Mucosal-Associated Invariant T Cells Augment Immunopathology and Gastritis in Chronic <i>Helicobacter pylori</i> Infection
Bibliographic record
Abstract
Abstract Mucosal-associated invariant T (MAIT) cells produce inflammatory cytokines and cytotoxic granzymes in response to by-products of microbial riboflavin synthesis. Although MAIT cells are protective against some pathogens, we reasoned that they might contribute to pathology in chronic bacterial infection. We observed MAIT cells in proximity to Helicobacter pylori bacteria in human gastric tissue, and so, using MR1-tetramers, we examined whether MAIT cells contribute to chronic gastritis in a mouse H. pylori SS1 infection model. Following infection, MAIT cells accumulated to high numbers in the gastric mucosa of wild-type C57BL/6 mice, and this was even more pronounced in MAIT TCR transgenic mice or in C57BL/6 mice where MAIT cells were preprimed by Ag exposure or prior infection. Gastric MAIT cells possessed an effector memory Tc1/Tc17 phenotype, and were associated with accelerated gastritis characterized by augmented recruitment of neutrophils, macrophages, dendritic cells, eosinophils, and non-MAIT T cells and by marked gastric atrophy. Similarly treated MR1−/− mice, which lack MAIT cells, showed significantly less gastric pathology. Thus, we demonstrate the pathogenic potential of MAIT cells in Helicobacter-associated immunopathology, with implications for other chronic bacterial infections.
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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) | 0.001 | 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".