Iron Is Critical for Mucosal-Associated Invariant T Cell Metabolism and Effector Functions
Bibliographic record
Abstract
Mucosal-Associated Invariant T (MAIT) cells are a population of innate T cells that play a critical role in host protection against bacterial and viral pathogens. Upon activation, MAIT cells can rapidly respond via both TCR-dependent and -independent mechanisms, resulting in robust cytokine production. The metabolic and nutritional requirements for optimal MAIT cell effector responses are still emerging. Iron is an important micronutrient and is essential for cellular fitness, in particular cellular metabolism. Iron is also critical for many pathogenic microbes, including those that activate MAIT cells. However, iron has not been investigated with respect to MAIT cell metabolic or functional responses. In this study, we show that human MAIT cells require exogenous iron, transported via CD71 for optimal metabolic activity in MAIT cells, including their production of ATP. We demonstrate that restricting iron availability by either chelating environmental iron or blocking CD71 on MAIT cells results in impaired cytokine production and proliferation. These data collectively highlight the importance of a CD71-iron axis for human MAIT cell metabolism and functionality, an axis that may have implications in conditions where iron availability is limited.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".