Conformational specificity of opioid receptors is determined by subcellular location irrespective of agonist

A given G protein-coupled receptor (GPCR) can generate a diverse array of signaling responses, underscoring the physiological and clinical relevance of this class of proteins. Endogenous and synthetic ligands that ‘bias’ the responses of a given receptor toward one response or another are a key aspect of this signaling diversity (Wootten et al., 2018). This diversity in responses provides several opportunities to target specific GPCR signaling responses to reduce potential adverse effects while managing a variety of clinical conditions. However, using bias to precisely tune GPCR signaling has been difficult, suggesting that we are missing some key pieces in our understanding. Understanding the cellular mechanisms that contribute to individual components of the integrated signaling response is therefore of profound importance to understanding GPCR pharmacology.

The specific conformations adopted by GPCRs, which preferentially allow coupling to distinct effectors, are likely key determinants of which specific downstream signaling response is amplified (Latorraca et al., 2017; Wingler et al., 2019; Suomivuori et al., 2020; Okude et al., 2015). Recent studies support an allosteric model of coupling in which binding to both agonist and G protein stabilizes an active state of the receptor, among a number of states that a given receptor may sample (Okude et al., 2015; Weis and Kobilka, 2018; Nygaard et al., 2013; Ye et al., 2016; Manglik et al., 2015). In addition to canonical G protein effectors, β-arrestins interact with GPCRs through additional receptor conformations and can serve as scaffolds for kinase signaling pathways (Gurevich and Gurevich, 2019a; Liu et al., 2012; Wingler et al., 2020). However, efforts to develop compounds that stabilize one set of conformations and therefore bias the receptor response to specific pathways have been promising but difficult to translate to in vivo models (Gillis et al., 2020; Luttrell et al., 2015; Viscusi et al., 2019).

One hypothesis that could explain this difficulty is that the same agonist could drive coupling of the same receptor to different core signaling proteins based on the immediate subcellular environment of the receptor. Although an exciting idea with profound implications, this hypothesis has been difficult to test using traditional methods, because receptor signaling readouts are complex and have been difficult to separate based on location.

Here we use the δ-opioid receptor (DOR), a physiologically and clinically relevant GPCR, as a model to test this hypothesis. DOR localizes primarily to the Golgi in neuronal cells, with a small amount on the plasma membrane (PM) (Kim and von Zastrow, 2003; Shiwarski et al., 2017a; Zhang et al., 1998; Cahill et al., 2001a; Wang and Pickel, 2001; Mittal et al., 2013; Shiwarski et al., 2017b). DOR can be activated both on the PM and the Golgi by synthetic agonists, but whether the two activation states are different is not clear (Stoeber et al., 2018). Relocating DOR from intracellular compartments to the PM increases the ability of DOR agonists to relieve pain (Shiwarski et al., 2017a; Mittal et al., 2013; Cahill et al., 2001b; Patwardhan et al., 2005), illustrating the importance of understanding whether DOR activation on the Golgi is different from that on the PM.

Here we leverage conformational biosensors and high-resolution imaging to test whether DOR activation on the Golgi is different from that on the PM. We have shown that DOR on the PM, when activated by the selective DOR agonist SNC80, can recruit both a nanobody-based sensor and a G protein-based sensor that read out active DOR conformations, as well as β-arrestins. In contrast, DOR in the Golgi apparatus, when activated by the same ligand, recruits the nanobody sensor, but not the G protein-based sensor or β-arrestins. Nevertheless, Golgi-localized DOR is competent to inhibit cAMP. Together, these data demonstrate that these biosensors could be used to read out subtle differences in GPCR conformations even if signaling readouts are similar. Our results that the downstream effectors recruited by the same GPCR, activated by the same ligand, depend on the location of the receptor, suggest that subcellular location could be a master regulator of GPCR coupling to specific effectors and signaling for any given GPCR-ligand pair.

Nanobody and miniG protein biosensors are emerging as powerful tools to study the effects of ligand-induced receptor conformational changes at the molecular and cellular level (Manglik et al., 2017; Crilly and Puthenveedu, 2020; Wan et al., 2018). These sensors differentially engage the μ-opioid receptor (MOR) and the κ-opioid receptor (KOR) activated by different agonists in vitro (Livingston et al., 2018) or in the PM (Stoeber et al., 2020), suggesting that they can provide a readout of conformational heterogeneity in agonist-stabilized active states. Because DOR is highly similar to MOR and KOR in the intracellular regions recognized by the sensors (Chen et al., 1993), we asked whether these sensors could be optimized to read out specific active DOR conformations at distinct subcellular locations. We used the nanobody biosensor Nb39, which recognizes opioid receptor active conformations through residues conserved across MOR, KOR, and DOR (Che et al., 2018; Huang et al., 2015), and the miniGsi biosensor, which mimics the interaction of the Gαi protein with GPCRs (Wan et al., 2018; Nehmé et al., 2017), as two orthogonal readouts of DOR conformations.

As an initial step, we first tested whether these sensors report active DOR conformations on the PM, similar to what has been reported for MOR and KOR. We used total internal fluorescence reflection microscopy (TIR-FM), which uses an evanescent wave to specifically excite fluorescent proteins on the PM to a depth of approximately 100 nm into the cell (Hellen and Axelrod, 1991), to visualize sensor recruitment to activated DOR on the PM with high sensitivity (Figure 1A). When cells were treated with either small molecule agonist SNC80 or peptide DPDPE, Nb39 (Figure 1B–C) and miniGsi (Figure 1D–E) were rapidly recruited to DOR on the plasma membrane (PM DOR), as observed by a rapid increase in fluorescence. Fluorescence of both sensors increased significantly after treatment with either agonist, but not inverse agonist ICI174864 (ICI) (Figure 1F), indicating specificity of both sensors for an agonist-induced active conformation. Furthermore, recruitment of Nb39 and miniGsi to PM DOR was concentration-dependent and saturated at the concentration of SNC80 used in these experiments (10 μM) (Figure 1G). Concentration-response curves also revealed that miniGsi was more potently recruited to PM DOR than Nb39 in response to the agonist SNC80.

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Interestingly, DPDPE recruited Nb39 to PM DOR more strongly than SNC80 (Figure 1C,F), whereas the opposite trend was observed for miniGsi (Figure 1E–F), suggesting that these sensors might report selective conformations of DOR induced by different agonists. MiniGs, which mimics Gαs protein interaction with Gs-coupled GPCRs, was not recruited to PM DOR (Figure 1F), suggesting that the miniGsi sensor specifically reports activation of the Gi-coupled DOR. DOR expression levels were, overall, comparable across conditions (Figure 1—figure supplement 1A). Overall, our data show that Nb39 and miniGsi report agonist-induced active conformations of PM DOR.

To test whether DOR localized to an intracellular compartment engages Nb39 or miniGsi differently upon activation by the same agonist, we took advantage of the fact that newly synthesized DOR is retained in an intracellular compartment in neurons and pheochromocytoma-12 cells (PC12 cells) (Figure 1H and I, yellow arrows) acutely treated with nerve growth factor (NGF) (Kim and von Zastrow, 2003; Shiwarski et al., 2017a). The presence of both plasma membrane and intracellular pools of DOR in these cells allowed us to measure sensor recruitment to DOR at both of these locations.

