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Effector membrane translocation biosensors reveal G protein and βarrestin coupling profiles of 100 therapeutically relevant GPCRs

Institute for Research in Immunology and Cancer (IRIC), and Department of Biochemistry and Molecular Medicine, Université de Montréal, Canada
Domain Therapeutics North America, Canada
Department of Drug Design and Pharmacology, University of Copenhagen, Denmark
Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, United States
Pfizer Global R&D, United States
Domain Therapeutics, France

Mar 18, 2022

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Abstract
Editor's evaluation
Introduction
Results
Discussion
Materials and methods
Appendix 1
Data availability
References
Article and author information
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Abstract

The recognition that individual GPCRs can activate multiple signaling pathways has raised the possibility of developing drugs selectively targeting therapeutically relevant ones. This requires tools to determine which G proteins and βarrestins are activated by a given receptor. Here, we present a set of BRET sensors monitoring the activation of the 12 G protein subtypes based on the translocation of their effectors to the plasma membrane (EMTA). Unlike most of the existing detection systems, EMTA does not require modification of receptors or G proteins (except for G_s). EMTA was found to be suitable for the detection of constitutive activity, inverse agonism, biased signaling and polypharmacology. Profiling of 100 therapeutically relevant human GPCRs resulted in 1500 pathway-specific concentration-response curves and revealed a great diversity of coupling profiles ranging from exquisite selectivity to broad promiscuity. Overall, this work describes unique resources for studying the complexities underlying GPCR signaling and pharmacology.

Editor's evaluation

The authors describe a novel set of biosensors to assess the coupling specificity of 100 therapeutically relevant G proteins-coupled receptors (GPCRs) to various G proteins. The utility of the assay system is well-supported by the data. These tools are likely to be useful for many specific studies of individual receptors, including efforts to discover ligands that display functional selectivity (bias) between G protein pathways or between G proteins and arrestins. The work provides a rich repository of data informing on the possible effector coupling of 100 GPCRs and a set of analytical tools that could guide the development of new drugs, including efforts to discover ligands that display functional selectivity (bias) between G protein pathways or between G proteins and arrestins.

https://doi.org/10.7554/eLife.74101.sa0

Introduction

G protein-coupled receptors (GPCRs) play crucial roles in the regulation of a wide variety of physiological processes and represent one-third of clinically prescribed drugs (Hauser et al., 2017). Classically, GPCR-mediated signal transduction was believed to rely on linear signaling pathways whereby a given GPCR selectively activates a single G protein family, defined by the nature of its Gα subunit (Oldham and Hamm, 2008). Gα proteins are divided into four major families (G_s, G_i/o, G_q/11, and G_12/13) encoded by 16 human genes. Once activated, these proteins each trigger different downstream effectors yielding different biological outcomes. It has now become evident that many GPCRs can couple to more than one G protein family and that ligands can selectively promote the activation of different subsets of these pathways (Namkung et al., 2018; Quoyer et al., 2013). These observations extended the concept of ligand-biased signaling, which was first established for ligand-directed selectivity between βarrestin and G protein (Azzi et al., 2003; Wei et al., 2003), to functional selectivity between G proteins. Ligand-directed functional selectivity represents a promising avenue for GPCRs drug discovery since it offers the opportunity of activating pathways important for therapeutic efficacy while minimizing activation of pathways responsible for undesirable side effects (Galandrin et al., 2007; Kenakin, 2019).

To fully explore the potential of functional selectivity, it is essential to have an exhaustive description of the signaling partners that can be activated by a given receptor, providing receptor- and ligand-specific signaling signatures. Currently, few assays allow for an exhaustive pathway-specific analysis of GPCR signaling; these include BRET-based G protein activation sensors platforms (Galés et al., 2005; Masuho et al., 2015; Maziarz et al., 2020; Mende et al., 2018; Olsen et al., 2020) and the TGF-α shedding assay (Inoue et al., 2019). However, several of these platforms require modification of G protein subunits that may create functional distortions. Moreover, these assays may detect non-productive conformational rearrangements of the G protein heterotrimer as was recently reported for G₁₂ (Okashah et al., 2020).

Here, we describe unique sensors that do not require modification of receptors or G proteins (except for G_s) for interrogating the signaling profiles of GPCRs. The platform includes 15 pathway-selective enhanced bystander bioluminescence resonance energy transfer (ebBRET) biosensors monitoring the translocation of downstream effectors to the plasma membrane for G_i/o, G_q/11, and G_12/13, the dissociation of the Gα subunit from the plasma membrane for G_s and the recruitment of βarrestin to the plasma membrane. Overall, the new ebBRET-based Effector Membrane Translocation Assays, named EMTA, provide a readily accessible large scale and comprehensive platform to study constitutive and ligand-directed GPCR signaling. The signaling signatures of 100 GPCRs using the EMTA platform also provides a rich source of information to explore the principles underlying receptor/G protein/βarrestin coupling selectivity relationships. It thus provides a unique set of tools that is complementary to previously described platforms and existing datasets, and offers a map of the coupling potentials for individual GPCR that will stimulate future studies investigating the relevance of these couplings in different physiological systems.

Results

ebBRET-based G protein effector membrane translocation assay (EMTA) allows detection of each Gα protein subunit activation

To detect the activation of Gα subtypes, we created an EMTA biosensor platform based on ebBRET (Namkung et al., 2016; Figure 1A). The biosensors at the heart of EMTA consist of sub-domains of the G protein-effector proteins p63-RhoGEF, Rap1GAP and PDZ-RhoGEF that selectively interact with activated G_q/11, G_i/o, or G_12/13, respectively. These domains were fused at their C-terminus to Renilla luciferase (RlucII) and co-expressed with different unmodified receptor and Gα protein subtypes. Upon GPCR activation, the energy donor-fused effectors translocate to the plasma membrane to bind activated Gα proteins, bringing RlucII in close proximity to the energy acceptor, Renilla green fluorescent protein, targeted to the plasma membrane through a CAAX motif (rGFP-CAAX), thus leading to an increase in ebBRET. The same plasma membrane translocation principle is used to measure βarrestin recruitment (Namkung et al., 2016; Figure 1B, top). Because no selective soluble downstream effector of G_s exists, the assay was modified taking advantage of Gα_s dissociation from the plasma membrane following its activation (Wedegaertner et al., 1996). In this configuration, the RlucII is directly fused to Gα_s (Carr et al., 2014). Its activation upon GPCR stimulation leads to its dissociation from the plasma membrane (Martin and Lambert, 2016), resulting in a reduction in ebBRET (Figure 1B, bottom).

Figure 1

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EMTA ebBRET platform to monitor G protein activation and βarrestin recruitment.

(A) Schematic of the G protein Effector Membrane Translocation Assay (GEMTA) to monitor Gα protein activation. Upon receptor activation, RlucII-tagged effector proteins (Effector-RlucII) translocate towards and interact with active Gα subunits from each G protein family, leading to increased ebBRET. (B) Principle of the Effector Membrane Translocation Assay (EMTA) monitoring βarrestin recruitment to the plasma membrane (*top*) and Gα_s activation (*bottom*). *Top*; upon receptor activation, RlucII-tagged βarrestins (βarrestin-RlucII) translocate to the plasma membrane, thus increasing ebBRET with rGFP-CAAX. *Bottom*; Internalization of activated RlucII-tagged Gα_s (Gα_s-RlucII) following receptor stimulation decreases ebBRET with the membrane-anchored rGFP-CAAX.

The sensitivity and selectivity of the newly created G protein EMTA biosensors, were validated using prototypical GPCRs known to activate specific Gα subtypes. The responses were monitored upon heterologous expression of specific Gα subunits belonging to G_i/o, G_q/11, or G_12/13 families in the absence or presence of pharmacological inhibitors and using engineered cells lacking selected Gα subtypes. The dopamine D₂ receptor was used to validate the ability of the G_i/o binding domain of Rap1GAP (Jordan et al., 1999; Meng et al., 1999) to selectively detect G_i/o activation. The dopamine-promoted increase in ebBRET between Rap1GAP-RlucII and rGFP-CAAX in the presence of Gα_i/o subunits was not affected by the G_q/11-selective inhibitor UBO-QIC (a.k.a., FR900359; Schrage et al., 2015; Figure 2A, left), whereas the Gα_i/o family inhibitor, pertussis toxin (PTX), completely blocked the response for all members of Gα_i/o family except for Gα_z, known to be insensitive to PTX (Casey et al., 1990; Figure 2A, right). Gonadotropin-releasing hormone (GnRH) stimulation of the GnRH receptor (GnRHR), used as a prototypical G_q/11-coupled receptor, promoted ebBRET between the RlucII-fused G_q/11 binding domain of p63-RhoGEF (p63-RhoGEF-RlucII; Lutz et al., 2007; Rojas et al., 2007) and rGFP-CAAX. The ebBRET increase observed in the presence of different Gα_q/11 subunits was not significantly (p = 0.077, 0.0636 and 0.073 for G_q, G₁₁, and G₁₄, respectively) affected by PTX (Figure 2B, right), whereas UBO-QIC completely blocked the response for all members of Gα_q/11 family except for Gα₁₅, known to be insensitive to UBO-QIC (Schrage et al., 2015; Figure 2B, left). These two G protein-specific EMTA were sensitive enough to detect responses elicited by endogenous G proteins since deletion of G_i/o (ΔG_i/o) or G_q/11 (ΔG_q/11) subtypes completely abolished the responses induced by D₂ or GnRHR activation in the absence of heterologously expressed G proteins (Figure 2—figure supplement 1I). It should however be noted that relying on endogenous proteins does not allow the identification of specific members of G_i/o (i.e.: G_i1, G_i2, G_i3, G_oA, G_oB, or G_z) or G_q/11 (i.e.: G_q, G₁₁, G₁₄, or G₁₅) families.

Figure 2 with 6 supplements see all

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Validation of EMTA ebBRET-based platform to monitor Gα protein activation.

(A) Pharmacological validation of the Gα_i/o activation sensor. HEK293 cells were transfected with the D₂ receptor and the Gα_i/o family-specific sensor, along with each Gα_i/o subunit. Concentration-response curve using the Gα_i/o activation sensor, in the presence or absence of UBO-QIC (*left*) or PTX (*right*) inhibitors. *Insets*; E_max values determined from concentration-response curves of inhibitor-pretreated cells. (B) Pharmacological validation of the Gα_q/11 activation sensor. HEK293 cells were transfected with the GnRH receptor and the Gα_q/11 family-specific sensor, along with each Gα_q/11 subunit. Concentration-response curve using Gα_q/11 activation sensor, in the presence or absence of UBO-QIC (*left*) or PTX (*right*) inhibitors. *Insets*; E_max values determined from dose-response curves of inhibitor-pretreated cells. (C) Validation of the Gα_12/13 activation sensor. Cells were transfected with the CB₁ receptor and one of the Gα_12/13 activation sensors, along with the Gα₁₂ or Gα₁₃ subunits. Concentration-response curves of HEK293 cells (*top*) or the parental and devoid of G_12/13 (ΔG_12/13) HEK293 cells (*bottom*) using the PDZ-RhoGEF-RlucII/rGFP-CAAX (*top and bottom left*) or PKN-RBD-RlucII/rGFP-CAAX (*bottom right*) sensors, pretreated or not with UBO-QIC or PTX (*top*). (D) Pharmacological validation of the Gα_s activation sensor. HEK293 cells were transfected with the GPBA receptor and the Gα_s activation (*left and central*) or the EPAC (*right*) sensors. *Left:* Concentration-response curves using the Gα_s activation sensor in the presence or absence of UBO-QIC or PTX, inhibitors of Gα_q or Gα_i/o, respectively. *Central*: Concentration-response activation of the Gα_s sensor using CTX, a Gα_s activator. *Right*: Concentration-response curve using the EPAC sensor. *Inset*; E_max values determined from dose-response curves of inhibitors-pretreated cells. Data are expressed as BRET ratio for the concentration-response curves or expressed in % of respective control cells (E_max graphs) and are the mean ± SEM of 3 (**A–C**) or 4 (D) independent experiments performed in one replicate. Unpaired t-test (**A–D**): *p < 0.05 and ***p < 0.001 compared to control cells.

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The selectivity of the G_12/13 binding domain of PDZ-RhoGEF (Fukuhara et al., 2001) was confirmed using the cannabinoid receptor type 1 (CB₁). The ebBRET between PDZ-RhoGEF-RlucII and rGFP-CAAX in the presence of Gα₁₂ or Gα₁₃ promoted by the cannabinoid agonist WIN-55,212–2 was not affected by UBO-QIC (Figure 2C, top left), nor PTX (Figure 2C, top right). Given the lack of selective G_12/13 pharmacological inhibitor, we used HEK293 cells genetically deleted for Gα₁₂ and Gα₁₃ proteins (ΔG_12/13) to further confirm the response selectivity. As expected, PDZ-RhoGEF-RlucII/rGFP-CAAX ebBRET was observed only following reintroduction of either Gα₁₂ (ΔG_12/13_+G₁₂) or Gα₁₃ (ΔG_12/13_+G₁₃) (Figure 2C, bottom left). The G_12/13 coupling of CB₁ was further confirmed by monitoring the recruitment of PKN to the plasma membrane (Figure 2C, bottom right) in agreement with previous reports (Inoue et al., 2019).

To further assess the selectivity of each EMTA biosensor, we took advantage of the fact that the endothelin-1 receptor (ET_A) can activate G_q/11, G_i/o, and G_12/13 family members. As shown in Figure 2—figure supplement 2, only over-expression of the Gα family members corresponding to their selective effectors (Rap1GAP for G_i/o, p63-RhoGEF for G_q/11, and PDZ-RhoGEF for G_12/13) significantly increased the recruitment of the effector-RlucII to the plasma membrane. A recent study (Chandan et al., 2021) showed that G_i/o can also activate full length PDZ-RhoGEF. Although the domain of PDZ-RhoGEF required for this activation has not been identified yet, the selectivity of our PDZ-RhoGEF sensor for G_12/13 vs. all other G protein families most likely results from the fact that we used a truncated version of PDZ-RhoGEF that only contains the G_12/13 binding domain and lacks the PDZ domain involved in protein-protein interaction, the actin-binding domain and the DH/PH domains involved in GEF activity and RhoA activation (Aittaleb et al., 2010).

It should be noted that in the heterologous expression configuration, competition with endogenous G proteins did not occur to a significant extent since the potencies of the responses to a given G protein subtype were not affected by genetic deletion of the different G protein family members (Figure 2—figure supplement 1 and Supplementary file 1A). Similarly, overexpression of G proteins, GPCRs or effectors-RlucII did not affect the potencies of the responses observed (Figure 2—figure supplement 3 and Supplementary file 1B-D), indicating that, in our experimental conditions, overexpression of the different components of EMTA sensors must likely not bias the coupling response. In addition to spectrometric assessment of coupling selectivity (above) and activation kinetics (Figure 2—figure supplement 4), EMTA allows to image the real-time recruitment of the G protein effectors to the plasma membrane (Videos 1–3) thus providing spatiotemporal resolution for the imaging detection of Gα_i/o, Gα_q/11, and Gα_12/13 activation.

Video 1

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posterframe for video — BRET-based imagery of p63-RhoGEF-RlucII recruitment to the plasma membrane upon AT₁ activation.

