Signal profiling of the β1AR reveals coupling to novel signalling pathways and distinct phenotypic responses mediated by β1AR and β2AR

Lukasheva, Viktoriya; Devost, Dominic; Le Gouill, Christian; Namkung, Yoon; Martin, Ryan D.; Longpré, Jean-Michel; Amraei, Mohammad; Shinjo, Yuji; Hogue, Mireille; Lagacé, Monique; Breton, Billy; Aoki, Junken; Tanny, Jason C.; Laporte, Stéphane A.; Pineyro, Graciela; Inoue, Asuka; Bouvier, Michel; Hébert, Terence E.

doi:10.1038/s41598-020-65636-3

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Published: 29 May 2020

Signal profiling of the β₁AR reveals coupling to novel signalling pathways and distinct phenotypic responses mediated by β₁AR and β₂AR

Viktoriya Lukasheva¹^na1,
Dominic Devost²^na1,
Christian Le Gouill¹^na1,
Yoon Namkung³,
Ryan D. Martin²,
Jean-Michel Longpré⁴,
Mohammad Amraei⁵,
Yuji Shinjo⁶,
Mireille Hogue¹,
Monique Lagacé¹,
Billy Breton¹,
Junken Aoki⁶,
Jason C. Tanny²,
Stéphane A. Laporte^2,3,
Graciela Pineyro⁵,
Asuka Inoue⁶,
Michel Bouvier¹ &
…
Terence E. Hébert²

Scientific Reports volume 10, Article number: 8779 (2020) Cite this article

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Abstract

A comprehensive understanding of signalling downstream of GPCRs requires a broad approach to capture novel signalling modalities in addition to established pathways. Here, using an array of sixteen validated BRET-based biosensors, we analyzed the ability of seven different β-adrenergic ligands to engage five distinct signalling pathways downstream of the β₁-adrenergic receptor (β₁AR). In addition to generating signalling signatures and capturing functional selectivity for the different ligands toward these pathways, we also revealed coupling to signalling pathways that have not previously been ascribed to the βAR. These include coupling to G_z and G₁₂ pathways. The signalling cascade linking the β₁AR to calcium mobilization was also characterized using a combination of BRET-based biosensors and CRISPR-engineered HEK 293 cells lacking the Gαs subunit or with pharmacological or genetically engineered pathway inhibitors. We show that both G_s and G₁₂ are required for the full calcium response. Our work highlights the power of combining signal profiling with genome editing approaches to capture the full complement of GPCR signalling activities in a given cell type and to probe their underlying mechanisms.

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Introduction

Recent studies have established that GPCRs engage multiple signalling pathways and that ligands can discriminate between these pathways, which is now defined as functional selectivity or biased agonism^1,2,3,4. Such effects have been characterized by examining the production of individual second messengers or more recently direct examination of proximal effector activation. Identification of signalling pathways beyond canonical effector systems has demonstrated the need to cast a broader net in studying signalling outcomes downstream of a given receptor. One such example is the recent recognition that, in addition to cAMP production, β-arrestin recruitment and ERK1/2 activation, the β₂-adrenergic receptor (β₂AR) can also promote calcium mobilization in a ligand-selective manner^5,6. However, relatively little is known about functional selectivity for the β₁AR. Although several papers have described functional selectivity for β₁AR ligands^7,8, a more comprehensive analysis of distinct pathways operating in living cells and the elaboration of a signalling signature is still lacking. Certain βAR antagonists, likely acting on cardiac β₁AR, can activate a cardioprotective pathway involving β-arrestin signalling. Notably, alprenolol and carvedilol both acted as agonists for this pathway in a manner similar to clinically used agonists such as dobutamine and isoproterenol^9,10. Thus carvedilol and alprenolol may be agonists for β₁AR signalling pathways which might underlie their cardioprotective mechanisms in heart failure, in contrast to numerous other βAR antagonists without such pleiotropic activity. Carvedilol is known to activate ERK1/2 via a pathway involving β-arrestin¹¹. These observations argue that such functional selectively is worth exploring further for both receptors which may have clinical impact in a number of diseases.

Historically, most GPCRs have been defined as being coupled to a primary heterotrimer G protein partner¹². However, it is clear that such definitions are oversimplifications of the actual wiring of receptors into multiple signalling pathways¹³. Therefore, to generate a more global understanding of the repertoire of signalling pathways that can be engaged by a given receptor, and of the possible implications of signalling diversity generated by different ligands, a broader approach taking into account multiple GPCR effectors and downstream signalling cascades is needed^14,15,16,17. Toward that end, we present here an integrated platform of BRET-based biosensors detecting both proximal and distal outputs downstream of the β₁AR. We combine this with CRISPR/Cas9 genome editing^{18,19,20,21,22} and genetically engineered dominant negative approaches to target G proteins to examine the functional consequences of removing individual G proteins on signalling outcomes.

BRET-based biosensors have become a convenient and robust method for characterizing both canonical and novel signalling pathways modulated by GPCRs^{15,16,17,23,24}. These biosensors have distinct designs that allow detection of intermolecular interactions between proteins, intramolecular structural reorganization resulting from protein partners, and ligand or second messenger binding. We show that the combination of such biosensors, including two specifically developed for the present study, with genome editing approaches facilitates detection of new signalling pathways downstream of a given receptor as well as their functional consequences.

