Coronary artery disease (CAD) is a type of heart disease with a high morbidity rate. This study is aimed at identifying potential biomarkers closely related to the progression of CAD.  A microarray dataset of GSE59867 was downloaded from a public database, Gene Expression Omnibus, which included 46 cases of stable CAD without a history of myocardial infarction (MI), 30 cases of MI without heart failure (HF), and 34 cases of MI with HF. Differentially expressed long noncoding RNAs (DElncRNAs) and mRNAs (DEmRNAs) were identified by the limma package, and functions of DEmRNAs were annotated by Gene Ontology and KEGG pathways. In addition, weighed gene coexpression network analysis (WGCNA) was used to construct a coexpression network of DEmRNAs, and a disease-related lncRNAs-mRNAs-pathway network was constructed. Finally, the datasets of GSE61145 and GSE57338 were used to verify the expression levels of the above highly correlated candidates. FASN in “insulin signaling pathway,” DGKZ in “phosphatidylinositol signaling system,” and TNFRSF1A in “MAPK signaling pathway” were involved in MI.  A total of 2362 upregulated mRNAs and 2816 downregulated mRNAs, as well as 235 upregulated lncRNAs and 113 downregulated lncRNAs were screened. These genes were significantly enriched in “cytokine-cytokine receptor interaction,” “RIG-I-like receptor signaling pathway,” and “natural killer cell-mediated cytotoxicity.” Five modules including 1201 DEmRNAs were enriched in WGCNA. A coexpression network including 19 DElncRNAs and 413 DEmRNAs was constructed. These genes were significantly enriched in “phosphatidylinositol signaling system,” “insulin signaling pathway,” and “MAPK signaling pathway”. Disease-related gene-pathway network suggested FASN, DGKZ, and TNFRSF1A were revealed to be CAD progression-associated genes by WGCNA coexpression network analysis. Coronary artery disease (CAD) is the most common type of cardiovascular event . The morTREML4 (triggering receptor expressed on myeloid cell-like 4), which was upregulated in coronary artery calcification, was reported to be involved in the formation of calcified atheromatous plaque [GLO1  and PPIL1 (peptidylprolyl isomerase I), play important regulatory roles in CAD risk processes including metabolism, signal transduction, coagulation, immunity, and proteolysis [FMN1, JDP2, and RNASE1, which were transcriptomic biomarkers of HF development. The candidate genes involved in the molecular mechanism of CAD with clinical implications would be used as potential novel biomarkers and targets for therapeutic intervention of CAD. However, no effective genes could be used in a clinical setting currently, and the cellular mechanisms of genes involved in the pathological process of CAD remain largely unexplained.Though considerable efforts have been made during the past decades, the pathophysiologic mechanisms in the development and progression of MI and HF remain elusive and require further investigation. Molecular biology studies have demonstrated that several genes are associated with the development and progression of CAD . TREML4 s plaque . Some geteolysis . In additeolysis  used michttp://www.ncbi.nlm.nih.gov/geo) and was reanalyzed using the widely used bioinformatics methods to identify potential genes related with CAD progression. This study may help to reveal the relationship between candidate genes with altered expression and the development and progression of CAD.In the current study, a microarray dataset was downloaded from the public database of Gene Expression Omnibus  [http://www.genenames.org/) [https://www.gencodegenes.org/human/) was used to realign, and the unique alignment sequence remained. Meanwhile, based on the corresponding gtf gene annotation files, we kept annotation information for “protein-coding” genes as mRNA and genes of “antisense,” “sense-intronic,” “lincRNA,” “sense-overlapping,” “processed transcript,” “3prime–overlapping-ncRNA,” and “noncoding” as lncRNA. After removing the unmatched probes of gene symbol, 595 lncRNAs and 17790 mRNAs were obtained.The original data GSE59867 (.txt) were downloaded and log 3.32.5)  in R3.4. 3.32.5) , 13. Thees.org/)  which inhttps://cran.r-project.org/package=pheatmap) in R 3.4.1. Further, the DEmRNAs were used to perform Gene Ontology (GO) and KEGG pathway enrichment analyses using DAVID 6.8 (https://david.ncifcrf.gov/) [All samples were divided into three groups: stable CAD samples, MI without HF, and MI with HF. The limma package in R 3.4.1  was usedrf.gov/) , 17.P < 0.05. A WGCNA map was constructed which divided the DEGs into several modules based on the above analysis. For the lncRNAs and mRNAs in the modules screened from the previous step, cor function (http://77.66.12.57/R-help/cor.test.html) in R was utilized to calculate the Pearson correlation coefficient (PCC) among their expression levels. A coexpression network was then built and visualized by Cytoscape 3.6.1 [http://www.cytoscape.org/). The genes in the coexpression network were submitted for pathway enrichment analysis using DAVID.A coexpression network was built using weighed gene coexpression network analysis (WGCNA) package (version 1.61) in R . The ovepe 3.6.1  (http://http://ctdbase.org/) by searching words of “heart failure” or “myocardial infarction”. The resulting pathways and genes were compared with those pathways and genes in the coexpression network. The overlapped pathways and genes were used to construct a disease-related gene and pathway network. Finally, the expression levels of important genes in the network were plotted at different times (1 d (admission), 4-6 d (discharge), and 1 m and 6 m (stable phase)) under different disease representations.The MI- or HF-related KEGG pathways and genes were searched in the Comparative Toxicogenomics Database (CTD) 2017 update  were used to statistically analyze. The dataset GSE59867 was downloaded from the GEO database, and DEGs between stable CAD patients and MI patients without HF, stable CAD patients and MI patients with HF, and MI patients without HF and MI patients with HF were identified using the limma package. The number of DEmRNAs and DElncRNAs was displayed in The DEmRNAs were submitted to GO and KEGG pathway enrichment analyses further. Twenty-four, 26, and 23 GO biological processes and 13, 13, and 14 KEGG pathways, respectively, were enriched by the DEmRNAs between the three comparisons. As shown in The 4039 overlapped DEGs were submitted to WGCNA analysis. To qualify the scale-free network distribution, the minimum power value was set as the value when the squared correlation coefficients first reached 0.8. As shown in The PCCs among the 1201 DEGs were calculated, and the relationships with PCC > 0.7 were retained. A coexpression network was built using these relationships . This neDGKZ, SLC9A1, TNFRSF1A, and FASN), and 32 genes in the disease pathways. From this network, the disease gene FASN was involved in the “insulin signaling pathway,” DGKZ was involved in the “phosphatidylinositol signaling system,” SLC9A1 was involved in “regulation of actin cytoskeleton,” and TNFRSF1A was involved in the “MAPK signaling pathway.” The lncRNA ARRDC1-AS1 might coexpress with FASN and DGKZ to be involved in the “insulin signaling pathway” and “phosphatidylinositol signaling system.” The lncRNA AATBC might coexpress with TNFRSF1A to be involved in “cytokine-cytokine receptor interaction.” The lncRNA MIRLET7BHG might coexpress with FASN to be involved in the “insulin signaling pathway.” These lncRNAs and pathways might be closely related with the progression of CAD.A total of 201 HF-related KEGG pathways and 104 HF-related genes as well as 190 MI-related KEGGs and MI-related genes were obtained from CTD. Five KEGG pathways including 4 disease genes related to both HF and MI were extracted. A disease-related lncRNA-mRNA-pathway network was built . This neTNFRSF1A was the highest both in different phases and at different times in the same disease phase, while the expression level of FASN was the lowest compared with the other genes  and at different times in the same disease phase were shown in er genes .ARRDC1-AS1, AATBC, and MIRLET7BHG, as well as four mRNAs DGKZ, SLC9A1, TNFRSF1A, and FASN were verified in the validation datasets (GSE61145 and GSE57338). The results showed that in the GSE61145 dataset, the expression levels of AATBC, DGKZ, FASN, SLC9A1, and TNFRSF1A were significantly upregulated in the STEMI group, compared with the normal group  and three coexpressed lncRNAs  were identified as the potential candidates in the development of CAD. The differential expressions of the seven genes were successfully validated by the independent datasets of GSE61145 and GSE57338.CAD has become a major health concern in the last few decades; therefore, future studies need to concentrate on controlling CAD risk factors . In the NCF2, MYO1F, S1PR4, and FCN1) identified by WGCNA in GSE90074. A recent study by Niu et al. identified 6 hub genes  by analyzing GSE59867 using WGCNA [In this study, we used WGCNA to build a DElncRNA-DEmRNA coexpression network and analyzed the DEGs in this network. WGCNA, a bioinformatics application, describes the correlation patterns between gene chip samples and provides direct biologically functional interpretations of gene network modules . It has ng WGCNA . Though FASN was involved in the “insulin signaling pathway,” DGKZ was involved in the “phosphatidylinositol signaling system,” and TNFRSF1A was involved in the “MAPK signaling pathway.” Fatty acid synthase (FASN) is the sole cytosolic mammalian enzyme for de novo lipid synthesis. One of the features of cancer cells is the increased de novo lipogenesis, and FASN is part of the metabolic reprogramming cancer hallmark [FASN is significantly upregulated in many cancer types while it is extremely low in nonmalignant tissues [FASN could suppress the expression of NF-κB but increase the expression of specificity protein 1 and regulate DNA repair to increase survival against genotoxic insults [FASN has received much attention as a cancer therapeutic target [FASN in CAD has few been reported. Myocardial FA substrate metabolism is a feature of late-stage HF [ARRDC1-AS1 and MIRLET7BHG could coexpress with FASN to be involved in “insulin signaling pathway.” The lncRNA ARRDC1-AS1 was involved in a 9-lncRNA signature to predict recurrence of breast cancer [ARRDC1-AS1 was not reported in CAD previously. MIRLET7BHG is the miRNA let-7b host gene and has been previously reported to be implicated in metabolic disorders [MIRLET7BHG, has been reported to be associated with cardiovascular diseases by many studies. hsa-let-7b was identified as a potential candidate regulator in acute MI [ARRDC1-AS1 and MIRLET7BHG might have interacted with FASN to be involved in regulating insulin signaling pathways in patients with CAD.In the disease-related lncRNA-mRNA-pathway network, hallmark –26. The  tissues . Numerou insults . FASN hac target . Severalc target . Howeverstage HF . The prostage HF . In thist cancer . Howeverisorders . It was isorders . miRNA lacute MI , 36. It acute MI . Our resDGKZ was involved in the “phosphatidylinositol signaling system,” and TNFRSF1A was involved in the “MAPK signaling pathway.” The PI3K/Akt signaling pathway is a critical pathway in the “phosphatidylinositol signaling system” and is a key signaling pathway involved in many life activities including cell division, differentiation, and apoptosis. PI3K/Akt signaling pathway was closely related to cardiovascular disease [ disease . It was  disease . PI3K si disease  and for  disease . The PI3 disease . In-deptin vivo or in vitro experiments. Future studies should be conducted to confirm the results of this study.The major limitation of this study is that the identified critical lncRNAs and genes were not verified on independent patients as well as FASN, DGKZ, and TNFRSF1A, in “insulin signaling pathway,” “phosphatidylinositol signaling system,” and “MAPK signaling pathway”, would be potentially associated with the path mechanism of CAD. In addition, the cross-action effects among genetic and molecular processes would closely work in CAD. These three genes would be potentially represented by a novel molecular, mechanistic explanation for the pathologic basis of CAD and may be used as candidate targets for therapeutic discovery in CAD.From the above,


