Dendritic Cells Genetically Engineered to Express Fas Ligand Induce Donor-Specific Hyporesponsiveness and Prolong Allograft Survival
Bibliographic record
Abstract
Polarization of an immune response toward tolerance or immunity is dictated by the interactions between T cells and dendritic cells (DC), which in turn are modulated by the expression of distinct cell surface molecules, and the cytokine milieu in which these interactions are taking place. Genetic modification of DC with genes coding for specific immunoregulatory cell surface molecules and cytokines offers the potential of inhibiting immune responses by selectively targeting Ag-specific T cells. In this study, the immunomodulatory effects of transfecting murine bone marrow-derived DC with Fas ligand (FasL) were investigated. In this study, we show that FasL transfection of DC markedly augmented their capacity to induce apoptosis of Fas+ cells. FasL-transfected DC inhibited allogeneic MLR in vitro, and induced hyporesponsiveness to alloantigen in vivo. The induction of hyporesponsiveness was Ag specific and was dependent on the interaction between FasL on DC and Fas on T cells. Finally, we show that transfusion of FasL-DC significantly prolonged the survival of fully MHC-mismatched vascularized cardiac allografts. Our findings suggest that DC transduced with FasL may facilitate the development of Ag-specific unresponsiveness for the prevention of organ rejection. Moreover, they highlight the potential of genetically engineering DC to express other genes that affect immune responses.
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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".