Glycolipids from the gut symbiont Bacteroides fragilis are agonists for natural killer T cells and induce their regulatory differentiation
Bibliographic record
Abstract
Natural Killer T (NKT) cells are a lipid-antigen reactive, CD1d-restricted T cell subset with diverse functional properties that contribute to inflammatory and regulatory immune responses. The most studied lipid antigen target for these cells is alpha-galactosylceramide (GC). The commensal organism Bacteroides fragilis (B. fragilis) produces several forms of GC, but conflicting information exists about the influence of these lipids on NKT cells. Herein, we report the total synthesis of a major form of alpha-GC from B. fragilis (Bf alpha-GC), and several analogues thereof. We confirm the T cell receptor (TCR)-mediated recognition of these glycolipids by mouse and human NKT cells. Despite the natural structure of Bf alpha-GC containing lipid branching that limits potency, we demonstrate that Bf alpha-GC drives mouse NKT cells to proliferate and differentiate into producers of the immunoregulatory cytokine, interleukin-10 (IL-10). These Bf alpha-GC-experienced NKT cells display regulatory function by inhibiting the expansion of naïve NKT cells upon subsequent exposure to this antigen. Moreover, this regulatory activity impacts more than just NKT cells, as demonstrated by the NKT cell-mediated inhibition of antigen-stimulated mucosal-associated invariant T (MAIT) cells. These findings reveal that B. fragilis-derived NKT cell agonists may have broad immunoregulatory activity, providing insight into the mechanisms influencing immune tolerance to commensal bacteria and highlighting a potential means to manipulate NKT cell function for therapeutic benefit.
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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".