文章基本信息

标题：Systematic errors in detecting biased agonism: Analysis of current methods and development of a new model-free approach
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作者：H. Ongun Onaran ; Caterina Ambrosio ; Özlem Uğur 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2017
卷号：7
期号：1
DOI：10.1038/srep44247
语种：English
出版社：Springer Nature
摘要：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.