Requirement for Both H-2Db and H-2Kd for the Induction of Diabetes by the Promiscuous CD8+ T Cell Clonotype AI4
Bibliographic record
Abstract
The NOD mouse is a model for autoimmune type 1 diabetes in humans. CD8(+) T cells are essential for the destruction of the insulin-producing pancreatic beta cells characterizing this disease. AI4 is a pathogenic CD8(+) T cell clone, isolated from the islets of a 5-wk-old female NOD mouse, which is capable of mediating overt diabetes in the absence of CD4(+) T cell help. Recent studies using MHC-congenic NOD mice revealed marked promiscuity of the AI4 TCR, as the selection of this clonotype can be influenced by multiple MHC molecules, including some class II variants. The present work was designed, in part, to determine whether similar promiscuity also characterizes the effector function of mature AI4 CTL. Using splenocyte and bone marrow disease transfer models and in vitro islet-killing assays, we report that efficient recognition and destruction of beta cells by AI4 requires the beta cells to simultaneously express both H-2D(b) and H-2K(d) class I MHC molecules. The ability of the AI4 TCR to interact with both H-2D(b) and H-2K(d) was confirmed using recombinant peptide libraries. This approach also allowed us to define a mimotope peptide recognized by AI4 in an H-2D(b)-restricted manner. Using ELISPOT and mimotope/H-2D(b) tetramer analyses, we demonstrate for the first time that AI4 represents a readily detectable T cell population in the islet infiltrates of prediabetic NOD mice. Our identification of a ligand for AI4-like T cells will facilitate further characterization and manipulation of this pathogenic and promiscuous T cell population.
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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 | 0.001 |
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 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".