SOT 2017 P225 AOP Poster Luettich Et Al Final

Total Page:16

File Type:pdf, Size:1020Kb

SOT 2017 P225 AOP Poster Luettich Et Al Final The Adverse Outcome Pathway For Mucus Hypersecretion in Chronic Bronchitis Introduction Karsta Luettich1, Marja Talikka1, FrazerIntroduction J. Lowe2, Linsey E. Haswell2, Justyna Szostak1, Florian Martin1, Marianna Gaça2, Julia Hoeng1 Introduction Poster Board P225 Abstract 2823 1 Philip Morris Products S.A. (part of Philip Morris International group of companies), Neuchatel, Switzerland 2 British American Tobacco (Investments) Ltd, Southampton, UK Introduction Adverse Outcome Pathway - Description and Evaluation Biological Network Analysis as Potential Tool for Quantitative AOP Evaluation complex(SCOMP:"NADPH act(p(HGNC:"Proteases, composite(p(HGNC:TNF),p(HGNC:EGF)) Oxidase act(p(HGNC:ELANE)) p(HGNC:CAMP) serine")) Chronic obstructive pulmonary disease (COPD) is one of the adverse outcomes resulting from cigarette smoking composite(p(HGNC:TNF),p(HGNC:TGFA)) Complex") composite(p(HGNC:SPDEF),p(HGNC:FOXA3))p(HGNC:CLCA1) and manifests as obstruction of the small airways, chronic bronchitis, or emphysema. There is a need to establish p(HGNC:FOXA3) act(p(HGNC:P2RY2)) p(HGNC:ADAM17)p(HGNC:RPS6KA1,pmod(P)) a(CHEBI:"hyaluronic tloc(p(SFAM:"PRKC acid") a mechanistic understanding of COPD development in response to chronic exposure to inhaled toxicants for risk KE2 IL4/13 KE3 KE8 Family"),MESHCS:Cytoplasm,MESHCS:"Cell Decreased Membrane") p(SFAM:"PRKC assessment and regulatory decision-making. The Adverse Outcome Pathway (AOP) framework provides a means Transdifferentiation act(p(HGNC:FOXA3)) act(p(HGNC:ADAM17)) Apoptosis of Goblet cell Family") to outline a knowledge-driven sequence of events from exposure to adverse outcome (AO). Here, we describe CASP3↓ of Ciliated Epithelial Ciliated Epithelial metaplasia act(p(SFAM:"PRKC sec(p(HGNC:TGFA))sec(p(HGNC:AREG)) an AOP for EGFR-mediated mucus hypersecretion that leads to chronic bronchitis and COPD. The AOP is based Cells Cells p(HGNC:AGR2) Family")) on existing experimental findings with the activation of EGFR by its ligands in response to oxidative stress as the p(HGNC:TGFA) molecular initiating event (MIE). In response to EGFR activation, the key events (KEs) include decreased PI3K ↑ AKT ↑ HIF1A ↑ tloc(p(HGNC:NCF2),MESHCS:Cytosol,MESHCS:"Cell HB-EGF↑ Membrane") tloc(p(HGNC:NCF1),MESHCS:Cytosol,MESHCS:"Cell apoptosis of ciliated epithelial cells, mediated by PI3K/AKT signaling, transdifferentiation of ciliated cells into Membrane") act(p(HGNC:DUOX1)) Figure 2. Cytoscape Visualization of goblet cells by IL-4 and IL-13, and increased goblet cell proliferation. These KEs lead to goblet cell hyperplasia or MIE TGFA ↑ FOXA2 ↓ AO Goblet Cell Hyperplasia Network Model. metaplasia. Additionally, EGFR stimulation leads to the activation of SP-1 that upregulates MUC5AC, increasing AREG ↑ act(p(HGNC:EGFR)) p(HGNC:EGFR,pmod(P)) BEL statements were extracted from over mucin production by goblet cells. Together these processes ultimately result in mucus hypersecretion that, when KE1 KE5 KE6 KE9 p(HGNC:BCL2) complex(GOCC:"phosphatidylinositol act(p(HGNC:RAC1)) act(p(SFAM:"RAS NRG↑ 3-kinase act(p(HGNC:SRC)) 60 scientific publications on goblet cell p(HGNC:BAX) Family")) chronic, results in declining lung function and allows for COPD diagnosis. We describe the biological plausibility ROS Increase in Decreased complex") act(p(HGNC:NOTCH1)) ADAM↑ EGFR Transcriptional Mucus act(p(HGNC:FOXA2)) hyperplasia using BELIEF and imported Introduction RAS ↑ RAF↑ MEK1/2 ↑ ERK1/2 ↑ Mucin lung act(p(SFAM:"AKT act(p(SFAM:"RAF of the AOP and the weight of each evidence supporting the KEs and the key even relationships (KERs). Finally, we hypersecretion