Variant in NHLRC2 leads to increased hnRNP C2 in developing neurons and the hippocampus of a mouse model of FINCA disease
Bibliographic record
Abstract
BACKGROUND: FINCA disease is a pediatric cerebropulmonary disease caused by variants in the NHL repeat-containing 2 (NHLRC2) gene. Neurological symptoms are among the first manifestations of FINCA disease, but the consequences of NHLRC2 deficiency in the central nervous system are currently unexplored. METHODS: The orthologous mouse gene is essential for development, and its complete loss leads to early embryonic lethality. In the current study, we used CRISPR/Cas9 to generate an Nhlrc2 knockin (KI) mouse line, harboring the FINCA patient missense mutation (c.442G > T, p.Asp148Tyr). A FINCA mouse model, resembling the compound heterozygote genotype of FINCA patients, was obtained by crossing the KI and Nhlrc2 knockout mouse lines. To reveal NHLRC2-interacting proteins in developing neurons, we compared cortical neuronal precursor cells of E13.5 FINCA and wild-type mouse embryos by two-dimensional difference gel electrophoresis. RESULTS: Despite the significant decrease in NHLRC2, the mice did not develop severe early onset multiorgan disease in either sex. We discovered 19 altered proteins in FINCA neuronal precursor cells; several of which are involved in vesicular transport pathways and actin dynamics which have been previously reported in other cell types including human to have an association with dysfunctional NHLRC2. Interestingly, isoform C2 of hnRNP C1/C2 was significantly increased in both developing neurons and the hippocampus of adult female FINCA mice, connecting NHLRC2 dysfunction with accumulation of RNA binding protein. CONCLUSIONS: We describe here the first NHLRC2-deficient mouse model to overcome embryonic lethality, enabling further studies on predisposing and causative mechanisms behind FINCA disease. Our novel findings suggest that disrupted RNA metabolism may contribute to the neurodegeneration observed in FINCA patients.
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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 | 0.001 |
| Bibliometrics | 0.002 | 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".