We first tested whether the two different biosensors were differentially recruited to DOR in intracellular compartments (IC DOR) vs PM DOR by confocal imaging (Figure 1H). When cells were treated with 10 µM SNC80, a membrane-permeable, small molecule agonist, Nb39 was rapidly recruited to intracellular SNAP-tagged DOR, within 30 s (Figure 1I, Video 1). When quantitated, Nb39 fluorescence in the region of the cell defined by IC DOR rapidly and significantly increased after SNC80 addition (Figure 1K–L). This Nb39 recruitment was dynamic and required DOR activation, as the DOR antagonist naltrindole rapidly reversed this effect (Figure 1—figure supplement 2A–B). In striking contrast to Nb39, miniGsi was not recruited to IC DOR in the same time frame (Figure 1J–K, Video 2), despite comparable levels of IC DOR (Figure 1—figure supplement 1B). MiniGsi fluorescence in the region of the cell defined by IC DOR did not increase in cells treated with SNC80 (Figure 1K–L). As a control, miniGs was also not recruited to IC DOR in cells treated with SNC80 (Figure 1L).

Video 1

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Both sensors were recruited to PM DOR, as seen by confocal imaging in the same cells in which we measured recruitment to IC DOR, although the sensitivity of detection was lower in confocal imaging (Figure 1I–J, yellow arrowheads, and Figure 1M). MiniGsi was recruited to PM DOR more strongly than Nb39 (Figure 1F,M), consistent with the TIR-FM results, which makes the absence of miniGsi recruitment to IC DOR in response to SNC80 even more striking. These results indicate that SNC80 promotes an active DOR conformation preferentially recognized by Nb39 in intracellular compartments and a distinct active DOR conformation preferentially recognized by miniGsi at the PM, suggesting location-specific conformational effects of SNC80 on DOR.

Differential biosensor recruitment to IC DOR did not depend on the method used to cause DOR retention in this compartment (Figure 1—figure supplement 3A), and recruitment to PM DOR was unaffected by the presence of IC DOR (Figure 1—figure supplement 3B). Further, recruitment of either sensor was not significantly correlated with sensor expression level (Figure 1—figure supplement 3C–D), as miniGsi failed to show recruitment to IC DOR across a broad range of expression levels (Figure 1—figure supplement 3D).

When cells were treated with 10 µM peptide agonist DPDPE, which does not readily cross the PM over short time scales (Stoeber et al., 2018), Nb39 was not recruited to IC DOR (Figure 1L). This suggests that activation of PM DOR is not sufficient for recruitment of Nb39 to IC DOR. We next tested whether PM DOR activation was required for differential sensor recruitment to IC DOR. Cells were pretreated with a high concentration (100 µM) of DOR inverse agonist ICI, a peptide restricted to the extracellular space (Stoeber et al., 2018), to pharmacologically block PM DOR. Nb39 recruitment to IC DOR after 100 nM SNC80 addition was then measured. Nb39 was robustly recruited to IC DOR, even when PM DOR was pharmacologically blocked, indicating that recruitment to IC DOR does not require activation of PM DOR (Figure 2A, top, 2B, C). When PM DOR was pharmacologically blocked, miniGsi again remained diffuse throughout the cell and was not recruited to IC DOR (Figure 2A, bottom, 2B, C), indicating that the absence of miniGsi recruitment to IC DOR is not due to sequestration of sensor at PM DOR. To test whether activation of endogenous G proteins was restricting miniGsi recruitment, cells were pretreated with pertussis toxin (PTX) to inactivate endogenous Gαi/o proteins. Even in PTX-treated cells, miniGsi was not recruited to IC DOR, suggesting that competition with endogenous Gαi/o protein effectors for interaction with DOR is not responsible for the lack of recruitment of miniGsi to IC DOR (Figure 2C).

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Immunofluorescence microscopy showed that Nb39 was recruited to IC DOR localized to the Golgi. Using a similar approach as described above, PC12 cells expressing Flag-tagged DOR and either Nb39 or miniGsi were pretreated for 15 min with 10 µM β-chlornaltrexamine (CNA), an irreversible, cell-impermeable antagonist (Shiwarski et al., 2017a; Virk and Williams, 2008), to irreversibly block PM DOR, before treating with 10 µM SNC80 for 5 min. Cells were stained for TGN-38, a marker for the trans-Golgi network, which was previously shown to colocalize with IC DOR (Shiwarski et al., 2017a). Consistent with live-cell imaging data, only Nb39, and not miniGsi, was recruited to IC DOR in a region of the cell colocalizing with the TGN-38 marker (Figure 2D,E) in cells treated with CNA and SNC80. CNA alone did not cause recruitment of either sensor. Sensor fluorescence in the region of the cell defined by TGN-38 staining was normalized to sensor fluorescence in the cell outside this region, as a measure of sensor enrichment in the Golgi. Treatment with CNA and SNC80 significantly increased Nb39 Golgi enrichment (Figure 2F), whereas miniGsi enrichment was not significantly different from control cells (Figure 2G). These results confirm differential biosensor recruitment to IC DOR specifically localized to the Golgi and reiterate that PM DOR activation is not required for differential biosensor recruitment to Golgi DOR.

In addition to heterotrimeric G proteins, DOR and other GPCRs interact with other proteins and signaling effectors after agonist-induced conformational changes. Agonist-dependent differential biosensor recruitment to MOR and KOR in the PM correlates with recruitment of other receptor effectors, specifically G protein-coupled receptor kinase 2 (GRK2), which mediates receptor desensitization (Stoeber et al., 2020). Given differential recruitment of Nb39 and miniGsi to IC DOR, we hypothesized that Golgi localization may also influence coupling to other downstream signaling effectors. β-arrestins interact with DOR and other GPCRs after activation by agonists and receptor phosphorylation by GRKs to mediate receptor desensitization and internalization from the PM (Zhang et al., 1999; Zhang et al., 2005; Gurevich and Gurevich, 2019b). β-arrestins can also scaffold kinase-signaling complexes from GPCRs at the PM and endosomes (Peterson and Luttrell, 2017; DeFea et al., 2000a; DeFea et al., 2000b; McDonald et al., 2000; Weinberg et al., 2017).

To test whether β-arrestin effectors are recruited to active IC DOR, we monitored recruitment of fluorescently tagged β-arrestin-1 or β-arrestin-2 to IC DOR. Similar to miniGsi, neither β-arrestin-1 nor β-arrestin-2 was recruited to IC DOR in cells treated with SNC80, and no increase in fluorescence in the region of the cell defined by IC DOR was detected (Figure 3A–C). In contrast, both β-arrestins were visibly recruited to the PM by confocal imaging (Figure 3A,B, yellow arrowheads) and by quantitation of TIR-FM imaging (Figure 3D–F) in response to SNC80 treatment. Together, these data indicate that like miniGsi, β-arrestins interact with only agonist-activated DOR present in the PM.