act(p(HGNC:BCL2)) act(p(HGNC:BAX)) act(p(HGNC:CYBB)) MMP ↑ activation activation of SP-1 Family")) Family")) a(SCHEM:NADPH) into Cytoscape for visualization of the H2O2 Production function p(HGNC:MUC5B) have evaluated the mechanisms described in the AOP with transcriptomics data from respiratory tissue of causal relationships, and to verify content act(p(SFAM:"MEK1/2 a(CHEBI:"hydrogen KE4 KE7 act(p(HGNC:CASP3)) human smokers and rodents exposed to cigarette smoke. These results can be further compared to Family")) peroxide") and directionality. Increased experimental in vitro data in the context of the AOP for translational purposes to reduce animal experimentation Goblet cell a(CHEBI:"reactivep(HGNC:SRC,pmod(P)) Proliferation of oxygen species") in toxicological assessment. hyperplasia translocation (tloc), secretion (sec) Epithelial Cells chemical p(HGNC:MMP9) complex(p(HGNC:CD44),p(HGNC:EGFR)) p(HGNC:AREG) biological process (bp) Decreased protein abundance (p) act(p(SFAM:"MAPK act(p(HGNC:MMP9)) p(HGNC:EGFR) p(HGNC:CD44) protein activity (act) Bcl-2↑ Bax↓ Apoptosis of Erk1/2 Family")) complex Goblet Cells p(HGNC:IL8) complex(p(HGNC:BCL2L11),p(HGNC:BCL2L1)) composite Methods p(HGNC:HIF1A) p(HGNC:PARP1)act(p(HGNC:SP1)) act(p(HGNC:CD44)) increases Figure 1. Schematic presentation of the decreased lung function AOP. MIE; molecular initiating event, KE; key event, AO; adverse outcome, ROS; reactive oxygen species, H O ; hydrogen peroxide, ADAM; a disintegrin and 2 2 bp(GOBP:"neutrophil act(p(SFAM:"KLK act(p(HGNC:HIF1A)) act(p(HGNC:PARP1)) directly increases activation") p(HGNC:BCL2L11) Family")) metalloproteinase, MMP; matrix metalloproteinase, HB-EGF; heparin-binding EGF-like growth factor, TGFA; transforming growth factor alpha, AREG; amphiregulin, NRG; neuregulin, EGFR; epidermal growth factor receptor, PI3K; p(HGNC:MUC2) p(HGNC:BCL2L1) decreases directly decreases bp(GOBP:"programmed phosphoinositide 3-kinase, AKT; protein kinase B, CASP3; caspase 3, RAS; p21/Ras GTPase, RAF; RAF proto-oncogene serine/threonine-protein kinase, MEK1/2; mitogen-activated protein kinase kinase 1/2, ERK1/2; extracellular p(HGNC:MUC5AC) p(HGNC:TNF) The AOP for decreased lung function that arises from oxidative stress-mediated EGFR activation in the airway cell death") p(HGNC:EGF) non-causal relationships, e.g. between protein epithelium was developed based on the guidance provided by the Organisation for Economic Cooperation and signal–regulated kinases 1/2, HIF1A; hypoxia-inducible factor 1 alpha, Bcl-2; B-cell lymphoma 2, Bax; bcl-2-like protein 4, IL4; interleukin 4, IL13; interleukin 13, HIF1A; hypoxia-inducible factor 1-alpha, FOXA2; forkhead box A2 p(HGNC:BCL2L11,pmod(P)) abundance and protein activity or complex and bp(GOBP:"regulation of lung goblet cell act(p(HGNC:PTPN1)) its component Development (OECD) using currently available mechanistic evidence from in vitro, in vivo and clinical studies. differentiation") Table 1. Summary overview of biological plausibility and empirical support of KERs, essentiality of KEs and applicability of AOP. The evidence has been summarized and evaluated using a modified weight-of-evidence approach and will be bp(GOBP:"mucus secretion") available for review and comment on the AOPwiki (www.aopwiki.org). Biological Plausibility of KERs Empirical support for KERs Essentiality of KEs A computable network model that describes the biological signaling pathways regulating the increase in the Is there a mechanistic relationship between KEs consistent with established biological knowledge? Does the empirical evidence support that a change in KEup leads to an appropriate change in Are KEsdown and/or the AO prevented if a KEup is blocked? number of airway goblet cells by increased proliferation or transdifferentiation, clinically