Ischemia-Reperfusion Injury in a Simulated Lung Transplant Setting Differentially Regulates Transcriptomic Profiles between Human Lung Endothelial and Epithelial Cells
Bibliographic record
Abstract
Current understanding of mechanisms of ischemia-reperfusion-induced lung injury during lung preservation and transplantation is mainly based on clinical observations and animal studies. Herein, we used cell and systems biology approaches to explore these mechanisms at transcriptomics levels, especially by focusing on the differences between human lung endothelial and epithelial cells, which are crucial for maintaining essential lung structure and function. Human pulmonary microvascular endothelial cells and human lung epithelial cells were cultured to confluent, subjected to different cold ischemic times (CIT) to mimic static cold storage with preservation solution, and then subjected to warm reperfusion with a serum containing culture medium to simulate lung transplantation. Cell morphology, viability, and transcriptomic profiles were studied. Ischemia-reperfusion injury induced a CIT time-dependent cell death, which was associated with dramatic changes in gene expression. Under normal control conditions, endothelial cells showed gene clusters enriched in the vascular process and inflammation, while epithelial cells showed gene clusters enriched in protein biosynthesis and metabolism. CIT 6 h alone or after reperfusion had little effect on these phenotypic characteristics. After CIT 18 h, protein-biosynthesis-related gene clusters disappeared in epithelial cells; after reperfusion, metabolism-related gene clusters in epithelial cells and multiple gene clusters in the endothelial cells also disappeared. Human pulmonary endothelial and epithelial cells have distinct phenotypic transcriptomic signatures. Severe cellular injury reduces these gene expression signatures in a cell-type-dependent manner. Therapeutics that preserve these transcriptomic signatures may represent new treatment to prevent acute lung injury during lung transplantation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".