Dissecting the Signaling Events That Impact Classical Nuclear Import and Target Nuclear Transport Factors
Bibliographic record
Abstract
BACKGROUND: Signaling through MEK-->ERK1/2 and PI3 kinases is implicated in many aspects of cell physiology, including the survival of oxidant exposure. Oxidants play a role in numerous physiological and pathophysiological processes, many of which rely on transport in and out of the nucleus. However, how oxidative stress impacts nuclear trafficking is not well defined. METHODOLOGY/PRINCIPAL FINDINGS: To better understand the effect of stress on nucleocytoplasmic trafficking, we exposed cells to the oxidant diethyl maleate. This treatment activated MEK-->ERK1/2 as well as PI3 kinase-->Akt cascades and triggered the inhibition of classical nuclear import. To define the molecular mechanisms that regulate nuclear transport, we examined whether MEK and PI3 kinase signaling affected the localization of key transport factors. Using recently developed tools for image acquisition and analysis, the subcellular distributions of importin-alpha, CAS, and nucleoporins Nup153 and Nup88 were quantified in different cellular compartments. These studies identified specific profiles for the localization of transport factors in the nucleus and cytoplasm, and at the nuclear envelope. Our results demonstrate that MEK and PI3 kinase signaling as well as oxidative stress control nuclear trafficking and the localization of transport components. Furthermore, stress not only induced changes in transport factor distribution, but also upregulated post-translational modification of transport factors. Our results are consistent with the idea that the phosphorylation of importin-alpha, CAS, Nup153, and Nup88, and the O-GlcNAc modification of Nup153 increase when cells are exposed to oxidant. CONCLUSIONS/SIGNIFICANCE: Our studies defined the complex regulation of classical nuclear import and identified key transport factors that are targeted by stress, MEK, and PI3 kinase signaling.
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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".