The DUSP domain of pseudophosphatase MK-STYX interacts with G3BP1 to decrease stress granules
Bibliographic record
Abstract
R). We previously reported that MK-STYX interacts with G3BP1 [Ras-GAP (GTPase-activating protein) SH3 (Src homology 3) domain-binding-1] and reduces stress granules, stalled mRNA. To determine how MK-STYX reduces stress granules, truncated domains, CH2 (cell division cycle 25 phosphatase homology 2) and DUSP, of MK-STYX were used. Wild-type MK-STYX and the DUSP domain significantly decreased stressed granules that were induced by sodium arsenite, in which G3BP1 (a stress granule nucleator) was used as the marker. In addition, HEK/293 and HeLa cells co-expressing G3BP1-GFP and mCherry-MK-STYX, mCherry-MK-STYX-CH2, mCherry-MK-STYX-DUSP or mCherry showed that stress granules were significantly decreased in the presence of wild-type MK-STYX and the DUSP domain of MK-STYX. Further characterization of these dynamics in HeLa cells showed that the CH2 domain increased the number of stress granules within a cell, relative to wild-type and DUSP domain of MK-STYX. To further analyze the interaction of G3BP1 and the domains of MK-STYX, coimmunoprecipitation experiments were performed. Cells co-expressing G3BP1-GFP and mCherry, mCherry-MK-STYX, mCherry-MK-STYX-CH2, or mCherry-MK-STYX-DUSP demonstrated that the DUSP domain of MK-STYX interacts with both G3BP1-GFP and endogenous G3BP1, whereas the CH2 domain of MK-STYX did not coimmunoprecipitate with G3BP1. In addition, G3BP1 tyrosine phosphorylation, which is required for stress granule formation, was decreased in the presence of wild-type MK-STYX or the DUSP domain but increased in the presence of CH2. These data highlight a model for how MK-STYX decreases G3BP1-induced stress granules. The DUSP domain of MK-STYX interacts with G3BP1 and negatively alters its tyrosine phosphorylation- decreasing stress granule formation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".