Natural killer T cells regulate the homing of chemokine CXC receptor 3-positive regulatory T cells to the liver in mice #
Bibliographic record
Abstract
UNLABELLED: Natural killer T (NKT) cells and regulatory T cells (Tregs) are both found within the liver and are known to exhibit immune regulatory functions. Hepatic NKT cells are activated early during inflammatory responses and release cytokines, including interferon gamma (IFN-gamma), which we speculated could regulate Treg recruitment to the liver. To examine this, we treated C57BL/6 mice with a specific NKT cell activating ligand alpha galactosyl-C18-ceramide (alphaGal-C18-Cer) and examined the hepatic recruitment of Tregs. We found a time-dependant increase in the hepatic recruitment of Tregs after NKT cell activation, which was absent in NKT cell-deficient mice. Most recruited Tregs expressed interleukin (IL) 10, and to a lesser extent transforming growth factor beta (TGF-beta). Because IFN-gamma induces the production of chemokine (C-X-C motif) ligand 10 (CXCL10), and Tregs can express the cognate receptor for CXCL10 (that is, CXCR3), we considered that CXCL10 might mediate the hepatic recruitment of Tregs after NKT cell activation. Hepatic CXCL10 levels were markedly increased after alphaGal-C18-Cer administration in wild-type but not in NKT cell-deficient mice. Moreover, approximately 50% of Tregs recruited to the liver after alphaGal-C18-Cer administration expressed CXCR3 and CXCR3+ Treg recruitment into the liver was significantly inhibited in IFN-gamma KO mice, and after CXCL10 neutralization. In addition, prevention of CXCR3+ Treg recruitment into the liver enhanced inflammatory effector cell recruitment into the liver after alphaGal-C18-Cer treatment. CONCLUSION: These results show that activated NKT cells can induce the hepatic recruitment of Tregs through a cytokine-to-chemokine pathway, which could be relevant in the development of chemokine blocking or NKT cell activating strategies to treat liver diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".