Abstract
The μ-opioid receptor (μOR) represents an important target of therapeutic and abused drugs. So far, most understanding of μOR activity has focused on a subset of known signal transducers and regulatory molecules. Yet μOR signaling is coordinated by additional proteins in the interaction network of the activated receptor, which have largely remained invisible given the lack of technologies to interrogate these networks systematically. Here we describe a proteomics and computational approach to map the proximal proteome of the activated μOR and to extract subcellular location, trafficking and functional partners of G-protein-coupled receptor (GPCR) activity. We demonstrate that distinct opioid agonists exert differences in the μOR proximal proteome mediated by endocytosis and endosomal sorting. Moreover, we identify two new μOR network components, EYA4 and KCTD12, which are recruited on the basis of receptor-triggered G-protein activation and might form a previously unrecognized buffering system for G-protein activity broadly modulating cellular GPCR signaling.
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Data availability
Shotgun proteomics data access: RAW data and database search results have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository46 with the dataset identifier PXD031415. Targeted proteomics data access: Raw data and SRM transition files can be accessed, queried and downloaded via Panorama47 https://panoramaweb.org/MOR-APEX.url. Source data are provided with this paper.
References
Kieffer, B. L. & Evans, C. J. Opioid receptors: from binding sites to visible molecules in vivo. Neuropharmacology 56, 205–212 (2009).
Corder, G., Castro, D. C., Bruchas, M. R. & Scherrer, G. Endogenous and exogenous opioids in pain. Annu. Rev. Neurosci. 41, 453–473 (2018).
Hilger, D., Masureel, M. & Kobilka, B. K. Structure and dynamics of GPCR signaling complexes. Nat. Struct. Mol. Biol. 25, 4–12 (2018).
von Zastrow, M. Proteomic approaches to investigate regulated trafficking and signaling of G protein-coupled receptors. Mol. Pharmacol. 99, 392–398 (2021).
Degrandmaison, J., Rochon-Haché, S., Parent, J.-L. & Gendron, L. Knock-in mouse models to investigate the functions of opioid receptors in vivo. Front. Cell. Neurosci. 16, 807549 (2022).
Rhee, H.-W. et al. Proteomic mapping of mitochondria in living cells via spatially restricted enzymatic tagging. Science 339, 1328–1331 (2013).
Hung, V. et al. Proteomic mapping of the human mitochondrial intermembrane space in live cells via ratiometric APEX tagging. Mol. Cell 55, 332–341 (2014).
Lobingier, B. T. et al. An approach to spatiotemporally resolve protein interaction networks in living cells. Cell 169, 350–360 (2017).
Paek, J. et al. Multidimensional tracking of GPCR signaling via peroxidase-catalyzed proximity labeling. Cell 169, 338–349 (2017).
Manglik, A. et al. Structure-based discovery of opioid analgesics with reduced side effects. Nature 537, 185–190 (2016).
McPherson, J. et al. μ-Opioid receptors: correlation of agonist efficacy for signalling with ability to activate internalization. Mol. Pharmacol. 78, 756–766 (2010).
Lau, E. K. et al. Quantitative encoding of the effect of a partial agonist on individual opioid receptors by multisite phosphorylation and threshold detection. Sci. Signal. 4, ra52 (2011).
Choi, M. et al. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30, 2524–2526 (2014).
Ehrlich, A. T. et al. Biased signaling of the mu opioid receptor revealed in native neurons. iScience 14, 47–57 (2019).
Hegde, R. S., Roychoudhury, K. & Pandey, R. N. The multi-functional eyes absent proteins. Crit. Rev. Biochem. Mol. Biol. 55, 372–385 (2020).
Fan, X. et al. The alpha subunits of Gz and Gi interact with the eyes absent transcription cofactor Eya2, preventing its interaction with the six class of homeodomain-containing proteins. J. Biol. Chem. 275, 32129–32134 (2000).
Embry, A. C., Glick, J. L., Linder, M. E. & Casey, P. J. Reciprocal signaling between the transcriptional co-factor Eya2 and specific members of the Gαi family. Mol. Pharmacol. 66, 1325–1331 (2004).
