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

  1. Charlotte Avet
  2. Arturo Mancini
  3. Billy Breton
  4. Christian Le Gouill
  5. Alexander S Hauser
  6. Claire Normand
  7. Hiroyuki Kobayashi
  8. Florence Gross
  9. Mireille Hogue
  10. Viktoriya Lukasheva
  11. Stéphane St-Onge
  12. Marilyn Carrier
  13. Madeleine Héroux
  14. Sandra Morissette
  15. Eric B Fauman
  16. Jean-Philippe Fortin
  17. Stephan Schann
  18. Xavier Leroy  Is a corresponding author
  19. David E Gloriam  Is a corresponding author
  20. Michel Bouvier  Is a corresponding author
  1. Institute for Research in Immunology and Cancer (IRIC), and Department of Biochemistry and Molecular Medicine, Université de Montréal, Canada
  2. Domain Therapeutics North America, Canada
  3. Department of Drug Design and Pharmacology, University of Copenhagen, Denmark
  4. Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, United States
  5. Pfizer Global R&D, United States
  6. Domain Therapeutics, France

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 Gs). 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 (Gs, Gi/o, Gq/11, and G12/13) 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 Gs) 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 Gi/o, Gq/11, and G12/13, the dissociation of the Gα subunit from the plasma membrane for Gs 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 Gq/11, Gi/o, or G12/13, 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 Gs exists, the assay was modified taking advantage of Gαs dissociation from the plasma membrane following its activation (Wedegaertner et al., 1996). In this configuration, the RlucII is directly fused to Gαs (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).

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 Gαs 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 Gαs (Gαs-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 Gi/o, Gq/11, or G12/13 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 Gi/o binding domain of Rap1GAP (Jordan et al., 1999; Meng et al., 1999) to selectively detect Gi/o activation. The dopamine-promoted increase in ebBRET between Rap1GAP-RlucII and rGFP-CAAX in the presence of Gαi/o subunits was not affected by the Gq/11-selective inhibitor UBO-QIC (a.k.a., FR900359; Schrage et al., 2015; Figure 2A, left), whereas the Gαi/o family inhibitor, pertussis toxin (PTX), completely blocked the response for all members of Gαi/o family except for Gαz, 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 Gq/11-coupled receptor, promoted ebBRET between the RlucII-fused Gq/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 Gαq/11 subunits was not significantly (p = 0.077, 0.0636 and 0.073 for Gq, G11, and G14, respectively) affected by PTX (Figure 2B, right), whereas UBO-QIC completely blocked the response for all members of Gαq/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 Gi/o (ΔGi/o) or Gq/11 (ΔGq/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 Gi/o (i.e.: Gi1, Gi2, Gi3, GoA, GoB, or Gz) or Gq/11 (i.e.: Gq, G11, G14, or G15) families.

Figure 2 with 6 supplements see all
Validation of EMTA ebBRET-based platform to monitor Gα protein activation.

(A) Pharmacological validation of the Gαi/o activation sensor. HEK293 cells were transfected with the D2 receptor and the Gαi/o family-specific sensor, along with each Gαi/o subunit. Concentration-response curve using the Gαi/o activation sensor, in the presence or absence of UBO-QIC (left) or PTX (right) inhibitors. Insets; Emax values determined from concentration-response curves of inhibitor-pretreated cells. (B) Pharmacological validation of the Gαq/11 activation sensor. HEK293 cells were transfected with the GnRH receptor and the Gαq/11 family-specific sensor, along with each Gαq/11 subunit. Concentration-response curve using Gαq/11 activation sensor, in the presence or absence of UBO-QIC (left) or PTX (right) inhibitors. Insets; Emax values determined from dose-response curves of inhibitor-pretreated cells. (C) Validation of the Gα12/13 activation sensor. Cells were transfected with the CB1 receptor and one of the Gα12/13 activation sensors, along with the Gα12 or Gα13 subunits. Concentration-response curves of HEK293 cells (top) or the parental and devoid of G12/13 (ΔG12/13) 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 Gαs activation sensor. HEK293 cells were transfected with the GPBA receptor and the Gαs activation (left and central) or the EPAC (right) sensors. Left: Concentration-response curves using the Gαs activation sensor in the presence or absence of UBO-QIC or PTX, inhibitors of Gαq or Gαi/o, respectively. Central: Concentration-response activation of the Gαs sensor using CTX, a Gαs activator. Right: Concentration-response curve using the EPAC sensor. Inset; Emax 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 (Emax 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.

The selectivity of the G12/13 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 G12/13 pharmacological inhibitor, we used HEK293 cells genetically deleted for Gα12 and Gα13 proteins (ΔG12/13) to further confirm the response selectivity. As expected, PDZ-RhoGEF-RlucII/rGFP-CAAX ebBRET was observed only following reintroduction of either Gα12 (ΔG12/13_+G12) or Gα13 (ΔG12/13_+G13) (Figure 2C, bottom left). The G12/13 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 (ETA) can activate Gq/11, Gi/o, and G12/13 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 Gi/o, p63-RhoGEF for Gq/11, and PDZ-RhoGEF for G12/13) significantly increased the recruitment of the effector-RlucII to the plasma membrane. A recent study (Chandan et al., 2021) showed that Gi/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 G12/13 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 G12/13 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 13) thus providing spatiotemporal resolution for the imaging detection of Gαi/o, Gαq/11, and Gα12/13 activation.

Video 1
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 Gαq 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
BRET-based imagery of Rap1GAP-RlucII recruitment to the plasma membrane upon D2 activation.

HEK293 cells expressing the Rap1GAP-RlucII/rGFP-CAAX sensors with Gαi2 and D2 were stimulated with dopamine. 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 3
BRET-based imagery of PDZ-RhoGEF-RlucII recruitment to the plasma membrane upon TPαR activation.

HEK293 cells expressing the PDZ-RhoGEF-RlucII/rGFP-CAAX + Gα13 and TPαR were stimulated with U46619. 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).

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 Gαs 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 Gαs-RlucII and rGFP-CAAX (Figure 2D, left). Cholera toxin (CTX), which directly activates Gαs (De Haan and Hirst, 2004), led to a decrease in ebBRET (Figure 2D, center), confirming that loss of Gαs plasma membrane localization results from its activation. The potency of lithocholic acid to promote Gs 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 Gs-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: Gαs, Gαi1, Gαi2, GαoA, GαoB, Gαz, Gα12, Gα13, Gαq, Gα11, Gα14, Gα15 and βarrestin 2 as well as βarrestin 1 and 2 in the presence of GRK2 (Supplementary file 3). Emax 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 Emax 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 Gs), 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 Gi/o family. Indeed, 27% of the receptors coupling to Gi/o only activated this subtype family in comparison to 0, 2.4 and 9.1% for receptors activating G12/13, Gq/11, and Gs, 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-HT2C; G12/13 for adenosine 2A (A2A) and prostaglandin E1 (EP1) receptors; Gi/o for corticotropin-releasing hormone receptor 1 (CRFR1), ETA and G protein-coupled receptor 39 (GPR39)) or higher (ex: Gz for serotonin 5-HT2B; G15 for adenosine 3 (A3) and melanocortin 3 (MC3R) receptors; G12 for bradykinin 2 (B2), cholecystokinin A (CCK1), chemokine receptor 6 (CCR6) and ETA receptors; G12/13 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.

Figure 3 with 3 supplements see all
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 Emax (left) and pEC50 (right) data. Empty cells (grey) indicate no detected coupling. IUPHAR receptor names are displayed.

Figure 4 with 1 supplement see all
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 Emax values from Figure 3.

When examining the frequency of coupling for each Gα subunit family (Figure 4C), the Gi/o family members were the most commonly activated, with 89% of the tested receptors activating a Gi/o family member. In contrast, only 33%, 49%, and 45% of the receptors activate Gs, G12/13, or Gq/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 Gi/o-coupled receptors can couple to only a subpopulation of the family (Figure 4—figure supplement 1A). For the Gq/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 Gi/o family was observed. The greatest similarities were observed between GαoB and either GαoA or Gαz, and the lowest between Gαi1 and Gαz. A striking example of intra-family coupling selectivity is the serotonin 5-HT2B that activates only GαoB and Gαz and GPR65 that selectively activates GαoB. Similarly, when considering the ligand-promoted responses above our threshold criteria (see Materials and methods), histamine H2 and MC3R receptors show preferred activation of GαoB and Gαz, whereas the prostaglandin F (FP) and neuropeptide Y5 (Y5) receptors preferentially activate GαoB, GαoA, and Gαz. 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 Emax 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 Gαs (21 vs. 28), Gαi1 (54 vs. 56), Gαq (31 vs. 34), and Gα14 (36 vs. 40). In contrast, more receptors activating Gα12 (29 vs. 23), Gαo (59 vs. 41), Gα13 (30 vs. 15), Gαz (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.: Gs, Gi/o, Gq/11, or G12/13) and not at the single G protein subtype level, we detected less couplings than what was reported in GtP for Gαs (32 vs. 37), but more for Gαi/o (89 vs. 69), Gαq/11 (81 vs. 48), and Gα12/13 (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 G12/13 and G15 specifically, the difference with the GtP dataset most likely results from the fact that in most cases G12/13 or G15 activation were determined indirectly since, until their recent description (G12/13: 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 G12/13 and G15 couplings

Given the overrepresentation of both G12/13 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 G12/13, we used the PKN-based BRET biosensor detecting Rho activation downstream of either G12/13 or Gq/11 (Namkung et al., 2018) and the MyrPB-Ezrin-based BRET biosensor detecting the activation of Ezrin downstream of G12/13 (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 Gq/11 inhibitor YM-254890, confirming that these receptors activate Gα12/13.

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 Gq/11 members, expression of Gα15 significantly increased the PGE2- and Dynorphin A- promoted calcium responses. For α2A adrenergic (α2AAR) and vasopressin 2 (V2) receptors that couple other Gq/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 α2AAR 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 Gαi2-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 Gαi2 (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.

Figure 5 with 1 supplement see all
Multiple applications using the EMTA ebBRET platform.

(A) Inverse agonist activity detection. Left: Gαi2 activation in HEK293 cells transfected with the Rap1GAP-RlucII/rGFP-CAAX sensors with untagged Gαi2 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 Gαi2 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:z activation in HEK293 cells transfected with the Rap1GAP-RlucII/rGFP-CAAX sensors with untagged Gαz 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 Gαz 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.

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 Gz, 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 Gαq, Gαi2, 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 Gαi2, it lost the ability to activate Gαq and βarrestin2. In contrast, variant Arg129His at the neighboring position resulted in an increased constitutive βarrestin2 recruitment and a loss of Gαi2 and Gαq protein signaling.

Combining Gz 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 Gαz (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 Gz/G15 sensor captured the activation of receptors largely or uniquely coupled to either Gαz (e.g. CB2) or Gα15 (e.g. A2A and A2B), as well as receptors coupled (to varying degrees) to both pathways. The usefulness of the Gz/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 α2AAR 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 Gz/G15 or the Gi2- and GoB-selective sensors and βarrestin2 sensor (Figure 6B, top). Similarly, use of the α2AR selective antagonist, WB4101, allowed to confirm that dopamine can activate Gαi2, GαoB and βarrestin2 through the α2AAR (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 α2AAR by dopamine was confirmed by showing that treatment with the D2-family receptor selective antagonist eticlopride had negligible effect on dopamine-mediated activation of Gαi2 and GαoB in cells heterologously expressing α2AAR, confirming that the response did not result from the activation of endogenously expressed dopamine receptor. In contrast, eticlopride blocked the activation of Gαi2 and GαoB in cells heterologously expressing D2 (Figure 6—figure supplement 2).

