An E2F/miR-20a Autoregulatory Feedback Loop
Bibliographic record
Abstract
The E2F family of transcription factors is essential in the regulation of the cell cycle and apoptosis. While the activity of E2F1-3 is tightly controlled by the retinoblastoma family of proteins, the expression of these factors is also regulated at the level of transcription, post-translational modifications and protein stability. Recently, a new level of regulation of E2Fs has been identified, where micro-RNAs (miRNAs) from the mir-17-92 cluster influence the translation of the E2F1 mRNA. We now report that miR-20a, a member of the mir-17-92 cluster, modulates the translation of the E2F2 and E2F3 mRNAs via binding sites in their 3'-untranslated region. We also found that the endogenous E2F1, E2F2, and E2F3 directly bind the promoter of the mir-17-92 cluster activating its transcription, suggesting an autoregulatory feedback loop between E2F factors and miRNAs from the mir-17-92 cluster. Our data also point toward an anti-apoptotic role for miR-20a, since overexpression of this miRNA decreased apoptosis in a prostate cancer cell line, while inhibition of miR-20a by an antisense oligonucleotide resulted in increased cell death after doxorubicin treatment. This anti-apoptotic role of miR-20a may explain some of the oncogenic capacities of the mir-17-92 cluster. Altogether, these results suggest that the autoregulation between E2F1-3 and miR-20a is important for preventing an abnormal accumulation of E2F1-3 and may play a role in the regulation of cellular proliferation and apoptosis.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".