NKT Cell–Deficient Mice Harbor an Altered Microbiota That Fuels Intestinal Inflammation during Chemically Induced Colitis
Bibliographic record
Abstract
NKT cells are unconventional T cells that respond to self and microbe-derived lipid and glycolipid Ags presented by the CD1d molecule. Invariant NKT (iNKT) cells influence immune responses in numerous diseases. Although only a few studies have examined their role during intestinal inflammation, it appears that iNKT cells protect from Th1-mediated inflammation but exacerbate Th2-mediated inflammation. Studies using iNKT cell-deficient mice and chemically induced dextran sodium sulfate (DSS) colitis have led to inconsistent results. In this study, we show that CD1d-deficient mice, which lack all NKT cells, harbor an altered intestinal microbiota that is associated with exacerbated intestinal inflammation at steady-state and following DSS treatment. This altered microbiota, characterized by increased abundance of the bacterial phyla Proteobacteria, Deferribacteres, and TM7, among which the mucin-eating Mucispirillum, as well as members of the genus Prevotella and segmented filamentous bacteria, was transmissible upon fecal transplant, along with the procolitogenic phenotype. Our results also demonstrate that this proinflammatory microbiota influences iNKT cell function upon activation during DSS colitis. Collectively, alterations of the microbiota have a major influence on colitis outcome and therefore have to be accounted for in such experimental settings and in studies focusing on iNKT cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".