Role of adipose and hepatic atypical protein kinase C lambda (PKCλ) in the development of obesity and glucose intolerance
Bibliographic record
Abstract
PKCλ, an atypical member of the multifunctional protein kinase C family, has been implicated in the regulation of insulin-stimulated glucose transport and of the intracellular immune response. To further elucidate the role of this cellular regulator in diet-induced obesity and insulin resistance, we generated both liver (PKC-Alb) and adipose tissue (PKC-Ap2) specific knockout mice. Body weight, fat mass, food intake, glucose homeostasis and energy expenditure were evaluated in mice maintained on either chow or high fat diet (HFD). Ablation of PKCλ from the adipose tissue resulted in mice that were indistinguishable from their wild-type littermates. However, PKC-Alb mice were resistant to diet-induced obesity (DIO). Surprisingly this DIO resistance was not associated with either a reduction in caloric intake or an increase in energy expenditure as compared with their wild-type littermates. Furthermore, these mice displayed an improvement in glucose tolerance. When maintained on chow diet, these mice were similar to wild types in respect to body weight and fat mass, yet insulin sensitivity was impaired compared with wt littermates. Taken together these data suggest that hepatic PKCλ is modulating insulin-mediated glucose turnover and response to high fat diet feeding, thus offering a deeper understanding of an important target for anti-obesity therapeutics.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".