INVESTIGATING THE ROLE AND REGULATION OF THE SPECIFIC ANDGENERAL PRB-E2F1 INTERACTIONS
Bibliographic record
Abstract
Background/Purpose. The retinoblastoma tumor suppressor protein (pRB) plays a central role in proliferative control and is a frequent target for inactivation in cancer. The G1-Sphase transition of the cell cycle is regulated by pRB, which is capable of interacting with E2F family members and inhibiting the transcription of genesrequired to progress into S-phase. E2F1 is unique from other E2F family members as it can induce both apoptosis and proliferation. To control these contrasting functions of E2F1, a second E2F1 binding site exists in the C-terminus of pRB that can control apoptosis separately from proliferation. This anti-apoptotic function of pRBcan in some circumstances promote tumorigenesis, which leads to the question; is pRB a tumor suppressor or an oncogene? Methods. To investigate this, a gene-targeted mouse model is being engineered to selectively disrupt the ability of pRB to control proliferation through the general E2F binding site while still retaining the ability tocontrol apoptosis through the specific E2F1 site. Results. A series of novel mutants were engineered to selectively disrupt the binding of E2Fs at the general site, and prevent pRB from controlling proliferation. The mutants retain the ability to bind E2F1 and control apoptosis through thespecific binding site, which is not disrupted. Conclusions. By separating the ability of pRB to control proliferation and apoptosis under in vivo conditions, a better understanding into the significance of these two functions in development and tumorigenesis can be gained.
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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 | 0.001 | 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".