Abstract 4997: Specialized microRNP and translation mechanisms in quiescent cancer cells
Bibliographic record
Abstract
Abstract Quiescence (G0) represents an assortment of reversible, cell cycle-arrested states that are resistant to unfavorable conditions and associated with cancer persistence. G0 involves regulated gene expression with selective mRNA expression and decreased canonical translation. Low mTOR activity in G0 activates the cap complex inhibitor, eIF4EBP, and impairs canonical translation. The alternative translation mechanisms in G0 remain to be uncovered. Our data show that microRNAs, regulatory, non-coding RNAs that target distinct mRNAs to alter gene expression, can associate with alternative complexes and translation factors to regulate specific mRNA translation in G0. One subset of transcripts expressed in G0 includes specific mRNAs recruited by an FXR1a-associated microRNP (microRNA-protein complex) for translation activation in G0 mammalian cells. MicroRNPs predominantly mediate repression and downregulation; however, FXR1a-microRNP lacks conventional microRNP repressors, and instead, contains a specific RNA binding protein isoform, FXR1a. FXR1a promotes translation and is overexpressed and associated with poor prognosis in several cancers. Our data reveal that microRNA-mediated activation requires target mRNAs with unadenylated/ shortened poly(A) tails to avoid the roles of PABP in enhancing microRNA-mediated downregulation and in canonical translation that is impaired in G0. Instead of canonical translation factors that are inhibited by eIF4EBP in G0, we find alternative translation factors—a non-canonical 5’cap binding factor and an eIF4G homolog that interacts with the ribosome—are recruited by the 3’-UTR binding FXR1a-microRNP, and promote specific mRNA translation. Our data show that G0 leukemic cells are chemoresistant and their translation profile is similar to surviving leukemic cells that are isolated after clinical therapy. We find expression of critical cytokines and immune regulators in G0. Significantly, inhibiting these immune regulators in resistant G0 cancer cells reduces their survival and chemoresistance. These data reveal a specialized translation mechanism in G0 cancer cells that promotes specific mRNA translation in these conditions of reduced canonical translation, and is important for chemoresistance. Citation Format: Syed I. Bukhari, Samuel S. Truesdell, Sooncheol Lee, Swapna Kollu, Anthony Classon, Myriam Boukhali, Esha Jain, Richard D. Mortensen, Akiko Yanagiya, Ruslan Sadreyev, Wilhelm Haas, Shobha Vasudevan. Specialized microRNP and translation mechanisms in quiescent cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4997. doi:10.1158/1538-7445.AM2017-4997
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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".