HEK293 cells expressing the p63-RhoGEF-RlucII/rGFP-CAAX sensors with Gα_q and AT₁ were stimulated with Angiotensin II. BRET levels (the ratio of the acceptor photon count to the total photon count) are expressed as a color code (lowest being black and purple, and highest being red and white).

Video 2

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Video 3

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The sensitivity of the EMTA platform is illustrated by a direct side-by-side comparison of the signals detected with EMTA vs. BRET assays based on Gαβγ dissociation (Gαβγ) (Galés et al., 2005; Galés et al., 2006; Olsen et al., 2020), that reveals a significantly larger assay windows for EMTA for the 6 Gα subunits tested for eight selected receptors, (Figure 2—figure supplement 5).

For the Gα_s translocation biosensor, the bile acid receptor (GPBA) was chosen for validation (Kawamata et al., 2003). As expected, lithocholic acid stimulation resulted in a concentration-dependent decrease in ebBRET between Gα_s-RlucII and rGFP-CAAX (Figure 2D, left). Cholera toxin (CTX), which directly activates Gα_s (De Haan and Hirst, 2004), led to a decrease in ebBRET (Figure 2D, center), confirming that loss of Gα_s plasma membrane localization results from its activation. The potency of lithocholic acid to promote G_s dissociation from the plasma membrane was well in line with its potency to increase cAMP production as assessed using a BRET²-based EPAC biosensor (Leduc et al., 2009; Figure 2D, right). The G_s-plasma membrane dissociation ebBRET signal was not affected by UBO-QIC or PTX (Figure 2D, left), confirming the selectivity of the biosensor.

Signaling signatures of one hundred therapeutically relevant receptors reveals distinct G protein and βarrestin selectivity profiles

We used EMTA to assess the signaling signature of a panel of 100 human GPCRs that are either already the target of clinically used drugs (74 receptors), considered for pre- or clinical drug development (6 receptors), or pathophysiologically relevant (Supplementary file 2A). To establish the coupling potentials for each receptor, we quantified its ability to activate 15 pathways: Gα_s, Gα_i1, Gα_i2, Gα_oA, Gα_oB, Gα_z, Gα₁₂, Gα₁₃, Gα_q, Gα₁₁, Gα₁₄, Gα₁₅ and βarrestin 2 as well as βarrestin 1 and 2 in the presence of GRK2 (Supplementary file 3). E_max and pEC₅₀ values were determined (Supplementary file 2) and, based on the pre-determined threshold criteria (Emax ≥mean of vehicle-stimulated +2*SD; see Materials and methods), a ‘yes or no’ agonist-dependent activation was assigned to each signaling pathway and summarized using radial graph representations (Figure 3—figure supplement 1). To assess whether endogenous receptors could contribute to the observed responses, assays were also carried out in cells not transfected with the studied receptor (Figure 3—figure supplement 2). When an agonist-promoted response was observed in non-transfected parental HEK293 cells, this response was not considered as a receptor-specific response (see Materials and methods).

To compare the signaling profiles across all receptors and pathways and to overcome differences in receptor expression levels and individual biosensor dynamic windows, we first min-max normalized E_max and pEC₅₀ values (between 0 and 1) across receptors as a function of a reference receptor yielding the largest response for a given pathway (Figure 3A, left). Then, these values were again min-max normalized (between 0 and 1) for the same receptor across pathways, using the pathway with the largest response for this receptor as the reference (Figure 3A, right; see description in Materials and methods). Such double normalization allows direct comparison of the coupling efficiency to different G proteins for a given receptor and across receptors for a given G protein. This coupling efficiency is summarized as heatmaps (Figure 3B) that reveals a high diversity of signaling profiles. The selectivity toward the different G protein families varies considerably among GPCRs (Figure 4). In our dataset, which is the first using unmodified GPCRs and Gα proteins (except for G_s), 29% of the receptors coupled to only one family, whereas others displayed more promiscuity by coupling to 2, 3, or 4 families (36%, 25%, and 10%, respectively). Receptors coupling to a single G protein family favored the members of the G_i/o family. Indeed, 27% of the receptors coupling to G_i/o only activated this subtype family in comparison to 0, 2.4 and 9.1% for receptors activating G_12/13, G_q/11, and G_s, respectively, thus displaying more promiscuous coupling. A detailed comparative analysis of the selectivity profiles that we observed using the EMTA sensors with that of the chimeric G protein-based assay developed by Inoue et al., 2019 and the IUPHAR/BPS Guide to Pharmacology database (GtP; https://www.guidetopharmacology.org/) is presented in the accompanying paper (Hauser et al., 2022). Supplementary file 2C allows a direct comparison of the relative potency determined using EMTA for both the new and the already known (i.e.: identified in GtP database) couplings. As can be seen in the table, although in many cases the potency for the novel couplings is lower, this is not a universal finding since for some receptors, the pEC₅₀s for the new couplings are similar (ex: G₁₂ for CB₁; G₁₃ for serotonin 5-HT_2C; G_12/13 for adenosine 2A (A_2A) and prostaglandin E1 (EP₁) receptors; G_i/o for corticotropin-releasing hormone receptor 1 (CRFR1), ET_A and G protein-coupled receptor 39 (GPR39)) or higher (ex: G_z for serotonin 5-HT_2B; G₁₅ for adenosine 3 (A₃) and melanocortin 3 (MC3R) receptors; G₁₂ for bradykinin 2 (B₂), cholecystokinin A (CCK₁), chemokine receptor 6 (CCR6) and ET_A receptors; G_12/13 for CRFR1 and GPR68) than those for the canonical ones. Interestingly, in many instances the potency for the newly uncovered couplings are similar to those for βarrestins, which is generally lower than for their canonical G proteins, a finding consistent with the role of βarrestins in signaling arrest at the plasma membrane. The potency differences observed for the activation of different G protein subtypes by a given receptor may lead to preferential activation of some pathways over others. This relative selectivity is likely to be influenced by tissue-dependent G protein subtype expression levels. The physiological consequences of such selectivity remain to be investigated.

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Heatmaps illustrating the diversity of receptor-specific signaling signatures detected with the EMTA ebBRET platform.

(A) First, values within each pathway were normalized relative to the maximal response observed across all receptors (max = 1; *left*). These values were then normalized across pathways for the same receptor, with the highest-ranking pathway serving as the reference (max = 1; *right*). (B) Heatmap representation of double normalized E_max (*left*) and pEC₅₀ (*right*) data. Empty cells (grey) indicate no detected coupling. IUPHAR receptor names are displayed.

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The EMTA ebBRET platform has a unique ability to uncover coupling selectivity between G protein families.

(A) Venn diagram showing the numbers of receptors coupled to each G protein family in the EMTA ebBRET biosensor assay. (B) Evaluation of receptors coupling promiscuity: number of receptors that couple to members of 1, 2, 3, or 4 G protein families. (C) Determination of G protein subunit coupling frequency: number of receptors that activate each Gα subunit. (D) Proportion of receptors recruiting βarrestins: number of receptors that do not recruit (-/-) or that recruit either (+/- or -/+) or both (+/+) βarrestin isotypes. All data are based on double normalized E_max values from Figure 3.

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When examining the frequency of coupling for each Gα subunit family (Figure 4C), the G_i/o family members were the most commonly activated, with 89% of the tested receptors activating a G_i/o family member. In contrast, only 33%, 49%, and 45% of the receptors activate G_s, G_12/13, or G_q/11 (excluding Gα₁₅) family members, respectively. Not surprisingly, and consistent with its reported promiscuous coupling, Gα₁₅ was found to be activated by 81% of the receptors. For some receptors, we also observed preferential coupling of distinct members within a subtype family (Figure 3—figure supplement 1). For instance, 33% of G_i/o-coupled receptors can couple to only a subpopulation of the family (Figure 4—figure supplement 1A). For the G_q/11 family, only 44% activate all family members with 45% activating only Gα₁₅ and 11% engaging only two or three members of the family. A matrix expressing the % of receptors engaging a specific Gα subtype that also activated another subtype, is illustrated in Figure 4—figure supplement 1B. When considering individual families, considerable variation within the G_i/o family was observed. The greatest similarities were observed between Gα_oB and either Gα_oA or Gα_z, and the lowest between Gα_i1 and Gα_z. A striking example of intra-family coupling selectivity is the serotonin 5-HT_2B that activates only Gα_oB and Gα_z and GPR65 that selectively activates Gα_oB. Similarly, when considering the ligand-promoted responses above our threshold criteria (see Materials and methods), histamine H₂ and MC3R receptors show preferred activation of Gα_oB and Gα_z, whereas the prostaglandin F (FP) and neuropeptide Y5 (Y₅) receptors preferentially activate Gα_oB, Gα_oA, and Gα_z. Even when all members of a given family are found to be activated, some receptors activate specific family members with greater potencies (Supplementary file 2C).

When considering βarrestin recruitment, our analysis shows that 22% of receptors did not recruit βarrestin 1 or 2, even in the presence of overexpressed GRK2 (Figure 4D). Among the receptors able to recruit βarrestins, only a very small number selectively recruited βarrestin1 (1.3%) or βarrestin2 (6.4%), most of them recruiting both βarrestins in the presence of GRK2 (92.3%) (Figure 4D). Overexpression of GRK2 potentiated the recruitment of βarrestin2 for 68% of receptors highlighting the importance of GRK2 expression level in determining βarrestin activation (Supplementary files 3 and 2).

Comparison with previous datasets reveals commonalities and crucial differences

We compared the signaling profiles obtained here with those presented by Inoue et al., 2019 and the GtP dataset. Of note, this comparison only considers the final reported couplings that in the Inoue’s study were based on the criteria of positive coupling if LogRAi ≥ –1 and negative coupling if LogRAi ≤ –1, and is influenced by the different cut-offs and normalization used in the two studies. A comparison of couplings using common E_max standard deviation cut-off, quantitative normalization and aggregation of G proteins into families is provided in the accompanying paper (Hauser et al., 2022). As can be seen in Supplementary file 4A, among the 70 receptors common to both studies, less couplings were detected in our study than reported in Inoue et al. for Gα_s (21 vs. 28), Gα_i1 (54 vs. 56), Gα_q (31 vs. 34), and Gα₁₄ (36 vs. 40). In contrast, more receptors activating Gα₁₂ (29 vs. 23), Gα_o (59 vs. 41), Gα₁₃ (30 vs. 15), Gα_z (52 vs. 37), and Gα₁₅ (62 vs. 15) were detected in our study. When comparing with data collected in GtP, that reports couplings grouped for G protein families (i.e.: G_s, G_i/o, G_q/11, or G_12/13) and not at the single G protein subtype level, we detected less couplings than what was reported in GtP for Gα_s (32 vs. 37), but more for Gα_i/o (89 vs. 69), Gα_q/11 (81 vs. 48), and Gα_12/13 (47 vs. 10), among the 99 receptors common to both datasets (Supplementary file 4B).

Altogether, the comparative analysis reveals 64% and 69% identity of couplings between the EMTA and Inoue’s or GtP datasets, respectively. Each dataset reporting unique couplings and missing couplings found in the other two datasets. The reasons for these differences are plausibly due to intrinsic differences in the assays used. For instance, for G_12/13 and G₁₅ specifically, the difference with the GtP dataset most likely results from the fact that in most cases G_12/13 or G₁₅ activation were determined indirectly since, until their recent description (G_12/13: Quoyer et al., 2013; Schrage et al., 2015; G₁₅:Inoue et al., 2019; Olsen et al., 2020), no robust readily available assay existed to monitor the activation of these G proteins.

Validation of newly identified G_12/13 and G₁₅ couplings

Given the overrepresentation of both G_12/13 and G₁₅ couplings, obtained with the EMTA assays vs. those reported by Inoue et al. and the GtP datasets, the validity of the EMTA assay to detect real productive couplings, was confirmed using orthogonal assays for selective examples not reported in the two other datasets. For G_12/13, we used the PKN-based BRET biosensor detecting Rho activation downstream of either G_12/13 or G_q/11 (Namkung et al., 2018) and the MyrPB-Ezrin-based BRET biosensor detecting the activation of Ezrin downstream of G_12/13 (Leguay et al., 2021), both in the absence of heterologously expressed G proteins. Ligand stimulation of FP and CysLT₂ receptors led to Rho and ezrin activation (Figure 3—figure supplement 3A), that were insensitive to the G_q/11 inhibitor YM-254890, confirming that these receptors activate Gα_12/13.

For newly identified G₁₅ couplings, we took advantage of the lack of Gα₁₅ in HEK293 cells and assessed the impact of Gα₁₅ heterologous expression on receptor-mediated calcium responses (Figure 3—figure supplement 3B). For prostaglandin E2 (EP₂) and κ-opioid (κOR) receptors, which couple to G₁₅ but no other G_q/11 members, expression of Gα₁₅ significantly increased the PGE2- and Dynorphin A- promoted calcium responses. For α_2A adrenergic (α_2AAR) and vasopressin 2 (V₂) receptors that couple other G_q/11 family members, treatment with YM-254890 completely abolished the agonist-promoted calcium response in the absence of Gα₁₅. In contrast, the calcium response evoked by α_2AAR and V₂ agonists following Gα₁₅ expression was completely insensitive to YM-254890 (Figure 3—figure supplement 3B), confirming that these receptors can activate this YM-254890-insensitive G protein subtype (Takasaki et al., 2004).

EMTA platform detects constitutive receptor activity and biased signaling

We went on to assess the ability of the EMTA platform to detect receptor constitutive activity. Transfection of increasing amounts of adenosine A₁ receptor (A₁) led to a receptor-dependent increase in basal ebBRET of the Gα_i2-activation sensor (Figure 5A, left), reflecting A₁ constitutive activity. The A₁ inverse agonist DPCPX (Lu et al., 2014) dose-dependently decreased the constitutive A₁-mediated activation of Gα_i2 (Figure 5A, left), indicating that EMTA can detect inverse agonism. Although we can not exclude that the high basal activity resulted from activation by adenosine in the cell culture medium, the fact that high basal activity was observed for A₁ but not A₃, despite a similar potency of adenosine to activate these two receptors subtypes (see Figure 5—figure supplement 1A), supports the notion that the increased basal activity reflects A₁ constitutive activity.

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Multiple applications using the EMTA ebBRET platform.

