Combinatorial expression of GPCR isoforms affects signalling and drug responses

An Author Correction to this article was published on 25 November 2020

This article has been updated

Abstract

G-protein-coupled receptors (GPCRs) are membrane proteins that modulate physiology across human tissues in response to extracellular signals. GPCR-mediated signalling can differ because of changes in the sequence1,2 or expression3 of the receptors, leading to signalling bias when comparing diverse physiological systems4. An underexplored source of such bias is the generation of functionally diverse GPCR isoforms with different patterns of expression across different tissues. Here we integrate data from human tissue-level transcriptomes, GPCR sequences and structures, proteomics, single-cell transcriptomics, population-wide genetic association studies and pharmacological experiments. We show how a single GPCR gene can diversify into several isoforms with distinct signalling properties, and how unique isoform combinations expressed in different tissues can generate distinct signalling states. Depending on their structural changes and expression patterns, some of the detected isoforms may influence cellular responses to drugs and represent new targets for developing drugs with improved tissue selectivity. Our findings highlight the need to move from a canonical to a context-specific view of GPCR signalling that considers how combinatorial expression of isoforms in a particular cell type, tissue or organism collectively influences receptor signalling and drug responses.

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Fig. 1: Structural diversity and functional impact of receptor isoforms.
Fig. 2: Tissue distribution of GPCR isoforms.
Fig. 3: Combinatorial isoform expression influences pharmacological responses.
Fig. 4: GPCR isoforms as potential drug targets.
Fig. 5: From a canonical to a context-specific view of GPCR signalling.

Data availability

All data accessed from public repositories—namely the GTEx database (www.gtexportal.org, version 7), GPCRdb (https://gpcrdb.org), Ensembl (www.ensembl.org, GRCh37 assembly), Uniprot (www.uniprot.org), Protein DataBank (PDB; www.rcsb.org), PRIDE (www.ebi.ac.uk/pride, accession numbers PXD0079985 and PXD010154), ProteomicsDB (www.proteomicsdb.org), Guide to Pharmacology (www.guidetopharmacology.org), GeneAtlas (geneatlas.roslin.ed.ac.uk) and BioProject (www.ncbi.nlm.nih.gov/bioproject, accession numbers PRJNA183192 and PRJNA322355)—are detailed in the Methods. We declare that all data supporting the findings of this study are available within the paper, via the GPCRdb (https://gpcrdb.org/protein/isoforms) and in the Supplementary Information files.

Change history

  • 25 November 2020

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. 1.

    Hauser, A. S. et al. Pharmacogenomics of GPCR drug targets. Cell 172, 41–54 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Thompson, M. D. et al. Pharmacogenetics of the G protein-coupled receptors. Methods Mol. Biol. 1175, 189–242 (2014).

    PubMed  Google Scholar 

  3. 3.

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

    CAS  PubMed  Google Scholar 

  4. 4.

    Smith, J. S., Lefkowitz, R. J. & Rajagopal, S. Biased signalling: from simple switches to allosteric microprocessors. Nat. Rev. Drug Discov. 17, 243–260 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    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 

  6. 6.

    Stallaert, W., Christopoulos, A. & Bouvier, M. Ligand functional selectivity and quantitative pharmacology at G protein-coupled receptors. Expert Opin. Drug Discov. 6, 811–825 (2011).

    CAS  PubMed  Google Scholar 

  7. 7.

    Kenakin, T. & Christopoulos, A. Signalling bias in new drug discovery: detection, quantification and therapeutic impact. Nat. Rev. Drug Discov. 12, 205–216 (2013).

    CAS  PubMed  Google Scholar 

  8. 8.

    Buljan, M. et al. Alternative splicing of intrinsically disordered regions and rewiring of protein interactions. Curr. Opin. Struct. Biol. 23, 443–450 (2013).

    CAS  PubMed  Google Scholar 

  9. 9.

    Kilpatrick, G. J., Dautzenberg, F. M., Martin, G. R. & Eglen, R. M. 7TM receptors: the splicing on the cake. Trends Pharmacol. Sci. 20, 294–301 (1999).

    CAS  PubMed  Google Scholar 

  10. 10.

    Markovic, D. & Challiss, R. A. J. Alternative splicing of G protein-coupled receptors: physiology and pathophysiology. Cell. Mol. Life Sci. 66, 3337–3352 (2009).

    CAS  PubMed  Google Scholar 

  11. 11.

    Zhou, J., Zhao, S. & Dunker, A. K. Intrinsically disordered proteins link alternative splicing and post-translational modifications to complex cell signaling and regulation. J. Mol. Biol. 430, 2342–2359 (2018).

