Regulation of the nuclear speckle localization and function of Rac1
Bibliographic record
Abstract
Despite significant evidence that Rac1 is localized to the nucleus, little is known regarding the function and biological significance of nuclear Rac1. Here, we showed that in response to EGF Rac1 was translocated to nuclear speckles and co-localized with the nuclear speckle marker Serine/arginine-rich splicing factor 2 (SRSF2) in Cos-7 cells. We also showed that the nuclear speckle localization of Rac1 was dependent on its T108 phosphorylation and facilitated by Rac1 polybasic region (PBR) that contains a nuclear localization signal and Rac1 GTPase activity. To gain insight into the function of Rac1 in nuclear speckles, we searched for Rac1 binding proteins in the nucleus. We isolated nuclear fraction of HEK 293 cells and incubated with GST-Rac1 and the phosphomimetic GST-Rac1T108E. We identified 463 proteins that were associated with GST-Rac1T108E, but not with GST-Rac1 by LC-MS/MS. Three notable groups of these proteins are: the heterogeneous nuclear ribonucleoproteins (hnRNPs), small nuclear ribonucleoproteins (snRNPs), and SRSFs, all of which are involved in pre-mRNA splicing and associated with nuclear speckles. We further showed by co-immunoprecipitation that Rac1 interacts with SRSF2, hnRNPA1, and U2A' in response to EGF. The interaction is dependent on T108 phosphorylation and facilitated by Rac1 PBR and GTPase activity. We showed that hnRNPA1 translocated in and out of nucleus in response to EGF in a similar pattern to Rac1. Rac1 only partially colocalized with U2A' that localizes to the actual splicing sites adjacent to nuclear speckle. Finally, we showed that Rac1 regulated EGF-induced pre-mRNA splicing and this is mediated by T108 phosphorylation. We conclude that in response to EGF, T108 phosphorylated Rac1 is targeted to nuclear speckles, interacts with multiple groups of proteins involved in pre-mRNA splicing, and regulates EGF-induced pre-mRNA splicing.
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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 | 0.000 | 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) | 0.000 | 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".