(A) Inverse agonist activity detection. *Left*: Gα_i2 activation in HEK293 cells transfected with the Rap1GAP-RlucII/rGFP-CAAX sensors with untagged Gα_i2 and increasing amount of A₁ receptor plasmid. Data are expressed in % of response obtained in control cells (0 ng of A₁) and are the mean ± SEM of 4–6 independent experiments performed in two replicates. One Way ANOVA test: ***p < 0.001 compared to control cells. HEK293 cells expressing the Gα_i2 activation sensor and control (Mock) or A₁ receptor plasmid were stimulated (10 min) with increasing concentrations of the indicated compound. Data are expressed in % of constitutive response obtained in vehicle-treated A₁ transfected cells and are the mean ± SEM of 4-6 independent experiments performed in one replicate. *Right:* Gα_z activation in HEK293 cells transfected with the Rap1GAP-RlucII/rGFP-CAAX sensors with untagged Gα_z and increasing amount of CB₁ receptor plasmid. Data are expressed in % of response obtained in control cells (0 ng of CB₁) and are the mean ± SEM of 4 independent experiments performed in one replicate. One Way ANOVA test: ***p < 0.001 compared to control cells. HEK293 cells expressing the Gα_z activation sensor and increasing amount of CB₁ receptor plasmid were directly stimulated (10 min) with increasing concentrations of the CB₁ inverse agonist rimonabant. Data are expressed as % of the response obtained in control cells (0 ng of CB₁) treated with vehicle and are the mean ± SEM of 4 independent experiments performed in one replicate. (B) Ligand-biased detection. Concentration-response curves of AT₁ for the endogenous ligand (Angiotensin II, AngII) and biased agonists [Sar1-Ile4-Ile8] AngII (SII), saralasin or TRV027. G protein and βarrestin2 signaling activity were assessed by EMTA platform. Data are expressed in % of maximal response elicited by AngII and are the mean ± SEM of 3–6 independent experiments performed in one replicate. (C) Functional selectivity of naturally occurring receptor variants. Concentration-response curves for WT or E/DRY motif Asp128Asn and Arg129His variants of GPR17 upon agonist stimulation in HEK293 cells co-expressing the indicated EMTA biosensor. Data are expressed in % of maximal response elicited by WT receptor and are the mean ± SEM of 3 independent experiments performed in one replicate.

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To further confirm that the platform can adequately detect inverse agonism, a second receptor for which no endogenous ligand should be present in the media, the CB₁ receptor, was used. As illustrated in Figure 5A (right), increase CB₁ expression led to a ligand-independent constitutive activation of G_z, that could be completely blocked by the CB₁ inverse agonist rimonabant.

EMTA also faithfully detected biased signaling. Indeed, as previously reported (Namkung et al., 2018; Wei et al., 2003), angiotensin analogs such as SII, saralasin or TRV027 displayed biased signaling by promoting efficient βarrestin2 recruitment but marginal or no Gα_q, Gα_i2, or Gα₁₃ activation as compared to angiotensin II that activated all G proteins and βarrestin2 (Figure 5B). The platform was also used to identify biased signaling resulting from single nucleotide polymorphisms. As shown in Figure 5C, two naturally occurring variants of human GPR17 (isoform 2) localised in the TM3 E/DRY motif resulted in altered functional selectivity profiles. Whereas the Asp128Asn variant displayed WT-like activity on Gα_i2, it lost the ability to activate Gα_q and βarrestin2. In contrast, variant Arg129His at the neighboring position resulted in an increased constitutive βarrestin2 recruitment and a loss of Gα_i2 and Gα_q protein signaling.

Combining G_z and G₁₅ biosensors for safety panels and systems pharmacology

The G protein coupling profiles obtained for the 100 GPCRs revealed that 95% of receptors activate either Gα_z (73%) or Gα₁₅ (81%). Measuring activation of both pathways simultaneously provides an almost universal sensor applicable to screening. Combining the two sensors (Rap1GAP-RlucII/p63-RhoGEF-RlucII/rGFP-CAAX) in the same cells allowed to detect ligand concentration-dependent activation of a safety panel of 24 GPCRs, that are well established as contributors to clinical adverse drug reactions (Bowes et al., 2012; Figure 6—figure supplement 1). Indeed, the G_z/G₁₅ sensor captured the activation of receptors largely or uniquely coupled to either Gα_z (e.g. CB₂) or Gα₁₅ (e.g. A_2A and A_2B), as well as receptors coupled (to varying degrees) to both pathways. The usefulness of the G_z/G₁₅ combined sensor to detect off-target ligand activity is illustrated in Figure 6A. Most ligands tested were specific for their primary target(s). However, certain ligands displayed functional cross-reactivity with GPCRs other than their cognate targets. These included the activation of the α_2AAR by dopamine and serotonin, the D₂ by noradrenaline and serotonin, and of the CB₁ and CB₂ receptors by acetylcholine (Figure 6B–C). The activation of D₂ by noradrenaline and serotonin was confirmed by the ability of the D₂-family selective antagonist eticlopride to block the dopamine-, serotonin-, and noradrenaline-promoted responses detected using the combined G_z/G₁₅ or the G_i2- and G_oB-selective sensors and βarrestin2 sensor (Figure 6B, top). Similarly, use of the α₂AR selective antagonist, WB4101, allowed to confirm that dopamine can activate Gα_i2, Gα_oB and βarrestin2 through the α_2AAR (Figure 6B, bottom). Such pleiotropic activation of different monoaminergic receptors by catecholamines and serotonin has been previously observed (Roth et al., 2004; Sánchez-Soto et al., 2016; Sunahara et al., 1991). Direct activation of the α_2AAR by dopamine was confirmed by showing that treatment with the D₂-family receptor selective antagonist eticlopride had negligible effect on dopamine-mediated activation of Gα_i2 and Gα_oB in cells heterologously expressing α_2AAR, confirming that the response did not result from the activation of endogenously expressed dopamine receptor. In contrast, eticlopride blocked the activation of Gα_i2 and Gα_oB in cells heterologously expressing D₂ (Figure 6—figure supplement 2).

Figure 6 with 2 supplements see all

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Detection of direct and indirect (*trans*) mechanisms of ligand polypharmacology using the G_z/G₁₅ biosensor.

(A) Test of the G_z/G₁₅ biosensor on a safety target panel. ebBRET signal was measured before and after stimulation with the indicated ligand in HEK293 cells transfected with the combined G_z/G₁₅ biosensor and one of the 24 receptors listed. (B) Cross-activation of D₂ and α_2AAR by other natural ligands. For the agonist mode read, HEK293 cells expressing D₂ or α_2AAR and either the Gα_i2, Gα_oB, or the βarrestin2 + GRK2 sensors were stimulated with increasing concentrations of the indicated ligand. For the antagonist mode read, cells were pretreated with increasing concentrations of the selective D₂ antagonist eticlopride or the selective α_2AAR antagonist WB4101 before stimulation with an EC₈₀ of the indicated ligand. Data are the mean ± SEM from 3-4 independent experiments performed in one replicate and expressed in % of the response elicited by dopamine or noradrenaline for D₂ and α_2AAR expressing cells, respectively. (C) Indirect (*trans*) activation of CB₁ by acetylcholine. For the agonist mode read, HEK293 cells expressing CB₁ and the Rap1GAP-RlucII/rGFP-CAAX sensors with untagged Gα_oB were stimulated with increasing concentrations of the indicated ligand. For the antagonist mode read, same cells were pretreated or not with increasing concentrations of the CB inverse agonist AM-630 (*left*) or the cholinergic antagonist atropine (*central*) before stimulation with an EC₈₀ of the indicated ligand. To evaluate the contribution of G_q/11-coupled receptor, cells were pretreated with the Gα_q inhibitor UBO-QIC and then stimulated with increasing concentrations of the indicated ligand (*right*). Data are the mean ± SEM from 3-5 independent experiments performed in one replicate and expressed in % of the response elicited by WIN55,212–2.

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These cross-reactivity may be direct (i.e. via direct binding of a ligand to its non-cognate receptor) as suggested above, or indirect (e.g. ‘trans’, via ligand activation of its canonical receptor, leading to subsequent secretion of factors that activate the non-canonical target). One such example of trans-activation is provided by the activation of cannabinoid CB₁ and CB₂ receptors by acetylcholine (detected by the G_z/15 and confirmed with the G_oB sensors; Figure 6A and C). Indeed, the activation was completely inhibited by both the CB inverse agonist AM-630 and by the cholinergic antagonist atropine (Figure 6C, left). Yet the response evoked by the CB selective agonist WIN55,212 2 was not blocked by atropine (Figure 6C, center). Gα_oB activation by acetylcholine did not result from direct activation of endogenous muscarinic receptors since no Gα_oB response was observed in parental cells (Figure 3—figure supplement 2). Given that the M₃ muscarinic receptor, which is endogenously expressed at relatively high levels in HEK293 cells (Atwood et al., 2011), is strongly coupled to the G_q/11, CB₁-expressing cells were pretreated with G_q/11/14 inhibitor UBO-QIC prior to stimulation with acetylcholine. UBO-QIC pre-treatment blocked acetylcholine- but not WIN55,212–2-mediated Gα_oB activation (Figure 6C, right). These results demonstrate that CB₁ activation by acetylcholine is indirect and potentially involves the secretion of an endogenous CBR ligand following activation of G_q/11 by endogenous muscarinic acetylcholine receptors. The combined G_z/G₁₅ sensor is therefore a useful tool to identify interplay between receptors and to explore systems pharmacology resulting from such cross-talks.

Discussion

This study describes the development and validation of a genetically encoded ebBRET-based biosensor platform allowing live-cell mapping of GPCR-G protein coupling preferences covering 12 heterotrimeric G proteins. The novel EMTA biosensors were combined with previously described ebBRET-based βarrestin trafficking sensors (Namkung et al., 2016), providing an unprecedented description of GPCR signaling partner couplings. In addition to providing a resource to study GPCR functional selectivity (Pándy-Szekeres et al., 2022) , the sensors provide versatile and readily usable tools to study, on a large-scale, pharmacological processes such as constitutive activity, inverse agonism, ligand-biased signaling, and signaling cross-talk.

Our EMTA-based biosensor platform offers several advantages relative to other available approaches. First, EMTA provides direct real-time measurement of proximal signaling events following GPCR activation (i.e. Gα protein activation and βarrestin recruitment) and resulting in lower level of amplification than those of assays relying on enzymatic activity of downstream effectors (i.e.: adenylyl cyclase or phospholipase C) or artificial detection systems (i.e.: gene-reporter or TGF-α shedding assays) that measure signal accumulation sometimes following extended incubation times. In addition, measuring proximal activity reduces the risk of cross-talks between pathways that may complicate data interpretation when considering downstream signaling as the readout (Mancini et al., 2015).

Second, EMTA uses native untagged GPCRs and G protein subunits (except for G_s), contrary to protein complementation (Laschet et al., 2019), FRET/BRET-based Gαβγ dissociation/receptor-G protein interaction (Bünemann et al., 2003; Galés et al., 2005; Galés et al., 2006; Hoffmann et al., 2005; Namkung et al., 2018; Olsen et al., 2020) or TGF-α shedding (Inoue et al., 2019) assays. Modifying these core-signaling components could alter responses, complicate interpretation and explain some of the discrepancies observed between the EMTA platform and other approaches used to study G protein activation. Moreover, the ability to work with unmodified receptors and G proteins (except for G_s) offers numerous advantages. First, it allows for the detection of endogenous GPCR signaling in either generic HEK293 cells (Figure 3—figure supplement 2) or more physiologically relevant cell lines such as induced pluripotent stem cell (iPSC)-derived cardiomyocytes (Figure 7A) and promyelocytic HL-60 cells (Figure 7B). Further it allows, in cells expressing sufficient endogenous level of the G proteins of interest, to detect activation of both native receptor and G proteins with no need of overexpression (Figure 7C–D). This is illustrated by the ability to detect the recruitment of Rap1GAP upon activation of the endogenous G_i/o family members by the formyl peptide receptor 2 (FPR2) in HL-60 cells (Figure 7C) or protease-activated receptor-2 (PAR2) in HEK293 cells (Figure 7D). The ability to detect the activation of endogenous G protein was also illustrated in Figure 2—figure supplement 1I, where the responses elicited by agonist stimulation were lost in cells genetically deleted of the G protein engaged by the studied receptor (i.e.: G_q/11 or G_i/o families). Recently, another BRET-based approach (Maziarz et al., 2020), taking advantage of a synthetic peptide recognizing the GTP-bound form of Gα subunits, also allows the detection of native G protein activation, offering alternative means to probe coupling selectivity profiles for both endogenously and heterologously expressed GPCRs.

Figure 7

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Detection of endogenous receptor- and/or G protein-mediated responses in cells with the EMTA ebBRET platform.

Concentration-dependent activation of Gα_i2 protein by (A) endogenous S1P₁ receptor in iPSC-derived cardiomyocytes transfected with heterologous Gα_i2, (B) endogenous FPR2 in promyelocytic HL-60 cells transfected with heterologous Gα_i2, (C) endogenous FPR2 in promyelocytic HL-60 cells with endogenous G_i/o proteins and (D) endogenous PAR2 receptor in HEK293 cells with endogenous G_i/o proteins. In all cases, cells were co-transfected with the Rap1GAP-RlucII/rGFP-CAAX biosensor. Data are the mean ± SEM of 3-4 independent experiments performed in one replicate and are expressed as BRET² ratio in percentage of response induced by vehicle.

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Finally, similarly to BERKY, the EMTA assay platform detects the active form of the Gα subunits rather than the surrogate measurement of Gαβγ dissociation (Galés et al., 2005; Masuho et al., 2015; Maziarz et al., 2020; Mende et al., 2018), which can also detect non-productive binding as recently described for the V₂ engagement of G₁₂ (Okashah et al., 2020).

A potential caveat of EMTA is the use of common downstream effectors for all members of a given G protein family. Indeed, one cannot exclude that distinct members of a given family may display different relative affinities for their common effector. However, such differences are compensated by our data normalization that establishes the maximal response observed for a given subtype as the reference for this pathway (Figure 3A), as long as the number of the diversity of receptors included in the analysis is sufficient.

A second potential caveat of EMTA is that, when using heterologously expressed GPCRs and G proteins, some of the responses could result from favorable stoichiometries that may not exist under physiological conditions. It follows that such profiling represents the coupling possibilities of a given GPCR and not necessarily the coupling that will be observed in all cell types. Any couplings observed in such high-throughput studies requires further validation to conclude on their physiological relevance in cells or tissues of interest, and to form hypothesis for futures studies. Because we elected to use unmodified receptors (i.e.: not bearing any tags), the expression level of receptors could not be directly monitored. However, the double normalization method developed (see Materials and methods) allows quantitative comparison of coupling preferences across different receptors curtailing the influence of the assay response windows as well as receptor expression levels. Indeed, the double normalization allows ranking the coupling propensity of the receptors first as a function of the receptor which shows the strongest coupling to a specific G protein subtype, and then establishing the maximal response observed for a given G protein subtype as the reference for all G protein activated by a given receptor. In addition, as illustrated using the ET_A receptor as example, titrating receptor levels did not influence the pEC₅₀ for the activation of the different G protein coupled to this receptor (Figure 2—figure supplement 3B and Supplementary file 1C). Similarly, the pEC₅₀ was not affected when titrating the amount of G protein subtype expressed (Figure 2—figure supplement 3A and Supplementary file 1B). As expected, only the amplitude of the response was affected.

It could be argued that overexpressing the G protein effectors (i.e.: p63-RhoGEF, Rap1GAP or PDZ-RhoGEF) used as sensors could influence the couplings observed. This potential caveat is mitigated by the fact that we used truncated part and/or modified versions of these effectors that limit the possibilities of interference with other components of the signaling machinery, and served essentially as a binding detector of the active forms of the G proteins (see Materials and methods). Supporting this notion, titrating the amount of the biosensor effector component did not affect the pEC₅₀ of G protein activation (Figure 2—figure supplement 3C and Supplementary file 1D).