    CAS  PubMed  Google Scholar 

  12. 12.

    Sriram, K. & Insel, P. A. G. G protein-coupled receptors as targets for approved drugs: how many targets and how many drugs? Mol. Pharmacol. 93, 251–258 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Hauser, A. S., Attwood, M. M., Rask-Andersen, M., Schiöth, H. B. & Gloriam, D. E. Trends in GPCR drug discovery: new agents, targets and indications. Nat. Rev. Drug Discov. 16, 829–842 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Melé, M. et al. The human transcriptome across tissues and individuals. Science 348, 660–665 (2015).

    ADS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Kooistra, A. J., Mordalski, S., Pándy-Szekeres, G., Esguerra, M., Mamyrbekov, A., Munk, C., Keserű, G. M. & Gloriam, D. E. GPCRdb in 2021: integrating GPCR sequence, structure and function. Nucleic Acids Res. (in the press).

  16. 16.

    Langenhan, T. Adhesion G protein–coupled receptors—candidate metabotropic mechanosensors and novel drug targets. Basic Clin. Pharmacol. Toxicol. 126 (Suppl. 6), 5–16 (2019).

    PubMed  Google Scholar 

  17. 17.

    Knierim, A. B. et al. Genetic basis of functional variability in adhesion G protein-coupled receptors. Sci. Rep. 9, 11036 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Milligan, G. A day in the life of a G protein-coupled receptor: the contribution to function of G protein-coupled receptor dimerization. Br. J. Pharmacol. 153 (Suppl 1), S216–S229 (2008).

    CAS  PubMed  Google Scholar 

  19. 19.

    Berchiche, Y. A. & Sakmar, T. P. CXC chemokine receptor 3 alternative splice variants selectively activate different signaling pathways. Mol. Pharmacol. 90, 483–495 (2016).

    CAS  PubMed  Google Scholar 

  20. 20.

    Hirata, T., Ushikubi, F., Kakizuka, A., Okuma, M. & Narumiya, S. Two thromboxane A2 receptor isoforms in human platelets. Opposite coupling to adenylyl cyclase with different sensitivity to Arg60 to Leu mutation. J. Clin. Invest. 97, 949–956 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Venkatakrishnan, A. J. et al. Structured and disordered facets of the GPCR fold. Curr. Opin. Struct. Biol. 27, 129–137 (2014).

    CAS  PubMed  Google Scholar 

  22. 22.

    Martin, C. B. P. et al. RNA splicing and editing modulation of 5-HT2C receptor function: relevance to anxiety and aggression in VGV mice. Mol. Psychiatry 18, 656–665 (2013).

    CAS  PubMed  Google Scholar 

  23. 23.

    Grosse, R., Schöneberg, T., Schultz, G. & Gudermann, T. Inhibition of gonadotropin-releasing hormone receptor signaling by expression of a splice variant of the human receptor. Mol. Endocrinol. 11, 1305–1318 (1997).

    CAS  PubMed  Google Scholar 

  24. 24.

    Marrone, G. F. et al. Truncated mu opioid GPCR variant involvement in opioid-dependent and opioid-independent pain modulatory systems within the CNS. Proc. Natl Acad. Sci. USA 113, 3663–3668 (2016).

    ADS  CAS  PubMed  Google Scholar 

  25. 25.

    Howlett, A. C. et al. Endocannabinoid tone versus constitutive activity of cannabinoid receptors. Br. J. Pharmacol. 163, 1329–1343 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Finan, B. et al. Reappraisal of GIP pharmacology for metabolic diseases. Trends Mol. Med. 22, 359–376 (2016).

    CAS  PubMed  Google Scholar 

  27. 27.

    Schwarzschild, M. A., Agnati, L., Fuxe, K., Chen, J. F. & Morelli, M. Targeting adenosine A2A receptors in Parkinson’s disease. Trends Neurosci. 29, 647–654 (2006).

    CAS  PubMed  Google Scholar 

  28. 28.

    Canela-Xandri, O., Rawlik, K. & Tenesa, A. An atlas of genetic associations in UK Biobank. Nat. Genet. 50, 1593–1599 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    MacKenzie, A. E. et al. The antiallergic mast cell stabilizers lodoxamide and bufrolin as the first high and equipotent agonists of human and rat GPR35. Mol. Pharmacol. 85, 91–104 (2014).

