Stretch‐activated signaling pathways responsible for early response gene expression in fetal lung epithelial cells
Bibliographic record
Abstract
High-tidal volume ventilation has been shown to increase the expression of several inflammation-associated genes prior to overt physiologic lung injury. Herein, using an in vitro stretch system, we investigated the mechanotransduction pathways involved in ventilation-induced expression of these early response genes (i.e., early growth response gene (Egr)1, heat-shock protein (HSP)70, and the pro-inflammatory cytokines interleukin (IL)-1beta, IL-6, and MIP-2). Mechanical stretch of fetal lung epithelial cells activated various signaling pathways, resulting in transient or progressive increases in gene expression of the early response genes. The transient increase in Egr1 and IL-6 expression was mediated via p44/42 mitogen-activated protein kinase (p44/42 MAPK), while nuclear factor-kappaB (NF-kappaB) was responsible for the sustained and progressive increase in expression of HSP70 and MIP-2. Blockage of Egr-1 expression did not affect the upregulation of IL-6, HSP70, MIP-2, and itself by stretch. Inhibition of calcium mobilization abolished stretch-induced p44/42 MAPK activation and NF-kappaB nuclear translocation as well as increased expression of all early response genes. Similar results were obtained with an inhibitor of Ras. These results suggest that mechanical stretch of fetal lung epithelial cells evokes a complex network of signaling molecules, which diverge downstream to regulate the temporal expression of a unique set of early response genes, but upstream converge at calcium. Thus, calcium mobilization may be a point of hierarchical integration of mechanotransduction in lung epithelial cells.
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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".