Another limitation of the EMTA platform is the lack of a soluble effector protein selective for activated Gα_s thus requiring tagging of the Gα_s subunit (Figure 1B, bottom) and monitoring its dissociation from the plasma membrane. Yet, our data show that this translocation reflects G_s activation state, justifying its use in a G protein activation detection platform.

Finally, because EMTA is able to detect constitutive activity, high receptor expression levels may lead to an elevated basal signal level that may obscure an agonist-promoted response. Such an example can be appreciated for the A₁ receptor for which the agonist-promoted Gα_i2 response did not reach the activation threshold criteria because of a very high constitutive activity level (Figure 5A). The impact of receptor expression on the constitutive activity and the narrowing on the agonist-promoted response is illustrated for Gα_q activation by the 5-HT_2C (Figure 5—figure supplement 1B).

A limitation of any large-scale signaling study and drug discovery program is that ligands may elicit responses downstream of receptors other than the one under study. The development of a G_z/G₁₅ quasi-universal biosensor enables efficient screening and detection of such polypharmacology and cross-talk. Using a combination of EMTA and appropriate pharmacological tools, we also proposed a systematic approach to distinguish off-target action of ligands from cross-talk. Interestingly, the cross-talk between the M₃ and CB receptors detected (Figure 6) may have physiological relevance since activation of muscarinic acetylcholine receptors has been shown to enhance the release of endocannabinoids in the hippocampus (Kim et al., 2002). The combined G_z/G₁₅ biosensor should be particularly useful for early profiling of compound activity on safety panels and for the design of drugs displaying polypharmacology, an approach that is increasingly considered for the development of neuropsychiatric drugs (Roth et al., 2004).

The EMTA platform undoubtedly represents a novel tool-set that could be amenable for high throughput screening of small molecules and biologics across an array of signaling pathways, allowing for the discovery of functionally selective molecules or for GPCR deorphanization campaigns. The ability of the EMTA platform to quantitatively assess G protein coupling selectivity firmly expands the concept of functional selectivity and potential ligand bias beyond the dichotomic G protein vs. βarrestin view and provides plausible functional selectivity profiles that could be tested for their biological and pharmacological outcomes.

Materials and methods

Cells

HEK293 clonal cell line (HEK293SL cells), hereafter referred as HEK293 cells, were a gift from S. Laporte (McGill University, Montreal, Quebec, Canada) and previously described (Namkung et al., 2016). HEK293 cells devoid of functional Gα_s (ΔG_s), Gα₁₂ and Gα₁₃ (ΔG_12/13), Gα_q, Gα₁₁, Gα₁₄ and Gα₁₅ (ΔG_q/11) and, Gα_i, and Gα_o (ΔG_i/o) proteins were a gift from Dr. A. Inoue (Tohoku University, Sendai, Miyagi, Japan) and previously described (Devost et al., 2017; Namkung et al., 2018; Schrage et al., 2015; Stallaert et al., 2017). Cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM, Wisent, Saint-Jean-Baptiste, QC, Canada) supplemented with 10% fetal bovine serum (FBS, Wisent) and 1% antibiotics (100 U/mL penicillin and 100 μg/mL streptomycin (PS); Wisent). HL-60 cells were obtained from ATCC and maintained in RPMI 1640 medium containing L-Glutamine and 25 mM HEPES (Gibco) supplemented with 20% FBS (Wisent) and 1/100 volume PS (Wisent). Differentiation of HL-60 cells into neutrophil-like cells was induced by maintaining the cells in growth medium containing 1.3% DMSO (Bioshop) during 5 days. Cardiomyocytes derived from induced pluripotent stem cells (iPSCs; iCell Cardiomyocytes) were obtained from FUJIFILM Cellular Dynamics (Madison, WI, USA) and maintained in maintenance medium provided with the cells (special formulation by FujiFilm). Cells were grown at 37 °C in 5% CO₂ and 90% humidity and checked for mycoplasma contamination.

Plasmids and ebBRET biosensor constructs

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Only human GPCRs and human Gα subunits were used in this study. An open reading frame of each full-length GPCR was cloned into pcDNA3.1(+) expression plasmid. Except when otherwise specified, GPCRs sequences were devoid of epitope tags.

Gα_s-67-RlucII (Carr et al., 2014), Gα_i1-loop-RlucII and GFP10-Gγ₁ (Armando et al., 2014), Gα_i2-loop-RlucII and βarrestin2-RlucII (Quoyer et al., 2013), Gα_oB-99-RlucII (Mende et al., 2018), Gα_q-118-RlucII (Breton et al., 2010), Gα₁₂-136-RlucII and PKN-RBD-RlucII (Namkung et al., 2018), Gα₁₃-130-RlucII (Avet et al., 2020), GFP10-Gγ₂ (Galés et al., 2006), βarrestin1-RlucII (Zimmerman et al., 2012), rGFP-CAAX (Namkung et al., 2016), EPAC (Leduc et al., 2009), MyrPB-Ezrin-RlucII (Leguay et al., 2021), HA-β₂AR (Lavoie et al., 2002), signal peptide-Flag-AT₁ (Goupil et al., 2015), and EAAC-1 (Brabet et al., 1998) were previously described. Full-length, untagged Gα subunits, Gβ₁ and Gγ₉ were purchased from cDNA Resource Center. GRK2 was generously provided by Dr. Antonio De Blasi (Istituto Neurologico Mediterraneo Neuromed, Pozzilli, Italy).

To selectively detect G_i/o activation, a construct coding for aa 1–442 of Rap1 GTPase-activating protein (comprising a G_i/o binding domain) fused to Rluc8, was sequence-optimized, synthetized and subcloned at TopGenetech (St-Laurent, QC, Canada). From this construct, a RlucII-tagged version of Rap1GAP (1-442) with a linker sequence (GSAGTGGRAIDIKLPAT) between Rap1GAP and RlucII was created by Gibson assembly in pCDNA3.1_Hygro (+) GFP10-RlucII, replacing GFP10. Three substitutions (i.e. S437A/S439A/S441A) were introduced into the Rap1GAP sequence by PCR-mediated mutagenesis. These putative (S437 and S439) and documented (S441) (McAvoy et al., 2009) protein kinase A phosphorylation sites were removed in order to eliminate any G_s-mediated Rap1GAP recruitment to the plasma-membrane.

To selectively detect G_q/11 activation, a construct encoding the G_q binding domain of the human p63 Rho guanine nucleotide exchange factor (p63RhoGEF; residues: 295–502) tagged with RlucII was done from IMAGE clones (OpenBiosystems; Burlington, ON, Canada) and subcloned by Gibson assembly in pCDNA3.1_Hygro (+) GFP10-RlucII, replacing GFP10. The G_q binding domain of p63RhoGEF and RlucII were separated by the peptidic linker ASGSAGTGGRAIDIKLPAT. N-term part containing palmitoylation sites maintaining p63 to plasma membrane and part of its DH domain involved in RhoA binding/activation (Aittaleb et al., 2010; Aittaleb et al., 2011) are absent of the sensor.

To selectively detect G_12/13 activation, a construct encoding the G_12/13 binding domain of the human PDZ-RhoGEF (residues: 281–483) tagged with RlucII was done by PCR amplification from IMAGE clones (OpenBiosystems) and subcloned by Gibson assembly in pCDNA3.1_Hygro (+) GFP10-RlucII, replacing GFP10. The peptidic linker GIRLREALKLPAT is present between RlucII and the G_12/13 binding domain of PDZ-RhoGEF. The sensor is lacking the PDZ domain of PDZ-RhoGEF involved in protein-protein interaction, as well as actin-binding domain and DH/PH domains involved in GEF activity and RhoA activation (Aittaleb et al., 2010).

The sequence of each EMTA biosensors is provided in the Supplementary file 5.

Transfection

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For BRET experiments, HEK293 cells (1.2 mL at 3.5 × 10⁵ cells per mL) were transfected with a fixed final amount of pre-mixed biosensor-encoding DNA (0.57 μg, adjusted with salmon sperm DNA; Invitrogen) and human receptor DNA. Transfections were performed using a polyethylenimine solution (PEI, 1 mg/mL; Polysciences, Warrington, PA, USA) diluted in NaCl (150 mM, pH 7.0; 3:1 PEI/DNA ratio). Gelatin solution (1%; Sigma-Aldrich, Saint-Louis, Missouri) was used to stabilize DNA/PEI transfection mixes. Following addition of cells to the stabilized DNA/PEI transfection mix, cells were immediately seeded (3.5 × 10⁴ cells/well) into 96-well white microplates (Greiner Bio-one; Monroe, NC, USA) and maintained in culture for the next 48 hr in DMEM containing 2% FBS and 1% PS. DMEM medium without L-glutamine (Wisent) was used for transfection of cells with mGluR to avoid receptor activation and desensitization. For Neutrophil-like differentiated HL-60 cells, cells were resuspended in electroporation medium (growth medium containing an extra 15 mM of HEPES pH 7.0) at 25 × 10⁶ cells/mL. Electroporation reactions were prepared by adding 50 µL of DNA mastermix (20 µg total of DNA adjusted with salmon sperm DNA, supplemented with 210 mM NaCl) to 200 µL of cell suspension and transferring into 0.4 cm gap electroporation cuvettes (Bio-Rad). The cells were electroporated at 350 µF/400 V using a Bio-Rad Gene Pulser II electroporation system, washed in electroporation medium, and seeded in 96-well plates at 0.8 × 10⁶ cells/well in 200 µL of growth medium. BRET assays were performed 6 hr post-electroporation. For iPSC Cardiomyocytes, cells were seeded in 96-well plates pretreated with fibronectin (10 µg/ml 60 min; Sigma-Aldrich) at 3.5 × 10⁴ cells /well. After 48 hr, attached iPSCs cells were transfected with the indicated biosensor components, using TransIT-LT1 reagent (Mirus; Madison, WI, USA), according to manufacturer recommendation. BRET assays were performed 48 hr after transfection.

For Ca²⁺ experiments, cells (3.5 × 10⁴ cells/well) were co-transfected with the indicated receptor, with or without Gα₁₅ protein, using PEI and seeded in poly-ornithine-coated 96-well clear-bottom black microplates (Greiner Bio-one) and maintained in culture for the next 48 hr.

For BRET-based imagery, cells (4 × 10⁵ cells/dish) were seeded into 35 mm poly-d-lysine-coated glass-bottom culture dishes (Mattek Corporation; Ashland, MA, USA) in 2 ml of fresh medium and incubated at 37 °C in 5% CO₂, 3 day before imaging experiments. Twenty-four hours later, cells were transfected with EMTA ebBRET biosensors and the indicated receptor (i.e. p63-RhoGEF-RlucII/rGFP-CAAX + Gα_q and AT₁, Rap1GAP-RlucII/rGFP-CAAX + Gα_i2 and D₂ or PDZ-RhoGEF-RlucII/rGFP-CAAX + Gα₁₃ and TPαR) using X-tremeGENE 9 DNA transfection reagent (3:1 reagent/DNA ratio; Roche) diluted in OptiMEM (Gibco) and maintained in culture for the next 48 hr in DMEM containing 10% FBS and 1% PS.

Bioluminescence resonance energy transfer measurement

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Enhanced bystander BRET (ebBRET) was used to monitor the activation of each Gα protein, as well as βarrestin 1 and 2 recruitment to the plasma membrane. Gα_s protein activation was measured between the plasma membrane marker rGFP-CAAX and human Gα_s-RlucII in the presence of human Gβ₁, Gγ₉ and the tested receptor. Gα_s downstream cAMP production was determined using the EPAC biosensor and GPBA receptor. Gα_i/o protein family activation was followed using the selective-G_i/o effector Rap1GAP-RlucII and rGFP-CAAX along with the human Gα_i1, Gα_i2, Gα_oA, Gα_oB, or Gα_z subunits and the tested receptor. Gα_q/11 protein family activation was determined using the selective-G_q/11 effector p63-RhoGEF-RlucII and rGFP-CAAX along with the human Gα_q, Gα₁₁, Gα₁₄, or Gα_15/16 subunits and the tested receptor. Gα_12/13 protein family activation was monitored using the selective-G_12/13 effector PDZ-RhoGEF-RlucII and rGFP-CAAX in the presence of either Gα₁₂ or Gα₁₃ and the tested receptor. The expression level of the Gα subunits was monitored by western blot in HEK293 cells that endogenously expressed Gα_i1, Gα_i2, Gα₁₂, Gα₁₃, Gα_q, Gα₁₁, Gα₁₄, and Gαs but not Gα_oA, Gα_oB, Gα_z, and Gα₁₅ (Figure 2—figure supplement 6). Gα_12/13-downstream activation of the Rho pathway was measured using PKN-RBD-RlucII or Ezrin-RlucII and rGFP-CAAX with the indicated receptor. βarrestin recruitment to the plasma membrane was determined using DNA mix containing rGFP-CAAX and βarrestin1-RlucII with GRK2 or βarrestin2-RlucII alone or with GRK2 and the tested receptor. Glutamate transporters EAAC-1 and EAAT-1 were systematically co-transfected with the mGluR to prevent receptor activation and desensitization by glutamate secreted in the medium by the cells (Brabet et al., 1998). All ligands were also tested for potential activation of endogenous receptors by transfecting the biosensors without receptor DNA. The G_z/G₁₅ biosensor consists of a combination of the following plasmids: rGFP-CAAX, Rap1GAP-RlucII, Gα_z, p63-RhoGEF-RlucII and Gα₁₅. For G protein activation detection using the BRET-based Gαβγ dissociation sensors, cells were co-transfected with untagged Gβ₁ and Gα_q-118-RlucII, Gα₁₂-136-RlucII or Gα₁₃-130-RlucII with GFP10-Gγ₁, or Gα_i1-loop-RlucII, Gα_i2-loop-RlucII or Gα_oB-99-RlucII with GFP10-Gγ₂, along with the indicated receptor.