Dinoflagellates, a major class of marine eukaryote microalgae composing the phytoplankton, are widely recognised as producers of a large variety of toxic molecules, particularly neurotoxins, which can also act as potent bioactive pharmacological mediators. In addition, similarly to other microalgae, they are also good producers of polyunsaturated fatty acids (PUFAs), important precursors of key molecules involved in cell physiology. Among PUFA derivatives are the prostaglandins (Pgs), important physiological mediators in several physiological and pathological processes in humans, also used as “biological” drugs. Their synthesis is very expensive because of the elevated number of reaction steps required, thus the search for new Pgs production methods is of great relevance. One possibility is their extraction from microorganisms , which have been proved to produce the same Pgs as humans. In the present study, we took advantage of the available transcriptomes for dinoflagellates in the iMicrobe database to search for the Pgs biosynthetic pathway using a bioinformatic approach. Here we show that dinoflagellates express nine Pg-metabolism related enzymes involved in both Pgs synthesis and reduction. Not all of the enzymes were expressed simultaneously in all the species analysed and their expression was influenced by culturing conditions, especially salinity of the growth medium. These results confirm the existence of a biosynthetic pathway for these important molecules in unicellular microalgae other than diatoms, suggesting a broad diffusion and conservation of the Pgs pathway, which further strengthen their importance in living organisms. Fatty acids in general and PUFAs in particular have essential functions in living organisms from all taxa, ensuring fundamental homeostatic functions . Their p2 (PgG2), which is then rapidly reduced to prostaglandin H2 (PgH2). PgH2 is transformed in turn by specific synthases, namely prostaglandin E, F, D and I synthases  into prostaglandin E2, F2α, D2, I2 , respectively  (15-PGDH), and prostaglandin reductase 1 and 2 (PTGR1 and PTGR2). The functions annotated as COX2, PTGES2, HPGDS, PGE2-9-OR, PTGFS1 and PTGFS2 are associated to enzymes involved in Pgs synthesis, while the ones annotated as 15-PGDH, PTGR1 and PTGR2 are associated to enzymes involved in Pgs catabolism /([length of transcript]/1000)/([total reads pairs]/10K. brevis possesses an almost complete set of enzymes involved in the pathway, although no annotation for COX was identified. This enzyme, catalysing the initial step of Pg biosynthesis, was found annotated only in two species, A. massartii and P. glacialis. The CD-HIT analysis excluded transcript redundancy (The transcriptome mining approach used in this work succeeded in revealing the presence of Pg pathway in 14 out of 19 dinoflagellate species analysed. Only dundancy  and idendundancy . G. foliaceum under different light conditions, while in S. hangoei HPGDS and PTGR1 were differentially expressed in different salinity conditions.In-silico analysis of gene expression levels showed differences only in These findings pave the way for future investigations through experimental gene expression analysis and chemical identification, in order to confirm Pgs presence in dinoflagellates and stimulate further researches toward the understanding of their ecological role in nature.


Metabolic pathways are an important part of systems biology research since they illustrate complex interactions between metabolites, enzymes, and regulators. Pathway maps are drawn to elucidate metabolism or to set data in a metabolic context. We present MetaboMAPS, a web-based platform to visualize numerical data on individual metabolic pathway maps. Metabolic maps can be stored, distributed and downloaded in SVG-format. MetaboMAPS was designed for users without computational background and supports pathway sharing without strict conventions. In addition to existing applications that established standards for well-studied pathways, MetaboMAPS offers a niche for individual, customized pathways beyond common knowledge, supporting ongoing research by creating publication-ready visualizations of experimental data. Clostridioides difficile uses an incomplete TCA cycle  and users can continue to work with their preferred software.BKMS  provided to allow replication of the software development and its use by others?YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?YesReviewer Expertise:Computational Biology, Systems Biology, Data Integration, Evolutionary Biology, MitochondriaI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. MetaboMAPS is an interesting small application for making available and displaying numerical data on non-standard pathways. It can be seen as complementing the well-known and standard pathway resources, such as KEGG and others. The advancement of MetaboMAPS is the possibility to store and present incomplete or so far not described pathways. Moreover, it has a nice visualization toolbox to display numerical data on the pathways.The way the introduction is formulated currently indicates that well-established pathway resources are ignorant concerning species-specific differences of pathways. This is not true. KEGG for instance offers a rich selection of organisms and indicates that genes are missing/added to the classical pathway. It is true however when it comes to the overall display of the pathway map, which is – I think – what the authors want to express here. They should reformulate this to reflect what they mean to say properly.I do not fully understand, why it is necessary to point out the disadvantages of printed pathway maps  – which by default cannot be used for any form of computerized work? Sparse information is given – also on the website – of how to upload user-provided pathways. For instance, it seems necessary to add an organism, before adding a pathway. This is very un-intuitively presented at the website and should be improved.The SVG format offers many advantages. However, it is not so easy to e.g. build on an existing pathway from other resources, as they often do not offer download in SVG format. Do the authors have any thoughts on how the interoperability with existing pathway resources could be improved, e.g. by allowing more and different formats for upload?Should the resource become more widely used for pathways that are incomplete, it will become a problem that users don’t stick to the same nomenclature. How do the authors think about consolidating novel pathways coming from multiple users that illustrate the same biological pathway, yet under a different name?Whether or not it is an advantage to offer such great flexibility with respect to the pathway map is questionable. Often, users don’t sick to official gene symbols, but use synonyms or other, officially not recognized gene names. The authors should comment on how they could address this issue of unconventional gene names or rarely used synonyms. There are some points that should be addressed: Are the conclusions about the tool and its performance adequately supported by the findings presented in the article?YesIs the rationale for developing the new software tool clearly explained?YesIs the description of the software tool technically sound?YesAre sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others?YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?YesReviewer Expertise:Computational Biology, Systems Biology, Data IntegrationI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. We highly appreciate your feedback regarding MetaboMAPS and the quality of our paper. We uploaded a new version of the latter to address your concerns. Point 1 and 2: We changed to the manuscript accordingly. Point 3: Thank you for your helpful feedback regarding the upload of pathway maps. We reimplemented the upload procedure to be more intuitive and user friendly. Basically, the user can now define which organisms are associated after uploading a pathway. Furthermore, we added a submenu to the navigation bar. Point 4: Interoperability is an important topic. The SVG format was our favourite choice because it can be exported from nearly every tool that users eventually use to draw their pathways. Additionally, there exists a number of conversion tools to convert nearly every vector-based format to SVG (we provide How-Tos on the website). Also existing platforms often provide export to SVG, e.g. we tried to add pathways exported from WikiPathways and BRENDA and it worked perfectly fine. However, we are open to include other data formats (e.g. KGML) in the future. Therefore, we would like to rely on user surveys once the tool has a larger community.https://www.ebi.ac.uk/ols/ontologies/pw) at a later time point. Point 5: Regarding concerns about pathway nomenclature, we agree that this will be an issue in the future. At this point, we group pathways roughly using 19 pathway categories. Pathways can furthermore be found using identifiers such as EC numbers or metabolites using the search function. However, with increasing numbers of pathways, we will consider implementing a system based on pathway ontologies  provided to allow replication of the software development and its use by others?YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool?YesReviewer Expertise:multi-omics integration, pathway analysis, omics data visualisation.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you for your feedback regarding our manuscript. We uploaded a new version of our paper and included a review on other resources as suggested.


