Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

TRUPATH, an open-source biosensor platform for interrogating the GPCR transducerome

Nature Chemical Biology volume 16, pages 841–849 (2020)Cite this article

Subjects

Abstract

G-protein-coupled receptors (GPCRs) remain major drug targets, despite our incomplete understanding of how they signal through 16 non-visual G-protein signal transducers (collectively named the transducerome) to exert their actions. To address this gap, we have developed an open-source suite of 14 optimized bioluminescence resonance energy transfer (BRET) Gαβγ biosensors (named TRUPATH) to interrogate the transducerome with single pathway resolution in cells. Generated through exhaustive protein engineering and empirical testing, the TRUPATH suite of Gαβγ biosensors includes the first Gα15 and GαGustducin probes. In head-to-head studies, TRUPATH biosensors outperformed first-generation sensors at multiple GPCRs and in different cell lines. Benchmarking studies with TRUPATH biosensors recapitulated previously documented signaling bias and revealed new coupling preferences for prototypic and understudied GPCRs with potential in vivo relevance. To enable a greater understanding of GPCR molecular pharmacology by the scientific community, we have made TRUPATH biosensors easily accessible as a kit through Addgene.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Optimization workflow for the exemplar Gαq biosensor.
Fig. 2: Switch III in the Gα-subunit is a novel region for protein engineering.
Fig. 3: Head-to-head comparisons of TRUPATH biosensors to first-generation BRET2 biosensors.
Fig. 4: TRUPATH screens of prototypic and understudied GPCRs reveal varying degrees of transducer promiscuity.
Fig. 5: TRUPATH screens of κOR agonists reveal unappreciated transducer-selective effects on potency and efficacy.

Similar content being viewed by others

Data availability

All data that were generated or analyzed during this study are included in this Article and its Supplementary Information files or are available from the corresponding authors upon reasonable request. All TRUPATH sensors are available to academic and non-profit institutions as a kit through Addgene (https://www.addgene.org/).

References

  1. Kroeze, W. K. et al. PRESTO-TANGO: an open-source resource for interrogation of the druggable human GPCR-ome. Nat. Struct. Mol. Biol. 22, 362–369 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Rodgers, G. et al. Glimmers in illuminating the druggable genome. Nat. Rev. Drug Discov. 17, 301–302 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Roth, B. L. & Chuang, D.-M. Multiple mechanisms of serotonergic signal transduction. Life Sci. 41, 1051–1064 (1987).

    CAS  PubMed  Google Scholar 

  4. Urban, J. D. et al. Functional selectivity and classical concepts of quantitative pharmacology. J. Pharmacol. Exp. Ther. 320, 1–13 (2007).

    CAS  PubMed  Google Scholar 

  5. White, K. L. et al. The G protein-biased κ-opioid receptor agonist RB-64 is analgesic with a unique spectrum of activities in vivo. J. Pharm. Exp. Ther. 352, 98–109 (2015).

    Google Scholar 

  6. Manglik, A. et al. Structure-based discovery of opioid analgesics with reduced side effects. Nature 537, 185–190 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Wisler, J. W. et al. A unique mechanism of β-blocker action: carvedilol stimulates β-arrestin signaling. Proc. Natl Acad. Sci. USA 104, 16657–16662 (2007).

    CAS  PubMed  Google Scholar 

  8. Wacker, D., Stevens, R. C. & Roth, B. L. How ligands illuminate GPCR molecular pharmacology. Cell 170, 414–427 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Viscusi, E. R. et al. A randomized, phase 2 study investigating TRV130, a biased ligand of the μ-opioid receptor, for the intravenous treatment of acute pain. Pain 157, 264–272 (2016).

    CAS  PubMed  Google Scholar 

  10. Kenakin, T. Biased receptor signaling in drug discovery. Pharm. Rev. 71, 267–315 (2019).

    CAS  PubMed  Google Scholar 

  11. Masuho, I., Martemyanov, K. A. & Lambert, N. A. in G Protein-Coupled Receptors in Drug Discovery: Methods and Protocols vol. 1335 (ed. Filizola, M.) 107–113 (Springer, 2015).