The day of the BRET experiment, cells were incubated in HBSS for 1 hr at room temperature (RT). Cells were then co-treated with increasing concentrations of ligand (see Appendix 1—key resources table and Supplementary file 2 for details) and the luciferase substrate coelenterazine prolume purple (1 µM, NanoLight Technologies; Pinetop, AZ, USA) for 10 min at RT. Plates were read on a Synergy Neo microplate reader (BioTek Instruments, Inc; Winooski, VT, USA) equipped with 410 ± 80 nm donor and 515 ± 30 nm acceptor filters or with a Spark microplate reader (Tecan; Männedorf, Switzerland) using the BRET² manufacturer settings. The BRET signal (BRET²) was determined by calculating the ratio of the light intensity emitted by the acceptor over the light intensity emitted by the donor. To validate the specificity of the biosensor responses, cells were pretreated in the absence or presence of either the Gα_q inhibitor UBO-QIC (100 nM, 30 min; Institute for Pharmaceutical Biology of the University of Bonn, Germany), the Gα_i/o inhibitor PTX (100 ng/mL, 18 hr; List Biological Laboratories, Campbell, California, USA) or the Gα_s activator CTX (0–200 ng/mL, 4 hr; Sigma-Aldrich) before stimulation with agonist. For inverse agonist activity detection of A₁ or CB₁ receptors, cells were stimulated during 10 min with increasing concentrations of DPCPX or rimonabant, respectively. For ligand-cross receptor activation experiments, cells were pretreated for 10 min with increasing concentrations of antagonists or inverse agonist (eticlopride for D₂, WB4101 for α_2AAR, atropine for muscarinic receptors and AM-630 for CB₁) before a 10 min stimulation with an EC₈₀ concentration of the indicated agonist. BRET was measured as described above. For the safety target panel ligand screen using the combined G_z/G₁₅ sensor, basal ebBRET level was first measured 10 min following the addition of coelenterazine prolume purple (1 µM) and ebBRET level was measured again following a 10 min stimulation with a single dose of the indicated ligand (1 μM for endothelin-1 and 10 μM for all other ligands). Technical replicates for each receptor were included on the same 96-well plate. For kinetics experiment of ET_A activation, basal BRET was measured during 150 s before cells stimulation with either vehicle (DMSO) or 1 µM of endothelin-1 (at time 0 sec) and BRET signal was recorded each 30 s during 3570 s. For the validation of G_12/13-mediated signal by new identified G_12/13-coupled receptor using PKN- or Ezrin-based BRET biosensors, cells were pretreated or not with the Gα_q inhibitor YM-254890 (1 µM, 30 min; Wako Pure Chemical Industries (Fujifilm), Osaka, Japan) before agonist stimulation for 10 min. For G protein activation detection using the BRET-based Gαβγ dissociation sensors, and for titration experiments of either Gα proteins subunit with GEMTA sensors, GPCRs with GEMTA sensors or Effector-RlucII (p63-RhoGEF-RlucII for Gα_q/11, Rap1GAP-RlucII for Gα_i/o or PDZ-RhoGEF-RlucII for Gα_12/13) from GEMTA sensors, cells were stimulated with increasing concentrations of the indicated agonist in the presence of prolume purple for 10 min before BRET measurement. For BRET in iPSC cardiomyocytes and HL-60 cells, cells were incubated in Tyrode Hepes buffer (137 mM NaCl, 0.9 mM KCl, 1 mM MgCl₂, 11.9 mM NaHCO₃, 3.6 mM NaH₂PO₄, 25 mM HEPES, 5.5 mM D-Glucose and 1 mM CaCl₂, pH 7.4) 30 min at RT before being treated with increasing concentrations of agonist for 15 min, using prolume purple (2 µM) as luciferase substrate, and BRET measured.

BRET data analyses and coupling efficiency evaluation

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All BRET ratios were standardized using the equation below and represented as universal BRET (uBRET) values: uBRET = ((BRET ratio – A)/(B-A)) * 10,000. Constants A and B correspond to the following values:

A = pre-established BRET ratio obtained from transfection of negative control (vector coding for RlucII alone).

B = pre-established BRET ratio obtained from transfection of positive control (vector coding for a GFP10-RlucII fusion protein).

For a given signaling pathway, uBRET values at each agonist concentration were normalized as the % of the response obtained in the absence of agonist (vehicle) and concentration-response curves were fitted in GraphPad Prism 8.3 software using a four-parameter logistic nonlinear regression model. Results are expressed as mean ± SEM of at least three independent experiments.

A ligand-promoted response was considered real when the E_max value was ≥to the mean + 2*SD of the response obtained in vehicle condition and that a pEC₅₀ value could be determined in the agonist concentration range used to stimulate the receptor. Consequently, a score of 0 or 1 was assigned to each signaling pathway depending on an agonist’s ability to activate the tested pathway (0 = no activation; 1 = activation). In the case were responses associated to endogenous receptor were detectable, we considered as ‘distorted’ and exclude all the responses observed in the presence of transfected receptor for which E_max was ≤to 2*mean of the E_max value obtained with endogenous receptors or pEC₅₀ was ≥to 2*mean of the pEC₅₀ value obtained with endogenous receptors. Consequently, a score of 0 was assigned for these distorted responses in radial graph representation (Figure 3—figure supplement 1) and concentration-response curves were placed on a gray background in signaling signature profile panels (Supplementary file 3). Whenever transfected receptors produced an increase in E_max or a left-shift in pEC₅₀ values compared to endogenous receptors, responses were considered ‘true’ and were assigned with a score of 1 for radial graph representation (Figure 3—figure supplement 1) and concentration-response curves were placed on a yellow background in signaling signature profile panels to indicate a partial contribution of endogenous receptors (Supplementary file 3).

We used a double normalization of E_max and pEC₅₀ values to compare the signaling efficiency obtained for the 100 GPCRs across all receptors and pathways. E_max and pEC₅₀ values deduced from concentration-response curves were first normalized between 0 and 1 across receptors by ranking the receptors as a function of the receptor that most efficiently activate a given pathway and then using the activation value for the pathway (including G protein and βarrestin subtypes) that a given receptor most efficiently activate as a reference for the other pathways that can be activated by this receptor. This double normalization can be translated in the following formalized equation:

STEP1: For each receptor and for each pathway:

$[\frac{E_{m a x} {G P C R}_{x}}{E_{m a x} {G P C R}_{R e f}}]$ _{Pathway A} = Pathway specific normalized score for GPCR_x on pathway A ([PSNS GPCR_x]_{Pathway A})

where: GPCR_x is receptor being analyzed, GPCR_Ref is the receptor giving greatest E_max on pathway A of all receptors studied (i.e. reference receptor for pathway A). A PSNS was determined for every receptor and every pathway coupled to that receptor.
STEP2: For any given receptor:

$\frac{[P S N S {G P C R}_{x}]_{P a t h w a y A}}{[P S N S {G P C R}_{x}]_{R e f p a t h w a y}}$ = Normalized pathway A coupling score for GPCR_x

where: [PSNS GPCR_x] _{Pathway A} is the pathway specific normalized score for GPCR_x on pathway A, and [PSNS GPCR_x] _{Ref pathway} is the pathway specific normalized score for the pathway giving the highest PSNS for GPCR_x (i.e., reference pathway for GPCR_x).

For the safety target panel ligand screen using the combined G_z/G₁₅ sensor, the fold ligand-induced stimulation was calculated for each receptor by dividing the BRET ratio after ligand addition (measured at 10 min post stimulation) by the basal BRET ratio prior to receptor stimulation. Activation thresholds were defined as the mean + 2*SD of the ligand-stimulated response obtained with receptor-null cells expressing only the combined G_z/G₁₅ sensor.

Ca²⁺ mobilization assay

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The day of the experiment, cells were incubated with 100 μL of a Ca²⁺-sensitive dye-loading buffer (FLIPR calcium five assay kit, Molecular Devices; Sunnyvale, CA, USA) containing 2.5 mM probenecid (Sigma-Aldrich) for 1 hr at 37 °C in a 5% CO₂ incubator. During a data run, cells in individual wells were exposed to an EC₈₀ concentration of agonist, and fluorescent signals were recorded every 1.5 s for 3 min using the FlexStation II microplate reader (Molecular Devices). For receptors that also activate other G_q/11 family members, cells were pretreated with the G_q/11 inhibitor YM-254890 (1 µM, 30 min) before agonist stimulation. Gα₁₅ is resistant to inhibition by YM-254890, thus allowing to measure Ca²⁺ responses generated specifically by Gα₁₅.

BRET-based imaging

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BRET images were obtained as previously described (Kobayashi et al., 2019). Briefly, the day of imaging experiment, cells were carefully rinsed with HBSS, and images were acquired before and after agonists addition (100 nM for Angiotensin II and U46619, and 1 µM for dopamine) diluted in HBSS in the presence of the luciferase substrate coelenterazine prolume purple (20 µM).

Images were recorded using an inverted microscope (Nikon Eclipse Ti-U) equipped with x60 objective lens (Nikon CFI Apochromat TIRF) and EM-CCD camera (Nuvu HNu 512). Measurements were carried out in photon counting mode with EM gain 3000. Exposure time of each photon counting was 100ms. Successive 100 frames were acquired alternatively with 480 nm longpass filter (acceptor frames) or without filter (total luminescence frames), and integrated. Image integrations were repeated 10 times and 1000 frames (Video 1) or 5 times and 500 frames (Videos 2 and 3) of acceptor and total luminescence were used to generate each image.

BRET values were obtained by dividing acceptor counts by total luminescence counts pixelwise. BRET values from 0.0 to 0.8 (Video 1) or 0.0–0.5 (Videos 2 and 3) were allocated to ‘jet’ heatmap array using MATLAB 2019b. Brightness of each pixel was mapped from the signal level of total luminescence image. 0% and 99.9% signal strength were allocated to the lowest and highest brightness to exclude the influence of defective pixels with gamma correction factor of 2.0.

The movies were generated using ImageJ 1.52 a. Frame rate is 10 (Video 1) or 3 (Videos 2 and 3) frames/s, and frame interval is 20 or 100 s for Videos 1 and 2–3, respectively. The field of view of the movie is 137 µm x 137 µm.

Western blot analysis

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Cells were transfected or not with the indicated biosensors mix as previously described and whole-cell extracts were prepared 48 hr later. Briefly, cells were washed with ice-cold PBS and lysed in a buffer containing 10 mM Tris buffer (pH 7.4), 100 mM NaCl, 1 mM EDTA, 1 mM EGTA, 0.1% SDS, 1% Triton X-100, 10% Glycerol supplemented with protease inhibitors cocktails (Thermo Fisher Scientific). Cell lysates were centrifuged at 13,000 × g for 30 min at 4 °C. Equal amounts of proteins were separated by SDS-PAGE and transferred onto polyvinylidene fluoride membrane. The membranes were blocked (1 hr incubation at RT in TBS, 0.1% Tween-20, 5% BSA) and successively probed with primary antibody and appropriate goat secondary antibodies coupled to horseradish peroxidase (see Appendix 1-Key Resources Table). Western blots were visualized using enhanced chemiluminescence and detection was performed using a ChemiDoc MP Imaging System (BioRad). Relative densitometry analysis on protein bands was performed using MultiGauge software (Fujifilm). Results were normalized against control bands.

Statistical analyses

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Curve fitting and statistical analyses were performed using GraphPad Prism 8.3 software and methods are described in the legends of the figures. Significance was determined as p < 0.05.

