids were generated in Glide with the default settings and the OPLS3 force field. A cubic box with a boundary box (14 Å × 14 Å × 14 Å) was specified for the receptors. All docking studies were conducted using Glide's standard precision (SP) and flexible docking modes, and the lowest docking score for each ligand was recorded.G; kcal/mol) for each ligand and the target receptors were calculated using the Schrödinger software package Prime/MM-GBSA module (OPLS3) [G to the number of heavy atoms (NHA): LE = −(ΔG)/NHA [The free energies of binding (Δ (OPLS3) , 38. The(ΔG)/NHA .The molecular dynamics simulation study was conducted in YASARA dynamics by the aid of AMBER14 force field , 41. TheA. capitiformis stem methanolic extract revealed 49 compounds with different retention times and peak areas  revealed signaling pathways related with anti-inflammatory effects see .  FigureA total of 47 compounds docking results are presented in Tables 31Plants have been used throughout the history of traditional medicine to induce a variety of biological effects, and extensive pharmaceutical resources have recently been devoted to the identification and investigation of new remedies, including those derived from plants. A critical issue encountered by researchers who perform phytoscience is that a single plant can harbor a wide range of bioactive chemicals , 56. Thep = 0.0447) and similar to that for PKC. Also, retinoic acid binding has a great impact on the immune system and the inflammatory response. Our findings identified the genes UGT1A1, UGT1A3, UGT1A7, UGT1A8, and UGT1A9 as being the most significant interactions for the compounds in our extract (p = 2.69E − 06).The network pharmacology analysis was performed to evaluate potential interactions between the identified chemical compounds and proteins, followed by multiple comparisons to determine the number of genes responsible for the anti-inflammatory effects, which was represented by the STITCH platform . Table 2IL1R1, IRAK4, MYD88, TIRAP, TLR4, and TRAF6 in the NF-κB signaling pathway, which has a p-value of 1.35E − 05. Similar results were demonstrated for the inflammatory response in the biological process analysis, which identified the MYD88-dependent TLR signaling pathway , the TLR4 signaling pathway , the TLR1:TLR2 signaling pathway , the TLR 2 pathway , the TLR signaling pathway , positive regulation of interleukin-6 production , NF-κB signaling , NF-κB signaling , TLR5 signaling pathway , and TLR10 signaling pathway . Six genes were identified as being associated with the NF-κB signaling pathway, suggesting anti-inflammatory effects, in support of a previous study that identified the inhibition of NF-κB functional pathways in RAW 264.7 cells following treatment with a methanolic extract of A. capitiformis leaves [As shown in IL1R1, IRAK4, IRAK2, MYD88, TIRAP, TRAF6, TLR3, TLR4, TLR5, and TLR6) were used to analyze a PPI network (p < 1.0e − 16) related to anti-inflammatory functions. Signals from the various TLRs are diverse, but all TLRs are activated by the binding of TIR-containing adaptor proteins . IRAK4, IRAK1, IRAK2, and TRAF6 are all activated by MYD88. MYD88 binding results in the phosphorylation and release of IRAK1 from the cell membrane into the cytoplasm, where it interacts with and activates the transforming growth factor-β-activated kinase 1 (TAK1) downstream. TAK1 activates the IKKβ-to-IκB-α-to-NF-κB pathway, inducing the transcription of proinflammatory genes. TAK1 also influences gene expression by activating mitogen-activated protein kinase (MAPK) cascades. TRAF6, βRIP1, and TAK1 are activated by the binding of TLR with TRIF, resulting in the activation of MAPK, interferon regulatory factor 3 (IRF3), NF-κB, and interferon- transcription activation. The TRIF pathway also promotes the release of proinflammatory cytokines, although to a lesser degree than the MYD88 pathway. TLRs are a class of specific receptors that are key players in mounting an effective innate immune response to infection [Although a remarkable amount of functional and structural data has been compiled for each identified protein, our knowledge regarding protein-protein relationships remain scattered. The purpose of the STRING database is the collection, scoring, integration, and complementation with computational predictions for all public sources of PPIs –66. In t network , which wnfection –69.Homo sapiens. In addition, coexpression (transferred) of three more genes was observed in Gallus gallus, Mus musculus, and Rattus norvegicus.Subsequently, gene co-occurrence can be used to identify gene families whose patterns exhibit similarity across genomes. Three types of analyses have been used to examine genomes  based on the systemic comparison of all-against-all genomes to evaluate the impacts of historical genome restructurings, genetic gains and losses, and gene fusion , 71. In Structure-based drug discovery is gradually becoming a key technique for facilitating the rapid and cost-effective discovery and optimization of lead compounds. The use of a rational, structure-based drug design strategy is more efficient than conventional drug development techniques because this approach seeks to understand the molecular basis of diseases and incorporates