Transcription Factor IRF-1 in Kidney Transplants Mediates Resistance to Graft Necrosis during Rejection
Bibliographic record
Abstract
In many circumstances kidney transplants remain viable despite extensive inflammation, permitting rejection episodes to be reversed. The mechanisms by which the kidney resists host effector mechanisms are not known. In mouse kidney transplants, resistance requires interferon-gamma (IFN-gamma), which acts on the graft to protect the graft from necrosis during the first days of rejection as well as inducing major histocompatibility complex (MHC) expression. Because some effects of IFN-gamma are mediated by transcription factor IRF-1, the role of IRF-1 in the donor tissue early phases of rejection of mouse kidney allografts was studied. H-2(b) kidneys were transplanted from mice with wild-type IRF-1 genes (WT) or mice with disrupted IRF-1 genes (IRF-1KO) into CBA (H-2(k)) recipients. At day 5 and day 7, IRF-1KO and WT kidneys were functioning despite typical rejection pathology: interstitial infiltration and tubulitis. However, function deteriorated rapidly in rejecting IRF-1KO allografts, associated with widespread epithelial necrosis, peritubular capillary congestion, glomerulitis, and fibrin thrombi in small veins by day 7. At day 21, WT kidneys were viable despite severe tubulitis and arteritis, whereas IRF-1KO kidneys showed massive necrosis of the epithelium despite patent large vessels. Compared with WT kidneys, rejecting IRF-1KO kidneys showed less induction of donor MHC yet had similar mRNA levels of perforin, granzyme B, and Fas ligand and evoked host alloantibody responses. Thus in rejecting kidney transplants, IRF-1 in the graft mediates MHC induction, but it also mediates resistance to necrosis, an effect that could be crucial to permit success in interventions against 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 | 0.001 | 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".