Results

General approach

The purpose of the study was to obtain a broad profiling of the signalling activity of the β1-adrenergic receptor to generate a more comprehensive map of its signalling potential. We chose the β₁AR as an example of an extensively studied GPCR to explore the possibility that, in addition to well characterized signalling pathways which have been studied for many decades (i.e.: G_s/cAMP and β-arrestin), new pathways might also be revealed (Supplementary Fig. S1). To explore the potential pleiotropic receptor signalling, seven β-adrenergic ligands were tested across a panel of identified signalling cascades. For this purpose, we used a variety of BRET-based sensors that allowed us to monitor 10 different G protein subtypes (including at least one from each of the 4 Gα subunit classes), β-arrestin2, and the activity of 4 effectors downstream of engaged G proteins. To probe the functional significance of the newly identified β-adrenergic-activated signalling pathways, we combined the use of the biosensors with a CRISPR-Cas9-generated cell line lacking Gα_s, the cognate G protein subtypes engaged by β₁AR as well as a novel engineered dominant negative construct selectively inhibiting Gα_12/13.

G protein pathways engaged by the β₁AR

In order to assess which G protein subtypes were activated by the β₁AR, we used a panel of biosensors designed to capture the activation state of the heterotrimer by measuring the physical separation of Gα and Gβγ subunits via BRET²⁵. Figure 1 shows BRET titration curves for 10 different Gα subunits, indicating that the βAR agonist isoproterenol leads to a robust activation of G_s, G₁₂ and G_z, as measured by a decrease in BRET. Smaller decreases in BRET in response to isoproterenol were also observed for other G proteins (G_i2, G_oA and G_oB). No responses were observed for G_q, G₁₃, G_i1 or G_i3. The lack of activation of different G protein isoforms did not result from insufficient biosensor sensitivity since robust responses were detected for the D2 dopamine receptor toward all Gα_i subtypes (Supplementary Fig. S2), for TPα receptors toward both G₁₂ and G₁₃ (Supplementary Fig. S3a,c) as well as for other GPCRs toward G_q¹⁷, G₁₃²⁶ and G_i^17,27. To further characterize the activation of these G proteins and their downstream effector pathways by a panel of βAR ligands, full concentration-response curves and kinetic measurements were obtained using both the G protein sensors described above and biosensors detecting downstream events representative for each of these pathways.

G_s signalling

To probe the G_s signalling pathway, an EPAC biosensor sensitive to cAMP levels^28,29 was used in conjunction with the G_s activation biosensor (Fig. 2a,b). Similar activation kinetics were detected using both biosensors (Fig. 2c,d) and the effects of both full and partial agonists were revealed by the concentration-responses curves (Fig. 2e,f). A general pattern of concordance for both biosensors (Supplementary Tables S1 and S2) was observed with respect to efficacy and potency, although the differences between full and partial agonists were reduced when using the EPAC biosensor, consistent with the notion of signal amplification at the level of cAMP production.

G_i signalling

For each of the canonical PTX-sensitive Gi family members (G_i1–3, G_oA and G_oB, Fig. 3a), concentration-response curves were generated to further characterize the weak activation detected for some of these isoforms in BRET titration experiments. Reproducible kinetic responses and concentration/response curves could only be obtained for G_i2 (Fig. 3b,c, Supplementary Tables S1, S2). Interestingly, only three agonists (isoproterenol, epinephrine and norepinephrine) could elicit responses from G_i2, suggesting more efficient coupling to G_s. Of note, isoetharine and indacaterol, which were close to full agonist on G_s activation and cAMP production could not evoke any G_i activation. The fact that we only detected G_i2 is not because this biosensor is more sensitive than those for other G_i family members, as we could detect a more robust response for G_i1 than G_i2 for the D2-dopamine receptor which can activate all G_i subfamily members (Supplementary Fig. S2). Although not previously reported for the β₁AR, previous studies have also demonstrated coupling of the β₂AR to G_i using conventional assays^30,31 and we could also detect β₂AR coupling to G_i2 using the BRET-based sensor (Fig. 3d,e).

G_z signalling

G_z is an atypical member of the G_i family as it is insensitive to pertussis toxin. We next investigated if this G protein could be activated by the β₁AR. Our BRET-based biosensor (Fig. 4a) detected robust time- and concentration-dependent activation of G_z by the β₁AR (Fig. 4b,d). The concentration-response curves in Fig. 4d show that, in addition to the three full agonists for G_s, indacaterol also acted as a weak partial agonist for G_z. The lack of detection of the effects of other partial agonists was not caused by differential dynamic windows in each assay as isoproterenol responses were similar in both the G_s and G_z sensors (compare Fig. 1b with Fig. 1i). As G_z coupling has never previously been documented for any βAR subtype, we also examined the ability of the β₂AR to engage G_z. The kinetics of activation in response to isoproterenol were similar for both receptor subtypes (Fig. 4b,c). Further, as expected, the rank order of potency of the ligands was distinct for each receptor subtype, generally reflecting their relative affinities for the two receptors (Fig. 4d,e). However, one notable exception was that despite its characterization as a β₂AR-selective ligand, indacaterol^32,33 only produced a response in the β₁AR, indicating that selectivity also depends on the signalling pathway examined; a further manifestation of functional selectivity. A similar effect was observed for isoetharine, activating G_s through both β₁- and β₂AR (albeit with lower potency for the β₁AR), but only resulting in G_z coupling for the β₂AR (Fig. 4e).