Figure 6 with 2 supplements see all
Detection of direct and indirect (trans) mechanisms of ligand polypharmacology using the Gz/G15 biosensor.

(A) Test of the Gz/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 Gz/G15 biosensor and one of the 24 receptors listed. (B) Cross-activation of D2 and α2AAR by other natural ligands. For the agonist mode read, HEK293 cells expressing D2 or α2AAR and either the Gαi2, GαoB, 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 α2AAR 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 α2AAR 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 GαoB 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 Gq/11-coupled receptor, cells were pretreated with the Gαq 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.

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 Gz/15 and confirmed with the GoB 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). GαoB activation by acetylcholine did not result from direct activation of endogenous muscarinic receptors since no GαoB 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 Gq/11, CB1-expressing cells were pretreated with Gq/11/14 inhibitor UBO-QIC prior to stimulation with acetylcholine. UBO-QIC pre-treatment blocked acetylcholine- but not WIN55,212–2-mediated GαoB 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 Gq/11 by endogenous muscarinic acetylcholine receptors. The combined Gz/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 Gs), 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 Gs) 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 Gi/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.: Gq/11 or Gi/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.

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

Concentration-dependent activation of Gαi2 protein by (A) endogenous S1P1 receptor in iPSC-derived cardiomyocytes transfected with heterologous Gαi2, (B) endogenous FPR2 in promyelocytic HL-60 cells transfected with heterologous Gαi2, (C) endogenous FPR2 in promyelocytic HL-60 cells with endogenous Gi/o proteins and (D) endogenous PAR2 receptor in HEK293 cells with endogenous Gi/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.

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 ETA 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 Gαs thus requiring tagging of the Gαs subunit (Figure 1B, bottom) and monitoring its dissociation from the plasma membrane. Yet, our data show that this translocation reflects Gs 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 Gαi2 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 Gαq activation by the 5-HT2C (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 Gz/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 Gz/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 Gαs (ΔGs), Gα12 and Gα13 (ΔG12/13), Gαq, Gα11, Gα14 and Gα15 (ΔGq/11) and, Gαi, and Gαo (ΔGi/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.

s-67-RlucII (Carr et al., 2014), Gαi1-loop-RlucII and GFP10-Gγ1 (Armando et al., 2014), Gαi2-loop-RlucII and βarrestin2-RlucII (Quoyer et al., 2013), GαoB-99-RlucII (Mende et al., 2018), Gαq-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 Gi/o activation, a construct coding for aa 1–442 of Rap1 GTPase-activating protein (comprising a Gi/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 Gs-mediated Rap1GAP recruitment to the plasma-membrane.

To selectively detect Gq/11 activation, a construct encoding the Gq 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 Gq 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 G12/13 activation, a construct encoding the G12/13 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 G12/13 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 + Gαq and AT1, Rap1GAP-RlucII/rGFP-CAAX + Gαi2 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. Gαs protein activation was measured between the plasma membrane marker rGFP-CAAX and human Gαs-RlucII in the presence of human Gβ1, Gγ9 and the tested receptor. Gαs downstream cAMP production was determined using the EPAC biosensor and GPBA receptor. Gαi/o protein family activation was followed using the selective-Gi/o effector Rap1GAP-RlucII and rGFP-CAAX along with the human Gαi1, Gαi2, GαoA, GαoB, or Gαz subunits and the tested receptor. Gαq/11 protein family activation was determined using the selective-Gq/11 effector p63-RhoGEF-RlucII and rGFP-CAAX along with the human Gαq, Gα11, Gα14, or Gα15/16 subunits and the tested receptor. Gα12/13 protein family activation was monitored using the selective-G12/13 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 Gαi1, Gαi2, Gα12, Gα13, Gαq, Gα11, Gα14, and Gαs but not GαoA, GαoB, Gαz, and Gα15 (Figure 2—figure supplement 6). Gα12/13-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 Gz/G15 biosensor consists of a combination of the following plasmids: rGFP-CAAX, Rap1GAP-RlucII, Gαz, 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 Gαq-118-RlucII, Gα12-136-RlucII or Gα13-130-RlucII with GFP10-Gγ1, or Gαi1-loop-RlucII, Gαi2-loop-RlucII or GαoB-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 Gαq inhibitor UBO-QIC (100 nM, 30 min; Institute for Pharmaceutical Biology of the University of Bonn, Germany), the Gαi/o inhibitor PTX (100 ng/mL, 18 hr; List Biological Laboratories, Campbell, California, USA) or the Gαs 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 α2AAR, 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 Gz/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 ETA 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 G12/13-mediated signal by new identified G12/13-coupled receptor using PKN- or Ezrin-based BRET biosensors, cells were pretreated or not with the Gαq 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 Gαq/11, Rap1GAP-RlucII for Gαi/o or PDZ-RhoGEF-RlucII for Gα12/13) 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 Emax 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 Emax was ≤to 2*mean of the Emax 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 Emax 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 Emax and pEC50 values to compare the signaling efficiency obtained for the 100 GPCRs across all receptors and pathways. Emax 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:

    Emax GPCRxEmax GPCRRefPathway A = Pathway specific normalized score for GPCRx on pathway A ([PSNS GPCRx]Pathway A)

    where: GPCRx is receptor being analyzed, GPCRRef is the receptor giving greatest Emax 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:

    PSNS GPCRxPathway APSNS GPCRxRef pathway= Normalized pathway A coupling score for GPCRx

