Molecular Regulation of Gluconeogenesis by the Nuclear Receptors GR and LXRβ
Bibliographic record
Abstract
The long-term use of immunosuppressive glucocorticoid (GC) drugs is limited by undesirable side effects including osteoporosis, obesity, and type 2 diabetes. The potent induction of hepatic glucose production by GCs is known to significantly contribute to the development of type 2 diabetes. Understanding the molecular mediators contributing to the metabolic effects of GCs in the liver will provide a basis from which to generate novel therapeutics to treat this disease. At the cellular level, GCs exert both therapeutic and adverse effects through the activation of the glucocorticoid receptor (GR). Herein, we showed that the gluconeogenic and immune suppressive effects of GC administration can be separated by the liver X receptor β (LXRβ) in mice. Notably, using ChIP assays we demonstrated that either genomic knockdown or antagonism of LXRβ decreases GC-mediated GR recruitment to the GRE of Pepck, a key gluconeogenic gene in mouse liver. This causes decreased expression of Pepck and loss of glucose production following GC- administration in mouse liver (Chapter 2 and 3). We also demonstrated that LXRβ is dispensable for the GC-mediated immune suppression in mice. Gene expression and glucose production studies in mouse primary macrophages and hepatocytes demonstrated that during GC administration, the beneficial effect of either LXRβ knockdown or LXRβ antagonism is cell autonomous. FGF21 is a hepatokine that regulates whole body insulin sensitivity, and glucose and lipid metabolism. In normal physiology, FGF21 levels are elevated by long term fasting to coordinate the adaptive starvation response (i.e., glucose homeostasis). Using gene expression and ChIP studies we showed that the GC activated-GR directly regulates the starvation hepatokine FGF21 in mouse liver (Chapter 4). In conclusion, we have identified novel modulators that contribute to GC-mediated gluconeogenesis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".