Selective activation of Gαob by an adenosine A1 receptor agonist elicits analgesia without cardiorespiratory depression.

Wall, Mark J; Hill, Emily; Huckstepp, Robert; Barkan, Kerry; Deganutti, Giuseppe; Leuenberger, Michele; Preti, Barbara; Winfield, Ian; Carvalho, Sabrina; Suchankova, Anna; Wei, Haifeng; Safitri, Dewi; Huang, Xianglin; Imlach, Wendy; La Mache, Circe; Dean, Eve; Hume, Cherise; Hayward, Stephanie; Oliver, Jess; Zhao, Fei-Yue; ... (2022). Selective activation of Gαob by an adenosine A1 receptor agonist elicits analgesia without cardiorespiratory depression. Nature communications, 13(1), p. 4150. Nature Publishing Group 10.1038/s41467-022-31652-2

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The development of therapeutic agonists for G protein-coupled receptors (GPCRs) is hampered by the propensity of GPCRs to couple to multiple intracellular signalling pathways. This promiscuous coupling leads to numerous downstream cellular effects, some of which are therapeutically undesirable. This is especially the case for adenosine A1 receptors (A1Rs) whose clinical potential is undermined by the sedation and cardiorespiratory depression caused by conventional agonists. We have discovered that the A1R-selective agonist, benzyloxy-cyclopentyladenosine (BnOCPA), is a potent and powerful analgesic but does not cause sedation, bradycardia, hypotension or respiratory depression. This unprecedented discrimination between native A1Rs arises from BnOCPA's unique and exquisitely selective activation of Gob among the six Gαi/o subtypes, and in the absence of β-arrestin recruitment. BnOCPA thus demonstrates a highly-specific Gα-selective activation of the native A1R, sheds new light on GPCR signalling, and reveals new possibilities for the development of novel therapeutics based on the far-reaching concept of selective Gα agonism.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Biochemistry and Molecular Medicine

UniBE Contributor:

Leuenberger, Michele, Preti, Barbara, Lochner, Martin

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
000 Computer science, knowledge & systems

ISSN:

2041-1723

Publisher:

Nature Publishing Group

Language:

English

Submitter:

Pubmed Import

Date Deposited:

25 Jul 2022 13:32

Last Modified:

05 Dec 2022 16:22

Publisher DOI:

10.1038/s41467-022-31652-2

PubMed ID:

35851064

BORIS DOI:

10.48350/171482

URI:

https://boris.unibe.ch/id/eprint/171482

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