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

For the safety target panel ligand screen using the combined Gz/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 Gz/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 Gq/11 family members, cells were pretreated with the Gq/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 resourceDesignationSource or referenceIdentifiersAdditional information
Cell line (Homo-sapiens)HEK29310.1038/ncomms12178 (Namkung et al., 2016)HEK293 clonal cell line (HEK293SL cells)
Cell line (Homo-sapiens)ΔGs HEK293 cellsDr. A. Inoue (Tohoku University, Sendai, Miyagi, Japan) 10.1124 /mol.116.106419 (Stallaert et al., 2017)HEK293 cells devoid of functional Gαs
Cell line (Homo-sapiens)ΔG12/13 HEK293 cellsDr. 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)ΔGq/11 HEK293 cellsDr. A. Inoue (Tohoku University, Sendai, Miyagi, Japan) 10.1038/ncomms10156 (Schrage et al., 2015)HEK293 cells devoid of functional Gαq, Gα11, Gα14 and Gα15
Cell line (Homo-sapiens)ΔGi/o HEK293 cellsDr. A. Inoue (Tohoku University, Sendai, Miyagi, Japan)HEK293 cells devoid of functional Gαi and Gαo
Cell line (Homo-sapiens)HL-60ATCCCat. #: CCL-240
Cell line (Homo-sapiens)iCell Cardiomyocytes, 01434FUJIFILM Cellular DynamicsCat. #: R1057
Transfected construct (Homo sapiens)Human Gα subunits-encoding plasmid libraryMissouri 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)Gβ1Missouri S&T cDNA Resource Center (https://www.cdna.org/)Cat. #: GNB0100000
Transfected construct (Homo sapiens)Gγ9Missouri S&T cDNA Resource Center (https://www.cdna.org/)Cat. #: GNG0900000
Transfected construct (Homo sapiens)Gαs-67-RlucII10.1074/jbc.M114.618819(Carr et al., 2014)
Transfected construct (Homo sapiens)Gαi1-loop-RlucII10.1096/fj.13242446 (Armando et al., 2014)
Transfected construct (Homo sapiens)Gαi2-loop-RlucII10.1073/pnas.1312515110 (Quoyer et al., 2013)
Transfected construct (Homo sapiens)GαoB-99-RlucII10.1073/pnas.1804003115(Mende et al., 2018)
Transfected construct (Homo sapiens)Gαq-118-RlucII10.1016 /j.bpj.2010.10.025 (Breton et al., 2010)
Transfected construct (Homo sapiens)Gα12–136-RlucII10.1126/scisignal.aat1631(Namkung et al., 2018)
Transfected construct (Homo sapiens)Gα13–130-RlucII10.1038 /s42003-020-01453-8 (Avet et al., 2020)
Transfected construct (Homo sapiens)GFP10-Gγ110.1096/fj.13242446 (Armando et al., 2014)
Transfected construct (Homo sapiens)GFP10-Gγ210.1038/nsmb1134(Galés et al., 2006)
Transfected construct (Homo sapiens)EPAC10.1124/jpet.109.156398(Leduc et al., 2009)
Transfected construct (Homo sapiens)rGFP-CAAX10.1038/ncomms12178(Namkung et al., 2016)
Transfected construct (Homo sapiens)Rap1GAP-RlucIIThis paperSee Materials and Methods
Transfected construct (Homo sapiens)p63-RhoGEF-RlucIIThis paperSee Materials and Methods
Transfected construct (Homo sapiens)PDZ-RhoGEF-RlucIIThis paperSee Materials and Methods
Transfected construct (Homo sapiens)PKN-RBD-RlucII10.1126/scisignal.aat1631(Namkung et al., 2018)
Transfected construct (Homo sapiens)MyrPB-Ezrin-RlucII10.1242/jcs.255307(Leguay et al., 2021)
Transfected construct (Homo sapiens)βarrestin1-RlucII10.1126/scisignal.2002522(Zimmerman et al., 2012)
Transfected construct (Homo sapiens)βarrestin2-RlucII10.1073/pnas.1312515110 (Quoyer et al., 2013)
Transfected construct (Homo sapiens)GRK2This paperSee Materials and Methods
Transfected construct (Homo sapiens)RAMP3Domain Therapeutics North AmericaN/A
Transfected construct (Homo sapiens)EAAC-110.1016/s0028-3908(98)00091-4 (Brabet et al., 1998)
Transfected construct (Homo sapiens)EAAT-1Domain Therapeutics North AmericaN/A
Transfected construct (Homo sapiens)signal peptide-Flag-AT110.1074/jbc.M114.631119(Goupil et al., 2015)
Transfected construct (Homo sapiens)FLAG-α2BARDomain Therapeutics North AmericaN/A
Transfected construct (Homo sapiens)HA-β2AR10.1074/jbc.M204163200(Lavoie et al., 2002)
AntibodyGαi1 (I-20) (Rabbit polyclonal)Santa CruzCat. #: sc-391 RRID: AB_2247692WB (1:500)
AntibodyGαi2 (T-19) (Rabbit polyclonal)Santa CruzCat. #: sc-7276 RRID:AB_2111472WB (1:500)
AntibodyGαo (K-20) (Rabbit polyclonal)Santa CruzCat. #: sc-387 RRID:AB_2111641WB (1:500)
AntibodyGαz (Rabbit monoclonal)AbcamCat. #: ab154846WB (1:1000)
AntibodyGαs (K-20) (Rabbit polyclonal)Santa CruzCat. #: sc-823 RRID:AB_631538WB (1:500)
AntibodyGα12 (S-20) (Rabbit polyclonal)Santa CruzCat. #: sc-409 RRID:AB_2263416WB (1:500)
AntibodyGα13 (A-20) (Rabbit polyclonal)Santa CruzCat. #: sc-410 RRID:AB_2279044WB (1:500)
AntibodyGαq (E-17) (Rabbit polyclonal)Santa CruzCat. #: sc-393 RRID:AB_631536WB (1:500)
AntibodyGα11 (C-terminal) (Rabbit polyclonal)Sigma-AldrichCat. #: SAB2109181WB (1:500)
AntibodyGα14 (Rabbit polyclonal)Sigma-AldrichCat. #: SAB4300771WB (1:500)
AntibodyGα15 (Rabbit polyclonal)ThermoFisher scientific (Pierce)Cat. #: PA1-29022 RRID:AB_1958024WB (1:5,000)
Antibodyβactin (Mouse monoclonal)Sigma-AldrichCat. #: A5441 RRID:AB_476744WB (1:5,000)
AntibodyAnti-rabbit HRP-coupled (Donkey polyclonal)GE HealthcareCat. #: NA934 RRID:AB_772206WB (1:5,000)
AntibodyAnti-mouse HRP-coupled (Sheep polyclonal)GE HealthcareCat. #: NA931 RRID:AB_772210WB (1:10,000)
Commercial assay or kitFLIPR Calcium 5 Assay KitMolecular DevicesCat. #: R8185
Chemical compound, drugα-linolenic acidCayman ChemicalCat. #: 21,910
Chemical compound, drugα-MSHSigma-AldrichCat. #: M4135
Chemical compound, drugγ-MSHTocrisCat. #: 4,272
Chemical compound, drug[Pyr1]-Apelin 13TocrisCat. #: 2,420
Chemical compound, drug[Sar1, Ile4,8]-Angiotensin IIPeptides InternationalCat. #: PAN-4476-V-1EA
Chemical compound, drug3-hydroxyoctanoic acid (3-HOA)Sigma-AldrichCat. #: H3898
Chemical compound, drug7α–25 dihydroxycholesterolSigma-AldrichCat. #: SML0541
Chemical compound, drugAcetylcholine chlorideTocrisCat. #: 2,809
Chemical compound, drugACT-389949Provided by Bristol-Myers SquibbN/A
Chemical compound, drugAdenosineSigma-AldrichCat. #: A9251
Chemical compound, drugAM-630TocrisCat. #: 1,120
Chemical compound, drugAmylinTocrisCat. #: 3,418
Chemical compound, drugAngiotensin II (Ang II)Sigma-AldrichCat. #: A9525
Chemical compound, drugArginine vasopressin (AVP)Sigma-AldrichCat. #: V9879
Chemical compound, drugAtropineSigma-AldrichCat. #: A0132
Chemical compound, drugBovine serum albuminSigma-AldrichCat. #: A7030
Chemical compound, drugC5aComplement TechnologyCat. #: A144(300)
Chemical compound, drugCalcitoninBachemCat. #: H-2250
Chemical compound, drugCCK Octapeptide, sulfated (CCK8)TocrisCat. #: 1,166
Chemical compound, drugCCL20R&D SystemsCat. #: 360-MP/CF
Chemical compound, drugCCL3 (MIP-1a)R&D SystemsCat. #: 270-LD/CF
Chemical compound, drugCholera Toxin (CTX) from Vibrio choleraeSigma-AldrichCat. #: C8052
Chemical compound, drugCmpd43Provided by Bristol-Myers SquibbN/A
Chemical compound, drugCorticotropin-Releasing Factor (CRF)BachemCat. #: H-2435
Chemical compound, drugCXCL12R&D SystemsCat. #: 350-NS
Chemical compound, drugCXCL13R&D SystemsCat. #: 801 CX/CF
Chemical compound, drugCXCL8R&D SystemsCat. #: 208-IL/CF
Chemical compound, drugDAMGOTocrisCat. #: 1,171
Chemical compound, drugDopamineSigma-AldrichCat. #: H8502
Chemical compound, drugDPCPXTocrisCat. #: 0439
Chemical compound, drugDynorphin ATocrisCat. #: 3,195
Chemical compound, drugEndothelin-1TocrisCat. #: 1,160
Chemical compound, drugEticloprideTocrisCat. #: 1,847
Chemical compound, drugFingolimodProvided by Bristol-Myers SquibbN/A
Chemical compound, drugGastric Inhibitory Peptide (GIP)BachemCat. #: H-5645
Chemical compound, drugGhrelinTocrisCat. #: 1,463
Chemical compound, drugGlucagon (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)BachemCat. #: H-6790
Chemical compound, drugGlucagon-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)BachemCat. #: H-6795
Chemical compound, drugGlucagon-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)BachemCat. #: H-7742
Chemical compound, drugGlutamateSigma-AldrichCat. #: 49,621
Chemical compound, drugGnRH (LH-RH)Peptides InternationalCat. #: PLR-4013
Chemical compound, drugHistamineTocrisCat. #: 3,545
Chemical compound, drugKallidinAnaspecCat. #: 22,853(AN)
Chemical compound, drugLeukotriene B4 (LTB4)Cayman ChemicalCat. #: 20,110
Chemical compound, drugLeukotriene D4 (LTD4)Cayman ChemicalCat. #: 20,310
Chemical compound, drugLitocholic acidSigma-AldrichCat. #: L6250
Chemical compound, drugMelatoninBachemCat. #: Q-1300
Chemical compound, drugMDL 29,951Cayman ChemicalCat. #: 16,266
Chemical compound, drugNeuropeptide FF (NPFF)TocrisCat. #: 3,137
Chemical compound, drugNeuropeptide Y (NPY)BachemCat. #: H-6375
Chemical compound, drugNicotinic acidAbcamCat. #: ab120145
Chemical compound, drugNociceptinTocrisCat. #: 910
Chemical compound, drugNoradrenalineTocrisCat. #: 5,169
Chemical compound, drugOleoyl-Lysophosphatidic acid (O-LPA)Sigma-AldrichCat. #: L7260
Chemical compound, drugOrexin-ABachemCat. #: H-4172
Chemical compound, drugOxytocinTocrisCat. #: 1910
Chemical compound, drugParathyroid 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-AldrichCat. #: P3796
Chemical compound, drugPertussis toxin (PTX) from Bordetella pertussisList Biological LaboratoriesCat. #: 179 A(LB)
Chemical compound, drugpH (proton) (Hydrochloric acid)Sigma-AldrichCat. #: 320,331
Chemical compound, drugProbenecidSigma-AldrichCat. #: P8761
Chemical compound, drugProlume Purple (methoxy e-Coelenterazine; Me-O-e-CTZ)NanolightCat. #: 369
Chemical compound, drugPropionate (sodium salt)Sigma-AldrichCat. #: P1880
Chemical compound, drugProstaglandin D2 (PGD2)Cayman ChemicalCat. #: 12,010
Chemical compound, drugProstaglandin E2 (PGE2)Sigma-AldrichCat. #: P0409
Chemical compound, drugRFamide-related peptide 3 (RFRP3)TocrisCat. #: 4,683
Chemical compound, drugRimonabantCayman ChemicalCat. #: 9000484
Chemical compound, drugSaralasinApexBioCat. #: B5063
Chemical compound, drugSerotoninCayman ChemicalCat. #: 14,332
Chemical compound, drugSLIGKV-NH2 (PAR2 AP)TocrisCat. #: 3,010
Chemical compound, drugSNC80Sigma-AldrichCat. #: S2812
Chemical compound, drugSomatostatin-14BachemCat. #: H-6276
Chemical compound, drugSphingosine 1-phosphateCaymanCat. #: 62,570
Chemical compound, drugTFLLR-NH2 (PAR1 AP)TocrisCat. #: 1,464
Chemical compound, drugTRV027Provided by Bristol-Myers SquibbN/A
Chemical compound, drugUBO-QIC (FR900359)Institute for Pharmaceutical Biology of the University of BonnN/A
Chemical compound, drugUndecanoic acidSigma-AldrichCat. #: 171,476
Chemical compound, drugUrocortin IIPhoenix PharmaceuticalCat. #: 019–30
Chemical compound, drugUTPSigma-AldrichCat. #: U1006
Chemical compound, drugVasoactive Intestinal Peptide (VIP)TocrisCat. #: 1911
Chemical compound, drugWB4101TocrisCat. #: 946
Chemical compound, drugWIN55,212–2Enzo Life SciencesCat. #: BMLCR105
Chemical compound, drugYM-254890Wako Pure Chemical Industries (Fujifilm)Cat. #: 257–00631
Chemical compound, drugZinc chloride (Zn2+)Sigma-AldrichCat. #: 229,997
Software, algorithmPrism, Version 8.3GraphPad
Software, algorithmMATLAB, Version R2019bMathWorks
Software, algorithmImageJ, Version 1.52 aNIH https://imagej.nih.gov/ij/
Software, algorithmScipy, Version 1.4.1https://www.scipy.org
Other96 W white plateGreiner Bio-oneCat. #: 655,083
Other96 W black plate, clear-bottomGreiner Bio-oneCat. #: 655,090
OtherOptiPlate-384, White Opaque 384-well MicroplatePerkin ElmerCat. #: 6007290
Other35 mm poly-d-lysine-coated glass-bottom culture dishesMattekCat. #: P35GC-1.5–14 C
OtherMicroplate washerBioTek InstrumentsCat. #: 405TSUS
OtherD300e Digital DispenserTecan
OtherT8 + Dispensehead CassettesHp (Tecan)Cat. #: 30097370
OtherSynergy NEO Luminescence microplate readerBioTek Instruments
OtherFlexStation 2 Multi-mode, auto-pipetting microplate readerMolecular Devices
OtherInverted microscopeNikon Eclipse Ti-U
Otherx60 objective lensNikon CFI Apochromat TIRF
OtherEMCCD cameraNuvu 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

    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.

Decision letter

  1. William I Weis
    Reviewing Editor; Stanford University School of Medicine, United States
  2. Richard W Aldrich
    Senior Editor; The University of Texas at Austin, United States
  3. Roger Sunahara
    Reviewer; University of California, San Diego, United States

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Effector membrane translocation biosensors reveal G protein and βarrestin coupling profiles of 100 therapeutically relevant GPCRs" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Richard Aldrich as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Roger Sunahara (Reviewer #1).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

The reviewers agree that the authors have generated a useful set of reagents and analysis tools for investigating G protein coupling by GPCRs, and is in principle appropriate as a Tools and Resources contribution, but it was felt that the strengths and weaknesses need to be presented in a more cautious manner. The need for overexpression of receptors, G proteins and effectors (points 2, 3 and 4) is not adequately discussed, and the authors need to make clear to the general reader that the assays indicate what GPCR-G couplings are possible, as distinct from what happens physiologically. It is also felt that the "non modified" G protein argument is overstated given that Gs is modified; nonetheless, there is very likely significant advantage in using unmodified receptors.

1. The authors should show an example in which this assay system shows a clear benefit versus other available high-throughput assays. The authors refer to the companion paper that presents a detailed analysis of selectivity profiles and comparison with existing data sets. It would be better if this analysis (or at least the main points) was simply included in the present manuscript, as this might better demonstrate cases where the EMTA assays outperform other assays, a key claim of the present manuscript. Moreover, the authors say that their "comparative analysis" (line 275) revealed a number of couplings not found in IUPHAR or Inoue, but simple inspection of the data does the same thing. For completeness, that should at least mention that their dataset also lacks some couplings found in other datasets.

