Changes in the Transcriptome in Allograft Rejection:IFN-γ-Induced Transcripts in Mouse Kidney Allografts
Bibliographic record
Abstract
We used Affymetrix Microarrays to define interferon-gamma (IFN-gamma)-dependent, rejection-induced transcripts (GRITs) in mouse kidney allografts. The algorithm included inducibility by recombinant IFN-gamma in kidneys of three normal mouse strains, increase in kidney allografts in three strain combinations and less induction in IFN-gamma-deficient allografts. We identified 40 transcripts, which were highly IFN-gamma inducible (e.g. Cxcl9, ubiquitin D, MHC), and 168 less sensitive to IFN-gamma in normal kidney. In allografts, expression of GRITs was intense and consistent at all time points (day 3 through 42). These transcripts were partially dependent on donor IFN-gamma receptors (IFN-gammars): receptor-deficient allografts manifested up to 76% less expression, but some transcripts were highly dependent (ubiquitin D) and others relatively independent (Cxcl9). Kidneys of hosts rejecting allografts showed expression similar to that observed with IFN-gamma injections. Many GRITs showed transient IFN-gamma-dependent increase in isografts, peaking at day 4-5. GRITs were increased in heart allografts, indicating them as generalized feature of alloresponse. Thus, expression of rejection-induced transcripts is robust and consistent in allografts, reflecting the IFN-gamma produced by the alloresponse locally and systemically, acting via host and donor IFN-gammar, as well as local IFN-gamma production induced by post-operative stress.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".