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Given the differential recruitment of active conformation biosensors, Nb39 and miniGsi, and the absence of β-arrestin recruitment to IC DOR, we asked whether the active conformation of IC DOR allows for signaling through G proteins. Like the other opioid receptors, DOR couples primarily to Gαi/o proteins, which inhibit adenylyl cyclase activity to decrease cAMP (Gendron et al., 2016). We used a Forster resonance energy transfer (FRET) sensor, ICUE3, to monitor cAMP levels in single cells in real time (DiPilato and Zhang, 2009). In PC12 cells expressing ICUE3 and SNAP-DOR, addition of adenylyl cyclase activator forskolin (Fsk, 2 µM) caused a rapid increase in the cyan fluorescent protein (CFP/FRET) ratio over the baseline ratio (Figure 4A–B). Pretreatment with 100 nM SNC80 prior to Fsk addition decreased the Fsk-stimulated cAMP response (Figure 4C–D), consistent with DOR activation inhibiting adenylyl cyclase activity.

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We next specifically tested whether IC DOR was sufficient for cAMP inhibition. Cells expressing DOR were treated with cycloheximide (CHX) to ensure that, in the absence of NGF, newly synthesized DOR transiting the Golgi was cleared out and that no residual DOR remained in the Golgi. In cells pretreated with NGF before CHX, the pool of IC DOR was maintained even after CHX treatment, consistent with previous results (Kim and von Zastrow, 2003; Shiwarski et al., 2017a). Under these conditions, the overall Fsk response and SNC80-mediated inhibition in cells treated with NGF were comparable to untreated cells (Figure 4—figure supplement 1A). To isolate the contribution of IC DOR to cAMP inhibition, we pharmacologically blocked PM DOR with 100 µM ICI. In cells with PM DOR only, SNC80 failed to decrease Fsk-stimulated cAMP in the presence of ICI (Figure 4E–F). Neither the total cAMP levels, measured as area under the curve, nor the endpoint cAMP levels, measured as the change in endpoint cAMP levels over baseline, were significantly different from cells treated with Fsk alone (Figure 4I–J). In contrast, in cells with IC DOR, SNC80 decreased Fsk-stimulated cAMP even in the presence of ICI (Figure 4G–H), and significantly decreased endpoint and total cAMP levels (Figure 4I–J). IC DOR activation suppressed Fsk-stimulated cAMP to approximately half the degree suppressed by combined PM and IC DOR (Figure 4I–J, Figure 4—figure supplement 1A). As a control, ICI alone did not significantly affect endpoint or total cAMP levels (Figure 4I–J). These results indicate that Golgi DOR activation is sufficient for cAMP inhibition.

As an independent method to induce an intracellular pool of DOR, we used LY294002, a small molecule inhibitor of PI3K that causes DOR retention in the Golgi independent of NGF (Shiwarski et al., 2017b). Similar to results obtained in NGF-treated cells, the permeable small molecule agonist SNC80 decreased Fsk-stimulated cAMP (Figure 4—figure supplement 1B–D) even in the presence of ICI, reiterating that Golgi DOR activation is sufficient for cAMP inhibition. The total and endpoint cAMP levels decreased significantly compared to cells treated with Fsk alone (Figure 4—figure supplement 1G–H). Again, IC DOR alone suppressed Fsk-stimulated cAMP to approximately half the degree suppressed by combined PM and IC DOR (Figure 4—figure supplement 1B,G–H). As a control, the peptide DPDPE agonist did not decrease the Fsk response in the presence of ICI (Figure 4—figure supplement 1B,E–F).

Together, our data suggest that DOR activation by the same agonist in different subcellular compartments promotes distinct active conformations recognized differentially by biosensors. The conventional model suggests that distinct GPCR conformations can drive coupling to distinct effectors, which determine subsequent downstream signaling responses. This relationship between structure and function has been a subject of great interest due to its potential to explain the pleiotropic effects of GPCR activation by any given ligand. Our results suggest that the subcellular location in which receptors are activated might determine the conformational landscapes that receptors can adopt upon activation, because the immediate environment of receptors varies between these locations.

Conformational biosensors like Nb39 and miniGsi used here are valuable tools to study how location can bias receptor conformations. The possibility that Nb39 and miniGsi could recognize distinct conformations is supported by structures of agonist-bound homologous MOR and KOR in complex with Nb39 or MOR in complex with the heterotrimeric G protein complex, Gα_i1β₁γ₂. Structures of MOR with agonist BU72 and KOR with agonist MP1104 share the outward shift of transmembrane helix (TM)6, which is characteristic of active GPCR structures (Che et al., 2018; Huang et al., 2015). Nb39 appears to stabilize this conformation via contacts with intracellular loop (ICL)2 and ICL3, as well as the eighth helix through residues conserved across MOR, KOR, and DOR (Che et al., 2018; Huang et al., 2015). The structure of MOR in complex with agonist [D-Ala2, N-MePhe4, Gly-ol5]-enkephalin (DAMGO) and the nucleotide-free Gαi protein is very similar to the MOR-Nb39 structure, with the exception of a greater displacement of TM6 toward TM7 and a decreased extension of ICL3 (Koehl et al., 2018). The miniGsi sensor does not contain all regions of Gαi that contact the receptor, but many of the residues that interact with MOR ICL2 and ICL3 via the Gαi C-terminal α5 helix are present in miniGsi, and previous reports show that miniGs and Gαs contact B2AR similarly (Nehmé et al., 2017; Carpenter et al., 2016). Though Nb39 and Gαi contact opioid receptors in similar regions and share two interaction residues, each also makes additional distinct contacts with TM domains and the eighth helix. Distinct interactions with these intracellular domains important for effector coupling and unique stabilization of TM6 and ICL3 by Nb39 could suggest that these sensors differentially report distinct conformations relevant to receptor function. Additionally, these structures are limited to a single static view of opioid receptor-active conformation, and the ability of Nb39 and miniGsi to discriminate between additional distinct intermediate or active conformations will be an exciting area for future study.

One clear difference between compartments is the composition of specific phospholipids that make up the membranes (Ikonen, 2008; van Meer et al., 2008; Balla, 2013). Phospholipids differing in charge can stabilize active or inactive conformations of the β₂-adrenergic receptor, stabilize G protein coupling, and modulate G protein selectivity (Dawaliby et al., 2016; Strohman et al., 2019; Yen et al., 2018). Lipid composition can also influence recruitment of effectors like β-arrestins, which bind PI4,5P2, a phospholipid species enriched in the PM (Gaidarov et al., 1999), potentially contributing to the lack of observed arrestin recruitment to Golgi DOR. To date, β-arrestin recruitment to active GPCRs in the Golgi has not been reported, and the impact of receptor localization to this compartment on desensitization, β-arrestin recruitment, and β-arrestin-biased signaling is not known.