2. The authors discuss the caveats with the EMTA approach, stating that the assays require overexpression of both G protein isoforms and receptors, although they can be used with endogenous receptors. It is unclear how overexpression of GPCRs and G proteins could influence the engagement of downstream effectors and possible mislead interpretation about potential biased signaling or polypharmacology. The authors need to present a balanced discussion of the relationship between receptor and G proteins overexpression. Notably, the authors have previously claimed that ligand biased signaling is dependent on cellular context, where various levels of expression of receptor, G proteins, effectors are found. With this in mind, how valuable is the information presented since the measurements were acquired in a non-natural context? Some experimental examples varying receptor and G protein expression and monitoring effector engagement would be ideal to address these issues.

3. Related to point 2, the assays are limited to a subset of effectors. The authors still need to address whether overexpression of the effector-sensor also biases the response. Typical endogenous effectors are expressed at very low levels owing to their roles as catalysts. In this assay, however, overexpression of the sensor is required as a BRET pair. Thus, are the authors confident that over expression of the effector-sensor does not bias the coupling response, either by altering the potency or efficacy of agonist activation?

4. The authors state that effector coupling between G proteins from the same subclass are observed (i.e. different Galphai, G12 vs G13 etc…)? In order to claim this, the authors would need to carefully monitor the level of expression of the G proteins (if not possible by WB then qPCR). For example, in figure 2C, the authors present data that CB1 seems to couple to Galpha 13 more than Galpha 12, two G proteins that have been thought to be functionally equivalent. Could G protein isoforms have different affinities for the biosensors?

5. The authors are for the most part clear about the fact that Gs needed to be modified in their experiments, but in several places they gloss over this in a way that tends to overstate their results. For example, on line 219 they refer to theirs as the first dataset to use "unmodified GPCRs and G proteins" in the same sentence as promiscuous coupling to 2,3 or 4 families. Some of this promiscuous coupling must have been demonstrated with modified Gs. In general the authors should be cautious about the broad claim that the assay uses unmodified G proteins when one of the four major families is indeed modified.

6. The authors considered the effects of glutamate in media for the mGluR studies, but the authors use A1 adenosine receptors to demonstrate constitutive activity, with larger inverse agonist responses than agonist responses. However, a limitation of this example is the ubiquity of adenosine in cell cultures; studying adenosine receptors in HEK cells is not straightforward without testing the effects of adenosine deaminase to metabolize adenine-containing molecules released into the media by the cells themselves. Either deaminase treatment should be performed for this experiment, or a better receptor example with known constitutive activity (e.g. beta2AR or CB1), where the possibility of an endogenous ligand being present is more remote.

7. When discussing the coupling efficiency of GPCRs to subsets of Galpha subunits, the authors highlight the adenosine A2A receptor as one which preferentially couples to Galpha15. The data in the table really suggest that the A2B receptor might be more selective for Galpha15 then A2A.

8. To compare the receptor coupling efficiencies, the authors normalized the dose-response curves of the agonists for each G protein (or arrestin) against a reference receptor on that given pathway. Similarly, the authors illustrated the Emax for each coupling relationship as a normalized response to a reference receptor that yields a maximal response. These are rational interpretations for comparison purposes. For physiological and pharmacological purposes, it might be useful if the authors expressed the coupling specificity in a slightly different manner. It would be useful for readers to see how an effector response would be for a non-canonical coupling event relative to a cognate coupling relationship. For example, what would be the effector response for norepinephrine stimulation of beta1AR with the non-cognate G proteins using an EC50 concentration determined from the Galphas (cognate) measurements? Here the physiological concentrations of norepinephrine that are necessary for Galphas activation (coupling) can be compared to the coupling to other Galphas (or arrestin) at the same concentration. At least under non-pathological conditions, coupling with hormone or even therapeutically relevant drug concentrations could then be compared fairly. The data are already collected by the authors and assembling a table illustrating this comparison should not be too difficult. This sort of interpretation/presentation should be quite informative.

9. The normalization procedure that compares all responses as a percentage of baseline may have unintended consequences for receptors with high constitutive activity or where endogenous ligands may be present in HEK cell cultures (e.g. adenosine, 5-HT, glutamate). Basal responses may be high for the most efficient G proteins but lower for secondary couplers, which could paradoxically give the latter a larger response window. This might skew the results, e.g. does A1 really activate G12 better than Gi subunits, or rather does the G12 sensor simply have a higher window due to lower baseline. This might also explain why Gi looks like a better coupler to mGluR5 than Gq; perhaps the Gq signal is already close to saturation, as glutamate is also difficult to remove.

10. Line 418 the authors claim that EMTA does not require amplification. It's unclear precisely what they mean in this context, but amplification could certainly occur with this assay system, as a single active receptor could maintain many G proteins in the active state. This might be especially true if the EMTA probes themselves prevent binding of RGS proteins, thus prolonging the active lifetime of each G protein. It is now clear that assays that lack amplification are advantageous for studies of ligand bias, therefore the authors should be careful to avoid making this claim.

11. Line 505 the authors refer to the use of EMTA for HTS. BRET is rarely used for true HTS, so this statement might be softened somewhat.

12. The authors should directly address a recent study showing that PDZ-RhoGEF is activated by Gi subunits (https://www.biorxiv.org/content/10.1101/2021.07.15.452545v1.full). Were all experiments with their PDZ-RhoGEF sensor done in the presence of PTX, as in Figure 2C?

13. The authors claim that EMTA allows for imaging of the spatiotemporal activation of the different Biosensors. Although this is convincing for video 2 and video 3, it is less so for video 1 (galphaI). Can the authors provide additional information or an alternative representative video?

14. On lines 300-301, the authors mention that HEK293 cells do not express Galpha15 and that the EP2 and opioid receptors do not couple to Gq/11 family members but couple to Galpha15. However, in Figure 3 supp 3B, there is a clear calcium response upon PGE2 or Dynorphin exposure. Galpha15 expression only potentiated the response. How is calcium signaling activated if it is not Galphaq/11 mediated?

15. The authors suggest that the cross-activation of the D2 and alpha2 receptors by noradrenaline and serotonin is direct (through direct engagement of D2 and alpha2) as the response is inhibited by D2 and alpha2 antagonist. This conclusion would be further strengthened if the response was not inhibited by adrenergic or serotoninergic antagonists (as done with the muscarininc antagonist for CB1-CB2 cross talk).

Reviewer #1 (Recommendations for the authors):

There are some issues that should to addressed by the authors. None are deal breakers but they should be addressed, most likely possible without any further experimentation.

Comments:

The authors nicely state the caveats with the EMTA approach stating that the assays require overexpression of both G protein isoforms and receptors, although can be used with endogenous receptors. As well, they state that the assays are limited to a subset of effectors. The authors still need to address whether overexpression of the effector-sensor also biases the response. Typical endogenous effectors are expressed at very low levels owing to their roles as catalysts. In this assay, however, overexpression of the sensor is required as a BRET pair. Thus, are the authors confident that over expression of the effector-sensor does not bias the coupling response, either by altering the potency or efficacy of agonist activation?

The coupling results are interesting with the adenosine receptors. It appears that most of the adenosine receptors tested displayed high constitutive activity. Is this due to adenosine released in the media? Studying adenosine receptors in HEK cells is not straight forward without testing the effects of adenosine deaminase to metabolize adenine-containing molecules released into the media by the cells themselves. The authors did consider the effects of glutamate in media for the mGluR studies but I'm not certain they considered it for the adenosine receptors.

Also, when discussing the coupling efficiency of GPCRs to subsets of Galpha subunits, the authors highlight the adenosine A2A receptor as one which preferentially couples to Galpha15. The data in the table really suggest that the A2B receptor might be more selective for Galpha15 then A2A.

To compare the receptor coupling efficiencies the authors normalized the dose-response curves of the agonists for each G protein (or arrestin) against a reference receptor on that given pathway. Similarly, the authors illustrated the Emax for each coupling relationship as a normalized response to a reference receptor that yields a maximal response. These are rational interpretations for comparison purposes. For physiological and pharmacological purposes it might be useful if the authors expressed the coupling specificity in a slightly different manner. It would be useful for readers to see how an effector response would be for a non-canonical coupling event relative to a cognate coupling relationship. For example, what would be the effector response for norepinephrine stimulation of beta1AR with the non-cognate G proteins using an EC50 concentration determined from the Galphas (cognate) measurements? Here the physiological concentrations of norepinephrine that are necessary for Galphas activation (coupling) can be compared to the coupling to other Galphas (or arrestin) at the same concentration. At least under non-pathological conditions, coupling with hormone or even therapeutically relevant drug concentrations could then be compared fairly. The data are already collected by the authors and assembling a table illustrating this comparison should not be too difficult. This sort of interpretation/presentation should be quite informative.

Reviewer #2 (Recommendations for the authors):

The authors are for the most part clear about the fact that Gs needed to be modified in their experiments, but in several places they gloss over this in a way that tends to overstate their results. For example, on line 219 they refer to theirs as the first dataset to use "unmodified GPCRs and G proteins" in the same sentence as promiscuous coupling to 2,3 or 4 families. Some of this promiscuous coupling must have been demonstrated with modified Gs.

The authors refer to a companion paper that presents a detailed analysis of selectivity profiles and comparison with existing data sets. It would be better if this analysis (or at least the main points) was simply included in the present manuscript, as this might better demonstrate cases where the EMTA assays outperform other assays, a key claim of the present manuscript.

The authors say that their "comparative analysis" (line 275) revealed a number of couplings not found in IUPHAR or Inoue, but simple inspection of the data does the same thing. For completeness, that should at least mention that their dataset also lacks some couplings found in other datasets.

The term "therapeutically-relevant" is not defined, isn't necessary, and seems a stretch for many of the receptors sampled here, many of which are not demonstrated therapeutic targets.

The authors make frequent use of the term "engage". This is semantics, but the term is not as precise as "bind" or "activate” and seems closer to "bind". In this case I much prefer "activate", after all a key advantage of the EMTA format is that G proteins must be activated, not just bound or engaged by a GPCR.

The authors use A1 adenosine receptors to demonstrate constitutive activity, with larger inverse agonist responses than agonist responses. However, a limitation of this example is the ubiquity of adenosine in cell cultures, which often requires adenosine deaminase treatment to remove completely. A better example might be another receptor with known constitutive activity (e.g. beta2AR or CB1), where the possibility of an endogenous ligand being present is more remote.

Line 364 refers to prior observation of pleiotropic activation of monoamine receptors. A citation is needed.

Line 418 the authors claim that EMTA does not require amplification. It's unclear precisely what they mean in this context, but amplification could certainly occur with this assay system, as a single active receptor could maintain many G proteins in the active state. This might be especially true if the EMTA probes themselves prevent binding of RGS proteins, thus prolonging the active lifetime of each G protein. It is now clear that assays that lack amplification are advantageous for studies of ligand bias, therefore the authors should be careful to avoid making this claim.

Line 505 the authors refer to the use of EMTA for HTS. BRET is rarely used for true HTS, so this statement might be softened somewhat.

