The hormone-bound vitamin D receptor enhances the FBW7-dependent turnover of NF-κB subunits
Bibliographic record
Abstract
Signaling by hormonal vitamin D, 1,25-dihydroxyvitamin D (1,25D) has attracted increasing interest because of its non-classical actions, particularly its putative anticancer properties and its role in controlling immune system function. Notably, the hormone-bound vitamin D receptor (VDR) suppresses signaling by pro-inflammatory NF-κB transcription factors, although the underlying mechanisms have remained elusive. Recently, the VDR was shown to enhance the turnover of the oncogenic transcription factor cMYC mediated by the E3 ligase and tumor suppressor FBW7. As FBW7 also controls the turnover of the p100 (NF-κB2) subunit of the family, we determined whether the 1,25D enhanced FBW7-dependent turnover of NF-κB subunits p100, p105 (NF-κB1) and p65 (RELA). Protein levels of all three subunits declined markedly in the presence of 1,25D in multiple cell lines in the absence of substantial changes in mRNA expression. The VDR coimmunoprecipitated with all three subunits, and 1,25D treatment accelerated subunit turnover in cycloheximide-treated cells. Importantly, we observed an association of FBW7 with p105 and p65, as well as p100, and knockdown of FBW7 eliminated 1,25D-dependent subunit turnover. Moreover, expression of NF-κB target genes was elevated in FBW7-depleted cells. These results reveal that 1,25D signaling suppresses NF-κB function by enhancing FBW7-dependent subunit turnover.
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 | 0.001 |
| 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".