Defining the minimal peptide sequence of the ING1b tumour suppressor capable of efficiently inducing apoptosis
Bibliographic record
Abstract
The ING1b protein is a type-II tumour suppressor and stoichiometric member of the Sin3 histone deacetylase (HDAC) protein complex in which it acts to target HDAC activity to regulate chromatin structure. Altering ING1 levels by ectopic expression of ING1b in cancer cells promotes apoptosis, whereas altering levels by knockout in normal murine fibroblasts alters sensitivity to doxorubicin-induced apoptosis. We have identified a minimal region of ING1b capable of inducing levels of apoptosis in targeted cells as effectively as full-length ING1b, using transient overexpression of ING1b fragments followed by the Annexin V assay. We observed high levels of apoptosis in 14 of 14 cancer cell lines tested. Infecting triple-negative tumorigenic MDA-MB-468 breast cancer, U2OS or Saos-2 cells at multiplicities of infection (MOIs) ranging from 10 to 20 rapidly triggered apoptosis in ~80% of infected cells within 48 h. This was not due to the effects of virus, as infection at the same MOI with a control adenovirus expressing GFP was not effective in inducing apoptosis. When used at low MOIs, the ING1b fragment showed a cell-killing efficacy that was higher than native, full-length ING1b. Using a doxycycline-regulated inducible p53 expression system demonstrated that apoptosis induced by the ING1b fragment was p53 independent. Given the growing importance of combination therapies, we evaluated whether there was synergism between the ING1b fragment and HDAC inhibitors. Combination treatments with TSA, LBH 589 and SAHA reduced cancer cell survival by 3.9-4.7-fold as compared with single-drug treatment, and resulted in ~90% reduction in cell survival. Normalized isobologram analysis confirmed strong synergism between the ING1b fragment and drugs tested. These findings provide support for using ING1b-derived therapeutics as adjuvant treatments in combination with existing epigenetic therapies.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".