known as goblet cell KEdown? Temporality? Inconsistencies? A *o*k* B C hyperplasia (GCH) was constructed using a semi-automated knowledge extraction workflow (BELIEF). BELIEF MIE KE1: Oxidative stress directly leading to EGFR High: Considered critical for mucus hypersecretion and 1 Strong Strong KE1 allows for the transformation of unstructured information available in the literature and published datasets activation GCH/GCM into a structured, cause-effect, scientific representation in Biological Expression Language (BEL; Szostaket al.). KE1 KE2: EGFR activation indirectly leading to decreased Moderate: While the evidence supports essentiality of EGFR Moderate Moderate KE2 *o * k* To date, the network model contains causal relationships from over 60 scientific publications and focuses on epithelial cell apoptosis activation for decreased ciliated cell apoptosis, there is also 1 EGFR-mediated goblet cell proliferation and mucin production. KE2 KE3: Decreased epithelial cell apoptosis directly evidence supporting decreased apoptosis in airway goblet 0.75 Moderate Weak There is only correlative evidence for this KER. KE3 Publicly available gene expression datasets were used to validate the network model using the NPA algorithm leading to transdifferentiation into goblet cells cells in vitro. (Martinet al.). Dataset 1 (GSE22430), summarizes the effects of pyocyanin (PYO), a redox-active toxin, on gene KE1 KE4: EGFR activation directly leading to increased 0.75 *o k* Moderate Moderate KE4 High: Blocking EGFR signaling suppresses GCH/GCM. *o*k* 1 expression in H292 lung epithelial cells. Dataset 2 (GSE5264) reflects gene expression changes in airway basal epithelial cell proliferation 0.5 epithelial cells undergoing mucociliary differentiation at early, intermediate and late time points, respectively. Moderate: The evidence suggests that other transcription NPA Score 0.5 Dataset 3 (GSE37693) was derived from bronchial epithelial cells treated with IL-13. KE1 KE5: EGFR activation directly leading to Sp-1 activation Moderate Weak There is little direct evidence for this KER. KE5 0.75 factors could contribute to increased MUC5AC expression. *o * k*
Recommended publications
  • The Structure and Antioxidant Properties
    materials Review Recent Developments in Effective Antioxidants: The Structure and Antioxidant Properties Monika Parcheta 1 , Renata Swisłocka´ 1,* , Sylwia Orzechowska 2,3 , Monika Akimowicz 4 , Renata Choi ´nska 4 and Włodzimierz Lewandowski 1 1 Department of Chemistry, Biology and Biotechnology, Bialystok University of Technology, Wiejska 45E, 15-351 Bialystok, Poland; [email protected] (M.P.); [email protected] (W.L.) 2 Solaris National Synchrotron Radiation Centre, Jagiellonian University, Czerwone Maki 98, 30-392 Krakow, Poland; [email protected] 3 M. Smoluchowski Institute of Physics, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków, Poland 4 Prof. Waclaw Dabrowski Institute of Agriculture and Food Biotechnology–State Research Institute, Rakowiecka 36, 02-532 Warsaw, Poland; [email protected] (M.A.); [email protected] (R.C.) * Correspondence: [email protected] Abstract: Since the last few years, the growing interest in the use of natural and synthetic antioxidants as functional food ingredients and dietary supplements, is observed. The imbalance between the number of antioxidants and free radicals is the cause of oxidative damages of proteins, lipids, and DNA. The aim of the study was the review of recent developments in antioxidants. One of the crucial issues in food technology, medicine, and biotechnology is the excess free radicals reduction to obtain healthy food. The major problem is receiving more effective antioxidants. The study aimed to analyze the properties of efficient antioxidants and a better understanding of the molecular ´ Citation: Parcheta, M.; Swisłocka, R.; mechanism of antioxidant processes. Our researches and sparing literature data prove that the Orzechowska, S.; Akimowicz, M.; ligand antioxidant properties complexed by selected metals may significantly affect the free radical Choi´nska,R.; Lewandowski, W.
