Identification of nonferritin mitochondrial iron deposits in a mouse model of Friedreich ataxia
Bibliographic record
Abstract
There is no effective treatment for the cardiomyopathy of the most common autosomal recessive ataxia, Friedreich ataxia (FA). This disease is due to decreased expression of the mitochondrial protein, frataxin, which leads to alterations in mitochondrial iron (Fe) metabolism. The identification of potentially toxic mitochondrial Fe deposits in FA suggests Fe plays a role in its pathogenesis. Studies using the muscle creatine kinase (MCK) conditional frataxin knockout mouse that mirrors the disease have demonstrated frataxin deletion alters cardiac Fe metabolism. Indeed, there are pronounced changes in Fe trafficking away from the cytosol to the mitochondrion, leading to a cytosolic Fe deficiency. Considering Fe deficiency can induce apoptosis and cell death, we examined the effect of dietary Fe supplementation, which led to body Fe loading and limited the cardiac hypertrophy in MCK mutants. Furthermore, this study indicates a unique effect of heart and skeletal muscle-specific frataxin deletion on systemic Fe metabolism. Namely, frataxin deletion induces a signaling mechanism to increase systemic Fe levels and Fe loading in tissues where frataxin expression is intact (i.e., liver, kidney, and spleen). Examining the mutant heart, native size-exclusion chromatography, transmission electron microscopy, Mössbauer spectroscopy, and magnetic susceptibility measurements demonstrated that in the absence of frataxin, mitochondria contained biomineral Fe aggregates, which were distinctly different from isolated mammalian ferritin molecules. These mitochondrial aggregates of Fe, phosphorus, and sulfur, probably contribute to the oxidative stress and pathology observed in the absence of frataxin.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| 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".