The authors should directly address a recent study showing that PDZ-RhoGEF is activated by Gi subunits (https://www.biorxiv.org/content/10.1101/2021.07.15.452545v1.full). Were all experiments with their PDZ-RhoGEF sensor done in the presence of PTX, as in Figure 2C?

The normalization procedure which compares all responses as a percentage of baseline may have unintended consequences for receptors with high constitutive activity or where endogenous ligands may be present in HEK cell cultures (e.g. adenosine, 5-HT, glutamate). Basal responses may be high for the most efficient G proteins but lower for secondary couplers, which could paradoxically give the latter a larger response window. This might skew the results, e.g. does A1 really activate G12 better than Gi subunits, or rather does the G12 sensor simply have a higher window due to lower baseline. This might also explain why Gi looks like a better coupler to mGluR5 than Gq; perhaps the Gq signal is already close to saturation, as glutamate is also difficult to remove.

Reviewer #3 (Recommendations for the authors):

1) The authors highlight that one of the strengths of EMTA is the use of untagged receptor and G proteins but fail to present an example where this constitutes a true benefit when compared to other biosensors or signaling assays.

2) although EMTA functions in some endogenous cases where receptor expression and effector coupling are optimal, in most contexts overexpression of both GPCRs and G proteins is required. It is unclear how overexpression of GPCRs and G proteins could influence the engagement of downstream effectors and possible mislead interpretation about potential biased signaling or polypharmacology. The authors need to present a careful analysis of the relationship between receptor and G proteins overexpression (using qPCR) and effector engagement? This could be done by varying receptor and g protein expression and monitoring effector engagement.

3) The authors have previously claimed that ligand biased signaling is dependent on cellular contexts, where various levels of expression of receptor, G proteins, effectors are found. With this in mind, how valuable is the information presented since the measurements were acquired in a non-natural context?

4) In figure 2B on the right, it appears that PTX leads to a significant inhibition of the G protein coupling (non Galphai) in response to GnRH, is this significant? The authors state on lines 128-129 that the response is not affected by PTX. This should be clarified.

5) The authors state that effector coupling between G proteins from the same subclass are observed (i.e. different Galphai, G12 vs G13 etc…)? In order to claim this, the authors need to carefully monitor the level of expression of the G proteins (if not possible by WB then qPCR). For example, in figure 2C, the authors present data that CB1 seems to couple to Galpha 13 more than Galpha 12, two G proteins that have been thought to be functionally equivalent. Could G protein isoforms have different affinities for the biosensors?

6) The authors claim that EMTA allows for imaging of the spatiotemporal activation of the different Biosensors. Although this is convincing for video 2 and video 3, it is less so for video 1 (galphaI). Can the authors provide additional information or alternative representative video?

7) On lines 300-301, the authors mention that HEK293 cells do not express Galpha15 and that the EP2 and opioid receptors do not couple to Gq/11 family members but couple to Galpha15. However, in Figure 3 supp 3B, there is a clear calcium response upon PGE2 or Dynorphin exposure. Galpha15 expression only potentiated the response. How is calcium signaling activated if it is not Galphaq/11 mediated?

8) The authors suggest that the cross-activation of the D2 and alpha2 receptors by noradrenaline and serotonin is direct (through direct engagement of D2 and alpha2) as the response is inhibited by D2 and alpha2 antagonist. This conclusion would be further strengthened if the response was not inhibited by adrenergic or serotoninergic antagonists (as done with the muscarininc antagonist for CB1-CB2 cross talk).

9) Perhaps the most interesting new biology uncovered in this study is the indirect activation of CB1 and CB2 signaling by muscarinic agonist, a mechanism the authors suggest could involve the secretion of a cannabinoid ligand by muscarinic receptor activation. The authors however stop short of directly demonstrating that this is the case.

https://doi.org/10.7554/eLife.74101.sa1

Author response

Essential revisions:

The reviewers agree that the authors have generated a useful set of reagents and analysis tools for investigating G protein coupling by GPCRs, and is in principle appropriate as a Tools and Resources contribution, but it was felt that the strengths and weaknesses need to be presented in a more cautious manner. The need for overexpression of receptors, G proteins and effectors (points 2, 3 and 4) is not adequately discussed, and the authors need to make clear to the general reader that the assays indicate what GPCR-G couplings are possible, as distinct from what happens physiologically. It is also felt that the "non modified" G protein argument is overstated given that Gs is modified; nonetheless, there is very likely significant advantage in using unmodified receptors.

We thank the reviewers for their positive comments concerning the usefulness of the reagent set presented and validated in our study. We agree with the reviewers that, as for most assays, the EMTA platform has its limitations. Concerning, the notion that the assays indicate what GPCR-G couplings are possible, and not necessarily what happens physiologically, we agree with the reviewers and we had mentioned this point in the discussion of the original manuscript in the following way:

‘A second potential caveat of EMTA is the use of overexpressed GPCRs and G proteins. Some of the responses detected could indeed result from favorable stoichiometries that may not exist under physiological conditions. It follows that the profiling represents the coupling possibilities of a given GPCR and not necessarily the coupling that will be observed in all cell types.’ (lines 472-476 of the revised manuscript).

To emphasize it even further, we now also addressed this point in a more general way as early as the introduction (lines 82-86 of the revised manuscript) and more directly linked it to the EMTA assays in the Results section (line 195 of the revised manuscript).

The reviewer is right that the possibility to use the assay with endogenous levels of receptors or G proteins (discussed below) should not detract from the fact that, in our manuscript, the EMTA platform was mainly used with heterologously expressed receptors and G proteins, since the goal was to perform a large-scale scanning of the G protein coupling potentials for a large collection of GPCRs. Any couplings observed in such high-throughput studies will require further validation to conclude on their physiological relevance in cells or tissues of interest. This has now been reiterated in the discussion on lines 476-478. Yet, it should be noted that the assays for Gq/11 and, Gi/o families can be used in the absence of heterologously expressed G proteins (lines 127-131 of the revised manuscript). However, as we have now indicated on lines 131-133, it is impossible under these conditions to distinguish which members of the family is activated by the receptor. As also noted on lines 442-454 of the revised manuscript, the assay can be used to profile endogenously expressed receptor and G proteins, with no need for overexpression of either the receptor or G proteins, given a sufficient endogenous expression level.

Concerning the special case of Gs that needs to be modified to detect its activation in an EMTA format, this had been mentioned in the original discussion:

‘Another limitation is the lack of a soluble effector protein selective for activated Gαs thus requiring tagging of the Gαs subunit (Figure 1B, bottom) and monitoring its dissociation from the plasma membrane. Yet, our data show that this translocation reflects Gs activation state, justifying its use in a G protein activation detection platform’ (lines 503-507).

For clarity sake, we have now stated the exception of Gs everytime we evoked the ability of detecting G protein activation without modification of either Gα or Gβγ proteins.

1. The authors should show an example in which this assay system shows a clear benefit versus other available high-throughput assays. The authors refer to the companion paper that presents a detailed analysis of selectivity profiles and comparison with existing data sets. It would be better if this analysis (or at least the main points) was simply included in the present manuscript, as this might better demonstrate cases where the EMTA assays outperform other assays, a key claim of the present manuscript. Moreover, the authors say that their "comparative analysis" (line 275) revealed a number of couplings not found in IUPHAR or Inoue, but simple inspection of the data does the same thing. For completeness, that should at least mention that their dataset also lacks some couplings found in other datasets.

We would like to respectfully emphasize that we did not meant to say that our assay system universally outperforms others. To more clearly present the advantages and limitations of EMTA in a more balance matter, the section of advantages, limitations and caveats have been significantly expanded (lines 82-86, 127-133, 163-167, 173-177, 195, 343-347, 442-454, 476-478, 483-501, 589-592 and 597-600 of the revised manuscript).

Yet, to directly address the question of the reviewer regarding examples in which EMTA shows a clear benefit, new data presenting side-by-side comparison of the signals detected with EMTA vs. BRET assays based on Gαβγ dissociation (Gαβγ) (Gales et al., Nat Methods 2005; Gales et al., Nat Struct Mol Biol 2006; Olsen et al., Nat Chem Biol 2020) had been added. As shown in new Figure 2—figure supplement 5, EMTA generated significantly larger assay windows than Gαβγ assays for the 6 Gα subunits tested for the 8 receptors selected (D2, GIP, PTH1, M3, ETA, B1, FP or Cys-LT2). In some cases, a robust response could be observed only with EMTA. These data are now presented in the Results section (lines 173-177).

Given that some of the G12/13 couplings had not been reported by Inoue et al., or in the GtP dataset, the validity of these couplings was confirmed using orthogonal assays downstream of G12/13 in the absence of heterologously expressed G proteins. As shown in modified Figure 3—figure supplement 4A, G12/13 activation by FP and Cys-LT2 was detected using the PKN-based (Namkung et al., Sci Signal 2018) and MyrPB-Ezrin-based (Leguay et al., J. Cell Sci 2021) BRET biosensors, two biosensors detecting Rho activation downstream of G12/13; a response not affected by Gq/11 inhibition. These data are now described in the revised manuscript (lines 314-316). Such confirmation of new couplings compared to Inoue et al., and GtP dataset was also provided for G15 by monitoring calcium-induced responses (see Figure 3—figure supplement 4B).

As requested by the reviewer, although an extensive comparison between datasets is presented in the companion paper, we included a new table directly comparing couplings identified in our study vs. that of Inoue et al., (Supplementary File 4A). This table clearly shows that EMTA not only detected couplings that were not reported in Inoue et al., but also did not detect some of the couplings seen in Inoue et al. This is now discussed more extensively on lines 288-292. Direct comparison with the GtP dataset is less straightforward since all members of a given family are clustered in one category in this database. Yet, when considering the family clusters, some differences can be observed between the couplings identified in our study, and those reported in GtP database. This is now also discussed in lines 292-296 and in Supplementary File 4B, and a more detailed analysis can be found in the companion paper (Hauser et al., 2021 bioRxiv. doi: https://doi.org/10.1101/2021.09.07.459250).

2. The authors discuss the caveats with the EMTA approach, stating that the assays require overexpression of both G protein isoforms and receptors, although they can be used with endogenous receptors. It is unclear how overexpression of GPCRs and G proteins could influence the engagement of downstream effectors and possible mislead interpretation about potential biased signaling or polypharmacology. The authors need to present a balanced discussion of the relationship between receptor and G proteins overexpression. Notably, the authors have previously claimed that ligand biased signaling is dependent on cellular context, where various levels of expression of receptor, G proteins, effectors are found. With this in mind, how valuable is the information presented since the measurements were acquired in a non-natural context? Some experimental examples varying receptor and G protein expression and monitoring effector engagement would be ideal to address these issues.

