A cross-sectional study of the role of epithelial cell injury in kidney transplant outcomes
Bibliographic record
Abstract
Background. Expression of acute kidney injury–associated (AKI-associated) transcripts in kidney transplants may reflect recent injury and accumulation of epithelial cells in “failed repair” states. We hypothesized that the phenomenon of failed repair could be associated with deterioration and failure in kidney transplants. Methods. We defined injury-induced transcriptome states in 4,502 kidney transplant biopsies injury-induced gene sets and classifiers previously developed in transplants. Results. In principal component analysis (PCA), PC1 correlated with both acute and chronic kidney injury and related inflammation and PC2 with time posttransplant. Positive PC3 was a dimension that correlated with epithelial remodeling pathways and anticorrelated with inflammation. Both PC1 and PC3 correlated with reduced survival, with PC1 effects strongly increasing over time whereas PC3 effects were independent of time. In this model, we studied the expression of 12 “new” gene sets annotated in single-nucleus RNA-sequencing studies of epithelial cells with failed repair in native kidneys. The new gene sets reflecting epithelial-mesenchymal transition correlated with injury PC1 and PC3, lower estimated glomerular filtration rate, higher donor age, and future failure as strongly as any gene sets previously derived in transplants and were independent of nephron segment of origin and graft rejection. Conclusion. These results suggest 2 dimensions in the kidney transplant response to injury: PC1, AKI-induced changes, failed repair, and inflammation; and PC3, a response involving epithelial remodeling without inflammation. Increasing kidney age amplifies PC1 and PC3. Trial registration. INTERCOMEX (ClinicalTrials.gov NCT01299168); Trifecta-Kidney (ClinicalTrials.gov NCT04239703). Funding. Genome Canada; Natera, Inc.; and Thermo Fisher Scientific.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".