Transcriptome Analysis of Kidney Grafts Subjected to Normothermic Ex Vivo Perfusion Demonstrates an Enrichment of Mitochondrial Metabolism Genes
Bibliographic record
Abstract
Background. Normothermic ex vivo kidney perfusion (NEVKP) has demonstrated superior outcomes for donation-after-cardiovascular death grafts compared with static cold storage (SCS). To determine the mechanisms responsible for this, we performed an unbiased genome-wide microarray analysis. Methods. Kidneys from 30-kg Yorkshire pigs were subjected to 30 min of warm ischemia followed by 8 h of NEVKP or SCS, or no storage, before autotransplantation. mRNA expression was analyzed on renal biopsies on postoperative day 3. Gene set enrichment analysis was performed using hallmark gene sets, Gene Ontology, and pathway analysis. Results. The gene expression profile of NEVKP-stored grafts closely resembled no storage kidneys. Gene set enrichment analysis demonstrated enrichment of fatty acid metabolism and oxidative phosphorylation following NEVKP, whereas SCS-enriched gene sets were related to mitosis, cell cycle checkpoint, and reactive oxygen species (q < 0.05). Pathway analysis demonstrated enrichment of lipid oxidation/metabolism, the Krebs cycle, and pyruvate metabolism in NEVKP compared with SCS (q < 0.05). Comparison of our findings with external data sets of renal ischemia-reperfusion injury revealed that SCS-stored grafts demonstrated similar gene expression profiles to ischemia-reperfusion injury, whereas the profile of NEVKP-stored grafts resembled recovered kidneys. Conclusions. Increased transcripts of key mitochondrial metabolic pathways following NEVKP storage may account for improved donation-after-cardiovascular death graft function, compared with SCS, which promoted expression of genes typically perturbed during IRI.
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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 | 0.000 | 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".