Iron homeostasis in astrocytes and microglia is differentially regulated by TNF‐α and TGF‐β1
Bibliographic record
Abstract
Abnormal iron homeostasis is increasingly thought to contribute to the pathogenesis of several neurodegenerative disorders. We have previously reported impaired iron homeostasis in a mouse model of spinal cord injury and in a mouse model of amyotrophic lateral sclerosis. Both these disorders are associated with CNS inflammation. However, what effect inflammation, and in particular, inflammatory cytokines have on iron homeostasis in CNS glia remains largely unknown. Here we report that the proinflammatory cytokine TNF-α, and the anti-inflammatory cytokine TGF-β1 affect iron homeostasis in astrocytes and microglia in distinct ways. Treatment of astrocytes in vitro with TNF-α induced the expression of the iron importer "divalent iron transporter 1" (DMT1) and suppressed the expression of the iron exporter ferroportin (FPN). However, TGF-β1 had no effect on DMT1 expression but increased the expression of FPN in astrocytes. In microglia, on the other hand, both cytokines caused induction of DMT1 and suppression of FPN expression. Iron influx and efflux assays in vitro confirmed that iron homeostasis in astrocytes and microglia is differentially regulated by these cytokines. In particular, TNF-α caused an increase in iron uptake and retention by both astrocytes and microglia, while TGF-β1 promoted iron efflux from astrocytes but caused iron retention in microglia. These data suggest that these two cytokines, which are expressed in CNS inflammation in injury and disease, can have profound and divergent effects on iron homeostasis in astrocytes and microglia.
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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) | 0.000 | 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".