    [Show full text]
  • S41467-020-16549-2.Pdf
    ARTICLE https://doi.org/10.1038/s41467-020-16549-2 OPEN Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN ✉ Maria Masid 1, Meric Ataman 2 & Vassily Hatzimanikatis 1 Altered metabolism is associated with many human diseases. Human genome-scale meta- bolic models (GEMs) were reconstructed within systems biology to study the biochemistry 1234567890():,; occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation. We present here redHUMAN, a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology using the recently established methods redGEM and lumpGEM. The reductions include the thermodynamic properties of compounds and reactions guaranteeing the con- sistency of predictions with the bioenergetics of the cell. We introduce a method (redGEMX) to incorporate the pathways used by cells to adapt to the medium. We provide the ther- modynamic curation of the human GEMs Recon2 and Recon3D and we apply the redHUMAN workflow to derive leukemia-specific reduced models. The reduced models are powerful platforms for studying metabolic differences between phenotypes, such as diseased and healthy cells. 1 Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. 2 Computational and ✉ Systems Biology, Biozentrum, University of Basel, Basel, Switzerland. email: vassily.hatzimanikatis@epfl.ch NATURE COMMUNICATIONS | (2020)11:2821 | https://doi.org/10.1038/s41467-020-16549-2 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-16549-2 n altered metabolism is a hallmark of several human compartmentalized as Recon 2 with an additional compartment diseases, such as cancer, diabetes, obesity, Alzheimer’s, for the mitochondria intermembrane space.
    [Show full text]
  • DATABASE Report April 2019 [email protected] TABLE of CONTENTS TABLE of CONTENTS
    DATABASE Report April 2019 [email protected] TABLE OF CONTENTS TABLE OF CONTENTS .................................................................................................................. 2 INTRODUCTION ............................................................................................................................. 3 GENERAL OVERVIEW OF DATABASE TEAM ACTIVITIES ......................................................... 3 PUBLIC ENGAGEMENT – PROMOTING OUR RESOURCES ............................................................ 3 PUBLICATIONS ................................................................................................................................. 3 OUTREACH AND SOCIAL MEDIA ......................................................................................................... 4 THE GUIDE TO PHARMACOLOGY DATABASE (GTOPDB) ........................................................ 6 GTOPDB WEB SITE ACCESS STATISTICS ............................................................................................ 6 ACQUISITION, BROWSERS & DEVICES ............................................................................................... 7 GTOPDB CONTENT .......................................................................................................................... 8 DOWNLOAD STATISTICS ............................................................................................................... 9 SUPPLIER LINKS ...........................................................................................................................
