Quantifying immunoregulation by autoantigen‐specific T‐regulatory type 1 cells in mice with simultaneous hepatic and extra‐hepatic autoimmune disorders
Bibliographic record
Abstract
Nanoparticles (NPs) displaying autoimmune disease-relevant peptide-major histocompatibility complex class II molecules (pMHCII-NPs) trigger cognate T-regulatory type 1 (Tr1)-cell formation and expansion, capable of reversing organ-specific autoimmune responses. These pMHCII-NPs that display epitopes from mitochondrial protein can blunt the progression of both autoimmune hepatitis (AIH) and experimental autoimmune encephalomyelitis (EAE) in mice carrying either disease. However, with co-morbid mice having both diseases, these pMHCII-NPs selectively treat AIH. In contrast, pMHCII-NPs displaying central nervous system (CNS)-specific epitopes can efficiently treat CNS autoimmunity, both in the absence and presence of AIH, without having any effects on the progression of the latter. Here, we develop a compartmentalized population model of T-cells in co-morbid mice to identify the mechanisms by which Tr1 cells mediate organ-specific immunoregulation. We perform time-series simulations and bifurcation analyses to study how varying physiological parameters, including local cognate antigenic load and rates of Tr1-cell recruitment and retention, affect T-cell allocation and Tr1-mediated immunoregulation. Various regimes of behaviour, including 'competitive autoimmunity' where pMHCII-NP-treatment fails against both diseases, are identified and compared with experimental observations. Our results reveal that a transient delay in Tr1-cell recruitment to the CNS, resulting from inflammation-dependent Tr1-cell allocation, accounts for the liver-centric effects of AIH-specific pMHCII-NPs in co-morbid mice as compared with mice exclusively having EAE. They also suggest that cognate autoantigen expression and local Tr1-cell retention are key determinants of effective regulatory-cell function. These results thus provide new insights into the rules that govern Tr1-cell recruitment and their autoregulatory function.
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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) | 0.001 | 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".