Iron Efflux from Oligodendrocytes Is Differentially Regulated in Gray and White Matter
Bibliographic record
Abstract
Accumulation of iron occurs in the CNS in several neurodegenerative diseases. Iron is essential for life but also has the ability to generate toxic free radicals if not properly handled. Iron homeostasis at the cellular level is therefore important to maintain proper cellular function, and its dysregulation can contribute to neurodegenerative diseases. Iron export, a key mechanism to maintain proper levels in cells, occurs via ferroportin, a ubiquitously expressed transmembrane protein that partners with a ferroxidase. A membrane-bound form of the ferroxidase ceruloplasmin is expressed by astrocytes in the CNS and regulates iron efflux. We now show that oligodendrocytes use another ferroxidase, called hephaestin, which was first identified in enterocytes in the gut. Mice with mutations in the hephaestin gene (sex-linked anemia mice) show iron accumulation in oligodendrocytes in the gray matter, but not in the white matter, and exhibit motor deficits. This was accompanied by a marked reduction in the levels of the paranodal proteins contactin-associated protein 1 (Caspr) and reticulon-4 (Nogo A). We show that the sparing of iron accumulation in white matter oligodendrocytes in sex-linked anemia mice is due to compensatory upregulation of ceruloplasmin in these cells. This was further confirmed in ceruloplasmin/hephaestin double-mutant mice, which show iron accumulation in both gray and white matter oligodendrocytes. These data indicate that gray and white matter oligodendrocytes can use different iron efflux mechanisms to maintain iron homeostasis. Dysregulation of such efflux mechanisms leads to iron accumulation in the CNS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".