A20 is an immune tolerance factor that can determine islet transplant outcomes
Bibliographic record
Abstract
Abstract Islet transplantation can restore lost glycemic control in type 1 diabetes subjects, but is restricted in its clinical application by limiting supplies of islets and the need for heavy immune suppression to prevent rejection. TNFAIP3 , encoding the ubiquitin editing enzyme A20, regulates the activation of immune cells by raising NF-κB signalling thresholds. Here we show that increasing A20 expression in allogeneic islet grafts resulted in permanent survival for ~45 % of recipients, and >80% survival when combined with subtherapeutic rapamycin. Allograft survival was dependent upon regulatory T cells, was antigen-specific and grafts showed reduced expression of inflammatory factors, but increased TGFβ and IL-10. By analysing islets expressing an A20 coding mutation (I325N) that cripples A20’s OTU ubiquitin editing domain, we found that A20 regulates intra-graft RIPK1 levels to modulate NF-κB signalling. Transplantation of I325N islets resulted in increased NF-κB signalling, graft hyper-inflammation and acute allograft rejection. Neonatal porcine islets (NPI) represent a clinical alternative islet source but are readily rejected. However, forced A20 expression reduced NPI inflammation and increased their function after transplantation. Therapeutic administration of A20 raises NF-κB signalling thresholds and promotes islet allogeneic survival. Clinically this would allow for reduced immunosuppression supporting the use of alternate islet sources.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".