Schwenk, J. et al. Native GABAB receptors are heteromultimers with a family of auxiliary subunits. Nature 465, 231–235 (2010).
Turecek, R. et al. Auxiliary GABAB receptor subunits uncouple G protein βγ subunits from effector channels to induce desensitization. Neuron 82, 1032–1044 (2014).
Zheng, S., Abreu, N., Levitz, J. & Kruse, A. C. Structural basis for KCTD-mediated rapid desensitization of GABAB signalling. Nature 567, 127–131 (2019).
DeWire, S. M. et al. A G protein-biased ligand at the μ-opioid receptor is potently analgesic with reduced gastrointestinal and respiratory dysfunction compared with morphine. J. Pharmacol. Exp. Ther. 344, 708–717 (2013).
Kliewer, A. et al. Morphine‐induced respiratory depression is independent of ß‐arrestin2 signalling. Br. J. Pharmacol. 177, 2923–2931 (2020).
Bachmutsky, I., Wei, X. P., Durand, A. & Yackle, K. ß-arrestin 2 germline knockout does not attenuate opioid respiratory depression. eLife 10, e62552 (2021).
Cullen, P. J. & Steinberg, F. To degrade or not to degrade: mechanisms and significance of endocytic recycling. Nat. Rev. Mol. Cell Biol. 19, 679–696 (2018).
Yong, X., Mao, L., Seaman, M. N. J. & Jia, D. An evolving understanding of sorting signals for endosomal retrieval. iScience 25, 104254 (2022).
Lauffer, B. E. L. et al. SNX27 mediates PDZ-directed sorting from endosomes to the plasma membrane. J. Cell Biol. 190, 565–574 (2010).
Temkin, P. et al. SNX27 mediates retromer tubule entry and endosome-to-plasma membrane trafficking of signalling receptors. Nat. Cell Biol. 13, 715–721 (2011).
McGarvey, J. C. et al. Actin-sorting nexin 27 (SNX27)-retromer complex mediates rapid parathyroid hormone receptor recycling. J. Biol. Chem. 291, 10986–11002 (2016).
Chan, A. S. M. et al. Sorting nexin 27 couples PTHR trafficking to retromer for signal regulation in osteoblasts during bone growth. Mol. Biol. Cell 27, 1367–1382 (2016).
McNally, K. E. et al. Retriever is a multiprotein complex for retromer-independent endosomal cargo recycling. Nat. Cell Biol. 19, 1214–1225 (2017).
Healy, M. D. et al. Structure of the endosomal Commander complex linked to Ritscher–Schinzel syndrome. Cell 186, 2219–2237 (2023).
Muntean, B. S. et al. Members of the KCTD family are major regulators of cAMP signaling. Proc. Natl Acad. Sci. USA 119, e2119237119 (2022).
Civciristov, S. et al. Ligand-dependent spatiotemporal signaling profiles of the μ-opioid receptor are controlled by distinct protein-interaction networks. J. Biol. Chem. 294, 16198–16213 (2019).
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
Tsai, T.-H. et al. Selection of features with consistent profiles improves relative protein quantification in mass spectrometry experiments. Mol. Cell. Proteomics 19, 944–959 (2020).
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2016).
MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26, 966–968 (2010).
Choi, H. et al. SAINT: probabilistic scoring of affinity purification-mass spectrometry data. Nat. Methods 8, 70–73 (2011).
Sowa, M. E., Bennett, E. J., Gygi, S. P. & Harper, J. W. Defining the human deubiquitinating enzyme interaction landscape. Cell 138, 389–403 (2009).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Matreyek, K. A., Stephany, J. J., Chiasson, M. A., Hasle, N. & Fowler, D. M. An improved platform for functional assessment of large protein libraries in mammalian cells. Nucleic Acids Res. 48, e1 (2020).
Hermann, M. et al. Binary recombinase systems for high-resolution conditional mutagenesis. Nucleic Acids Res. 42, 3894–3907 (2014).
Abreu, N., Acosta-Ruiz, A., Xiang, G. & Levitz, J. Mechanisms of differential desensitization of metabotropic glutamate receptors. Cell Rep. 35, 109050 (2021).