Sang-Xing-Zhi-Ke-Fang (SXZKF) demonstrates good therapeutic effect against pharyngitis. Nevertheless, the pharmacological mechanism underlying its effectiveness is still unclear.  To investigate the underlying mechanisms of SXZKF against pharyngitis using network pharmacology method.  Bioactive ingredients of SXZKF were collected and screened using published literature and two public databases. Using four public databases, the overlapping genes between these bioactive compound-related and pharyngitis-related genes were identified by Venn diagram. Protein-protein interaction (PPI) was obtained using “Search Tool for the Retrieval of Interacting Genes (STRING)” database. “Database for Annotation, Visualization, and Integrated Discovery ver. 6.8 (DAVID 6.8)” was used to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to explore the molecular mechanisms of SXZKF against pharyngitis. Finally, Cytoscape 3.7.2 software was used to construct and visualize the networks. γ, MAPK3, EGF, and PTGS2. Signaling pathways closely related to mechanisms of SXZKF for pharyngitis were identified, including serotonergic synapse, VEGF signaling pathway, Fc epsilon RI signaling pathway, Ras signaling pathway, MAPK signaling pathway, and influenza A.  A total of 102 bioactive compounds were identified. Among them, 886 compounds-related and 6258 pharyngitis-related genes were identified, including 387 overlapping genes. Sixty-three core targets were obtained, including ALB, PPAR This is the first identification of in-depth study of SXZKF against pharyngitis using network pharmacology. This new evidence could be informative in providing new support on the clinical effects of SXZKF on pharyngitis and for the development of personalized medicine for pharyngitis. Pharyngitis, a collective term for inflammation caused by various microorganisms in the pharynx, still has a high incidence even with the rapid development of modern medicine . In ChinSang-Xing-Zhi-Ke-Fang is a concoction based on Sang-Xing-Tang, which is originated form Wen Bing Tiao BiannBia that had been widely used for more than 200 years in China. SXZKF has been patented in China (patent number: 201910302604.4) in 2019 and the oral lozenge based on it is being developed. SXZKF is composed of Mulberry Leaf, Armeniacae Amarum Semen, Radix Glehniae, Radix Rehmanniae Praeparata, Loguat Leaf, Fritillary Bulb, Eriobotrya japonica Thunb, Ligusticum chuanxiong hort, Earthworm Lumbricus, Mentha haplocalyx Briq, and Exocarpium Citrus Grandis. Although we have previously verified that SXZKF was effective against pharyngitis [Network pharmacology is a new discipline based on the theory of systems biology, analyzing the network of biological systems, and selecting specific signal nodes for multitarget drug molecular design . Based ohttp://lsp.nwu.edu.cn/tcmsp.php) and Bioinformatics Analysis Tool for Molecular mechanism of Traditional Chinese Medicine  [Information of the bioactive compounds in SXZKF was obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  . The actan-tcm/) , 10. Othan-tcm/) , 12.https://www.uniprot.org/) was then used to remove nonhuman target gene names. To ensure the reliability of prediction, only target genes with “Reviewed/Swiss-Prot” rots “Human” in UniProt would be selected [http://ctdbase.org/) [http://www.genecards.org/). By searching the keyword “pharyngitis,” target genes of pharyngitis were identified.Target genes of bioactive compounds of SXZKF were obtained from TCMSP, and the Universal Protein Database   and The http://bioinformatics.psb.ugent.be/webtools/Venn/). Then, Cytoscape 3.7.2 software was used to visualize and analyze the network of interactions between overlapping genes, bioactive compounds, and herbs; we named it the H-C-T network.The overlapping target genes relating to both the bioactive compounds and pharyngitis were identified and visualized by Venn diagrams (https://string-db.org/) was used to obtain the protein-protein interactions (PPI) by uploading 387 overlapping targets between active compounds and pharyngitis. Species was limited to “Homo sapiens” with a confidence score >0.4. Cytoscape 3.7.2 was used to construct the network of PPI, and then the topological features of the network were analyzed to screen out the core targets that play a crucial part in the PPI network. The degree centrality (DC), betweenness centrality (BC), and closeness centrality (CC) were analyzed for each node using the plugin cytoNCA in Cytoscape 3.7.2 [STRING database (pe 3.7.2 . Prelimipe 3.7.2 .https://david.ncifcrf.gov/) with the “Homo sapiens” setting. Generally, the results of KEGG pathway enrichment were considered to have statistically significant and necessary functional mechanisms of pharyngitis when P < 0.05. To ensure the accuracy of this study, we set the screening criteria as P < 0.01. Bubble chart of the concerned KEGG pathways was plotted using the OmicShare tools (https://www.omicshare.com/tools). Finally, Cytoscape 3.7.2 was used to establish and visualize the network of interactions between compounds, targets, and signaling pathways that are closely related to pharyngitis. The complete workflow of the analysis is given in Pathway enrichment analysis for overlapping genes was performed using DAVID ver. 6.8 , betweenness centrality (BC) and closeness centrality (CC) for each node in PPI network, the results are shown in P < 0.01  and 158 edges, as demonstrated in As seen in Figures α-IκB-NF-κB signaling pathway in inflammatory cells to inhibit the inflammatory response due to the strong anti-inflammatory effects of resolvin and protectin [in vitro, especially against Streptococcus aureus, Streptococcus pneumoniae, Streptococcus epidermidis, Bacillus cereus, and Bacillus subtilis [In our current study, a total of 102 bioactive ingredients were identified in SXZKF. The main active ingredients are arachidonic acid, quercetin, kaempferol, eicosapentaenoic acid, and luteolin, the compounds with higher degree value of node in the H-C-T and C-T-P network, whose chemical structures were shown in rotectin . The prorotectin . Besidesrotectin . Kim et rotectin . In termrotectin . In termsubtilis .γ (peroxisome proliferator-activated receptor gamma), MAPK3 (mitogen-activated protein kinase 3), EGF , and PTGS2 (prostaglandin G/H synthase 2) are closely related to pharyngitis. ALB is an important substance to maintain plasma colloid osmotic pressure, but bacterial infection in the throat can cause local vascular permeability to increase, lead to vasodilatation and exudation of the serous fluid, and finally result in hyperemia, pain, and even thickening of the mucosa. When localized albumin synthesis increases when the throat is inflamed, ALB acts to maintain intravascular osmotic pressure, reduce serous exudation, and alleviate the symptoms as well as processes of pharyngeal inflammation [γ is an essential transcription factor, which can act as an inhibitor for inflammatory gene expression, blocking the reverses inflammation [A total of 63 key targets for SXZKF in the treatment of pharyngitis were identified. DC, CC, and BC analysis indicated that ALB , PPARammation . EGF proammation . Studiesammation . PPARγ iammation , 32. MAPammation , 34. PTGammation .εRI and Fc epsilon RI signal transduction pathway are the keys in causing allergic diseases [This study analyzes the six pathways that are highly relevant to the pathogenesis of pharyngitis. Vasodilation and serous exudation are important links in the pathological process of pharyngitis. Vascular endothelial growth factor (VEGF) is the most effective angiogenesis factor in the body, which can bind to specific receptors of vascular endothelial cells, promote the division and proliferation of vascular endothelial cells, and promote the generation of new blood vessels and increase vascular permeability . VEGF-mediseases . The Fc diseases . Last buγ, MAPK3, EGF, and PTGS2 recognized as core targets. The main molecular mechanisms of SXZKF for pharyngitis consisted of 28 signaling pathways, and the key pathways that were closely related to pharyngitis were found to be related to analgesia, inhibition of inflammation response, inhibition of viral replication, and inhibition of anaphylactic reaction through inactivating serotonergic synaptic, VEGF signaling pathway, Fc epsilon RI signaling pathway, Ras signaling pathway, MAPK signaling pathway, and influenza A. Taken together, this study provides an insight on the cellular and pathway mechanism of SXZKF in the treatment of pharyngitis. This new technique could also be used to understand many other traditional or alternative medicine to provide a new horizon for the development of personalized TCM in the near future.In conclusion, this study identified the main bioactive ingredients, core target genes and molecular mechanisms of SXZKF in the treatment of pharyngitis through network pharmacology. A total of 102 bioactive compounds were found, with arachidonic acid, quercetin, kaempferol, eicosapentaenoic acid, and luteolin identified as main active compounds. Among them, 63 key target genes were identified, with ALB, PPAR


In biological terms, the term "pathway" is used to describe a collection of processes within a cell that lead to one or more actions. The graphical representation of these processes enables the reader to understand complex relationships and interactions much more easily compared to free-text descriptions. While there is usually agreement on the existence and function of these high-level processes, the specific molecules and their interactions are often disputed and a matter of current research. A standardized computational representation of biological networks has become desirable, especially with the recent surge in new knowledge generation in biology and medicine. Our work is influenced by challenges emerging from previous work on biological pathways, knowledge encoding, and visualization as well as pathway databases. Our main motivation is the difficulty of reproducing pathway knowledge used within publications, even in top-tier journals. We propose a new way of integrating and modeling pathways and other influencing knowledge, such as drugs, and documenting their modifications using multilayered networks. We provide a tool that transforms encoded pathway data to multilayered graphs, with the possibility to modify them, and integrate other knowledge from external databases.Biological pathway data integration has become a topic of interest in the past years. This interest originates essentially from the continuously increasing size of existing prior knowledge as well as from the many challenges scientists face when studying biological pathways. Multipath is a framework that aims at helping re-trace the use of specific pathway knowledge in specific publications, and easing the data integration of multiple pathway types and further influencing knowledge sources. Multipath thus helps scientists to increase the reproducibility of their code and analysis by allowing the integration of numerous data sources and documentation of their integration steps while doing so. In this paper, we present the package Multipath, and we describe how it can be used for data integration and tracking pathway modifications. We present a multilayer model built from the Wnt Pathway as a demonstration. Deciphering the inner workings of cells fascinates researchers all over the world. However, these processes are highly complex and heterogeneous, with countless players constantly interacting via biochemical reactions, signaling cascades and feedback loops. Knowledge about these processes can be organized into so-called pathways by grouping sets of interactions, which share a common goal or function . Over thOur aim is to provide a generic modelling framework to integrate multiple pathway types and further knowledge sources influencing these pathways. This framework is defined by a multi-layered model allowing automatic pathway transformations and documentation. By providing a tool that generates this model, we aim to facilitate the data integration, boost the reproducibility and increase the interoperability between different sources and databases in the field of pathways. The transformation of the pathway models helps structuring and condensing knowledge in specific areas of interests, e.g., removing layers with irrelevant information, coupling expression and pathway knowledge, and connecting drug-target and mutation layer connections, while documenting every step of this process. In this paper, we present the R package Multipath that creates these multilayer extendable models from BioPAX-encoded pathway files , and extThis package uses freely available data sources like Reactome or other ontology-encoded pathway databases, using our R Packages rBiopaxParser  and mullMultipath is an R package that aims at creating reproducible pathway models. It allows the user to transform BioPAX encoded pathways into multilayered graphs, using the R packages mully  and rBioRead the BioPAX file using the rBiopaxparser function readBiopax and provide the path to the BioPAX file containing the pathway information as an argument. The file can be downloaded manually or using the function downloadPathway.Fetch the pathway’s internal ID using the function getPathwayID from the BioPAX object returned in step 1 which might contain multiple pathways.Provide the pathway’s ID alongside the BioPAX object as arguments to the Multipath function pathway2Mully which returns the mully graph created from the parsed pathway information in the BioPAX object.To build the BioPAX mully model, the user has to follow the following steps :Read theThe processing time depends on the size of the BioPAX file being imported. A larger pathway may require a larger memory space and a longer time to be parsed, and transformed into a mully object.We currently offer the function downloadPathway, to download a pathway from the database Reactome  in BioPAThe generated mully graph is modifiable and retraceable. All modifications applied to the model can be stored in a view object and tracked using the feature track and undo. The view object contains different information concerning the modifications, including the timestamp of creation and last modification, the original graph and its final modified version, as well as the list of applied steps. To track the modification, the user has to call the function addStep, to which a list of arguments should be provided: the action (add or remove), the element , the name of the element, and more arguments depending on the type of the element. It returns the view with the added steps. Removing a layer results removing a list of nodes and edges, which are all considered a single step.The function undo, which reverses the latest changes applied to the graph, requires the view and the number of steps to undo. The steps are reversed based on their id in the view. For instance, undoing a deletion of a node, will re-add the node and all its connections, and of a layer will re-add the layer, the nodes of this layer, and all their connections.Multipath also offers data integration functions to extract any additional information needed from DrugBank  and UniPFunctions querying other databases such as ChEBI  and PubCWe present functions in Multipath that fetch information from external databases. Essential information on each node are taken from the original database of the given IDs; for example, information on proteins with UniProtKB/Swiss-Prot IDs are extracted from UniProt. The interactions are proved from different databases, and the source of the interactions added to the graph is assigned as an attribute to the edges. DrugBank is an online freely accessible database for drug data and drug products . The comAfter calling loadDBXML, the functions getDBDrug and getDBInteractions can be called to fetch respectively the drug entries’ information and the interactions between them from DrugBank, using the parsed DrugBank object, and the list of DrugBank IDs. The addition of a Drug Layer to an existing mully graph can be achieved using the function addDBLayer, with the mully graph, the parsed DrugBank object, and the list of DrugBank IDs as arguments.The connections to the protein nodes on the protein Layer are returned by the function getDBtoUPKB, which combines the results of the four following functions: getDBTargets, getDBTransporters, getDBCarriers, and getDBEnzymes.The Multipath package uses the R interface UniProt.ws  to queryTo add the protein layer, the information on a list of proteins can be extracted using addUPKBInfo, with the UniProt.ws object, the list of UniProt IDs and the columns’ names representing the attributes as an input. To get the list of possible columns, the UniProt.ws function columns can be called. To get the edges between the protein nodes, the function getUPKBInteractions can be called, returning the list of edges, needed alongside the list of nodes to build the protein layer. This task can be accomplished using the function addUPKBLayer, with the mully graph, the UniProt.ws object, the list of UniProt IDs, and the list of attributes as arguments.The connections to the Drug Layer can be obtained by calling the function getUPKBtoDB.The addition of the Protein and Drug Layers can also be automatically achieved by calling the function multipath, with the following arguments: the name of the returned mully graph, the UniProt.ws object, the list of UniProt IDs, the DrugBank parsed object, and the list of DrugBank IDs.The main feature of Multipath is the possibility to track modifications applied to the multilayered graph built from the pathway BioPAX file. The user can apply a set of standard procedures, i.e., the addition or removal of nodes, edges, and layers. Every modification is stored in a pathwayView object, which can be used later to undo these modifications, retrace the transformation of the original graph, apply further modifications or compare to other views generated from the same pathway.The Wnt signaling pathway is an ancient and evolutionarily conserved pathway that regulates crucial aspects of cell fate determination, cell migration, cell polarity, neural patterning, and organogenesis during embryonic development . We geneTo build the model and generate the views, we used the R Script in The downloaded pathway file (4.19 MB) was parsed in 24.56 s and occupies ~5.05 MB of memory space. The transformation to a mully graph was conducted in 19.23 s, and the resulted graph’s size is 121.744 KB.We applied different changes to the model and generated three different views from the model , by deleAfter creating the Signaling by Wnt Pathway model, we retrieved the Drugs targeting the proteins on the protein layer. We extracted the mappings of the internal IDs to UniProt using the function getExternalIDs. We obtained the list of Drugs targeting these proteins using getUPKBtoDB. These IDs were used to add a Drug layer to our model using addDBLayer, which automatically adds the Drugs and their interactions to the graph. To get the merged Drug–Protein targets from both databases, we used the function getUPKBDBRelations using the list of external Protein IDs and the list of DrugBank IDs. The edges were added then one by one .To retrieve drug information from DrugBank, parsing the downloaded data from DrugBank is required, which is a relatively slow process, fortunately needed only once before calling any drug-related functions. The parsed data is a large list occupying ~553MB of memory space. However, extracting the drug information and interactions, as well as their targets is fast. On the other hand, the retrieval of protein information is accomplished by means of the UniProt.ws R Package. This process does not require any memory space, but we faced a very slow response from the UniProt server multiple times, with no detection of response speed patterns, i.e. specific time of the day, or the amount of information to be retrieved. The web service also recommends querying using a maximum number of 50 UniProt entries’ IDs in each single query. Multipath could be updated in the future to support a better API/Tool for this process. Multipath is an R package to generate multilayered models from BioPAX encoded pathway knowledge. The models are modifiable, and all modifications are tracked to allow the reproduction of the graphs. Multipath can also be used to query influencing knowledge databases such as UniProt and DrugBank and integrate them into a multilayered graph. In this paper, we presented the different features of Multipath. We described how to use our package to generate multilayered models from BioPAX files, and integrate different pathway knowledge. We used the Signaling by Wnt pathway as a demonstration, and displayed three different views derived from it. Multipath is open source, and can be downloaded from our GitHub Repository . The ref


The present study focused on the expression patterns, prognostic values and potential mechanism of the PDI family in gliomas. Most PDI family members’ mRNA expressions were observed significantly different between gliomas classified by clinical features. Construction of the PDI signature, cluster and risk score models of glioma was done using GSVA, consensus clustering analysis, and LASSO Cox regression analysis respectively. High values of PDI signature/ risk score and cluster 1 in gliomas were associated with malignant clinicopathological characteristics and poor prognosis. Analysis of the distinctive genomic alterations in gliomas revealed that many cases having high PDI signature and risk score were associated with genomic aberrations of driver oncogenes. GSVA analysis showed that PDI family was involved in many signaling pathways in ERAD, apoptosis, and MHC class I among many more. Prognostic nomogram revealed that the risk score was a good prognosis indicator for gliomas. The qRT-PCR and immunohistochemistry confirmed that P4HB, PDIA4 and PDIA5 were overexpressed in gliomas. In summary, this research highlighted the clinical importance of PDI family in tumorigenesis and progression in gliomas. Gliomas are the most common brain and central nervous system (CNS) tumors. According to cancer statistics, gliomas account for approximately 80% of all malignant primary brain tumors and 57.3% of gliomas was glioblastoma (GBM) , 2. HideProtein disulfide isomerase (PDI) family consists of twenty-one members sharing a common structure (the TRX-like domain). Their proteins have an N-terminal signal peptide consisting of 15–30 amino acids which are cleaved upon entry into the endoplasmic reticulum . The PDISeveral studies have linked protein disulfide isomerases (PDIs) to the regulation of proliferation, invasion, and metastasis of various cancers, such as brain, lymphoma, kidney, ovarian, prostate, and lung cancers . PDIA6 wPDIs have been associated with the progression of GBM, however little is known on the relationship between PDIs expression and the clinical outcome in gliomas. In the present study, the aim was to conduct a comprehensive analysis of twenty-one PDI family members. Bioinformatic tools and qRT-PCR were used for mRNA expression in gliomas. Therefore, data obtained revealed several new potential signaling pathways of PDIs involved in glioma progression. Furthermore, a prognostic nomogram using PDIs-based survival risk score and other clinical factors was established, and these provided