On How Monospecific Memory-Like Autoregulatory CD8+ T Cells Can Blunt Diabetogenic Autoimmunity: A Computational Approach
Bibliographic record
Abstract
We have recently shown that during progression to autoimmune diabetes in NOD mice, memory autoreactive regulatory CD8(+) T cells arising from low-avidity precursors can be expanded to therapeutic levels using nanoparticles coated with disease-relevant peptide-major histocompatibility complexes (pMHCs). Here we examine the dynamics of memory autoregulatory CD8(+) T cells specific for islet-specific glucose-6-phosphatase catalytic subunit-related protein(206-214), a prevalent β cell autoantigen; their high-avidity counterparts (dominant effectors); and all other autoreactive non-islet-specific glucose-6-phosphatase catalytic subunit-related protein(206-214)-specific CD8(+) T cell specificities (subdominant effectors) in response to pMHC-coated nanoparticle (pMHC-nanoparticle) therapy. We combine experimental data with mathematical modeling to investigate the clonal competition dynamics of these T cell pools. To mimic the response diversity observed in NOD mice, we simulated many individual mice, using a wide range of parameters, and averaged the results as done experimentally. We find that under certain circumstances, pMHC-nanoparticle-induced expansion of autoregulatory CD8(+) T cells can effectively suppress the expansion of dominant and subdominant effectors simultaneously but, in some few cases, can lead to the substitution (or switching) of one effector population by another. The model supports the idea that disease suppression is based on the elimination of autoantigen-loaded APCs by the expanded autoregulatory CD8(+) T cells. The model also predicts that treatment strategies that operate by selectively inhibiting autoantigen-loaded APCs, such as the pMHC-nanoparticle approach, have the highest promise to blunt polyclonal, multiantigen-specific autoimmune responses in vivo without impairing systemic immunity.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".