Concerning the idea that the signaling profile of a receptor can be influence by the expression levels of its downstream transducers and effectors (a concept known as system bias), the reviewer is right. Again, the point of the manuscript is not to determine the signaling profiles of a given receptor in specific physiologically relevant tissues but rather to provide a broad view of the coupling potentials of a large number of receptors. As indicated in the response to the previous comments, this potential caveat of the assays has been emphasized in lines 82-86, 127-133, 465-470, 472-501, 589-592 and 597-600 of the revised manuscript. Given that for all receptors tested the same amount of a given G protein was used to assess their coupling potential, the relative propensity of each of these receptors to activate the different G proteins can be assessed. Such comparison is greatly facilitated by the double normalization used in the study (see lines 208-216) since it 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 proteins activated by a given receptor. This should greatly diminish, if not obliterate, the influence of the G protein and receptor expression levels. Nevertheless, we added a new supplemental figure (Figure 2—figure supplement 3A), demonstrating that varying G protein expression level can modify the amplitude of the response (as expected since the purpose of our platform is to identifying specific Gα subunits activated by a given receptor by overexpressing them) but not the potency (pEC50) of ligand-promoted effectors recruitment by activated-G proteins (Supplementary File 1B). Similarly, varying GPCR expression level only modified the amplitude of the response but not the pEC50 (Figure 2—figure supplement 3B and Supplementary File 1C). These results are now discussed in the revised manuscript (lines 163-167 and 487-489). It should also be noted that in the original manuscript (lines 159-163 of the revised manuscript), we provided evidence that no competition with endogenous G proteins occurs to a significant extent in the heterologous expression configuration, since the potencies of the responses to a given G protein subtype were not affected by the genetic deletion of the different G protein family members (see lines 490-492 of the revised manuscript, Figure 2—figure supplement 1 and Supplementary File 1A).

The question of ligand-biased signaling is a different one. This is a comparison between the propensity of two ligands to lead to the activation of two different pathways. This is theoretically an intrinsic property of the ligand-receptor pair and should be independent of the absolute expression of the partners as long as the two ligands are compared under equivalent conditions. No systematic analysis of ligand-bias has been attempted in the present study. The only reference to ligand-biased signaling concerns an example that was given for type-1 angiotensin II receptor (AT1; see Figure 5B and text lines 349-353 of the manuscript) to illustrate that biases previously reported could be detected using EMTA. We also provided an example showing that mutations of a given receptor (i.e.: GPR17) could affect the signaling preference of this receptor (See Figure 5C and text lines 353-359 of the manuscript). Again, these experiments were performed at equal quantities of effectors and cannot be explain by difference in receptor expression levels since signaling to specific pathway was either untouched or compromised in a mutation-depend manner.

3. Related to point 2, the assays are limited to a subset of effectors. The authors still need to address whether overexpression of the effector-sensor also biases the response. Typical endogenous effectors are expressed at very low levels owing to their roles as catalysts. In this assay, however, overexpression of the sensor is required as a BRET pair. Thus, are the authors confident that over expression of the effector-sensor does not bias the coupling response, either by altering the potency or efficacy of agonist activation?

The reviewer is right in stating that the RlucII-fused effector constructs need to be heterologously expressed in the EMTA assay as they are part of the biosensors. It should however be specified that only part of the PDZ and p63 sequences are included in the biosensors. For the PDZ biosensor, the sequence fused to RlucII include the G12/13 protein binding domain but is lacking the PDZ domain involved in protein-protein interaction, the actin-binding domain and the DH/PH domains involved in GEF activity and RhoA activation (Mohamed Aittaleb et al., Molecular Pharmacology 2010). For the p63 biosensor, the sequence fused to RlucII included the Gq binding domain (which is included in the PH domain), but lacked the N-term part, containing the palmitoylation sites maintaining p63 to plasma membrane, and part of the DH domain involved in RhoA binding/activation (Mohamed Aittaleb et al., Molecular Pharmacology 2010; Mohamed Aittaleb et al., JBC 2011). For Rap1GAP, a bigger part of the protein was kept as two regions are important for the interaction with Gi/o proteins. However, mutations of protein kinase A phosphorylation sites (i.e.: S437A/S439A/S441A) were introduced in order to eliminate any Gs-mediated Rap1GAP recruitment to the plasma-membrane (McAvoy et al., PNAS 2009). Altogether, the use of truncated part and/or modified version of these effector limits the possibilities of interference with other components of the signaling machinery, and serves essentially as a binding detector of the active forms of the G proteins. These precisions have now been added to the Material and Method section of the revised manuscript (see lines 589-592 and 597-600).

Although the ratio of effector-RlucII to rGFP-CAAX is important to define the assay window (maximal response), as is the case in any BRET assay, the expression level of the RlucII-effector should not affect the measured pEC50 of the response. In agreement with this statement, similar pEC50 were obtained for different levels of effector-RlucII expression as illustrated in the new Figure 2—figure supplement 3C, with corresponding pEC50 reported in Supplementary File 1D, and discussed in the section validating the assay platform (lines 163-167 and 499-501 of revised manuscript). These data indicate that heterologous expression of the biosensors did not influence the propensity of a receptor to activate a given G protein. Consistent with this notion, recruitment of the effectors was entirely G protein selective (Figure 2—figure supplement 1 and Figure 2—figure supplement 2) in agreement with the documented selectivity of the G protein effectors used.

4. The authors state that effector coupling between G proteins from the same subclass are observed (i.e. different Galphai, G12 vs G13 etc…) ? In order to claim this, the authors would need to carefully monitor the level of expression of the G proteins (if not possible by WB then qPCR). For example, in figure 2C, the authors present data that CB1 seems to couple to Galpha 13 more than Galpha 12, two G proteins that have been thought to be functionally equivalent. Could G protein isoforms have different affinities for the biosensors?

The reviewer is right that the expression levels of the G proteins belonging to the same subclass (ex: G12 and G13) could theoretically lead to apparent difference in coupling that would be driven by mass action. However, it should be noted that all experiments were carried out using the same amount of G protein cDNA (using the same plasmid backbone) for all receptors tested. Yet, differences could still occur. This is why we used the double normalization approach (see lines 208-216 of the revised manuscript) that allows ranking the coupling propensity of the receptors as a function of the receptor which shows the strongest coupling to a specific G protein subtype. This should greatly diminish, if not obliterate, the influence of the G protein expression level.

In the specific case of G12 and G13, the fact that we observe receptors that couple to G13 but not G12 (14) and reciprocally to G12 but not G13 (6) indicates that a simple difference in expression levels cannot explain the selectivity between the subtypes. Similarly, for receptors that were found to couple to both G12 and G13, some showed greater potencies for G12 whereas others for G13.

It should also be noted that western blot analyses of the G protein expression levels were presented in the original Figure 2—figure supplement 4 (Figure 2—figure supplement 6 of the revised manuscript) to illustrate the level of overexpression vs. endogenous levels for each of the G protein subtype. However, this cannot be use to compare the expression levels of the different G proteins since the different antibodies may have different affinities/avidities. qPCR would also be an inadequate estimate as it would not consider differences in the translation and stability of the individual G proteins.

As indicated above, we believed that our double normalization methods alleviate the possible distortions promoted by different expression levels. The lack of effect of increasing G protein levels on the pEC50 of ligand-promoted G protein activation observed in the new Figure 2—figure supplement 3A, also suggests that the differences observed do not result from different expression levels (see response to question 2).

Another variable that could impact the apparent preference among G protein of the same subclass that is mitigated by the double normalization, is the possibility that the effectors-RlucII constructs may have different affinities for the members of the G protein family that they recognize. This was already acknowledged in the original discussion:

‘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 the 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).’ (lines 465-470 of the revised manuscript).

5. The authors are for the most part clear about the fact that Gs needed to be modified in their experiments, but in several places they gloss over this in a way that tends to overstate their results. For example, on line 219 they refer to theirs as the first dataset to use "unmodified GPCRs and G proteins" in the same sentence as promiscuous coupling to 2,3 or 4 families. Some of this promiscuous coupling must have been demonstrated with modified Gs. In general the authors should be cautious about the broad claim that the assay uses unmodified G proteins when one of the four major families is indeed modified.

We carefully revised the text not to lead the readers to believe that all G protein activities could be detected without modification of the Gα subunit. In every case, we made it clear that this was possible for all Gα subunits except Gαs.

6. The authors considered the effects of glutamate in media for the mGluR studies, but the authors use A1 adenosine receptors to demonstrate constitutive activity, with larger inverse agonist responses than agonist responses. However, a limitation of this example is the ubiquity of adenosine in cell cultures; studying adenosine receptors in HEK cells is not straightforward without testing the effects of adenosine deaminase to metabolize adenine-containing molecules released into the media by the cells themselves. Either deaminase treatment should be performed for this experiment, or a better receptor example with known constitutive activity (e.g. beta2AR or CB1), where the possibility of an endogenous ligand being present is more remote.

We agree with the reviewer that the high level of basal activity observed for the A1 adenosine receptor could result from activation by adenosine in cell culture medium rather than constitutive activity. Yet, despite the fact that adenosine was found to have the same potency to activate A1 and A3 receptors (see Figure 5—figure supplement 1A), high basal activity was observed for A1 but not A3, supporting the notion that it is truly constitutive activity that has been observed. This is now included in the manuscript lines 336-342. Nevertheless, we chose to also provide another example where inverse efficacy could be observed with the EMTA assay. A new panel in Figure 5A indeed describes the constitutive activity of CB1 on Gz activation and the inverse agonist activity of rimonabant, and is described lines 343-347.

7. When discussing the coupling efficiency of GPCRs to subsets of Galpha subunits, the authors highlight the adenosine A2A receptor as one which preferentially couples to Galpha15. The data in the table really suggest that the A2B receptor might be more selective for Galpha15 then A2A.

When we referred to the selectivity of A2A in the manuscript (line 370 of the revised manuscript), it was done in the context of the Gz/G15 dual biosensor. It was presented to provide an example of a receptor that couples to only one of the two pathways (G15 and not Gz) and which activity could be detected by the dual biosensor. Nevertheless, the reviewer is right that this is also the case for A2B, which we now added as an additional example (line 370).

8. To compare the receptor coupling efficiencies, the authors normalized the dose-response curves of the agonists for each G protein (or arrestin) against a reference receptor on that given pathway. Similarly, the authors illustrated the Emax for each coupling relationship as a normalized response to a reference receptor that yields a maximal response. These are rational interpretations for comparison purposes. For physiological and pharmacological purposes, it might be useful if the authors expressed the coupling specificity in a slightly different manner. It would be useful for readers to see how an effector response would be for a non-canonical coupling event relative to a cognate coupling relationship. For example, what would be the effector response for norepinephrine stimulation of beta1AR with the non-cognate G proteins using an EC50 concentration determined from the Galphas (cognate) measurements ? Here the physiological concentrations of norepinephrine that are necessary for Galphas activation (coupling) can be compared to the coupling to other Galphas (or arrestin) at the same concentration. At least under non-pathological conditions, coupling with hormone or even therapeutically relevant drug concentrations could then be compared fairly. The data are already collected by the authors and assembling a table illustrating this comparison should not be too difficult. This sort of interpretation/presentation should be quite informative.

We thank the reviewer for this comment. The information requested by the reviewer can be found in the Supplementary File 2C. 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 (non-canonical couplings) are similar to those of the canonical ones (for example: G12 for CB1; G13 for 5-HT2C; G12/13 for A2A and EP1; Gi/o for CRFR1, ETA and GPR39). Interestingly, in a few cases, it is the potency for the non-canonical pathways that is higher (for example: Gz for 5-HT2B; G15 for A3 and MC3R; G12 for B2, CCK1, CCR6 and ETA; G12/13 for CRFR1 and GPR68). A paragraph has now been added in the manuscript (lines 229-246) to discuss the implications of these potency differences for the potential physiological roles of the newly identified couplings. A note concerning the differences between the members of the same family was also made (lines 267-269). Also, the results have been discussed considering that for most receptors, the potency toward βarrestins is lower than for their canonical G protein, an observation that has also been made in the past using different assays and that is consistent with the role of βarrestins into signaling arrest at the plasma membrane.