novel insights for future diagnostics and therapeutic targets of glioma.In humans, twenty-one members of the PDI family have been identified. In this study, TCGA and CGGA databases data revealed the correlation between PDIs mRNA expression and clinical-pathological features in gliomas. High transcription levels of P4HB, PDIA3, PDIA4, PDIA5, PDIA6, ERP27, ERP29, ERP44, TMX1, TMX3, TXNDC5, TXNDC12, AGR3, and DNAJC10 were found in GBM tissues. However, the transcription levels of PDIA2, AGR2, CASQ1, and CASQ2 were lower in GBM tissues than in LGG tissues –1F. TherTo construct a PDIs-based signature model for both in training and validation groups GSVA was performed. Heat maps presented the expression profiles of PDI family members ranked according to their PDI signatures from the TCGA and CGGA datasets , 2B. In The patients were divided into two groups (high vs. low group) using the median value of PDI signature as the cut-off value to investigate the relationship between the value of PDI signature and patients’ prognosis. In the TCGA LGGGBM cohort, the Kaplan–Meier plot revealed that the high value of PDI signature was associated with poor OS, PFI and DSS –2C. Simist vs. 4th). High mutation frequency in IDH1, TP53, and ATRX were associated with low PDI signature in gliomas , whereas TTN, MUC16, and PIK3CA were associated with high PDI signature   –3B. The vs. 11%)  while thvs. 11%) .GSVA was performed to characterize the potential function of PDIs in gliomas. Most of the functional pathways were enriched in higher PDI signature group. Some important biological processes in which PDI was known to be involved were identified, such as unfolded protein response (UPR), endoplasmic reticulum-associated degradation (ERAD), endoplasmic reticulum stress (ERS). Many pathways were also associated with cancer pathogenesis, invasion, and metastasis, including WNT signaling pathways, positive regulation of NF kappa B transcription factor activity, vascular endothelial growth factor receptor (VEGFR) signaling pathway, apoptotic signaling pathway, regulation of MAPK cascade, cell cycle, p53 signaling pathway, cytosolic DNA sensing, pathways in cancer, and JAK-STAT signaling pathway. There were also immune-related regulatory pathways identified which did not traditionally focus on PDIs such as antigen processing and presentation, chemokine signaling pathway, B cell receptor signaling pathway, T cell receptor signaling pathway, and natural killer cell-mediated cytotoxicity .Tumor samples from TCGA and CGGA datasets were grouped into two clusters  using consensus clustering analysis to explore PDIs’ potential predictive and prognostic value. Cumulative distribution function (CDF) curves and consensus matrixes –3D deterApart from differences in clinical characteristics between cluster 1 and cluster 2, there were also differences in prognosis in the two groups. In the TCGA datasets, Cluster 2 significantly correlated with longer OS, PFI and DSS in contrast to cluster 1 –4G.p < 0.05 were selected through the univariate COX regression analysis   –5B. Thes, CASQ2) –5E. The , CASQ2) , 6B. In , CASQ2) , 6J, 6E., CASQ2) . However, CASQ2) . There w, CASQ2) –3K.The prognostic differences between low and high-risk groups divided by the median risk score were compared. As shown in SCNAs between samples with low and high-risk scores was investigated considering the somatic copy number alternations’ roles in oncogenesis. The incidence of the amplification of Chr 7 together with a deletion of Chr 10 increased with an increase in the value of risk score in gliomas, while the codeletion of 1p/19q decreased . GISTIC The GSVA analysis identified the biological functions of the PDI gene family in gliomas. From the GO and KEGG enrichment analysis, 10 signaling pathways having statistical significance and high correlation coefficient were selected. The higher gene set enrichment scores of all the selected pathways were associated with higher risk scores, excepting for neuron cell-cell adhesion. Some pathways were related to immune response, including T cell apoptotic process, antigen processing and presentation of peptide antigen via MHC class I, regulation of T cell-mediated cytotoxicity, negative regulation of immune response, cytokine-cytokine receptor interaction, and antigen processing and presentation. The cell adhesion-related pathways were also enriched, including neuron cell-cell adhesion, cell adhesion mediated by integrin, cell adhesion molecules CAMs, and focal adhesion. Biological processes significantly associated with the mechanisms of PDIs in gliomas included protein folding in endoplasmic reticulum, regulation of intrinsic apoptotic signaling pathway in response to DNA damage by P53 class mediator, tumor necrosis factor-mediated signaling pathway, regulation of tyrosine phosphorylation of STAT3 protein, ECM receptor interaction, JAK-STAT signaling pathway, amino sugar and nucleotide sugar metabolism, P53 signaling pathway, DNA replication, and mismatch repair .p < 0.001) , MGMT status, 1p19q codel status and IDH status. Consensus clustering analysis model was the least performer in predicting prognosis and clinical features –8F. The < 0.001) .p = 0·0198, HR = 1·66; CGGA p = 0·0072, HR = 1·56) and 1p19q  were independent prognostic indicators for OS time. Considering parameters of grade III and grade IV with statistical significance, the grade was adopted as one of the prognostic indicators as well  test . There was no statistical significance in IDH and age in the multivariate Cox regression analysis in CGGA datasets . Through Cox regression analyses, risk score, WHO grade, IDH, 1p19q, and age were identified as independent factors in predicting DSS, while risk score and IDH were identified as significant prognostic factors of PFI . Risk score . There was a significant difference in the OS between the two groups (p < 0·0001) (p < 0·0001)  . Similar 0·0001) .The qRT-PCR assay was performed to identify significant genes for construction of the risk score model in gliomas. AGR3 was eliminated as it played a non-significant role in this model. As shown in Furthermore, the immunohistochemistry-based expression data of PDI gene family members was obtained from the online Human Protein Atlas. As shown in In the last three decades, the incidence rate of primary malignant brain and other CNS tumor has increased year by year, with an annual rate of 1%-2%, especially in the elderly population . HoweverGSVA analysis investigated the mechanism of PDIs in gliomas. Consistent with previous studies, the common biological functions of PDIs identified in tumorigenesis and development, included unfolded protein response, endoplasmic reticulum-associated degradation, endoplasmic reticulum stress, cell adhesion, apoptosis, WNT signaling pathways, and cytosolic DNA sensing. There is sufficient evidence supporting that PDI family members are considered as potential targets for tumors due to their functions in UPR, ERAD and ERS signaling pathways. Integrins have been confirmed to be associated with glioma cell invasion, migration and temozolomide resistance –23. ThioThe findings of function annotation revealed a number of unreported signaling pathways in gliomas which PDIs may be involved in and included P53 signaling pathway, cell cycle, tumor necrosis factor (TNF) mediated signaling pathway, regulation of MAPK cascade, JAK-STAT signaling pathway, positive regulation of NF kappa B transcription factor activity, regulation of tyrosine phosphorylation of STAT3 protein, vascular endothelial growth factor receptor signaling pathway, DNA replication, and mismatch repair. Green et al. noted that PDIA3 was able to interact with P53 to inhibit P53-meditated apoptosis . SimilarAn important finding was that PDIs were associated with immune regulation in gliomas, such as negative regulation of immune response, antigen processing and presentation of peptide antigen via MHC class I, T cell receptor signaling pathway, T cell apoptotic process, regulation of T cell-mediated cytotoxicity, antigen processing and presentation, chemokine signaling pathway, B cell receptor signaling pathway, and natural killer cell-mediated cytotoxicity. Gastric cancer patients having high expression of PDIA3 had a favorable prognosis and this was associated with the involvement of PDIA3 in antigen processing and formation of a complex with MCH class I which induced an immune response . HoweverConsidering the important roles of PDI family members in cancer progression, some studies have designed PDIs’ inhibitors. Herein, we have briefly reviewed the previously reported inhibitors for PDI family members. Bacitracin, interacting with reduced PDI and then interfering the integrin, inhibits the glioma cell migration and facilitates apoptosis induced by chemotherapy agents in melanoma cells. BAP2 and analogs play key roles in inducing ER stress, decreasing DNA repair proteins’ expression, inhibiting cell migration and growth in GBM via binding to His256 in the b domain of PDI . The comIn summary, this study the expression patterns, prognostic value, and potential mechanisms of the PDI gene family in gliomas were analyzed. This study was the first to construct clinical models in predicting prognosis and clinicopathological characteristics of glioma and revealed potential signaling pathways. However, further research should be shed light on the validation of PDIs biological impacts on gliomas.http://cancergenome.nih.gov) as training set and from CGGA database (http://www.cgga.org.cn) as the validation set, which was summarized in RNA-seq data and corresponding clinical information were downloaded from the TCGA database  with 10-fold cross validation . SubsequGlioma patients were divided into high and low groups on the basis of median PDI signature or risk score. Survival curves were created by the Kaplan-Meier method with log-rank test, comparing the overall survival (OS), progression free interval (PFI), and disease specific survival (DSS) rates of patients in low and high group. Additionally, survival analyses were conducted between clusters of gliomas.st (lowest) and 4th (highest) groups were screened and presented. In addition, Genomic Identification of Significant Targets in Cancer 2·0 (GISTIC) was used to explore SCNAs between the two groups classified by median value of risk score, according to the gene copy number variation data and Human genome reference consortium h19 downloaded from TCGA and GISTIC 2·0 [We further investigated genetic alterations in gliomas, including somatic mutations and somatic copy number alternations (SCNAs), based on samples obtained from TCGA database. Glioma samples were divided into four groups according to the values of PDI signature and risk scores of low and high groups. Thereafter, the top 20 genes with the highest mutation frequency in the 1STIC 2·0 . The thrROC and the area under the curve (AUC) were used to compare the prediction performance of PDI signature, risk score, clustering and grade in several aspects, including 3-year OS, subtype , MGMT status , 1p19q codel status , and IDH status .The univariate and multivariate Cox regression analyses were performed to identify prognostic factors with P value < 0·05 for designing a prognostic model. A nomogram was constructed using the “RMS” , 51 in R2.Human glioma cell lines  were obtained from the Chinese Academy of Sciences . All cells were maintained in Dulbecco's modified Eagle's medium (DMEM)  supplemented with 10% fetal bovine serum (FBS)  at 37°C with 5% CO® Premix DimerEraserTM  on the LightCycler® 480 system . The following primer sequences were used: P4HB forward primer, 5′-GGTGCTGCGGAAAAGCAAC-3′ and reverse primer, 