Appendix 1

Key Resources Table

Appendix 1—key resources table

Reagent type (species) or resource	Designation	Source or reference	Identifiers	Additional information
Cell line (Homo-sapiens)	HEK293	10.1038/ncomms12178 (Namkung et al., 2016)		HEK293 clonal cell line (HEK293SL cells)
Cell line (Homo-sapiens)	ΔG_s HEK293 cells	Dr. A. Inoue (Tohoku University, Sendai, Miyagi, Japan) 10.1124 /mol .116.106419 (Stallaert et al., 2017)		HEK293 cells devoid of functional Gα_s
Cell line (Homo-sapiens)	ΔG_12/13 HEK293 cells	Dr. A. Inoue (Tohoku University, Sendai, Miyagi, Japan) 10.1074/jbc.M116.763854 (Devost et al., 2017)		HEK293 cells devoid of functional Gα₁₂ and Gα₁₃
Cell line (Homo-sapiens)	ΔG_q/11 HEK293 cells	Dr. A. Inoue (Tohoku University, Sendai, Miyagi, Japan) 10.1038/ncomms10156 (Schrage et al., 2015)		HEK293 cells devoid of functional Gα_q, Gα₁₁, Gα₁₄ and Gα₁₅
Cell line (Homo-sapiens)	ΔG_i/o HEK293 cells	Dr. A. Inoue (Tohoku University, Sendai, Miyagi, Japan)		HEK293 cells devoid of functional Gα_i and Gα_o
Cell line (Homo-sapiens)	HL-60	ATCC	Cat. #: CCL-240
Cell line (Homo-sapiens)	iCell Cardiomyocytes, 01434	FUJIFILM Cellular Dynamics	Cat. #: R1057
Transfected construct (Homo sapiens)	Human Gα subunits-encoding plasmid library	Missouri S&T cDNA Resource Center (https://www.cdna.org/)	Cat. #: GNAI100000; GNAI200000; GNA0OA0000; GNA0OB0000; GNA0Z00000; GNA1200000; GNA1300001; GNA0Q00000; GNA1100000; GNA1400000; GNA1500000; GNA0SL0000
Transfected construct (Homo sapiens)	Gβ1	Missouri S&T cDNA Resource Center (https://www.cdna.org/)	Cat. #: GNB0100000
Transfected construct (Homo sapiens)	Gγ9	Missouri S&T cDNA Resource Center (https://www.cdna.org/)	Cat. #: GNG0900000
Transfected construct (Homo sapiens)	Gαs-67-RlucII	10.1074/jbc.M114.618819(Carr et al., 2014)
Transfected construct (Homo sapiens)	Gαi1-loop-RlucII	10.1096/fj.13 –242446 (Armando et al., 2014)
Transfected construct (Homo sapiens)	Gαi2-loop-RlucII	10.1073/pnas.1312515110 (Quoyer et al., 2013)
Transfected construct (Homo sapiens)	GαoB-99-RlucII	10.1073/pnas.1804003115(Mende et al., 2018)
Transfected construct (Homo sapiens)	Gαq-118-RlucII	10.1016 /j .bpj.2010.10.025 (Breton et al., 2010)
Transfected construct (Homo sapiens)	Gα12–136-RlucII	10.1126/scisignal.aat1631(Namkung et al., 2018)
Transfected construct (Homo sapiens)	Gα13–130-RlucII	10.1038 /s42003-020-01453-8 (Avet et al., 2020)
Transfected construct (Homo sapiens)	GFP10-Gγ1	10.1096/fj.13 –242446 (Armando et al., 2014)
Transfected construct (Homo sapiens)	GFP10-Gγ2	10.1038/nsmb1134(Galés et al., 2006)
Transfected construct (Homo sapiens)	EPAC	10.1124/jpet.109.156398(Leduc et al., 2009)
Transfected construct (Homo sapiens)	rGFP-CAAX	10.1038/ncomms12178(Namkung et al., 2016)
Transfected construct (Homo sapiens)	Rap1GAP-RlucII	This paper		See Materials and Methods
Transfected construct (Homo sapiens)	p63-RhoGEF-RlucII	This paper		See Materials and Methods
Transfected construct (Homo sapiens)	PDZ-RhoGEF-RlucII	This paper		See Materials and Methods
Transfected construct (Homo sapiens)	PKN-RBD-RlucII	10.1126/scisignal.aat1631(Namkung et al., 2018)
Transfected construct (Homo sapiens)	MyrPB-Ezrin-RlucII	10.1242/jcs.255307(Leguay et al., 2021)
Transfected construct (Homo sapiens)	βarrestin1-RlucII	10.1126/scisignal.2002522(Zimmerman et al., 2012)
Transfected construct (Homo sapiens)	βarrestin2-RlucII	10.1073/pnas.1312515110 (Quoyer et al., 2013)
Transfected construct (Homo sapiens)	GRK2	This paper		See Materials and Methods
Transfected construct (Homo sapiens)	RAMP3	Domain Therapeutics North America	N/A
Transfected construct (Homo sapiens)	EAAC-1	10.1016 /s0028-3908 (98 )00091-4 (Brabet et al., 1998)
Transfected construct (Homo sapiens)	EAAT-1	Domain Therapeutics North America	N/A
Transfected construct (Homo sapiens)	signal peptide-Flag-AT₁	10.1074/jbc.M114.631119(Goupil et al., 2015)
Transfected construct (Homo sapiens)	FLAG-α_2BAR	Domain Therapeutics North America	N/A
Transfected construct (Homo sapiens)	HA-β₂AR	10.1074/jbc.M204163200(Lavoie et al., 2002)
Antibody	Gαi1 (I-20) (Rabbit polyclonal)	Santa Cruz	Cat. #: sc-391 RRID: AB_2247692	WB (1:500)
Antibody	Gαi2 (T-19) (Rabbit polyclonal)	Santa Cruz	Cat. #: sc-7276 RRID:AB_2111472	WB (1:500)
Antibody	Gαo (K-20) (Rabbit polyclonal)	Santa Cruz	Cat. #: sc-387 RRID:AB_2111641	WB (1:500)
Antibody	Gαz (Rabbit monoclonal)	Abcam	Cat. #: ab154846	WB (1:1000)
Antibody	Gαs (K-20) (Rabbit polyclonal)	Santa Cruz	Cat. #: sc-823 RRID:AB_631538	WB (1:500)
Antibody	Gα12 (S-20) (Rabbit polyclonal)	Santa Cruz	Cat. #: sc-409 RRID:AB_2263416	WB (1:500)
Antibody	Gα13 (A-20) (Rabbit polyclonal)	Santa Cruz	Cat. #: sc-410 RRID:AB_2279044	WB (1:500)
Antibody	Gαq (E-17) (Rabbit polyclonal)	Santa Cruz	Cat. #: sc-393 RRID:AB_631536	WB (1:500)
Antibody	Gα11 (C-terminal) (Rabbit polyclonal)	Sigma-Aldrich	Cat. #: SAB2109181	WB (1:500)
Antibody	Gα14 (Rabbit polyclonal)	Sigma-Aldrich	Cat. #: SAB4300771	WB (1:500)
Antibody	Gα15 (Rabbit polyclonal)	ThermoFisher scientific (Pierce)	Cat. #: PA1-29022 RRID:AB_1958024	WB (1:5,000)
Antibody	βactin (Mouse monoclonal)	Sigma-Aldrich	Cat. #: A5441 RRID:AB_476744	WB (1:5,000)
Antibody	Anti-rabbit HRP-coupled (Donkey polyclonal)	GE Healthcare	Cat. #: NA934 RRID:AB_772206	WB (1:5,000)
Antibody	Anti-mouse HRP-coupled (Sheep polyclonal)	GE Healthcare	Cat. #: NA931 RRID:AB_772210	WB (1:10,000)
Commercial assay or kit	FLIPR Calcium 5 Assay Kit	Molecular Devices	Cat. #: R8185
Chemical compound, drug	α-linolenic acid	Cayman Chemical	Cat. #: 21,910
Chemical compound, drug	α-MSH	Sigma-Aldrich	Cat. #: M4135
Chemical compound, drug	γ-MSH	Tocris	Cat. #: 4,272
Chemical compound, drug	[Pyr1]-Apelin 13	Tocris	Cat. #: 2,420
Chemical compound, drug	[Sar1, Ile4,8]-Angiotensin II	Peptides International	Cat. #: PAN-4476-V-1EA
Chemical compound, drug	3-hydroxyoctanoic acid (3-HOA)	Sigma-Aldrich	Cat. #: H3898
Chemical compound, drug	7α–25 dihydroxycholesterol	Sigma-Aldrich	Cat. #: SML0541
Chemical compound, drug	Acetylcholine chloride	Tocris	Cat. #: 2,809
Chemical compound, drug	ACT-389949	Provided by Bristol-Myers Squibb	N/A
Chemical compound, drug	Adenosine	Sigma-Aldrich	Cat. #: A9251
Chemical compound, drug	AM-630	Tocris	Cat. #: 1,120
Chemical compound, drug	Amylin	Tocris	Cat. #: 3,418
Chemical compound, drug	Angiotensin II (Ang II)	Sigma-Aldrich	Cat. #: A9525
Chemical compound, drug	Arginine vasopressin (AVP)	Sigma-Aldrich	Cat. #: V9879
Chemical compound, drug	Atropine	Sigma-Aldrich	Cat. #: A0132
Chemical compound, drug	Bovine serum albumin	Sigma-Aldrich	Cat. #: A7030
Chemical compound, drug	C5a	Complement Technology	Cat. #: A144(300)
Chemical compound, drug	Calcitonin	Bachem	Cat. #: H-2250
Chemical compound, drug	CCK Octapeptide, sulfated (CCK8)	Tocris	Cat. #: 1,166
Chemical compound, drug	CCL20	R&D Systems	Cat. #: 360-MP/CF
Chemical compound, drug	CCL3 (MIP-1a)	R&D Systems	Cat. #: 270-LD/CF
Chemical compound, drug	Cholera Toxin (CTX) from Vibrio cholerae	Sigma-Aldrich	Cat. #: C8052
Chemical compound, drug	Cmpd43	Provided by Bristol-Myers Squibb	N/A
Chemical compound, drug	Corticotropin-Releasing Factor (CRF)	Bachem	Cat. #: H-2435
Chemical compound, drug	CXCL12	R&D Systems	Cat. #: 350-NS
Chemical compound, drug	CXCL13	R&D Systems	Cat. #: 801 CX/CF
Chemical compound, drug	CXCL8	R&D Systems	Cat. #: 208-IL/CF
Chemical compound, drug	DAMGO	Tocris	Cat. #: 1,171
Chemical compound, drug	Dopamine	Sigma-Aldrich	Cat. #: H8502
Chemical compound, drug	DPCPX	Tocris	Cat. #: 0439
Chemical compound, drug	Dynorphin A	Tocris	Cat. #: 3,195
Chemical compound, drug	Endothelin-1	Tocris	Cat. #: 1,160
Chemical compound, drug	Eticlopride	Tocris	Cat. #: 1,847
Chemical compound, drug	Fingolimod	Provided by Bristol-Myers Squibb	N/A
Chemical compound, drug	Gastric Inhibitory Peptide (GIP)	Bachem	Cat. #: H-5645
Chemical compound, drug	Ghrelin	Tocris	Cat. #: 1,463
Chemical compound, drug	Glucagon (Aittaleb et al., 2010; Aittaleb et al., 2011; Armando et al., 2014; Atwood et al., 2011; Avet et al., 2020; Azzi et al., 2003; Bowes et al., 2012; Brabet et al., 1998; Breton et al., 2010; Bünemann et al., 2003; Carr et al., 2014; Casey et al., 1990; Chandan et al., 2021; De Haan and Hirst, 2004; Devost et al., 2017; Fukuhara et al., 2001; Galandrin et al., 2007; Galés et al., 2005; Galés et al., 2006; Goupil et al., 2015; Hauser et al., 2017; Hauser et al., 2022; Hoffmann et al., 2005; Inoue et al., 2019; Jordan et al., 1999; Kawamata et al., 2003; Kenakin, 2019; Kim et al., 2002)	Bachem	Cat. #: H-6790
Chemical compound, drug	Glucagon-like peptide-1 GLP-1 (Bowes et al., 2012; Brabet et al., 1998; Breton et al., 2010; Bünemann et al., 2003; Carr et al., 2014; Casey et al., 1990; Chandan et al., 2021; De Haan and Hirst, 2004; Devost et al., 2017; Fukuhara et al., 2001; Galandrin et al., 2007; Galés et al., 2005; Galés et al., 2006; Goupil et al., 2015; Hauser et al., 2017; Hauser et al., 2022; Hoffmann et al., 2005; Inoue et al., 2019; Jordan et al., 1999; Kawamata et al., 2003; Kenakin, 2019; Kim et al., 2002; Kobayashi et al., 2019; Laschet et al., 2019; Lavoie et al., 2002; Leduc et al., 2009; Leguay et al., 2021; Lu et al., 2014; Lutz et al., 2007)	Bachem	Cat. #: H-6795
Chemical compound, drug	Glucagon-like peptide-2 GLP-2 (Aittaleb et al., 2010; Aittaleb et al., 2011; Armando et al., 2014; Atwood et al., 2011; Avet et al., 2020; Azzi et al., 2003; Bowes et al., 2012; Brabet et al., 1998; Breton et al., 2010; Bünemann et al., 2003; Carr et al., 2014; Casey et al., 1990; Chandan et al., 2021; De Haan and Hirst, 2004; Devost et al., 2017; Fukuhara et al., 2001; Galandrin et al., 2007; Galés et al., 2005; Galés et al., 2006; Goupil et al., 2015; Hauser et al., 2017; Hauser et al., 2022; Hoffmann et al., 2005; Inoue et al., 2019; Jordan et al., 1999; Kawamata et al., 2003; Kenakin, 2019; Kim et al., 2002; Kobayashi et al., 2019; Laschet et al., 2019; Lavoie et al., 2002; Leduc et al., 2009)	Bachem	Cat. #: H-7742
Chemical compound, drug	Glutamate	Sigma-Aldrich	Cat. #: 49,621
Chemical compound, drug	GnRH (LH-RH)	Peptides International	Cat. #: PLR-4013
Chemical compound, drug	Histamine	Tocris	Cat. #: 3,545
Chemical compound, drug	Kallidin	Anaspec	Cat. #: 22,853(AN)
Chemical compound, drug	Leukotriene B4 (LTB4)	Cayman Chemical	Cat. #: 20,110
Chemical compound, drug	Leukotriene D4 (LTD4)	Cayman Chemical	Cat. #: 20,310
Chemical compound, drug	Litocholic acid	Sigma-Aldrich	Cat. #: L6250
Chemical compound, drug	Melatonin	Bachem	Cat. #: Q-1300
Chemical compound, drug	MDL 29,951	Cayman Chemical	Cat. #: 16,266
Chemical compound, drug	Neuropeptide FF (NPFF)	Tocris	Cat. #: 3,137
Chemical compound, drug	Neuropeptide Y (NPY)	Bachem	Cat. #: H-6375
Chemical compound, drug	Nicotinic acid	Abcam	Cat. #: ab120145
Chemical compound, drug	Nociceptin	Tocris	Cat. #: 910
Chemical compound, drug	Noradrenaline	Tocris	Cat. #: 5,169
Chemical compound, drug	Oleoyl-Lysophosphatidic acid (O-LPA)	Sigma-Aldrich	Cat. #: L7260
Chemical compound, drug	Orexin-A	Bachem	Cat. #: H-4172
Chemical compound, drug	Oxytocin	Tocris	Cat. #: 1910
Chemical compound, drug	Parathyroid Hormone (Aittaleb et al., 2010; Aittaleb et al., 2011; Armando et al., 2014; Atwood et al., 2011; Avet et al., 2020; Azzi et al., 2003; Bowes et al., 2012; Brabet et al., 1998; Breton et al., 2010; Bünemann et al., 2003; Carr et al., 2014; Casey et al., 1990; Chandan et al., 2021; De Haan and Hirst, 2004; Devost et al., 2017; Fukuhara et al., 2001; Galandrin et al., 2007; Galés et al., 2005; Galés et al., 2006; Goupil et al., 2015; Hauser et al., 2017; Hauser et al., 2022; Hoffmann et al., 2005; Inoue et al., 2019; Jordan et al., 1999; Kawamata et al., 2003; Kenakin, 2019; Kim et al., 2002; Kobayashi et al., 2019; Laschet et al., 2019; Lavoie et al., 2002; Leduc et al., 2009; Leguay et al., 2021)	Sigma-Aldrich	Cat. #: P3796
Chemical compound, drug	Pertussis toxin (PTX) from Bordetella pertussis	List Biological Laboratories	Cat. #: 179 A(LB)
Chemical compound, drug	pH (proton) (Hydrochloric acid)	Sigma-Aldrich	Cat. #: 320,331
Chemical compound, drug	Probenecid	Sigma-Aldrich	Cat. #: P8761
Chemical compound, drug	Prolume Purple (methoxy e-Coelenterazine; Me-O-e-CTZ)	Nanolight	Cat. #: 369
Chemical compound, drug	Propionate (sodium salt)	Sigma-Aldrich	Cat. #: P1880
Chemical compound, drug	Prostaglandin D2 (PGD2)	Cayman Chemical	Cat. #: 12,010
Chemical compound, drug	Prostaglandin E2 (PGE2)	Sigma-Aldrich	Cat. #: P0409
Chemical compound, drug	RFamide-related peptide 3 (RFRP3)	Tocris	Cat. #: 4,683
Chemical compound, drug	Rimonabant	Cayman Chemical	Cat. #: 9000484
Chemical compound, drug	Saralasin	ApexBio	Cat. #: B5063
Chemical compound, drug	Serotonin	Cayman Chemical	Cat. #: 14,332
Chemical compound, drug	SLIGKV-NH2 (PAR2 AP)	Tocris	Cat. #: 3,010
Chemical compound, drug	SNC80	Sigma-Aldrich	Cat. #: S2812
Chemical compound, drug	Somatostatin-14	Bachem	Cat. #: H-6276
Chemical compound, drug	Sphingosine 1-phosphate	Cayman	Cat. #: 62,570
Chemical compound, drug	TFLLR-NH2 (PAR1 AP)	Tocris	Cat. #: 1,464
Chemical compound, drug	TRV027	Provided by Bristol-Myers Squibb	N/A
Chemical compound, drug	UBO-QIC (FR900359)	Institute for Pharmaceutical Biology of the University of Bonn	N/A
Chemical compound, drug	Undecanoic acid	Sigma-Aldrich	Cat. #: 171,476
Chemical compound, drug	Urocortin II	Phoenix Pharmaceutical	Cat. #: 019–30
Chemical compound, drug	UTP	Sigma-Aldrich	Cat. #: U1006
Chemical compound, drug	Vasoactive Intestinal Peptide (VIP)	Tocris	Cat. #: 1911
Chemical compound, drug	WB4101	Tocris	Cat. #: 946
Chemical compound, drug	WIN55,212–2	Enzo Life Sciences	Cat. #: BMLCR105
Chemical compound, drug	YM-254890	Wako Pure Chemical Industries (Fujifilm)	Cat. #: 257–00631
Chemical compound, drug	Zinc chloride (Zn2+)	Sigma-Aldrich	Cat. #: 229,997
Software, algorithm	Prism, Version 8.3	GraphPad
Software, algorithm	MATLAB, Version R2019b	MathWorks
Software, algorithm	ImageJ, Version 1.52 a	NIH https://imagej.nih.gov/ij/
Software, algorithm	Scipy, Version 1.4.1	https://www.scipy.org
Other	96 W white plate	Greiner Bio-one	Cat. #: 655,083
Other	96 W black plate, clear-bottom	Greiner Bio-one	Cat. #: 655,090
Other	OptiPlate-384, White Opaque 384-well Microplate	Perkin Elmer	Cat. #: 6007290
Other	35 mm poly-d-lysine-coated glass-bottom culture dishes	Mattek	Cat. #: P35GC-1.5–14 C
Other	Microplate washer	BioTek Instruments	Cat. #: 405TSUS
Other	D300e Digital Dispenser	Tecan
Other	T8 + Dispensehead Cassettes	Hp (Tecan)	Cat. #: 30097370
Other	Synergy NEO Luminescence microplate reader	BioTek Instruments
Other	FlexStation 2 Multi-mode, auto-pipetting microplate reader	Molecular Devices
Other	Inverted microscope	Nikon Eclipse Ti-U
Other	x60 objective lens	Nikon CFI Apochromat TIRF
Other	EMCCD camera	Nuvu HNu 512

Data availability

All data generated or analysed during this study are included in the manuscript and supporting file; Source Data files have been provided for Figures 2, 4, 5, 6 and 7 and associated figure supplements. Supplementary file 1 contains the numerical data used to generate Figure 2-figure supplement 1 and Figure 2-figure supplement 3. Supplementary File 2 contains the numerical data used to generate figure 3. Further data are available in the companion paper co-published in eLife: https://doi.org/10.7554/eLife.74107.

