Reduced SKP1 Expression Induces Chromosome Instability through Aberrant Cyclin E1 Protein Turnover
Bibliographic record
Abstract
Chromosome instability (CIN), or progressive changes in chromosome numbers, is an enabling feature of many cancers; however, the mechanisms giving rise to CIN remain poorly understood. To expand our mechanistic understanding of the molecular determinants of CIN in humans, we employed a cross-species approach to identify 164 human candidates to screen. Using quantitative imaging microscopy (QuantIM), we show that silencing 148 genes resulted in significant changes in CIN-associated phenotypes in two distinct cellular contexts. Ten genes were prioritized for validation based on cancer patient datasets revealing frequent gene copy number losses and associations with worse patient outcomes. QuantIM determined silencing of each gene-induced CIN, identifying novel roles for each as chromosome stability genes. SKP1 was selected for in-depth analyses as it forms part of SCF (SKP1, CUL1, FBox) complex, an E3 ubiquitin ligase that targets proteins for proteolytic degradation. Remarkably, SKP1 silencing induced increases in replication stress, DNA double strand breaks and chromothriptic events that were ascribed to aberrant increases in Cyclin E1 levels arising from reduced SKP1 expression. Collectively, these data reveal a high degree of evolutionary conservation between human and budding yeast CIN genes and further identify aberrant mechanisms associated with increases in chromothriptic events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".