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Zhao, P. et al. Targeting of the orphan receptor GPR35 by pamoic acid: a potent activator of extracellular signal-regulated kinase and β-arrestin2 with antinociceptive activity. Mol. Pharmacol. 78, 560–568 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Thal, D. M., Glukhova, A., Sexton, P. M. & Christopoulos, A. Structural insights into G-protein-coupled receptor allostery. Nature 559, 45–53 (2018).

    ADS  CAS  PubMed  Google Scholar 

  32. 32.

    Shin, H. et al. Recent advances in RNA therapeutics and RNA delivery systems based on nanoparticles. Adv. Ther. 1, 1800065 (2018).

    Google Scholar 

  33. 33.

    Adhikari, S., Sharma, S., Ahn, S. B. & Baker, M. S. In silico peptide repertoire of human olfactory receptor proteome on high-stringency mass spectrometry. J. Proteome Res. 18, 4117–4123 (2019).

    PubMed  Google Scholar 

  34. 34.

    Insel, P. A. et al. G protein-coupled Receptor (GPCR) expression in native cells: “novel” endoGPCRs as physiologic regulators and therapeutic targets. Mol. Pharmacol. 88, 181–187 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Wang, D. et al. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Mol. Syst. Biol. 15, e8503 (2019).

    PubMed  PubMed Central  Google Scholar 

  36. 36.

    Enge, M. et al. Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns. Cell 171, 321–330 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    De Mei, C., Ramos, M., Iitaka, C. & Borrelli, E. Getting specialized: presynaptic and postsynaptic dopamine D2 receptors. Curr. Opin. Pharmacol. 9, 53–58 (2009).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

    Kopelman, N. M., Lancet, D. & Yanai, I. Alternative splicing and gene duplication are inversely correlated evolutionary mechanisms. Nat. Genet. 37, 588–589 (2005).

    CAS  PubMed  Google Scholar 

  39. 39.

    Zerbino, D. R. et al. Ensembl 2018. Nucleic Acids Res. 46 (D1), D754–D761 (2018).

    CAS  Google Scholar 

  40. 40.

    Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    The GTEx Consortium. The Genotype-tissue expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 348, 648–660 (2015).

    Google Scholar 

  42. 42.

    Saha, A. et al. Co-expression networks reveal the tissue-specific regulation of transcription and splicing. Genome Res. 27, 1843–1858 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Harding, S. D. et al. The IUPHAR/BPS Guide to PHARMACOLOGY in 2018: updates and expansion to encompass the new guide to IMMUNOPHARMACOLOGY. Nucleic Acids Res. 46 (D1), D1091–D1106 (2018).

    CAS  PubMed  Google Scholar 

  44. 44.

    Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Isberg, V. et al. Generic GPCR residue numbers—aligning topology maps while minding the gaps. Trends Pharmacol. Sci. 36, 22–31 (2015).

    CAS  PubMed  Google Scholar 

  46. 46.

    DiPilato, L. M. & Zhang, J. The role of membrane microdomains in shaping β2-adrenergic receptor-mediated cAMP dynamics. Mol. Biosyst. 5, 832–837 (2009).

    CAS  PubMed  Google Scholar 

  47. 47.

    Weinberg, Z. Y., Zajac, A. S., Phan, T., Shiwarski, D. J. & Puthenveedu, M. A. Sequence-specific regulation of endocytic lifetimes modulates arrestin-mediated signaling at the μ opioid receptor. Mol. Pharmacol. 91, 416–427 (2017).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Bailey, S. et al. Interactions between RAMP2 and CRF receptors: the effect of receptor subtypes, splice variants and cell context. Biochim. Biophys. Acta Biomembr. 1861, 997–1003 (2019).

    CAS  PubMed  Google Scholar 

  49. 49.

    Weston, C. et al. Receptor activity-modifying protein-directed G protein signaling specificity for the calcitonin gene-related peptide family of receptors. J. Biol. Chem. 291, 21925–21944 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Knight, A. et al. Discovery of novel adenosine receptor agonists that exhibit subtype selectivity. J. Med. Chem. 59, 947–964 (2016).

    CAS  PubMed  Google Scholar 

  51. 51.

    Griffin, M. T., Figueroa, K. W., Liller, S. & Ehlert, F. J. Estimation of agonist activity at G protein-coupled receptors: analysis of M2 muscarinic receptor signaling through Gi/o,Gs, and G15. J. Pharmacol. Exp. Ther. 321, 1193–1207 (2007).

    CAS  PubMed  Google Scholar 

  52. 52.

    Mackenzie, A. E. et al. Receptor selectivity between the G proteins Gα12 and Gα13 is defined by a single leucine-to-isoleucine variation. FASEB J. 33, 5005–5017 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Milligan, G. Orthologue selectivity and ligand bias: translating the pharmacology of GPR35. Trends Pharmacol. Sci. 32, 317–325 (2011).