References

(2010) Structure and function of heterotrimeric G protein-regulated Rho guanine nucleotide exchange factors
Molecular Pharmacology 77:111–125.

https://doi.org/10.1124/mol.109.061234
- PubMed
- Google Scholar
(2011) Plasma membrane association of p63 Rho guanine nucleotide exchange factor (p63RhoGEF) is mediated by palmitoylation and is required for basal activity in cells
The Journal of Biological Chemistry 286:34448–34456.

https://doi.org/10.1074/jbc.M111.273342
- PubMed
- Google Scholar
1. Armando S
2. Quoyer J
3. Lukashova V
4. Maiga A
5. Percherancier Y
6. Heveker N
7. Pin J-P
8. Prézeau L
9. Bouvier M
(2014) The chemokine CXC4 and CC2 receptors form homo- and heterooligomers that can engage their signaling G-protein effectors and betaarrestin
FASEB Journal 28:4509–4523.

https://doi.org/10.1096/fj.13-242446
- PubMed
- Google Scholar
(2011) 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.

https://doi.org/10.1186/1471-2164-12-14
- PubMed
- Google Scholar
1. Avet C
2. Sturino C
3. Grastilleur S
4. Gouill CL
5. Semache M
6. Gross F
7. Gendron L
8. Bennani Y
9. Mancini JA
10. Sayegh CE
11. Bouvier M
(2020) The PAR2 inhibitor I-287 selectively targets Galphaq and Galpha12/13 signaling and has anti-inflammatory effects
Communications Biology 3:719.

https://doi.org/10.1038/s42003-020-01453-8
- Google Scholar
1. Azzi M
2. Charest PG
3. Angers S
4. Rousseau G
5. Kohout T
6. Bouvier M
7. Piñeyro G
(2003) Beta-arrestin-mediated activation of MAPK by inverse agonists reveals distinct active conformations for G protein-coupled receptors
PNAS 100:11406–11411.

https://doi.org/10.1073/pnas.1936664100
- PubMed
- Google Scholar
1. Bowes J
2. Brown AJ
3. Hamon J
4. Jarolimek W
5. Sridhar A
6. Waldron G
7. Whitebread S
(2012) Reducing safety-related drug attrition: the use of in vitro pharmacological profiling
Nature Reviews. Drug Discovery 11:909–922.

https://doi.org/10.1038/nrd3845
- PubMed
- Google Scholar
1. Brabet I
2. Parmentier ML
3. De Colle C
4. Bockaert J
5. Acher F
6. Pin JP
(1998) Comparative effect of L-CCG-I, DCG-IV and gamma-carboxy-L-glutamate on all cloned metabotropic glutamate receptor subtypes
Neuropharmacology 37:1043–1051.

https://doi.org/10.1016/s0028-3908(98)00091-4
- PubMed
- Google Scholar
1. Breton B
2. Sauvageau É
3. Zhou J
4. Bonin H
5. Le Gouill C
6. Bouvier M
(2010) Multiplexing of multicolor bioluminescence resonance energy transfer
Biophysical Journal 99:4037–4046.

https://doi.org/10.1016/j.bpj.2010.10.025
- PubMed
- Google Scholar
(2003) Gi protein activation in intact cells involves subunit rearrangement rather than dissociation
PNAS 100:16077–16082.

https://doi.org/10.1073/pnas.2536719100
- PubMed
- Google Scholar
1. Carr R
2. Du Y
3. Quoyer J
4. Panettieri RA
5. Janz JM
6. Bouvier M
7. Kobilka BK
8. Benovic JL
(2014) Development and characterization of pepducins as Gs-biased allosteric agonists
The Journal of Biological Chemistry 289:35668–35684.

https://doi.org/10.1074/jbc.M114.618819
- PubMed
- Google Scholar
1. Casey PJ
2. Fong HK
3. Simon MI
4. Gilman AG
(1990)
Gz, a guanine nucleotide-binding protein with unique biochemical properties

The Journal of Biological Chemistry 265:2383–2390.
- PubMed
- Google Scholar
(2021) Identification of G Protein α I Signaling Partners by Proximity Labeling Reveals a Network of Interactions That Includes PDZ-RhoGEF
Pharmacology and Toxicology 1:452545.

https://doi.org/10.1101/2021.07.15.452545
- Google Scholar
1. De Haan L
2. Hirst TR
(2004) Cholera toxin: a paradigm for multi-functional engagement of cellular mechanisms (Review)
Molecular Membrane Biology 21:77–92.

https://doi.org/10.1080/09687680410001663267
- PubMed
- Google Scholar
1. Devost D
2. Sleno R
3. Petrin D
4. Zhang A
5. Shinjo Y
6. Okde R
7. Hebert TE
(2017) Conformational Profiling of the AT1 Angiotensin II Receptor Reflects Biased Agonism
G Protein Coupling, and Cellular Context. J Biol Chem 292:5443–5456.

https://doi.org/10.1074/jbc.M116.763854
- Google Scholar
(2001) RGS-containing RhoGEFs: the missing link between transforming G proteins and Rho
Oncogene 20:1661–1668.

https://doi.org/10.1038/sj.onc.1204182
- PubMed
- Google Scholar
(2007) The evasive nature of drug efficacy: implications for drug discovery
Trends in Pharmacological Sciences 28:423–430.

https://doi.org/10.1016/j.tips.2007.06.005
- PubMed
- Google Scholar
1. Galés C
2. Rebois RV
3. Hogue M
4. Trieu P
5. Breit A
6. Hébert TE
7. Bouvier M
(2005) Real-time monitoring of receptor and G-protein interactions in living cells
Nature Methods 2:177–184.

https://doi.org/10.1038/nmeth743
- PubMed
- Google Scholar
1. Galés C
2. Van Durm JJJ
3. Schaak S
4. Pontier S
5. Percherancier Y
6. Audet M
7. Paris H
8. Bouvier M
(2006) Probing the activation-promoted structural rearrangements in preassembled receptor-G protein complexes
Nature Structural & Molecular Biology 13:778–786.

https://doi.org/10.1038/nsmb1134
- PubMed
- Google Scholar
1. Goupil E
2. Fillion D
3. Clément S
4. Luo X
5. Devost D
6. Sleno R
7. Pétrin D
8. Saragovi HU
9. Thorin É
10. Laporte SA
11. Hébert TE
(2015) Angiotensin II type I and prostaglandin F2alpha receptors cooperatively modulate signaling in vascular smooth muscle cells
The Journal of Biological Chemistry 290:3137–3148.

https://doi.org/10.1074/jbc.M114.631119
- PubMed
- Google Scholar
(2017) Trends in GPCR drug discovery: new agents, targets and indications
Nature Reviews. Drug Discovery 16:829–842.

https://doi.org/10.1038/nrd.2017.178
- PubMed
- Google Scholar
1. Hauser AS
2. Avet C
3. Normand C
4. Mancini A
5. Inoue A
6. Bouvier M
7. Gloriam DE
(2022) Common coupling map advances GPCR-G protein selectivity
eLife 11:e74107.

https://doi.org/10.7554/eLife.74107
- PubMed
- Google Scholar
1. Hoffmann C
2. Gaietta G
3. Bünemann M
4. Adams SR
5. Oberdorff-Maass S
6. Behr B
7. Vilardaga J-P
8. Tsien RY
9. Ellisman MH
10. Lohse MJ
(2005) A FlAsH-based FRET approach to determine G protein-coupled receptor activation in living cells
Nature Methods 2:171–176.

https://doi.org/10.1038/nmeth742
- PubMed
- Google Scholar
1. Inoue A
2. Raimondi F
3. Kadji FMN
4. Singh G
5. Kishi T
6. Uwamizu A
7. Ono Y
8. Shinjo Y
9. Ishida S
10. Arang N
11. Kawakami K
12. Gutkind JS
13. Aoki J
14. Russell RB
(2019) Illuminating G-Protein-Coupling Selectivity of GPCRs
Cell 177:1933–1947.

https://doi.org/10.1016/j.cell.2019.04.044
- PubMed
- Google Scholar
1. Jordan JD
2. Carey KD
3. Stork PJ
4. Iyengar R
(1999) Modulation of rap activity by direct interaction of Galpha(o) with Rap1 GTPase-activating protein
The Journal of Biological Chemistry 274:21507–21510.

https://doi.org/10.1074/jbc.274.31.21507
- PubMed
- Google Scholar
1. Kawamata Y
2. Fujii R
3. Hosoya M
4. Harada M
5. Yoshida H
6. Miwa M
7. Fukusumi S
8. Habata Y
9. Itoh T
10. Shintani Y
11. Hinuma S
12. Fujisawa Y
13. Fujino M
(2003) A G protein-coupled receptor responsive to bile acids
The Journal of Biological Chemistry 278:9435–9440.

https://doi.org/10.1074/jbc.M209706200
- PubMed
- Google Scholar
1. Kenakin T
(2019) Biased Receptor Signaling in Drug Discovery
Pharmacological Reviews 71:267–315.

https://doi.org/10.1124/pr.118.016790
- PubMed
- Google Scholar
1. Kim J
2. Isokawa M
3. Ledent C
4. Alger BE
(2002) Activation of muscarinic acetylcholine receptors enhances the release of endogenous cannabinoids in the hippocampus
The Journal of Neuroscience 22:10182–10191.

https://doi.org/10.1523/JNEUROSCI.22-23-10182.2002
- PubMed
- Google Scholar
(2019) Bioluminescence resonance energy transfer-based imaging of protein-protein interactions in living cells
Nature Protocols 14:1084–1107.

https://doi.org/10.1038/s41596-019-0129-7
- PubMed
- Google Scholar
(2019) A dynamic and screening-compatible nanoluciferase-based complementation assay enables profiling of individual GPCR-G protein interactions
The Journal of Biological Chemistry 294:4079–4090.

https://doi.org/10.1074/jbc.RA118.006231
- PubMed
- Google Scholar
1. Lavoie C
2. Mercier J-F
3. Salahpour A
4. Umapathy D
5. Breit A
6. Villeneuve L-R
7. Zhu W-Z
8. Xiao R-P
9. Lakatta EG
10. Bouvier M
11. Hébert TE
(2002) Beta 1/beta 2-adrenergic receptor heterodimerization regulates beta 2-adrenergic receptor internalization and ERK signaling efficacy
The Journal of Biological Chemistry 277:35402–35410.

https://doi.org/10.1074/jbc.M204163200
- PubMed
- Google Scholar
1. Leduc M
2. Breton B
3. Galés C
4. Le Gouill C
5. Bouvier M
6. Chemtob S
7. Heveker N
(2009) Functional selectivity of natural and synthetic prostaglandin EP4 receptor ligands
The Journal of Pharmacology and Experimental Therapeutics 331:297–307.

https://doi.org/10.1124/jpet.109.156398
- PubMed
- Google Scholar
1. Leguay K
2. Decelle B
3. He YY
4. Pagniez A
5. Hogue M
6. Kobayashi H
7. Le Gouill C
8. Bouvier M
9. Carréno S
(2021) Development of conformational BRET biosensors that monitor ezrin, radixin and moesin activation in real time
Journal of Cell Science 134:jcs255307.

https://doi.org/10.1242/jcs.255307
- PubMed
- Google Scholar
1. Lu M
2. Wang B
3. Zhang C
4. Zhuang X
5. Yuan M
6. Wang H
7. Li W
8. Su R
9. Li J
(2014) PQ-69, a novel and selective adenosine A1 receptor antagonist with inverse agonist activity
Purinergic Signalling 10:619–629.

https://doi.org/10.1007/s11302-014-9424-5
- PubMed
- Google Scholar
1. Lutz S
2. Shankaranarayanan A
3. Coco C
4. Ridilla M
5. Nance MR
6. Vettel C
7. Baltus D
8. Evelyn CR
9. Neubig RR
10. Wieland T
11. Tesmer JJG
(2007) Structure of Galphaq-p63RhoGEF-RhoA complex reveals a pathway for the activation of RhoA by GPCRs
Science (New York, N.Y.) 318:1923–1927.

https://doi.org/10.1126/science.1147554
- PubMed
- Google Scholar
(2015) Exploring the Technology Landscape of 7TMR Drug Signaling Profiling
Current Topics in Medicinal Chemistry 15:2528–2542.

https://doi.org/10.2174/1568026615666150701113344
- PubMed
- Google Scholar
1. Martin BR
2. Lambert NA
(2016) Activated G Protein Galphas Samples Multiple Endomembrane Compartments
The Journal of Biological Chemistry 291:20295–20302.

https://doi.org/10.1074/jbc.M116.729731
- PubMed
- Google Scholar
(2015) Distinct profiles of functional discrimination among G proteins determine the actions of G protein-coupled receptors
Science Signaling 8:ra123.

https://doi.org/10.1126/scisignal.aab4068
- PubMed
- Google Scholar
(2020) Revealing the Activity of Trimeric G-proteins in Live Cells with a Versatile Biosensor Design
Cell 182:770–785.

https://doi.org/10.1016/j.cell.2020.06.020
- PubMed
- Google Scholar
1. McAvoy T
2. Zhou M
3. Greengard P
4. Nairn AC
(2009) Phosphorylation of Rap1GAP, a striatally enriched protein, by protein kinase A controls Rap1 activity and dendritic spine morphology
PNAS 106:3531–3536.

https://doi.org/10.1073/pnas.0813263106
- PubMed
- Google Scholar
1. Mende F
2. Hundahl C
3. Plouffe B
4. Skov LJ
5. Sivertsen B
6. Madsen AN
7. Lückmann M
8. Diep TA
9. Offermanns S
10. Frimurer TM
11. Bouvier M
12. Holst B
(2018) Translating biased signaling in the ghrelin receptor system into differential in vivo functions
PNAS 115:E10255–E10264.

https://doi.org/10.1073/pnas.1804003115
- PubMed
- Google Scholar
1. Meng J
2. Glick JL
3. Polakis P
4. Casey PJ
(1999) Functional interaction between Galpha(z) and Rap1GAP suggests a novel form of cellular cross-talk
The Journal of Biological Chemistry 274:36663–36669.

https://doi.org/10.1074/jbc.274.51.36663
- PubMed
- Google Scholar
1. Namkung Y
2. Le Gouill C
3. Lukashova V
4. Kobayashi H
5. Hogue M
6. Khoury E
7. Song M
8. Bouvier M
9. Laporte SA
(2016) Monitoring G protein-coupled receptor and beta-arrestin trafficking in live cells using enhanced bystander BRET
Nature Communications 7:12178.

