Repression of Hh signaling in the absence of ligand depends on the transmembrane receptor protein Patched (PTCH), which inhibits Smoothened (SMO) activity by an unknown mechanism. This promotes the proteolytic processing and/or degradation of the GLI family of transcription factors and maintains the pathway in a transcriptionally repressed state (reviewed in Briscoe and Therond, 2013). In the absence of ligand, PTCH is localized in the cilium, while SMO is largely concentrated in intracellular compartments. Upon binding of Hh to the PTCH receptor, PTCH is endocytosed, relieving SMO inhibition and allowing it to accumulate in the primary cilium (Marigo et al, 1996; Chen and Struhl, 1996; Stone et al, 1996; Rohatgi et al, 2007; Corbit et al, 2005; reviewed in Goetz and Anderson, 2010). In the cilium, SMO is activated by an unknown mechanism, allowing the full length transcriptional activator forms of the GLI proteins to accumulate and translocate to the nucleus, where they bind to the promoters of Hh-responsive genes (reviewed in Briscoe and Therond, 2013).
In addition to PTCH, three additional membrane proteins have been shown to bind Hh and to be required for Hh-dependent signaling in vertebrates: CDON (CAM-related/downregulated by oncogenes), BOC (brother of CDO) and GAS1 (growth arrest specific 1) (Yao et al, 2006; Okada et al, 2006; Tenzen et al, 2006; McLellan et al, 2008; reviewed in Kang et al, 2007; Beachy et al, 2010; Sanchez-Arrones et al, 2012). CDON and BOC, homologues of Drosophila Ihog and Boi respectively, are evolutionarily conserved transmembrane glycoproteins that have been shown to bind both to Hh ligand and to the canonical receptor PTCH to promote Hh signaling (Okada et al, 2006; Yao et al, 2006; Tenzen et al, 2006, McLellan et al, 2008; Izzi et al, 2011; reviewed in Sanchez-Arrones et al, 2012). Despite the evolutionary conservation, the mode of ligand binding by CDON/Ihog and BOC/Boi is distinct in vertebrates and invertebrates. High affinity ligand-binding by CDON and BOC requires Ca2+, while invertebrate ligand-binding is heparin-dependent (Okada et al, 2006; Tenzen et al, 2006; McLellan et al, 2008; Yao et al, 2006; Kavran et al, 2010). GAS1 is a vertebrate-specific GPI-anchored protein that similarly binds both to Hh ligand and to the PTCH receptor to promote Hh signaling (Martinelli and Fan, 2007; Izzi et al, 2011; reviewed in Kang et al, 2007). CDON, BOC and GAS1 have partially overlapping but not totally redundant roles, and knock-out of all three is required to abrogate Hh signaling in mice (Allen et al, 2011; Izzi et al, 2011; reviewed in Briscoe and Therond, 2013).

Collection of pathways and pathway annotations. The core unit of the Reactome data model is the reaction. Entities (nucleic acids, proteins, complexes and small molecules) participating in reactions form a network of biological interactions and are grouped into pathways (signaling, innate and acquired immune function, transcriptional regulation, translation, apoptosis and classical intermediary metabolism) . Provides website to navigate pathway knowledge and a suite of data analysis tools to support the pathway-based analysis of complex experimental and computational data sets.

This event has been computationally inferred from an event that has been demonstrated in another species.

The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.

More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp

This event has been computationally inferred from an event that has been demonstrated in another species.

The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.

More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp

Host responses to vaccines are complex but important to investigate. To facilitate the study, we have developed a tool called Vaccine Induced Gene Expression Analysis Tool (VIGET), with the aim to provide an interactive online tool for users to efficiently and robustly analyze the host immune response gene expression data collected in the ImmPort/GEO databases. VIGET allows users to select vaccines, choose ImmPort studies, set up analysis models by choosing confounding variables and two groups of samples having different vaccination times, and then perform differential expression analysis to select genes for pathway enrichment analysis and functional interaction network construction using the Reactome’s web services. VIGET provides features for users to compare results from two analyses, facilitating comparative response analysis across different demographic groups. VIGET uses the Vaccine Ontology (VO) to classify various types of vaccines such as live or inactivated flu vaccines, yellow fever vaccines, etc. To showcase the utilities of VIGET, we conducted a longitudinal analysis of immune responses to yellow fever vaccines and found an intriguing complex activity response pattern of pathways in the immune system annotated in Reactome, demonstrating that VIGET is a valuable web portal that supports effective vaccine response studies using Reactome pathways and ImmPort data.

