A Fanci knockout mouse model reveals common and distinct functions for FANCI and FANCD2
Bibliographic record
Abstract
Fanconi Anemia (FA) clinical phenotypes are heterogenous and rely on a mutation in one of the 22 FANC genes (FANCA-W) involved in a common interstrand DNA crosslink-repair pathway. A critical step in the activation of FA pathway is the monoubiquitination of FANCD2 and its binding partner FANCI. To better address the clinical phenotype associated with FANCI and the epistatic relationship with FANCD2, we created the first conditional inactivation model for FANCI in mouse. Fanci -/- mice displayed typical FA features such as delayed development in utero, microphtalmia, cellular sensitivity to mitomycin C, occasional limb abnormalities and hematological deficiencies. Interestingly, the deletion of Fanci leads to a strong meiotic phenotype and severe hypogonadism. FANCI was localized in spermatocytes and spermatids and in the nucleus of oocytes. Both FANCI and FANCD2 proteins co-localized with RPA along meiotic chromosomes, albeit at different levels. Consistent with a role in meiotic recombination, FANCI interacted with RAD51 and stimulated D-loop formation, unlike FANCD2. The double knockout Fanci-/- Fancd2-/- also showed epistatic relationship for hematological defects while being not epistatic with respect to generating viable mice in crosses of double heterozygotes. Collectively, this study highlights common and distinct functions of FANCI and FANCD2 during mouse development, meiotic recombination and hematopoiesis.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".