    [Show full text]
  • Using Natural Language Processing Methods to Support Curation of a Chemical Ontology., Doctor of Philosophy, May 2014 DECLARATION
    USINGNATURALLANGUAGEPROCESSING METHODSTOSUPPORTCURATIONOFA CHEMICAL ONTOLOGY. adam bernard Homerton College, University of Cambridge & European Bioinformatics Institute May 2014 This dissertation is submitted for the degree of Doctor of Philosophy. Adam Bernard: Using Natural Language Processing methods to support curation of a chemical ontology., Doctor of Philosophy, May 2014 DECLARATION This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except where specifically indi- cated in the text. This dissertation does not exceed the word limit as specified by the Degree Committee for the Faculty of Biology. Cambridge, May 2014 Adam Bernard To Hugh R. S. Jones, who taught me the value of back-of-the-envelope calculations, and to the memory of my grandparents Elise Kersh and Sidney &Norma Bernard. SUMMARY Adam Bernard Using Natural Language Processing methods to support curation of a chemical on- tology. This thesis describes various techniques to assist the curation of a chemi- cal ontology (ChEBI) using a combination of textmining techniques and the resources of the ontology itself. ChEBI is an ontology of small molecules that are either produced by, or otherwise relevant to, biological organisms. It is manually expert-curated, and as such has high reliablity but incomplete coverage. To make efficient use of curator time, it is desirable to have auto- matic suggestions for chemical species and their properties, to be assessed for inclusion in ChEBI. Having developed a system to identify chemicals within biological text, I use a combination of a syntactic parser and a small set of textual patterns to extract hypernyms of these chemicals (categories of chemicals where there is an is-a relationship e.g.
    [Show full text]
  • The Experimental Factor Ontology Utilities in Knowledge Discovery and Sharing
    The Experimental Factor Ontology Utilities in Knowledge Discovery and Sharing Sirarat Sarntivijai, Tony Burdett, Simon Jupp, and Helen Parkinson http://www.ebi.ac.uk/efo/ [email protected] @SSirarat Outline • Ontologies at the EBI • Using and building ontologies with the real data at the Centre for Therapeutic Target Validation (CTTV) • The ontology toolkit • Challenges Pre-RDA Plenary – 22/09/2015 Data resources at EMBL-EBI Genes, genomes & variation European Nucleotide Archive Ensembl European Genome-phenome Archive 1000 Genomes Ensembl Genomes Metagenomics portal Gene, protein & metabolite expression ArrayExpress Metabolights Expression Atlas PRIDE Literature & ontologies Protein sequences, families & motifs Europe PubMed Central Gene Ontology InterPro Pfam UniProt Experimental Factor Ontology BioSamples Database Molecular structures Protein Data Bank in Europe Electron Microscopy Data Bank +~10, BAO, CTO, ORDO, ChEBI, Fly Anatomy, SWO, Chemical biology OBI, …. ChEMBL ChEBI Reactions, interactions & pathways Systems BioModels BioSamples IntAct Reactome MetaboLights Enzyme Portal Pre-RDA Plenary – 22/09/2015 The Experimental Factor Ontology • We build the EFO in OWL • EFO is an application ontology, built for use in production services • Lots of external dependencies - reuses where possible and appropriate other references Chemical Entities of Biological Interest Cell Type Ontology (ChEBI) Gene Ontology PATO Pre-RDA Plenary – 22/09/2015 EFO reuses existing knowledge Pre-RDA Plenary – 22/09/2015 Working the abundance of data • BioMedBridges,
    [Show full text]
  • Expanding Opportunities for Mining Bioactive Chemistry from Patents, Drug Discov Today: Technol (2015), 10.1016/J.Ddtec.2014.12.001
