Re-programming mouse liver-resident invariant natural killer T cells for suppressing hepatic and diabetogenic autoimmunity
Bibliographic record
Abstract
Abstract Invariant NKT (iNKT) cells comprise a heterogeneous group of non-circulating, tissue-resident T lymphocytes that recognize glycolipids, including alpha-galactosylceramide (αGalCer), in the context of CD1d, but whether peripheral iNKT cell subsets are terminally differentiated remains unclear. Here we show that mouse and human liver-resident αGalCer/CD1d-binding iNKTs largely correspond to a novel Zbtb16+Tbx21+Gata3+MaflowRorc– subset that exhibits profound transcriptional, phenotypic and functional plasticity. Repetitive in vivo encounters of these liver iNKT (LiNKT) cells with intravenously delivered αGalCer/CD1d-coated nanoparticles (NP) trigger their differentiation into immunoregulatory, IL-10+IL-21-producing Zbtb16highMafhighTbx21+Gata3+Rorc– cells, termed LiNKTR1, expressing a T regulatory type 1 (TR1)-like transcriptional signature. This response is LiNKT-specific, since neither lung nor splenic tissue-resident iNKT cells from αGalCer/CD1d-NP-treated mice produce IL-10 or IL-21. Additionally, these LiNKTR1 cells suppress autoantigen presentation, and recognize CD1d expressed on conventional B cells to induce IL-10+IL-35-producing regulatory B (Breg) cells, leading to the suppression of liver and pancreas autoimmunity. Our results thus suggest that LiNKT cells are plastic for further functional diversification, with such plasticity potentially targetable for suppressing tissue-specific inflammatory phenomena.
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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 | 0.001 |
| 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".