    CAS  PubMed  Google Scholar 

  54. 54.

    Jenkins, L. et al. Agonist activation of the G protein-coupled receptor GPR35 involves transmembrane domain III and is transduced via Gα13 and β-arrestin-2. Br. J. Pharmacol. 162, 733–748 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Hornbeck, P. V. et al. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 43, D512–D520 (2015).

    CAS  PubMed  Google Scholar 

  56. 56.

    Bai, B. et al. Deep multilayer brain proteomics identifies molecular networks in Alzheimer’s disease progression. Neuron 105, 975–991 (2020).

    CAS  PubMed  Google Scholar 

  57. 57.

    Peng, J., Elias, J. E., Thoreen, C. C., Licklider, L. J. & Gygi, S. P. Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome. J. Proteome Res. 2, 43–50 (2003).

    CAS  PubMed  Google Scholar 

  58. 58.

    Wang, X. et al. JUMP: a tag-based database search tool for peptide identification with high sensitivity and accuracy. Mol. Cell. Proteomics 13, 3663–3673 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Eng, J. K., Jahan, T. A. & Hoopmann, M. R. Comet: an open-source MS/MS sequence database search tool. Proteomics 13, 22–24 (2013).

    CAS  PubMed  Google Scholar 

  60. 60.

    Samaras, P. et al. ProteomicsDB: a multi-omics and multi-organism resource for life science research. Nucleic Acids Res. 48 (D1), D1153–D1163 (2020).

    CAS  PubMed  Google Scholar 

  61. 61.

    Barrett, T. et al. BioProject and BioSample databases at NCBI: facilitating capture and organization of metadata. Nucleic Acids Res. 40, D57–D63 (2012).

    CAS  PubMed  Google Scholar 

  62. 62.

    Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank A. Hummer, R. Henderson, F. Heidenreich, W. R. Orchard and E. Villanueva for reading the manuscript. M.M.-S. and M.M.B. acknowledge the UK Medical Research Council (MRC; grant number MC_U105185859) for support. M.M.-S. is a Wolfson College Junior Research Fellow and has been supported by a Federation of European Biochemical Societies Long-Term Fellowship and a Marie Skłodowska-Curie Individual Fellowship from the European Union’s Horizon 2020 research and innovation programme under grant agreement 832620. D.M. acknowledges the support of the Swiss National Science foundation (SNF) under grant P2ELP3_18910. J.P. is partially supported by National Institutes of Health (NIH) grant R01AG053987. X.W. is partially supported by a pilot grant from the NIH Center of Biomedical Research Excellence (COBRE) for epigenomics of development and disease, and a pilot grant from the Core National Institute on Drug Abuse (NIDA) Center of Excellence in Omics, Systems Genetics, and the Addictome. M.A.P. was supported by NIH grant GM117425 and National Science Foundation (NSF) grant 1935926. M.H. and G.L. were supported by an MRC confidence in concept award (MC_PC_17156). A.P. was supported by a UK Biotechnology and Biological Sciences Research Council (BBSRC)-iCase studentship (BB/JO14540/1), co-funded with AstraZeneca. D.E.G. acknowledges the Lundbeck Foundation (R313-2019-526) and Novo Nordisk Foundation (NNF17OC0031226) for financial support. C.M. acknowledges the Lundbeck Foundation (R218-2016-1266). S.E.C. was supported by an NSF Graduate Research Fellowship under grant DGE 1256260. G.M. and A.B.T. were supported by BBSRC grants BB/P000649/1 and BB/P00069X/1, respectively. M.M.B. is a Lister Institute Fellow and is also supported by the European Research Council (ERC; ERC-COG-2015-682414), the Novo Nordisk Foundation Challenge Grant (ADIPOSIGN) and the American Lebanese Syrian Associated Charities (ALSAC).

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Affiliations

Authors

Contributions

M.M.-S. collected data, wrote scripts and performed all of the computational analyses. S.E.C. and M.A.P. experimentally characterized the pharmacology of CNR1 isoforms. M.H., A.P. and G.L. experimentally characterized the pharmacology of GIPR isoforms. T.Q., A.E.M., G.M. and A.B.T. experimentally characterized the pharmacology of GPR35 isoforms. D.M. collected data and carried out genomics analyses. C.M. and D.E.G. designed and implemented the GPCRdb receptor-isoform browser. X.W. and J.P. collected data and analysed MS data. D.E.G. helped with aspects of data interpretation. M.M.-S. and M.M.B. designed the project, analysed and interpreted the results, and wrote the manuscript. All authors read, and provided comments on, the draft. M.M.-S. led the project. M.M.B. initiated, managed and set the direction of research.

