Currently active biological databases aiming to archive data related to oral biology.

Semaphorins are a large family of cell surface and secreted guidance molecules divided into eight classes on the basis of their structures. They all have an N-terminal conserved sema domain. Semaphorins signal through multimeric receptor complexes that include other proteins such as plexins and neuropilins. Original Pathway at Reactome: http://www.reactome.org/PathwayBrowser/#DB=gk_current&FOCUS_SPECIES_ID=48887&FOCUS_PATHWAY_ID=373755

Link: https://figshare.com/articles/dataset/S2_Table/25546426. (XLSX)

RELEASE V2.1.0 KNOWLEDGE GRAPH: ORIGINAL DATA SOURCES

Release: v2.1.0

The goal of this build was to create a knowledge graph that represented human disease mechanisms and included the central dogma. The data sources utilized in this release include many of the sources used in the initial release, as well as some new data made available by the Comparative Toxicogenomics Database and experimental data from the Human Protein Atlas.

Data sources are listed by type (Ontology and Data not represented in an ontology [Database Sources]). Additional details are provided for each data source below. Please see documentation on the primary release (https://github.com/callahantiff/PheKnowLator/wiki/v2-Data-Sources) for additional details on each data source as well as citation information.

Data Access:

https://console.cloud.google.com/storage/browser/pheknowlator/archived_builds/release_v2.1.0/build_01MAY2021

ONTOLOGIES

Cell Ontology

Cell Line Ontology

Chemical Entities of Biological Interest (ChEBI) Ontology

Gene Ontology

Human Phenotype Ontology

Mondo Disease Ontology

Pathway Ontology

Protein Ontology

Relations Ontology

Sequence Ontology

Uber-Anatomy Ontology

Vaccine Ontology

Cell Ontology (CL)

Homepage: GitHub Citation:

Bard J, Rhee SY, Ashburner M. An ontology for cell types. Genome Biology. 2005;6(2):R21

Usage: Utilized to connect transcripts and proteins to cells. Additionally, the edges between this ontology and its dependencies are utilized:

ChEBI

GO

PATO

PRO

RO

UBERON

Cell Line Ontology (CLO)

Homepage: http://www.clo-ontology.org/ Citation:

Sarntivijai S, Lin Y, Xiang Z, Meehan TF, Diehl AD, Vempati UD, Schürer SC, Pang C, Malone J, Parkinson H, Liu Y. CLO: the cell line ontology. Journal of Biomedical Semantics. 2014;5(1):37

Usage: Utilized this ontology to map cell lines to transcripts and proteins. Additionally, the edges between this ontology and its dependencies are utilized:

CL

DOID

NCBITaxon

UBERON

Chemical Entities of Biological Interest (ChEBI)

Homepage: https://www.ebi.ac.uk/chebi/ Citation:

Hastings J, Owen G, Dekker A, Ennis M, Kale N, Muthukrishnan V, Turner S, Swainston N, Mendes P, Steinbeck C. ChEBI in 2016: Improved services and an expanding collection of metabolites. Nucleic Acids Research. 2015;44(D1):D1214-9

Usage: Utilized to connect chemicals to complexes, diseases, genes, GO biological processes, GO cellular components, GO molecular functions, pathways, phenotypes, reactions, and transcripts.

Gene Ontology (GO)

Homepage: http://geneontology.org/ Citations:

Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA. Gene ontology: tool for the unification of biology. Nature Genetics. 2000;25(1):25

The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Research. 2018;47(D1):D330-8

Usage: Utilized to connect biological processes, cellular components, and molecular functions to chemicals, pathways, and proteins. Additionally, the edges between this ontology and its dependencies are utilized:

CL

NCBITaxon

RO

UBERON

Other Gene Ontology Data Used: goa_human.gaf.gz

Human Phenotype Ontology (HPO)

Homepage: https://hpo.jax.org/ Citation:

Köhler S, Carmody L, Vasilevsky N, Jacobsen JO, Danis D, Gourdine JP, Gargano M, Harris NL, Matentzoglu N, McMurry JA, Osumi-Sutherland D. Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Research. 2018;47(D1):D1018-27

