Abstract
A ligand that acts on a target receptor to activate particular multiple signalling pathways with activity that is distinct from other ligands is termed ligand bias. Quantification of ligand bias is based on applying the operational model to each pathway separately and subsequent calculation of the ligand bias metric (ΔΔlogR). This approach implies independence among different pathways and causes propagation of error in the calculation. Here, we propose a semi-mechanism-based model which allows for receptor selectivity across all the pathways simultaneously (termed the ‘intact operational model’). The power of the intact model for detecting ligand bias was evaluated via stochastic simulation estimation studies. It was also applied to two examples: (1) opposing effects of Gi/Gs signalling of α2-adrenergic receptors and (2) simultaneous measurement of arachidonic acid release and inositol phosphate accumulation following 5-HT2C receptor activation. The intact operational model demonstrated greater power to detect ligand bias in the simulation. In the applications, it provided better precision of estimation and identified biased ligands that were missed by analysis of traditional methods. Issues identified in both examples might lead to different interpretations of the data. The intact operational model may elucidate greater understanding of the underlying mechanisms of functional selectivity.
Highlights
A ligand that acts on a target receptor to activate particular multiple signalling pathways with activity that is distinct from other ligands is termed ligand bias
It is seen that the intact operational model had greater power to detect ligand bias compared to the marginal operational model for all values of ΔΔlogR1−2
It was shown that ΔΔlogR1−2 had to be greater than 0.8 for the marginal operational model to achieve 80% power for the ligand bias, while, for the intact operational model, the requirement of ΔΔlogR1−2 was approximately 0.4
Summary
A ligand that acts on a target receptor to activate particular multiple signalling pathways with activity that is distinct from other ligands is termed ligand bias. The intact operational model demonstrated greater power to detect ligand bias in the simulation In the applications, it provided better precision of estimation and identified biased ligands that were missed by analysis of traditional methods. Different ligands can differentially activate multiple signalling pathways when coupled to a single receptor This feature is termed functional selectivity[1]. This value is further normalised such that a ligand’s transduction coefficient for one pathway is related to its ΔlogR for a second pathway This second normalisation step provides a widely reported metric for ligand bias, ΔΔlogR. The use of the marginal operational model makes the assumption that each signalling pathway is independent This precludes insights pertaining to functional selectivity from observed phenomena, such as the natural correlation of a ligand’s C50 values in different pathways. This means that C50 values should be exactly the www.nature.com/scientificreports/
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