Common coupling map advances GPCR-G protein selectivity

  1. Alexander S Hauser
  2. Charlotte Avet
  3. Claire Normand
  4. Arturo Mancini
  5. Asuka Inoue
  6. Michel Bouvier
  7. David E Gloriam

  • Curated by eLife

    eLife logo

    Evaluation Summary:

    This study is a meta-analysis of previously reported studies on G protein-coupled receptor (GPCR) coupling to G proteins. Three separate data sets that describe the coupling of members of the superfamily of non-sensory GPCRs (~200 genes) to the large family of G protein alpha subunits (~20 genes). The authors try to arrive at a consensus for receptor-G protein coupling from the three data sets, as well as identify and highlight differences or incongruencies. Compiling these vast data sets into a unified format will be extremely useful for investigators to understand receptor and effector relationships. The meta-analysis will help to deconvolute the complex physiology and pharmacology underlying hormone or drug actions acting on receptor superfamilies.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Two-thirds of human hormones and one-third of clinical drugs act on membrane receptors that couple to G proteins to achieve appropriate functional responses. While G protein transducers from literature are annotated in the Guide to Pharmacology database, two recent large-scale datasets now expand the receptor-G protein ‘couplome’. However, these three datasets differ in scope and reported G protein couplings giving different coverage and conclusions on G protein-coupled receptor (GPCR)-G protein signaling. Here, we report a common coupling map uncovering novel couplings supported by both large-scale studies, the selectivity/promiscuity of GPCRs and G proteins, and how the co-coupling and co-expression of G proteins compare to the families from phylogenetic relationships. The coupling map and insights on GPCR-G protein selectivity will catalyze advances in receptor research and cellular signaling toward the exploitation of G protein signaling bias in design of safer drugs.

Article activity feed

  1. Version published to 10.7554/elife.74107 on eLife
  2. eLife

    Author Response:

    Reviewer #1:

    Hauser et al, analyze two large datasets of GPCR-G protein interactions/couplings ("Inoue" and "Bouvier"), comparing and combining them with the widely-used literature-based Guide to Pharmacology (GtP) database. As the Inoue and Bouvier datasets were based on different experimental setups, this enables the identification of which couplings are supported by more than one method. The authors also establish a normalization protocol that enables to move from qualitative to quantitative comparisons and identify couplings that might be either below are above a rigid threshold. Overall, the paper describes a new resource and the methodologies used to build this resource. The resulting coupling map is available through the GPCRdb website, a widely used resource in the field.

    The authors have thus improved the ability of researchers to assess prior results and compare them to their own new data. This resource clearly and significantly upgrades options currently available and will likely be of interest and prove quite useful to scientists both in academia and in industry.

    We thank the reviewer for so nicely describing the study and its prospective application.

    Weaknesses include:

    • The data is described mostly by broad numbers, such as the number of receptors or coupling in a subset, or percentages. While this is helpful to understand the data, this reviewer found it hard to follow the mountain of numbers. A suggestion would be to add a section where the authors pick selected examples of particular experimental data and show how their combine database can resolve previously unanswered (or wrongly answered) questions of GPCR/G protein coupling.

    We have removed numbers in several places throughout Results where we had included multiple measures e.g., absolute numbers and percentages. Furthermore, where an overall number has been broken down into distributions, e.g., across different G proteins of families thereof, we moved other numbers to parentheses.

    The different sections of Results that answer questions of GPCR-G protein coupling have now been presented more clearly by updating their headings and grouping them all in a subsection of part of Results called “Research Advances – Insights on GPCR-G protein selectivity”. These sections are all based on our “combined database”/coupling map. In each such section, we start at the overall level – covering all GPCRs and/or G proteins – but then give selected examples thereof that are weaved into and exemplifies the text. This approach has also been used in the new Results section “Differential tissue expression gives G proteins in the same family large spatial selectivity”, which gives selected examples of G proteins with specific tissue expression profiles.

    Given that the paper has already exceeded the maximum of 5,000 words by quite a bit, we think that this approach of weaving selected examples into each selectivity insight section is the most appropriate, and that it brings most clarity. Furthermore, we hope that readers will be inspired to use our coupling map to generate additional questions for future experiments.

    • The paper does not reveal new biological findings. For example, while some emphasis is placed on new data on G15, it would be helpful to take the extra step and use this to suggest new biological insights.

    eLife’s author guidelines (https://reviewer.elifesciences.org/author-guide/types) state that “Tools and Resources articles do not have to report major new biological insights or mechanisms, but it must be clear that they will enable such advances to take place, for example, through exploratory or proof-of-concept experiments.” In case this manuscript is published as a Tools and Resources paper, it may therefore be sufficient to provide the foundation for future studies to reveal new biological findings.

