Regulation of B- and T-cell Mediated Xenogeneic Transplant Rejection by Interleukin 12
Bibliographic record
Abstract
BACKGROUND: Xenotransplantation may provide a solution to the increasing shortage of donor organs. Acute vascular rejection and cell-mediated rejection remain the primary barriers to successful xenotransplantation. In animal models where acute vascular rejection can be attenuated, xenografts succumb to cell-mediated rejection. The mechanisms of acute vascular rejection and cell-mediated rejection are poorly understood. METHODS: Using a heterotopic rat-to-mouse cardiac transplantation model, we demonstrate that IL-12p40 attenuates both allogeneic and xenogeneic acute vascular rejection pathology by suppressing B-cell activation and anti-rat isotype switching. To study the mechanism of xenogeneic cell-mediated rejection, we use B-cell deficient mice that only develop cell-mediated rejection pathology. To elucidate the role of IL-12 in cell-mediated rejection, we generated B cell/ IL-12p40 double knockout mice. RESULTS: We demonstrate that xenogeneic cell-mediated rejection is mediated by CD4+ T cells, and is accompanied by elevated FasL and granzyme mRNA expression. Strikingly, by generating B cell/IL-12p40 double knockout mice, we demonstrate that xenogeneic cell-mediated rejection is IL-12p40 dependent. In contrast, we demonstrate that allogeneic cellular rejection is IL-12p40 independent. CONCLUSIONS: We conclude that IL-12 plays a dual role in xenotransplantation by driving xenogeneic CD4+ T cell responses but suppressing both allogeneic and xenogeneic B cell responses. Therefore, the mechanism of allogeneic and xenogeneic transplantation rejection is differentially regulated by IL-12.
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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 | 0.001 |
| 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".