https://doi.org/10.1038/ncomms12178
- PubMed
- Google Scholar
1. Namkung Y
2. LeGouill C
3. Kumar S
4. Cao Y
5. Teixeira LB
6. Lukasheva V
7. Giubilaro J
8. Simões SC
9. Longpré JM
10. Devost D
11. Hébert TE
12. Piñeyro G
13. Leduc R
14. Costa-Neto CM
15. Bouvier M
16. Laporte SA
(2018) Functional selectivity profiling of the angiotensin II type 1 receptor using pathway-wide BRET signaling sensors
Science Signaling 11:559.

https://doi.org/10.1126/scisignal.aat1631
- Google Scholar
1. Okashah N
2. Wright SC
3. Kawakami K
4. Mathiasen S
5. Zhou J
6. Lu S
7. Javitch JA
8. Inoue A
9. Bouvier M
10. Lambert NA
(2020) Agonist-induced formation of unproductive receptor-G12 complexes
PNAS 117:21723–21730.

https://doi.org/10.1073/pnas.2003787117
- PubMed
- Google Scholar
1. Oldham WM
2. Hamm HE
(2008) Heterotrimeric G protein activation by G-protein-coupled receptors
Nature Reviews. Molecular Cell Biology 9:60–71.

https://doi.org/10.1038/nrm2299
- PubMed
- Google Scholar
1. Olsen RHJ
2. DiBerto JF
3. English JG
4. Glaudin AM
5. Krumm BE
6. Slocum ST
7. Che T
8. Gavin AC
9. McCorvy JD
10. Roth BL
11. Strachan RT
(2020) TRUPATH, an open-source biosensor platform for interrogating the GPCR transducerome
Nature Chemical Biology 16:841–849.

https://doi.org/10.1038/s41589-020-0535-8
- PubMed
- Google Scholar
1. Pándy-Szekeres G
2. Esguerra M
3. Hauser AS
4. Caroli J
5. Munk C
6. Pilger S
7. Keserű GM
8. Kooistra AJ
9. Gloriam DE
(2022) The G protein database, GproteinDb
Nucleic Acids Research 50:D518–D525.

https://doi.org/10.1093/nar/gkab852
- PubMed
- Google Scholar
1. Quoyer J
2. Janz JM
3. Luo J
4. Ren Y
5. Armando S
6. Lukashova V
7. Benovic JL
8. Carlson KE
9. Hunt SW
10. Bouvier M
(2013) Pepducin targeting the C-X-C chemokine receptor type 4 acts as a biased agonist favoring activation of the inhibitory G protein
PNAS 110:E5088–E5097.

https://doi.org/10.1073/pnas.1312515110
- PubMed
- Google Scholar
1. Rojas RJ
2. Yohe ME
3. Gershburg S
4. Kawano T
5. Kozasa T
6. Sondek J
(2007) Galphaq directly activates p63RhoGEF and Trio via a conserved extension of the Dbl homology-associated pleckstrin homology domain
The Journal of Biological Chemistry 282:29201–29210.

https://doi.org/10.1074/jbc.M703458200
- PubMed
- Google Scholar
(2004) Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia
Nature Reviews. Drug Discovery 3:353–359.

https://doi.org/10.1038/nrd1346
- PubMed
- Google Scholar
1. Sánchez-Soto M
2. Bonifazi A
3. Cai NS
4. Ellenberger MP
5. Newman AH
6. Ferré S
7. Yano H
(2016) Evidence for Noncanonical Neurotransmitter Activation: Norepinephrine as a Dopamine D2-Like Receptor Agonist
Molecular Pharmacology 89:457–466.

https://doi.org/10.1124/mol.115.101808
- PubMed
- Google Scholar
1. Schrage R
2. Schmitz A-L
3. Gaffal E
4. Annala S
5. Kehraus S
6. Wenzel D
7. Büllesbach KM
8. Bald T
9. Inoue A
10. Shinjo Y
11. Galandrin S
12. Shridhar N
13. Hesse M
14. Grundmann M
15. Merten N
16. Charpentier TH
17. Martz M
18. Butcher AJ
19. Slodczyk T
20. Armando S
21. Effern M
22. Namkung Y
23. Jenkins L
24. Horn V
25. Stößel A
26. Dargatz H
27. Tietze D
28. Imhof D
29. Galés C
30. Drewke C
31. Müller CE
32. Hölzel M
33. Milligan G
34. Tobin AB
35. Gomeza J
36. Dohlman HG
37. Sondek J
38. Harden TK
39. Bouvier M
40. Laporte SA
41. Aoki J
42. Fleischmann BK
43. Mohr K
44. König GM
45. Tüting T
46. Kostenis E
(2015) The experimental power of FR900359 to study Gq-regulated biological processes
Nature Communications 6:10156.

https://doi.org/10.1038/ncomms10156
- PubMed
- Google Scholar
(2017) Purinergic Receptor Transactivation by the beta2-Adrenergic Receptor Increases Intracellular Ca(2+) in Nonexcitable Cells
Molecular Pharmacology 91:533–544.

https://doi.org/10.1124/mol.116.106419
- Google Scholar
1. Sunahara RK
2. Guan HC
3. O’Dowd BF
4. Seeman P
5. Laurier LG
6. Ng G
7. George SR
8. Torchia J
9. Van Tol HH
10. Niznik HB
(1991) Cloning of the gene for a human dopamine D5 receptor with higher affinity for dopamine than D1
Nature 350:614–619.

https://doi.org/10.1038/350614a0
- PubMed
- Google Scholar
1. Takasaki J
2. Saito T
3. Taniguchi M
4. Kawasaki T
5. Moritani Y
6. Hayashi K
7. Kobori M
(2004) A novel Galphaq/11-selective inhibitor
The Journal of Biological Chemistry 279:47438–47445.

https://doi.org/10.1074/jbc.M408846200
- PubMed
- Google Scholar
(1996) Activation-induced subcellular redistribution of Gs alpha
Molecular Biology of the Cell 7:1225–1233.

https://doi.org/10.1091/mbc.7.8.1225
- PubMed
- Google Scholar
1. Wei H
2. Ahn S
3. Shenoy SK
4. Karnik SS
5. Hunyady L
6. Luttrell LM
7. Lefkowitz RJ
(2003) Independent beta-arrestin 2 and G protein-mediated pathways for angiotensin II activation of extracellular signal-regulated kinases 1 and 2
PNAS 100:10782–10787.

https://doi.org/10.1073/pnas.1834556100
- PubMed
- Google Scholar
1. Zimmerman B
2. Beautrait A
3. Aguila B
4. Charles R
5. Escher E
6. Claing A
7. Bouvier M
8. Laporte SA
(2012) Differential beta-arrestin-dependent conformational signaling and cellular responses revealed by angiotensin analogs
Science Signaling 5:ra33.

https://doi.org/10.1126/scisignal.2002522
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Article and author information

Author details

Charlotte Avet

Institute for Research in Immunology and Cancer (IRIC), and Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, Canada

Contribution
Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

Contributed equally with
Arturo Mancini

Competing interests
No competing interests declared
Arturo Mancini

Domain Therapeutics North America, Montréal, Canada

Contribution
Conceptualization, Formal analysis, Investigation, Methodology, Resources, Writing – original draft, Writing – review and editing

Contributed equally with
Charlotte Avet

Competing interests
was employee of Domain Therapeutics North America during this research
Billy Breton

Domain Therapeutics North America, Montréal, Canada

Present address
Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montréal, Canada

Contribution
Conceptualization, Investigation, Methodology

Contributed equally with
Christian Le Gouill and Alexander S Hauser

Competing interests
was employee of Domain Therapeutics North America during this research. Has filed patent application (US20200256869A1) related to the biosensors used in this work and the technology has been licensed to Domain Therapeutics
Christian Le Gouill

Institute for Research in Immunology and Cancer (IRIC), and Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, Canada

Contribution
Conceptualization, Methodology

Contributed equally with
Billy Breton and Alexander S Hauser

Competing interests
has filed patent application (US20200256869A1) related to the biosensors used in this work and the technology has been licensed to Domain Therapeutics
Alexander S Hauser

Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark

Contribution
Formal analysis, Writing – review and editing

Contributed equally with
Billy Breton and Christian Le Gouill

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0003-1098-6419
Claire Normand

Domain Therapeutics North America, Montréal, Canada

Contribution
Investigation

Competing interests
was employee of Domain Therapeutics North America during this research
Hiroyuki Kobayashi

Institute for Research in Immunology and Cancer (IRIC), and Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, Canada

Contribution
Investigation

Competing interests
has filed patent application (US20200256869A1) related to the biosensors used in this work and the technology has been licensed to Domain Therapeutics

"This ORCID iD identifies the author of this article:" 0000-0003-4965-0883
Florence Gross

Domain Therapeutics North America, Montréal, Canada

Contribution
Investigation

Competing interests
was employee of Domain Therapeutics North America during this research
Mireille Hogue

Institute for Research in Immunology and Cancer (IRIC), and Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, Canada

Contribution
Investigation

Competing interests
has filed patent application (US20200256869A1) related to the biosensors used in this work and the technology has been licensed to Domain Therapeutics
Viktoriya Lukasheva

Institute for Research in Immunology and Cancer (IRIC), and Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, Canada

Contribution
Investigation

Competing interests
has filed patent application (US20200256869A1) related to the biosensors used in this work and the technology has been licensed to Domain Therapeutics
Stéphane St-Onge

Institute for Research in Immunology and Cancer (IRIC), and Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, Canada

Contribution
Investigation

Competing interests
No competing interests declared
Marilyn Carrier

Institute for Research in Immunology and Cancer (IRIC), and Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, Canada

Contribution
Investigation

Competing interests
No competing interests declared
Madeleine Héroux

Institute for Research in Immunology and Cancer (IRIC), and Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, Canada

Contribution
Investigation, Supervision

Competing interests
No competing interests declared
Sandra Morissette

Domain Therapeutics North America, Montréal, Canada

Contribution
Investigation

Competing interests
was employee of Domain Therapeutics North America during this research
Eric B Fauman

Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, United States

Contribution
Resources

Competing interests
is employee and shares holders of Pfizer
Jean-Philippe Fortin

Pfizer Global R&D, Cambridge, United States

Contribution
Resources

Competing interests
is employee and shares holders of Pfizer
Stephan Schann

Domain Therapeutics, Illkirch-Strasbourg, France

Contribution
Funding acquisition, Resources

Competing interests
is employee and is part of the management of Domain Therapeutics
Xavier Leroy

Domain Therapeutics, Illkirch-Strasbourg, France

Contribution
Conceptualization, Resources, Supervision

For correspondence
xleroy@domaintherapeutics.com

Competing interests
is employee and is part of the management of Domain Therapeutics
David E Gloriam

Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark

Contribution
Formal analysis, Funding acquisition, Supervision, Writing – original draft, Writing – review and editing

For correspondence
david.gloriam@sund.ku.dk

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-4299-7561
Michel Bouvier

Institute for Research in Immunology and Cancer (IRIC), and Department of Biochemistry and Molecular Medicine, Université de Montréal, Montréal, Canada

Contribution
Conceptualization, Formal analysis, Funding acquisition, Methodology, Resources, Supervision, Writing – original draft, Writing – review and editing

For correspondence
michel.bouvier@umontreal.ca

Competing interests
is the president of Domain Therapeutics scientific advisory board. Has filed patent application (US20200256869A1) related to the biosensors used in this work and the technology has been licensed to Domain Therapeutics

"This ORCID iD identifies the author of this article:" 0000-0003-1128-0100

Funding

Canada Research Chairs

Michel Bouvier

Canadian Institutes of Health Research (FDN-148431)

Michel Bouvier

Lundbeckfonden (R218-2016-1266)

David E Gloriam

Lundbeckfonden (R313-2019-526)

David E Gloriam

Novo Nordisk Fonden (NNF18OC0031226)

David E Gloriam

Lundbeckfonden (R278-2018-180)

Alexander S Hauser

Bristol-Myers Squibb

Michel Bouvier

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Shane C Wright for scientific discussion and Monique Lagacé for critical reading of the manuscript. The authors are grateful to the funding from Bristol-Myers Squibb that supported the detection of Gα proteins by endogenous receptors in iPSC cardiomyocytes and promyelocytic HL-60 cells.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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Charlotte Avet
Arturo Mancini
Billy Breton
Christian Le Gouill
Alexander S Hauser
Claire Normand
Hiroyuki Kobayashi
Florence Gross
Mireille Hogue
Viktoriya Lukasheva
Stéphane St-Onge
Marilyn Carrier
Madeleine Héroux
Sandra Morissette
Eric B Fauman
Jean-Philippe Fortin
Stephan Schann
Xavier Leroy
David E Gloriam
Michel Bouvier

(2022)

Effector membrane translocation biosensors reveal G protein and βarrestin coupling profiles of 100 therapeutically relevant GPCRs

eLife 11:e74101.

https://doi.org/10.7554/eLife.74101

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Human

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Cite this article

EMTA ebBRET platform to monitor G protein activation and βarrestin recruitment.

Validation of EMTA ebBRET-based platform to monitor Gα protein activation.

Figure 2—source data 1

BRET-based imagery of p63-RhoGEF-RlucII recruitment to the plasma membrane upon AT1 activation.

BRET-based imagery of Rap1GAP-RlucII recruitment to the plasma membrane upon D2 activation.

BRET-based imagery of PDZ-RhoGEF-RlucII recruitment to the plasma membrane upon TPαR activation.

Heatmaps illustrating the diversity of receptor-specific signaling signatures detected with the EMTA ebBRET platform.

The EMTA ebBRET platform has a unique ability to uncover coupling selectivity between G protein families.

Figure 4—source data 1

Multiple applications using the EMTA ebBRET platform.

Figure 5—source data 1

Detection of direct and indirect (trans) mechanisms of ligand polypharmacology using the Gz/G15 biosensor.

Figure 6—source data 1

Detection of endogenous receptor- and/or G protein-mediated responses in cells with the EMTA ebBRET platform.

Figure 7—source data 1

Author details

Charlotte Avet

Contribution

Contributed equally with

Competing interests

Arturo Mancini

Contribution

Contributed equally with

Competing interests

Billy Breton

Present address

Contribution

Contributed equally with

Competing interests

Christian Le Gouill

Contribution

Contributed equally with

Competing interests

Alexander S Hauser

Contribution

Contributed equally with

Competing interests

Claire Normand

Contribution

Competing interests

Hiroyuki Kobayashi

Contribution

Competing interests

Florence Gross

Contribution

Competing interests

Mireille Hogue

Contribution

Competing interests

Viktoriya Lukasheva

Contribution

Competing interests

Stéphane St-Onge

Contribution

Competing interests

Marilyn Carrier

Contribution

Competing interests

Madeleine Héroux

Contribution

Competing interests

Sandra Morissette

Contribution

Competing interests

Eric B Fauman

Contribution

Competing interests

Jean-Philippe Fortin

Contribution

Competing interests

Stephan Schann

Contribution

Competing interests

Xavier Leroy

Contribution

For correspondence

Competing interests

David E Gloriam

BRET-based imagery of p63-RhoGEF-RlucII recruitment to the plasma membrane upon AT₁ activation.

BRET-based imagery of Rap1GAP-RlucII recruitment to the plasma membrane upon D₂ activation.

Detection of direct and indirect (trans) mechanisms of ligand polypharmacology using the G_z/G₁₅ biosensor.