    Nevertheless, the coupling map led to biological findings relating to patterns and mechanisms of GPCR-G protein selectivity that were not described in the original studies. I.e., while this study did not generate new data, it arrived at new insights based on published data. This seems to be in line with eLife’s publication format “Research Advances” (https://reviewer.elifesciences.org/author-guide/types), and the Analysis format of several other journals. Some insights described herein have not been presented before while others have been updated in scope and precision. Furthermore, we have added a new section of Results with insights on G protein expression profiles and co-expression.

    We have clarified this by updating the headings of the sections that present these insights, and grouped them under a common subheading of Results termed “Research Advances – Insights on GPCR-G protein selectivity”. However, in case we have overlooked very recent studies describing some of the same biological insights, we would please like to ask for their references and would be more than willing to revise the manuscript again to incorporate them. Furthermore, if the Reviewer is missing a particular analysis that is critical to understand GPCR-G protein coupling, please let us know.

    • The authors cautiously label couplings supported by only one dataset as "unsupported". It would seem more helpful to grade couplings by a reliability scale, providing users with a wider set of data. Perhaps only couplings that are directly conflicted by negative data should be labeled as unsupported?

    We understand that the term “unsupported” has been used in a confusing way. We have now replaced this term with “unique” and explained all terms in Table 1 of the revised manuscript.

    To address the need for a means to grade or filter couplings by reliability, we have added the following paragraph to the manuscript:

    “To enable any researcher to use the coupling map, we have availed a “G protein couplings” browser (https://gproteindb.org/signprot/couplings) in GproteinDb (2). By default, this browser only shows “supported” couplings with evidence from two datasets, but there is an option (first blue button) to changes the level of support to only one (for most complete coverage of GPCRs) or to three (for the highest confidence) sources. We propose a standardized terminology to describe couplings based on their level of experimental support from independent groups (Table 1). The criterion of supporting independent data, and the terms “proposed” and “supported”, are already used by the Nomenclature Committee of the International Union of Basic and Clinical Pharmacology (NC-IUPHAR) for GPCR deorphanization. Furthermore, the online coupling browser allows any researcher to use only a subset of datasets, or to apply filters to the Log(Emax/EC50), Emax, and EC50 values. Finally, users can filter datapoints based on a statistical reliability score in the form of the number of SDs from basal response."

    Furthermore, we have added references to the online G protein coupling browser in the:

    (1) Introduction ending: “On this basis, we develop a unified map of GPCR-G protein couplings that can be filtered or intersected in GproteinDb …”, (2) Fig. 2 legend ending: “Note: Researchers wishing to use this coupling map, optionally after applying own reliability criteria or cut-offs, can do so for any set of couplings in GproteinDb (1).” (3) Fig. S2 ending: “Unique couplings are hidden by default in the online G protein couplings browser in GproteinDb, as they await the independent support by a second group.”

    To many scientists the most reliable option is to involve NC-IUPHAR. Gloriam is a corresponding member of NC-IUPHAR, which has mentioned the possibility of involving its many worldwide pharmacological experts to update GtP on a case-by-case basis for receptors. For example, many of the “novel” couplings jointly supported by Bouvier and Inoue may be added. This option is advantageous as it involves experts in each receptor system (often with knowledge of other relevant studies) and is backed by the authoritative organization.

    • Given that this manuscript includes authors from both the Inoue and Bouvier studies, I can understand why they are not directly assessing which of the two datasets (in relation to the GtP) might be more accurate. Nevertheless, I believe this assessment should be done and that the advantages and disadvantages of the two experimental systems discussed clearly.

    We believe that the three-way intersection of couplings is the most informative and therefore preferred over individual comparison of each of the Inoue and Bouvier datasets to GtP. GtP is unfortunately not suitable as a stand-alone resource – neither to contradict nor support couplings (on the G protein subtype level). This is because GtP is incomplete (especially for G12/13) and does not provide any information on the level of G protein subtypes, only families. The three-way interactions will always use GtP but adds a second dataset on top of this when validating a third dataset. Our manuscript already included a three-way intersection of datasets, allowing readers to conclude which dataset might be more accurate (then Fig. 3 and Spreadsheet 3) on a per-G protein basis.