G₁₂ signalling

Another novel feature of β₁AR signalling captured by our biosensor panel was the activation of G₁₂ (Fig. 5a,c,e,g) with no detectable activation of G₁₃ (compare Fig. 1d,e). A previous report³⁴ showed a similar ability of GPR35 to discriminate between G₁₂ and G₁₃. The lack of activation of G₁₃ by the β₁AR did not result from lower sensitivity of the biosensor as we could readily detect its activation by a known activator, the TPα receptor (TPαR, Supplementary Fig. S3a,c). To confirm and further characterize G₁₂ activation, an additional assay monitoring downstream engagement of p115-RhoGEF was designed (Fig. 5b) and again validated using TPαR (Supplementary Fig. S3b,d). This biosensor monitors recruitment of the rgRGS domain of the G_12/13 effector, p115-RhoGEF, directly to Gα₁₂ by monitoring BRET between Gα₁₂-Rluc and p115-RhoGEF-GFP10. The expression of the biosensor components did not affect cell surface receptor expression (Supplementary Fig. S4a,b). Rapid G₁₂ activation kinetics and recruitment of p115-RhoGEF were noted in response to isoproterenol (Fig. 5d,f). The kinetics of the two biosensors were quite different. It is difficult to make direct kinetic comparisons between biosensors based on different designs. In the case of Fig. 5c, the G12 activation biosensor is based on dissociation of Gα from Gβγ whereas the biosensor for RhoGEF is based on recruitment of a subdomain of p115-RhoGEF to the Gα (Fig. 5d). The dynamics of such interactions are most likely different and possibly explain the difference in the kinetics observed. The other compounds tested displayed similar potencies and efficacies for either of the two G₁₂ pathway biosensors, with the exception of the weak partial agonist isoetharine, where activity could only be detected for the G₁₂/Gβγ sensor (compare Fig. 5e,f). Compounds had similar rank order of potencies and efficacies as those observed for G_z.

As for G_z coupling discussed above, engagement of G₁₂ had never been previously described for any βAR subtype. Therefore, the ability of the β₂AR to activate this pathway was also examined. The β₂AR was also capable of activating G₁₂ with potencies that were similar between the two receptors (Fig. 5g). To further characterize the downstream consequences of the engagement of Gα₁₂ by the β₁AR, we next assessed activation of the Rho pathway, a known downstream effector of G₁₂. Recruitment of PKN to the plasma membrane was measured using an ebBRET¹⁷ biosensor based on PKN translocation to the plasma membrane upon Rho activation. This sensor monitors RlucII-tagged PKN (Rluc-PKN) density at the plasma membrane, which is labelled with a BRET acceptor, Renilla reniformis GFP (rGFP-CAAX, Fig. 6a). TPαR, a known activator of the G₁₂/Rho/PKN pathway^35,36,37, was used to validate this biosensor. As shown in Fig. 6c, agonist-mediated activation of TPαR led to an increased BRET signal, reflecting membrane recruitment of PKN. This response was blocked by the TPαR antagonist, SQ 29548. Consistent with the ability of this biosensor to detect Rho activity, a constitutively active mutant form of RhoA, Q63L-RhoA³⁸, promoted robust BRET from the PKN-based sensor (Fig. 6d). To further validate the PKN recruitment assay as a faithful readout of G_12/13 activity, we assessed the effect of co-expressing either WT or constitutively active forms (CAM) of Gα₁₂ or Gα₁₃. As seen in Fig. 6e, expression of WT Gα₁₂ and to a greater extent of CAM Gα₁₂ and CAM Gα₁₃ significantly increased recruitment of PKN to the plasma membrane. Finally, we designed a plasma membrane-targeted inhibitor of G_12/13 activity by fusing the rgRGS domain of P115-RhoGEF (p115-RGS) to the membrane anchoring CAAX domain (p115-RGS-CAAX) (Fig. 6b). As shown in Fig. 6f, expression of p115-RGS-CAAX completely blocked the ability of U46619 to promote PKN recruitment to the plasma membrane as assessed by ebBRET³⁹. The G_q inhibitor YM254890 did not affect the response, confirming that PKN recruitment resulted mainly from G_12/13 activation. The selectivity of p115-RGS-CAAX inhibitory action on G_12/13 was confirmed by its lack of effect on the activation of G_q, G_i2 or G_s-mediated cAMP production stimulated by TPαR, D4R and β₁AR, respectively (Supplementary Fig. S5b–d). Expression of the biosensor component did not influence the cell surface receptor expression (Supplementary Fig. S4c). As shown in Fig. 6g,h, stimulation of the β₁AR promoted PKN recruitment to the plasma membrane that was blocked by the expression of the G_12/13 activity dominant negative p115-RGS-CAAX, confirming engagement and activation of G₁₂ by the β1AR.

Calcium signalling

Using a bioluminescent obelin calcium biosensor⁴⁰, we observed that similar to β₂AR activation²⁰, β₁AR stimulation leads to a calcium response⁴⁰. As shown in Fig. 7, only three of the seven ligands tested (isoproterenol, epinephrine and norepinephrine) were able to stimulate a calcium response (Fig. 7c,d). Interestingly, these three compounds were also identified as full agonists using the G₁₂ biosensor. Ligands such as isoetharine and indacaterol that were almost full agonists for the G_s pathway but only weakly activated G₁₂ were unable to promote significant calcium mobilization, suggesting that G₁₂ could play a role in the calcium response. To test this notion, we assessed βAR-mediated calcium mobilization in the presence or absence of the G_12/13 dominant negative p115-RGS-CAAX construct. Isoproterenol-stimulated calcium mobilization through either βAR isoform (Fig. 7e) was blunted in the presence of p115-RGS-CAAX, confirming the functional relevance of G₁₂ coupling. Again, both receptors were expressed at similar levels which was not altered by the presence of p115-CAAX (Supplementary Fig. S4d). Given that the receptor calcium responses were only partially affected by the inhibition of G_12/13 signalling, we next examined potential contributions of other G proteins activated by these receptors. As shown in Fig. 7f, the isoproterenol-mediated response for either β₁AR or β₂AR was significantly reduced in CRISPR-Cas9-generated cells lacking Gα_s²⁰(ΔGs), again with no change in levels of receptor expression (Supplementary Fig. S4e). Restoration of G_s expression in the ΔGs cells rescued β₁AR and β₂AR-mediated calcium influx, confirming a role for G_s in this response. Taken together, the data indicate that both G_12/13 and G_s contribute to βAR-mediated calcium mobilization.

