A method for assaying the sensitivity of<i>Drosophila</i>replication checkpoint mutants to anti-cancer and DNA-damaging drugs.
Bibliographic record
Abstract
In multi-cellular organisms, failure to properly regulate cell-cycle progression can result in inappropriate cell death or uncontrolled cell division leading to tumor formation. To guard against such events, conserved regulatory mechanisms called "checkpoints" block progression into mitosis in response to DNA damage and incomplete replication, as well as in response to other signals. Checkpoint mutants in organisms as diverse as yeast and humans are sensitive to various chemical agents that inhibit DNA replication or cause DNA damage. This phenomenon is the primary rationale for chemotherapy, which uses drugs that preferentially target tumor cells with compromised checkpoints. In this study, we demonstrate the use of Drosophila checkpoint mutants as a system for assaying the effects of various DNA-damaging and anti-cancer agents in a developing multicellular organism. Dwee1, grp and mei-41 are genes that encode kinases that function in the DNA replication checkpoint. We tested zygotic mutants of each gene for sensitivity to the DNA replication inhibitor hydroxyurea (HU), methyl methanosulfonate (MMS), ara-C, cisplatin, and the oxygen radical generating compound paraquat. The mutants show distinct differences in their sensitivity to each of the drugs tested, suggesting an underlying complexity in the responses of individual checkpoint genes to genotoxic stress.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".