Acute invariant NKT cell activation triggers an immune response that drives prominent changes in iron homeostasis
Bibliographic record
Abstract
Iron homeostasis is an essential biological process that ensures the tissue distribution of iron for various cellular processes. As the major producer of hepcidin, the liver is central to the regulation of iron metabolism. The liver is also home to many immune cells, which upon activation may greatly impact iron metabolism. Here, we focus on the role of invariant natural killer T (iNKT) cells, a subset of T lymphocytes that, in mice, is most abundant in the liver. Activation of iNKT cells with the prototypical glycosphingolipid antigen, α-galactosylceramide, resulted in immune cell proliferation and biphasic changes in iron metabolism. This involved an early phase characterized by hypoferremia, hepcidin induction and ferroportin suppression, and a second phase associated with strong suppression of hepcidin despite elevated levels of circulating and tissue iron. We further show that these changes in iron metabolism are fully dependent on iNKT cell activation. Finally, we demonstrate that the biphasic regulation of hepcidin is independent of NK and Kupffer cells, and is initially driven by the STAT3 inflammatory pathway, whereas the second phase is regulated by repression of the BMP/SMAD signaling pathway. These findings indicate that iNKT activation and the resulting cell proliferation influence iron homeostasis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".