Transcriptome Analysis Reveals Heterogeneity in the Injury Response of Kidney Transplants
Bibliographic record
Abstract
We studied the transcripts that are increased by stress and injury in mouse kidney transplants, focusing on transcripts increased in parenchymal cells-injury and repair-induced transcripts (IRITs). We compared four types of stressed kidneys: isografts, allografts, host kidneys of mice with isografts and nontransplant kidneys with ischemic acute tubular necrosis (ATN). After excluding transcripts associated with infiltrating cells and interferon-gamma-induced transcripts, we defined 790 IRITs in isografts. IRITs were remarkably heterogeneous in timing and mechanisms. Some were increased in host as well as donor kidneys, reflecting systemic influences (wounding, anesthetic). Most reflected local stress, resembling changes in ATN despite the lack of ATN histopathology. Mathematical decomposition of IRIT expression patterns confirmed heterogeneity, separating IRIT changes into component subsets, with an early peak (day 1) showing systemic effects and late peaks that resembled ATN, manifested Tgf-ss1 effects and recapitulated embryonic development. In allografts IRITs were initially similar to isografts but diverged due to allogeneic injury. The allospecific induction of IRITs was T-cell-dependent but perforin-granzyme-independent, compatible with delayed type hypersensitivity. The alloresponse strikingly and selectively increased the late IRITs but not the IRITs that peak early, indicating that rejection triggers parenchymal responses similar to those in ATN.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".