Vivaudou, M. et al. Probing the G-protein regulation of GIRK1 and GIRK4, the two subunits of the KACh channel, using functional homomeric mutants. J. Biol. Chem. 272, 31553–31560 (1997).
Vizcaíno, J. A. et al. 2016 update of the PRIDE database and its related tools. Nucleic Acids Res. 44, 11033 (2016).
Sharma, V. et al. Panorama: a targeted proteomics knowledge base. J. Proteome Res. 13, 4205–4210 (2014).
Acknowledgements
This work was supported by funding from the Defense Advanced Research Projects Agency (DARPA) under the Cooperative Agreements HR0011-19-2-0020 (to B.K.S., N.J.K., M.V.Z. and R.H.) and HR0011-20-2-0029 (to N.J.K. and R.H.). The views, opinions and/or findings contained in this material are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the US Government. This work further received funding from the NIH (R01DA056354 to R.H., W.C.-M., and M.V.Z.; P01HL146366 to N.J.K. and R.H.; 1U01MH115747 to N.J.K. and M.V.Z.; R35GM124731 to J.L; R01DA010711, DA012864 and MH120212 to M.V.Z.) and an NSF Graduate Research Fellowship (to N.A). B.T.L. was a recipient of a K99/R00 (DA043607). E.B. was initially supported by an NIH/NRSA Postdoctoral Fellowship (F32CA260118) and is currently supported by a K99 (K99GM151441). M.K.H. was supported by a training grant from NIH (5T32GM139786). A.G.-H. is funded by the Margarita Salas Fellowship from the Spanish Ministry of Universities. J.L. is also supported by the Rohr Family Research Scholar Award and the Irma T. Hirschl and Monique Weill-Caulier Award. The work was carried out in the Thermo Fisher Scientific Mass Spectrometry Facility for Disease Target Discovery at the J. David Gladstone Institutes and the UCSF Center for Advanced Technology. We thank Luke D. Lavis and Claire Deo (Janelia / HHMI) for critical advice and materials in supporting the development of the Janelia Fluor (JF) dye-based receptor trafficking assay. We thank K. Obernier and M. Eckhardt for reading the manuscript and providing critical feedback and members of both the von Zastrow and Krogan laboratories for helpful advice and comments.
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B.T.L., B.J.P., M.V.Z. and R.H. conceived and directed the study with input from B.K.S. and N.J.K. B.T.L., Q.L. and P.K. performed APEX proximity labeling. B.J.P. developed the data analysis workflow and performed APEX data analysis with input from R.H. M.K.H., W.C.-M. and R.H. performed HaloTag-based trafficking assay with input from E.E.B. and M.V.Z. Electrophysiology experiments were performed by N.A., A.J.G.-H. and J.L. Constructs were cloned by B.T.L., J.X. and P.K. AP–MS experiments were performed by J.X. with input from R.H. AP–MS data analysis was conducted by Z.Z.C.N. with input from R.H. J.X. and R.H. generated and characterized knockout cell lines. E.E.B., B.T.L. and P.K. performed cAMP measurements with input from M.V.Z. B.N., B.T.L. and M.V.Z. performed imaging.
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The Krogan laboratory has received research support from Vir Biotechnology, F. Hoffmann-La Roche and Rezo Therapeutics. N.J.K. has financially compensated consulting agreements with Maze Therapeutics and Interline Therapeutics. He is on the Board of Directors and is President of Rezo Therapeutics and is a shareholder in Tenaya Therapeutics, Maze Therapeutics, Rezo Therapeutics, GEn1E Lifesciences and Interline Therapeutics. B.K.S. is a founder of Epiodyne Inc., BlueDolphin LLC and Deep Apple Therapeutics, serves on the SAB of Schrodinger LLC and of Vilya Therapeutics, on the SRB of Genentech, and consults for Levator Therapeutics, Hyku Therapeutics and Great Point Ventures. The remaining authors declare no competing interests.
