ERK3 and DGKζ interact to modulate cell motility in lung cancer cells
Bibliographic record
Abstract
Extracellular signal-regulated kinase 3 (ERK3) promotes cell migration and tumor metastasis in multiple cancer types, including lung cancer. The extracellular-regulated kinase 3 protein has a unique structure. In addition to the N-terminal kinase domain, ERK3 includes a central conserved in extracellular-regulated kinase 3 and ERK4 (C34) domain and an extended C-terminus. However, relatively little is known regarding the role(s) of the C34 domain. A yeast two-hybrid assay using extracellular-regulated kinase 3 as bait identified diacylglycerol kinase ζ (DGKζ) as a binding partner. DGKζ was shown to promote migration and invasion in some cancer cell types, but its role in lung cancer cells is yet to be described. The interaction of extracellular-regulated kinase 3 and DGKζ was confirmed by co-immunoprecipitation and in vitro binding assays, consistent with their co-localization at the periphery of lung cancer cells. The C34 domain of ERK3 was sufficient for binding to DGKζ, while extracellular-regulated kinase 3 bound to the N-terminal and C1 domains of DGKζ. Surprisingly, in contrast to extracellular-regulated kinase 3, DGKζ suppresses lung cancer cell migration, suggesting DGKζ might inhibit ERK3-mediated cell motility. Indeed, co-overexpression of exogenous DGKζ and extracellular-regulated kinase 3 completely blocked the ability of ERK3 to promote cell migration, but DGKζ did not affect the migration of cells with stable ERK3 knockdown. Furthermore, DGKζ had little effect on cell migration induced by overexpression of an ERK3 mutant missing the C34 domain, suggesting DGKζ requires this domain to prevent ERK3-mediated increase in cell migration. In summary, this study has identified DGKζ as a new binding partner and negative regulator of extracellular-regulated kinase 3 in controlling lung cancer cell 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".