Identification of Novel Stress Granule Components That Are Involved in Nuclear Transport
Bibliographic record
Abstract
BACKGROUND: Importin-α1 belongs to a subfamily of nuclear transport adaptors and participates in diverse cellular functions. Best understood for its role in protein transport, importin-α1 also contributes to other biological processes. For instance, arsenite treatment causes importin-α1 to associate with cytoplasmic stress granules (SGs) in mammalian cells. These stress-induced compartments contain translationally arrested mRNAs, small ribosomal subunits and numerous proteins involved in mRNA transport and metabolism. At present, it is not known whether members of all three importin-α subfamilies locate to SGs in response to stress. RESULTS: Here, we demonstrate that the oxidant diethyl maleate (DEM), arsenite and heat shock, promote the formation of cytoplasmic SGs that contain nuclear transport factors. Specifically, importin-α1, α4 and α5, which belong to distinct subfamilies, and importin-β1 were targeted by all of these stressors to cytoplasmic SGs, but not to P-bodies. Importin-α family members have been implicated in transcriptional regulation, which prompted us to analyze their ability to interact with poly(A)-RNA in growing cells. Our studies show that importin-α1, but not α4, α5, importin-β1 or CAS, associated with poly(A)-RNA under nonstress conditions. Notably, this interaction was significantly reduced when cells were treated with DEM. Additional studies suggest that importin-α1 is likely connected to poly(A)-RNA through an indirect interaction, as the adaptor did not bind homopolymer RNA specifically in vitro. SIGNIFICANCE: Our studies establish that members of three importin-α subfamilies are bona fide SG components under different stress conditions. Furthermore, importin-α1 is unique in its ability to interact with poly(A)-RNA in a stress-dependent fashion, and in vitro experiments indicate that this association is indirect. Collectively, our data emphasize that nuclear transport factors participate in a growing number of cellular activities that are modulated by stress.
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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".