Polymorphism in the Innate Immune Receptor SIRPα Controls CD47 Binding and Autoimmunity in the Nonobese Diabetic Mouse
Bibliographic record
Abstract
The signal regulatory protein (SIRP) locus encodes a family of paired receptors that mediate both activating and inhibitory signals and is associated with type 1 diabetes (T1D) risk. The NOD mouse model recapitulates multiple features of human T1D and enables mechanistic analysis of the impact of genetic variations on disease. In this study, we identify Sirpa encoding an inhibitory receptor on myeloid cells as a gene in the insulin-dependent diabetes locus 13.2 (Idd13.2) that drives islet inflammation and T1D. Compared to T1D-resistant strains, the NOD variant of SIRPα displayed greater binding to its ligand CD47, as well as enhanced T cell proliferation and diabetogenic potency. Myeloid cell-restricted expression of a Sirpa transgene accelerated disease in a dose-dependent manner and displayed genetic and functional interaction with the Idd5 locus to potentiate insulitis progression. Our study demonstrates that variations in both SIRPα sequence and expression level modulate T1D immunopathogenesis. Thus, we identify Sirpa as a T1D risk gene and provide insight into the complex mechanisms by which disease-associated variants act in concert to drive defined stages in disease progression.
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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 | 0.001 | 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 | 0.001 |
| 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".