Differing patterns of genetic instability in mice deficient in the mismatch repair genes Pms2, Mlh1, Msh2, Msh3 and Msh6
Bibliographic record
Abstract
Defects in genes associated with DNA mismatch repair (MMR) have been linked to hereditary colon cancer. Because the MMR pathway includes multiple factors with both overlapping and divergent functions, we sought to compare the impact of deficiencies in each of several MMR genes on genetic instability using a collection of knock-out mouse models. We investigated mutation frequencies and patterns in MMR-deficient mice using two transgenic reporter genes, supFG1 and cII, in the context of mice deficient for Pms2, Mlh1, Msh2, Msh3 or Msh6 or both Msh2 and Msh3 or both Msh3 and Msh6. We found that the mean mutation frequencies of all of the MMR-deficient mice were significantly higher than the mean mutation frequencies of wild-type mice. Mlh1-deficient mice and Msh2-deficient mice had the highest mutation frequencies in a comparison of the single nullizygous mice. Of all the mice studied, mice nullizygous for both Msh2 and Msh3 and those nullizygous for both Msh3 and Msh6 displayed the greatest overall increases in mutation frequencies compared with wild-type mice. Sequence analysis of the mutated reporter genes revealed significant differences between the individual groups of MMR-deficient mice. Taken together, our results further characterize the functions of the MMR factors in mutation avoidance and provide in vivo correlation to biochemical models of the MMR pathway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".