This event has been computationally inferred from an event that has been demonstrated in another species.

The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.

More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp

Database for molecular interaction information integrated with various other bio-entity information, including pathways, diseases, gene ontology (GO) terms, species and molecular types. The information is obtained from several manually curated databases and automatic extraction from literature. There are protein-protein interaction, gene/protein regulation and protein-small molecule interaction information stored in the database. The interaction information is linked with relevant GO terms, pathway, disease and species names. Interactions are also linked to the PubMed IDs of the corresponding abstracts the interactions were obtained from. Manually curated molecular interaction information was obtained from BioGRID, IntAct, NCBI Gene, and STITCH database. Pathway related information was obtained from KEGG database, Pathway Interaction database and Reactome. Disease information was obtained from PharmGKB and KEGG database. Gene ontology terms and related information was obtained from Gene Ontology database and GOA database.

STRING protein-protein interaction networks for WT-C vs. WT-D.

This event has been computationally inferred from an event that has been demonstrated in another species.

The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.

More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp

PKT Human Disease Knowledge Graph Benchmark Builds (v1.0.0) Build Date: September 03, 2019 The KG Benchmark Builds can also be downloaded from Zenodo:👉 KGs: https://doi.org/10.5281/zenodo.7030200👉 Embeddings: https://zenodo.org/record/7030189

Required Input Documents

resource_info.txt class_source_list.txt instance_source_list.txt ontology_source_list.txt

Data Data Download Date: November 30, 2018 Ontologies

Gene Ontology Human Phenotype Ontology Classes

Human Disease Ontology Gene Ontology: gene associations Reactome: gene associations Human Phenotype Ontology: all source annotations - genes to phenotypes Human Phenotype Ontology: all source annotations - diseases to genes to phenotypes Instances

CTD: chemicals-genes CTD: chemicals-pathways CTD: chemicals-diseases CTD: genes-pathways CTD: diseases-pathways STRING DB: Proteins String DB: entrez gene mappings

Knowledge Graphs Knowledge RepresentationWe worked with a PhD-level biologist to develop a knowledge representation (see the figure below) that modeled mechanisms underlying human disease.

To do this, we manually mapped all possible combinations of the following six node types:

Humans Diseases Human Phenotypes Human Genes Gene Ontology concepts Reactome Pathways Chemicals As shown in the figure above, the Basic Formal Ontology and Relation Ontology ontologies were then used to create edges between the node types.

As shown in this figure, the following edge-types were created:

Phenotypes-Genes: The Human Phenotype Ontology (HP) provides phenotype-Entrez gene annotations that were used to map 6,651 HP classes to 120,288 Entrez genes. Phenotypes-Diseases: The HP provides HP-DOID-Gene annotations that were used to map 5,438 HP concepts to 43,817 DOID concepts. Biological processes, Molecular Functions, and Cellular Locations-Genes: The Gene Ontology (GO) provides GO-Gene annotations that were used to map 17,505 GO concepts to 265,002 Entrez genes. Biological processes, Molecular Functions, and Cellular Locations-Pathways-Pathways: Reactome provides GO-Gene links that were used to map 17,906 pathways to 1,910 biological processes, molecular functions, and cellular locations. Chemicals-Pathways: The Comparative Toxicogenomics Database (CTD) provides Chemical-pathway links that were used to map 8,886 MESH concepts to 711,043 Reactome pathways. Chemicals-Genes: The Comparative Toxicogenomics Database (CTD) provides Chemical-Gene links that were used to map 8,881 MESH concepts 410,379 Entrez genes. Chemicals-Diseases: The Comparative Toxicogenomics Database (CTD) provides Chemical-Disease links that were used to map 14,238 MESH concepts 1,216,900 DOID concepts. Genes-Genes: TheSTRING Database provides Gene-Gene links that were used to create 594,100 gene-gene interactions. When generating these mappings, only the inferred protein-protein relationships considered to be high confidence were used (score of 700 or better). Genes-Disease: Mappings between genes and diseases were retrieved from DisGeNet via SPARQL endpoint and used to map 6,051 Entrez genes to 20,452 DOID concepts. Genes-Pathways: The Comparative Toxicogenomics Database (CTD) provides Gene-Pathway links that were used to map 110,370 Entrez genes to 107,029 Reactome pathways. Pathways-Disease: The Comparative Toxicogenomics Database (CTD) provides Pathway-Disease links that were used to map 1,818 Reactome pathways to 106,727 DOID concepts.