β-arrestin recruitment

Finally, we examined recruitment of β-arrestin2 to the β₁AR in response to various agonists using a previously described β-arrestin biosensor⁴¹ (Fig. 8). Interestingly, only isoproterenol, epinephrine and norepinephrine acted as full agonists, indacaterol being a partial agonist (Fig. 8c), while isoetharine, xamoterol and salmeterol showed no β-arrestin recruitment.

Global signalling profiles

Given the granularity of the signalling profiles obtained, we analyzed the potential for functional selectivity amongst the pathways engaged by the β₁AR. The operational model⁴² was used to capture signalling efficiency in the form of Log(τ/K_A) (Supplementary Fig. S6, Supplementary Table S3). The propensity of each compound to activate the pathways considered was illustrated using a radial graph format in which each of the vertices represented activity towards one of the biosensors tested. Both maximal response and the efficiency expressed as Log(τ/K_A) are shown. This analysis revealed that maximal response and efficiency profiles of isoproterenol, epinephrine or norepinephrine were practically identical for all the pathways analyzed. However, the patterns were quite different when considering the effects of isoetharine, salmeterol, indacaterol and xamoterol. These 4 compounds were partial agonists to different extents toward the G_s/cAMP pathway and they displayed different abilities to promote detectable activation of G_i2, G_z, G₁₂ and β-arrestin2. The most efficient of the four partial agonists toward G_s/cAMP, indacaterol, was also able to recruit β-arrestin2, and activate Gα₁₂, and Gα_z. Isoetharine could activate G₁₂ and G_s, and salmeterol and xamoterol could only activate G_s. These different signalling profiles are illustrated in Supplementary Fig. S7 in which the relative efficiency is color-coded for each of the signalling pathways studied. Whether this reflects true functional bias or pure partial agonism towards pathways coupled to the activated receptor is difficult to unambiguously determine. When comparing the efficiency of each ligand towards the different pathways using the operational model (log(τ/K_A)), the rank order of coupling efficiency of each compound (except for xamoterol) was respected for all pathways measured (Supplementary Fig. S7). However, although indacaterol and xamoterol had similar efficiencies as epinephrine and norepinephrine to activate the G_s/cAMP pathways, they were much weaker than these two compounds at activating G_i2, G_z, G₁₂ or promoting the recruitment of β-arrestin2, in many cases not activating them at all. To a lesser extent, a similar comment can be made for isoetharine and salmeterol, although their efficiency to activate G_s and cAMP production is slightly less than norepinephrine and epinephrine. In any case, the data clearly reveals distinct signalling profiles for the different ligands that translate into distinct cellular outputs.

Discussion

The β₁AR was chosen here as prototypical GPCR where functional selectivity and biased signalling has been examined, albeit to a limited extent^7,8. In previous studies, different ligands were reported to act in a biased fashion toward either ERK1/2 MAPK or adenylyl cyclase signalling and, in some cases antagonists or inverse agonists for the adenylyl cyclase pathway acted as agonists for the MAPK pathway. These studies and similar ones for other GPCRs^15,17,43 open the possibility that fine-tuning receptor-specific signalling outputs with functionally selective ligands could generate superior therapeutic options for a number of diseases by disfavouring signalling cascades associated with adverse effects without compromising beneficial pathways. To do this would first require a broader approach to the signalosome downstream of the receptors to capture the ensemble of pathways that can be engaged by any given receptor. However, in most cases, only a limited number of pathways have been examined. In addition, comprehensive signal profiling of proximal and distal outputs can reveal novel signalling pathways downstream of a given receptor and how these pathways might interact. The approach proposed in the present study was to combine BRET-based signal profiling with genome editing and the use of pharmacological and genetically engineered inhibitors to allow a more detailed dissection of the relevant signalling architecture in a model cell type, establishing proof of principle.

G protein profiling was first used to identify novel signalling partners for the β₁AR. Our data shows for the first time that both the β₁AR and β₂AR are coupled to the G₁₂ signalling pathway. This was revealed not only by the activity of G₁₂ itself, but also using novel biosensors detecting the engagement of Rho-GEF and the recruitment of PKN downstream of G₁₂ activation, which was also found to contribute to βAR-stimulated calcium mobilization. Interestingly, our data suggest that neither receptor was coupled to G₁₃, despite the fact that robust activation of G₁₃ could be detected in response to TPαR activation using our biosensors. Such selectivity between the two members of the G_12/13 family was also noted for GPR35³⁴, which was found to be better coupled to G₁₃ over G₁₂. The implications for G₁₂ signalling downstream of βAR activation may include cell shape changes as well as cell migration, mediated through activation of the Rho pathway.

Interestingly, we noted that both G_s and G₁₂ seem to be involved in βAR-mediated calcium mobilization, suggesting a role in calcium signalling, beyond the well-characterized activation of voltage-gated calcium channels by βAR in excitable tissues^44,45,46. β₂AR-mediated calcium mobilization in non-excitable HEK 293 cells was previously reported to require transactivation of the purinergic P2Y receptor through a G_s-dependent but cAMP-independent mechanism²⁰. Whether G₁₂ activation is also involved in this transactivation remains to be determined. For compounds such as isoproterenol that activated both G_s and G₁₂, we found that the two pathways contributed to rise in cytoplasmic calcium, as cells deleted for either G_s or in which G_12/13 was inhibited by the G_12/13 dominant negative construct, p115-RGS-CAAX showed calcium responses significantly lower than those observed in the parental cells. Interestingly, compounds such as indacaterol, isoetharine and salmeterol that were strong partial agonists for G_s but weak partial agonist (indacaterol, isoetharine) or unable to activate G₁₂ (salmeterol) could not generate significant calcium mobilization. This is consistent with an important role of G₁₂ in calcium mobilization. Dominant-negative RhoA or the ROCK inhibitor Y-27632 were previously shown to block GPR55-mediated Ca²⁺ transients^47,48, consistent with a role for the G_12/13 pathway in calcium responses. In a recent paper, the β₁AR was proposed to couple to G₁₄ using DREADDs in combination with CRISPR gene deletions³⁹. In theory, part of the calcium response could originate from this pathway as well. Although this cannot be excluded, this is unlikely since RNA-Seq analysis did not show detectable expression of G₁₄ in the cells we used (Supplementary Table S4).

