Elucidation of the mechanism of mitochondrial iron loading in Friedreich's ataxia by analysis of a mouse mutant
Bibliographic record
Abstract
We used the muscle creatine kinase (MCK) conditional frataxin knockout mouse to elucidate how frataxin deficiency alters iron metabolism. This is of significance because frataxin deficiency leads to Friedreich's ataxia, a disease marked by neurologic and cardiologic degeneration. Using cardiac tissues, we demonstrate that frataxin deficiency leads to down-regulation of key molecules involved in 3 mitochondrial utilization pathways: iron-sulfur cluster (ISC) synthesis (iron-sulfur cluster scaffold protein1/2 and the cysteine desulferase Nfs1), mitochondrial iron storage (mitochondrial ferritin), and heme synthesis (5-aminolevulinate dehydratase, coproporphyrinogen oxidase, hydroxymethylbilane synthase, uroporphyrinogen III synthase, and ferrochelatase). This marked decrease in mitochondrial iron utilization and resultant reduced release of heme and ISC from the mitochondrion could contribute to the excessive mitochondrial iron observed. This effect is compounded by increased iron availability for mitochondrial uptake through (i) transferrin receptor1 up-regulation, increasing iron uptake from transferrin; (ii) decreased ferroportin1 expression, limiting iron export; (iii) increased expression of the heme catabolism enzyme heme oxygenase1 and down-regulation of ferritin-H and -L, both likely leading to increased "free iron" for mitochondrial uptake; and (iv) increased expression of the mammalian exocyst protein Sec15l1 and the mitochondrial iron importer mitoferrin-2 (Mfrn2), which facilitate cellular iron uptake and mitochondrial iron influx, respectively. Our results enable the construction of a model explaining the cytosolic iron deficiency and mitochondrial iron loading in the absence of frataxin, which is important for understanding the pathogenesis of Friedreich's ataxia.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".