Mesenchymal stem cells protect from hypoxia-induced alveolar epithelial-mesenchymal transition
Bibliographic record
Abstract
Administration of bone marrow-derived human mesenchymal stem cells (hMSC) reduces lung inflammation, fibrosis, and mortality in animal models of lung injury, by a mechanism not completely understood. We investigated whether hMSC would prevent epithelial-mesenchymal transition (EMT) induced by hypoxia in primary rat alveolar epithelial cell (AEC). In AEC cultured on semipermeable filters, prolonged hypoxic exposure (1.5% O2 for up to 12 days) induced phenotypic changes consistent with EMT, i.e., a change in cell morphology, a decrease in transepithelial resistance (Rte) and in the expression of epithelial markers [zonula occludens-1 (ZO-1), E-cadherin, AQP-5, TTF-1], together with an increase in mesenchymal markers [vimentin, α-smooth muscle actin (α-SMA)]. Expression of transcription factors driving EMT such as SNAIL1, ZEB1, and TWIST1 increased after 2, 24, and 48 h of hypoxia, respectively. Hypoxia also induced TGF-β1 mRNA expression and the secretion of active TGF-β1 in apical medium, and the expression of connective tissue growth factor (CTGF), two inducers of EMT. Coculture of AEC with hMSC partially prevented the decrease in Rte and in ZO-1, E-cadherin, and TTF-1 expression, and the increase in vimentin expression induced by hypoxia. It also abolished the increase in TGF-β1 expression and in TGF-β1-induced genes ZEB1, TWIST1, and CTGF. Finally, incubation with human recombinant KGF at a concentration similar to what was measured in hMSC-conditioned media restored the expression of TTF-1 and prevented the increase in TWIST1, TGF-β1, and CTGF in hypoxic AEC. Our results indicate that hMSC prevent hypoxia-induced alveolar EMT through the paracrine modulation of EMT signaling pathways and suggest that this effect is partly mediated by KGF.
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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 | 0.001 |
| 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".