Characteristics of induced mutations in offspring derived from irradiated mouse spermatogonia and mature oocytes
Bibliographic record
Abstract
The exposure of germ cells to radiation introduces mutations in the genomes of offspring, and a previous whole-genome sequencing study indicated that the irradiation of mouse sperm induces insertions/deletions (indels) and multisite mutations (clustered single nucleotide variants and indels). However, the current knowledge on the mutation spectra is limited, and the effects of radiation exposure on germ cells at stages other than the sperm stage remain unknown. Here, we performed whole-genome sequencing experiments to investigate the exposure of spermatogonia and mature oocytes. We compared de novo mutations in a total of 24 F1 mice conceived before and after the irradiation of their parents. The results indicated that radiation exposure, 4 Gy of gamma rays, induced 9.6 indels and 2.5 multisite mutations in spermatogonia and 4.7 indels and 3.1 multisite mutations in mature oocytes in the autosomal regions of each F1 individual. Notably, we found two types of deletions, namely, small deletions (mainly 1~12 nucleotides) in non-repeat sequences, many of which showed microhomology at the breakpoint junction, and single-nucleotide deletions in mononucleotide repeat sequences. The results suggest that these deletions and multisite mutations could be a typical signature of mutations induced by parental irradiation in mammals.
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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 | 0.000 |
| 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".