High Levels of Adenosine Deaminase on Dendritic Cells Promote Autoreactive T Cell Activation and Diabetes in Nonobese Diabetic Mice
Bibliographic record
Abstract
Adenosine has been established as an important regulator of immune activation. It signals through P1 adenosine receptors to suppress activation of T cells and professional APCs. Adenosine deaminase (ADA) counters this effect by catabolizing adenosine. This regulatory mechanism has not been tested in a disease model in vivo. Questions also remain as to which cell types are most sensitive to this regulation and whether its dysregulation contributes to any autoimmune conditions. We approached this issue using the NOD model. We report that ADA is upregulated in NOD dendritic cells, which results in their exuberant and spontaneous activation. This, in turn, triggers autoimmune T cell activation. NOD DCs deficient in ADA expression have a greatly reduced capacity to trigger type I diabetes. We also provide evidence that although many cell types, particularly T cells, have been implicated as the suppression targets by adenosine in an in vitro setting, DCs also seem to be affected by this regulatory mechanism. Therefore, this report illustrates a role of ADA in autoimmunity and suggests a potential target for therapeutic intervention.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".