Interpreting Neonatal Lethal Phenotypes in Mouse Mutants: Insights Into Gene Function and Human Diseases
Bibliographic record
Abstract
The mouse represents the model of choice to study the biological function of mammalian genes through mutation of its genome. However, the biggest challenge of mouse geneticists remains the phenotypic analysis of mouse mutants. A survey of mouse mutant databases reveals a surprisingly high number of gene mutations leading to neonatal death. These genetically modified mouse mutants have been instrumental in elucidating gene function and have become important models of congenital human diseases. The main complication when phenotyping mutant mice dying during the neonatal period is the large spectrum of physiological systems whose defects can challenge neonatal survival. Here, we present a comprehensive review of gene mutations leading to neonatal lethality and discuss the impact of these mutations on the major physiological processes critical to mouse newborn survival: parturition, breathing, suckling, and homeostasis. Selected examples of mouse mutants are highlighted to illustrate how the precise identification of the timing and cause of death associated with these physiological processes allows for a more profound understanding of the underlying cellular and molecular defects. This review provides a guide for the analysis of neonatal lethal phenotypes in mutant mice that will be helpful for dissecting out the function of specific genes during mouse development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".