Other compartment-specific factors including ion concentrations and GPCR-interacting proteins could influence receptor conformations and effector coupling. The Golgi lumen is more acidic than the extracellular space, pH 6.4 vs pH 7.4 (Kim et al., 1996; Llopis et al., 1998; Miesenböck et al., 1998), which could affect ligand binding and GPCR activation (Ghanouni et al., 2000; Meyer et al., 2019; Vetter et al., 2006; Pert and Snyder, 1973). Estimated concentrations of sodium in the Golgi are closer to cytosolic sodium concentrations (12–27 mM) than high extracellular sodium concentrations (100 mM) (Chandra et al., 1991; Hooper and Dick, 1976). Sodium acts as an allosteric modulator of class A GPCRs, and DOR specifically has been crystallized with a coordinated sodium ion, which stabilizes the inactive receptor conformation, and is required for receptor activation and signaling (Fenalti et al., 2014; Zarzycka et al., 2019), suggesting Golgi sodium concentrations could affect DOR activity. Ligand concentrations of a permeable agonist like SNC80 may also differ between the Golgi lumen and the extracellular space. A lower SNC80 concentration in the Golgi, however, is unlikely to explain the differential recruitment we observed, as miniGsi is more potently recruited to PM DOR than Nb39 (Figure 1G). Additionally, DOR-interacting proteins, which regulate DOR trafficking and localize to the Golgi, like the coatomer protein I (COPI) complex and Rab10, could also regulate receptor conformations and effector coupling (Degrandmaison et al., 2020; St-Louis et al., 2017; Shiwarski et al., 2019).

Our results suggest that the conformational space sampled by any given GPCR, even when activated by the same agonist, differs based on the precise subcellular location of the receptor. Compartmental effects on GPCR conformations may also be specific to individual GPCRs. In contrast to DOR, both an active-state nanobody and miniGs are recruited to active Gαs-coupled β₁-adrenergic receptor (B1AR) in the Golgi, suggesting that the local Golgi environment may influence DOR and B1AR energy landscapes differently (Irannejad et al., 2017; Nash et al., 2019). Additionally, the A₁-adenosine receptor, when expressed exogenously, can localize to the Golgi and recruit miniGsi to this compartment upon adenosine treatment (Wan et al., 2018). This result demonstrates that miniGsi can in fact report active conformations of Gαi-coupled receptors in the Golgi, which emphasizes the absence of miniGsi recruitment to Golgi DOR that we observed. These GPCR-specific effects may reflect important differences in pharmacology among individual GPCRs and emphasize the importance of characterizing compartmental effects for each GPCR.

These results also provide a new perspective into drug development efforts, by highlighting the effects that the subcellular location of receptors could have on the integrated effects of any given drug. The majority of these efforts largely rely on assays using conventional readouts of signaling in model cells, where GPCR localization could be different from that of physiologically relevant cells in vivo. This difference is especially true for DOR, which exhibits robust surface localization in model cell lines, but high levels of intracellular pools in many neuronal subtypes. Traditional signaling assays, which rely on whole-cell readouts of primary signaling pathways such as cAMP, will not distinguish between the contributions of different pools of receptors, which could signal differently via pathways outside the primary readouts (Costa-Neto et al., 2016). Therefore, the potentially distinct effects of ligands at spatially distinct pools of receptors in the integrated response should be an important consideration for measuring the outcomes of receptor activation.

All data generated and analyzed have been included in this study. No new sequencing data or structural data are reported.

1. 1. Balla T

   (2013) Phosphoinositides: tiny lipids with giant impact on cell regulation

   Physiological Reviews 93:1019–1137.

   https://doi.org/10.1152/physrev.00028.2012

   • PubMed
   • Google Scholar
2. 1. Bindels DS
   2. Haarbosch L
   3. van Weeren L
   4. Postma M
   5. Wiese KE
   6. Mastop M
   7. Aumonier S
   8. Gotthard G
   9. Royant A
   10. Hink MA
   11. Gadella TW

   (2017) mScarlet: a bright monomeric red fluorescent protein for cellular imaging

   Nature Methods 14:53–56.

   https://doi.org/10.1038/nmeth.4074

   • PubMed
   • Google Scholar
3. 1. Cahill CM
   2. McClellan KA
   3. Morinville A
   4. Hoffert C
   5. Hubatsch D
   6. O'Donnell D
   7. Beaudet A

   (2001a) Immunohistochemical distribution of Delta opioid receptors in the rat central nervous system: evidence for somatodendritic labeling and antigen-specific cellular compartmentalization

   The Journal of Comparative Neurology 440:65–84.

   https://doi.org/10.1002/cne.1370

   • PubMed
   • Google Scholar
4. 1. Cahill CM
   2. Morinville A
   3. Lee M-C
   4. Vincent J-P
   5. Collier B
   6. Beaudet A

   (2001b) Prolonged morphine treatment targets δ opioid receptors to neuronal plasma membranes and enhances δ-Mediated antinociception

   The Journal of Neuroscience 21:7598–7607.

   https://doi.org/10.1523/JNEUROSCI.21-19-07598.2001

   • Google Scholar
5. 1. Carpenter B
   2. Nehmé R
   3. Warne T
   4. Leslie AG
   5. Tate CG

   (2016) Structure of the adenosine A(2A) receptor bound to an engineered G protein

   Nature 536:104–107.

   https://doi.org/10.1038/nature18966

   • PubMed
   • Google Scholar
6. 1. Chandra S
   2. Kable EP
   3. Morrison GH
   4. Webb WW

   (1991) Calcium sequestration in the golgi apparatus of cultured mammalian cells revealed by laser scanning confocal microscopy and ion microscopy

   Journal of Cell Science 100:747–752.

   https://doi.org/10.1242/jcs.100.4.747

   • PubMed
   • Google Scholar
7. 1. Che T
   2. Majumdar S
   3. Zaidi SA
   4. Ondachi P
   5. McCorvy JD
   6. Wang S
   7. Mosier PD
   8. Uprety R
   9. Vardy E
   10. Krumm BE
   11. Han GW
   12. Lee MY
   13. Pardon E
   14. Steyaert J
   15. Huang XP
   16. Strachan RT
   17. Tribo AR
   18. Pasternak GW
   19. Carroll FI
   20. Stevens RC
   21. Cherezov V
   22. Katritch V
   23. Wacker D
   24. Roth BL

   (2018) Structure of the Nanobody-Stabilized active state of the kappa opioid receptor

   Cell 172:55–67.

   https://doi.org/10.1016/j.cell.2017.12.011

   • PubMed
   • Google Scholar
8. 1. Che T
   2. English J
   3. Krumm BE
   4. Kim K
   5. Pardon E
   6. Olsen RHJ
   7. Wang S
   8. Zhang S
   9. Diberto JF
   10. Sciaky N
   11. Carroll FI
   12. Steyaert J
   13. Wacker D
   14. Roth BL

   (2020) Nanobody-enabled monitoring of kappa opioid receptor states

   Nature Communications 11:1145.

   https://doi.org/10.1038/s41467-020-14889-7

   • PubMed
   • Google Scholar
9. 1. Chen Y
   2. Mestek A
   3. Liu J
   4. Yu L

   (1993) Molecular cloning of a rat kappa opioid receptor reveals sequence similarities to the mu and Delta opioid receptors

   Biochemical Journal 295:625–628.

   https://doi.org/10.1042/bj2950625

   • PubMed
   • Google Scholar
10. 1. Costa-Neto CM
    2. Parreiras-E-Silva LT
    3. Bouvier M

