Unexpected embryonic stem (ES) cell mutations represent a concern in gene targeting: Lessons from “fierce” mice
Bibliographic record
Abstract
The exceptional value of gene targeting technology to generate mouse models of human disease exists under the shadow of potential genetic errors. We previously observed an unexpected brain-behavior phenotype that resulted from a gene-targeting experiment designed to delete the Zfa gene. Given that the transcription of Zfa is restricted to the germ cell lineage of adult testis, it was both a surprise and a concern when the resulting mice had a phenotype present in both sexes that included abnormal brains and violent behavior. We hypothesized that an unrelated mutation may have been responsible for the unexpected phenotype. Here we show that the single gene mutation, Nr2e1(frc) (fierce), which was responsible for the brain-behavior phenotype, existed in the embryonic stem (ES) cell even before the derivation of the Zfa knockout mice. Our work thus highlights a concern in gene targeting, namely, that ES cells can harbor unexpected mutations, which can lead to genotype-phenotype misattribution. Based on our findings, we caution the gene-targeting community to use low-passage ES cells, to characterize mice derived from more than one independently targeted ES cell clone, and to backcross mice to allow for segregation of distant but linked mutations.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".