SUMO Proteomics Analyses Identify Protein Inhibitor of Activated STAT-Mediated Regulatory Networks Involved in Cell Cycle and Cell Proliferation
Bibliographic record
Abstract
Protein inhibitor of activated STAT (PIAS) proteins are E3 SUMO ligases playing important roles in protein stability and signaling transduction pathways. PIAS proteins are overexpressed in the triple-negative breast cancer cell line MDA-MB-231, and PIAS knockout (KO) results in a reduction in cell proliferation and cell arrest in the S phase. However, the molecular mechanisms underlying PIAS functions in cell proliferation and cell cycle remain largely unknown. Here, we used quantitative SUMO proteomics to explore the regulatory role of PIAS SUMO E3 ligases upon CRISPR/Cas9 KO of individual PIAS. A total of 1422 sites were identified, and around 10% of SUMO sites were regulated following KO of one or more PIAS genes. We identified protein substrates that were either specific to individual PIAS ligase or regulated by several PIAS ligases. Ki-67 and TOP2A, which are involved in cell proliferation and epithelial-to-mesenchymal transition, are SUMOylated at several lysine residues by all PIAS ligases, suggesting a level of redundancy between these proteins. Confocal microscopy and biochemical experiments revealed that SUMOylation regulated TOP2A protein stability, while this modification is involved in the recruitment of Ki-67 nucleolar proteins containing the SUMO interacting motif. These results provide novel insights into both the redundant and specific regulatory mechanisms of cell proliferation and cell cycle mediated by PIAS SUMO E3 ligases.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".