Increased RNA and protein degradation is required for counteracting transcriptional burden and proteotoxic stress in human aneuploid cells
Bibliographic record
Abstract
Abstract Aneuploidy, an abnormal chromosome composition, results in a stoichiometric imbalance of protein complexes, which jeopardizes the fitness of aneuploid cells. Aneuploid cells thus need to compensate for the imbalanced DNA levels by regulating their RNA and protein levels, a phenomenon known as dosage compensation. However, the molecular mechanisms involved in dosage compensation in human cells – and whether they can be targeted to selectively kill aneuploid cancer cells – remain unknown. Here, we addressed this question via molecular dissection of multiple diploid vs. aneuploid cell models. Using genomic and functional profiling of a novel isogenic system of RPE1-hTERT cells with various degrees of aneuploidy, we found that aneuploid cells cope with both transcriptional burden and proteotoxic stress. At the mRNA level, aneuploid cells increased RNA synthesis, but concomitantly elevated several RNA degradation pathways, in particular the nonsense-mediated decay (NMD) and the microRNA-mediated mRNA silencing pathways. Consequently, aneuploid cells were more sensitive to the genetic or chemical perturbation of several key components of these RNA degradation pathways. At the protein level, aneuploid cells experienced proteotoxic stress, resulting in reduced translation and increased protein degradation, rendering them more sensitive to proteasome inhibition. These findings were recapitulated across hundreds of human cancer cell lines and primary tumors, confirming that both non-transformed and transformed cells alter their RNA and protein metabolism in order to adapt to the aneuploid state. Our results reveal that aneuploid cells are dependent on the over- or under-activation of several nodes along the gene expression process, identifying these pathways as clinically-actionable vulnerabilities of aneuploid cells.
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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".