Profiling of HCAR1 signaling 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
Abstract Lactate was long considered a byproduct of glycolysis and associated with various harmful effects. However, the role of lactate was expanded with the finding that it also can act as a signaling molecule through the G protein–coupled receptor Hydroxycarboxylic Acid Receptor 1 (HCAR1). The receptor was shown to be primarily expressed in adipocytes but is also expressed in many other tissues and cell types. Activation of HCAR1 can help regulate lipolysis and improve insulin sensitivity, making it a promising target for managing obesity and other metabolic disorders. While HCAR1 activation offers therapeutic benefits for metabolic diseases, it can also promote cancer cell survival and metastasis, necessitating a nuanced approach to avoid unintended tumor growth. However, only a few ligands have been reported for HCAR1, and their signaling pathways remain unexplored. Using enhanced bystander bioluminescence resonance energy transfer (ebBRET) to study G protein activation and β-arrestin recruitment following ligand addition, we were able to identify compounds such as AZ7136, a potent HCAR1 agonist, AZ2114 a partial agonist, and establish GPR81 agonist 1 as an ago-positive allosteric modulator. We also show that HCAR1 preferentially activates the Gα i/o and Gα s pathways without recruiting β-arrestins. These findings enhance our understanding of the signaling profile of HCAR1 and the newly characterized ligands could be used as molecular tools to understand more about HCAR1 in metabolic disease. One Sentence Summary This study used the ebBRET platform to identify and characterize several synthetic ligands for the lactate receptor HCAR1, significantly advancing our understanding of HCAR1 signaling.
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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".