Ischemia/Reperfusion Upregulates Genes Related to PANoptosis in Human Lung Transplants
Bibliographic record
Abstract
BACKGROUND: Activation of multiple programmed cell death (PCD) pathways has been reported in cellular and animal studies of ischemia/reperfusion injury in lung transplantation. However, the status of these pathways in human lung transplants remains unknown. This study investigates the involvement of PCD pathways and their relationship with inflammation and signaling pathways in human lung transplants. METHODS: Transcriptomic analysis was conducted on 54 paired human lung tissue samples at the end of cold preservation time and 2 h after reperfusion, collected between 2008 and 2011. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA were used to examine the activation of genes in 6 PCD pathways. The relationships between PCD pathways and inflammation, as well as signaling pathways, were assessed via single-gene GSEA. RESULTS: GSEA results indicated that apoptosis and necroptosis were significantly upregulated after reperfusion in human lung transplants, whereas the gene sets related to pyroptosis, ferroptosis, autophagy, and cuproptosis were not significantly upregulated. Notably, single-sample GSEA demonstrated an intricate interplay among pyroptosis, apoptosis, and necroptosis, collectively referred to as PANoptosis, which is further supported by enrichment of genes related to PANoptosome, inflammatory response, and nuclear factor-κB and interferon signaling pathways, via single-gene GSEA assays. CONCLUSIONS: This study demonstrated the genes of PANoptosis are upregulated in human lung grafts during reperfusion. The discovery of PANoptosis as an underlying mechanism of cell death in human lung grafts implies that effective therapeutics to prevent or reduce PANoptosis may alleviate ischemia/reperfusion injury and improve clinical lung transplant outcomes.
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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 | 0.001 | 0.001 |
| 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".