    DDTEC-426; No of Pages 7 Vol. xxx, No. xx 2015 Drug Discovery Today: Technologies Editors-in-Chief Kelvin Lam – Simplex Pharma Advisors, Inc., Boston, MA, USA Henk Timmerman – Vrije Universiteit, The Netherlands DRUG DISCOVERY TODAY TECHNOLOGIES From Chemistry to Biology Database Curation Expanding opportunities for mining bioactive chemistry from patents Christopher Southan IUPHAR/BPS Database and Guide to PHARMACOLOGY Web Portal Group, Centre for Integrative Physiology, School of Biomedical 1 Sciences, University of Edinburgh, Hugh Robson Building, Edinburgh EH8 9XD, UK Bioactive structures published in medicinal chemistry Section editor: patents typically exceed those in papers by at least Antony Williams – Royal Society of Chemistry, Wake twofold and may precede them by several years. The Forest, NC, USA. Big-Bang of open automated extraction since 2012 has contributed to over 15 million patent-derived com- (both commercial and academic) but also contain substan- tially more structure–activity relationship (SAR) results than pounds in PubChem. While mapping between chemical journals. This article will focus on exploring scientific value structures, assay results and protein targets from patent rather than intellectual property (IP) because, while both documents is challenging, these relationships can be aspects are intertwined, the analytical approaches diverge. harvested using open tools and are beginning to be Value curated into databases. The question needs to be posed as to what data-centric patent mining has to offer practitioners in cheminformatics, phar- Introduction macology, medicinal chemistry or chemical biology. To an- Compared to papers, patents in the biological sciences have swer this, it is necessary to compare the availability of data hitherto been an underexploited information source, prin- from non-patent sources.
    [Show full text]
  • The Role of Uniprot's Protein Sequence Databases in Biomedical Research
    Andrew Nightingale1, Tunca Dogan1, Diego Poggioli1, Maria Martin1 and the UniProt Consortium1,2,3 1 EMBL-European Bioinformatics Institute, Cambridge, UK 2 SIB Swiss Institute of Bioinformatics, Geneva, Switzerland 3 Protein Information Resource, Georgetown University, Washington DC & University od Delaware, USA The Role of UniProt's Protein Sequence Databases in Biomedical Research Introduction Mapping diseases to InterPro UniProt provides human disease information with extensive Domains, Variants and ChEMBL cross-references to disease relevant databases such as: Medical Subject Headings (MeSH)1 and Online Mendelian Inheritance in Man Compounds (OMIM)2, Figure 1. In order to further enhance the functional annotations relevance to biomedical research; UniProt has recently developed a pipeline for importing protein altering variants from globally ● 4,246 diseases mapped to 2,337 InterPro domains. recognised genetic variant repositories with the aim to extend the ● 316 InterPro domains from 510 protein entries matched to 3,601 ChEMBL manually curated set of natural protein altering variants provided by ligands. UniProt. By combining these resources UniProt has become a more relevant resource for biomedical research and drug target identification. ● Somatic variants have been found within the binding pockets of proteins Here we describe how users of UniProt can develop methodologies to associated to specific cancer types. utilise the described cross-references, protein structure and functional annotations to explore how structural, functional and chemical ligand annotations can be utilised to identify relationships between a protein and disease causing variants. Mitogen-activated Protein Kinase 4 Example Figure 1: Disease and natural variant annotation for UniProtKB/SwissProt entry for Human BRCA1 Figure 3: MAPK4 binding pocket with analogue inhibitor bound and p.Ser233Ala variant.
