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dc.creatorKontou P., Pavlopoulou A., Dimou N., Theodoropoulou M., Braliou G., Tsaousis G., Pavlopoulos G., Hamodrakas S., Bagos P.en
dc.date.accessioned2023-01-31T08:44:09Z
dc.date.available2023-01-31T08:44:09Z
dc.date.issued2021
dc.identifier10.1007/s13721-020-00278-z
dc.identifier.issn21926662
dc.identifier.urihttp://hdl.handle.net/11615/75102
dc.description.abstractThe eukaryotic cell surface G protein-coupled receptors (GPCRs) interact with a wide spectrum of ligands. The intracellular transmission of the extracellular signal is mediated by the selective coupling of GPCRs to G proteins, which, in turn, activate downstream effectors. GPCRs are of paramount pharmacological importance, with approximately 40% of all commercial drugs targeting these proteins. Herein, we have made an effort to unravel the molecular mechanisms underlying the GPCR-mediated signaling pathway and the way this pathway is associated with diseases. Network-based approaches were utilized to delineate the GPCR pathway, incorporating data from gene expression profiles across eleven healthy tissues and disease–gene associations from three diverse resources. The associations between the tissue-specific expression profiles of the disease-related genes along with the relative risk of disease development were further investigated. In the GPCR-activated pathway, the signal was found to be amplified at the successive steps of the pathway so that the effector molecules are highly expressed compared to ligands. This amplification effect was more pronounced when the respective genes encoding the particular proteins were associated with diseases. It was also found that co-expressed genes, corresponding to interacting molecules in affected tissues, may constitute powerful predictive markers for disease development. A disease risk prediction model based on tissue-specific expression profiles of the disease-associated genes was also generated. These findings could be applied to clinical settings. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH, AT part of Springer Nature.en
dc.language.isoenen
dc.sourceNetwork Modeling Analysis in Health Informatics and Bioinformaticsen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85099349302&doi=10.1007%2fs13721-020-00278-z&partnerID=40&md5=6d39e86765a59895267bb0d966362def
dc.subjectCell membranesen
dc.subjectGene expressionen
dc.subjectHistologyen
dc.subjectLigandsen
dc.subjectMoleculesen
dc.subjectRisk assessmenten
dc.subjectSignal transductionen
dc.subjectTissueen
dc.subjectDisease developmenten
dc.subjectDisease risk prediction modelen
dc.subjectDisease risksen
dc.subjectExpression profileen
dc.subjectG protein coupled receptorsen
dc.subjectG protein-coupled receptor network analyseen
dc.subjectGene expression profilesen
dc.subjectRisk prediction modelsen
dc.subjectTissue specificityen
dc.subjectTissue-specific expressionen
dc.subjectProteinsen
dc.subjectamyloid beta proteinen
dc.subjectangiotensinogenen
dc.subjectcomplement component C3en
dc.subjectendothelin 1en
dc.subjectG protein coupled receptoren
dc.subjectglutamate receptoren
dc.subjectkininogenen
dc.subjectlipocortin 1en
dc.subjectlow density lipoprotein cholesterolen
dc.subjectmessenger RNAen
dc.subjectArticleen
dc.subjectdiastolic blood pressureen
dc.subjectdisease associationen
dc.subjectgeneen
dc.subjectgene expressionen
dc.subjectphenotypeen
dc.subjectprescriptionen
dc.subjectprotein protein interactionen
dc.subjectrisk factoren
dc.subjectRNA sequenceen
dc.subjectsignal transductionen
dc.subjectsystolic blood pressureen
dc.subjecttissue specificityen
dc.subjectSpringeren
dc.titleThe human GPCR signal transduction networken
dc.typejournalArticleen


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