2034-P: The Role of 14-3-3zeta in the “Beiging” of White Adipose Tissue
Bibliographic record
Abstract
Adipocyte hypertrophy and hyperplasia is a hallmark of obesity, and treatments that induce the oxidation of lipid stores may represent a potential therapy. When stimulated by cold or beta-adrenergic agonists, adipocytes in inguinal white adipose tissue (iWAT) can convert to beige cells, which resemble brown adipocytes. This process can reduce body weight in rodents and improve glucose homeostasis through increased Ucp1-dependent lipid oxidation. 14-3-3?, a molecular scaffold we found to be essential for adipogenesis, regulates the enzymatic activities of tryptophan and tyrosine hydroxylases, both of which influence beiging. Thus, the aim of the current study is to investigate whether 14-3-3? influences the beiging process. Compared to control mice, acute (3 hr) and chronic (72 hr) cold (4 C) exposure in male transgenic mice over-expressing 14-3-3? led to improved cold tolerance due to significantly increased Ucp1 mRNA and protein in iWAT. Following chronic exposure, they were also able to maintain their body weight by increasing their food intake. Consistent with these data, analysis of adipocyte area revealed a decrease in the size of inguinal adipocytes in transgenic mice, suggesting increased lipid oxidation. In contrast, gonadal adipocytes were larger in transgenic mice, which may explain their ability to maintain their body weight. Systemic 14-3-3? knockout mice had significantly lower levels of Ucp1 mRNA in iWAT and BAT but did not display differences in tolerance to acute cold. Depletion of 14-3-3? by siRNA in brown adipocytes did not impair isoproterenol-mediated induction of Ucp1 mRNA, suggesting that 14-3-3? is not required for Ucp1 expression in this cell type. In future studies, we will examine if 14-3-3? influences the responsiveness of iWAT to chronic β-adrenergic stimuli and assess the impact of 14-3-3? overexpression in brown adipocyte function. Collectively, our results point to a novel role of 14-3-3? in the beiging of inguinal adipocytes and increase our understanding of how beiging is regulated. Disclosure K. Diallo: None. G.E. Lim: None. Funding Canadian Institutes of Health Research; Fonds de la recherche en santé du Québec; Centre de recherche du CHUM; Montreal Diabetes Research Center
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".