Dose Dependent Effects on Cell Cycle Checkpoints and DNA Repair by Bendamustine
Bibliographic record
Abstract
Bendamustine (BDM) is an active chemotherapeutic agent approved in the U. S. for treating chronic lymphocytic leukemia and non-Hodgkin lymphoma. Its chemical structure suggests it may have alkylator and anti-metabolite activities; however the precise mechanism of action is not well understood. Here we report the concentration-dependent effects of BDM on cell cycle, DNA damage, checkpoint response and cell death in HeLa cells. Low concentrations of BDM transiently arrested cells in G2, while a 4-fold higher concentration arrested cells in S phase. DNA damage at 50, but not 200 µM, was efficiently repaired after 48 h treatment, suggesting a difference in DNA repair efficiency at the two concentrations. Indeed, perturbing base-excision repair sensitized cells to lower concentrations of BDM. Timelapse studies of the checkpoint response to BDM showed that inhibiting Chk1 caused both the S- and G2-arrested cells to prematurely enter mitosis. However, whereas the cells arrested in G2 (low dose BDM) entered mitosis, segregated their chromosomes and divided normally, the S-phase arrested cells (high dose BDM) exhibited a highly aberrant mitosis, whereby EM images showed highly fragmented chromosomes. The vast majority of these cells died without ever exiting mitosis. Inhibiting the Chk1-dependent DNA damage checkpoint accelerated the time of killing by BDM. Our studies suggest that BDM may affect different biological processes depending on drug concentration. Sensitizing cells to killing by BDM can be achieved by inhibiting base-excision repair or disrupting the DNA damage checkpoint pathway.
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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) | 0.002 | 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".