5′-ACCTGATCTCGGAACCTTCTG-3′; PDIA4 forward primer, 5′-GGCAGGCTGTAGACTACGAG-3′ and reverse primer, 5′-TTGGTCAACACAAGCGTGACT-3′; PDIA5 forward primer, 5′-AAAGGTCTCCTCGCTCATTGA-3′ and reverse primer, 5′-CACCAGTACATTATTCCGGGTTC-3′; TXNDC12 forward primer, 5′-TGGCAAGGTGCATCCTGAAAT-3′ and reverse primer, 5′-TGCTCGGCACTGACATAAAAA-3′. GAPDH was used as the internal reference gene for data normalization. The qRT-PCR reaction steps were as follows: denaturation at 95°C for 30 sec, followed by 45 cycles of 95°C for 5 sec and 60°C for 20 sec. The relative mRNA expression was analyzed using the 2-ΔΔCt method.Total RNA was extracted from cells using RNAiso Plus reagent  according to manufacturer’s instructions. The total RNA was reverse-transcribed into cDNA using the PrimeScript RT Master Mix  kit following the manufacturer’s recommendations. The qRT-PCR was conducted with the SYBRhttps://www.proteinatlas.org) [The immunohistochemical assay images of different PDI family members in the normal and high grade glioma tissues were downloaded from the Human Protein Atlas website (las.org) , 53.p < 0·05 was considered to indicate statistical significance.All statistical analyses were conducted using R software (version 3·5·3). Differences between groups were assessed using the two-tailed Students’ t-test. Multiple groups were compared by one-way ANOVA test. The Schoenfeld residual plots was performed to assess the PH assumption. Univariate and multivariate Cox regression analyses were performed to determine the prognostic value of the risk score and clinical features. The partition around medoids (PAM) algorithm has been conducted in the consensus clustering analysis. The chi-squared test was performed, while analyzing clinical characteristics’ difference between cluster 1 and 2. Kaplan-Meier method with log-rank test was used to compare the OS, PFI and DSS rates of glioma patients . The correlation between two variables was analyzed with Spearman rank. Supplementary FiguresSupplementary Table 1Supplementary Table 2


In vitro experiments indicated that MDM2-mediated p53 degradation induced osteoblast differentiation, and resveratrol could partially reverse p53-dependent inhibition of osteogenic differentiation. These findings suggest resveratrol may alleviate osteoporosis at least in part by modulating the MDM2/p53 signaling pathway.Although osteoporosis is one of the most common chronic age-related diseases, there is currently no gold standard for treatment. Evidence suggests resveratrol, a natural polyphenolic compound, may be helpful in the treatment of osteoporosis and other diseases. However, the molecular mechanisms underlying the anti-osteoporotic effects of resveratrol remain largely unknown. In the present study, KEGG pathway enrichment analysis of resveratrol-targeted genes identified 33 associated pathways, 12 of which were also involved in osteoporosis. In particular, the MDM2/p53 signaling pathway was identified as a potential key pathway among the shared pathways.  Osteoporosis (OP) is a common metabolic bone disease in postmenopausal women and the elderly . The patResveratrol  is a natural polyphenolic compound found in several plants, including grapes, berries, and peanuts . Growingin vitro study to further examine whether resveratrol could counteract the negative effects of p53 on MDM2-mediated osteogenic differentiation.Bioinformatics, which uses computational techniques to organize and integrate experimental data, has been used to great effect in many studies , 12. In In total, 30 resveratrol-targeted genes were identified in Search Tool for Interacting Chemicals (STITCH) using a three shell limit. Interaction networks among resveratrol-targeted genes were then constructed in STITCH . TP53, SEnrichment analysis of the resveratrol-targeted genes using Database for Annotation, Visualization, and Integrated Discovery (DAVID) identified 33 KEGG pathways with p <0.05. 110 KEGG pathways involved in human osteoporosis were retrieved using miRWalk2.0. Twelve KEGG pathways associated with both osteoporosis and resveratrol-targeted genes were identified and visualized using a Venn Diagram online tool . Among tAmong the 30 resveratrol-targeted genes, TP53, AKT1, EP300, CDKN1A, CREBBP, PPARG, MAPK8, FOXO1, MTOR, HSP90AA1, ATM, MDM2, CDKN2A, PTGS2, and RCHY1 were involved in the top five shared KEGG pathways . TP53, CThe downstream cellular responses of the top five KEGG pathways associated with both osteoporosis and resveratrol-targeted genes are shown in p53 gene and protein expression were measured in non-OP (n=10) and OP patients (n=10) using qRT-PCR and western blotting, respectively. p53 gene and protein expression were significantly higher in OP patients  is a natural polyphenolic compound found in several plants . Accumuleriments . These rSome limitations in this study should be considered when interpreting the results. Firstly, the effects of different durations of resveratrol treatment were not investigated. Furthermore, potential differences in p53 enrichment in different osteoporosis subtypes were not examined. Finally, additional studies are needed to compare the effects of resveratrol on osteoporosis development and progression with those of other drugs.in vitro experiment further indicated that resveratrol exerts anti-osteoporosis effects by inhibiting the p53 signaling pathway, and may thus serve as a promising osteoporosis treatment.In conclusion, bioinformatics analysis revealed that the protective effects of resveratrol against osteoporosis were associated with its interaction with the prostate cancer pathway, pathway in cancer, glioma pathway, p53 signaling pathway, and cell cycle signaling pathway. Our The STITCH database integrates data on the effects of over 430000 chemicals on gene expression . Resverahttp://bioinfogp.cnb.csic.es/tools/venny/index.html).miRWalk2.0, a comprehensive archive containing the largest existing collection of predicted and experimentally verified miRNA-target gene interactions , was useEnrichment information for the top five KEGG pathways was presented with GOplot, an R package that visually combines expression data with functional analysis . Genes ihttp://www.proteinlounge.com). Hub genes and their mechanisms of action are shown in the schematic diagrams.The top five shared KEGG pathways with the smallest p values were selected, and KEGG pathways related to resveratrol-targeted genes were established, using Pathway Builder Tool 2.0  was used to transfect cells with siRNAs according to provided directions. MDM2 siRNA constructs  were transfected at 50 nM. Plasmid NC and plasmid MDM2 were synthesized by GenePharma company . All in vitro experiments using cells were independently repeated three times.BMSCs were kindly donated by the Huazhong University of Science and Technology, Wuhan, China. Cells were grown in a specific media designed for C57BL/6 mouse mesenchymal stem cells  at 37°C in a 5% COTM cDNA Synthesis Kit , and qRT-PCR reactions were performed using a Thermal Cycler C-1000 Touch system . GAPDH was used as a housekeeping gene. Target gene expression was quantified using the ΔΔCT method. The primers used in this study are listed in Total tissue/cell RNA was extracted using Trizol reagent . RNA was reverse transcribed using the Versop53 , and GAPDH . Blots were then stained with appropriate horseradish peroxidase (HRP)-conjugated secondary antibodies  and proteins were detected with a chemiluminescence detection system. Each experiment was independently repeated three times.Lysis buffer  containing 1% protease inhibitor  was used to lyse cells and tissue samples. Proteins were separated via SDS-PAGE, transferred to NC membranes , blocked with 5% nonfat milk, and stained overnight at 4°C with antibodies specific for ALP , Osteocalcin , RunX2 , BMSCs were grown in 6-well plates in media containing 100 nM dexamethasone, 50 mM ascorbic acid, and 10 mM b-glycerophosphate to promote osteogenesis . Briefly, cells were washed twice with PBS and 10% formalin was added to fix the cells for 15 minutes. Subsequently, 1 mL 0.5% alizarin red staining solution was used to stain the cells at room temperature for 15 minutes. After rinsing with distilled water for 5 minutes, red mineralized nodules were analyzed via a charge-coupled device microscope. Absorbance was measured at 570 nm. Experiments were repeated in triplicate.± SD. GraphPad Prism 8.0  was used to perform statistical analyses. One-way analysis of variance (ANOVA) with Tukey’s post hoc test was used to compare three or more groups. Unpaired Student’s t-test was used to compare data between two groups. P < 0.05 was considered statistically significant. The study schema is shown in Data are shown as means


Depression is a widespread chronic medical illness affecting thoughts, mood, and physical health. However, the limited and delayed therapeutic efficacy of monoaminergic drugs has led to intensive research efforts to develop novel antidepressants. ARN-3236 is the first potent and selective inhibitor of salt-inducible kinase 2 (SIK2). In this study, a multidisciplinary approach was used to explore the antidepressant-like actions of ARN-3236 in mice. Chronic social defeat stress (CSDS) and chronic unpredictable mild stress (CUMS) models of depression, various behavioral tests, high performance liquid chromatography-tandem mass spectrometry, stereotactic infusion, viral-mediated gene transfer, western blotting, co-immunoprecipitation and immunofluorescence were used together. It was found that ARN-3236 could penetrate the blood-brain barrier. Repeated ARN-3236 administration induced significant antidepressant-like effects in both the CSDS and CUMS models of depression, accompanied with fully preventing the stress-enhanced SIK2 expression and cytoplasmic translocation of cyclic adenosine monophosphate response element binding protein (CREB)-regulated transcription coactivator 1 (CRTC1) in the hippocampus. ARN-3236 treatment also completely reversed the down-regulating effects of CSDS and CUMS on the hippocampal brain-derived neurotrophic factor (BDNF) system and neurogenesis. Moreover, we demonstrated that the hippocampal CRTC1-CREB-BDNF pathway mediated the antidepressant-like efficacy of ARN-3236. Collectively, ARN-3236 possesses strong protecting effects against chronic stress, and could be a novel antidepressant beyond monoaminergic drugs. ARN-3236 administration prevented both the CSDS-induced and CUMS-induced depressive-like behaviors in mice.2. ARN-3236 administration antagonized the effects of both CSDS and CUMS on the hippocampal SIK2-CRTC1-CREB system.3. ARN-3236 administration reversed the decreasing effects of both CSDS and CUMS on the hippocampal BDNF signaling cascade and neurogenesis.As a widespread chronic medical illness affecting thoughts, mood, and physical health, depression is characterized by low mood, lack of energy, sadness, insomnia, and an inability to enjoy life . HoweverSalt-inducible kinases  are serine/threonine kinases which belong to the family of AMP-activated protein kinases (AMPKs), and have wide expression in many tissues such as adipocytes, hypothalamus, hippocampus, cerebral cortex and so on . SIKs haARN-3236 is the first potent, orally active and selective inhibitor of SIK2, with IC50s of <1 nM, 21.63 nM and 6.63 nM for SIK2, SIK1 and SIK3, respectively. However, so far there are few reports involving the pharmacological effects of ARN-3236. Here we speculated that ARN-3236 may have antidepressant-like actions due to SIK2 inhibition. In this study, a multidisciplinary approach was used to explore our assumption.The experimental procedures involving animals and their care were conducted in compliance with the ARRIVE guidelines , and appad libitum access to water and rodent chow, as we previously described  and according to our previous reports  and CD1 mice  were bought from SLAC Laboratory Animal Co., Ltd. . C57BL/6J mice were the experimental subjects and subjected to stratified randomization according to body weight. Before use, all C57BL/6J mice were acclimatized to standard housing for 1 week with escribed . The beh reports . For behFluoxetine and ARN-3236 (Molecular Weight: 336.41) were obtained from Target Mol  and MedKoo Biosciences , respectively. For intraperitoneal injection  of fluoxetine/ARN-3236, the vehicle was 5% DMSO + 95% diluents . For hippocampal infusion of ARN-3236, the vehicle was 5% DMSO + 95% diluents (30% SBE-β-CD in ACSF). 