Transcriptomic investigation reveals donor-specific gene signatures in human lung transplants
Bibliographic record
Abstract
Introduction Transplantation of lungs from donation after circulatory death (DCD) in addition to donation after brain death (DBD) became routine worldwide to address the global organ shortage. The development of ex vivo lung perfusion (EVLP) for donor lung assessment and repair contributed to the increased use of DCD lungs. We hypothesise that a better understanding of the differences between lungs from DBD and DCD donors, and between EVLP and directly transplanted (non-EVLP) lungs, will lead to the discovery of the injury-specific targets for donor lung repair and reconditioning. Methods Tissue biopsies from human DBD (n=177) and DCD (n=65) donor lungs, assessed with or without EVLP, were collected at the end of cold ischaemic time. All samples were processed with microarray assays. Gene expression, network and pathway analyses were performed using R, Ingenuity Pathway Analysis and STRING. Results were validated with protein assays, multiple logistic regression and 10-fold cross-validation. Results Our analyses showed that lungs from DBD donors have upregulation of inflammatory cytokines and pathways. In contrast, DCD lungs display a transcriptome signature of pathways associated with cell death, apoptosis and necrosis. Network centrality revealed specific drug targets to rehabilitate DBD lungs. Moreover, in DBD lungs, tumour necrosis factor receptor-1/2 signalling pathways and macrophage migration inhibitory factor-associated pathways were activated in the EVLP group . A panel of genes that differentiate the EVLP from the non-EVLP group in DBD lungs was identified. Conclusion The examination of gene expression profiling indicates that DBD and DCD lungs have distinguishable biological transcriptome signatures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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".