Classic costimulatory interactions in MAIT cell responses: from gene expression to immune regulation
Bibliographic record
Abstract
Mucosa-associated invariant T (MAIT) cells are evolutionarily conserved, innate-like T lymphocytes with enormous immunomodulatory potentials. Due to their strategic localization, their invariant T cell receptor (iTCR) specificity for major histocompatibility complex-related protein 1 (MR1) ligands of commensal and pathogenic bacterial origin, and their sensitivity to infection-elicited cytokines, MAIT cells are best known for their antimicrobial characteristics. However, they are thought to also play important parts in the contexts of cancer, autoimmunity, vaccine-induced immunity, and tissue repair. While cognate MR1 ligands and cytokine cues govern MAIT cell maturation, polarization, and peripheral activation, other signal transduction pathways, including those mediated by costimulatory interactions, regulate MAIT cell responses. Activated MAIT cells exhibit cytolytic activities and secrete potent inflammatory cytokines of their own, thus transregulating the biological behaviors of several other cell types, including dendritic cells, macrophages, natural killer cells, conventional T cells, and B cells, with significant implications in health and disease. Therefore, an in-depth understanding of how costimulatory pathways control MAIT cell responses may introduce new targets for optimized MR1/MAIT cell-based interventions. Herein, we compare and contrast MAIT cells and mainstream T cells for their expression of classic costimulatory molecules belonging to the immunoglobulin superfamily and the tumor necrosis factor (TNF)/TNF receptor superfamily, based not only on the available literature but also on our transcriptomic analyses. We discuss how these molecules participate in MAIT cells' development and activities. Finally, we introduce several pressing questions vis-à-vis MAIT cell costimulation and offer new directions for future research in this area.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".