    [Show full text]
  • Carbon Dioxide - Wikipedia
    5/20/2020 Carbon dioxide - Wikipedia Carbon dioxide Carbon dioxide (chemical formula CO2) is a colorless gas with Carbon dioxide a density about 60% higher than that of dry air. Carbon dioxide consists of a carbon atom covalently double bonded to two oxygen atoms. It occurs naturally in Earth's atmosphere as a trace gas. The current concentration is about 0.04% (412 ppm) by volume, having risen from pre-industrial levels of 280 ppm.[8] Natural sources include volcanoes, hot springs and geysers, and it is freed from carbonate rocks by dissolution in water and acids. Because carbon dioxide is soluble in water, it occurs naturally in groundwater, rivers and lakes, ice caps, glaciers and seawater. It is present in deposits of petroleum and natural gas. Carbon dioxide is odorless at normally encountered concentrations, but at high concentrations, it has a sharp and acidic odor.[1] At such Names concentrations it generates the taste of soda water in the Other names [9] mouth. Carbonic acid gas As the source of available carbon in the carbon cycle, atmospheric Carbonic anhydride carbon dioxide is the primary carbon source for life on Earth and Carbonic oxide its concentration in Earth's pre-industrial atmosphere since late Carbon oxide in the Precambrian has been regulated by photosynthetic organisms and geological phenomena. Plants, algae and Carbon(IV) oxide cyanobacteria use light energy to photosynthesize carbohydrate Dry ice (solid phase) from carbon dioxide and water, with oxygen produced as a waste Identifiers product.[10] CAS Number 124-38-9 (http://ww w.commonchemistr CO2 is produced by all aerobic organisms when they metabolize carbohydrates and lipids to produce energy by respiration.[11] It is y.org/ChemicalDeta returned to water via the gills of fish and to the air via the lungs of il.aspx?ref=124-38- air-breathing land animals, including humans.
    [Show full text]
  • Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (Lexebi)
    Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (LexEBI) Dietrich Rebholz-Schuhmann1,2*, Jee-Hyub Kim2, Ying Yan2, Abhishek Dixit2, Caroline Friteyre2, Robert Hoehndorf3, Rolf Backofen4, Ian Lewin2 1 Department of Computational Linguistics, University of Zu¨rich, Zu¨rich, Switzerland, 2 European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL- EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom, 3 Department of Genetics, University of Cambridge, Downing Street, Cambridge, United Kingdom, 4 Albert-Ludwigs-University Freiburg, Fahnenbergplatz, Freiburg, Germany Abstract Motivation: Biomedical entities, their identifiers and names, are essential in the representation of biomedical facts and knowledge. In the same way, the complete set of biomedical and chemical terms, i.e. the biomedical ‘‘term space’’ (the ‘‘Lexeome’’), forms a key resource to achieve the full integration of the scientific literature with biomedical data resources: any identified named entity can immediately be normalized to the correct database entry. This goal does not only require that we are aware of all existing terms, but would also profit from knowing all their senses and their semantic interpretation (ambiguities, nestedness). Result: This study compiles a resource for lexical terms of biomedical interest in a standard format (called ‘‘LexEBI’’), determines the overall number of terms, their reuse in different resources and the nestedness of terms. LexEBI comprises references for protein and gene entries and their term variants and chemical entities amongst other terms. In addition, disease terms have been identified from Medline and PubmedCentral and added to LexEBI. Our analysis demonstrates that the baseforms of terms from the different semantic types show only little polysemous use.
    [Show full text]
  • Chemistry Requirements
    OBO: chemistry requirements Colin Batchelor July 2, 2008 Abstract We list some urgent corrections which are needed to make ChEBI compatible with BFO, RO and the other OBO Foundry ontologies, as well as outlining developments which will minimize future curatorial effort. The urgent corrections are to ensure: is a completeness, that the is a relation is transitive, that the parthood relations are compatible with RO, that granular and determinate parthood are distinguished, that the names of important terms reflect biomedical usage rather than IUPAC's prescriptions, and that the top-level terms are rearranged to reflect the new parthood relations. The future developments outlined are to replace most of the asserted structure of ChEBI with an inferred structure, and to introduce a cross-product structure compatible with postcomposition of terms. 1 Introduction Not only are organisms made of chemical entities, but fundamental processes in biological organisms such as the Krebs cycle, DNA transcription, RNA transla- tion and post-translational modification are defined in terms of transformations of chemical entities. It is for this reason that the Open Biomedical Ontologies (OBO) need a biochemical ontology. In order to interoperate effectively with other OBO ontologies, any ontology should • be is a complete • have an is a relation that is transitive • reuse relations from RO where appropriate • distinguish between granular and determinate parthood (Rogers et al.) • distinguish between dependent and independent continuants (BFO) A biochemical ontology that is part of OBO we needs to provide a chemical framework for biomedical ontologies such as the molecular function and biolog- ical process ontologies in GO, the types of molecule that are implied by the 1 Sequence Ontology, and the chemical classes in the Systems Biology Ontology.