Usage: Utilized to connect phenotypes to chemicals, diseases, genes, and variants. Additionally, the edges between this ontology and its dependencies are utilized:

CL

ChEBI

GO

UBERON

Files

Other Human Phenotype Ontology Data Used: phenotype.hpoa

Mondo Disease Ontology (Mondo)

Homepage: https://mondo.monarchinitiative.org/ Citation:

Mungall CJ, McMurry JA, Köhler S, Balhoff JP, Borromeo C, Brush M, Carbon S, Conlin T, Dunn N, Engelstad M, Foster E. The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species. Nucleic Acids Research. 2017;45(D1):D712-22

Usage: Utilized to connect diseases to chemicals, phenotypes, genes, and variants. Additionally, the edges between this ontology and its dependencies are utilized:

CL

NCBITaxon

GO

HPO

UBERON

Pathway Ontology (PW)

Homepage: rgd.mcw.edu Citation:

Petri V, Jayaraman P, Tutaj M, Hayman GT, Smith JR, De Pons J, Laulederkind SJ, Lowry TF, Nigam R, Wang SJ, Shimoyama M. The pathway ontology–updates and applications. Journal of Biomedical Semantics. 2014;5(1):7.

Usage: Utilized to connect pathways to GO biological processes, GO cellular components, GO molecular functions, Reactome pathways. Several steps are taken in order to connect Pathway Ontology identifiers to Reactome pathways and GO biological processes. To connect Pathway Ontology identifiers to Reactome pathways, we use ComPath Pathway Database Mappings developed by Daniel Domingo-Fernández (PMID:30564458).

Files

Downloaded Mapping Data

curated_mappings.txt

kegg_reactome.csv

Generated Mapping Data

REACTOME_PW_GO_MAPPINGS.txt

Protein Ontology (PRO)

Homepage: https://proconsortium.org/ Citation:

Natale DA, Arighi CN, Barker WC, Blake JA, Bult CJ, Caudy M, Drabkin HJ, D’Eustachio P, Evsikov AV, Huang H, Nchoutmboube J. The Protein Ontology: a structured representation of protein forms and complexes. Nucleic Acids Research. 2010;39(suppl_1):D539-45

Usage: Utilized to connect proteins to chemicals, genes, anatomy, catalysts, cell lines, cofactors, complexes, GO biological processes, GO cellular components, GO molecular functions, pathways, proteins, reactions, and transcripts. Additionally, the edges between this ontology and its dependencies are utilized:

ChEBI

DOID

GO

Notes: A partial, human-only version of this ontology was used. Details on how this version of the ontology was generated can be found under the Protein Ontology section of the Data_Preparation.ipynb Jupyter Notebook.

Files

Generated Human Version Protein Ontology (PRO)

human_pro.owl (closed with hermit reasoner)

Other PRO Data Used: promapping.txt

Generated Mapping Data

Merged Gene, RNA, Protein Map: Merged_gene_rna_protein_identifiers.pkl

Ensembl Transcript-PRO Identifier Mapping: ENSEMBL_TRANSCRIPT_PROTEIN_ONTOLOGY_MAP.txt

Entrez Gene-PRO Identifier Mapping: ENTREZ_GENE_PRO_ONTOLOGY_MAP.txt

UniProt Accession-PRO Identifier Mapping: UNIPROT_ACCESSION_PRO_ONTOLOGY_MAP.txt

STRING-PRO Identifier Mapping: STRING_PRO_ONTOLOGY_MAP.txt

Relations Ontology (RO)

Homepage: GitHub Citation:

Smith B, Ceusters W, Klagges B, Köhler J, Kumar A, Lomax J, Mungall C, Neuhaus F, Rector AL, Rosse C. Relations in biomedical ontologies. Genome Biology. 2005;6(5):R46.

Usage: Utilizing this ontology to connect all data sources in knowledge graph. Additionally, the ontology is queried prior to building the knowledge graph to identify all relations, their inverse properties, and their labels.

