Suppression of islet allogeneic immune response by indoleamine 2,3 dioxygenase‐expressing fibroblasts
Bibliographic record
Abstract
Success of transplantation of pancreatic islets which is a promising way for restoring efficient insulin regulation in type 1 diabetes depends on lifelong use of immunosuppressive drugs. To eliminate the use of systemic immunosuppressive drugs for islet transplantation, we examined the potential use of a local immunosuppressive factor, indoleamine 2,3-dioxygenase (IDO). Thus, the aim of this study was to determine whether local expression of IDO in bystander syngeneic fibroblasts could prevent islet allogeneic immune response in vitro. C57BL/6 (B6) mouse fibroblasts were induced to express IDO by either IFN-gamma treatment or transduction with an adenoviral vector and were co-cultured with B6 mouse lymphocytes and BALB/c mouse pancreatic islets in the presence or absence of an IDO inhibitor. Proliferation of lymphocytes were then assessed using [(3)H]-thymidine incorporation assay. IDO-expression by co-cultured syngeneic fibroblasts resulted in a five-fold decrease in lymphocyte proliferation rate upon stimulation of lymphocytes by allogeneic mouse pancreatic islets (21.9% +/- 5.3 and 22.1% +/- 4.9 in the preparations with IFN-gamma treated and genetically modified IDO-expressing fibroblasts, respectively vs. 100% in control groups, P < 0.01). Allogeneic response was restored when IDO inhibitor was added to the culture indicating that suppression was due to IDO. In conclusion, this study shows that local expression of IDO by syngeneic bystander fibroblasts can suppress in vitro proliferation of lymphocytes in response to stimulation with allogeneic pancreatic islets. This local immunosuppressive function of IDO may be employed for development of a novel alternative strategy for preventing allogeneic islet graft rejection.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".