CSIG-23. CYTOPLASMIC RNA STRESS GRANULES: A PUTATIVE TRANSLATIONAL MECHANISM OF mTOR REGULATION IN GLIOBLASTOMA
Bibliographic record
Abstract
We performed RNA immunoprecipitation followed by microarray profiling of mRNAs pulled down by Stress Granule (SG) markers TIAR and G3BP1 in normal and oxidative stress conditions to enrich for mRNA contained in SGs during stress in Glioblastoma cell lines. Interestingly, components of the mTOR Ragulator, RRAGD and LAMTOR were enriched in G3BP1 precipitates in stress conditions. We confirmed localization of RRAGD mRNA to SGs utilizing single molecule RNA FISH in arsenite stressed Glioblastoma cell lines. We therefore hypothesized that perhaps Ragulator mRNAs are sheltered in SG for rapid translation after stress release. We confirmed that protein levels of RRAGD and LAMTOR increase at 25-35 minutes post-release from arsenite induced stress concomitant with a 50% decrease in SGs. We also observed similar increases in protein levels of other Ragulator components (RRAGA/B/C). This increase differed from protein levels of other mRNAs identified in our screen, which showed no increase in protein levels after stress release (TIAM1, WAVE1/2, FOXK2, FOXN3). The increase in protein levels of the Ragulator complex components remained despite the addition of actinomycin D, suggesting that the increase protein levels are a result of translation of a stabilized cohort of mRNA and not de novo transcription. Interestingly, preliminary data utilizing a mCherry-LAMP construct suggests that mTOR colocalizes to the lysosome at 25-35 minutes post release from stress, a necessary step for mTOR activation. Utilizing Glioblastoma cells that we have engineered to display a delay in SG dissolution, we aim to demonstrate impaired SG release of mRNA results in a shift in the Ragulator protein spike after stress release. Taken together this data suggests a novel translational control mechanism of mTOR activation by SGs.
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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 | 0.001 |
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".