Files

Generated RO Data

INVERSE_RELATIONS.txt

RELATIONS_LABELS.txt

Sequence Ontology (SO)

Homepage: GitHub Citation:

Eilbeck K, Lewis SE, Mungall CJ, Yandell M, Stein L, Durbin R, Ashburner M. The Sequence Ontology: a tool for the unification of genome annotations. Genome Biology. 2005;6(5):R44

Usage: Utilized to connect transcripts and other genomic material like genes and variants.

Files

Generated Mapping Data

genomic_sequence_ontology_mappings.xlsx

SO_GENE_TRANSCRIPT_VARIANT_TYPE_MAPPING.txt

Uber-Anatomy Ontology (Uberon)

Homepage: GitHub Citation:

Mungall CJ, Torniai C, Gkoutos GV, Lewis SE, Haendel MA. Uberon, an integrative multi-species anatomy ontology. Genome Biology. 2012;13(1):R5

Usage: Utilized to connect tissues, fluids, and cells to proteins and transcripts. Additionally, the edges between this ontology and its dependencies are utilized:

ChEBI

CL

GO

PRO

Vaccine Ontology (VO)

Homepage: http://www.violinet.org/vaccineontology/ Citations:

He Y, Racz R, Sayers S, Lin Y, Todd T, Hur J, Li X, Patel M, Zhao B, Chung M, Ostrow J. Updates on the web-based VIOLIN vaccine database and analysis system. Nucleic Acids Research. 2013;42(D1):D1124-32

Xiang Z, Todd T, Ku KP, Kovacic BL, Larson CB, Chen F, Hodges AP, Tian Y, Olenzek EA, Zhao B, Colby LA. VIOLIN: vaccine investigation and online information network. Nucleic Acids Research. 2007;36(suppl_1):D923-8

Usage: Utilized the edges between this ontology and its dependencies:

ChEBI

DOID

GO

PRO

UBERON

DATABASE SOURCES

BioPortal

ClinVar

Comparative Toxicogenomics Database

DisGeNET

Ensembl

GeneMANIA

Genotype-Tissue Expression Project

Human Genome Organisation Gene Nomenclature Committee

Human Protein Atlas

National Center for Biotechnology Information Gene

Reactome Pathway Database

Search Tool for Recurring Instances of Neighbouring Genes Database

Universal Protein Resource Knowledgebase

BioPortal

Homepage: BioPortal Citation:

BioPortal. Lexical OWL Ontology Matcher (LOOM)

Ghazvinian A, Noy NF, Musen MA. Creating mappings for ontologies in biomedicine: simple methods work. In AMIA Annual Symposium Proceedings 2009 (Vol. 2009, p. 198). American Medical Informatics Association

Usage: BioPortal was utilized to obtain mappings between MeSH identifiers and ChEBI identifiers for chemicals-diseases, chemicals-genes, chemical-GO biological processes, chemicals-GO cellular components, chemicals-GO molecular functions, chemicals-phenotypes, chemicals-proteins, and chemicals-transcripts. Additional information on how this data was processed can be obtained

Content of the Bioinformatics for Dentistry, with its respective primary sources.

Neurotransmitter is stored in the synaptic vesicle in the pre-synaptic terminal prior to its release in the synaptic cleft upon depolarization of the pre-synaptic membrane. The release of the neurotransmitter is a multi-step process that is controlled by electrical signals passing through the axons in form of action potential. Neurotransmitters include glutamate, acetylcholine, nor-epinephrine, dopamine and seratonin. Each of the neurotransmitter cycle is independently described.Original Pathway at Reactome: http://www.reactome.org/PathwayBrowser/#DB=gk_current&FOCUS_SPECIES_ID=48887&FOCUS_PATHWAY_ID=112310