Another novel observation of our study is the subtype-selective coupling of β₁AR to G_i family members. Although coupling to G_i had already been shown for the β₂AR^30,31, our study is the first report showing that the β₁AR can also couple to G_i family members. When considering the pertussis toxin-sensitive G_i isoforms, β₁AR-mediated activation could only be detected for G_i2, indicating a possible selectivity among the G_i protein family members. This apparent selectivity did not result from a different sensitivity of the BRET biosensors themselves, since dopamine D2R-mediated activation of G_i1 was more robust than that of G_i2 while those of G_i3, G_oA and G_oB were equivalent to G_i2. Regardless of whether this also reflects functional selectivity for the D2R G_i/o subtypes engagement, our biosensors have the dynamic range to capture distinct levels of G protein activation by different GPCRs. Thus, as observed for the G_12/13 family, β₁AR can display selectivity among members of a same G protein subfamily. This observation may provide interesting avenues to further explore functional differences among different members of the same Gα subfamily and start to understand the evolutionary pressures that maintained these closely related subtypes.

Our data also reveal for the first-time robust coupling of both the β₁- and β₂AR to G_z, a member of the G_i subfamily lacking an ADP ribosylation site, making them pertussis toxin-insensitive. This subtype has been shown to inhibit adenylyl cyclase types I, V and VI⁴⁹ and has been found to have a very low intrinsic GTPase activity compared to other G protein subtypes, including other G_i isoforms. It is widely expressed in many tissues and is found in high levels in the central and peripheral nervous systems as well as in the platelets. In these circulating cells, G_z knockout mice were found to display abnormal platelet aggregation at physiological concentrations of epinephrine⁵⁰. The physiological implications of this G_z coupling to the βARs remain to be investigated but the possibility of a counter regulatory action on the G_s-stimulated production of cAMP in tissues expressing G_z and the adenylyl cyclase sensitive to this G_i subunit will be worth exploring. It should be noted that the potency of ligands to activate G_z is an order of magnitude lower than that for G_s activation, which would be consistent with a biphasic regulation of adenylyl cyclase, allowing for tight regulation and possibly oscillatory behaviours.

The present paper clearly illustrates that broader profiling of signalling pathways that can be engaged by a receptor can reveal signalling pathways that had not been anticipated before, even for receptors as well studied as the β₁- and β₂AR. We acknowledge that apparent differences in coupling efficacy between the β₁- and β₂AR could result from different expression levels, as they were not directly compared. The data also reveals that different ligands produce distinct signalling profiles leading to the activation to specific subsets of a given receptor’s repertoire. Whether these different profiles result from bona-fide biased signalling or are consequences of different level of partial agonism and different coupling strengths of the different effectors engaged is difficult to unambiguously determine at this point, especially when no bias factor can be calculated because of the absence of response for some of the pathways. Nevertheless, the selective activation of weakly coupled pathways seems to correlate with the overall relative efficacy of the compounds tested, indicating that partial agonism plays an important role in the apparent functional selectivity. However, whether the different signalling profiles result from biased signalling or partial agonism, the end result is that some ligands activate certain pathways but not others and that this is bound to have functional consequences. Many of our biosensors are based on overexpression of G protein subunits which may alter native receptor/G protein stoichiometries. An additional caveat must be considered when using gene deleted lines where rewiring of signalling pathways might occur. Resolving such issues will require a closer focus using distinct approaches.

What impact might the novel signalling pathways uncovered in the present study and the ligand-selective signalling profiles observed have on clinical indications where β-adrenergic agonists or antagonists are used will be an interesting area for further study. For this purpose, it will be critical to move biosensors into more physiologically relevant cell systems as the HEK 293 cell may not reflect important differences in signalling pathways in disease-relevant cells. Our results showed a limited functional bias for the various agonists tested among the different pathways tested downstream of the β₁AR, but was able to robustly discriminate between partial and full agonists. Thus, as a homogenous platform, our approach allows a more global appreciation of the signalling profile of a given cell.

Material and Methods

Cell culture and transfection

HEK 293 and ΔGα cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum, 100 U/ml penicillin/streptomycin at 37 °C in 5% CO₂. The HA-β₁AR HEK 293 stable cell line was maintained in complete medium supplemented with 1 μg/ml of puromycin. Except for G_s titration and concentration-response curves with β₁AR, all transfections were carried out as follows: 48 h before the experiments, cells were transfected in suspension using polyethylenimine at 3:1 PEI/DNA ratio and seeded (~3×10⁴ cells/well) in 96-well/plates pre-treated with poly-D-lysine. Each of the expression vectors and biosensor constructs were diluted in PBS, and the total quantity of DNA was completed to 1 µg/row with salmon sperm DNA. For G_s titration and concentration-response curves, stable HA-β₁AR cells were plated at 9000 cells/well in a poly-ornithine treated white 96-well plate the day before transfection and were transfected using PEI (2.5ug PEI:1ug DNA ratio) at 150 ng total DNA (i.e. biosensor constructs, HA-β₁AR vector and empty pcDNA3.1 vector) per well. 24 h post-transfection, the medium was changed. The HA-β₁AR HEK 293 stable cell line was used for all β₁AR experiments, except for G_q and G₁₃ saturation curves and when experiments were done in parental and ΔGα (ΔGα_s) cell lines. β₁AR-encoding plasmid was also added in the transfection mix for all experiments, except for EPAC and obelin biosensors, where native responses were measured.