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Extended data
Extended Data Fig. 1 APEX2-tagged μOR remains functional and ligand-dependent proximal interaction networks of μOR are enriched for proteins indicating cellular location.
a. Receptor signaling was measured using a commercial cAMP biosensor (pGloSensor-20F). cAMP accumulation was measured after ~10 minutes of DAMGO/isoproterenol incubation and normalized to isoproterenol alone. Data from three independent experiments are presented as mean ± SEM. b. Comparison of agonist-stimulated receptor internalization as assayed by loss of cell surface immunoreactivity and measured by flow cytometry comparing untreated (control) and treated samples (10 μM DAMGO, 30 min). Data from four independent experiments are presented as mean ± SEM. c. Comparison of cell surface recovery of receptors (‘recycling’) following 30 min of DAMGO application (10 μM), agonist removal, and a 30 min recovery period in the presence of antagonist (10 μM). Data from four independent experiments are presented as mean ± SEM. d. The heatmap shows all significantly enriched gene ontology terms (adjusted P value < 0.05) among the proteins that significantly change in the proximal protein environment of the μOR upon activation with DAMGO, morphine, or PZM21 including the number of proteins that match the gene ontology terms. Cluster 1-4 refer to the clustering of the heatmap in Fig. 1b. e. Colocalization of μOR with endosomes to monitor receptor trafficking following activation. HEK293 cells stable expressing the μOR with an N-terminal Flag-tag were activated with 10 μM DAMGO, morphine, or PZM21 for 10 min. The receptor was imaged using anti-Flag. Endosomes were marked with anti-EEA1. n = 3 independent biological replicates, representative example shown, Scale bar is 10μm.
Extended Data Fig. 2 Validation of spatial reference cell lines and μOR trafficking.
a. Colocalization of location markers with APEX2 spatial references. HEK293 cells stably expressing PM-APEX2, Endo-APEX2, or Lyso-APEX2 were imaged with either Cell Mask to mark the plasma membrane, anti-EEA1 to mark endosomes, or Lyso-Tracker to mark lysosomes. n = 3 independent biological replicates, representative example shown, scale bar is 5μm. b. Colocalization of biotin with APEX2 spatial reference constructs. Localization of biotin following APEX-mediated proximity labeling was probed with Neutravidin and APEX2 constructs were detected with anti-APEX. n = 3 independent biological replicates, representative example shown, scale bar is 5μm.
Extended Data Fig. 3 Prediction of expected protein intensities based on location coefficients.
The heatmap shows the location specific proteins that were selected by pairwise comparison of the spatial reference data and their scaled intensity measured across the spatial references (left side of the heatmap). Agonist and time point dependent expected protein intensities were estimated by summing the spatial reference protein intensities that were weighted with their respective location coefficient. Observed protein intensities are shown as comparison (right side of the heatmap). Data from three independent experiments are presented as mean.
Extended Data Fig. 4 Effect of data detrending for AP2 complex subunits.
Data detrending process to dissect localization specific effect from effect of interaction with the receptor for AP2A1 (a) and AP2B1 (b), members of the adaptor protein complex. Three different temporal profiles are depicted for each protein, ligand, and replicate: the initial observed intensities, the expected intensities based on the location specific references, and the intensities after detrending. Data from three independent experiments are presented.
Extended Data Fig. 5 Correlation between ARRB2 engagement upon μOR activation with receptor trafficking.
Correlation between the minimum location coefficient calculated for the plasma membrane (PM) and the maximum ARRB2 log2FC over the time course of μOR activation with DAMGO (red), morphine (yellow) and PZM21 (green).
Extended Data Fig. 6 Comparing the DAMGO-dependent proximal proteome changes of the μOR in HEK293 and SH-SY5Y cells.
a. Comparison of μOR-APEX2 experiment upon activation with DAMGO in HEK293 and SH-SY5Y neuroblastoma cells. Heatmap focuses on all proteins significant for DAMGO in HEK293 data depicted in Fig. 3a. Data from three biological replicates are presented as mean. b. Temporal profile for selected proximal interactors of the μOR in HEK293 and SH-SY5Y cells. Line charts represent the log2FC over the time course of receptor activation with DAMGO in HEK293 (black) and SH-SY5Y (red) cells. Data from three biological replicates are presented.
