A 20-Mb Region of Chromosome 4 Controls TNF-α-Mediated CD8+ T Cell Aggression Toward β Cells in Type 1 Diabetes
Bibliographic record
Abstract
Identification of candidate genes and their immunological mechanisms that control autoaggressive T cells in inflamed environments, may lead to novel therapies for autoimmune diseases, like type 1 diabetes (T1D). In this study, we used transgenic NOD mice that constitutively express TNF-alpha in their islets from neonatal life (TNF-alpha-NOD) to identify protective alleles that control T1D in the presence of a proinflammatory environment. We show that TNF-alpha-mediated breakdown in T cell tolerance requires recessive NOD alleles. To identify some of these recessive alleles, we crossed TNF-alpha-NOD mice to diabetes-resistant congenic NOD mice having protective alleles at insulin-dependent diabetes (Idd) loci that control spontaneous T1D at either the preinsulitis (Idd3.Idd5) or postinsulitis (Idd9) phases. No protection from TNF-alpha-accelerated T1D was afforded by resistance alleles at Idd3.Idd5. Lack of protection was not at the level of T cell priming, the efficacy of islet-infiltrating APCs to present islet peptides, nor the ability of high levels of CD4+ Foxp3+ T cells to accumulate in the islets. In contrast, protective alleles at Idd9 significantly increased the age at which TNF-alpha-NOD mice developed T1D. Disease delay was associated with a decreased ability of CD8+ T cells to respond to islet Ags presented by islet-infiltrating APCs. Finally, we demonstrate that the protective region on chromosome 4 that controls T1D in TNF-alpha-Idd9 mice is restricted to the Idd9.1 region. These data provide new evidence of the mechanisms by which selective genetic loci control autoimmune diseases in the presence of a strong inflammatory assault.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".