Knowledge GraphThe knowledge graph represented above was built using the following steps: Merge Ontologies: Merge ontologies using the OWL Tools APIExpress New Ontology Concept Annotations: Create new ontology annotations by asserting a relation between the instance and an instance of the ontology class. For example to assert the following relations:

Morphine --> is substance that treats --> Migraine We would need to create two axioms:

isSubstanceThatTreats(Morphine, x1) instanceOf(x1, Migraine) While the instance of the HP class hemiplegic migraines can be treated as an anonymous node in the knowledge graph, we generate a new international resource identifier for each newly generated instance. Deductively Close Knowledge Graph: The knowledge graph is deductively closed by using the OWL 2 EL reasoner, ELK via Protégé v5.1.1. ELK is able to classify instances and supports inferences over class hierarchies and object properties. inference over disjointness, intersection, and existential quantification (ontology class hierarchies). Generate Edge List: The final step before exporting the edge list is to remove any nodes that are not biologically meaningful or would otherwise reduce the performance of machine learning algorithms and the algorithm used to generate embeddings.

🚨 AVAILABLE FILES 🚨Available KG benchmark files are zipped and listed below. For additional details on what each file contains, please see the associated Wiki page 👉 here.

IntroductionAndrogenetic alopecia (AGA) is a common progressive scalp hair loss disorder that leads to baldness. This study aimed to identify core genes and pathways involved in premature AGA through an in-silico approach.MethodsGene expression data (GSE90594) from vertex scalps of men with premature AGA and men without pattern hair loss was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the bald and haired samples were identified using the limma package in R. Gene ontology and Reactome pathway enrichment analyses were conducted separately for the up-regulated and down-regulated genes. The DEGs were annotated with the AGA risk loci, and motif analysis in the promoters of the DEGs was also carried out. STRING Protein-protein interaction (PPI) and Reactome Functional Interaction (FI) networks were constructed using the DEGs, and the networks were analyzed to identify hub genes that play could play crucial roles in AGA pathogenesis.Results and discussionThe in-silico study revealed that genes involved in the structural makeup of the skin epidermis, hair follicle development, and hair cycle are down-regulated, while genes associated with the innate and adaptive immune systems, cytokine signaling, and interferon signaling pathways are up-regulated in the balding scalps of AGA. The PPI and FI network analyses identified 25 hub genes namely CTNNB1, EGF, GNAI3, NRAS, BTK, ESR1, HCK, ITGB7, LCK, LCP2, LYN, PDGFRB, PIK3CD, PTPN6, RAC2, SPI1, STAT3, STAT5A, VAV1, PSMB8, HLA-A, HLA-F, HLA-E, IRF4, and ITGAM that play crucial roles in AGA pathogenesis. The study also implicates that Src family tyrosine kinase genes such as LCK, and LYN in the up-regulation of the inflammatory process in the balding scalps of AGA highlighting their potential as therapeutic targets for future investigations.

This event has been computationally inferred from an event that has been demonstrated in another species.

The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.

More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp

hiPathDB is an integrated pathway database that combines the curated human pathway data of NCI-Nature PID, Reactome, BioCarta and KEGG. In total, it includes 1661 pathways consisting of 8976 distinct physical entities. (2010.03.09) hiPathDB provides two different types of integration. The pathway-level integration, conceptually a simple collection of individual pathways, was achieved by devising an elaborate model that takes distinct features of four databases into account and subsequently reformatting all pathways in accordance with our model. The entity-level integration creates a single unified pathway that encompasses all pathways by merging common components. Even though the detailed molecular-level information such as complex formation or post-translational modifications tends to be lost, such integration makes it possible to investigate signaling network over the entire pathways and allows identification of pathway cross-talks. Another strong merit of hiPathDB is the built-in pathway visualization module that supports explorative studies of complex networks in an interactive fashion. The layout algorithm is optimized for virtually automatic visualization of the pathways.