  12. Wan, Q. et al. Mini G protein probes for active G protein-coupled receptors (GPCRs) in live cells. J. Biol. Chem. 293, 7466–7473 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Janetopoulos, C., Jin, T. & Devreotes, P. Receptor-mediated activation of heterotrimeric G-proteins in living cells. Science 291, 2408–2411 (2001).

    CAS  PubMed  Google Scholar 

  14. Busnelli, M. et al. Functional selective oxytocin-derived agonists discriminate between individual G protein family subtypes. J. Biol. Chem. 287, 3617–3629 (2012).

    CAS  PubMed  Google Scholar 

  15. Saulière, A. et al. Deciphering biased-agonism complexity reveals a new active AT1 receptor entity. Nat. Chem. Biol. 8, 622–630 (2012).

    PubMed  Google Scholar 

  16. Yano, H. et al. Development of novel biosensors to study receptor-mediated activation of the G-protein α subunits Gs and Golf. J. Biol. Chem. 292, 19989–19998 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Adjobo-Hermans, M. J. et al. Real-time visualization of heterotrimeric G protein Gq activation in living cells. BMC Biol. 9, 32 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Mastop, M. et al. A FRET-based biosensor for measuring Gα13 activation in single cells. PLoS ONE 13, e0193705 (2018).

    PubMed  PubMed Central  Google Scholar 

  19. van Unen, J. et al. A new generation of FRET sensors for robust measurement of Gαi1, Gαi2 and Gαi3 activation kinetics in single cells. PLoS ONE 11, e0146789 (2016).

    PubMed  PubMed Central  Google Scholar 

  20. Gether, U. & Kobilka, B. K. G protein-coupled receptors. II. Mechanism of agonist activation. J. Biol. Chem. 273, 17979–17982 (1998).

    CAS  PubMed  Google Scholar 

  21. Smrcka, A. V. et al. NMR analysis of G-protein βγ subunit complexes reveals a dynamic Gα-Gβγ subunit interface and multiple protein recognition modes. Proc. Natl Acad. Sci. USA 107, 639–644 (2010).

    CAS  PubMed  Google Scholar 

  22. Hughes, T. E., Zhang, H., Logothetis, D. E. & Berlot, C. H. Visualization of a functional Gαq-green fluorescent protein fusion in living cells. Association with the plasma membrane is disrupted by mutational activation and by elimination of palmitoylation sites, but not be activation mediated by receptors or AlF4 −. J. Biol. Chem. 276, 4227–4235 (2001).

    CAS  PubMed  Google Scholar 

  23. Gibson, S. K. & Gilman, A. G. Giα and Gβ subunits both define selectivity of G protein activation by α2-adrenergic receptors. Proc. Natl Acad. Sci. USA 103, 212–217 (2006).

    CAS  PubMed  Google Scholar 

  24. Nishimura, A. et al. Structural basis for the specific inhibition of heterotrimeric Gq protein by a small molecule. Proc. Natl Acad. Sci. USA 107, 13666–13671 (2010).

    CAS  PubMed  Google Scholar 

  25. Schrage, R. et al. The experimental power of FR900359 to study Gq-regulated biological processes. Nat. Commun. 6, 10156 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Grundmann, M. et al. Lack of β-arrestin signaling in the absence of active G proteins. Nat. Commun. 9, 341 (2018).

    PubMed  PubMed Central  Google Scholar 

  27. Tesmer, J. J., Berman, D. M., Gilman, A. G. & Sprang, S. R. Structure of RGS4 bound to AlF4 −-activated Giα1: stabilization of the transition state for GTP hydrolysis. Cell 89, 251–261 (1997).

    CAS  PubMed  Google Scholar 

  28. Li, Q. & Cerione, R. A. Communication between Switch II and Switch III of the transducin α subunit is essential for target activation. J. Biol. Chem. 272, 21673–21676 (1997).