information regarding the biological target's 3D structure during the drug design process . A molecα or IL-1β ligands, whereas the T-lymphocytes, fibroblastic cells, epithelial cells, and endothelial cells have been indicated [β [IL-1 controls a range of innate immune pathways, making it a key regulator of inflammation . Two IL-ndicated –78. In tcated [β .IRAK4 (PDB: 6EGA) also exhibited similar findings in the docking experiment, with only 12 compounds that did not display any interactions. Catechol (4.45), phenol (4.35), p-vinylguaiacol (4.15), methylcyclohexane (4.08), 3-furaldehyde (4.06), and 4-methyl-1,5-heptadiene (3.94) showed the best LE values, as shown in A. capitiformis stems. In the molecular docking experiment, 29 compounds were found to interact with MYD88  was also studied as a potential target of the anti-inflammatory effects of the methanolic extract of th MYD88 . The besκB to translocate to the nucleus [κB activation through the activation of TLR4 and TLR2 signaling pathways [A. capitiformis stems, of which 35 demonstrated interactions and 10 did not , is an important link between MYD88 and the receptor complex formed by TLR2 and TLR4 activation following bacterial infection . MYD88 a nucleus , 81. TIRpathways –85. The  did not . The bes did not . The mol not  was compared against 45 compounds found in the methanolic extract of ith TLR4 . Phenol A. capitiformis stems, of which 34 were identified as interacting  was assessed against the 45 chemicals found in the methanolic extract of eracting . SulcatoThe root-mean-square deviations from the C-alpha atoms from the docked complexes IL1R1 (PDB: 1ITB) proteins are illustrated in The molecular dynamics simulation of IRAK4 (PDB: 6EGA) is presented in The RMSD of the MYD88 (4EO7) protein complexes had a lower level of fluctuations, and lower deviations were observed across the compounds. All compounds had a lower RMSD than 2.5 Å in whole simulation periods . The SASThe RMSD profile of TIRAP (PDB: 4FZ5) complexes is illustrated in The molecular dynamics simulation study of the TLR4 (PDB: 3FXI) and complexes was done to analyze the structural deviation in the docked structure. The root-mean-square deviations of all complexes are illustrated in The docked complexes from the TRAF6 (PDB: 3HCT) protein and their simulation descriptors are illustrated in A. capitiformis stem. Six inflammation pathways were obtained from the KEGG pathway analysis , and molecular docking studies of these pathways revealed that the identified chemical compounds had strong binding affinities with these pathway components. The current study discovered that 3-furaldehyde, phenol, catechol, and hydroquinone were effective anti-inflammatory compounds found within the methanolic extract of A. capitiformis and played an important part in the inflammation pathway by targeting these six proteins. Additional in vitro and in vivo experiments will be helpful to validate and optimize the findings of this study.The network pharmacology analysis revealed key pathways involved in the anti-inflammatory activities induced by the chemical compounds found in the methanolic extract of


Identifying the biological subclasses of septic shock might provide specific targeted therapies for the treatment and prognosis of septic shock. It might be possible to find biological markers for the early prediction of septic shock prognosis.The data were obtained from the Gene Expression Omnibus databases (GEO) in NCBI. GO enrichment and KEGG pathway analyses were performed to investigate the functional annotation of up- and downregulated DEGs. ROC curves were drawn, and their areas under the curves (AUCs) were determined to evaluate the predictive value of the key genes.MME gene was 0.879, as a key gene with the most obvious upregulation in septic shock. The AUC for the THBS1 gene was 0.889, as a key downregulated gene with the most obvious downregulation in septic shock.117 DEGs were obtained, including 36 up- and 81 downregulated DEGs. The AUC for the MME via the renin-angiotensin system pathway and the downregulation of THBS1 through the PI3K–Akt signaling pathway might have implications for the early prediction of prognosis of septic shock in patients with pneumopathies.The upregulation of  Septic shock, characterized by circulatory and cellular abnormalities, is associated with substantial morbidity and mortality . The worAlthough much progress has been made in diagnosing and treating septic shock, mortality remains unacceptably high . A consiWe hypothesized that key genes are involved in the early stage of septic shock. These genes might be involved in the initiation of septic shock, and the mediated molecular changes might affect the prognosis of the disease. Therefore, it could be possible to detect the changes in gene expression in the early stage of septic shock through gene chips. By correlating these changes with the prognosis of the disease, it might be possible to identify biological markers for the early prediction of the prognosis of septic shock.https://www.ncbi.nlm.nih.gov/geo/; GSE33118) was obtained from the Affymetrix GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array), which was submitted by Raffelsberger et al. Septic shock by pneumopathy