Corresponding authors

Correspondence to Maria Marti-Solano or M. Madan Babu.

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The authors declare no competing interests.

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Peer review information Nature thanks Sudarshan Rajagopal, Roser Vento and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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

Extended data figures and tables

Extended Data Fig. 1 Isoform diversity in GPCRs.

a, Alternative transcriptional start or termination sites, together with alternative splicing, can generate non-reference receptor isoforms (that is, isoforms with sequences that are different to those of the GPCRdb-annotated receptor), with altered structural and signalling properties. b, Analysis pipeline combining GTEx isoform-level data with GPCRdb, Ensembl and UniProt annotations to filter for highly expressed, protein-coding isoforms. Isoforms with truncated signal peptides, or those without at least one conserved transmembrane helix compared with the reference isoform, are not considered (see Methods). c, Number of isoforms in different receptor classes (left) and grouped by the type of natural ligand of each receptor (right). d, Number of isoforms per receptor, grouped by each receptor’s coupling partners (G-protein subunits), as annotated in the IUPHAR/BPS Guide To Pharmacology database (www.guidetopharmacology.org). Box plots in c, d show medians and 25th and 75th percentiles; whiskers represent 1.5 times the interquartile range from the 25th to the 75th quartiles.

Extended Data Fig. 2 Classification of receptor isoforms according to topology.

Structural classification of non-reference isoforms. Isoforms are considered topologically preserved if all transmembrane (TM) segments are conserved in sequence with respect to the reference isoform; this includes receptor isoforms with changes (including deletions and unique isoform sequences) in the receptor N- and C termini, as well as in intracellular and extracellular loops (ECLs) and the helix 8 (H8) segment. Isoforms are classified as topologically truncated if at least one TM segment is altered (that is, is partially or totally missing or has a different sequence to the reference isoform). Both topologically preserved and truncated isoforms can either have an alternative sequence in the segments that differ from the reference (unique sequence, represented as solid coloured lines) or lack parts of those segments altogether, thus representing a shorter version of the reference sequence (non-unique, represented as dotted coloured lines).

Extended Data Fig. 3 Non-reference isoforms with missing phosphorylation motifs.

a, Analysis of PhosphoSitePlus (https://www.phosphosite.org/) phosphorylation motifs with annotated regulatory function in non-reference receptor isoforms. Left, frequency of phosphosites related to regulatory effects (for example, receptor trafficking and interaction) that are found to be missing in non-reference isoforms. Right, examples of the potential consequences of this lack of regulation on receptor function for somatostatin receptor type 2 (SSR2), thromboxane A2 receptor (TA2R) and adenosine receptor A2A (AA2AR). b, Table showing all detected non-reference receptor isoforms in which a phosphorylation motif can be lost, together with details about the function of that particular phosphosite in the reference isoform and its associated literature evidence (with the examples in a highlighted in grey in the table).

Extended Data Fig. 4 Tissue distribution of different isoforms.

a, Binary representation of the presence or absence of particular receptor isoforms in the 30 different GTEx tissues according to their classification as reference isoform (grey), topologically preserved isoform (dark blue) or topologically truncated isoform (light blue). This figure shows that every tissue expresses all these types of isoforms, and that non-reference isoforms can be consistently found in multiple tissues. b, Heatmap showing whether the most prevalent isoform for a receptor in a particular tissue is a reference (grey), topologically preserved (dark blue) or topologically truncated (light blue) receptor isoform. The most prevalent isoform in a particular tissue is considered to be the one with the highest median expression (in TPM). Only tissues in which more than one isoform was expressed have been retained and represented. c, Mean number of isoforms expressed per receptor for every GTEx donor-tissue combination. All available combinations of donor-tissue expression had a mean number of isoforms per receptor of more than one (that is, reference and non-reference isoforms are coexpressed in each GTEx donor-tissue combination). Empty cells (white) represent donor-tissue combinations with no expression data. For the underlying data in ac, see Supplementary Table 2. d, For receptors with multiple isoforms, two scenarios are possible. In one scenario, all receptor isoforms could be consistently expressed in human tissues, resulting in a uniform expression pattern. In a second scenario, different isoform combinations may be expressed in different tissues, resulting in a complex expression pattern. In order to assess this, we calculated the number of tissue-expression signatures per receptor (see Fig. 2).