BackgroundChronic Chagas Cardiomyopathy (CCM) is characterized by a unique pathophysiology in which inflammatory, microvascular and neuroendocrine processes coalesce in the development of one of the most severe cardiomyopathies affecting humans. Despite significant advances in understanding the molecular mechanisms involved in this disease, scarce information is available regarding microRNAs and clinical parameters of disease severity. We aimed to evaluate the association between circulating levels of six microRNAs with markers of myocardial injury and prognosis in this population.MethodsPatients with CCM and reduced ejection fraction were included in a prospective exploratory cohort study. We assessed the association of natural log-transformed values of six circulating microRNAs (miR-34a-5p, miR-208a-5p, miR-185-5p, miR-223-5p, let-7d-5p, and miR-454-5p) with NT-proBNP levels and echocardiographic variables using linear regression models adjusted for potential confounders. By using Cox Proportional Hazard models, we examined whether levels of microRNAs could predict a composite outcome (CO), including all-cause mortality, cardiac transplantation, and implantation of a left ventricular assist device (LVAD). Finally, for mRNAs showing significant associations, we predicted the target genes and performed pathway analyses using Targetscan and Reactome Pathway Browser.ResultsSeventy-four patients were included (59% males, median age: 64 years). After adjustment for age, sex, body mass index, and heart failure medications, only increasing miR-223-5p relative expression levels were significantly associated with better myocardial function markers, including left atrium area (Coef. -10.2; 95% CI -16.35; -4.09), end-systolic (Coef. -45.3; 95% CI -74.06; -16.61) and end-diastolic volumes (Coef. -46.1; 95% CI -81.99; -10.26) of the left ventricle. Moreover, we observed that higher miR-223-5p levels were associated with better left-ventricle ejection fraction and lower NT-proBNP levels. No associations were observed between the six microRNAs and the composite outcome. A total of 123 target genes for miR-223-5p were obtained. From these, several target pathways mainly related to signaling by receptor tyrosine kinases were identified.ConclusionsThe present study found an association between miR-223-5p and clinical parameters of CCM, with signaling pathways related to receptor tyrosine kinases as a potential mechanism linking low levels of miR-223-5p with CCM worsening.

The transport of iron between cells is mediated by transferrin. However, iron can also enter and leave cells not only by itself, but also in the form of heme and siderophores. When entering the cell via the main path (by transferrin endocytosis), its goal is not the (still elusive) chelated iron pool in the cytosol nor the lysosomes but the mitochondria, where heme is synthesized and iron-sulfur clusters are assembled (Kurz et al,2008, Hower et al 2009, Richardson et al 2010).Original Pathway at Reactome: http://www.reactome.org/PathwayBrowser/#DB=gk_current&FOCUS_SPECIES_ID=48887&FOCUS_PATHWAY_ID=917937

Neurotransmitter released in the synaptic cleft binds to specific receptors on the post-synaptic cell and the excess of the neurotransmitter is cleared to prevent over activation of the post-synaptic cell. The neurotransmitter is cleared by either re-uptake by the pre-synaptic neuron, diffusion in the perisynaptic area, uptake by astrocytes surrounding the synaptic cleft or enzymatic degradation of the neurotransmitter.
This topic will be annotated in a future release.Original Pathway at Reactome: http://www.reactome.org/PathwayBrowser/#DB=gk_current&FOCUS_SPECIES_ID=48887&FOCUS_PATHWAY_ID=112311

The secretory membrane system allows a cell to regulate delivery of newly synthesized proteins, carbohydrates, and lipids to the cell surface, a necessity for growth and homeostasis. The system is made up of distinct organelles, including the endoplasmic reticulum (ER), Golgi complex, plasma membrane, and tubulovesicular transport intermediates. These organelles mediate intracellular membrane transport between themselves and the cell surface. Membrane traffic within this system flows along highly organized directional routes. Secretory cargo is synthesized and assembled in the ER and then transported to the Golgi complex for further processing and maturation. Upon arrival at the trans Golgi network (TGN), the cargo is sorted and packaged into post-Golgi carriers that move through the cytoplasm to fuse with the cell surface. This directional membrane flow is balanced by retrieval pathways that bring membrane and selected proteins back to the compartment of origin.Original Pathway at Reactome: http://www.reactome.org/PathwayBrowser/#DB=gk_current&FOCUS_SPECIES_ID=48887&FOCUS_PATHWAY_ID=199991

