Liang Yang (1), David Finlay (2), Xiao Zhu (1), Hayley Green (2), Michelle Glass (2), Stephen Duffull (1)
(1) Otago Pharmacometrics Group, School of Pharmacy, University of Otago, Dunedin, New Zealand (2) Department of Pharmacology and Toxicology, University of Otago, Dunedin, New Zealand
Objectives: The most intensively studied drug targets are G protein-coupled receptors (GPCRs). There are currently more than 400 drugs targeting GPCRs, accounting for 34% of all FDA approved drugs[1]. GPCRs have complex behaviour patterns where one receptor-type of a GPCR can elicit several different pathways at the same time[2]. Physiological conditions of cells, such as relative abundance of G protein subtypes and regulation of other receptors[3,4], are purported to affect the receptor pathway preference. The ability to selectively stimulate the desired pathways and avoid other pathways remains one of the most important considerations for GPCR-targeted drug discovery and development.
In this study, we consider cannabinoid receptor type 1 (CB1) binding to G-proteins as an example and explore how the competition between Gi and Gs proteins affects pathway preference through modelling. CB1 receptors can couple with both Gi and Gs proteins, thereby either reducing or stimulating cAMP, respectively. Under normal conditions, the CB1 receptor shows a preference for the Gi protein and produces a net inhibition of cAMP production. Previous experimental studies have found that CB1 switches its signalling after activation of agonist CP55940 from Gi preference to Gs preference under three conditions: (1) pertussis toxin block of Gi protein[5] (a standardised control experiment); (2) concurrent stimulation with type 2 dopamine receptor (D2) by quinpirole[5] and (3) a high level expression of CB1 receptor[6].
The third mechanism remains poorly understood. The current hypothesis suggests that when Gi abundance is sufficient relative to receptor abundance, the receptors preferentially bind to Gi, and inhibit cAMP. However, when the receptor abundance exceeds Gi, the excess receptors bind to Gs and stimulate cAMP. A quantitative understanding of this effect is lacking.
The aim of this study was to build a systems pharmacology model to describe the CB1 receptor preference for Gi or Gs proteins and downstream effect on cAMP and use the model to examine the hypothesis about the relative abundance of receptor and overall G protein causes a shift in the preference for Gi versus Gs proteins.
Methods: We developed a mechanistic model based on a set of ordinary differential equations (ODEs) to describe the GPCR signalling process. The model included four components: receptor activation, Gi and Gs protein cycle, adenylyl cyclase activation and, catalysation for cAMP production. Constitutive activity of the CB1 receptor and receptor internalisation were also included in the model. The model consisted of 27 ODEs with 62 parameters. The initial conditions for the model were solved by using a “burn-in” process from an arbitrary set of initial values. The mathematical model was built using SimBiology, MATLAB (2020b). Simulations were performed in MATLAB (2020b) and compared to real data of cAMP assays from a published paper[5,6].
Results: The model parameters were calibrated for normal levels of expression of the receptor and G proteins with an approximate molar ratio of CB1:Gi:Gs=1:1.5:1 as per[7] and a ratio of active Gi vs active Gs of 10:1 was identified based on this process. The model accurately predicted increasing cAMP inhibition (with decreasing cAMP concentrations) with increasing concentration of the agonist CP55940 in HEK cells with standard CB1 expression. The model was also used to predict the effects of PTX pre-treatment, D2 receptor co-stimulation and high CB1 expression (by 2.7-fold). In all cases the model predictions aligned quantitatively with observed real data showing an increase in cAMP concentrations.
The model illustrated that competition of CB1 for Gi and Gs affects pathway preference. When the CB1 receptor is over expressed, there is excess CB1 (relative to Gi) and result in subsequent binding to Gs yielding a final ratio of active Gi vs Gs as 10:4. The increased ratio of active Gs stimulates cAMP production. PTX pre-treatment or co-stimulation of D2 decreases Gi abundance which also frees up CB1 to bind to Gs.
Conclusions: A model describing pathway preference of CB1 receptor was developed. The model mathematically quantified the influence on receptor abundance on pathway preference. This model allows researchers to go beyond simple descriptions of “up-regulation” or “down-regulation”, and help understand the impact of complex nonlinear dynamic relationships on biological systems. We anticipate this model can be used to predict the need and results of future experiments.
References:
[1] Hauser, A. S., Attwood, M. M., Rask-Andersen, M., Schiöth, H. B. & Gloriam, D. E. Trends in GPCR drug discovery: new agents, targets and indications. Nature Reviews Drug Discovery 16, 829–842 (2017).
[2] Marinissen, M. J. & Gutkind, J. S. G-protein-coupled receptors and signaling networks: emerging paradigms. Trends in Pharmacological Sciences 22, 368–376 (2001).
[3] Lu, P. et al. Relative abundance of G protein-coupled receptor 30 and localization in testis and epididymis of sheep at different developmental stages. Anim Reprod Sci 175, 10–17 (2016).
[4] Neubig, R. R. Membrane organization in G-protein mechanisms. FASEB J 8, 939–946 (1994).
[5] Glass, M. & Felder, C. C. Concurrent Stimulation of Cannabinoid CB1 and Dopamine D2 Receptors Augments cAMP Accumulation in Striatal Neurons: Evidence for a Gs Linkage to the CB1 Receptor. J. Neurosci. 17, 5327–5333 (1997).
[6] Finlay, D. B. et al. Gαs signalling of the CB1 receptor and the influence of receptor number. Br J Pharmacol 174, 2545–2562 (2017).
[7] Atwood, B. K., Lopez, J., Wager-Miller, J., Mackie, K. & Straiker, A. Expression of G protein-coupled receptors and related proteins in HEK293, AtT20, BV2, and N18 cell lines as revealed by microarray analysis. BMC Genomics 12, 14 (2011).
Reference: PAGE 29 (2021) Abstr 9713 [www.page-meeting.org/?abstract=9713]
Poster: Drug/Disease Modelling - Other Topics