Proteomic Profiling of Hypoplastic Lungs Suggests an Underlying Inflammatory Response in the Pathogenesis of Abnormal Lung Development in Congenital Diaphragmatic Hernia
Bibliographic record
Abstract
Abstract The pathogenesis of lung hypoplasia in congenital diaphragmatic hernia (CDH), a common birth defect, is poorly understood. The diaphragmatic defect can be repaired surgically, but the abnormal lung development contributes to a high mortality in these patients. To better understand the underlying pathobiology, we used the nitrofen rat model of CDH and characterized the proteome of hypoplastic CDH lungs at the alveolar stage (E21). Amongst the 218 significantly altered proteins between CDH and control lungs were Tenascin C, CREBBP, LYN and STAT3. We showed that Tenascin C was decreased around the distal airway branches in nitrofen and human fetal CDH lungs. In contrast, STAT3 was significantly increased in the airway epithelium of nitrofen lungs at E21. STAT3 inhibition after direct nitrofen exposure to fetal rat lung explants (E14.5) partially reversed the hypoplastic lung phenotype ex vivo by increasing peripheral lung budding. Moreover, we demonstrated that several STAT3 associated cytokines (IL-15, IL-9, IL-2) are increased in fetal tracheal aspirates of CDH survivors compared to non-survivors after fetoscopic tracheal occlusion. Using pathway analysis for significantly altered proteins in our proteomic analysis, we observed an enrichment in inflammatory response associated with Epstein Barr Virus and cytokine signaling in nitrofen CDH lungs. However, we were unable to detect EBV mRNA via in-situ Hybridization in human CDH lungs. With our unbiased proteomics approach, we show for the first time that inflammatory processes are likely underlying the pathogenesis of abnormal lung development in CDH.
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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".