Extended Data Fig. 5 Characterization of the pharmacological and signalling effects of the coexpression of different isoforms of CNR1 and GIPR.

a, Alignment of the N-terminal segments of the reference and non-reference CNR1 isoforms. Regions that are missing or vary in isoforms 1 and 2 are highlighted in blue. b, Left, change in mean CFP/FRET ratios induced by 5 μM forskolin (Fsk) in HEK293 cells. Data from cells from the same coverslip expressing (grey line) and not expressing (black line) the CNR1 reference isoform are shown. Cells expressing CNR1 show a lower Fsk response, as expected for Gi signalling. CFP/FRET ratios were obtained in HEK293 cells expressing the CNR1 reference isoform (+CNR1, n = 30 cells) or not (−CNR1 n = 40 cells). Right, the baseline ratios are not significantly different (P = 0.527 by unpaired, two-sided t-test). Boxplot whiskers show minima and maxima; centres indicate medians; and box boundaries indicate the 25th and 75th percentiles. c, The increased response to Fsk in CNR1-expressing cells treated with Rimonabant (SR) is consistent with the superactivation of adenylyl cyclase, as found in cell lines stably expressing Gi-coupled receptors such as CNR1. d, Total cAMP response induced by Fsk in CNR1-expressing cells. The response is decreased in CNR1-expressing cells compared with non-expressing cells. Pretreatment with 10 μM Rimonabant reversed the CNR1-dependent decrease. e, The endpoint cAMP response (REndpoint), expressed as fold change over baseline cAMP (RBaseline), induced by Fsk in CNR1-expressing cells is significantly decreased in CNR1-expressing cells compared with non-expressing cells. Pretreatment with 10 μM Rimonabant reverses the CNR1-dependent decrease (−CNR1 −Rimonabant, n = 24 cells; +CNR1 −Rimonabant, n = 19 cells; −CNR1 +Rimonabant, n = 27 cells; +CNR1 +Rimonabant, n = 17 cells, from 3 independent experiments). f, Pretreatment with 5 μM of the neutral antagonist AM4113 significantly increased the Fsk-stimulated total cAMP levels in CNR1-expressing relative to non-expressing cells. g, Pretreatment with AM4113 significantly increased the Fsk-stimulated endpoint cAMP levels in CNR1-expressing cells relative to non-expressing cells (−CNR1 −AM4113, n = 55 cells; +CNR1 −AM4113, n = 50 cells; −CNR1 +AM4113, n = 44 cells; +CNR1 +AM4113, n = 48 cells, from 3 independent experiments). In dg, P-values were obtained by one-way ANOVA with Sidak’s multiple comparisons tests; boxplot whiskers show 10th and 90th percentiles; centre lines indicate medians; box bounds indicate the 25th and 75th percentile; + indicates means. h, Expression ratios of SNAP-tagged CNR1 isoforms to the Flag-tagged reference isoform are not significantly different across the combinatorial expression conditions (one-way ANOVA with Dunnett’s multiple comparisons test). In all cases, boxplot whiskers show 10th and 90th percentiles; centre lines indicate medians; box bounds indicate the 25th and 75th percentiles; + indicates mean. Individual data points are overlaid on the boxplot. i, The total cAMP response, normalized to cells expressing the reference isoform, is increased in cells coexpressing the reference isoform and non-reference isoform 2 compared with control cells (ref + ref (‘ref’), n = 23 cells; ref + iso1, n = 18 cells; ref + iso2, n = 27 cells from 3 independent experiments; one-way ANOVA with Dunnett’s multiple comparisons test). j, Endpoint cAMP levels are significantly decreased in cells coexpressing the reference isoform and non-reference isoform 1 compared with cells expressing only the reference isoform (one-way ANOVA with Dunnett’s multiple comparisons test). For box plots in i, j, whiskers show 10th and 90th percentiles; centre lines indicate medians; box bounds indicate 25th and 75th percentiles; + indicates mean. Individual data points are overlaid on the boxplot. k, Heatmap representation of cAMP levels after treatment with Fsk and Rimonabant over time in individual HEK293 cells expressing a Flag-tagged reference isoform and different SNAP-tagged isoforms. CFP/FRET ratios were normalized using the baseline ratio as 0 and the maximum ratio after Fsk, but before Rimonabant, treatment as 1. l, Alignment of the C-terminal segment of the reference and non-reference GIPR isoforms. Varying regions in isoform 1 are highlighted in blue. m, Concentration–response curves for cAMP accumulation, intracellular calcium ((Ca2+)i) mobilization and β-arrestin-1/2 recruitment in response to GIP(1–42) stimulation, in HEK293T cells expressing GIPR–Nluc reference alone (dark grey, n = 4 independent experiments performed in duplicate), GIPR–Nluc isoform 1 alone (pink, n = 4 independent experiments performed in duplicate) or GIPR–Nluc reference and GIPR–Nluc isoform 1 combined (magenta, n = 4 independent experiments performed in duplicate). Data normalized to GIPR–Nluc reference alone and expressed as mean ± s.e.m. n, Cell-surface expression of Nluc–GIPR reference (n = 7 independent experiments performed in duplicate) or Nluc–GIPR isoform 1 (n = 7 independent experiments performed in duplicate). Data normalized to Nluc–GIPR reference, with significance determined by Mann–Whitney test. o, Left part of graph, cell-surface expression of Nluc–GIPR reference alone (dark grey, n = 4 independent experiments performed in duplicate) versus Nluc–GIPR reference and Nluc–GIPR isoform 1 combined (magenta, n = 4 independent experiments performed in duplicate). Middle, cell-surface expression of Flag–GIPR reference in the absence (dark grey, n = 5 independent experiments performed in duplicate) versus presence (light grey, n = 4 independent experiments performed in duplicate) of Nluc–GIPR isoform 1. Right, cell-surface expression of Nluc–GIPR isoform 1 in the absence (pink, n = 5 independent experiments performed in duplicate) versus presence (light pink, n = 5 independent experiments performed in duplicate) of Flag–GIPR reference. Data normalized to Nluc–GIPR reference alone (left), Flag–GIPR reference alone (middle), or Nluc–GIPR isoform 1 alone (right). Significance in n, o was determined by a two-tailed Mann–Whitney test. Box plots in n, o show the maximum and minimum (whiskers), as well as medians, 25th and 75th percentiles and means (+).p, GIP(1–42)-stimulated β-arrestin-1 and β-arrestin-2 recruitment to GIPR–Nluc reference in the absence (dark grey) or presence (light grey) of Nluc–GIPR isoform 1, or to GIPR–Nluc isoform 1 in the absence (pink) or presence (light pink) of Nluc–GIPR reference. Data are means ± s.e.m. of n = 4 experiments performed in triplicate, normalized to GIPR–Nluc reference alone.

