Targeted overexpression of PPARγ in skeletal muscle by random insertion and CRISPR/Cas9 transgenic pig cloning enhances oxidative fiber formation and intramuscular fat deposition
Bibliographic record
Abstract
Abstract Peroxisome proliferator‐activated receptor gamma (PPARγ) is a master regulator of adipogenesis and lipogenesis. To understand its roles in fiber formation and fat deposition in skeletal muscle, we successfully generated muscle‐specific overexpression of PPARγ in two pig models by random insertion and CRISPR/Cas9 transgenic cloning procedures. The content of intramuscular fat was significantly increased in PPARγ pigs while had no changes on lean meat ratio. PPARγ could promote adipocyte differentiation by activating adipocyte differentiating regulators such as FABP4 and CCAAT/enhancer‐binding protein (C/EBP), along with enhanced expression of LPL, FABP4, and PLIN1 to proceed fat deposition. Proteomics analyses demonstrated that oxidative metabolism of fatty acids and respiratory chain were activated in PPARγ pigs, thus, gathered more Ca 2+ in PPARγ pigs. Raising of Ca 2+ could result in increased phosphorylation of CAMKII and p38 MAPK in PPARγ pigs, which can stimulate MEF2 and PGC1α to affect fiber type and oxidative capacity. These results support that skeletal muscle‐specific overexpression of PPARγ can promote oxidative fiber formation and intramuscular fat deposition in pigs.
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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 | 0.001 |
| 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) | 0.003 | 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".