Metabolic Profiling of Kidney Grafts: A Novel Approach for Allograft Monitoring in Transplantation
Bibliographic record
Abstract
Background: Kidney transplantation is the optimal treatment for end-stage kidney disease, but kidney grafts are often lost prematurely. In transplanted organs, ischemia reperfusion injury (IRI) predisposes to inferior graft outcomes. Delayed graft function (DGF) is the manifestation of severe IRI in kidney transplantation. DGF is manifested as acute tubular necrosis (ATN), and injury to the peritubular and glomerular vasculature, features of acute kidney injury (AKI). Increasing evidence suggests that altered metabolism in the graft mediates AKI and is the major cause of DGF. Deciphering the metabolic underpinnings of IRI will improve our capacity to diagnose, prevent, and treat AKI. Methods: We studied the tubulointerstitial and glomerular proteome of kidney transplant patients that developed ATN (n=12) and compared them to cases with antibodymediated rejection (n=7) or acute cellular rejection (n=11). In addition to kidney graft proteome, we studied metabolic function of biopsy-derived kidney cell suspensions, and the urinary excretion of lactate, in a pig kidney auto-transplantation model. Results: Patients with ATN showed increased expression of 8 glycolytic enzymes in the tubulointerstitium (ALDOA, ALDOC, GPI, LDHA, PFKP, PGM2, PKM, and TALDO1) and 5 in the glomeruli (ALDOA, G6PD, HK1, LDHA, and TALDO1), at the time of rejection. ATN was also linked to altered levels of mitochondrial proteins (P<0.05). In our pig model, ischemia followed by cold storage led to significantly reduced mitochondrial respiration in cells derived from the kidney graft, in comparison to the ‘healthy’ contralateral kidney. Functional changes after cold storage were linked to significantly reduced levels of kidney mitochondrial proteins (e.g., CPT2, ETFB; n=10; P<0.05), and significantly increased lactate levels in urine (n=10; P<0.05). Conclusions: Our work shows that increased glycolysis and reduced mitochondrial function may contribute to post-transplant AKI and solidifies the importance of monitoring metabolism in kidney transplantation. Our next goal is to delineate a glycolytic signature that identifies ‘high risk allografts’, facilitating early diagnosis of DGF in kidney transplantation. Such signature could facilitate future clinical interventions targeting the highest risk grafts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".