    CAS  PubMed  Google Scholar 

  29. Kwan, D. H. T., Yung, L. Y., Ye, R. D. & Wong, Y. H. Activation of Ras-dependent signaling pathways by G14-coupled receptors requires the adaptor protein TPR1. J. Cell. Biochem. 113, 3486–3497 (2012).

    CAS  PubMed  Google Scholar 

  30. Felder, C. C. et al. Comparison of the pharmacology and signal transduction of the human cannabinoid CB1 and CB2 receptors. Mol. Pharmacol. 48, 443–450 (1995).

    CAS  PubMed  Google Scholar 

  31. Kenakin, T. Gaddum Memorial Lecture 2014: Receptors as an evolving concept: from switches to biased microprocessors. Br. J. Pharmacol. 172, 4238–4253 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Rothman, R. B. et al. β-FNA binds irreversibly to the opiate receptor complex: in vivo and in vitro evidence. J. Pharmacol. Exp. Ther. 247, 405–416 (1988).

    CAS  PubMed  Google Scholar 

  33. Kenakin, T. Functional selectivity and biased receptor signaling. J. Pharm. Exp. Ther. 336, 296–302 (2011).

    CAS  Google Scholar 

  34. Innamorati, G. et al. Heterotrimeric G proteins demonstrate differential sensitivity to β-arrestin dependent desensitization. Cell. Signal. 21, 1135–1142 (2009).

    CAS  PubMed  Google Scholar 

  35. Kenakin, T., Watson, C., Muniz-Medina, V., Christopoulos, A. & Novick, S. A simple method for quantifying functional selectivity and agonist bias. ACS Chem. Neurosci. 3, 193–203 (2011).

    PubMed  PubMed Central  Google Scholar 

  36. White, K. L. et al. Identification of novel functionally selective κ-opioid receptor scaffolds. Mol. Pharmacol. 85, 83–90 (2014).

    PubMed  PubMed Central  Google Scholar 

  37. Mueller, K. L. et al. The receptors and coding logic for bitter taste. Nature 434, 225–229 (2005).

    CAS  PubMed  Google Scholar 

  38. Wacker, D. et al. Crystal structure of an LSD-bound human serotonin receptor. Cell 168, 377–389.e12 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Garibay, J. L. R. et al. Analysis by mRNA levels of the expression of six G protein α-subunit genes in mammalian cells and tissues. Biochim. Biophys. Acta Mol. Cell Res. 1094, 193–199 (1991).

    CAS  Google Scholar 

  40. Hinton, D. R. et al. Novel localization of a G protein, Gz-α, in neurons of brain and retina. J. Neurosci. 10, 2763–2770 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Inoue, A. et al. Illuminating G-protein-coupling selectivity of GPCRs. Cell 177, 1933–1947.e25 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Daaka, Y., Luttrell, L. M. & Lefkowitz, R. J. Switching of the coupling of the β2-adrenergic receptor to different G proteins by protein kinase A. Nature 390, 88–91 (1997).

    CAS  PubMed  Google Scholar 

  43. Sandhu, M. et al. Conformational plasticity of the intracellular cavity of GPCR-G-protein complexes leads to G-protein promiscuity and selectivity. Proc. Natl Acad. Sci. USA 116, 11956–11965 (2019).

    CAS  PubMed  Google Scholar 

  44. Okashah, N. et al. Variable G protein determinants of GPCR coupling selectivity. Proc. Natl Acad. Sci. USA 116, 12054–12059 (2019).

    CAS  PubMed  Google Scholar 

  45. Hervé, D. Identification of a specific assembly of the G protein golf as a critical and regulated module of dopamine and adenosine-activated cAMP pathways in the striatum. Front Neuroanat. 5, 48 (2011).

    PubMed  PubMed Central  Google Scholar 

  46. Roth, B. L. Molecular pharmacology of metabotropic receptors targeted by neuropsychiatric drugs. Nat. Struct. Mol. Biol. 26, 535–544 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Roth, B. L., Irwin, J. J. & Shoichet, B. K. Discovery of new GPCR ligands to illuminate new biology. Nat. Chem. Biol. 13, 1143–1151 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Navidi, W. C. Statistics for Engineers and Scientists (McGraw-Hill, 2008).

