On the Pathogenicity of Autoantigen-Specific T-Cell Receptors
Bibliographic record
Abstract
OBJECTIVE: Type 1 diabetes is mediated by T-cell entry into pancreatic islets and destruction of insulin-producing beta-cells. The relative contribution of T-cells specific for different autoantigens is largely unknown because relatively few have been assessed in vivo. RESEARCH DESIGN AND METHODS: We generated mice possessing a monoclonal population of T-cells expressing 1 of 17 T-cell receptors (TCR) specific for either known autoantigens (GAD65, insulinoma-associated protein 2 (IA2), IA2beta/phogrin, and insulin), unknown islet antigens, or control antigens on a NOD.scid background using retroviral-mediated stem cell gene transfer and 2A-linked multicistronic retroviral vectors (referred to herein as retrogenic [Rg] mice). The TCR Rg approach provides a mechanism by which T-cells with broad phenotypic differences can be directly compared. RESULTS: Neither GAD- nor IA2-specific TCRs mediated T-cell islet infiltration or diabetes even though T-cells developed in these Rg mice and responded to their cognate epitope. IA2beta/phogrin and insulin-specific Rg T-cells produced variable levels of insulitis, with one TCR producing delayed diabetes. Three TCRs specific for unknown islet antigens produced a hierarchy of insulitogenic and diabetogenic potential (BDC-2.5 > NY4.1 > BDC-6.9), while a fourth (BDC-10.1) mediated dramatically accelerated disease, with all mice diabetic by day 33, well before full T-cell reconstitution (days 42-56). Remarkably, as few as 1,000 BDC-10.1 Rg T-cells caused rapid diabetes following adoptive transfer into NOD.scid mice. CONCLUSIONS; Our data show that relatively few autoantigen-specific TCRs can mediate islet infiltration and beta-cell destruction on their own and that autoreactivity does not necessarily imply pathogenicity.
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 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) | 0.000 | 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) | 0.000 | 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 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".