MRCKα/CDC42BPA Is a Suppressor of GEF-H1/RhoA/MRTF Signaling in Tubular Cells
Bibliographic record
Abstract
Pro-fibrotic mediators derived from tubular cells have a central role in he development of kidney fibrosis. Our previous studies revealed that GEF-H1 (ARHGEF2)/RhoA signalling is a crucial regulator of tubular mediator release. Fibrotic stimuli elevate GEF-H1 expression and activity, but the molecular mechanisms controlling tubular GEF-H1 activity during fibrotic reprogramming are incompletely explored. Here we used immunoprecipitation and proximity ligation assay to show that GEF-H1 interacts with Myotonic Dystrophy Kinase-related Cdc42-binding kinase (MRCK)α in porcine and human tubular cells. Using GEF-H1 mutants we mapped the interacting domain to the N-terminus of GEF-H1 and showed the requirement for an intact DH domain. MRCKα silencing elevated GEF-H1 activity, induced GEF-H1-dependent RhoA activation and augmented stress fibre formation and phospho-cofilin levels. Interestingly, TNFα or TGFβ1 addition rapidly increased binding between GEF-H1 and MRCKα, suggesting a negative feedback role. Indeed, the effect of TNFα or TGFβ1 on GEF-H1 activation was augmented in the absence of MRCKα. Using an mRNA array, we found that MRCKα depletion elevated basal and TGFβ1-induced expression of key fibrosis-related genes. MRCKα silencing also promoted nuclear translocation of the profibrotic transcriptional co-activator Myocardin- related Transcription Factor (MRTF). Depletion of MRTF-A and B prevented the increase in ACTA-2 (smooth muscle actin) and transgelin (TAGLN), key markers of fibrotic reprogramming, induced by MRCKα-silencing and TGFβ1 treatment. Taken together, we identified MRCKα as a new suppressor of GEF-H1/RhoA/MRTF signaling and tubular fibrotic gene expression. Cytokines augment binding between the two proteins, thereby mitigating GEF-H1 activation in a negative feedback cycle. These effects could be crucial for preventing RhoA overactivation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".