Download references

Acknowledgements

We thank M. Bouvier for the gift of the Gβ1 and Gγ2-GFP2 constructs, T. Kenakin for insights regarding quantitative pharmacology and J. Aubé for the gift of ML139. We also thank A. Inoue for the donation of the HEK293ΔG cells. Due to space constraints we could not include all citations; for this we apologize.

Author information

Author notes

  1. These authors contributed equally: Reid H. J. Olsen, Jeffrey F. DiBerto.

Authors and Affiliations

  1. Department of Pharmacology, University of North Carolina Chapel Hill School of Medicine, Chapel Hill, NC, USA

    Reid H. J. Olsen, Jeffrey F. DiBerto, Justin G. English, Alexis M. Glaudin, Brian E. Krumm, Samuel T. Slocum, Tao Che, Ariana C. Gavin, John D. McCorvy, Bryan L. Roth & Ryan T. Strachan

  2. National Institute of Mental Health Psychoactive Drug Screening Program, University of North Carolina Chapel Hill School of Medicine, Chapel Hill, NC, USA

    Samuel T. Slocum & Bryan L. Roth

  3. Department of Cell Biology, Neurobiology, and Anatomy, Medical College of Wisconsin, Milwaukee, WI, USA

    John D. McCorvy

  4. Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA

    Bryan L. Roth

Authors
  1. Reid H. J. Olsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jeffrey F. DiBerto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Justin G. English
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alexis M. Glaudin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Brian E. Krumm
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Samuel T. Slocum
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Tao Che
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ariana C. Gavin
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. John D. McCorvy
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Bryan L. Roth
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ryan T. Strachan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.H.J.O., J.F.D., J.G.E., B.L.R. and R.T.S. developed the concept and designed the experiments. R.H.J.O. and J.F.D. performed the bulk of the molecular biology and experimentation, with A.M.G., B.E.K., S.T.S., T.C., J.D.M., A.C.G. and R.T.S. contributing to the in vitro pharmacology assays. J.G.E. established the cloning strategy and helped with construct generation. R.H.J.O., J.F.D., J.G.E., B.L.R. and R.T.S. wrote and edited the manuscript. This work was supported by NIH grants (nos. R37DA035764, U24DK116195 and RO1MH112205 to B.L.R., KO1MH109943 to R.T.S. and F31NS093917 to R.H.J.O.) and the Michael Hooker Distinguished Professorship to B.L.R.

Corresponding authors

Correspondence to Bryan L. Roth or Ryan T. Strachan.

Ethics declarations

Competing interests

R.H.J.O., J.F.D., J.G.E., B.L.R. and R.T.S. are inventors of the TRUPATH technology and could receive royalties. These relationships have been disclosed to and are under management by UNC-Chapel Hill.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Tables 1–8, Figs. 1–24 and Note.

Reporting Summary

Supplementary Dataset 1

TRUPATH transducerome profiling analysis of β2AR, NT1R, LPAR6 and 5-HT7.

Supplementary Dataset 2

Statistical analysis of transducer-specific efficacy (Emax) values reported in Supplementary Data Set 1.

Supplementary Dataset 3

Statistical analysis of transducer-specific potency (log EC50, M) values reported in Supplementary Data Set 1.

Supplementary Dataset 4

Comparisons of ligand efficacy (Emax, % maximal transducer response from Supplementary Table 4.2) between transducers, within each drug.

Supplementary Dataset 5

Comparisons of ligand potency (log EC50, M; from Supplementary Table 4.1) between transducers, within each drug.

Supplementary Dataset 6

Comparisons of agonist transduction coefficients (log (Emax/EC50) from Supplementary Table 4.3) between transducers, within each drug.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Olsen, R.H.J., DiBerto, J.F., English, J.G. et al. TRUPATH, an open-source biosensor platform for interrogating the GPCR transducerome. Nat Chem Biol 16, 841–849 (2020). https://doi.org/10.1038/s41589-020-0535-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41589-020-0535-8

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research