Extended Data Fig. 6 Receptor isoforms and drug targets.

a, Number of receptors categorized by the number of tissue-expression signatures for all GPCRs (grey) and for those belonging to the 111 targets of 474 FDA-approved drugs (in green). b, The number of approved drugs varies greatly for receptors with the same number of tissue-expression signatures. Count plot of the 111 GPCRs that are targeted by 474 FDA-approved drugs. The radius of each circle indicates the number of GPCR targets for each combination of ‘number of tissue-expression signatures’ (x-axis) versus ‘number of approved drugs’ (y-axis). c, Disease-related phenotypes that can be exclusively linked to non-reference receptor isoforms were extracted from Gene ATLAS (see Methods and Supplementary Table 3). Non-reference isoform sequences for the N-terminal segment of the chemokine receptor CXCR3 and C-terminal segment of the metabotropic glutamate receptor 8 (GRM8) are represented in blue; the reference isoform is in grey. Polymorphisms are indicated in bold, together with their associated phenotypes in light grey boxes. d, Filtering based on structural and expression considerations identifies non-reference isoforms with changes in extracellular structure and tissue distribution with respect to the reference isoform, potentially allowing the development of ligands that specifically target them. e, Alignment of the N-terminal segment of the reference and non-reference GPR35 isoforms. Varying regions in isoform 1 are highlighted in blue. f, pEC50 and Emax values for the coupling of Gα13 and β-arrestin-2 to the reference GPR35 isoform and isoform 1 in response to lodoxamide, pamoic acid and zaprinast. (n = 3 biologically independent samples; error values correspond to s.e.m.) g, BRET signals were monitored after treatment of HEK293T cells with varying concentrations of pamoic acid and zaprinast using a GPR35–Gα13 SPASM sensor (left panel; n = 3 biologically independent samples) or eYFP-tagged GPR35 isoforms and β-arrestin-2 tagged with nanoluciferase (right panel; n = 3 biologically independent samples; Methods). Error values correspond to s.e.m.

Extended Data Fig. 7 GPCRdb receptor-isoform browser.