    (2016) A pluridimensional view of biased agonism

    Molecular Pharmacology 90:587–595.

    https://doi.org/10.1124/mol.116.105940

    • PubMed
    • Google Scholar
11. 1. Crilly SE
    2. Puthenveedu MA

    (2020) Compartmentalized GPCR signaling from intracellular membranes

    The Journal of Membrane Biology 10:00158-7.

    https://doi.org/10.1007/s00232-020-00158-7

    • Google Scholar
12. 1. Dawaliby R
    2. Trubbia C
    3. Delporte C
    4. Masureel M
    5. Van Antwerpen P
    6. Kobilka BK
    7. Govaerts C

    (2016) Allosteric regulation of G protein-coupled receptor activity by phospholipids

    Nature Chemical Biology 12:35–39.

    https://doi.org/10.1038/nchembio.1960

    • PubMed
    • Google Scholar
13. 1. DeFea KA
    2. Zalevsky J
    3. Thoma MS
    4. Déry O
    5. Mullins RD
    6. Bunnett NW

    (2000a) β-Arrestin–Dependent Endocytosis of Proteinase-Activated Receptor 2 Is Required for Intracellular Targeting of Activated Erk1/2

    Journal of Cell Biology 148:1267–1282.

    https://doi.org/10.1083/jcb.148.6.1267

    • Google Scholar
14. 1. DeFea KA
    2. Vaughn ZD
    3. O'Bryan EM
    4. Nishijima D
    5. Déry O
    6. Bunnett NW

    (2000b) The proliferative and antiapoptotic effects of substance P are facilitated by formation of a beta -arrestin-dependent scaffolding complex

    PNAS 97:11086–11091.

    https://doi.org/10.1073/pnas.190276697

    • PubMed
    • Google Scholar
15. 1. Degrandmaison J
    2. Abdallah K
    3. Blais V
    4. Génier S
    5. Lalumière MP
    6. Bergeron F
    7. Cahill CM
    8. Boulter J
    9. Lavoie CL
    10. Parent JL
    11. Gendron L

    (2020) In vivo mapping of a GPCR interactome using knockin mice

    PNAS 117:13105–13116.

    https://doi.org/10.1073/pnas.1917906117

    • PubMed
    • Google Scholar
16. 1. DiPilato LM
    2. Zhang J

    (2009) The role of membrane microdomains in shaping β2-adrenergic receptor-mediated cAMP dynamics

    Molecular BioSystems 5:832–837.

    https://doi.org/10.1039/b823243a

    • Google Scholar
17. 1. Fenalti G
    2. Giguere PM
    3. Katritch V
    4. Huang X-P
    5. Thompson AA
    6. Cherezov V
    7. Roth BL
    8. Stevens RC

    (2014) Molecular control of δ-opioid receptor signalling

    Nature 506:191–196.

    https://doi.org/10.1038/nature12944

    • Google Scholar
18. 1. Gaidarov I
    2. Krupnick JG
    3. Falck JR
    4. Benovic JL
    5. Keen JH

    (1999) Arrestin function in G protein-coupled receptor endocytosis requires phosphoinositide binding

    The EMBO Journal 18:871–881.

    https://doi.org/10.1093/emboj/18.4.871

    • Google Scholar
19. 1. Gendron L
    2. Cahill CM
    3. von Zastrow M
    4. Schiller PW
    5. Pineyro G

    (2016) Molecular pharmacology of δ-Opioid receptors

    Pharmacological Reviews 68:631–700.

    https://doi.org/10.1124/pr.114.008979

    • PubMed
    • Google Scholar
20. 1. Ghanouni P
    2. Schambye H
    3. Seifert R
    4. Lee TW
    5. Rasmussen SG
    6. Gether U
    7. Kobilka BK

    (2000) The effect of pH on beta(2) adrenoceptor function. Evidence for protonation-dependent activation

    The Journal of Biological Chemistry 275:3121–3127.

    https://doi.org/10.1074/jbc.275.5.3121

    • PubMed
    • Google Scholar
21. 1. Gillis A
    2. Kliewer A
    3. Kelly E
    4. Henderson G
    5. Christie MJ
    6. Schulz S
    7. Canals M

    (2020) Critical assessment of G Protein-Biased agonism at the μ-Opioid receptor

    Trends in Pharmacological Sciences 41:947–959.

    https://doi.org/10.1016/j.tips.2020.09.009

    • PubMed
    • Google Scholar
22. 1. Gurevich VV
    2. Gurevich EV

    (2019a) The structural basis of the arrestin binding to GPCRs

    Molecular and Cellular Endocrinology 484:34–41.

    https://doi.org/10.1016/j.mce.2019.01.019

    • PubMed
    • Google Scholar
23. 1. Gurevich VV
    2. Gurevich EV

    (2019b) GPCR signaling regulation: the role of GRKs and arrestins

    Frontiers in Pharmacology 10:125.

    https://doi.org/10.3389/fphar.2019.00125

    • PubMed
    • Google Scholar
24. 1. Hellen EH
    2. Axelrod D

    (1991) Kinetics of epidermal growth factor/receptor binding on cells measured by total internal reflection/fluorescence recovery after photobleaching

    Journal of Fluorescence 1:113–128.

    https://doi.org/10.1007/BF00865207

    • PubMed
    • Google Scholar
25. 1. Hooper G
    2. Dick DA

    (1976) Nonuniform distribution of sodium in the rat hepatocyte

    Journal of General Physiology 67:469–474.

    https://doi.org/10.1085/jgp.67.4.469

    • PubMed
    • Google Scholar
26. 1. Huang W
    2. Manglik A
    3. Venkatakrishnan AJ
    4. Laeremans T
    5. Feinberg EN
    6. Sanborn AL
    7. Kato HE
    8. Livingston KE
    9. Thorsen TS
    10. Kling RC
    11. Granier S
    12. Gmeiner P
    13. Husbands SM
    14. Traynor JR
    15. Weis WI
    16. Steyaert J
    17. Dror RO
    18. Kobilka BK

    (2015) Structural insights into µ-opioid receptor activation

    Nature 524:315–321.

    https://doi.org/10.1038/nature14886

    • PubMed
    • Google Scholar
27. 1. Ikonen E

    (2008) Cellular cholesterol trafficking and compartmentalization

    Nature Reviews Molecular Cell Biology 9:125–138.

    https://doi.org/10.1038/nrm2336

    • PubMed
    • Google Scholar
28. 1. Irannejad R
    2. Pessino V
    3. Mika D
    4. Huang B
    5. Wedegaertner PB
    6. Conti M
    7. von Zastrow M

    (2017) Functional selectivity of GPCR-directed drug action through location Bias

    Nature Chemical Biology 13:799–806.

    https://doi.org/10.1038/nchembio.2389

    • PubMed
    • Google Scholar
29. 1. Kim JH
    2. Lingwood CA
    3. Williams DB
    4. Furuya W
    5. Manolson MF
    6. Grinstein S

    (1996) Dynamic measurement of the pH of the golgi complex in living cells using retrograde transport of the verotoxin receptor

    Journal of Cell Biology 134:1387–1399.

