A Type 1 Diabetes-related Protein from Wheat (Triticum aestivum)
Bibliographic record
Abstract
The development of autoimmune type 1 diabetes involves complex interactions among several genes and environmental agents. Human patients with type 1 diabetes show an unusually high frequency of wheat gluten-sensitive enteropathy; T-cell response to wheat proteins is increased in some patients, and high concentrations of wheat antibodies in blood have been reported. In both major models of spontaneous type 1 diabetes, the BioBreeding (BB) rat and non-obese diabetic mouse, at least half of the cases are diet-related. In studies of BB rats fed defined semipurified diets, wheat gluten was the most potent diabetes-inducing protein source. A major limitation in understanding how wheat or other dietary antigens affect type 1 diabetes has been the difficulty in identifying specific diabetes-related dietary proteins. To address this issue, we probed a wheat cDNA expression library with polyclonal IgG antibodies from diabetic BB rats. Three clones were identified, and the intensity of antibody binding to one of them, WP5212, was strongly associated with pancreatic islet inflammation and damage. The WP5212 putative protein has high amino acid sequence homology with a wheat storage globulin, Glb1. Serum IgG antibodies from diabetic rats and humans recognized low molecular mass (33-46 kDa) wheat proteins. Furthermore, antibodies to Glb1 protein were found in serum from diabetic patients but not in age-, sex-, and HLA-DQ-matched controls. This study raises the possibility that in some individuals, type 1 diabetes may be induced by wheat proteins. Also, it provides a first candidate wheat protein that is not only antigenic in diabetic rats and human patients but is also closely linked with the autoimmune attack in the pancreas.
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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 | 0.000 | 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".