Mucosal-Associated Invariant T Cell Effector Function Is an Intrinsic Cell Property That Can Be Augmented by the Metabolic Cofactor α-Ketoglutarate
Bibliographic record
Abstract
Abstract Mucosal-associated invariant T (MAIT) cells are an innate-like population of unconventional T cells that respond rapidly to microbial metabolite Ags or cytokine stimulation. Because of this reactivity and surface expression of CD45RO+, CD45RA−, and CD127+, they are described as effector memory cells. Yet, there is heterogeneity in MAIT cell effector response. It is unclear what factors control MAIT cell effector capacity, whether it is fixed or can be modified and if this differs based on whether activation is TCR dependent or independent. To address this, we have taken a systematic approach to examine human MAIT cell effector capacity across healthy individuals in response to ligand and cytokine stimulation. We demonstrate the heterogenous nature of MAIT cell effector capacity and that the ability to produce an effector response is not directly attributable to TCR clonotype or coreceptor expression. Global gene transcription analysis revealed that the MAIT cell effector capacity produced in response to TCR stimulation is associated with increased expression of the epigenetic regulator lysine demethylase 6B (KDM6B). Addition of a KDM6B inhibitor did not alter MAIT cell effector function to Ag or cytokine stimulation. However, addition of the KDM6B cofactor α-ketoglutarate greatly enhanced MAIT cell effector capacity to TCR-dependent stimulation in a partially KDM6B-dependent manner. These results demonstrate that the TCR-dependent effector response of MAIT cells is epigenetically regulated and dependent on the availability of metabolic cofactors.
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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) | 0.002 | 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".