was studied prospectively in 20 patients, whose blood samples were taken within 12 h of diagnosis. Preparation, processing, and analysis of data were performed using the R software (version 3.6.3). The flowchart of this study is shown in Fig. The data was obtained from the Gene Expression Omnibus databases (GEO) in NCBI, a public functional genomics data repository. The expression dataset GSE33118   enrichmeUp- and downregulated DEGs were considered candidate genes to predict the outcome. Receiver operating characteristic (ROC) curves were drawn. Their areas under the curve (AUCs) were determined to evaluate the predicted value of the key genes using the pROC package in R .There were 54,613 genes in the GSE33118 dataset, including 29,647 upregulated and 24,939 downregulated genes. Finally, 117 DEGs were identified, including 36 up- and 81 downregulated DEGs. The volcano map of all DEGs is displayed in Fig. MME, SGK1, and COL9A3 , such as phosphatidylinositol-3-phosphate binding, sodium channel regulator activity, potassium channel regulator activity, extracellular matrix structural constituent conferring tensile strength, chemokine binding, chemokine receptor activity, G protein-coupled chemoattractant receptor activity, and C–C chemokine binding, as well as in Biological Process (BP), such as regulation of polysaccharide metabolic process, amyloid-beta clearance, regulation of glycogen metabolic process, and long-term memory , such as protein kinase complex, endoplasmic reticulum-Golgi intermediate compartment, serine/threonine-protein kinase complex, and cyclin-dependent protein kinase holoenzyme complex Fig. A. The KEBUB1  might play an important role in the prognosis of septic shock through the renin-angiotensin system pathway [In this study, as a significantly upregulated gene,  pathway , which i pathway . Indeed, pathway . Ang-(1– pathway . Still,  pathway . On the  pathway . Excessi pathway . Fernand pathway . Further pathway , 30. StiTHBS1 might play an important part in the prognosis of septic shock through the PI3K–Akt signaling pathway. Mizuta et al. [THBS1, DDIT4, TCL1A, and AREG might be biological markers in the prognosis of septic shock by acting via PI3K/Akt signaling pathway. Still, the role of THBS1 in the regulation of the PI3K–Akt signaling pathway in cancer is relatively well defined [In this study, as a significantly downregulated gene, a et al.  suggestea et al. . It was a et al. . In this defined , but itsDifferent metabolomic patterns might have a major impact on the development of novel diagnostic methods for the early diagnosis and prognosis of septic shock . The hetThe potential limitations of our study should be considered. The datasets we used were obtained from a public database. This dataset only included data about septic shock from pneumopathies. All results were obtained using bioinformatics. Future studies are currently being designed for examining the exact roles of MME and THBS1 in the renin-angiotensin system and PI3K/Akt signaling pathways in septic shock from pneumopathies. Further experiments are needed to verify the results of this study and expand the generalizability to septic shock from other sources.MME and its role in the renin-angiotensin system pathway and the downregulation of THBS1 and its role in the PI3K–Akt signaling pathway might have important implications for the early diagnosis and prognosis of septic shock from pneumopathies. This study suggests that the molecular typing of septic shock could reveal diagnostic, prognostic, and therapeutic biomarkers for patients with septic shock, allowing early diagnosis and management. Still, the biomarkers need to be refined, and prognosis models need to be built.In summary, the upregulation of


Assignment of chemical compounds to biological pathways is a crucial step to understand the relationship between the chemical repertory of an organism and its biology. Protein sequence profiles are very successful in capturing the main structural and functional features of a protein family, and can be used to assign new members to it based on matching of their sequences against these profiles. In this work, we extend this idea to chemical compounds, constructing a profile-inspired model for a set of related metabolites , based on a fragment-based vectorial representation of their chemical structures.We use this representation to predict the biological pathway of a chemical compound with good overall accuracy (AUC 0.74–0.90 depending on the database tested), and analyzed some factors that affect performance. The approach, which is compared with equivalent methods, can in addition detect those molecular fragments characteristic of a pathway.http://csbg.cnb.csic.es/iFragMent.The method is available as a graphical interactive web server The online version contains supplementary material available at 10.1186/s12859-021-04252-y. Studying the roles of chemical compounds in a cellular context is fundamental for understanding living systems at the molecular level . This caKnowledge on biological pathways is still incomplete, so pathway databases are continuously updated, both by adding new pathways, as well as molecular components to existing pathways. Predicting the biological role of a