PKCδ/midkine pathway drives hypoxia-induced proliferation and differentiation of human lung epithelial cells
Bibliographic record
Abstract
Epithelial cells are key players in the pathobiology of numerous hypoxia-induced lung diseases. The mechanisms mediating such hypoxic responses of epithelial cells are not well characterized. Earlier studies reported that hypoxia stimulates protein kinase C (PKC)δ activation in renal cancer cells and an increase in expression of a heparin-binding growth factor, midkine (MK), in lung alveolar epithelial cells. We reasoned that hypoxia might regulate MK levels via a PKCδ-dependent pathway and hypothesized that PKCδ-driven MK expression is required for hypoxia-induced lung epithelial cell proliferation and differentiation. Replication of human lung epithelial cells (A549) was significantly increased by chronic hypoxia (1% O2) and was dependent on expression of PKCδ. Hypoxia-induced proliferation of epithelial cells was accompanied by translocation of PKCδ from Golgi into the nuclei. Marked attenuation in MK protein levels by rottlerin, a pharmacological antagonist of PKC, and by small interfering RNA-targeting PKCδ, revealed that PKCδ is required for MK expression in both normoxic and hypoxic lung epithelial cells. Sequestering MK secreted into the culture media with a neutralizing antibody reduced hypoxia-induced proliferation demonstrating that an increase in MK release from cells is linked with epithelial cell division under hypoxia. In addition, recombinant MK accelerated transition of hypoxic epithelial cells to cells of mesenchymal phenotype characterized by elongated morphology and increased expression of mesenchymal markers, α-smooth muscle actin, and vimentin. We conclude that PKCδ/MK axis mediates hypoxic proliferation and differentiation of lung epithelial cells. Manipulation of PKCδ and MK activity in epithelial cells might be beneficial for the treatment of hypoxia-mediated lung diseases.
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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".