    https://doi.org/10.1083/jcb.134.6.1387

    • PubMed
    • Google Scholar
30. 1. Kim KA
    2. von Zastrow M

    (2003) Neurotrophin-regulated sorting of opioid receptors in the biosynthetic pathway of neurosecretory cells

    The Journal of Neuroscience 23:2075–2085.

    https://doi.org/10.1523/JNEUROSCI.23-06-02075.2003

    • PubMed
    • Google Scholar
31. 1. Koehl A
    2. Hu H
    3. Maeda S
    4. Zhang Y
    5. Qu Q
    6. Paggi JM
    7. Latorraca NR
    8. Hilger D
    9. Dawson R
    10. Matile H
    11. Schertler GFX
    12. Granier S
    13. Weis WI
    14. Dror RO
    15. Manglik A
    16. Skiniotis G
    17. Kobilka BK

    (2018) Structure of the µ-opioid receptor-G_i protein complex

    Nature 558:547–552.

    https://doi.org/10.1038/s41586-018-0219-7

    • PubMed
    • Google Scholar
32. 1. Latorraca NR
    2. Venkatakrishnan AJ
    3. Dror RO

    (2017) GPCR dynamics: structures in motion

    Chemical Reviews 117:139–155.

    https://doi.org/10.1021/acs.chemrev.6b00177

    • PubMed
    • Google Scholar
33. 1. Liu JJ
    2. Horst R
    3. Katritch V
    4. Stevens RC
    5. Wüthrich K

    (2012) Biased signaling pathways in β2-adrenergic receptor characterized by 19F-NMR

    Science 335:1106–1110.

    https://doi.org/10.1126/science.1215802

    • PubMed
    • Google Scholar
34. 1. Livingston KE
    2. Mahoney JP
    3. Manglik A
    4. Sunahara RK
    5. Traynor JR

    (2018) Measuring ligand efficacy at the mu-opioid receptor using a conformational biosensor

    eLife 7:e32499.

    https://doi.org/10.7554/eLife.32499

    • PubMed
    • Google Scholar
35. 1. Llopis J
    2. McCaffery JM
    3. Miyawaki A
    4. Farquhar MG
    5. Tsien RY

    (1998) Measurement of Cytosolic, mitochondrial, and golgi pH in single living cells with green fluorescent proteins

    PNAS 95:6803–6808.

    https://doi.org/10.1073/pnas.95.12.6803

    • PubMed
    • Google Scholar
36. 1. Luttrell LM
    2. Maudsley S
    3. Bohn LM

    (2015) Fulfilling the promise of "Biased" G Protein-Coupled Receptor Agonism

    Molecular Pharmacology 88:579–588.

    https://doi.org/10.1124/mol.115.099630

    • PubMed
    • Google Scholar
37. 1. Malhotra V
    2. Campelo F

    (2011) PKD regulates membrane fission to generate TGN to cell surface transport carriers

    Cold Spring Harbor Perspectives in Biology 3:a005280–a005289.

    https://doi.org/10.1101/cshperspect.a005280

    • Google Scholar
38. 1. Manglik A
    2. Kim TH
    3. Masureel M
    4. Altenbach C
    5. Yang Z
    6. Hilger D
    7. Lerch MT
    8. Kobilka TS
    9. Thian FS
    10. Hubbell WL
    11. Prosser RS
    12. Kobilka BK

    (2015) Structural insights into the dynamic process of β2-Adrenergic receptor signaling

    Cell 161:1101–1111.

    https://doi.org/10.1016/j.cell.2015.04.043

    • PubMed
    • Google Scholar
39. 1. Manglik A
    2. Kobilka BK
    3. Steyaert J

    (2017) Nanobodies to study G Protein-Coupled receptor structure and function

    Annual Review of Pharmacology and Toxicology 57:19–37.

    https://doi.org/10.1146/annurev-pharmtox-010716-104710

    • PubMed
    • Google Scholar
40. 1. McDonald PH
    2. Chow CW
    3. Miller WE
    4. Laporte SA
    5. Field ME
    6. Lin FT
    7. Davis RJ
    8. Lefkowitz RJ

    (2000) Beta-arrestin 2: a receptor-regulated MAPK scaffold for the activation of JNK3

    Science 290:1574–1577.

    https://doi.org/10.1126/science.290.5496.1574

    • PubMed
    • Google Scholar
41. 1. Meyer J
    2. Del Vecchio G
    3. Seitz V
    4. Massaly N
    5. Stein C

    (2019) Modulation of μ-opioid receptor activation by acidic pH is dependent on ligand structure and an ionizable amino acid residue

    British Journal of Pharmacology 176:4510–4520.

    https://doi.org/10.1111/bph.14810

    • PubMed
    • Google Scholar
42. 1. Miesenböck G
    2. De Angelis DA
    3. Rothman JE

    (1998) Visualizing secretion and synaptic transmission with pH-sensitive green fluorescent proteins

    Nature 394:192–195.

    https://doi.org/10.1038/28190

    • PubMed
    • Google Scholar
43. 1. Mittal N
    2. Roberts K
    3. Pal K
    4. Bentolila LA
    5. Fultz E
    6. Minasyan A
    7. Cahill C
    8. Pradhan A
    9. Conner D
    10. DeFea K
    11. Evans C
    12. Walwyn W

    (2013) Select G-protein-coupled receptors modulate agonist-induced signaling via a ROCK, LIMK, and β-arrestin 1 pathway

    Cell Reports 5:1010–1021.

    https://doi.org/10.1016/j.celrep.2013.10.015

    • PubMed
    • Google Scholar
44. 1. Nash CA
    2. Wei W
    3. Irannejad R
    4. Smrcka AV

    (2019) Golgi localized β1-adrenergic receptors stimulate golgi PI4P hydrolysis by plcε to regulate cardiac hypertrophy

    eLife 8:e48167.

    https://doi.org/10.7554/eLife.48167

    • PubMed
    • Google Scholar
45. 1. Nehmé R
    2. Carpenter B
    3. Singhal A
    4. Strege A
    5. Edwards PC
    6. White CF
    7. Du H
    8. Grisshammer R
    9. Tate CG

    (2017) Mini-G proteins: novel tools for studying GPCRs in their active conformation

    PLOS ONE 12:e0175642.

    https://doi.org/10.1371/journal.pone.0175642

    • PubMed
    • Google Scholar
46. 1. Nygaard R
    2. Zou Y
    3. Dror RO
    4. Mildorf TJ
    5. Arlow DH
    6. Manglik A
    7. Pan AC
    8. Liu CW
    9. Fung JJ
    10. Bokoch MP
    11. Thian FS
    12. Kobilka TS
    13. Shaw DE
    14. Mueller L
    15. Prosser RS
    16. Kobilka BK

    (2013) The dynamic process of β(2)-adrenergic receptor activation

    Cell 152:532–542.

    https://doi.org/10.1016/j.cell.2013.01.008

    • PubMed
    • Google Scholar
47. 1. Okude J
    2. Ueda T
    3. Kofuku Y
    4. Sato M
    5. Nobuyama N
    6. Kondo K
    7. Shiraishi Y
    8. Mizumura T
    9. Onishi K
    10. Natsume M
    11. Maeda M
    12. Tsujishita H
    13. Kuranaga T
    14. Inoue M
    15. Shimada I

