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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".