The intestinal microbiota modulates the transcriptional landscape of iNKT cells at steady-state and following antigen exposure
Bibliographic record
Abstract
Invariant Natural Killer T (iNKT) cells are unconventional T cells that respond to microbe-derived glycolipid antigens. iNKT cells exert fast innate effector functions that regulate immune responses in a variety of contexts, including during infection, cancer, or inflammation. The roles these unconventional T cells play in intestinal inflammation remain poorly defined and vary based on the disease model and species. Our previous work suggested that the gut microbiota influenced iNKT cell functions during dextran sulfate sodium-induced colitis in mice. This study, shows that iNKT cell homeostasis and response following activation are altered in germ-free mice. Using prenatal fecal transplant in specific pathogen-free mice, we show that the transcriptional signatures of iNKT cells at steady state and following αGC-mediated activation in vivo are modulated by the microbiota. Our data suggest that iNKT cells sense the microbiota at homeostasis independently of their T cell receptors. Finally, iNKT cell transcriptional signatures are different in male and female mice. Collectively, our findings suggest that sex and the intestinal microbiota are important factors that regulate iNKT cell homeostasis and responses. A deeper understanding of microbiota-iNKT cell interactions and the impact of sex could improve the development of iNKT cell-based immunotherapies.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".