LCAT-null mice develop improved hepatic insulin sensitivity through altered regulation of transcription factors and suppressors of cytokine signaling
Bibliographic record
Abstract
We previously reported that LCAT-deficient mice develop not only low HDL-cholesterol but also hypertriglyceridemia, hepatic triglyceride (TG) overproduction, and, unexpectedly, improved hepatic insulin sensitivity and reduced hepatic TG content. Here, we examined the mechanistic links underlying this apparent paradox. The LDL receptor-deficient (Ldlr)(-/-)xLcat(-/-) mouse model and age- and sex-matched Ldlr(-/-)xLcat(+/+) littermates, both in C57Bl/6 background, were employed. Studies of hepatic insulin signal transduction showed an upregulation of hepatic Irs2 mRNA level (5.3-fold, P = 0.02), IRS-2 protein mass level (1.5-fold, P = 0.009) and pIRS-2 (1.8-fold. P = 0.02) in the Ldlr(-/-)xLcat(-/-) mice. There was a 1.2-fold increase in pAkt (P = 0.03) with a nonsignificant change in total Akt. We observed a significant shift in its downstream transcription factor FoxO-1 to the cytosolic compartment (2.3-fold increase in cytosolic/nuclear ratio, P = 0.04). We also observed a significant 3.1-fold increase in nuclear abundance of FoxA-2 mass (P = 0.017) and a 1.5-fold upregulation of its coactivator PGC-1beta (P = 0.002), the coordinated actions of which promotes hepatic TG production and beta-oxidation. Increased hepatic insulin signaling in the Ldlr(-/-)xLcat(-/-) mice was associated with an upregulation of the Tcfe3 gene (1.7-fold, P = 0.024), a selective downregulation of the Socs-1 gene by 60% (P = 0.01), and no change in PTP-1B protein mass. These data suggest that LCAT deficiency induces complex alterations in hepatic signal transduction cascades, which explain, at least in part, the observed enhanced insulin signaling in association with hepatic TG overproduction and reduced hepatic TG content.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".