Antigen-specific nanoparticle tolerance treatment actively induces regulatory mechanisms via the STING pathway
Bibliographic record
Abstract
Abstract Antigen (Ag)-specific T cells are the underlying cause of many autoimmune diseases. T cells found at the site of tissue destruction not only in the exacerbation of disease, but contribute to the release of additiotnal self-epitopes resulting in the activation of spread eptitope-specific T cell populations perpetuating disease. Treatment with Ag-containingbiodegradable poly(lactide-co-glycolide) (PLGA) nanoparticles, i.e. tolerogenic immune-modifying particles (TIMP), has been shown to be both safe and induces Ag-specific tolerance in mouse models of autoimmunity and allergy, as well as in a celiac disease Phase I/IIa clinical trial. We are in the process of assessing if the mechanism of action between Ag-specific TIMP treatment and Ag-coupled cell treatment, to thereby identify self-tolerance mechanisms in common between the two highly efficacious therapies. The PLP139–151 TCR transgenic model system was utilized to determine the cellular and molecular mechanisms driving Ag-specific tolerance mediated by tolerogenic nanoparticle treatment. These data show Ag-specific TIMP treatment induced both FoxP3+ iTregs and IL-10+ Tr1 regulatory in both naïve mice and mice pre-primed with Ag/CFA. Additionally, the expression of STING is required for the induction of tolerance. The involvement of the STING pathway for both TIMP and Ag-coupled cell treatment was confirmed in MOG35–55 EAE. The data also show that Ag-specific tolerance induction requires both Type I IFNs and PD-1/PD-L1 expression, which are downstream of STING activation and signaling. Treatment thus activates various Ag-specific Treg subsets capable of regulating responses to disease-relevant autoepitopes via a STING/IFNAR/PD-1/PD-L1 pathway.
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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".