The performance of different feature sets on the training datasets over five-cross validations.

*Canonical pathways include the Biocart pathway database, KEGG pathway database, Reactome pathway database.

This event has been computationally inferred from an event that has been demonstrated in another species.

The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.

More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp

Background: Many studies on circular RNAs (circRNAs) have recently been published. However, the function of circRNAs in recurrent implantation failure (RIF) is unknown and remains to be explored. This study aims to determine the regulatory mechanisms of circRNAs in RIF.Methods: Microarray data of RIF circRNA (GSE147442), microRNA (miRNA; GSE71332), and messenger RNA (mRNA; GSE103465) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed circRNA, miRNA, and mRNA. The circRNA–miRNA–mRNA network was constructed by Cytoscape 3.8.0 software, then the protein–protein interaction (PPI) network was constructed by STRING database, and the hub genes were identified by cytoHubba plug-in. The circRNA–miRNA–hub gene regulatory subnetwork was formed to understand the regulatory axis of hub genes in RIF. Finally, the Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the hub genes were performed by clusterProfiler package of Rstudio software, and Reactome Functional Interaction (FI) plug-in was used for reactome analysis to comprehensively analyze the mechanism of hub genes in RIF.Results: A total of eight upregulated differentially expressed circRNAs (DECs), five downregulated DECs, 56 downregulated differentially expressed miRNAs (DEmiRs), 104 upregulated DEmiRs, 429 upregulated differentially expressed genes (DEGs), and 1,067 downregulated DEGs were identified regarding RIF. The miRNA response elements of 13 DECs were then predicted. Seven overlapping miRNAs were obtained by intersecting the predicted miRNA and DEmiRs. Then, 56 overlapping mRNAs were obtained by intersecting the predicted target mRNAs of seven miRNAs with 1,496 DEGs. The circRNA–miRNA–mRNA network and PPI network were constructed through six circRNAs, seven miRNAs, and 56 mRNAs; and four hub genes (YWHAZ, JAK2, MYH9, and RAP2C) were identified. The circRNA–miRNA–hub gene regulatory subnetwork with nine regulatory axes was formed in RIF. Functional enrichment analysis and reactome analysis showed that these four hub genes were closely related to the biological functions and pathways of RIF.Conclusion: The results of this study provide further understanding of the potential pathogenesis from the perspective of circRNA-related competitive endogenous RNA network in RIF.

Druggable toll-like receptor (TLR) cascades. Drug–pathway interactions within TLR cascades associated with definitions of remission in systemic lupus erythematosus (DORIS) remission (from: https://idg.reactome.org, with modifications); only selected parts of pathways are shown, and irrelevant pathways or parts of pathways are omitted. The complete pathways are detailed in online supplemental figure S2. Panel A depicts TLR 7/8 and TLR9 pathways; pathway–drug interactions with TLR7 and TLR9 are highlighted with red squares (the number of related drugs is indicated) and demonstrated in panels B and C. Parts of the MyD88:MAL(TIRAP) cascade initiated on the plasma membrane belonging to the druggable TLR cascades are detailed in panel D; this cascade constitutes the terminal effector of TLR2, TLR5, and TLR10 pathways. Within this pathway, Bruton tyrosine kinase (BTK) is a key druggable component, whose inhibitors are shown in panel E

Main interaction nodes were correlated to the data reported by a manual curation in the REACTOME website.*Interaction Nodes;1Pyruvate metabolism and TCA cycle;2Metabolism of carbohydrates;3Metabolism of amino acids and derivatives;4Respiratory electron transport, APT synthesis by chemiosmotic coupling, and heat production by uncoupling proteins;5Integration of energy metabolism;6Mitochondrial fatty acid beta-oxidation.

This event has been computationally inferred from an event that has been demonstrated in another species.

The inference is based on the homology mapping from PANTHER. Briefly, reactions for which all involved PhysicalEntities (in input, output and catalyst) have a mapped orthologue/paralogue (for complexes at least 75% of components must have a mapping) are inferred to the other species. High level events are also inferred for these events to allow for easier navigation.

More details and caveats of the event inference in Reactome. For details on PANTHER see also: http://www.pantherdb.org/about.jsp