Deciphering Mitochondrial Iron Metabolism Using a Knockout Mouse.
Bibliographic record
Abstract
Abstract Abstract 1995 Poster Board I-1017 We utilized the muscle creatine kinase conditional frataxin knockout mouse to elucidate how frataxin-deficiency alters iron metabolism. This is of significance since frataxin-deficiency leads to the neuro- and cardio-degenerative disease, Friedreich's ataxia. Using cardiac tissues, we demonstrate that frataxin-deficiency leads to down-regulation of key molecules involved in three 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 excess mitochondrial-iron observed. Indeed, this effect is compounded by increased iron availability for mitochondrial uptake through: (1) transferrin receptor1 up-regulation that increases iron uptake from transferrin; (2) decreased ferroportin1 expression, limiting iron export; (3) increased expression of the heme catabolism enzyme, heme oxygenase1, and down-regulation of ferritin-H and —L, both of which likely lead to increased “free iron” for mitochondrial uptake; and (4) increased expression of the mammalian exocyst protein, Sec15l1, and the mitochondrial-iron importer, mitoferrin-2 (Mfrn2), that facilitate cellular iron uptake and mitochondrial-iron influx, respectively. This study enables construction of a model explaining the cytosolic iron-deficiency and mitochondrial-iron-loading in the absence of frataxin that is important for understanding the pathogenesis of Friedreich's ataxia. Disclosures: No relevant conflicts of interest to declare.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".