Systematic errors in detecting biased agonism: Analysis of current methods and development of a new model-free approach

H. Ongun Onaran, Caterina Ambrosio, Özlem Ugur, Erzsebet Madaras Koncz, Maria Cristina Grò, Vanessa Vezzi, Sudarshan Rajagopal, Tommaso Costa

Research output: Contribution to journalArticle

Abstract

Discovering biased agonists requires a method that can reliably distinguish the bias in signalling due to unbalanced activation of diverse transduction proteins from that of differential amplification inherent to the system being studied, which invariably results from the non-linear nature of biological signalling networks and their measurement. We have systematically compared the performance of seven methods of bias diagnostics, all of which are based on the analysis of concentration-response curves of ligands according to classical receptor theory. We computed bias factors for a number of β-adrenergic agonists by comparing BRET assays of receptor-transducer interactions with Gs, Gi and arrestin. Using the same ligands, we also compared responses at signalling steps originated from the same receptor-transducer interaction, among which no biased efficacy is theoretically possible. In either case, we found a high level of false positive results and a general lack of correlation among methods. Altogether this analysis shows that all tested methods, including some of the most widely used in the literature, fail to distinguish true ligand bias from "system bias" with confidence. We also propose two novel semi quantitative methods of bias diagnostics that appear to be more robust and reliable than currently available strategies.

Original languageEnglish
Article number44247
JournalScientific Reports
Volume7
DOIs
Publication statusPublished - Mar 14 2017

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