The Tumor Suppressor FBW7 and the Vitamin D Receptor Are Mutual Cofactors in Protein Turnover and Transcriptional Regulation
Bibliographic record
Abstract
Abstract The E3 ligase and tumor suppressor FBW7 targets drivers of cell-cycle progression such as the oncogenic transcription factor c-MYC, for proteasomal degradation. Vitamin D signaling regulates c-MYC expression and turnover in vitro and in vivo, which is highly significant as epidemiologic data link vitamin D deficiency to increased cancer incidence. We hypothesized that FBW7 and the vitamin D receptor (VDR) controlled each other's function as regulators of protein turnover and gene transcription, respectively. We found that hormonal 1,25-dihydroxyvitamin D3 (1,25D) rapidly enhanced the interaction of FBW7 with VDR and with c-MYC, whereas it blocked FBW7 binding to c-MYC antagonist MXD1. 1,25D stimulated the recruitment of FBW7, SCF complex subunits, and ubiquitin to DNA-bound c-MYC, consistent with 1,25D-regulated c-MYC degradation on DNA. 1,25D also accelerated the turnover of other FBW7 target proteins such as Cyclin E, c-JUN, MCL1, and AIB1, and, importantly, FBW7 depletion attenuated the 1,25D-induced cell-cycle arrest. Although the VDR contains a consensus FBW7 recognition motif in a VDR-specific insertion domain, its mutation did not affect FBW7–VDR interactions, and FBW7 ablation did not stabilize the VDR. Remarkably, however, FBW7 is essential for optimal VDR gene expression. In addition, the FBW7 and SCF complex subunits are recruited to 1,25D-induced genes and FBW7 depletion inhibited the 1,25D-dependent transactivation. Collectively, these data show that the VDR and FBW7 are mutual cofactors, and provide a mechanistic basis for the cancer-preventive actions of vitamin D. Implications: The key findings show that the VDR and the E3 ligase FBW7 regulate each other's functions in transcriptional regulation and control of protein turnover, respectively, and provide a molecular basis for cancer-preventive actions of vitamin D. Visual Overview: http://mcr.aacrjournals.org/content/17/3/709/F1.large.jpg.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".