Unexpected Acceleration of Type 1 Diabetes by Transgenic Expression of B7-H1 in NOD Mouse Peri-Islet Glia
Bibliographic record
Abstract
OBJECTIVE: Autoimmune target tissues in type 1 diabetes include pancreatic β-cells and peri-islet Schwann cells (pSC)--the latter active participants or passive bystanders in pre-diabetic autoimmune progression. To distinguish between these alternatives, we sought to suppress pSC autoimmunity by transgenic expression of the negative costimulatory molecule B7-H1 in NOD pSC. RESEARCH DESIGN AND METHODS: A B7-H1 transgene was placed under control of the glial fibrillary acidic protein (GFAP) promoter. Transgenic and wild-type NOD mice were compared for transgene PD-1 affinities, diabetes development, insulitis, and pSC survival. Mechanistic studies included adoptive type 1 diabetes transfer, B7-H1 blockade, and T-cell autoreactivity and sublineage distribution. RESULTS: Transgenic and endogenous B7-H1 bound PD-1 with equal affinities. Unexpectedly, the transgene generated islet-selective CD8(+) bias with accelerated rather than suppressed diabetes progression. T-cells of diabetic transgenics transferred type 1 diabetes faster. There were no earlier pSC losses due to conceivable transgene toxicity, but transgenic pSC loss was enhanced by 8 weeks, preceded by elevated GFAP autoreactivity, with high-affinity T-cells targeting the major NOD K(d)-GFAP epitope, p253-261. FoxP3(+) regulatory T- and CD11c(+) dendritic cell pools were unaffected. CONCLUSIONS: In contrast with transgenic B7-H1 in NOD mouse β-cells, transgenic B7-H1 in pSC promotes rather than protects from type 1 diabetes. Here, ectopic B7-H1 enhanced the pathogenicity of effector T-cells, demonstrating that pSC can actively impact diabetes progression-likely through modification of intraislet T-cell selection. Although pSC cells emerge as a new candidate for therapeutic targets, caution is warranted with regard to the B7-H1-PD1 axis, where B7-H1 overexpression can lead to accelerated autoimmune disease.
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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 | 0.001 |
| 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".