Glycolipid Stimulation of Invariant NKT Cells Expands a Unique Tissue-Resident Population of Precursors to Mature NK Cells Endowed with Oncolytic and Antimetastatic Properties
Bibliographic record
Abstract
Abstract Invariant NKT (iNKT) cells are innate-like T lymphocytes that recognize and respond to glycolipid Ags such as α-galactosylceramide (α-GalCer). This unique property has been exploited in clinical trials for multiple malignancies. While investigating mouse iNKT cell responses to α-GalCer in vivo, we found a dramatically enlarged tissue-resident population surprisingly coexpressing select dendritic cell, NK cell, and B cell markers. Further phenotypic and functional analyses revealed the identity of this B220+CD11c+MHC class II+NK1.1+ population as precursors to mature NK (pre-mNK) cells, which also expressed high levels of proliferation and tissue retention markers but diminished sphingosine-1-phosphate receptor 1, a receptor that facilitates tissue trafficking. Accordingly, FTY720, a sphingosine-1-phosphate receptor 1 antagonist, failed to prevent pre-mNK cells’ intrahepatic accumulation. We found iNKT cell–driven expansion of pre-mNK cells to be dependent on IL-12 and IL-18. Although α-GalCer–transactivated pre-mNK cells lost their capacity to process a model tumor Ag, they selectively expressed granzyme A and directly lysed YAC-1 thymoma cells through granule exocytosis. They also contributed to β2 microglobulin–deficient target cell destruction in vivo. Therefore, α-GalCer treatment skewed pre-mNK cell responses away from an APC-like phenotype and toward killer cell–like functions. Finally, the ability of α-GalCer to reduce the pulmonary metastatic burden of B16-F10 mouse melanoma was partially reversed by in vivo depletion of pre-mNK cells. To our knowledge, our findings shed new light on iNKT cells’ mechanism of action and glycolipid-based immunotherapies. Therefore, we introduce pre-mNK cells as a novel downstream effector cell type whose anticancer properties may have been overlooked in previous investigations.
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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".