5-bromo-2-deoxyuridine (Brdu) was bought from Sigma  and dissolved in 0.9% saline. The i.p. doses of fluoxetine (20 mg/kg), ARN-3236  and Brdu (75 mg/kg) were chosen based on previous reports . The steCSDS was done as previously described . Enough CUMS was done as previously described . In brieFST was performed as previously used . BrieflyTST was performed as previously used  and lastThis test was conducted as previously used . In brieThis test was conducted as previously used . It is ap < 0.05.Data were subjected to statistical analyses with SPSS 13.0 software  and represented as means ± S.E.M. Multiple group comparisons were performed using two-way ANONA + Bonferroni's test, or one-way ANONA + Tukey's test. Statistical significance was determined at See the n = 10). In contrast, i.p. treatment of fluoxetine and ARN-3236 fully reversed these behavioral changes (n = 10). Detailed analyses showed that ARN-3236 produced a dose-dependent antidepressant action in mice between 10 and 60 mg/kg, and the effects of 30 and 60 mg/kg ARN-3236 were comparable and slightly superior to that of 20 mg/kg fluoxetine, respectively. In addition, i.p. treatment of fluoxetine and ARN-3236 also reduced the immobility of naïve control mice in the FST and TST (n = 10).The possible antidepressant-like effects of ARN-3236 were first examined in both CSDS and CUMS, two well-validated models of depression, with fluoxetine used as the positive control . As shown = 5), but produced none effects on hippocampal SIK1 and SIK3 (n = 5), consistent with our previous report (n = 5), but also down-regulated such level in naïve control mice (n = 5), in parallel with the above behavioral results. Like CSDS and CUMS, ARN-3236 had none effects on hippocampal SIK1 and SIK3 (n = 5), proving its pharmacological specificity. Fluoxetine did not influence hippocampal SIK3 (n = 5), but had down-regulating actions on hippocampal SIK1 (n = 5).Subsequently, western blotting analyses were done to examine hippocampal SIK1-3 between all groups. As shown in s report . It was n = 12). Also, at 2 h after drug administration, 293.69 ± 43.17 ng (approximately 1 nmol) ARN-3236 was detected in the hippocampus tissues of mice (n = 11). Thus, ARN-3236 is able to penetrate BBB.To confirm whether ARN-3236 could cross the blood-brain barrier (BBB), the HPLC-MS method was adopted. As shown in p < 0.01; ARN-3236, F = 16.405, p < 0.01; Interaction, F = 12.694, p < 0.01] and social interaction , as well as reduced immobility in the FST  and TST , than those of the CSDS-treated mice (n = 10). p < 0.01; ARN-3236, F = 13.796, p < 0.01; Interaction, F = 17.435, p < 0.01] and less immobility in the FST  and TST  than those of the CUMS-treated mice (n = 10). Besides, hippocampal infusion of ARN-3236 significantly decreased the immobility of naïve control mice in the FST and TST (n = 10). All these data are in accordance with the i.p. behavioral results.Next, 1 or 2 nmol ARN-3236 was stereotactically infused into the hippocampus of the CSDS-treated and CUMS-treated mice. p < 0.01; ARN-3236, F = 28.223, p < 0.01; Interaction, F = 24.775, p < 0.01] (n = 5) and cytoplasmic pCRTC1  (n = 5) expression, but also notably reversed the CSDS-induced decrease in hippocampal levels of total CRTC1  (n = 5), nuclear CRTC1  (n = 5) and CRTC1-CREB binding  (n = 5). Similarly, ARN-3236 infusion significantly prevented the CUMS-induced effects on hippocampal SIK2  (n = 5), total CRTC1  (n = 5), nuclear CRTC1  (n = 5), cytoplasmic pCRTC1  (n = 5) and CRTC1-CREB binding  (n = 5). Moreover, ARN-3236 infusion down-regulated the level of cytoplasmic pCRTC1 (n = 5) and up-regulated the levels of total CRTC1 (n = 5), nuclear CRTC1 (n = 5) and CRTC1-CREB binding (n = 5) in the hippocampus of naive control mice.Afterward, the hippocampal SIK2-CRTC1 signaling changes of all groups were detected. It was found that ARN-3236 infusion not only fully restored the CSDS-induced increase in hippocampal SIK2  , n = 5 a < 0.01] , n = 5 e < 0.01] , n = 5,  < 0.01] , n = 5 a < 0.01] , n = 5.  < 0.01] , n = 5,  < 0.01] , n = 5,  < 0.01] , n = 5,  < 0.01] , n = 5 a < 0.01] , n = 5. c pCRTC1 , n = 5 aal CRTC1 , n = 5, ar CRTC1 , n = 5 a binding , n = 5 ip < 0.01; ARN-3236, F = 27.886, p < 0.01; Interaction, F = 21.075, p < 0.01. CUMS, F = 28.395, p < 0.01; ARN-3236, F = 24.432, p < 0.01; Interaction, F = 18.255, p < 0.01], pTrkB , pERK1/2 , pAKT , pCaMKIV  and pCREB  expression, but also enhanced these proteins in naive control mice (n = 5). In contrast, the levels of total β-actin, TrkB, ERK1/2, AKT, CaMKIV and CREB in the hippocampus were unchanged among all groups (n = 5). Similarly, ARN-3236 infusion markedly prevented the CSDS-induced  and CUMS-induced  decrease in hippocampal neurogenesis (n = 5). However, ARN-3236 infusion did not influence the hippocampal neurogenesis in naïve control mice (n = 5). Taken together, ARN-3236 possesses excellent antidepressant-like actions against chronic stress in mice.The hippocampal BDNF signaling cascade and neurogenesis among all groups were also examined. ARN-3236 infusion not only blocked the CSDS-induced and CUMS-induced decrease in the hippocampal BDNF  and social interaction , as well as increased immobility in the FST  and TST , than those of the (CSDS + ARN-3236)-treated and (CSDS + ARN-3236 + Control-shRNA)-treated mice (n = 10). Moreover, the CRTC1-shRNA-pretreated mice were subjected to CUMS and 2 nmol ARN-3236 treatment, followed by behavioral tests. As shown in p < 0.01] and more immobility in the FST  and TST  than those of the (CUMS + ARN-3236)-treated and (CUMS + ARN-3236 + Control-shRNA)-treated mice (n = 10).To explore the antidepressant mechanism of ARN-3236, AAV-CRTC1-shRNA was first used, and its silencing efficacy has been confirmed in our previous study . The CRTp < 0.01] and social interaction , as well as increased immobility in the FST  and TST , than those of the (CSDS + ARN-3236)-treated and (CSDS + ARN-3236 + Control-shRNA)-treated mice (n = 10). In addition, the CREB-shRNA-pretreated mice were subjected to CUMS and 2 nmol ARN-3236 treatment, followed by behavioral tests. p < 0.01] and more immobility in the FST  and TST  than those of the (CUMS + ARN-3236)-treated and (CUMS + ARN-3236 + Control-shRNA)-treated mice (n = 10).Then, AAV–CREB–shRNA was used, and its silencing efficacy has been confirmed in our previous study . The CRECollectively, the antidepressant-like effects of ARN-3236 in mice require the hippocampal CRTC1-CREB pathway.p < 0.01] and social interaction  in mice, but also markedly blocked the ARN-3236-reduced immobility of mice in the FST  and TST  (n = 10). Similar results were observed in the CUMS model of depression  (n = 10). Besides, AAV-TrkB-shRNA was also employed, and its silencing efficacy has been proved before  = 30.281, p < 0.01], SPT  and social interaction test  (n = 10). Similar results were observed again in the CUMS model of depression  (n = 10). Therefore, as well as CRTC1 and CREB, the BDNF system in the hippocampus also contributes to the antidepressant mechanism of ARN-3236.To further determine the downstream target underlying the antidepressant-like effects of ARN-3236, AAV-BDNF-shRNA was employed, and its silencing efficacy has been demonstrated before . The BDN < 0.01] , n = 10. < 0.01] , n = 10.d before . The Trk < 0.01] , n = 10.To the best of our knowledge, our study is the first comprehensive study showing that ARN-3236 has beneficial effects against depression, a most burdensome neuropsychiatric disease worldwide. This finding is very interesting and meaningful as it has identified a new potential antidepressant beyond monoaminergic drugs. As the two most widely used and accepted model of depression in rodents, CSDS and CUMS can simulate many core symptoms of depression, including helplessness, anhedonia, social avoidance, decreased appetite, etc. . The FSTARN-3236 is chemically named 3--4-thiophen-3-yl-1H-pyrrolopyridine, possessing no hydrophilic groups and should therefore be liposoluble and able to penetrate BBB. As expected, the HPLC-MS results confirmed that ARN-3236 has good ability to cross BBB. It is possible that the antidepressant actions induced by i.p. administration of ARN-3236 were due to its peripheral SIK2 inhibition. To conclude out this possibility, ARN-3236 was stereotactically infused into the hippocampus of mice and a similar antidepressant efficacy was got, indicating that central SIK2 inhibition underlay the effects of ARN-3236. The western blotting, Co-IP and immunofluorescence results together suggested that ARN-3236 was able to prevent the stress-induced dysfunction in the hippocampal SIK2-CRTC1 system, BDNF signaling cascade and neurogenesis. These findings are consistent with our previous data involving SIK2-knockdown and SIK2-knockout, and further supporting the role of hippocampal SIK2 and CRTC1 in the pathophysiology of depression . BesidesIt is interesting that ARN-3236 treatment reduced the expression of SIK2. Normally, the inhibition of the enzymatic activity of a certain protein could lead to the increased expression of this protein because of the negative feedback mechanism. Here, it should be noticed that ARN-3236 treatment significantly enhanced the level of pCaMKIV, an important downstream signaling molecule of BDNF, while Sasaki et al. demonstrated that pCaMKIV was capable of phosphorylating SIK2 at Thr484 site, resulting in SIK2 degradation in cortical neurons . Thus, wThe usage of CRTC1-shRNA, CREB-shRNA, BDNF-shRNA and TrkB-shRNA together confirmed that the hippocampal CRTC1-CREB-BDNF pathway is required for the antidepressant-like effects of ARN-3236 . In thisIndeed, the monoaminergic system is now still being targeted by drug companies in the search to find improved antidepressant agents with greater effectiveness and a faster onset of clinical action. A multitude of strategies and new promising compounds have been tested to potentiate monoaminergic neurotransmission. For example, one strategy is to target 5-HT receptors in combination with SERT inhibition in one molecule, and has led to two multimodal antidepressants (vilazodone and vortioxetine) which recently received market authorization for treating major depressive disorder . BesidesThe original contributions presented in the study are included in the article/The animal study was reviewed and approved by Animal Welfare Committee of Nantong University.BJ and JL designed this study. BJ wrote the manuscript. YL, WT, CJ, JG, and YC performed the experiments. XZ, YS, JH, CW, and WG helped collecting and analyzing the data. All authors read and approved this manuscript.This work was supported by a grant from the National Natural Science Foundation of China to BJ (No. 81873795), a grant from the Natural Science fund for colleges and universities in Jiangsu Province to BJ (No. 18KJB310013), and also a grant from the Science and Technology Projects of Nantong City to BJ (No. MS12018076).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.