9. The normalization procedure that compares all responses as a percentage of baseline may have unintended consequences for receptors with high constitutive activity or where endogenous ligands may be present in HEK cell cultures (e.g. adenosine, 5-HT, glutamate). Basal responses may be high for the most efficient G proteins but lower for secondary couplers, which could paradoxically give the latter a larger response window. This might skew the results, e.g. does A1 really activate G12 better than Gi subunits, or rather does the G12 sensor simply have a higher window due to lower baseline. This might also explain why Gi looks like a better coupler to mGluR5 than Gq; perhaps the Gq signal is already close to saturation, as glutamate is also difficult to remove.

The point raised by the reviewer is valid and important and we had already acknowledged this possibility in the original manuscript (lines 509-515 of the revised manuscript):

‘Finally, because EMTA is able to detect constitutive activity, high receptor expression level 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 Gαi2 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 Gαq activation by the 5-HT2C (Figure 5—figure supplement 1B).’

It should however be noted that although such increased in the basal activity (whether originating from the intrinsic constitutive activity of the receptors or from the endogenous presence of agonists in the media) could affect the maximal agonist-mediated response, it should not dramatically affect the potency determined. Yet, the reduced agonist-promoted response could introduce a bias in the evaluation of the relative efficacies for different pathways (a phenomenon which is true for any assay). Because we did not asses the extent of constitutive activity of each receptor for each pathway, and given that inverse agonists, which would be needed to unambiguously conclude to constitutive activity, do not exist for all receptors, we believed that stating this possible limitation in the manuscript (lines 509-515) is sufficient to alert readers to this potential caveat.

In addition to the examples of A1 and 5-HT2C already presented in the original submission, prompted by the reviewer’s comment, we assessed whether a higher basal activity for the Gq pathway vs. Gi2 for mGluR5 could explain the difference in the agonist-promoted response window. As shown in Author response image 1, the basal activities detected were very similar for Gq and Gi2 and the greater window observed for Gi2 was also observed when examining the non-normalized data. Not to detract for the main message and not to hamper the flow of the manuscript, we elected not to add this data in the main manuscript.

Author response image 1

10. Line 418 the authors claim that EMTA does not require amplification. It's unclear precisely what they mean in this context, but amplification could certainly occur with this assay system, as a single active receptor could maintain many G proteins in the active state. This might be especially true if the EMTA probes themselves prevent binding of RGS proteins, thus prolonging the active lifetime of each G protein. It is now clear that assays that lack amplification are advantageous for studies of ligand bias, therefore the authors should be careful to avoid making this claim.

The reviewer is right that, since one receptor can activate multiple G proteins, some level of amplification similar to those observed for assays monitoring the dissociation of the Gα from Gβγ subunits would occur with EMTA. Given that receptors can activate many G proteins before being inactivated, there are very few assays monitoring GPCR signaling that have no amplification factor. The comment made at line 418 of the original manuscript was in respect to assays that relies on the enzymatic activity of a downstream effectors such as adenylyl cyclase or phospholipase C or artificial detection systems such as gene-reporter assays or TGF-α shedding assay, which would lead to higher level of amplification than EMTA. To avoid confusion, we clarified our thinking by modifying the sentence lines 426-432.

11. Line 505 the authors refer to the use of EMTA for HTS. BRET is rarely used for true HTS, so this statement might be softened somewhat.

In fact, BRET is now regularly used in HTS formats both in academic HTS centers and in biopharmaceutical companies. In the HTS platform of IRIC alone, BRET-based assays have been used in more than 25 screens. Domain Therapeutics (DT) has also run around 50 screens and hit-to-lead campaigns in miniaturized assay format. Further, DT have profiled over 200 variants per receptor, for 60 receptors, each using 4 to 8 biosensors in full concentration-response curves, using the EMTA platform. Some examples of such screens have already been published (Hugo Lavoie et al., Nat Chem Biol. 2013; Leguay et al., J Cell Sci. 2021; Giubilaro et al., Nat Commun. 2021). It should also be noted that the Z’ obtained for many BRET-based assays, including EMTA, are larger than 0.6 and thus sufficiently robust for HTS. An example of Z’ obtained for the histamine 1 (H1) receptor using the combined Gz/G15 sensor performed in 384-wells plate is provided for the reviewer’s perusal (Author response image 2). We respectfully think that this is sufficient to mention that the assays would be amenable for HTS. Yet, to be more conservative, we replaced ‘…may be used for high throughput screening’ by ‘…could be amenable for high throughput screening’ (line 531).

Author response image 2

12. The authors should directly address a recent study showing that PDZ-RhoGEF is activated by Gi subunits (https://www.biorxiv.org/content/10.1101/2021.07.15.452545v1.full). Were all experiments with their PDZ-RhoGEF sensor done in the presence of PTX, as in Figure 2C?

In the original manuscript, we reported that our PDZ-RhoGEF sensor detected G12/13 activation but not Gi/o activation. Indeed, when using ETA receptor, which was found to activate all G protein families, we only detected the PDZ-RhoGEF sensor activity upon heterologous expression of Gα12 or Gα13 but none of other G proteins subtypes, including Gi/o (see Figure 2—figure supplement 2). The selectivity of our PDZ-RhoGEF for G12/13 vs. Gi/o is also illustrated by the fact that many receptors that strongly activate members of the Gi/o family did not promote a PDZ-RhoGEF response. The apparent paradox between the results of Chandan et al., indicating that PDZ-RhoGEF can be activated by Gi/o family members and the selectivity of our PDZ-RhoGEF construct for G12/13 may result from the fact that, in contrast to the full length PDZ-RhoGEF used by Chandan et al., we used a truncated version of PDZ-RhoGEF that only contains the G12/13 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 (Mohamed Aittaleb et al., Molecular Pharmacology 2010). The domain of PDZ-RhoGEF involved in the Gi/o-mediated activation proposed by Chandan et al., remains undetermined and may be different than the G12/13 binding domain of PDZ-RhoGEF used in the present study. A small sentence referring to the preprint of Chandan et al., and discussing the fact that we did not detect the recruitment of our PDZ-RhoGEF upon Gi/o activation has been added on lines 152-158. Given the selectivity of our PDZ-RhoGEF sensor for G12/13 vs. Gi/o and our finding that CB1-mediacted activation of G12/G13 was independent of Gi/o protein family, since the response was not affected by PTX (Figure 2C), PTX was omitted from further G12/G13 assessments.

13. The authors claim that EMTA allows for imaging of the spatiotemporal activation of the different Biosensors. Although this is convincing for video 2 and video 3, it is less so for video 1 (galphaI). Can the authors provide additional information or an alternative representative video?

An alternative representative video for the Gq-activation biosensor (video 1) using the AT1 receptor has been generated, providing a more convincing example than the original one.

14. On lines 300-301, the authors mention that HEK293 cells do not express Galpha15 and that the EP2 and opioid receptors do not couple to Gq/11 family members but couple to Galpha15. However, in Figure 3 supp 3B, there is a clear calcium response upon PGE2 or Dynorphin exposure. Galpha15 expression only potentiated the response. How is calcium signaling activated if it is not Galphaq/11 mediated?

Calcium signal has been shown to result from many distinct signaling cascades. For instance, activation of PLC by Gβγ originating from Gi/o activation has been well documented (WJ Koch et al., JBC 1994; MF Ethier and JM Madison, Am. J. Respir. Cell Mol. Biol., 2006; ZG Gao. and KA Jacobson, Biochem. Pharmacol. 2016; EM Pfeil, et al., Molecular Cell 2020). This could explain the calcium response observed upon κOR activation by Dynorphin A in the absence of overexpressed G15 in Figure 3—figure supplement 4 (of the revised manuscript). Gs-mediated calcium entry has also been described (Zhang et al., J Pharmacol Exp Ther 2001; Christ et al., Br J Pharmacol 2009; Benitah et al., J Mol Cell Cardiol 2010) and could explain the EP2 response in the absence of overexpressed G15. It should be emphasized that the experiment described in Figure 3—figure supplement 4B was to confirm the G15 coupling observed with EMTA using the calcium response promoted by heterologous expression of G15 as an orthogonal assay and not to assess other pathways that could be involved in the calcium response evoked by κOR and EP2.

15. The authors suggest that the cross-activation of the D2 and alpha2 receptors by noradrenaline and serotonin is direct (through direct engagement of D2 and alpha2) as the response is inhibited by D2 and alpha2 antagonist. This conclusion would be further strengthened if the response was not inhibited by adrenergic or serotoninergic antagonists (as done with the muscarininc antagonist for CB1-CB2 cross talk).

As requested by the reviewer, we took the example of dopamine and α2AAR to illustrate the direct activation of a receptor by a ligand other than its natural ligand. Direct activation of the α2AAR by dopamine was confirmed by showing that treatment with the D2-family receptor selective antagonist eticlopride had negligible effect on dopamine-mediated activation of Gαi2 and GαoB in cells heterologously expressing α2AAR. In contrast, eticlopride blocked the activation of Gαi2 and GαoB in cells heterologously expressing D2. These new results are now shown in Figure 6—figure supplement 2 and discussed in the Results section of the revised manuscript (lines 384-390). Because serotoninergic antagonists are notorious for the activity on different catecholamine receptors (Roth et al., Nat Rev Drug Discov. 2004), conducting a similar experiment for the serotonin-mediated activation of D2 and α2AAR would have been hazardous. We therefore, toned-down the conclusion regarding this example.

Reviewer #1 (Recommendations for the authors):

There are some issues that should to addressed by the authors. None are deal breakers but they should be addressed, most likely possible without any further experimentation.

The authors nicely state the caveats with the EMTA approach stating that the assays require overexpression of both G protein isoforms and receptors, although can be used with endogenous receptors. As well, they state that the assays are limited to a subset of effectors. The authors still need to address whether overexpression of the effector-sensor also biases the response. Typical endogenous effectors are expressed at very low levels owing to their roles as catalysts. In this assay, however, overexpression of the sensor is required as a BRET pair. Thus, are the authors confident that over expression of the effector-sensor does not bias the coupling response, either by altering the potency or efficacy of agonist activation?

See response to point 3 of the Essential

The coupling results are interesting with the adenosine receptors. It appears that most of the adenosine receptors tested displayed high constitutive activity. Is this due to adenosine released in the media? Studying adenosine receptors in HEK cells is not straight forward without testing the effects of adenosine deaminase to metabolize adenine-containing molecules released into the media by the cells themselves. The authors did consider the effects of glutamate in media for the mGluR studies but I'm not certain they considered it for the adenosine receptors.

See response to point 6 of the Essential

Also, when discussing the coupling efficiency of GPCRs to subsets of Galpha subunits, the authors highlight the adenosine A2A receptor as one which preferentially couples to Galpha15. The data in the table really suggest that the A2B receptor might be more selective for Galpha15 then A2A.

