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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".