Characterization of unstable microsatellites in mice: No evidence for germline mutation induction following gamma‐radiation exposure
Bibliographic record
Abstract
Large tandem repeat DNA loci such as expanded simple tandem repeats and minisatellites are efficient markers for detecting germline mutations; however, mutation detection using these loci can be imprecise and difficult to standardize across labs. Short-tandem repeats, such as microsatellites, offer more precise and high-throughput mutation detection, but germline mutation induction at these loci has not yet been studied in model organisms such as mice. In this study, we used microsatellite enrichment and large-scale DNA sequencing of several closely related inbred mouse lines to identify a panel of 19 polymorphic microsatellites with potentially high spontaneous mutation frequencies. We used this panel and four additional loci from other sources to quantify spontaneous mutation frequency in pedigrees of outbred Swiss-Webster mice. In addition, we also examined mutation induction in families in which sires were treated with acute doses of either 0.5 Gy or 1.0 Gy gamma-irradiation to spermatogonial stem cells. Per locus mutation frequencies ranged from 0 to 5.03 × 10(-3). Considering only the 11 loci with mutations, the mutation frequencies were: control 2.78 × 10(-3), 0.5 Gy 4.09 × 10(-3), and 1.0 Gy 1.82 × 10(-3). There were no statistically significant changes in mutation frequencies among treatment groups. Our study provides the first direct quantification of microsatellite mutation frequency in the mouse germline, but shows no evidence for mutation induction at pre-meiotic male germ cells following acute gamma-irradiation. Further work using the panel is needed to examine mutation induction at different doses of radiation, exposure durations, and stages during spermatogenesis.
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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 | 0.001 | 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 | 0.000 |
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".