Signalling biosensors

G protein activation biosensors: G protein activation was measured by monitoring the separation of Gα from Gβγ subunits. Cells were transiently transfected with the receptor (HA-β₁AR, HA-β₂AR, HA-TPαR or D2R (250–300 ng) along with the BRET-based biosensors composed of the specific Gα-Rluc (i.e. Gα_s-67RlucII (40 ng), Gα_z-94RlucII (60–100 ng), Gα₁₂-84RlucII (50 ng), Gα_i2-loopRlucII (40 ng), GFP10-Gγ1 (200–250 ng) and stoichiometric amounts of the untagged Gβ₁ subunit (100 ng)^51,52. Gα_s, Gα_q, Gα₁₂, Gα₁₃, Gα_i1, Gα_i2, Gα_i3, Gα_z, Gα_oA and Gα_oB (20–60 ng) were used to generate BRET saturation curves. However, only the Gα proteins giving a sufficient signal above vehicle, when activated with isoproterenol, were further studied with additional ligands in kinetics and concentration-response experiments. For saturation and concentration-response experiments, BRET values were monitored 1 to 3 min after agonist addition, except for G_s and G₁₂ where they were monitored 10 min after agonist addition.

EPAC cAMP biosensor: Another BRET-based biosensor was used to monitor changes in cytosolic cAMP was described previously²⁹. This consisted of an N-terminal GFP10 with a 5 amino acid residue (GSAGT) linker to a mutated EPAC1 (ΔDEP; T781A; F782A) biosensor⁵³ and a C-terminal-RlucII with a 5 residue linker (KLPAT)²⁹. Stable HA-β₁AR HEK 293 cells were transiently transfected with 50 ng of EPAC biosensor per row of a 96-well plate. For concentration-response experiments, BRET values were monitored 15 min after agonist addition.

p115RhoGEF biosensor to monitor Gα₁₂ activity: A BRET-based biosensor composed of RGS-homology (RH) domain (amino acids 1–246) of p115RhoGEF fused to GFP10 and G₁₂-84RlucII was used to measure Gα₁₂ activity. Cells were transfected with 40 ng of G₁₂-84RlucII, 500 ng of p115RhoGEF-GFP10 and 300 ng of receptor per row of a 96-well plate. For concentration response experiments, BRET was monitored 2 minutes after agonist addition.

PKN biosensors to monitor Rho activation: Upon receptor stimulation, activated G proteins promote Rho activation through RhoGEF recruitment to the plasma membrane. The activated Rho then recruits the RlucII-tagged effector (PKN) to the PM. Rho activity is monitored using the BRET between RlucII and a membrane-bound rGFP. The Rho binding domain of PKN1 was tagged with RlucII (BRET donor) to monitor its recruitment to the plasma membrane using enhanced bystander BRET with rGFP-CAAX (kRAS)²³. To validate PKN recruitment by TPαR, HEK 293 cells were transfected in suspension with 10–20 ng of PKN-RlucII, 300 ng of CAAX-rGFP and increased concentration of TPαR (0–200 ng) per row of a 96-well plate. Cells were pre-treated with TPαR antagonist SQ29, 548 (1 µM) for 24 min and stimulated for 6 min with U46619 (100 nm) and 5 min of coelenterazine 400a before monitoring the BRET signal. Kinetic experiments were performed in HEK 293 cells using 10 ng of HA-β₁AR, 1 ng of PKN-RlucII and 300 ng of CAAX-rGFP. Prolume purple (1 µM) was added for 6 min and BRET was measured following injection of isoproterenol (1 µM) for 30 sec at 37 °C. To confirm specificity of Rho activation, HEK 293 cells were co-transfected with or without constitutively active RhoA mutant Q63A (50 ng per row of a 96-well plate). To validate the effect of Gα₁₂/Gα₁₃ pathway on PKN recruitment to the plasma membrane, EE-tagged versions of Gα₁₂, Gα₁₃ and Gα₁₂/Gα₁₃ CAM mutants (Q231L and Q226L, respectively) (10 ng) were transfected along with 100 ng of TPαR, 0.5 ng of PKN-RLucII and 300 ng of CAAX-rGFP per row of a 96-well plate. Cells were stimulated for 6 min with either vehicle or U46619 (100 nM), and BRET values were collected using Prolume purple (1 µM final) as a substrate.

β-arrestin recruitment to the receptor: A BRET-based biosensor composed of hβarrestin2-RlucII and β₁AR-GP10 was used to monitor β-arrestin recruitment to the receptor. HEK 293 cells were transfected with 60 ng of hβarrestin2-RlucII and 1 µg of β₁AR-GFP10. For concentration-response experiments, BRET values were monitored 10 min after agonist addition.

p115-CAAX-based inhibitor of G protein-mediated Rho activation. p115-CAAX is composed of the G protein binding domains (rgRGS) of p115RhoGEF, targeted to the plasma membrane using the prenylated polybasic sequence from kRAS. Upon receptor stimulation, p115-CAAX is recruited to activated G₁₂ or G₁₃, preventing recruitment of WT RhoGEF and thus activation of Rho. The rgRGS domain is known to promote GTPase activity, further inhibiting G protein-mediated signalling. To validate p115-CAAX as an inhibitor of G₁₂/G₁₃ pathway, HEK 293 cells were transfected with HA-TPαR (10 ng), PKN-RlucII (1 ng) and rGFP-CAAX (300 ng) along with p115-CAAX or ssDNA (mock). Cells were pre-treated with YM254890 or vehicle for 30 min at 37 °C and stimulated with TPαR agonist U46619 (100 nM) or vehicle for 6 min. For calcium mobilization experiments, HEK 293 cells were transfected with 500 ng of Obelin biosensor, 10 ng of HA-β₁AR or HA-β₂AR and 20 ng of p115-CAAX. Kinetic data for isoproterenol (1 µM) and A23187 (5 µM) were collected for 30 sec at 37 °C, with an integration time of 0.5 sec. To assess the effect of p115-CAAX on G_q/G_i signalling, cells were transfected with 10 ng of receptor, 5 ng of Gα_q/Gα_i2-RlucII, 100 ng of Gβ₁WT, 200 ng of GFP10-Gγ₁ and 20 ng of p115-CAAX. BRET values were collected after 6 min of agonist stimulation (TPαR: U46619, D4R: dopamine) at 37 °C and addition of Prolume purple. The influence of p115-CAAX on cAMP response from endogenous β₂AR or overexpressed β₁AR (10 ng) was assessed using EPAC biosensor (25 ng). BRET values were collected after 10 min of Isoproterenol stimulation and addition of coelenterazine 400a, as described in calcium transfection (see below).

