Loss of Solute Carriers in T Cell‐Mediated Rejection in Mouse and Human Kidneys: An Active Epithelial Injury–Repair Response
Bibliographic record
Abstract
T cell-mediated rejection of kidney allografts causes epithelial deterioration, manifested by tubulitis, but the mechanism remains unclear. We hypothesized that interstitial inflammation triggers a stereotyped epithelial response similar to that triggered by other types of injury such as ischemia-reperfusion. We identified solute carrier transcripts with decreased expression in mouse allografts, and compared their behavior in T cell-mediated rejection to native kidneys with ischemic acute tubular necrosis (ATN). Average loss of solute carrier expression was similar in ATN (77%) and T cell-mediated rejection (75%) with high correlation of individual transcripts. Immunostaining of SLC6A19 confirmed loss of proteins. Analysis of human kidney transplant biopsies confirmed that T cell-mediated rejection and ATN showed similar loss of solute carrier mRNAs. The loss of solute carrier expression was weakly correlated with interstitial inflammation, but kidneys with ATN showed decreased solute carriers despite minimal inflammation. Loss of renal function correlated better with decreased solute carrier expression than with histologic lesions (r = 0.396, p < 0.001). Thus the loss of epithelial transcripts in rejection is not a unique consequence of T cell-mediated rejection but an active injury-repair response of epithelium, triggered by rejection but also by other injury mechanisms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".