A Genetic Model Therapy Proposes a Critical Role for Liver Dysfunction in Mitochondrial Biology and Disease
Bibliographic record
Abstract
Abstract The clinical and largely unpredictable heterogeneity of phenotypes in patients with mitochondrial disorders demonstrates the ongoing challenges in the understanding of this semi-autonomous organelle in biology and disease. Here we present a new animal model that recapitulates key components of Leigh Syndrome, French Canadian Type (LSFC), a mitochondrial disorder that includes diagnostic liver dysfunction. LSFC is caused by allelic variations in the Leucine Rich Pentatricopeptide repeat-containing motif ( LRPPRC ) gene. LRPPRC has native functions related to mitochondrial mRNA polyadenylation and translation as well as a role in gluconeogenesis. We used the Gene-Breaking Transposon (GBT) cassette to create a revertible, insertional mutant zebrafish line in the LRPPRC gene. lrpprc zebrafish homozygous mutants displayed impaired muscle development, liver function and lowered levels of mtDNA transcripts and are lethal by 12dpf, all outcomes similar to clinical phenotypes observed in patients. Investigations using an in vivo lipidomics approach demonstrated accumulation of non-polar lipids in these animals. Transcript profiling of the mutants revealed dysregulation of clinically important nuclearly encoded and mitochondrial transcripts. Using engineered liver-specific rescue as a genetic model therapy, we demonstrate survival past the initial larval lethality, as well as restored normal gut development, mitochondrial morphology and triglyceride levels functionally demonstrating a critical role for the liver in the pathophysiology of this model of mitochondrial disease. Understanding the molecular mechanism of the liver-mediated genetic rescue underscores the potential to improve the clinical diagnostic and therapeutic developments for patients suffering from these devastating disorders.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".