Evidence map›Paper›PMID 33654881›Full record

ArticleBio-protocol2019

Identification of Heteroreceptors Complexes and Signal Transduction Events Using Bioluminescence Resonance Energy Transfer (BRET).

Irene Reyes-Resina, Jasmina Jiménez, Gemma Navarro, Rafael Franco

Abstract read
In one paragraph

Article in Bio-protocol, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Irene Reyes-ResinaMolecular Neurobiology Laboratory, Universitat de Barcelona, Barcelona, Spain.
Jasmina JiménezCentro de Investigación en Red, Enfermedades Neurodegenerativas (CiberNed), Instituto de Salud Carlos III, Madrid, Spain.
Gemma NavarroCentro de Investigación en Red, Enfermedades Neurodegenerativas (CiberNed), Instituto de Salud Carlos III, Madrid, Spain.
Rafael FrancoMolecular Neurobiology Laboratory, Universitat de Barcelona, Barcelona, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detecting protein-protein interactions by co-immunoprecipitation provided a major advancement in the immunology research field. In the G-protein-coupled receptors (GPCRs) research field, colocalization and co-immunoprecipitation were used to detect interactions, but doubts arose due to specificity of the antibodies (monoclonal in the case of receptors related to immunology and polyclonal in the case of GPCRs) and due to the possibility of false positive due to the potential occurrence of bridging proteins. Accordingly, new methodological approaches were needed, and energy transfer techniques have been instrumental to detect direct protein-protein, protein-receptor or receptor-receptor interactions. Of the two most relevant methods (Förster, or fluorescence resonance energy transfer: FRET and Bioluminescence energy transfer: BRET), the protocol for BRET is here presented. BRET has been instrumental to detect direct interactions between GPCRs and has contributed to demonstrate that GPCR dimers/oligomer functionality is different from that exerted by individual receptors. Advantages outweigh those of FRET as no fluorescence source is needed. Interestingly, BRET is not only useful to validate interactions detected by other means or hypothesized in the basis of indirect evidence, but to measure signal transduction events. In fact, BRET may, for instance, be used to assess β-arrestin recruitment to activated GPCRs.

Indexed as

BioluminescenceBRETEnergy transferFluorescenceGPCRG-protein-coupled receptorsHeteromersProtein-protein interactions

Identifiers

PMID33654881
PMCPMC7853939

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.