Diacylglycerol kinase ζ regulates RhoA activation via a kinase-independent scaffolding mechanism
Bibliographic record
Abstract
Rho GTPases share a common inhibitor, Rho guanine nucleotide dissociation inhibitor (RhoGDI), which regulates their expression levels, membrane localization, and activation state. The selective dissociation of individual Rho GTPases from RhoGDI ensures appropriate responses to cellular signals, but the underlying mechanisms are unclear. Diacylglycerol kinase ζ (DGKζ), which phosphorylates diacylglycerol to yield phosphatidic acid, selectively dissociates Rac1 by stimulating PAK1-mediated phosphorylation of RhoGDI on Ser-101/174. Similarly, phosphorylation of RhoGDI on Ser-34 by protein kinase Cα (PKCα) selectively releases RhoA. Here we show DGKζ is required for RhoA activation and Ser-34 phosphorylation, which were decreased in DGKζ-deficient fibroblasts and rescued by wild-type DGKζ or a catalytically inactive mutant. DGKζ bound directly to the C-terminus of RhoA and the regulatory arm of RhoGDI and was required for efficient interaction of PKCα and RhoA. DGKζ-null fibroblasts had condensed F-actin bundles and altered focal adhesion distribution, indicative of aberrant RhoA signaling. Two targets of the RhoA effector ROCK showed reduced phosphorylation in DGKζ-null cells. Collectively our findings suggest DGKζ functions as a scaffold to assemble a signaling complex that functions as a RhoA-selective, GDI dissociation factor. As a regulator of Rac1 and RhoA activity, DGKζ is a critical factor linking changes in lipid signaling to actin reorganization.
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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) | 0.001 | 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".