Adaptations to excess choline in insulin resistant and <i>Pcyt2</i> deficient skeletal muscle
Bibliographic record
Abstract
It was hypothesized that choline supplementation in insulin resistant (IR) CTP:phosphoethanolamine cytidylyltransferase deficient (Pcyt2+/−) mice would ameliorate muscle function by remodeling glucose and fatty acid (FA) metabolism. Pcyt2+/− mice either received no treatment or were allowed access to 2 mg/mL choline in drinking water for 4 weeks. Skeletal muscle was harvested from choline treated and untreated mice. Lipid analysis and metabolic gene expression and signaling pathways were compared between untreated Pcyt2+/− mice, treated Pcyt2+/− mice, and Pcyt2+/+ mice. The major positive effect of choline supplementation on IR muscle was the reduction of glucose utilization for FA and triglyceride (TAG) synthesis and increased muscle glucose storage as glycogen. Choline reduced the expression of genes for FA and TAG formation (Scd1, Fas, Srebp1c, Dgat1/2), upregulated the genes for FA oxidation (Cpt1, Pparα, Pgc1α), and had minor effects on phospholipid and lipolysis genes. Pcyt2+/− muscle had reduced insulin signaling (IRS1), autophagy (LC3), and choline transport (CTL1) proteins that were restored by choline treatment. Additionally, choline activated AMPK and Akt while inhibiting mTORC1 phosphorylation. These data established that choline supplementation could restore muscle glucose metabolism by reducing lipogenesis and improving mitochondrial and intracellular signaling for protein and energy metabolism in insulin resistant Pcyt2 deficient mice.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".