The culture medium of senescent cells in enriched in secreted proteins when compared with the culture medium of quiescent i.e. presenescent cells and these secreted proteins constitute the so-called senescence-associated secretory phenotype (SASP), also known as the senescence messaging secretome (SMS). SASP components include inflammatory and immune-modulatory cytokines (e.g. IL6 and IL8), growth factors (e.g. IGFBPs), shed cell surface molecules (e.g. TNF receptors) and survival factors. While the SASP exhibits a wide ranging profile, it is not significantly affected by the type of senescence trigger (oncogenic signalling, oxidative stress or DNA damage) or the cell type (epithelial vs. mesenchymal) (Coppe et al. 2008). However, as both oxidative stress and oncogenic signaling induce DNA damage, the persistent DNA damage may be a deciding SASP initiator (Rodier et al. 2009). SASP components function in an autocrine manner, reinforcing the senescent phenotype (Kuilman et al. 2008, Acosta et al. 2008), and in the paracrine manner, where they may promote epithelial-to-mesenchymal transition (EMT) and malignancy in the nearby premalignant or malignant cells (Coppe et al. 2008). Interleukin-1-alpha (IL1A), a minor SASP component whose transcription is stimulated by the AP-1 (FOS:JUN) complex (Bailly et al. 1996), can cause paracrine senescence through IL1 and inflammasome signaling (Acosta et al. 2013).

Here, transcriptional regulatory processes that mediate the SASP are annotated. DNA damage triggers ATM-mediated activation of TP53, resulting in the increased level of CDKN1A (p21). CDKN1A-mediated inhibition of CDK2 prevents phosphorylation and inactivation of the Cdh1:APC/C complex, allowing it to ubiquitinate and target for degradation EHMT1 and EHMT2 histone methyltransferases. As EHMT1 and EHMT2 methylate and silence the promoters of IL6 and IL8 genes, degradation of these methyltransferases relieves the inhibition of IL6 and IL8 transcription (Takahashi et al. 2012). In addition, oncogenic RAS signaling activates the CEBPB (C/EBP-beta) transcription factor (Nakajima et al. 1993, Lee et al. 2010), which binds promoters of IL6 and IL8 genes and stimulates their transcription (Kuilman et al. 2008, Lee et al. 2010). CEBPB also stimulates the transcription of CDKN2B (p15-INK4B), reinforcing the cell cycle arrest (Kuilman et al. 2008). CEBPB transcription factor has three isoforms, due to three alternative translation start sites. The CEBPB-1 isoform (C/EBP-beta-1) seems to be exclusively involved in growth arrest and senescence, while the CEBPB-2 (C/EBP-beta-2) isoform may promote cellular proliferation (Atwood and Sealy 2010 and 2011). IL6 signaling stimulates the transcription of CEBPB (Niehof et al. 2001), creating a positive feedback loop (Kuilman et al. 2009, Lee et al. 2010). NF-kappa-B transcription factor is also activated in senescence (Chien et al. 2011) through IL1 signaling (Jimi et al. 1996, Hartupee et al. 2008, Orjalo et al. 2009). NF-kappa-B binds IL6 and IL8 promoters and cooperates with CEBPB transcription factor in the induction of IL6 and IL8 transcription (Matsusaka et al. 1993, Acosta et al. 2008). Besides IL6 and IL8, their receptors are also upregulated in senescence (Kuilman et al. 2008, Acosta et al. 2008) and IL6 and IL8 may be master regulators of the SASP.

IGFBP7 is also an SASP component that is upregulated in response to oncogenic RAS-RAF-MAPK signaling and oxidative stress, as its transcription is directly stimulated by the AP-1 (JUN:FOS) transcription factor. IGFBP7 negatively regulates RAS-RAF (BRAF)-MAPK signaling and is important for the establishment of senescence in melanocytes (Wajapeyee et al. 2008).

Please refer to Young and Narita 2009 for a recent review. View original pathway at Reactome.