In vivo dissection of the mouse tyrosine catabolic pathway with CRISPR-Cas9 identifies modifier genes affecting hereditary tyrosinemia type 1
Bibliographic record
Abstract
Hereditary tyrosinemia type 1 is an autosomal recessive disorder caused by mutations (pathogenic variants) in fumarylacetoacetate hydrolase, an enzyme involved in tyrosine degradation. Its loss results in the accumulation of toxic metabolites that mainly affect the liver and kidneys and can lead to severe liver disease and liver cancer. Tyrosinemia type 1 has a global prevalence of approximately 1 in 100,000 births but can reach up to 1 in 1,500 births in some regions of Québec, Canada. Mutating functionally related "modifier' genes (i.e. genes that, when mutated, affect the phenotypic impacts of mutations in other genes) is an emerging strategy for treating human genetic diseases. In vivo somatic genome editing in animal models of these diseases is a powerful means to identify modifier genes and fuel treatment development. In this study, we demonstrate that mutating additional enzymes in the tyrosine catabolic pathway through liver-specific genome editing can relieve or worsen the phenotypic severity of a murine model of tyrosinemia type 1. Neonatal gene delivery using recombinant adeno-associated viral vectors expressing Staphylococcus aureus Cas9 under the control of a liver-specific promoter led to efficient gene disruption and metabolic rewiring of the pathway, with systemic effects that were distinct from the phenotypes observed in whole-body knockout models. Our work illustrates the value of using in vivo genome editing in model organisms to study the direct effects of combining pathological mutations with modifier gene mutations in isogenic settings.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".