Cold exposure induces the constitutively active thermogenic receptor, GPR3, via ERRα and ERRγ
Bibliographic record
Abstract
OBJECTIVES: Despite transformative advances in obesity pharmacotherapy, safely increasing energy expenditure remains a key unmet need. Exploiting thermogenic adipocytes represents a promising target given their capacity for significant catabolic activity. We previously showed that G protein-coupled receptor 3 (GPR3) can drive energy expenditure in brown and white mouse and human adipocytes. GPR3 is a unique GPCR because it displays high intrinsic activity and leads to constitutive cAMP signaling upon reaching the cell surface. Therefore, the transcriptional induction of GPR3 is analogous to ligand-binding activation of most GPCRs. Gpr3 expression is physiologically induced in thermogenic adipocytes by cold exposure, and mimicking this event through overexpression in mice is fully sufficient to increase energy expenditure and counteract metabolic disease. Yet the factors mediating physiological Gpr3 expression remain unknown. METHODS: Here, we apply ATAC-Seq to identify cold-induced promoter elements of Gpr3. We uncover a role for the estrogen-related receptors, ERRα and ERRγ, in the physiological transcriptional control of Gpr3 using adipose-specific double knock-out mice with and without adeno-associated virus (AAV)-mediated rescue. RESULTS: We show that ERRα directly binds the cold-induced promoter element of Gpr3 and that ERRα, ERRβ, and ERRγ each activate the Gpr3 promoter in vitro when co-transfected with PGC-1α. Adipocyte ERRα and ERRγ are required for the in vivo transcriptional induction of Gpr3 during cold exposure. Importantly, deficient Gpr3 cold-inducibility in adipose-specific ERRα and ERRγ KO mice is fully rescued by delivery of AAVs re-expressing either ERRα or ERRγ directly into brown adipose tissue. CONCLUSIONS: ERRα and ERRγ are critical regulators of cold-induced transcription of Gpr3 and represent a targetable strategy for pharmacologically unlocking GPR3-induced energy expenditure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".