Profiling of HCAR1 signalling reveals Gα <sub>i/o</sub> and Gα <sub>s</sub> activation without β‐arrestin recruitment and the discovery of an allosteric agonist
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Lactate, historically viewed as a metabolic by-product, has emerged as a signalling molecule via the G protein-coupled receptor Hydroxycarboxylic Acid Receptor 1 (HCAR1). The receptor is primarily expressed in adipocytes but also found in various other tissues. HCAR1 activation has been shown to regulate lipolysis and improve insulin sensitivity, positioning it as a promising therapeutic target for metabolic disorders such as obesity and type 2 diabetes. Despite its potential, its role in cancer progression and the limited availability of characterized ligands necessitate further investigation into its signalling mechanisms. This study aimed to broaden the pharmacological understanding of HCAR1 by investigating previously uncharacterized ligands and profiling their signalling properties. EXPERIMENTAL APPROACH: We employed enhanced bystander bioluminescence resonance energy transfer (ebBRET) assays to investigate G protein activation and β-arrestin recruitment following ligand stimulation of HCAR1. A panel of compounds was screened to identify more potent agonists and modulators of HCAR1 signalling. KEY RESULTS: pathways without recruiting β-arrestins, revealing a distinct signalling profile. CONCLUSION AND IMPLICATIONS: These findings expand our understanding of HCAR1 signalling and introduce new molecular tools for probing its physiological and pathological roles. The characterized ligands may support future therapeutic strategies targeting HCAR1 in metabolic disorders while informing approaches to mitigate potential oncogenic effects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".