Abstract B55: MYCN promotes m6a-dependent translation of eEF2K mRNA during adaptation to ER stress
Bibliographic record
Abstract
Abstract MYCN-amplified neuroblastoma is one of the most common and malignant pediatric tumors with low survival rates. Attempts to develop specific inhibitors to target MYCN have not been successful, prioritizing research into MYCN downstream targets. It was previously shown that eukaryotic translation elongation factor 2 (eEF2K) is upregulated in MYCN-amplified neuroblastoma and facilitates its adaptation to the nutrient deprivation (ND). However, the mechanism of eEF2K overexpression and its role in such adaptation is unknown. Using SUnSET assays, we showed that MYCN overexpression increases protein synthesis rates, demonstrating that MYCN-amplified cells depend on protein biosynthesis to cover the needs of fast proliferation. Enhanced protein biosynthesis increases the susceptibility for protein aggregates and subsequent unfolded protein response (UPR) activation and ER stress. ER stress also occurs under ND due to accumulation of unglycosylated proteins in the ER. To check how MYCN-amplified tumors might adapt to ER stress, we performed gene set enrichment analyses (GSEA) of genes that are overexpressed in MYCN amplified neuroblastoma and found “UPR” in the top 10 gene sets. As eEF2K regulates protein synthesis rates by slowing down the elongation step of mRNA translation, we examined whether eEF2K is part of the ER stress adaptation mechanism in MYCN-amplified cells. We found that during ND, eEF2K KO cells are more sensitive to thapsigargin (Tg) and tunicamycin (Tm), ER stress-inducing compounds, and show higher levels of ATF4, a major ER stress marker. Notably, low doses of cycloheximide, which blocks translation elongation, rescued the phenotype in eEF2K KO cells, confirming that the observed effect of eEF2K KO is linked to translation elongation. Finally, GSEA of RNAseq data obtained from TET21N WT vs. eEF2K KO after ND showed upregulation of genes responsible for the UPR and ER stress in eEF2K KO cells. Therefore, eEF2K appears to be linked to ER stress adaptation. To find potential mechanisms of MYCN-dependent regulation of eEF2K expression, we performed bioinformatic analyses of the eEF2K mRNA and found potential binding sites for METTL3, an enzyme that functions in the methylation of adenosine residues in mRNAs, in the eEF2K 3´-UTR. METTL3 knockdown resulted in a marked decrease of eEF2K protein without significant changes at the mRNA level. We applied polysome fractionation to study the effect of m6a modification on eEF2K mRNA translation. We discovered that METTL3 KD brings eEF2K mRNA to lighter polysome fractions compared with WT cells. As YTHDF1 is the only known reader of m6a modification that increases translation, we performed YTHDF1 knockdown and observed a decrease in eEF2K protein levels. Our findings indicate that MYCN amplified neuroblastoma cells increase translation of eEF2K mRNAs via a METTL3-YTHDF1 axis, and that this potentially functions as part of an adaptation mechanism under increased protein synthesis and subsequent ER stress. Citation Format: Andrii Vislovukh, Cristopher Hughes, Michael Lizardo, Poul H. Sorensen. MYCN promotes m6a-dependent translation of eEF2K mRNA during adaptation to ER stress [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B55.
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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) | 0.002 | 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".