Heme is an iron ion-containing molecule found within hemoproteins such as hemoglobin and cytochromes that participates in diverse biological processes. Although excessive heme has been implicated in several diseases including malaria, sepsis, ischemia-reperfusion, and disseminated intravascular coagulation, little is known about its regulatory and signaling functions. Furthermore, the limited understanding of heme’s role in regulatory and signaling functions is in part due to the lack of curated pathway resources for heme cell biology. Here, we present two resources aimed to exploit this unexplored information to model heme biology. The first resource is a terminology covering heme-specific terms not yet included in standard controlled vocabularies. Using this terminology, we curated and modeled the second resource, a mechanistic knowledge graph representing the heme’s interactome based on a corpus of 46 scientific articles. Finally, we demonstrated the utility of these resources by investigating the role of heme in the Toll-like receptor signaling pathway. Our analysis proposed a series of crosstalk events that could explain the role of heme in activating the TLR4 signaling pathway. In summary, the presented work opens the door to the scientific community for exploring the published knowledge on heme biology. Heme is an iron ion-coordinating porphyrin derivative essential to aerobic organisms . It playBiomedical literature is an immense source of heterogeneous data that are dispersed throughout hundreds of journals. Furthermore, the majority of the results are scattered and published as unstructured free-text, or at best, presented in tables and cartoons representing the experimental study or biological processes and pathways. These shortcomings, combined with the exponential growth of biomedical literature, prevent the healthcare community and individual researchers from being aware of all the available information and knowledge in the literature. With the introduction of new technologies and experimental techniques, researchers have made significant advances in heme-related research and its role in the pathogenesis of numerous hemolytic diseases such as sepsis , malariain vitro and in vivo experiments, and by bioinformaticians to analyze high throughput -omics experiments , that is, a network comprising more than 700 nodes and more than 3,000 interactions. It was generated from 46 selected articles as the first attempt of modeling the knowledge, which is available from more than 20,000 heme-related publications. Finally, we demonstrate both resources by analyzing the crosstalk between heme biology and the TLR4 signaling pathway. The results of this analysis suggest that the activation profile for labile heme as an extracellular signaling molecule through TLR4 induces cytokine and chemokine production. However, the underlying molecular mechanism and individual pathway effectors are not fully understood and need further exploration.This section describes the methodology used to generate the mechanistic knowledge graph and its supporting terminology. Subsequently, it outlines the approach followed to conduct the pathway crosstalk analysis. A schematic diagram of the methodology is presented in http://openbel.org/language/version_2.0/bel_specification_version_2.0.html and http://language.bel.bio as well as additional guidelines from https://github.com/pharmacome/curation.In order to identify recently published articles  describing the role of heme in hemolytic disorders, PubMed was queried with the following: (“heme” AND “hemolysis”) OR (“heme” AND “thrombosis”) OR (“heme” AND “inflammation”) AND (“2009”[Date – Publication]: “3000”[Date – Publication]). The resulting 3,108 articles were manually filtered by removing articles that were deemed too general or lacked a biochemical focus, as judged by expert opinion. After this filtering step, 6 reviews and 40 original research articles were selected for knowledge extraction and modeling. Knowledge was manually extracted and curated from this selected corpus using the official Biological Expression Language (BEL) curation guidelines from Evidence from the selected corpus was manually translated into BEL statements together with their contextual information . For instance, the evidence “Heme/iron-mediated oxidative modification of LDL can cause endothelial cytotoxicity and – at sublethal doses – the expression of stress-response genes”  correspoSET Cell = “endothelial cell”a(CHEBI:“oxidised LDL”) pos bp.https://github.com/hemekg/terminology.During curation, a terminology was generated to support the standardization of domain-specific terminology encountered during the curation of articles related to the heme molecule. The aim of the terminology is to catalog and harmonize terms not present in other controlled vocabularies such as ChEBI  for chemCrosstalk analysis aims to study how two or more pathways communicate or influence each other. While there exist, numerous methodologies designed to investigate pathway crosstalk, the majority of these approaches exclusively quantify such crosstalk based on the overlap between a pair of pathways without delving into the nature of the crosstalk . In thisBecause of the amount of effort required to manually analyze crosstalk across multiple pathways, we conducted a pathway enrichment analysis on three pathway databases [i.e., KEGG First, the three different representations of this pathway were downloaded from each database and converted to BEL using PathMe . Next, tWe introduce the first knowledge graph made publicly available to the biomedical and bioinformatics community focused on heme biology . The preFollowing the guidelines outlined in the Methods section, knowledge was manually extracted and encoded from each of these 46 articles using BEL because of its ability to represent not only causal, but also correlative and associative relationships found in the literature, as well as corresponding provenance and experimental contextual information. This curation exercise resulted in HemeKG, a knowledge graph containing 775 nodes  and 3,05https://github.com/hemekg/hemekg.Finally, to facilitate the use of the curated content in this work, BEL documents are bundled with a dedicated Python package that enables direct access to the content, provides conversion utilities and allows for network exploration. Both the BEL documents and the Python package are available at https://github.com/hemekg/terminology.The specificity of our work, together with the lack of contextual terminologies related to heme biology, prompted us to generate a supporting terminology focused on heme. It contains more than 50 terms that delineate heme-related entities, such as biological processes, proteins, or pathologies that are not yet included in other standard resources such as GO . BuildinThe established heme knowledge graph can be used to study the crosstalk of heme biology with a pathway of interest. HemeKG is of special interest in the context of hemolytic disorders, such as malaria and sickle cell anemia, because these diseases are associated with the release of heme into circulation. Heme can then exert a detrimental role by regulating several proteins and signaling pathways . In ordeWe first investigated the consensus of the three different representations of the TLR4 signaling pathway . We obseSecond, in order to study the overlap between TLR4 signaling pathway and heme biology, we overlaid the consensus network of the pathway with HemeKG . SuperimThe activation profile for labile heme as an extracellular signaling molecule through TLR4 was suggested to be similar to the one established via Lipopolysaccharides (LPS) as signaling molecule from standard pathway databases . This paIn addition, the preceding discussion has excluded parameters such as the concentration of labile heme available in the respective environment. This aspect will be particularly important, if heme-triggered signaling pathways are dependent on, or determined by the concentration of heme. At lower concentrations of heme, TLR4 signaling has been described to be CD14 dependent, whereas at high concentrations of heme, TLR4 activation does not require CD14 . Also, tWe have presented HemeKG, the first mechanistic model in the context of heme biology, as a viable solution to comprehensively summarize heme-related processes by bringing knowledge from disparate literature together. Furthermore, we have demonstrated how combining the knowledge from the heme knowledge graph with information available in pathway databases provides new insights into the network of interactions that regulate heme pathophysiology.1 can be used to scale up this resource by enriching HemeKG with dozens of widely used biomedical databases. In order to make HemeKG accessible to a wider audience, we uploaded it to BEL Commons - a web application for curating, validating, and exploring knowledge assemblies encoded in BEL (Because HemeKG was curated using standard vocabularies, its content can be linked to the majority of public databases. Therefore, enriching the HemeKG network with external data or incorporating its integrated knowledge into other resources is feasible. For example, the entire Bio2BEL frameworkd in BEL . Users cd in BEL  and NDExd in BEL . In summd in BEL . For insd in BEL . Instead-omics data set.A potential limitation of this study is that it is constrained to a specific literature corpus as we are aware that the presented knowledge graph captures only a part of a much larger interaction network. This tends to be a common challenge when constructing contextualized maps and is further compounded by the difficulty in assessing the coverage of a network, explaining why some nodes are missing in HemeKG compared to the three pathway databases used in this study. Furthermore, the bias in the scientific community against publishing negative results must also be acknowledged. A clear example is how the hypotheses of our crosstalk analysis could be complemented by this knowledge gap that could reveal new interesting hypotheses. Thus, future updates in HemeKG, as in any work of this kind, will be required while prioritizing time and effort . FurtherAlthough numerous interactions between heme and TLRs have been described in the literature , their dhttps://github.com/hemekg.The data sets and scripts of this study can be found at DI, MH-A, and DD-F conceived and designed the study. FH curated the data and conducted the main analysis supervised by AP, DI, and DD-F. M-TH, BS, MD, and AP assisted in selecting the corpora and interpreting the results. CH designed the curation guidelines and implemented the Python package. DD-F, FH, CH, M-TH, BS, MD, and DI wrote and reviewed the manuscript.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.