    In the revised manuscript, we have rewritten this section, which now has the heading “Bouvier’s and Inoue’s biosensors appear more sensitive for G15 and, Gs and G12, respectively. We have also made a completely new figure, Fig. 7, which more clearly illustrates for which G proteins that Bouvier and Inoue may have overrepresented or underrepresented couplings. This section specifically investigates the question of whether differential sensitivity can explain “unique” couplings. However, such unique couplings can either be due to overrepresentation or instead be true positives that are missing in GtP because of incompleteness and in the other biosensor due to lower sensitivity. Unfortunately, we will not be able to distinguish these possibilities until the research community has gained additional datasets from independent biosensors with as high sensitivity.

    Whereas our study compares datasets rather than experimental systems, we have added a paragraph in the Discussion describing which aspects should be considered when choosing a biosensor. There, we reference a review from last year dedicated to biosensors and describing their pros and cons (3), and the accompanying paper by Bouvier et al. (4), comparing several aspects of the experimental system used by Inoue et al. It is also important to note that the most advantageous biosensor may be one of the two for which data is analyzed in our paper. For many studies, researchers may instead be better off with another biosensor, for example those from Lambert/Mamyrbekov (5), Roth (2) (Gαβγ sensors first described in (6-11)) or Inoue (unpublished dissociation assays using wt G proteins fused with LgBit and HiBit). These are all referenced in the Discussion.

    References:

    1. Pandy-Szekeres G, Esguerra M, Hauser AS, Caroli J, Munk C, Pilger S, et al. The G protein database, GproteinDb. Nucleic Acids Res. 2022;50(D1):D518-D25. 10.1093/nar/gkab852
    2. Olsen RHJ, DiBerto JF, English JG, Glaudin AM, Krumm BE, Slocum ST, et al. TRUPATH, an open-source biosensor platform for interrogating the GPCR transducerome. Nat Chem Biol. 2020;16(8):841-9. 10.1038/s41589-020-0535-8
    3. Wright SC, Bouvier M. Illuminating the complexity of GPCR pathway selectivity – advances in biosensor development. Curr Opin Struct Biol. 2021;69:142-9. https://doi.org/10.1016/j.sbi.2021.04.006
    4. Avet C, Mancini A, Breton B, Gouill CL, Hauser AS, Normand C, et al. Effector membrane translocation biosensors reveal G protein and B-arrestin profiles of 100 therapeutically relevant GPCRs. bioRxiv. 2021:2020.04.20.052027. 10.1101/2020.04.20.052027
    5. Masuho I, Martemyanov KA, Lambert NA. Monitoring G Protein Activation in Cells with BRET. Methods Mol Biol. 2015;1335:107-13. 10.1007/978-1-4939-2914-6_8
    6. Gales C, Rebois RV, Hogue M, Trieu P, Breit A, Hebert TE, et al. Real-time monitoring of receptor and G-protein interactions in living cells. Nat Methods. 2005;2(3):177-84. 10.1038/nmeth743
    7. Gales C, Van Durm JJ, Schaak S, Pontier S, Percherancier Y, Audet M, et al. Probing the activation-promoted structural rearrangements in preassembled receptor-G protein complexes. Nat Struct Mol Biol. 2006;13(9):778-86. 10.1038/nsmb1134
    8. Schrage R, Schmitz AL, Gaffal E, Annala S, Kehraus S, Wenzel D, et al. The experimental power of FR900359 to study Gq-regulated biological processes. Nat Commun. 2015;6:10156. 10.1038/ncomms10156
    9. Breton B, Sauvageau E, Zhou J, Bonin H, Le Gouill C, Bouvier M. Multiplexing of multicolor bioluminescence resonance energy transfer. Biophys J. 2010;99(12):4037-46. 10.1016/j.bpj.2010.10.025
    10. Bunemann M, Frank M, Lohse MJ. Gi protein activation in intact cells involves subunit rearrangement rather than dissociation. Proceedings of the National Academy of Sciences of the United States of America. 2003;100(26):16077-82. 10.1073/pnas.2536719100
    11. Janetopoulos C, Jin T, Devreotes P. Receptor-mediated activation of heterotrimeric G-proteins in living cells. Science. 2001;291(5512):2408-11. 10.1126/science.1055835

    Reviewer #2:

    This study is a meta-analysis of previously reported studies on G protein-coupled receptor (GPCR) coupling to G proteins. The data sets are from three distinct sources: a compendium compiled by the International Union of Basic & Clinical Pharmacology (IUPHAR), and two data sets compiled by two separate laboratories. Each of these data sets describes the coupling of members of the superfamily of non-sensory GPCRs (~200 genes) to the large family of G protein alpha subunits (~20 genes). The authors try to arrive at a consensus for receptor-G protein coupling from the three data sets, as well as identify and highlight differences or incongruencies. Compiling these vast data sets into a unified format will be extremely useful for investigators to understand receptor and effector relationships. The meta-analysis will help to deconvolute the complex physiology and pharmacology underlying hormone or drug actions acting on receptor superfamilies. A better understanding of receptor-G protein selectivity and/or promiscuity will ultimately help in identifying safer therapeutics.

