Structure-guided design of an invariant natural killer T cell agonist for optimum protection from type 1 diabetes in non-obese diabetic mice
Bibliographic record
Abstract
Because invariant natural killer T (iNK T) cells link innate and adaptive immunity, the structure-dependent design of iNK T cell agonists may have therapeutic value as vaccines for many indications, including autoimmune disease. Previously, we showed that treatment of non-obese diabetic (NOD) mice with the iNK T cell activating prototypic glycolipid α-galactosylceramide (α-GalCer) protects them from type 1 diabetes (T1D). However, α-GalCer is a strong agonist that can hyperactivate iNK T cells, elicit several side effects and has shown only limited success in clinical trials. Here, we used a structure-guided design approach to identify an iNK T cell agonist that optimally protects from T1D with minimal side effects. Analyses of the kinetics and function of a panel of synthetic α-GalCer fatty acyl chain derivatives (C8:0-C16:0) were performed in NOD mice. C16:0 elicited the highest protection from insulitis and T1D, which was associated with a higher frequency and survival of iNK T cells and enhanced activity of tolerogenic dendritic cells (DCs) in draining pancreatic lymph nodes (PLN), inability to transactivate NK cells and a more rapid kinetics of induction and recovery of iNK T cells from anergy. We conclude that the length and structure of the acyl chain of α-GalCer regulates the level of protection against T1D in mice, and propose that the extent of this protection depends on the relative capacity of the acyl chain to accommodate an endogenous spacer lipid of appropriate length and structure. Thus, our findings with the α-GalCer C16:0 derivative suggest strongly that it be considered as a lead glycolipid candidate in clinical trials of T1D.
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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) | 0.000 | 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".