    (2015) Identification of a conformational equilibrium that determines the efficacy and functional selectivity of the μ-Opioid receptor

    Angewandte Chemie International Edition 54:15771–15776.

    https://doi.org/10.1002/anie.201508794

    • Google Scholar
48. 1. Patwardhan AM
    2. Berg KA
    3. Akopain AN
    4. Jeske NA
    5. Gamper N
    6. Clarke WP
    7. Hargreaves KM

    (2005) Bradykinin-Induced functional competence and trafficking of the δ-Opioid

    Receptor in Trigeminal Nociceptors 25:8825–8832.

    https://doi.org/10.1523/JNEUROSCI.0160-05.2005

    • Google Scholar
49. 1. Pert CB
    2. Snyder SH

    (1973) Properties of opiate-receptor binding in rat brain

    PNAS 70:2243–2247.

    https://doi.org/10.1073/pnas.70.8.2243

    • PubMed
    • Google Scholar
50. 1. Peterson YK
    2. Luttrell LM

    (2017) The diverse roles of arrestin scaffolds in G Protein-Coupled receptor signaling

    Pharmacological Reviews 69:256–297.

    https://doi.org/10.1124/pr.116.013367

    • PubMed
    • Google Scholar
51. 1. Rueden CT
    2. Schindelin J
    3. Hiner MC
    4. DeZonia BE
    5. Walter AE
    6. Arena ET
    7. Eliceiri KW

    (2017) ImageJ2: imagej for the next generation of scientific image data

    BMC Bioinformatics 18:529.

    https://doi.org/10.1186/s12859-017-1934-z

    • PubMed
    • Google Scholar
52. 1. Schindelin J
    2. Arganda-Carreras I
    3. Frise E
    4. Kaynig V
    5. Longair M
    6. Pietzsch T
    7. Preibisch S
    8. Rueden C
    9. Saalfeld S
    10. Schmid B
    11. Tinevez JY
    12. White DJ
    13. Hartenstein V
    14. Eliceiri K
    15. Tomancak P
    16. Cardona A

    (2012) Fiji: an open-source platform for biological-image analysis

    Nature Methods 9:676–682.

    https://doi.org/10.1038/nmeth.2019

    • PubMed
    • Google Scholar
53. 1. Shiwarski DJ
    2. Tipton A
    3. Giraldo MD
    4. Schmidt BF
    5. Gold MS
    6. Pradhan AA
    7. Puthenveedu MA

    (2017a) A PTEN-Regulated checkpoint controls surface delivery of δ opioid receptors

    The Journal of Neuroscience 37:3741–3752.

    https://doi.org/10.1523/JNEUROSCI.2923-16.2017

    • PubMed
    • Google Scholar
54. 1. Shiwarski DJ
    2. Darr M
    3. Telmer CA
    4. Bruchez MP
    5. Puthenveedu MA

    (2017b) PI3K class II α regulates δ-opioid receptor export from the trans-Golgi network

    Molecular Biology of the Cell 28:2202–2219.

    https://doi.org/10.1091/mbc.E17-01-0030

    • Google Scholar
55. 1. Shiwarski DJ
    2. Crilly SE
    3. Dates A
    4. Puthenveedu MA

    (2019) Dual RXR motifs regulate nerve growth factor-mediated intracellular retention of the Delta opioid receptor

    Molecular Biology of the Cell 30:680–690.

    https://doi.org/10.1091/mbc.E18-05-0292

    • PubMed
    • Google Scholar
56. 1. St-Louis É
    2. Degrandmaison J
    3. Grastilleur S
    4. Génier S
    5. Blais V
    6. Lavoie C
    7. Parent JL
    8. Gendron L

    (2017) Involvement of the coatomer protein complex I in the intracellular traffic of the Delta opioid receptor

    Molecular and Cellular Neurosciences 79:53–63.

    https://doi.org/10.1016/j.mcn.2016.12.005

    • PubMed
    • Google Scholar
57. 1. Stoeber M
    2. Jullié D
    3. Lobingier BT
    4. Laeremans T
    5. Steyaert J
    6. Schiller PW
    7. Manglik A
    8. von Zastrow M

    (2018) A genetically encoded biosensor reveals location Bias of opioid drug action

    Neuron 98:963–976.

    https://doi.org/10.1016/j.neuron.2018.04.021

    • PubMed
    • Google Scholar
58. 1. Stoeber M
    2. Jullié D
    3. Li J
    4. Chakraborty S
    5. Majumdar S
    6. Lambert NA
    7. Manglik A
    8. von Zastrow M

    (2020) Agonist-selective recruitment of engineered protein probes and of GRK2 by opioid receptors in living cells

    eLife 9:e54208.

    https://doi.org/10.7554/eLife.54208

    • PubMed
    • Google Scholar
59. 1. Strohman MJ
    2. Maeda S
    3. Hilger D
    4. Masureel M
    5. Du Y
    6. Kobilka BK

    (2019) Local membrane charge regulates β₂ adrenergic receptor coupling to G_i3

    Nature Communications 10:1–10.

    https://doi.org/10.1038/s41467-019-10108-0

    • PubMed
    • Google Scholar
60. 1. Suomivuori CM
    2. Latorraca NR
    3. Wingler LM
    4. Eismann S
    5. King MC
    6. Kleinhenz ALW
    7. Skiba MA
    8. Staus DP
    9. Kruse AC
    10. Lefkowitz RJ
    11. Dror RO

    (2020) Molecular mechanism of biased signaling in a prototypical G protein-coupled receptor

    Science 367:881–887.

    https://doi.org/10.1126/science.aaz0326

    • PubMed
    • Google Scholar
61. 1. van Meer G
    2. Voelker DR
    3. Feigenson GW

    (2008) Membrane lipids: where they are and how they behave

    Nature Reviews Molecular Cell Biology 9:112–124.

    https://doi.org/10.1038/nrm2330

    • PubMed
    • Google Scholar
62. 1. Vetter I
    2. Kapitzke D
    3. Hermanussen S
    4. Monteith GR
    5. Cabot PJ

    (2006) The effects of pH on beta-endorphin and morphine inhibition of calcium transients in dorsal root ganglion neurons

    The Journal of Pain 7:488–499.

    https://doi.org/10.1016/j.jpain.2006.01.456

    • PubMed
    • Google Scholar
63. 1. Virk MS
    2. Williams JT

    (2008) Agonist-specific regulation of mu-opioid receptor desensitization and recovery from desensitization

    Molecular Pharmacology 73:1301–1308.

    https://doi.org/10.1124/mol.107.042952

    • PubMed
    • Google Scholar
64. 1. Viscusi ER
    2. Skobieranda F
    3. Soergel DG
    4. Cook E
    5. Burt DA
    6. Singla N

    (2019) APOLLO-1: a randomized placebo and active-controlled phase III study investigating oliceridine (TRV130), a G protein-biased ligand at the µ-opioid receptor, for management of moderate-to-severe acute pain following bunionectomy