    We appreciate the summary and the explanation of the usefulness of our meta-analysis and its potential impact.

  3. eLife

    Evaluation Summary:

    This study is a meta-analysis of previously reported studies on G protein-coupled receptor (GPCR) coupling to G proteins. Three separate data sets that describe the coupling of members of the superfamily of non-sensory GPCRs (~200 genes) to the large family of G protein alpha subunits (~20 genes). The authors try to arrive at a consensus for receptor-G protein coupling from the three data sets, as well as identify and highlight differences or incongruencies. Compiling these vast data sets into a unified format will be extremely useful for investigators to understand receptor and effector relationships. The meta-analysis will help to deconvolute the complex physiology and pharmacology underlying hormone or drug actions acting on receptor superfamilies.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

  4. eLife

    Reviewer #1 (Public Review):

    Hauser et al, analyze two large datasets of GPCR-G protein interactions/couplings ("Inoue" and "Bouvier"), comparing and combining them with the widely-used literature-based Guide to Pharmacology (GtP) database. As the Inoue and Bouvier datasets were based on different experimental setups, this enables the identification of which couplings are supported by more than one method. The authors also establish a normalization protocol that enables to move from qualitative to quantitative comparisons and identify couplings that might be either below are above a rigid threshold. Overall, the paper describes a new resource and the methodologies used to build this resource. The resulting coupling map is available through the GPCRdb website, a widely used resource in the field.

    The authors have thus improved the ability of researchers to assess prior results and compare them to their own new data. This resource clearly and significantly upgrades options currently available and will likely be of interest and prove quite useful to scientists both in academia and in industry.

    Weaknesses include:

    - The data is described mostly by broad numbers, such as the number of receptors or coupling in a subset, or percentages. While this is helpful to understand the data, this reviewer found it hard to follow the mountain of numbers. A suggestion would be to add a section where the authors pick selected examples of particular experimental data and show how their combine database can resolve previously unanswered (or wrongly answered) questions of GPCR/G protein coupling.

    - The paper does not reveal new biological findings. For example, while some emphasis is placed on new data on G15, it would be helpful to take the extra step and use this to suggest new biological insights.

    - The authors cautiously label couplings supported by only one dataset as "unsupported". It would seem more helpful to grade couplings by a reliability scale, providing users with a wider set of data. Perhaps only couplings that are directly conflicted by negative data should be labeled as unsupported?

    - Given that this manuscript includes authors from both the Inoue and Bouvier studies, I can understand why they are not directly assessing which of the two datasets (in relation to the GtP) might be more accurate. Nevertheless, I believe this assessment should be done and that the advantages and disadvantages of the two experimental systems discussed clearly.

    In contrast to the larger volume of the "weaknesses" section, the strengths of this manuscript are clear and robust - this is a very useful resource that is described well and with many details.

  5. eLife

    Reviewer #2 (Public Review):

    This study is a meta-analysis of previously reported studies on G protein-coupled receptor (GPCR) coupling to G proteins. The data sets are from three distinct sources: a compendium compiled by the International Union of Basic & Clinical Pharmacology (IUPHAR), and two data sets compiled by two separate laboratories. Each of these data sets describes the coupling of members of the superfamily of non-sensory GPCRs (~200 genes) to the large family of G protein alpha subunits (~20 genes). The authors try to arrive at a consensus for receptor-G protein coupling from the three data sets, as well as identify and highlight differences or incongruencies. Compiling these vast data sets into a unified format will be extremely useful for investigators to understand receptor and effector relationships. The meta-analysis will help to deconvolute the complex physiology and pharmacology underlying hormone or drug actions acting on receptor superfamilies. A better understanding of receptor-G protein selectivity and/or promiscuity will ultimately help in identifying safer therapeutics.

  6. Version published to 10.1101/2021.09.07.459250v2 on bioRxiv
  7. Version published to 10.1101/2021.09.07.459250v1 on bioRxiv