BRET experiments

BRET was in general performed as previously decribed^29,51,52. 48 h after transfection, cells were washed once with PBS and incubated for 1 h at 37 °C in Tyrode’s-HEPES buffer (137 mM NaCl, 0.9 mM KCl, 1 mM MgCl₂, 11.9 mM NaHCO₃, 5.5 mM glucose, 3.6 mM NaH₂PO₄, 25 mM HEPES, and 1 mM CaCl₂, pH 7.4). The expression levels of the energy acceptor GFP10-tagged proteins were measured as total fluorescence using a FlexStation II microplate reader (Molecular Devices, Sunnyvale, CA, USA) with excitation at 400 nm and emission at 510 nm. Before β₁AR activation, cells were exposed to ICI 118,551 (10 nM) for 30 min prior to the experiment in order to inhibit β₂AR activity. For concentration-response experiments, cells were treated, with or without ligands, for the indicated times, and BRET was measured using a TriStar2 LB 942 Multidetection Microplate Reader (Berthold Technologies), equipped with a BRET400-GFP2/10 filter set (acceptor, 515 ± 20 nm; and donor, 400 ± 70 nm filters), 5 min after the addition of 2.5 µM of coelenterazine 400a. Absolute BRET signals (BRET) were derived from emissions detected with the energy acceptor filter divided by emission detected using the energy donor filter, while netBRET signals were obtained by subtracting BRET signals obtained in cells expressing the Rluc-fused donor constructs alone. ΔBRET was calculated by subtracting ligand-induced BRET from vehicle BRET. For kinetic experiments, cells were preincubated with coelenterazine 400a for 5 min, followed by readings for the indicated times, following addition of drugs. For G_s and G₁₂ titration and concentration-response curves, 48 h post-transfection, wells were washed once with Kreb’s/HEPES buffer (146 mM NaCl, 4.2 mM KCl, 0.5 mM MgCl₂, 1 mM CaCl₂, 5.9 mM Glucose and 10 mM HEPES pH 7.4) and 80 μl of Kreb’s/HEPES buffer was added and plates left for 2–3 h at 37 °C. Then, 10 nM ICI 118,551 was added and plates left another 30 min at 37 °C to block endogenous β₂AR. Finally, agonists were added and BRET assessed similarly as described above, using a FLUOstar Optima (BMG) equipped with BRET2 filter set (410 nm/515 nm). Saturation assays were performed initially to determine optimal donor to acceptor ratios for kinetic experiments and concentration-response curves and read 1 to 3 minutes after agonist addition (concentration 1 uM isoproterenol). Kinetic measurements were performed 48 h post-transfection, 5 minutes after addition of coelenterazine 400a (2.5 µM) or Prolume purple (1 µM, 6 min).

Calcium mobilization

An obelin biosensor was used as a calcium reporter as described previously^6,54,55. Stable HA-β₁AR HEK 293 cells were transfected in suspension with 500 ng of WT obelin-pLVXi2H/per row. For experiments performed in parental and cells lacking Gα_s proteins (ΔG_s), cells were transiently transfected with 100 ng of receptor and WT obelin. 48 h after transfection, cells were washed once with Tyrode’s-HEPES buffer and incubated with the obelin substrate, coelenterazine cp (1 µM; Biotium) for $ \sim $2 h in the dark. For concentration-response experiments, increasing concentrations of agonists, diluted in Tyrode’s buffer, were injected into the wells and luminescence was measured using a SpectraMax L (Molecular Devices). Kinetics of activation were determined for each ligand concentration for 60 s and concentration-response curves were determined from the area under the curve (AUC). For Gα_S complementation experiments, HEK 293 T parental or cells lacking Gα_S were transfected with 10 ng of β₁ or β₂AR, 500 ng of obelin, in the presence or absence of 10 ng of Gα_S. Luminescence was measured using a Mithras LB940 microplate reader.

Cell surface ELISA

Parental HEK 293 cells or cells lacking Gα_s were transfected with HA-β₁AR, HA-β₂AR or HA-TPαR. 48 h after transfection cells were washed with PBS and fixed with 3% paraformaldehyde for 10 min at RT. Cells were labelled with mouse anti–HA-HRP antibody (3F10; 1:2000) for 1 h at RT in Wash B buffer (PBS supplemented with 0.5% BSA), followed by extensive washing (3×10 min). Vybrant DyeCycle Orange stain was added for 30 min at a final concentration of 10 μM at RT. Vybrant fluorescence was measured with excitation at 519 nm and emission at 563 nm using a FlexStationII microplate reader (Molecular Devices, Sunnyvale, CA, USA) to control for the number of cells/well. Total luminescence was measured 2 min after the addition of the HRP substrate Western lightning plus ECL using a Mithras LB940 microplate reader. Receptor cell surface expression was calculated as ratio of total luminescence to vibrant fluorescence.