chemical compound, namely the pathway(s) it is involved in, from its chemical structure would be valuable not only in the assignment of new compounds to known biological pathways, but also in other applications like the functional interpretation of metabolomics experiments or the prediction of possible biological roles of drugs. A number of studies aimed to predict the biological pathway of a compound from its chemical structure alone. Most biological pathways contain a reduced number of metabolites, what could be a problem for machine learning approaches. This is one of the reasons why most studies have tried to predict general pathway classes –7 , inspired by sequence profile approaches. Sequence profiles have been widely used in the study of DNA and protein sequences, and they are behind most modern methodologies for obtaining information from these biological polymers. Sequence profiles are formal models that capture the main characteristics of a set of related sequences. They are built from a multiple sequence alignment of these related sequences using different approaches, from simple “position specific scoring matrices” (PSSMs) to complex statistical models such as “hidden Markov models” (HMMs) . Once buWe explore the possibility of using a conceptually  related strategy for studying chemical compounds in the context of biological pathways. We take into account the chemical composition  of the whole set of compounds participating in a pathway . We calculate the enrichment of chemical fragments in the pathway, and use this information to score new compounds, given the presence of the pathway-enriched fragments in their structure. We evaluate our method in its ability to predict the correct pathway of a chemical compound, using 861 pathways defined in four databases: KEGG , Reactomp values and detects the chemical substructures responsible for a compound-pathway assignment. The method is implemented as a web server http://csbg.cnb.csic.es/iFragMent and code is available at https://github.com/jlopez-ibanez/iFragMent.Our results show that the method proposed predicts biological pathways for chemical compounds with global AUCs (Area Under the Curve) ranging from 0.74 to 0.90 depending of the database considered. We compare our method to previous aproaches , 10 and We tested the ability of our method to predict a compound’s biological pathway(s) as defined in four databases: KEGG pathway, Reactome, SMPDB and enviPath, which represent metabolic, signaling, and biodegradative routes among others. We use only those pathways with at least 10 distinct compounds.To explore the largest number of chemical substructures, while at the same time not imposing any a priori chemical knowledge, we represented compounds as binary fragment vectors . We obtained better overall results for enviPath , and KEGG  than for SMPDB  and Reactome  databases. Neither the number of compounds nor the number of pathways was found to be related to these global performances. More details of each database can be found in Additional file We found that results of individual pathways varied from random to excellent performance in all databases except enviPath  Fig. a.Fig. 1DTo compare our profile-inspired approach with a pair-wise one, we implemented a k-nearest neighbour classifier (k-NN) using the same vectorial (fingerprint) representation of compounds see “”. We calIn the following sections, we will analyse several factors that can affect prediction performance: pathway class, compound class (single or multi-pathway) and compound size.We evaluated the performance of our approach for all KEGG pathways of a given general class (same top-level of the BRITE taxonomy) Table . For thaWe also observed that iFragMent performance varies largely depending on the number of chemical fragments present in a compound (a proxy of compound size) Fig. . AUC valWe finally assessed the performance of compounds involved in a single pathway and those involved in multiple pathways. Compounds involved in multiple pathways achieved worse results than those involved in a single pathway Table . E.g. fo. [human_unique dataset (comprising 52 human pathways) and perform a tenfold cross validaton of our method.To compare our approach with that of Machiarulo et al. , which u. for their prediction of individual pathways (they did it only for the prediction of pathway classes). Instead, classification errors for individual pathways were graphically shown in a blox-plot organized in seven pathway classes . AssumiVisual comparison of both figures .Part of the errors obtained by iFragMent in the p value). We have analysed the p values obtained for each of the compound-pathway pairs (as defined in human_unique), and the rank at which they were predicted. As expected, classification errors (FDR) increase as p value increases .In addition to the predicted pathway, iFragMent also provides an statistical estimate for each compound-pathway prediction , but make less mistakes.For example, considering all top-1 predictions iFragMent achieved a 0.66 TPR with a classification error (FDR) of 0.34. If we consider only the predictions with p-val < 0.05 in the top-1 positions . By considering the top-n predictions, TPR increases from 0.66 (top-1) to 0.79 (top-2) and 0.86 (top-3), at the cost of increasing also the number of false positives.To compare our approach with TrackSM , we haveOur method allows not only to assign compounds to their biological pathways  but also to detect the chemical substructures (fragments) that contribute most in such assignments .p values for the KEGG pathway “sphingolipid metabolism” (map00600) on the structures of the compounds associated to this pathway. It can be seen that these three fragments clearly delineate the the hydrocarbon chain and the polar head of sphingosine (C00319), while they are also highlighted in other compounds of the pathway.Figure To highlight the advantages as well as the limitations of our approach, we discuss here two examples taken from iFragMent Server, corresponding to predictions of compounds with known biological pathways  include ‘Histidine metabolism’ as the top-ranking pathway, based on the recognition of fragments in the histidine side chain, and ‘Biosynthesis of amino acids’ based on fragments of the amino and carboxyl functional groups. As these later fragments are common to all amino acids, they also lead to incorrect predictions of other amino acid-related pathways .p values , based on fragments of its phosphate group attached to the hydrocarbon structure, a substructure commonly found in many metabolic pathways.iFragMent fails to predict ‘Quoring sensing’ for phospho-DPD (C20959), the signaling pathway in which it is involved according to the KEGG database. In contrast, it predicts some metabolic and signaling pathways with good Profile methods are extensively used in the analysis of biological sequences  –18. TheyThe ability of profile-inspired approaches to assign compounds to their biological pathways has not been explored. As metabolic reactions proceed step-wise, substrate-reactant pairs are structurally related. But given the linear or branched topology of reactions in pathways, we don’t know to what extend all compounds of a pathway are structurally related. Indeed, in some cases, structurally similar compounds tend to participate in the same metabolic pathway . AdditioWe demonstrate that a profile-inspired approach can be used to predict the potential biological pathway of a chemical compound from its chemical structure alone. We propose a method that relies on enrichment of structural fragments. Although performance varied largely depending on the pathway, we obtained good performances, especially for KEGG metabolic pathways and enviPath biotransformation pathways.In this work pathways have been defined according to four public databases. KEGG, Reactome and SMPDB include not only metabolic but also other types of pathways such as disease, drug actions, transporting, signaling, etc. Yet, we found some general trends. In all databases, results for compounds involved in a single pathway are much better than for multi-pathway compounds. The percentage of multi-pathway compounds varied largely in the four databases, partially explaining differences in overall database performance. This could be due to the fact that multi-pathway compounds have “mixed” characteristics from more than one pathway, what might confound the predictor. This is equivalent to multi-domain proteins, for example.Pathways for small compounds (e.g. < 32 fragments in KEGG) were identified with less accuracy than for medium-size compounds. This trend was observed in the four databases analyzed. This could be related to the absence of enough information in their chemical structures to discriminate the pathway they belong to.The performance of our profile-inspired approach is higher than that obtained for a pair-wise similarity approach (k-NN method) in all databases tested. We also compared our method with the two previously described approaches that addressed the prediction of individual pathways , 10. Thep value) to each prediction, which can be used to decrease classification errors (at the cost of missing some true positives).Testing a reduced set of single-pathway compounds involved in 52 human pathways, Marchiarulo et al.  reportedIn a real-world scenario, where newer compounds never seen by the systems were tested, iFragMent achieved higher PPV than TrakSM . Our metAs we accumulate more data on the landscape of small chemicals underlying biological systems, new methodologies are required to mine it so as to extract useful information. Sequence profiles of biological polymers  are behind most approaches that allow mining and interpreting the massive genomic datasets. Consequently, we need similar approaches for the metabolism. A profile-inspired approach grouping functionally related metabolites is useful for assigning new metabolites to the group, as well as for detecting the structural fragments associated to that group . The last allows getting insight into the chemical basis of the biological activity of a given group of functionally related compounds, in case it is unknown. Both, the assignment of chemical compounds to biological pathways and the detection of the informative fragments using this methodology can be performed by any interested user via the interactive graphical web interface developed.We compiled pathways and their associated compounds from four resources: KEGG , Reactome (version v61), SMPDB (release 2.75) and enviPath . Compounds were compiled regardless the organism they are eventually assigned to. We selected those pathways with at least ten chemical compounds, excluding the very general pathways (e.g. from KEGG ‘Metabolic pathways’ (map01100); ‘Biosynthesis of secondary metabolites’ (map01110); ‘Microbial metabolism in diverse environments’ (map01120); ‘Biosynthesis of antibiotics’ (map01130) and ‘Degradation of aromatic compounds’ (map01220)).https://github.com/jlopez-ibanez/iFragMent.Structural data files of compounds were downloaded from their respective database for KEGG, EnviPath and SMPDB pathways. For Reactome pathways, we retrieved chemical structure files from ChEBI  using thFrom their structural data file, we generated a vectorial representation of compounds using the molecular fragments obtained with ISIDA Fragmentor . We geneIn some cases, we found compounds with the same vectorial representation. These redundant compounds were merged and treated as a single compound in the evaluation of the method. This avoids identical compounds to be present both in the training and test sets.p values are calculated with the cumulative hypergeometric distribution:M is the total number of compounds in the database, K is the total number of compounds in the database with the fragment, N is the number of compounds in the pathway and x is the number of compounds in the pathway with the fragment.From the vectorial representation of the compounds in a given pathway, we obtain a vector representation for that pathway (Pv) Fig. . Each veX) against a pathway P, Sc, as:With this pathway representation, we define a score for quantifying the matching of a given compound  are taken into account with this formulation.where We observed, as expected, that scores Sc of compounds belonging to a pathway P tend to be higher than the scores of compounds not associated with that pathway  (data not shown). However, scores of the same compound in different pathways are not comparable, so they can not be directly used as to predict in which pathway a compound participates. To solve this, we devised a random statistical model.p values from z-scores.In order to compare and rank the scores of a compound X in different pathways we calculate the corresponding z-scores using a null model, using a similar approach to . For eacp values of the compound against all pathway pv models. We then rank the pathway p values by increasing value. We take those pathways in top-n ranking positions as the predictions, creating a whole family of predictors with increasing sensitivity and decreasing specificity. A Receiving Operating Characteristic curve (ROC curve) is calculated as n increases from 1 to the total number of pathways. Overall performance was quantified as the area under the ROC curve (AUC). A value of AUC = 0.5 would represent a random prediction (correct and incorrect pathways uniformly distributed in the ranked list), while values higher than that represent good predictions (correct pathways closer to the top of the list).To predict the pathway of a compound, we calculate the z-scores and corresponding p values and z-scores are reported by the iFragMent web server. The chemical substructures that are characteristic (enriched) in a biological pathway are obtained from the pathway Pv vector as those with the highest enrichment in that pathway . These are used to highlight matched enriched fragments in the query compounds.Both We implemented a k-nearest neighbour (k-NN) approach using the same vector representation of compounds. We calculate the structural similarity of a query compound to all the compounds in the database using the Tanimoto coefficient. We finally assign the pathways of the top-k most similar compounds. ROC curves and AUC values are calculated as previously explained.We evaluated our method, as well as the k-NN approach, with a tenfold cross validation, using exactly the same partition of training datasets for both methods.human_unique was downloaded from the Supplementary material of Marchiarulo et al. [https://dna.engr.uconn.edu/?page_id=648. TrackSM training dataset was obtained from the Config directory files, and were used to calculate iFragMent profiles, so that both methods can be compared trained in the same sets. Novel compounds in KEGG (release 83.0) not included in this TrackSM training dataset were used to predict pathways with both TraskSM and iFragMent.Dataset o et al. . We usedo et al. . TrackSMo et al.  was downAdditional file 1: Table S1. Composition of databases and fingerprints used. Table S6. Precision  and sensitivity of the prediction of a dataset of 1313 compounds using TrackSM and iFragMent methods. Figure S1. The associated p-value resulting from iFragMent predictions can be used to reduce the FDR. Figure S2. iFragMent server predictions for histidine (C00135 in KEGG). Figure S3. iFragMent server predictions for phospho-DPD (C20959 in KEGG).Additional file 2: Table S2. AUC results for individual pathways in KEGG dataset. Table S3. AUC results for individual pathways in enviPath dataset. Table S4. AUC results for individual pathways in Reactome dataset. Table S5. AUC results for individual pathways in SMPDB dataset.