MAIT cell-mediated cytotoxicity: Roles in host defense and therapeutic potentials in infectious diseases and cancer
Bibliographic record
Abstract
Mucosa-associated invariant T (MAIT) cells are unconventional, innate-like T lymphocytes that sense the presence of MHC-related protein 1 (MR1)-restricted ligands and select inflammatory cues. Consequently, they release potent immunomodulatory mediators, including IFN-γ, TNF-α, and/or IL-17. MAIT cells can also be viewed as killer cells. They display several NK cell-associated receptors, carry granules containing cytotoxic effector molecules, and swiftly upregulate perforin and granzymes upon activation. Accordingly, MAIT cells are capable of lysing MR1-expressing cells infected with a variety of pathogenic bacteria in in vitro settings and may also mount cytotoxic responses during microbial infections in vivo. Of note, MAIT cell hyperactivation during certain infections may impede their ability to elicit inflammatory and/or cytotoxic responses to secondary stimuli. In addition, MAIT cells isolated from within and from the margin of tumor masses exhibit diminished functions. We propose that MAIT cell-mediated cytotoxicity can be induced, bolstered, or restored to assist in clearing infections and potentially in reducing tumor loads. In this review, we discuss our current understanding of MAIT cells' lytic functions and highlight the pressing questions that need to be addressed in future investigations. We also offer a picture, however hypothetical at this point, of how harnessing the full cytotoxic potentials of MAIT cells may be a valuable approach in the immunotherapy of infectious and malignant diseases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".