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An intact model for quantifying functional selectivity
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 l...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6384912/ https://www.ncbi.nlm.nih.gov/pubmed/30796256 http://dx.doi.org/10.1038/s41598-019-39000-z |
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author | Zhu, Xiao Finlay, David B. Glass, Michelle Duffull, Stephen B. |
author_facet | Zhu, Xiao Finlay, David B. Glass, Michelle Duffull, Stephen B. |
author_sort | Zhu, Xiao |
collection | PubMed |
description | 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-HT(2C) 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. |
format | Online Article Text |
id | pubmed-6384912 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-63849122019-02-26 An intact model for quantifying functional selectivity Zhu, Xiao Finlay, David B. Glass, Michelle Duffull, Stephen B. Sci Rep Article 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-HT(2C) 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. Nature Publishing Group UK 2019-02-22 /pmc/articles/PMC6384912/ /pubmed/30796256 http://dx.doi.org/10.1038/s41598-019-39000-z Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Zhu, Xiao Finlay, David B. Glass, Michelle Duffull, Stephen B. An intact model for quantifying functional selectivity |
title | An intact model for quantifying functional selectivity |
title_full | An intact model for quantifying functional selectivity |
title_fullStr | An intact model for quantifying functional selectivity |
title_full_unstemmed | An intact model for quantifying functional selectivity |
title_short | An intact model for quantifying functional selectivity |
title_sort | intact model for quantifying functional selectivity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6384912/ https://www.ncbi.nlm.nih.gov/pubmed/30796256 http://dx.doi.org/10.1038/s41598-019-39000-z |
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