Ubiquitous antigen-specific T regulatory type 1 cells variably suppress hepatic and extrahepatic autoimmunity
Bibliographic record
Abstract
Peptide MHC class II-based (pMHCII-based) nanomedicines trigger the formation of multicellular regulatory networks by reprogramming autoantigen-experienced CD4+ T cells into autoimmune disease-suppressing T regulatory type 1 (TR1) cells. We have shown that pMHCII-based nanomedicines displaying liver autoimmune disease-relevant yet ubiquitously expressed antigens can blunt various liver autoimmune disorders in a non-disease-specific manner without suppressing local or systemic immunity against infectious agents or cancer. Here, we show that such ubiquitous autoantigen-specific T cells are also awakened by extrahepatic tissue damage and that the corresponding TR1 progeny can suppress experimental autoimmune encephalomyelitis (EAE) and pancreatic β cell autoreactivity. In mice having EAE, nanomedicines displaying either ubiquitous or CNS-specific epitopes triggered the formation and expansion of cognate TR1 cells and their recruitment to the CNS-draining lymph nodes, sparing their liver-draining counterparts. Surprisingly, in mice having both liver autoimmunity and EAE, liver inflammation sequestered these ubiquitous or even CNS-specific TR1 cells away from the CNS, abrogating their antiencephalitogenic activity. In these mice, only the ubiquitous antigen-specific TR1 cells suppressed liver autoimmunity. Thus, the scope of antigen spreading in autoimmune disorders is larger than previously anticipated, involving specificities expected to be silenced by mechanisms of tolerance; the regulatory activity, but not the retention of autoreactive TR1 cells, requires local autoantigen expression.
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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".