Lecithin:Cholesterol Acyltransferase (LCAT) Deficiency Promotes Differentiation of Satellite Cells to Brown Adipocytes in a Cholesterol-dependent Manner
Bibliographic record
Abstract
Our laboratory previously reported that lecithin:cholesterol acyltransferase (LCAT) and LDL receptor double knock-out mice (Ldlr(-/-)xLcat(-/-) or DKO) spontaneously develop functioning ectopic brown adipose tissue (BAT) in skeletal muscle, putatively contributing to protection from the diet-induced obesity phenotype. Here we further investigated their developmental origin and the mechanistic role of LCAT deficiency. Gene profiling of skeletal muscle in DKO newborns and adults revealed a classical lineage. Primary quiescent satellite cells (SC) from chow-fed DKO mice, not in Ldlr(-/-)xLcat(+/+) single-knock-out (SKO) or C57BL/6 wild type, were found to (i) express exclusively classical BAT-selective genes, (ii) be primed to express key functional BAT genes, and (iii) exhibit markedly increased ex vivo adipogenic differentiation into brown adipocytes. This gene priming effect was abrogated upon feeding the mice a 2% high cholesterol diet in association with accumulation of excess intracellular cholesterol. Ex vivo cholesterol loading of chow-fed DKO SC recapitulated the effect, indicating that cellular cholesterol is a key regulator of SC-to-BAT differentiation. Comparing adipogenicity of Ldlr(+/+)xLcat(-/-) (LCAT-KO) SC with DKO SC identified a role for LCAT deficiency in priming SC to express BAT genes. Additionally, we found that reduced cellular cholesterol is important for adipogenic differentiation, evidenced by increased induction of adipogenesis in cholesterol-depleted SC from both LCAT-KO and SKO mice. Taken together, we conclude that ectopic BAT in DKO mice is classical in origin, and its development begins in utero. We further showed complementary roles of LCAT deficiency and cellular cholesterol reduction in the SC-to-BAT adipogenesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".