Through a Glass, Darkly: Reflections of Mutation From <i>lacI</i> Transgenic Mice
Bibliographic record
Abstract
The study of mutational frequency (Mf) and specificity in aging Big Blue lacI transgenic mice provides a unique opportunity to determine mutation rates (MR) in vivo in different tissues. We found that MR are not static, but rather, vary with the age or developmental stage of the tissue. Although Mf increase more rapidly early in life, MR are actually lower in younger animals than in older animals. For example, we estimate that the changes in Mf are 4.9x10(-8) and 1.1 x 10(-8) mutations/base pair/month in the livers of younger mice (<1. 5 months old) and older mice (> or =1.5 months old), respectively (a 4-fold decrease), and that the MR are 3.9 x 10(-9) and 1.3 x 10(-7) mutations/base pair/cell division, respectively ( approximately 30-fold increase). These data also permit an estimate of the MR of GC --> AT transitions occurring at 5'-CpG-3' (CpG) dinucleotide sequences. Subsequently, the contribution of these transitions to age-related demethylation of genomic DNA can be evaluated. Finally, to better understand the origin of observed Mf, we consider the contribution of various factors, including DNA damage and repair, by constructing a descriptive mutational model. We then apply this model to estimate the efficiency of repair of deaminated 5-methylcytosine nucleosides occurring at CpG dinucleotide sequences, as well as the influence of the Msh2(-/-) DNA repair defect on overall DNA repair efficiency in Big Blue mice. We conclude that even slight changes in DNA repair efficiency could lead to significant increases in mutation frequencies, potentially contributing significantly to human pathogenesis, including cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".