Loss of glucagon signaling alters white adipose tissue browning
Bibliographic record
Abstract
ABSTRACT Various endocrine factors contribute to cold‐induced white adipose tissue (WAT) browning, but glucagon has largely been ignored. The purpose of the current investigation was to determine if glucagon was required for the effects of cold on WAT browning. Utilizing whole‐body glucagon receptor knockout (Gcgr −/− ) mice and their wild‐type (WT) littermate controls, we examined the response of inguinal WAT (iWAT) and interscapular brown adipose tissue (BAT) to an acute (48 h) cold stress or challenge with the β3‐adrenergic agonist CL316,243. The effects of glucagon alone on the induction of thermogenic genes in adipose tissue from C57BL6/J mice were also examined. Gcgr −/− mice displayed modest increases in indices of browning at room temperature while displaying a blunted induction of Ucp1, Cidea , and Ffg21 mRNA expression in iWAT following cold exposure. Similarly, cold induced increases in mitochondrial DNA copy number, and the protein content of mitochondrial respiratory chain complexes, UCP1, and PGC1α were attenuated in iWAT from Gcgr −/− mice. In BAT, the induction of thermogenic markers following cold exposure was reduced, but the effect was less pronounced than in iWAT. Glucagon treatment increased the expression of thermogenic genes in both iWAT and BAT of C57BL6/J mice. In response to CL316,243, circulating fatty acids, glycerol, and the phosphorylation of hormone‐sensitive lipase were attenuated in iWAT of Gcgr −/− mice. We provide evidence that glucagon is sufficient for the induction of thermogenic genes in iWAT, and the absence of intact glucagon signaling blunts the cold‐induced browning of WAT, possibly due, in part, to impaired adrenergic signaling.—Townsend, L. K., Medak, K. D., Knuth, C. M., Peppier, W. T., Charron, M. J., Wright, D. C. Loss of glucagon signaling alters white adipose tissue browning. FASEB J. 33, 4824–4835 (2019). www.f asebj.org
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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) | 0.003 | 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 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".