The impact of choline availability on muscle lipid metabolism
Bibliographic record
Abstract
Consumption of choline-rich foods is essential to ensure membrane integrity, neurotransmission and genomic methylation pathways. Insufficient dietary choline supply can cause choline deficiency (CD) which manifests in the development of non-alcoholic fatty liver disease. There is very limited information regarding the effect of CD on non-hepatic tissues such as muscle. In this study, we induced CD in muscle cells and investigated the effect on choline transport, phosphatidylcholine (PC), fatty acid and triacylglycerol (TAG, fat) metabolism. Choline transport was stable across the plasma membrane of CD cells but significantly impaired in mitochondria. The main choline-transporter SLC44A1 was down-regulated by CD at the mRNA level, and SLC44A1 protein was reduced in total cell lysates and isolated mitochondria. CD significantly reduced PC synthesis but PC degradation was unaffected. PC from CD muscle was modified and contained more monounsaturated fatty acids at the expense of saturated fatty acids. Surprisingly, CD muscle cells also accumulated TAG in the form of large lipid droplets. Those droplets were formed from endogenous fatty acids and by slower TAG metabolism. This study established for the first time that choline availability affects muscle membrane lipid composition and intracellular lipid metabolism, and underlines the significance of choline-rich foods for proper muscle function.
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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".