Screenshot of the isoform browser as implemented in GPCRdb. The classification tree (left) depicts the number of unique isoforms for each receptor, receptor family and GPCR class. In the structural annotation table (right), conserved, partially conserved and non-conserved structural segments are coloured in green, orange and red. Purple triangles indicate insertions. Each entry includes the receptor name, isoform number (assigned according to sequence length, with the longest non-reference isoform considered to be isoform 1), topological preservation or truncation status, the number of tissues in which it has been found to be expressed, an average value of segment completeness with respect to the reference receptor sequence (Ref(%)), its functional annotation (either inferred through structural fingerprint identity with other characterized isoforms, or derived from the literature for isoforms with an associated PubMed reference), and a link to its isoform-level proteomics evidence in ProteomicsDB.

Extended Data Fig. 8 Analysis of MS-based proteomics data for receptor isoforms.

a, Analysis pipeline for investigating proteomics data to detect GPCR isoforms that had been identified using transcriptomics data at an isoform level. All transcripts from GPCRs with two or more isoforms that were detected in brain tissue through our GTEx analysis were filtered to identify those with unique peptide sequences. These transcripts were then matched with brain proteomics hits to discover how many receptor isoforms with unique protein sequences detected by transcriptomics could be identified by proteomics. b, Analysis of the number of matching peptides found in all receptor isoforms, considering receptor segments. Non-reference isoforms are highlighted in blue shaded boxes. Consistent with previous studies, non-transmembrane receptor regions are the ones that are often detected by MS. c, Proteomic detection of isoforms using MS data from a study of 29 healthy human tissues and 50 MS runs (see Methods). Searching for GPCRs detected in GTEx in this data set identified a number of receptors with more than one isoform. Of these, we filtered isoforms that had peptides matching one isoform only. d, Analysis of the number of matching peptides found in all receptor isoforms in c, considering receptor segments. Non-reference isoforms are highlighted in violet. As seen in b, non-transmembrane receptor regions are the ones that are more often detected by MS. Isoforms with an asterisk are found in both data sets. For all data, see Supplementary Table 5.

Extended Data Fig. 9 Receptor-centric isoform distribution in cell lines and single cells.

a, Analysis pipeline combining isoform-level data extracted from transcriptomics experiments of 11 human cell lines deposited in BioProject, with GPCRdb, Ensembl and Uniprot annotations to filter for highly expressed, protein-coding isoforms. Isoforms with truncated signal peptides or those without at least one conserved transmembrane helix as compared with their GPCRdb reference are not considered (see Methods). b, Relationship between the number of GPCRs expressed in the 11 different cell lines (left axis, grey line) and the mean number of isoforms per receptor found in each tissue (right axis; means are shown as black dots and standard errors are grey lines). c, Relationship between the total number of isoforms per receptor and its number of cell-line expression signatures in the analysed cell lines. The dark grey regression line was obtained using a linear model; the light grey confidence interval represents the standard error. d, Mean number of isoforms per receptor expressed in single pancreatic cells as measured by single-cell RNA-seq, with each point representing the mean number of isoforms per receptor in a single cell whose transcriptome was sequenced. This analysis shows that, for every inferred cell type, there are several cells that express more than one isoform per receptor. See underlying data for ad in Supplementary Table 6. Boxplots show median and 25th and 75th percentiles; whiskers represent 1.5 times the interquartile range from the 25th and 75th quartiles.

Extended Data Fig. 10 Integration of several data sets provides an isoform-centric view of GPCR signalling.

For receptors with multiple isoforms, combining tissue-specific and cell-specific transcriptomics information, sequence and structural data, functional annotations, population-wide genetic-association studies, proteomics evidence and details on receptor pharmacology allows us to obtain a GPCR diversity map with which to assess the possible contribution of GPCR isoform diversity to physiological signalling bias and system-specific drug responses.

Supplementary information

Reporting Summary

Supplementary Table 1

Functional changes reported in the literature for GPCR isoforms with particular structural fingerprints.

Supplementary Table 2

Tissue expression distribution of the analysed GPCR isoforms.

Supplementary Table 3

Statistically significant associations between disease-related phenotypes and single nucleotide polymorphisms available via Gene ATLAS that map to non-reference GPCR isoforms.

Supplementary Table 4

Tissue distribution of non-reference GPCR isoforms with extracellular structural variation and different tissue distribution compared to the reference isoform.

Supplementary Table 5

Evidence from mass spectrometry experiments for reference and non-reference GPCR isoforms.

Supplementary Table 6

Cancer cell line and human pancreatic single cell RNA sequencing evidence for GPCR isoforms.

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Marti-Solano, M., Crilly, S.E., Malinverni, D. et al. Combinatorial expression of GPCR isoforms affects signalling and drug responses. Nature 587, 650–656 (2020). https://doi.org/10.1038/s41586-020-2888-2

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