DNA Repair Pathway Choice Is Influenced by the Health of <i>Drosophila melanogaster</i>
Bibliographic record
Abstract
In nature, individuals vary tremendously in condition and this may be an important source of variation in mutation rate. Condition is likely to affect cell state and thereby impact the amount of DNA damage sustained and/or the way it is repaired. Here, we focus on DNA repair. If low-condition individuals are less capable of devoting the same level of resources to accurate repair, they may suffer higher mutation rates. However, repair decisions are also governed by various aspects of cell physiology, which may render the prediction that "higher-condition individuals use better repair mechanisms" too simplistic. We use a larval diet manipulation in Drosophila melanogaster to create high- and low-condition individuals and then contrast their relative usage of three repair pathways [homologous recombination (HR), single-strand annealing (SSA), and nonhomologous end joining (NHEJ)] that differ in their mechanistic requirements and their mutational consequences. We find that low-condition flies are more likely than high-condition flies to use the most conservative of these three repair pathways, suggesting that physiological constraints on repair pathway usage may be more important than energetic costs. We also show that the repair differences between high- and low-condition flies resemble those between young and old flies, suggesting the underlying mechanisms may be similar. Finally, we observe that the effect of larval diet on adult repair increases as flies age, indicating that developmental differences early in life can have long-lasting consequences.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".