Post-Translational Modifications of Nuclear Receptors and Human Disease
Bibliographic record
Abstract
Nuclear receptors (NR) impact a myriad of physiological processes including homeostasis, reproduction, development, and metabolism. NRs are regulated by post-translational modifications (PTM) that markedly impact receptor function. Recent studies have identified NR PTMs that are involved in the onset and progression of human diseases, including cancer. The majority of evidence linking NR PTMs with disease has been demonstrated for phosphorylation, acetylation and sumoylation of androgen receptor (AR), estrogen receptor α (ERα), glucocorticoid receptor (GR) and peroxisome proliferator activated receptor γ (PPARγ). Phosphorylation of AR has been associated with hormone refractory prostate cancer and decreased disease-specific survival. AR acetylation and sumoylation increased growth of prostate cancer tumor models. AR phosphorylation reduced the toxicity of the expanded polyglutamine AR in Kennedy's Disease as a consequence of reduced ligand binding. A comprehensive evaluation of ERα phosphorylation in breast cancer revealed several sites associated with better clinical outcome to tamoxifen therapy, whereas other phosphorylation sites were associated with poorer clinical outcome. ERα acetylation and sumoylation may also have predictive value for breast cancer. GR phosphorylation and acetylation impact GR responsiveness to glucocorticoids that are used as anti-inflammatory drugs. PPARγ phosphorylation can regulate the balance between growth and differentiation in adipose tissue that is linked to obesity and insulin resistance. Sumoylation of PPARγ is linked to repression of inflammatory genes important in patients with inflammatory diseases. NR PTMs provide an additional measure of NR function that can be used as both biomarkers of disease progression, and predictive markers for patient response to NR-directed treatments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".