Extended Data Fig. 7 Changes in proximity labeling of EYA4 upon μOR activation.
a. Volcano plot depicting log10 P value and log2FC comparing biotin labeled proteins in the proximity of EYA4 in the presence and absence of μOR activation by treatment with 10 μm DAMGO for 10 min. Data from three biological replicates are presented as mean. b. Volcano plot depicting log10 P value and log2FC comparing biotin labeled proteins in the proximity of EYA4 in the presence and absence of μOR activation by treatment with 10 μm DAMGO for 10 min and treatment with PTX. Data from three independent replicates are presented as mean.
Extended Data Fig. 8 Generation and validation of EYA4 and KCTD12 KO cell lines.
KO was validated by PCR and sequencing (EYA4) or Western blot analysis (KCTD12). n = 2 independent biological replicates, representative examples shown.
Extended Data Fig. 9 EYA4 and KCTD12 functional validation.
a. cAMP activity in control non-targeting (NT) (closed) and EYA4 KO (open) HEK293 cells stably expressing the μOR. Change in fluorescence intensity of cAMP biosensor upon stimulation with 100 nM isoproterenol (Iso) is plotted. n = 3, *p = 0.0275. b. cAMP activity EYA4 KO cells upon stimulation with Iso without (black) and with co-application of 1 μM somatostatin (SST, green) or 10 μM DAMGO (blue) is plotted. Iso curve is repeated from panel A. n = 3. c. Percent inhibition of Iso-stimulated cAMP with co-application of DAMGO (left) and integrated Iso-stimulated cAMP (right) in control non-targeting (NT) and EYA4 KO cell lines stably expressing the μOR pretreated with PTX. n = 3, *p = 0.0252. d. cAMP activity in control (closed) and KCTD12 KO (open) HEK293 cells stably expressing the μOR upon stimulation with Iso. Control curve is repeated from panel A. n = 3, **p = 0.007. e. cAMP activity in control and KCTD12 KO cells upon stimulation with Iso and SST. Percent inhibition data for control is repeated from panel B. n = 3, **p = 0.0093. f. cAMP activity in control and KCTD12 KO cells upon stimulation with Iso and DAMGO for clones used in main figure (circles) with the main text control curve repeated from Fig. 5a. n = 3. g. cAMP activity in control and KCTD12 KO cells upon stimulation with Iso and DAMGO for clones used in supplemental figures (diamonds). n = 3. h. cAMP activity in WT cells stably overexpressing μOR-APEX2 and transiently overexpressing KCTD12 or an empty vector control upon stimulation with Iso and DAMGO. n = 3. For all panels, data represent biological replicates, shown as individual data points or mean ± SD, and significance was determined by unpaired, two-tailed t-test.
Extended Data Fig. 10 Electrophysiology measurements for KCTD12 KO.
a. Summary bar graphs showing the average peak amplitudes of μOR-mediated GIRK currents over 60 s 10 µM DAMGO application, in control and KCTD12 KO HEK cells. Each point represents an individual cell. Error bars represent SEM. b. Left, summary bar graphs showing the average peak amplitudes and percent desensitization of μOR-mediated GIRK currents over 60 s 100 nM DAMGO, in control and KCTD12 KO HEK cells. Each point represents an individual cell. Unpaired, two-tailed t-test, * p = 0.0116. Error bars represent SEM. Right, representative whole cell patch clamp recordings of μOR-mediated GIRK currents in response to 60 s 100 nM DAMGO, in control and KCTD12 KO cells. c. Left, Quantification of the tau of desensitization of μOR-mediated GIRK currents over 60 s 10 µM DAMGO application, without (control) and with KCTD12 overexpression in HEK 293 T cells. Each point represents an individual cell. Unpaired t-test, ** p = 0.0038. Error bars represent SEM. Right, representative whole cell patch clamp recordings showing GIRK currents mediated by μOR activation over 60 s 10 µM DAMGO.
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Polacco, B.J., Lobingier, B.T., Blythe, E.E. et al. Profiling the proximal proteome of the activated μ-opioid receptor. Nat Chem Biol (2024). https://doi.org/10.1038/s41589-024-01588-3
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DOI: https://doi.org/10.1038/s41589-024-01588-3
