A new murine <i>Rpl5</i> ( <i>uL18</i> ) mutation provides a unique model of variably penetrant Diamond-Blackfan anemia
Bibliographic record
Abstract
Ribosome dysfunction is implicated in multiple abnormal developmental and disease states in humans. Heterozygous germline mutations in genes encoding ribosomal proteins are found in most individuals with Diamond-Blackfan anemia (DBA), whereas somatic mutations have been implicated in a variety of cancers and other disorders. Ribosomal protein-deficient animal models show variable phenotypes and penetrance, similar to human patients with DBA. In this study, we characterized a novel ENU mouse mutant (Skax23m1Jus) with growth and skeletal defects, cardiac malformations, and increased mortality. After genetic mapping and whole-exome sequencing, we identified an intronic Rpl5 mutation, which segregated with all affected mice. This mutation was associated with decreased ribosome generation, consistent with Rpl5 haploinsufficiency. Rpl5Skax23-Jus/+ animals had a profound delay in erythroid maturation and increased mortality at embryonic day (E) 12.5, which improved by E14.5. Surviving mutant animals had macrocytic anemia at birth, as well as evidence of ventricular septal defect (VSD). Surviving adult and aged mice exhibited no hematopoietic defect or VSD. We propose that this novel Rpl5Skax23-Jus/+ mutant mouse will be useful in studying the factors influencing the variable penetrance that is observed in DBA.
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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 | 0.000 |
| 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) | 0.003 | 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".