Epithelial Injury Cell States Affect Kidney Transplant Survival After T Cell-Mediated Rejection
Bibliographic record
Abstract
Background: T-cell-mediated rejection (TCMR) remains a key obstacle to achieving long-term survival of kidney allografts. Although it is generally considered responsive to intensified immunosuppression, its occurrence still significantly impairs graft function and durability. This is largely due to an incomplete understanding of the molecular pathways activated during TCMR and their clinical implications. Methods: To investigate this, we induced acute TCMR in murine models of allogeneic kidney transplantation (C57BL/6 to BALB/c and BALB/c to C57BL/6), alongside syngeneic controls (C57BL/6 to C57BL/6 and BALB/c to BALB/c). Molecular alterations were examined 7 days post-transplant using single-nucleus RNA sequencing (snRNA-seq) and spatial transcriptomics. Findings were compared with snRNA-seq data from three human TCMR biopsies and three stable allografts without rejection. To assess clinical relevance, we analyzed bulk transcriptomic data from 1,292 kidney allografts—including 95 TCMR cases—using biomarker gene sets reflecting TCMR-associated epithelial injury and allograft outcomes. Results: Allogeneic mouse kidneys showed classical histopathological signs of TCMR. snRNA-seq revealed distinct injury-associated cell states with marked gene expression changes, especially in proximal tubules (PT) and thick ascending limbs (TAL). Spatial transcriptomics identified a heterogeneous distribution of these injured cells and their spatial association with infiltrating leukocytes. Cross-species comparison demonstrated conserved injured PT and TAL cell states in human TCMR. Importantly, graft outcomes were closely linked to the extent of TCMR-induced epithelial injury, which persisted in some cases despite apparent resolution of rejection. Conclusion: This study provides an in-depth analysis of cell type-specific molecular changes during TCMR across species. The findings underscore the need for improved diagnostic tools and targeted therapies to address epithelial injury and enhance long-term kidney allograft survival.
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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 | 0.001 | 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".