Data analysis

Concentration response curves describing ligand responses by different ligands were analyzed with Graphpad Prismv6 (GraphPad Software, La Jolla, CA), using built-in 3 or 4 parameter logistic equations to obtain independent pEC50 and maximal response values for different receptor-biosensor pairs y = a + (b-a)/(1 + 10^(logEC50-x)*c) where: y is the measured response; a is the minimal asymptote, b is the maximal asymptote; b-a is maximal response and c is the slope.

Data were also fit with the operational model of Black and Leff^42,56 using a set of equations kindly provided by Dr. Arthur Christopoulos. These equations were introduced into Graphpad Prism6:

$${\rm{A}}={10}^{{\rm{x}}}$$

$${\rm{operate}}1={((1+{\rm{A}}/(1))/(({10}^{{\rm{LogR}}})\ast {\rm{A}}))}^{{\rm{n}}}({\rm{used}}\,{\rm{to}}\,{\rm{fit}}\,{\rm{full}}\,{\rm{agonists}})$$

$${\rm{operate}}2={((1+{\rm{A}}/({10}^{{\rm{LogKA}}}))/(({10}^{{\rm{LogR}}})\ast {\rm{A}}))}^{{\rm{n}}}({\rm{used}}\,{\rm{to}}\,{\rm{fit}}\,{\rm{partial}}\,{\rm{agonists}})$$

$${\rm{Full}}\,{\rm{agonist}}={\rm{basal}}+({\rm{Emax}}-{\rm{basal}})/(1+{\rm{operate}}1)$$

$${\rm{Partial}}\,{\rm{agonist}}={\rm{basal}}+({\rm{Emax}}-{\rm{basal}})/(1+{\rm{operate}}2)$$

$${\rm{basal}}\to {\rm{response}}\,{\rm{observed}}\,{\rm{in}}\,{\rm{the}}\,{\rm{absence}}\,{\rm{of}}\,{\rm{agonist}}$$

$${\rm{Emax}}\to {\rm{maximal}}\,{\rm{response}}\,{\rm{of}}\,{\rm{the}}\,{\rm{system}}$$

$${\rm{n}}\to {\rm{slope}}\,{\rm{of}}\,{\rm{the}}\,{\rm{function}}\,{\rm{which}}\,{\rm{links}}\,{\rm{occupancy}}\,{\rm{to}}\,{\rm{response}}$$

$$KA\to {\rm{functional}}\,{\rm{affinity}}({\rm{partial}}\,{\rm{agonists}}).$$

A one-phase exponential decay was used to calculate the half-life in kinetic experiments. Statistical significance of ligand-induced changes at the different biosensors was established using one-way ANOVA to reveal concentration effects. pEC₅₀ and maximal responses of different ligands were compared to corresponding isoproterenol values by means of one-way ANOVA followed by post-hoc Dunnett test. Specific details on statistical comparisons are provided in legends of Figures and Tables.

Data availability

The authors declare that all data supporting the findings in this study are presented within the article and its associated Supplementary Information Files. These are available from the corresponding authors upon request. Our biosensors are licensed to Domain Therapeutics for commercial purposes but are freely available to the academic community.

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Acknowledgements

This work was supported by grants from the Canadian Institute for Health Research (CIHR) [MOP11215] and [FDN148431] to M.B. and [PJT159687] to T.E.H. M.B. holds a Canada Research Chair in Signal Transduction and Molecular Pharmacology.

Author information

These authors contributed equally: Viktoriya Lukasheva, Dominic Devost and Christian Le Gouill.

Authors and Affiliations

Department of Biochemistry and Molecular Medicine, Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montréal, Québec, Canada
Viktoriya Lukasheva, Christian Le Gouill, Mireille Hogue, Monique Lagacé, Billy Breton & Michel Bouvier
Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec, Canada
Dominic Devost, Ryan D. Martin, Jason C. Tanny, Stéphane A. Laporte & Terence E. Hébert
Department of Medicine, Research Institute of the McGill University Health Centre, McGill University, Montréal, Québec, Canada
Yoon Namkung & Stéphane A. Laporte
Institut de Pharmacologie and Department of Pharmacology-Physiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Canada
Jean-Michel Longpré
Department of Pharmacology and Physiology, Université de Montréal, Centre de Recherche de l’Hôpital Ste-Justine, Montréal, Canada
Mohammad Amraei & Graciela Pineyro
Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Japan
Yuji Shinjo, Junken Aoki & Asuka Inoue

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Viktoriya Lukasheva
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Dominic Devost
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Christian Le Gouill
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Ryan D. Martin
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Billy Breton
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Junken Aoki
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Jason C. Tanny
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Stéphane A. Laporte
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Asuka Inoue
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Michel Bouvier
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Terence E. Hébert
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Contributions

C.L.G., S.A.L., G.P., M.B. and T.E.H. designed the study. A.I. Y.S and J.A. generated and characterized the Gα_s KO line using CRISPR/Cas9. D.D., V.L., Y.N, J.-M.L., M.A, M.H. R.D.M. and B.B. performed the experiments as well as the analysis of the data. D.D, V.L. M.L. C.L.G, J.C.T., S.A.L., G.P., M.B. and T.E.H. interpreted the data and wrote the manuscript.

Corresponding authors

Correspondence to Michel Bouvier or Terence E. Hébert.

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Lukasheva, V., Devost, D., Le Gouill, C. et al. Signal profiling of the β₁AR reveals coupling to novel signalling pathways and distinct phenotypic responses mediated by β₁AR and β₂AR. Sci Rep 10, 8779 (2020). https://doi.org/10.1038/s41598-020-65636-3

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Received: 04 September 2019
Accepted: 07 May 2020
Published: 29 May 2020
DOI: https://doi.org/10.1038/s41598-020-65636-3

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