GEF-H1 Mediates Tumor Necrosis Factor-α-induced Rho Activation and Myosin Phosphorylation
Bibliographic record
Abstract
Tumor necrosis factor-alpha (TNF-alpha), an inflammatory cytokine, has been shown to activate the small GTPase Rho, but the underlying signaling mechanisms remained undefined. This general problem is particularly important in the kidney, because TNF-alpha, a major mediator of kidney injury, is known to increase paracellular permeability in tubular epithelia. Here we aimed to determine the effect of TNF-alpha on the Rho pathway in tubular cells (LLC-PK(1) and Madin-Darby canine kidney), define the upstream signaling, and investigate the role of the Rho pathway in the TNF-alpha-induced alterations of paracellular permeability. We show that TNF-alpha induced a rapid and sustained RhoA activation that led to stress fiber formation and Rho kinase-dependent myosin light chain (MLC) phosphorylation. To identify new regulators connecting the TNF receptor to Rho signaling, we applied an affinity precipitation assay with a Rho mutant (RhoG17A), which captures activated GDP-GTP exchange factors (GEFs). Mass spectrometry analysis of the RhoG17A-precipitated proteins identified GEF-H1 as a TNF-alpha-activated Rho GEF. Consistent with a central role of GEF-H1, its down-regulation by small interfering RNA prevented the activation of the Rho pathway. Moreover GEF-H1 and Rho activation are downstream of ERK signaling as the MEK1/2 inhibitor PD98059 mitigated TNF-alpha-induced activation of these proteins. Importantly TNF-alpha enhanced the ERK pathway-dependent phosphorylation of Thr-678 of GEF-H1 that was key for activation. Finally the TNF-alpha-induced paracellular permeability increase was absent in LLC-PK(1) cells stably expressing a non-phosphorylatable, dominant negative MLC. In summary, we have identified the ERK/GEF-H1/Rho/Rho kinase/phospho-MLC pathway as the mechanism mediating TNF-alpha-induced elevation of tubular epithelial permeability, which in turn might contribute to kidney injury.
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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 | 0.001 |
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".