Harnessing Regulatory invariant Natural Killer T cells in Autoimmune Liver Disease Treatment
Bibliographic record
Abstract
Abstract Invariant Natural Killer T cells (iNKTs) are pivotal innate T cells that regulate immunity through cytokine secretion. In extra-hepatic autoimmunity, distinct regulatory iNKT cell types (iNKTR) play crucial roles. Recently, we identified a novel liver-specific subset, Maf+LiNKTR1 cells, characterized by its CD4+ TR1-like transcriptional signature. This subset responds to lipid-antigen-presenting nanomedicines, suppressing liver autoimmunity; however, its role under homeostatic conditions, in autoimmunity development, and responsiveness to other iNKT-directed therapies are not known. Using mouse models of Primary Biliary Cholangitis (PBC), here we explored the immunoregulatory role and therapeutic potential of Maf+LiNKTR1 cells. We observed a significant decrease in Maf+LiNKTR1 in PBC, accompanied by increased proinflammatory LiNKT1 subset. Biliary epithelial IL-10 signaling and gut-derived short-chain fatty acids contributed to Maf+LiNKTR1 generation. The reduced Maf+LiNKTR1 frequency correlated with loss of regulatory networks, suggesting a role in maintaining liver tolerance. Importantly, the subset responded to other iNKT-directed drugs, significantly suppressing PBC. Our study extended to the broader regulatory network of Maf+LiNKTR1. Further, it enhances our understanding of Maf+LiNKTR1 in liver health and autoimmunity development, offering potential therapeutic insights for autoimmune and other inflammatory liver conditions.
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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".