Altered purine metabolism at reperfusion affects clinical outcome in lung transplantation
Bibliographic record
Abstract
Introduction Lung transplantation is an established treatment for patients with end-stage lung disease. However, ischaemia reperfusion injury remains a barrier to achieving better survival outcomes. Here, we aim to investigate the metabolomic and transcriptomic profiles in human lungs before and after reperfusion, to identify mechanisms relevant to clinical outcome. Methods We analysed 67 paired human lung tissue samples collected from 2008 to 2011, at the end of cold preservation and 2 hours after reperfusion. Gene expression analysis was performed with R. Pathway analysis was conducted with Ingenuity Pathway Analysis. MetaboAnalyst and OmicsNet were used for metabolomics analysis and omics data integration, respectively. Association of identified metabolites with transplant outcome was investigated with Kaplan-Meier estimate and Cox proportional hazard models. Results Activation of energy metabolism and reduced antioxidative biochemicals were found by metabolomics. Upregulation of genes related to cytokines and inflammatory mediators, together with major signalling pathways were revealed by transcriptomics. Purine metabolism was identified as the most significantly enriched pathway at reperfusion, based on integrative analysis of the two omics data sets. Elevated expression of purine nucleoside phosphorylase (PNP) could be attributed to activation of multiple transcriptional pathways.PNPcatabolised reactions were evidenced by changes in related metabolites, especially decreased levels of inosine and increased levels of uric acid. Multivariable analyses showed significant association of inosine and uric acid levels with intensive care unit length of stay and ventilation time. Conclusion Oxidative stress, especially through purine metabolism pathway, is a major metabolic event during reperfusion and may contribute to the ischaemia reperfusion injury of lung 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".