Interferon-γ and Donor MHC Class I Control Alternative Macrophage Activation and Activin Expression in Rejecting Kidney Allografts: A Shift in the Th1-Th2 Paradigm
Bibliographic record
Abstract
Organ allografts deficient in interferon-gamma (Ifng) or major histocompatibility complex (MHC) class I products develop accelerated necrosis when rejection develops, depending on perforin and granzymes. Thus Ifng-induced donor class I products deliver inhibitory signals to host inflammatory cells. We used microarrays to investigate whether Ifng-induced donor class I products also control inflammation patterns in mouse kidney allografts. Compared to wild-type (WT) allografts, many transcripts were increased in both Ifng-deficient allografts (Ifng-suppressed transcripts [GSTs]) and class I-deficient allografts (class I-suppressed transcripts [CISTs]), with 73% overlap between GSTs and CISTs. Some GSTs and CISTs reflected increased necrosis, including known injury-induced transcripts. However, many GSTs and CISTs were independent of perforin, granzymes and necrosis, and were associated with alternative macrophage activation (AMA) (e.g. arginase I [Arg1], macrophage elastase [Mmp12] and macrophage mannose receptor 1 [Mrc1]). AMA transcripts were induced despite absence of host interleukin (IL)4 and IL13 receptors. The AMA inducer may be activins, whose genes (inhibin A [InhbA] and inhibin B [InhbB]) were increased in all allografts with AMA. We conclude that in allograft rejection, Ifng acts via donor Ifng receptors (Ifngr) to induce donor class Ia and Ib products, which engage host inflammatory cells to limit perforin-granzyme-mediated damage and prevent AMA associated with inhibition of activin expression. Thus, Ifng may control T helper type 2 (Th2) cell inflammation by induction of class I products.
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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".