Antigen-specific nanoparticle tolerance treatment actively induces both FoxP3- and IL-10-dependent regulatory mechanisms
Bibliographic record
Abstract
Abstract There are many autoimmune diseases where pathologic self T cells are the underlying cause of disease symptoms and progression. T cells are found at the site of tissue destruction, resulting not only in the exacerbation of disease, but also the release of self-epitopes in the context of inflammation resulting in the activation of additional T cell populations perpetuating disease. Published data have shown treatment with antigen-containing biodegradablepoly(lactide-co-glycolide) (PLGA) nanoparticles, i.e. tolerogenic immune-modifying particles (TIMP), is both safe and induces antigen-specific tolerance in mouse models of autoimmunity and allergy, as well as in a celiac disease Phase I/IIa clinical trial. This study further investigated the role and mechanism of TIMP-induced tolerance. The present data show that TIMP treatment modulated T cells specific for spread epitopes associated with disease progression, but not encapsulated within the TIMP, i.e. tissue-specific bystander suppression. The PLP139–151 TCR transgenic model systems was utilized to determine the cellular and molecular mechanisms driving antigen specific tolerance mediated by tolerogenic nanoparticle treatment. These data show antigen-specific TIMP treatment induced both FoxP3+ iTregs and IL-10+ Tr1 regulatory phenotypes within the antigen-specific T cell populations in both naïve mice and mice pre-primed with antigen/CFA. Additionally, both functional Tregs and IL-10 are required for TIMP-induced tolerance. Treatment thus activates various antigen-specific Treg subsets capable of regulating responses to disease-relevant autoepitopes not encapsulated within the TIMP via release of immunoregulatory cytokines within the inflammatory site. Supported by funding provided by Cour Pharmaceuticals Development, Inc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".