See response to point 7 of the Essential

To compare the receptor coupling efficiencies the authors normalized the dose-response curves of the agonists for each G protein (or arrestin) against a reference receptor on that given pathway. Similarly, the authors illustrated the Emax for each coupling relationship as a normalized response to a reference receptor that yields a maximal response. These are rational interpretations for comparison purposes. For physiological and pharmacological purposes it might be useful if the authors expressed the coupling specificity in a slightly different manner. It would be useful for readers to see how an effector response would be for a non-canonical coupling event relative to a cognate coupling relationship. For example, what would be the effector response for norepinephrine stimulation of beta1AR with the non-cognate G proteins using an EC50 concentration determined from the Galphas (cognate) measurements ? Here the physiological concentrations of norepinephrine that are necessary for Galphas activation (coupling) can be compared to the coupling to other Galphas (or arrestin) at the same concentration. At least under non-pathological conditions, coupling with hormone or even therapeutically relevant drug concentrations could then be compared fairly. The data are already collected by the authors and assembling a table illustrating this comparison should not be too difficult. This sort of interpretation/presentation should be quite informative.

See response to point 8 of the Essential

Reviewer #2 (Recommendations for the authors):

The authors are for the most part clear about the fact that Gs needed to be modified in their experiments, but in several places they gloss over this in a way that tends to overstate their results. For example, on line 219 they refer to theirs as the first dataset to use "unmodified GPCRs and G proteins" in the same sentence as promiscuous coupling to 2,3 or 4 families. Some of this promiscuous coupling must have been demonstrated with modified Gs.

See response to point 5 of the Essential.

The authors refer to a companion paper that presents a detailed analysis of selectivity profiles and comparison with existing data sets. It would be better if this analysis (or at least the main points) was simply included in the present manuscript, as this might better demonstrate cases where the EMTA assays outperform other assays, a key claim of the present manuscript.

See response to point 1 of the Essential.

The authors say that their "comparative analysis" (line 275) revealed a number of couplings not found in IUPHAR or Inoue, but simple inspection of the data does the same thing. For completeness, that should at least mention that their dataset also lacks some couplings found in other datasets.

See response to point 1 of the Essential.

The term "therapeutically-relevant" is not defined, isn't necessary, and seems a stretch for many of the receptors sampled here, many of which are not demonstrated therapeutic targets.

Looking back at the list, we feel confident that all receptors studied are either already the target of clinically used drugs (74 receptors reported in Drugbank database), presently considered for the pre- or clinical development of drugs (6 receptors are currently investigational in clinical trials according to ClinicalTrials.gov database) or directly involved in specific pathophysiological processes. For more clarity, the term "therapeutically-relevant" is now defined lines 192-195 of the revised manuscript and receptors with approved drug or currently investigational in clinical trials reported in Drugbank database are now identified in the Supplementary File 2A.

The authors make frequent use of the term "engage". This is semantics, but the term is not as precise as "bind" or "activate", and seems closer to "bind". In this case I much prefer "activate", after all a key advantage of the EMTA format is that G proteins must be activated, not just bound or engaged by a GPCR.

We thank and agree with the reviewer and the term ‘engage’ has been changed for ‘activate’ in all cases except when describing the non-productive engagement of G12 by the V2 that we have changed for ‘non-productive binding’ (line 462 of the revised manuscript).

The authors use A1 adenosine receptors to demonstrate constitutive activity, with larger inverse agonist responses than agonist responses. However, a limitation of this example is the ubiquity of adenosine in cell cultures, which often requires adenosine deaminase treatment to remove completely. A better example might be another receptor with known constitutive activity (e.g. beta2AR or CB1), where the possibility of an endogenous ligand being present is more remote.

See response to point 6 of the Essential

Line 418 the authors claim that EMTA does not require amplification. It's unclear precisely what they mean in this context, but amplification could certainly occur with this assay system, as a single active receptor could maintain many G proteins in the active state. This might be especially true if the EMTA probes themselves prevent binding of RGS proteins, thus prolonging the active lifetime of each G protein. It is now clear that assays that lack amplification are advantageous for studies of ligand bias, therefore the authors should be careful to avoid making this claim.

See response to point 10 of the Essential

Line 505 the authors refer to the use of EMTA for HTS. BRET is rarely used for true HTS, so this statement might be softened somewhat.

See response to point 11 of the Essential

The authors should directly address a recent study showing that PDZ-RhoGEF is activated by Gi subunits (https://www.biorxiv.org/content/10.1101/2021.07.15.452545v1.full). Were all experiments with their PDZ-RhoGEF sensor done in the presence of PTX, as in Figure 2C?

See response to point 12 of the Essential

The normalization procedure which compares all responses as a percentage of baseline may have unintended consequences for receptors with high constitutive activity or where endogenous ligands may be present in HEK cell cultures (e.g. adenosine, 5-HT, glutamate). Basal responses may be high for the most efficient G proteins but lower for secondary couplers, which could paradoxically give the latter a larger response window. This might skew the results, e.g. does A1 really activate G12 better than Gi subunits, or rather does the G12 sensor simply have a higher window due to lower baseline. This might also explain why Gi looks like a better coupler to mGluR5 than Gq; perhaps the Gq signal is already close to saturation, as glutamate is also difficult to remove.

See response to point 9 of the Essential

Reviewer #3 (Recommendations for the authors):

1) The authors highlight that one of the strengths of EMTA is the use of untagged receptor and G proteins but fail to present an example where this constitutes a true benefit when compared to other biosensors or signaling assays.

See response to point 1 of the Essential

2) Although EMTA functions in some endogenous cases where receptor expression and effector coupling are optimal, in most contexts overexpression of both GPCRs and G proteins is required. It is unclear how overexpression of GPCRs and G proteins could influence the engagement of downstream effectors and possible mislead interpretation about potential biased signaling or polypharmacology. The authors need to present a careful analysis of the relationship between receptor and G proteins overexpression (using qPCR) and effector engagement? This could be done by varying receptor and g protein expression and monitoring effector engagement.

See response to point 2 of the Essential

3) The authors have previously claimed that ligand biased signaling is dependent on cellular contexts, where various levels of expression of receptor, G proteins, effectors are found. With this in mind, how valuable is the information presented since the measurements were acquired in a non-natural context?

See response to point 2 of the Essential

4) In figure 2B on the right, it appears that PTX leads to a significant inhibition of the G protein coupling (non Galphai) in response to GnRH, is this significant? The authors state on lines 128-129 that the response is not affected by PTX. This should be clarified.

Although there is a tendency for reduction of G protein activation in presence of PTX, no statistically significant difference was found compared to untreated cells (p=0.077, 0.0636 and 0.073 using Unpaired t-test for Gq, G11 and G14, respectively). The term ‘not significantly’ and p values have been added to the sentence (lines 124-125 of revised manuscript).

5) The authors state that effector coupling between G proteins from the same subclass are observed (i.e. different Galphai, G12 vs G13 etc…)? In order to claim this, the authors need to carefully monitor the level of expression of the G proteins (if not possible by WB then qPCR). For example, in figure 2C, the authors present data that CB1 seems to couple to Galpha 13 more than Galpha 12, two G proteins that have been thought to be functionally equivalent. Could G protein isoforms have different affinities for the biosensors?

See response to point 4 of the Essential

6) The authors claim that EMTA allows for imaging of the spatiotemporal activation of the different Biosensors. Although this is convincing for video 2 and video 3, it is less so for video 1 (galphaI). Can the authors provide additional information or alternative representative video?

See response to point 13 of the Essential

7) On lines 300-301, the authors mention that HEK293 cells do not express Galpha15 and that the EP2 and opioid receptors do not couple to Gq/11 family members but couple to Galpha15. However, in Figure 3 supp 3B, there is a clear calcium response upon PGE2 or Dynorphin exposure. Galpha15 expression only potentiated the response. How is calcium signaling activated if it is not Galphaq/11 mediated?

See response to point 14 of the Essential

8) The authors suggest that the cross-activation of the D2 and alpha2 receptors by noradrenaline and serotonin is direct (through direct engagement of D2 and alpha2) as the response is inhibited by D2 and alpha2 antagonist. This conclusion would be further strengthened if the response was not inhibited by adrenergic or serotoninergic antagonists (as done with the muscarininc antagonist for CB1-CB2 cross talk).

See response to point 15 of the Essential

9) Perhaps the most interesting new biology uncovered in this study is the indirect activation of CB1 and CB2 signaling by muscarinic agonist, a mechanism the authors suggest could involve the secretion of a cannabinoid ligand by muscarinic receptor activation. The authors however stop short of directly demonstrating that this is the case.

We agree with the reviewer that the identification of the indirect activation of CB1 and CB2 signaling by muscarinic agonist is particularly interesting and deserve a deeper investigation of the mechanisms involved. However, we believe that this goes beyond the scope of the present manuscript which is to present a new platform with the description of the signal profiling of 100 GPCRs and informs the community on how the platform could be used to detect different pharmacological phenomenon including crosstalk between receptors.

https://doi.org/10.7554/eLife.74101.sa2

Article and author information

Author details

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1098-6419
  6. Claire Normand

    Domain Therapeutics North America, Montréal, Canada
    Contribution
    Investigation
    Competing interests
    was employee of Domain Therapeutics North America during this research
  7. 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
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4965-0883
  8. Florence Gross

    Domain Therapeutics North America, Montréal, Canada
    Contribution
    Investigation
    Competing interests
    was employee of Domain Therapeutics North America during this research
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. Sandra Morissette

    Domain Therapeutics North America, Montréal, Canada
    Contribution
    Investigation
    Competing interests
    was employee of Domain Therapeutics North America during this research
  15. 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
  16. Jean-Philippe Fortin

    Pfizer Global R&D, Cambridge, United States
    Contribution
    Resources
    Competing interests
    is employee and shares holders of Pfizer
  17. Stephan Schann

    Domain Therapeutics, Illkirch-Strasbourg, France
    Contribution
    Funding acquisition, Resources
    Competing interests
    is employee and is part of the management of Domain Therapeutics
  18. 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
  19. 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
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4299-7561
  20. 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
    ORCID icon "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.

Senior Editor

  1. Richard W Aldrich, The University of Texas at Austin, United States

Reviewing Editor

  1. William I Weis, Stanford University School of Medicine, United States

Reviewer

  1. Roger Sunahara, University of California, San Diego, United States

Publication history

  1. Preprint posted: April 24, 2020 (view preprint)
  2. Received: September 21, 2021
  3. Accepted: March 17, 2022
  4. Accepted Manuscript published: March 18, 2022 (version 1)
  5. Version of Record published: April 12, 2022 (version 2)

Copyright

© 2022, Avet et al.

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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  1. Charlotte Avet
  2. Arturo Mancini
  3. Billy Breton
  4. Christian Le Gouill
  5. Alexander S Hauser
  6. Claire Normand
  7. Hiroyuki Kobayashi
  8. Florence Gross
  9. Mireille Hogue
  10. Viktoriya Lukasheva
  11. Stéphane St-Onge
  12. Marilyn Carrier
  13. Madeleine Héroux
  14. Sandra Morissette
  15. Eric B Fauman
  16. Jean-Philippe Fortin
  17. Stephan Schann
  18. Xavier Leroy
  19. David E Gloriam
  20. 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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