Liver X receptor regulates hepatic nuclear O-GlcNAc signaling and carbohydrate responsive element-binding protein activity
Bibliographic record
Abstract
Liver X receptor (LXR)α and LXRβ play key roles in hepatic de novo lipogenesis through their regulation of lipogenic genes, including sterol regulatory element-binding protein (SREBP)-1c and carbohydrate responsive element-binding protein (ChREBP). LXRs activate lipogenic gene transcription in response to feeding, which is believed to be mediated by insulin. We have previously shown that LXRs are targets for glucose-hexosamine-derived O-linked β-N-acetylglucosamine (O-GlcNAc) modification enhancing their ability to regulate SREBP-1c promoter activity in vitro. To elucidate insulin-independent effects of feeding on LXR-mediated lipogenic gene expression in vivo, we subjected control and streptozotocin-treated LXRα/β(+/+) and LXRα/β(-/-) mice to a fasting-refeeding regime. We show that under hyperglycemic and hypoinsulinemic conditions, LXRs maintain their ability to upregulate the expression of glycolytic and lipogenic enzymes, including glucokinase (GK), SREBP-1c, ChREBPα, and the newly identified shorter isoform ChREBPβ. Furthermore, glucose-dependent increases in LXR/retinoid X receptor-regulated luciferase activity driven by the ChREBPα promoter was mediated, at least in part, by O-GlcNAc transferase (OGT) signaling in Huh7 cells. Moreover, we show that LXR and OGT interact and colocalize in the nucleus and that loss of LXRs profoundly reduced nuclear O-GlcNAc signaling and ChREBPα promoter binding activity in vivo. In summary, our study provides evidence that LXRs act as nutrient and glucose metabolic sensors upstream of ChREBP by modulating GK expression, nuclear O-GlcNAc signaling, and ChREBP expression and activity.
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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) | 0.033 | 0.004 |
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".