    [Show full text]
  • Extension of Roles in the Chebi Ontology
    Extension of Roles in the ChEBI Ontology This manuscript (permalink) was automatically generated from chemical-roles/manuscript@03ed444 on June 30, 2020. Authors Charles Tapley Hoyt 0000-0003-4423-4370 · cthoyt · cthoyt Enveda Therapeutics Christopher J Mungall 0000-0002-6601-2165 · cmungall · chrismungall Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Nicole Vasilevsky 0000-0001-5208-3432 · nicolevasilevsky · N_Vasilevsky Oregon Health and Science University, Portland, OR 97239, USA Daniel Domingo-Fernández 0000-0002-2046-6145 · ddomingof · daniel_sunday Enveda Therapeutics Matthew Healy 0000-0001-6439-5038 Enveda Therapeutics Viswa Colluru 0000-0002-6208-9288 Enveda Therapeutics Motivation The Chemical Entities of Biological Interest [1,2] ontology models chemicals, their classes, their roles, and their interrelations (Figure 1, left). While many roles correspond to how their substituent chemicals aect proteins and other biological entities (see Figure 3), this information is not formalized nor structured in the ChEBI ontology. Ying et al. [3] previously described how these correspondences could be theoretically formalized. This article proposes a concrete schema and axioms through which these roles can be linked to their target entities (Figure 1, right), a suite of open source, reusable curation tools, and ultimately a manually curated database of relationships between chemical roles and their targets. Figure 1: Schema for inference of chemicals’ relations to targets via roles. Targets may be other chemicals, proteins, protein families, protein complexes, pathways, pathologies, or organisms. Throughout this article, the term role (in the context of the ChEBI ontology) will be used in the colloquial sense described by Batchelor et al.
    [Show full text]
  • Chebi Guidelines URL Description Chemical Entities of Biological Interest (Chebi) Is a Freely Avail
    ChEBI Guidelines URL https://www.ebi.ac.uk/chebi/ Description Chemical Entities of Biological Interest (ChEBI) is a freely available dictionary of molecular entities focused on 'small' chemical compounds. Small chemical compounds are usually drugs that help regulate biological processes. A Warning On Nomenclature As stated in other guideline sheets, some authors may use a word to describe a chemical compound that is not included in the ChEBI database. If you find that you are having a difficult time finding anything on a particular compound, consult Wikipedia or PubMed for alternate terms. Guidelines If your BioModel includes instances of pharmaceutical intervention, then this database will be of use to you. This guide will be using ‘caffeine’ as an example. The homepage contains a search bar at the center of the page, with two filters. The first filter says “Search for *** only”, and the second says “All in ChEBI”. You will be checking the circle that says “All in ChEBI”. The “***” indicate that the small molecule was curated by the ChEBI team, but we don’t want to limit our search to only this category. In the search bar, type in ‘caffeine’ (without the quotes). You will be taken to the search results page for your query. These results contain pure caffeine along with its metabolites. You’re interested in the “caffeine” hyperlink. When you click on this link, you will be taken to the record for caffeine. The record contains six tabs: Main, ChEBI Ontology, Automatic Xrefs, Reactions, Pathways, Models. Main This tab contains basic information, such as: definition, molecular weight, ChEBI ID (which is what you will be using to tag the BioModel), and a Wikipedia entry about the small molecule.
    [Show full text]