    Journal of Pain Research 12:927–943.

    https://doi.org/10.2147/JPR.S171013

    • PubMed
    • Google Scholar
65. 1. Wan Q
    2. Okashah N
    3. Inoue A
    4. Nehmé R
    5. Carpenter B
    6. Tate CG
    7. Lambert NA

    (2018) Mini G protein probes for active G protein-coupled receptors (GPCRs) in live cells

    Journal of Biological Chemistry 293:7466–7473.

    https://doi.org/10.1074/jbc.RA118.001975

    • PubMed
    • Google Scholar
66. 1. Wang YJ
    2. Wang J
    3. Sun HQ
    4. Martinez M
    5. Sun YX
    6. Macia E
    7. Kirchhausen T
    8. Albanesi JP
    9. Roth MG
    10. Yin HL

    (2003) Phosphatidylinositol 4 phosphate regulates targeting of clathrin adaptor AP-1 complexes to the golgi

    Cell 114:299–310.

    https://doi.org/10.1016/S0092-8674(03)00603-2

    • PubMed
    • Google Scholar
67. 1. Wang H
    2. Pickel VM

    (2001) Preferential cytoplasmic localization of δ-Opioid receptors in rat striatal patches: comparison with plasmalemmal μ-Opioid receptors

    The Journal of Neuroscience 21:3242–3250.

    https://doi.org/10.1523/JNEUROSCI.21-09-03242.2001

    • Google Scholar
68. 1. Weinberg ZY
    2. Zajac AS
    3. Phan T
    4. Shiwarski DJ
    5. Puthenveedu MA

    (2017) Sequence-Specific regulation of endocytic lifetimes modulates Arrestin-Mediated signaling at the µ Opioid Receptor

    Molecular Pharmacology 91:416–427.

    https://doi.org/10.1124/mol.116.106633

    • PubMed
    • Google Scholar
69. 1. Weis WI
    2. Kobilka BK

    (2018) The molecular basis of G Protein-Coupled receptor activation

    Annual Review of Biochemistry 87:897–919.

    https://doi.org/10.1146/annurev-biochem-060614-033910

    • PubMed
    • Google Scholar
70. 1. Wingler LM
    2. Elgeti M
    3. Hilger D
    4. Latorraca NR
    5. Lerch MT
    6. Staus DP
    7. Dror RO
    8. Kobilka BK
    9. Hubbell WL
    10. Lefkowitz RJ

    (2019) Angiotensin analogs with divergent Bias stabilize distinct receptor conformations

    Cell 176:468–478.

    https://doi.org/10.1016/j.cell.2018.12.005

    • PubMed
    • Google Scholar
71. 1. Wingler LM
    2. Skiba MA
    3. McMahon C
    4. Staus DP
    5. Kleinhenz ALW
    6. Suomivuori CM
    7. Latorraca NR
    8. Dror RO
    9. Lefkowitz RJ
    10. Kruse AC

    (2020) Angiotensin and biased analogs induce structurally distinct active conformations within a GPCR

    Science 367:888–892.

    https://doi.org/10.1126/science.aay9813

    • PubMed
    • Google Scholar
72. 1. Wootten D
    2. Christopoulos A
    3. Marti-Solano M
    4. Babu MM
    5. Sexton PM

    (2018) Mechanisms of signalling and biased agonism in G protein-coupled receptors

    Nature Reviews Molecular Cell Biology 19:638–653.

    https://doi.org/10.1038/s41580-018-0049-3

    • PubMed
    • Google Scholar
73. 1. Ye L
    2. Van Eps N
    3. Zimmer M
    4. Ernst OP
    5. Prosser RS

    (2016) Activation of the A2A adenosine G-protein-coupled receptor by conformational selection

    Nature 533:265–268.

    https://doi.org/10.1038/nature17668

    • PubMed
    • Google Scholar
74. 1. Yen HY
    2. Hoi KK
    3. Liko I
    4. Hedger G
    5. Horrell MR
    6. Song W
    7. Wu D
    8. Heine P
    9. Warne T
    10. Lee Y
    11. Carpenter B
    12. Plückthun A
    13. Tate CG
    14. Sansom MSP
    15. Robinson CV

    (2018) PtdIns(4,5)P₂ stabilizes active states of GPCRs and enhances selectivity of G-protein coupling

    Nature 559:423–427.

    https://doi.org/10.1038/s41586-018-0325-6

    • PubMed
    • Google Scholar
75. 1. Zarzycka B
    2. Zaidi SA
    3. Roth BL
    4. Katritch V

    (2019) Harnessing Ion-Binding sites for GPCR pharmacology

    Pharmacological Reviews 71:571–595.

    https://doi.org/10.1124/pr.119.017863

    • PubMed
    • Google Scholar
76. 1. Zhang X
    2. Bao L
    3. Arvidsson U
    4. Elde R
    5. Hökfelt T

    (1998) Localization and regulation of the delta-opioid receptor in dorsal root ganglia and spinal cord of the rat and monkey: evidence for association with the membrane of large dense-core vesicles

    Neuroscience 82:1225–1242.

    https://doi.org/10.1016/S0306-4522(97)00341-2

    • PubMed
    • Google Scholar
77. 1. Zhang J
    2. Ferguson SS
    3. Law PY
    4. Barak LS
    5. Caron MG

    (1999) Agonist-specific regulation of δ-opioid receptor trafficking by G protein-coupled receptor kinase and β-arrestin

    Journal of Receptors and Signal Transduction 19:301–313.

    https://doi.org/10.3109/10799899909036653

    • PubMed
    • Google Scholar
78. 1. Zhang X
    2. Wang F
    3. Chen X
    4. Li J
    5. Xiang B
    6. Zhang YQ
    7. Li BM
    8. Ma L

    (2005) Beta-arrestin1 and beta-arrestin2 are differentially required for phosphorylation-dependent and -independent internalization of delta-opioid receptors

    Journal of Neurochemistry 95:169–178.

    https://doi.org/10.1111/j.1471-4159.2005.03352.x

    • PubMed
    • Google Scholar

Author details

1. Stephanie E Crilly

   1. Cellular and Molecular Biology Program, University of Michigan, Ann Arbor, United States
   2. Department of Pharmacology University of Michigan Medical School, Ann Arbor, United States

   Contribution

   Conceptualization, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8151-290X

2. Wooree Ko

   Department of Pharmacology University of Michigan Medical School, Ann Arbor, United States

   Present address

   Department of Pharmacology and Physiology, University of Rochester Medical Center, Rochester, United States

   Contribution

   Resources, Formal analysis, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Zara Y Weinberg

   Department of Pharmacology University of Michigan Medical School, Ann Arbor, United States

   Present address

   Department of Biochemistry and Biophysics, California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, United States

   Contribution

   Resources, Data curation, Software, Formal analysis, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7176-038X

4. Manojkumar A Puthenveedu

   1. Cellular and Molecular Biology Program, University of Michigan, Ann Arbor, United States
   2. Department of Pharmacology University of Michigan Medical School, Ann Arbor, United States

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Project administration, Writing - review and editing

   For correspondence

   puthenve@umich.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3177-4231

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