Common coupling map advances GPCR-G protein selectivity

  1. Alexander Sebastian Hauser
  2. Charlotte Avet
  3. Claire Normand
  4. Arturo Mancini
  5. Asuka Inoue
  6. Michel Bouvier  Is a corresponding author
  7. David E Gloriam  Is a corresponding author
  1. University of Copenhagen, Denmark
  2. University of Montreal, Canada
  3. Domain Therapeutics North America, Canada
  4. Tohoku University, Japan

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 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 towards the exploitation of G protein signaling bias in design of safer drugs.

Data availability

All underlying data are available in Spreadsheets S1-5. The obtained common coupling map is available in the online database GproteinDb at https://gproteindb.org/signprot/couplings.

The following previously published data sets were used

Article and author information

Author details

  1. Alexander Sebastian Hauser

    Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1098-6419
  2. Charlotte Avet

    Department of Biochemistry and Molecular Medicine, University of Montreal, Montréal, Canada
    Competing interests
    No competing interests declared.
  3. Claire Normand

    Domain Therapeutics North America, Montréal, Canada
    Competing interests
    Claire Normand, was an employees of Domain Therapeutics North America during part or all of this research..
  4. Arturo Mancini

    Domain Therapeutics North America, Montréal, Canada
    Competing interests
    Arturo Mancini, was an employees of Domain Therapeutics North America during part or all of this research..
  5. Asuka Inoue

    Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Japan
    Competing interests
    No competing interests declared.
  6. Michel Bouvier

    Department of Biochemistry and Molecular Medicine, University of Montreal, Montréal, Canada
    For correspondence
    michel.bouvier@umontreal.ca
    Competing interests
    Michel Bouvier, is the president of Domain Therapeutics scientific advisory board..
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1128-0100
  7. David E Gloriam

    Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark
    For correspondence
    david.gloriam@sund.ku.dk
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4299-7561

Funding

Canadian Institutes of Health Research (FDN-148431)

  • Michel Bouvier

Lundbeckfonden (R218-2016-1266)

  • David E Gloriam

Lundbeckfonden (R313-2019-526)

  • David E Gloriam

Novo Nordisk Fonden (NNF18OC0031226)

  • David E Gloriam

Basis for Supporting Innovative Drug Discovery and Life Science Research (JP20am0101095)

  • Asuka Inoue

Leading Asia's Private Infrastructure Fund (JP20gm0010004)

  • Asuka Inoue

Japan Agency for Medical Research and Development

  • Asuka Inoue

Takeda Science Foundation

  • Asuka Inoue

Uehara Memorial Foundation

  • Asuka Inoue

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

© 2022, Hauser et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 5,299
    views
  • 942
    downloads
  • 82
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Alexander Sebastian Hauser
  2. Charlotte Avet
  3. Claire Normand
  4. Arturo Mancini
  5. Asuka Inoue
  6. Michel Bouvier
  7. David E Gloriam
(2022)
Common coupling map advances GPCR-G protein selectivity
eLife 11:e74107.
https://doi.org/10.7554/eLife.74107

Share this article

https://doi.org/10.7554/eLife.74107

Further reading

    1. Computational and Systems Biology
    2. Developmental Biology
    Rachael Kuintzle, Leah A Santat, Michael B Elowitz
    Research Article

    The Notch signaling pathway uses families of ligands and receptors to transmit signals to nearby cells. These components are expressed in diverse combinations in different cell types, interact in a many-to-many fashion, both within the same cell (in cis) and between cells (in trans), and their interactions are modulated by Fringe glycosyltransferases. A fundamental question is how the strength of Notch signaling depends on which pathway components are expressed, at what levels, and in which cells. Here, we used a quantitative, bottom-up, cell-based approach to systematically characterize trans-activation, cis-inhibition, and cis-activation signaling efficiencies across a range of ligand and Fringe expression levels in Chinese hamster and mouse cell lines. Each ligand (Dll1, Dll4, Jag1, and Jag2) and receptor variant (Notch1 and Notch2) analyzed here exhibited a unique profile of interactions, Fringe dependence, and signaling outcomes. All four ligands were able to bind receptors in cis and in trans, and all ligands trans-activated both receptors, although Jag1-Notch1 signaling was substantially weaker than other ligand-receptor combinations. Cis-interactions were predominantly inhibitory, with the exception of the Dll1- and Dll4-Notch2 pairs, which exhibited cis-activation stronger than trans-activation. Lfng strengthened Delta-mediated trans-activation and weakened Jagged-mediated trans-activation for both receptors. Finally, cis-ligands showed diverse cis-inhibition strengths, which depended on the identity of the trans-ligand as well as the receptor. The map of receptor-ligand-Fringe interaction outcomes revealed here should help guide rational perturbation and control of the Notch pathway.

    1. Computational and Systems Biology
    2. Evolutionary Biology
    Pierre Barrat-Charlaix, Richard A Neher
    Research Article

    As pathogens spread in a population of hosts, immunity is built up, and the pool of susceptible individuals are depleted. This generates selective pressure, to which many human RNA viruses, such as influenza virus or SARS-CoV-2, respond with rapid antigenic evolution and frequent emergence of immune evasive variants. However, the host’s immune systems adapt, and older immune responses wane, such that escape variants only enjoy a growth advantage for a limited time. If variant growth dynamics and reshaping of host-immunity operate on comparable time scales, viral adaptation is determined by eco-evolutionary interactions that are not captured by models of rapid evolution in a fixed environment. Here, we use a Susceptible/Infected model to describe the interaction between an evolving viral population in a dynamic but immunologically diverse host population. We show that depending on strain cross-immunity, heterogeneity of the host population, and durability of immune responses, escape variants initially grow exponentially, but lose their growth advantage before reaching high frequencies. Their subsequent dynamics follows an anomalous random walk determined by future escape variants and results in variant trajectories that are unpredictable. This model can explain the apparent contradiction between the clearly adaptive nature of antigenic evolution and the quasi-neutral dynamics of high-frequency variants observed for influenza viruses.