The human GPCR signal transduction network

被引:2
作者
Kontou, Panagiota [1 ,2 ]
Pavlopoulou, Athanasia [3 ,4 ]
Dimou, Niki [5 ]
Theodoropoulou, Margarita [1 ]
Braliou, Georgia [1 ]
Tsaousis, Georgios [6 ]
Pavlopoulos, Georgios [7 ]
Hamodrakas, Stavros [6 ]
Bagos, Pantelis [1 ]
机构
[1] Univ Thessaly, Dept Comp Sci & Biomed Informat, Papasiopoulou 2-4, Lamia 35131, Greece
[2] Hellen Mil Acad, Dept Math & Engn Sci, Athens 16673, Greece
[3] Izmir Biomed & Genome Ctr IBG, TR-35340 Izmir, Balcova, Turkey
[4] Dokuz Eylul Univ, Izmir Int Biomed & Genome Inst, TR-35340 Izmir, Turkey
[5] Int Agcy Res Canc, F-69372 Lyon, France
[6] Univ Athens, Dept Cell Biol & Biophys, Fac Biol, Athens 15701, Greece
[7] Biomed Sci Res Ctr BSRC Alexander Fleming, Athens 16672, Greece
来源
NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS | 2021年 / 10卷 / 01期
关键词
GPCR network analysis; Gene expression profiles; Tissue-specificity; Disease risk prediction model;
D O I
10.1007/s13721-020-00278-z
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
The 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.
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页数:12
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