Viral Impacts on MR1 Antigen Presentation to MAIT Cells
Bibliographic record
Abstract
Mucosal-associated invariant T (MAIT) cells are abundant innate-like T cells important in antimicrobial immunity. These cells express a semi-invariant T cell receptor that recognizes the Major Histocompatibility Complex (MHC) class I-related protein 1 (MR1) in complex with small metabolite antigens derived from a range of commensal and pathogenic bacteria and yeasts, but not other pathogens such as viruses. Thus, MR1 stimulation of MAIT cells was thought to act as a sensor of bacterial infection and was not directly involved in anti-viral immunity. Surprisingly, viruses have recently been shown to directly impair MR1 antigen presentation by targeting the intracellular pool of MR1 for degradation. In this review, we summarize our current understanding of viral evasion of MR1 presentation pathway, and contrast this to evasion of other related MHC molecules. We examine MAIT cell activity in viral infection with a focus on the role of TCR-mediated activation of these innate-like cells and speculate on the selective pressure for viral evasion of MR1 antigen presentation. Overall, viral evasion of MR1 presentation uncovers a new avenue of research and implies that the MR1-MAIT cell axis is more important in viral immunity than was previously appreciated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".