Central role of the exchange factor GEF-H1 in TNF-α–induced sequential activation of Rac, ADAM17/TACE, and RhoA in tubular epithelial cells
Bibliographic record
Abstract
Transactivation of the epidermal growth factor receptor (EGFR) by tumor necrosis factor-α (TNF-α) is a key step in mediating RhoA activation and cytoskeleton and junction remodeling in the tubular epithelium. In this study we explore the mechanisms underlying TNF-α-induced EGFR activation. We show that TNF-α stimulates the TNF-α convertase enzyme (TACE/a disintegrin and metalloproteinase-17), leading to activation of the EGFR/ERK pathway. TACE activation requires the mitogen-activated protein kinase p38, which is activated through the small GTPase Rac. TNF-α stimulates both Rac and RhoA through the guanine nucleotide exchange factor (GEF)-H1 but by different mechanisms. EGFR- and ERK-dependent phosphorylation at the T678 site of GEF-H1 is a prerequisite for RhoA activation only, whereas both Rac and RhoA activation require GEF-H1 phosphorylation on S885. Of interest, GEF-H1-mediated Rac activation is upstream from the TACE/EGFR/ERK pathway and regulates T678 phosphorylation. We also show that TNF-α enhances epithelial wound healing through TACE, ERK, and GEF-H1. Taken together, our findings can explain the mechanisms leading to hierarchical activation of Rac and RhoA by TNF-α through a single GEF. This mechanism could coordinate GEF functions and fine-tune Rac and RhoA activation in epithelial cells, thereby promoting complex functions such as sheet migration.
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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".